@ 0d97beae:c5274a14

This article hopes to complement the article by Lyn Alden on YouTube: https://www.youtube.com/watch?v=jk_HWmmwiAs ## The reason why we have broken money Before the invention of key technologies such as the printing press and electronic communications, even such as those as early as morse code transmitters, gold had won the competition for best medium of money around the world. In fact, it was not just gold by itself that became money, rulers and world leaders developed coins in order to help the economy grow. Gold nuggets were not as easy to transact with as coins with specific imprints and denominated sizes. However, these modern technologies created massive efficiencies that allowed us to communicate and perform services more efficiently and much faster, yet the medium of money could not benefit from these advancements. Gold was heavy, slow and expensive to move globally, even though requesting and performing services globally did not have this limitation anymore. Banks took initiative and created derivatives of gold: paper and electronic money; these new currencies allowed the economy to continue to grow and evolve, but it was not without its dark side. Today, no currency is denominated in gold at all, money is backed by nothing and its inherent value, the paper it is printed on, is worthless too. Banks and governments eventually transitioned from a money derivative to a system of debt that could be co-opted and controlled for political and personal reasons. Our money today is broken and is the cause of more expensive, poorer quality goods in the economy, a larger and ever growing wealth gap, and many of the follow-on problems that have come with it. ## Bitcoin overcomes the "transfer of hard money" problem Just like gold coins were created by man, Bitcoin too is a technology created by man. Bitcoin, however is a much more profound invention, possibly more of a discovery than an invention in fact. Bitcoin has proven to be unbreakable, incorruptible and has upheld its ability to keep its units scarce, inalienable and counterfeit proof through the nature of its own design. Since Bitcoin is a digital technology, it can be transferred across international borders almost as quickly as information itself. It therefore severely reduces the need for a derivative to be used to represent money to facilitate digital trade. This means that as the currency we use today continues to fare poorly for many people, bitcoin will continue to stand out as hard money, that just so happens to work as well, functionally, along side it. Bitcoin will also always be available to anyone who wishes to earn it directly; even China is unable to restrict its citizens from accessing it. The dollar has traditionally become the currency for people who discover that their local currency is unsustainable. Even when the dollar has become illegal to use, it is simply used privately and unofficially. However, because bitcoin does not require you to trade it at a bank in order to use it across borders and across the web, Bitcoin will continue to be a viable escape hatch until we one day hit some critical mass where the world has simply adopted Bitcoin globally and everyone else must adopt it to survive. Bitcoin has not yet proven that it can support the world at scale. However it can only be tested through real adoption, and just as gold coins were developed to help gold scale, tools will be developed to help overcome problems as they arise; ideally without the need for another derivative, but if necessary, hopefully with one that is more neutral and less corruptible than the derivatives used to represent gold. ## Bitcoin blurs the line between commodity and technology Bitcoin is a technology, it is a tool that requires human involvement to function, however it surprisingly does not allow for any concentration of power. Anyone can help to facilitate Bitcoin's operations, but no one can take control of its behaviour, its reach, or its prioritisation, as it operates autonomously based on a pre-determined, neutral set of rules. At the same time, its built-in incentive mechanism ensures that people do not have to operate bitcoin out of the good of their heart. Even though the system cannot be co-opted holistically, It will not stop operating while there are people motivated to trade their time and resources to keep it running and earn from others' transaction fees. Although it requires humans to operate it, it remains both neutral and sustainable. Never before have we developed or discovered a technology that could not be co-opted and used by one person or faction against another. Due to this nature, Bitcoin's units are often described as a commodity; they cannot be usurped or virtually cloned, and they cannot be affected by political biases. ## The dangers of derivatives A derivative is something created, designed or developed to represent another thing in order to solve a particular complication or problem. For example, paper and electronic money was once a derivative of gold. In the case of Bitcoin, if you cannot link your units of bitcoin to an "address" that you personally hold a cryptographically secure key to, then you very likely have a derivative of bitcoin, not bitcoin itself. If you buy bitcoin on an online exchange and do not withdraw the bitcoin to a wallet that you control, then you legally own an electronic derivative of bitcoin. Bitcoin is a new technology. It will have a learning curve and it will take time for humanity to learn how to comprehend, authenticate and take control of bitcoin collectively. Having said that, many people all over the world are already using and relying on Bitcoin natively. For many, it will require for people to find the need or a desire for a neutral money like bitcoin, and to have been burned by derivatives of it, before they start to understand the difference between the two. Eventually, it will become an essential part of what we regard as common sense. ## Learn for yourself If you wish to learn more about how to handle bitcoin and avoid derivatives, you can start by searching online for tutorials about "Bitcoin self custody". There are many options available, some more practical for you, and some more practical for others. Don't spend too much time trying to find the perfect solution; practice and learn. You may make mistakes along the way, so be careful not to experiment with large amounts of your bitcoin as you explore new ideas and technologies along the way. This is similar to learning anything, like riding a bicycle; you are sure to fall a few times, scuff the frame, so don't buy a high performance racing bike while you're still learning to balance.

@ 37fe9853:bcd1b039

yoyoaa

@ 62033ff8:e4471203

收录的内容中 kind=1的部分，实话说 质量不高。 所以我增加了kind=30023 长文的article，但是更新的太少，多个relays 的服务器也没有多少长文。 所有搜索nostr如果需要产生价值，需要有高质量的文章和新闻。 而且现在有很多机器人的文章充满着浪费空间的作用，其他作用都用不上。 https://www.duozhutuan.com 目前放的是给搜索引擎提供搜索的原材料。没有做UI给人类浏览。所以看上去是粗糙的。 我并没有打算去做一个发microblog的 web客户端，那类的客户端太多了。 我觉得nostr社区需要解决的还是应用。如果仅仅是microblog 感觉有点够呛 幸运的是npub.pro 建站这样的，我觉得有点意思。 yakihonne 智能widget 也有意思 我做的TaskQ5 我自己在用了。分布式的任务系统，也挺好的。

@ 23b0e2f8:d8af76fc

## **Necessário** - Um Android que você não use mais (a câmera deve estar funcionando). - Um cartão microSD (opcional, usado apenas uma vez). - Um dispositivo para acompanhar seus fundos (provavelmente você já tem um). ## **Algumas coisas que você precisa saber** - O dispositivo servirá como um assinador. Qualquer movimentação só será efetuada após ser assinada por ele. - O cartão microSD será usado para transferir o APK do Electrum e garantir que o aparelho não terá contato com outras fontes de dados externas após sua formatação. Contudo, é possível usar um cabo USB para o mesmo propósito. - A ideia é deixar sua chave privada em um dispositivo offline, que ficará desligado em 99% do tempo. Você poderá acompanhar seus fundos em outro dispositivo conectado à internet, como seu celular ou computador pessoal. --- ## **O tutorial será dividido em dois módulos:** - Módulo 1 - Criando uma carteira fria/assinador. - Módulo 2 - Configurando um dispositivo para visualizar seus fundos e assinando transações com o assinador. --- ## **No final, teremos:** - Uma carteira fria que também servirá como assinador. - Um dispositivo para acompanhar os fundos da carteira.  --- ## **Módulo 1 - Criando uma carteira fria/assinador** 1. Baixe o APK do Electrum na aba de **downloads** em <https://electrum.org/>. Fique à vontade para [verificar as assinaturas](https://electrum.readthedocs.io/en/latest/gpg-check.html) do software, garantindo sua autenticidade. 2. Formate o cartão microSD e coloque o APK do Electrum nele. Caso não tenha um cartão microSD, pule este passo.  3. Retire os chips e acessórios do aparelho que será usado como assinador, formate-o e aguarde a inicialização.  4. Durante a inicialização, pule a etapa de conexão ao Wi-Fi e rejeite todas as solicitações de conexão. Após isso, você pode desinstalar aplicativos desnecessários, pois precisará apenas do Electrum. Certifique-se de que Wi-Fi, Bluetooth e dados móveis estejam desligados. Você também pode ativar o **modo avião**.\ *(Curiosidade: algumas pessoas optam por abrir o aparelho e danificar a antena do Wi-Fi/Bluetooth, impossibilitando essas funcionalidades.)*  5. Insira o cartão microSD com o APK do Electrum no dispositivo e instale-o. Será necessário permitir instalações de fontes não oficiais.  6. No Electrum, crie uma carteira padrão e gere suas palavras-chave (seed). Anote-as em um local seguro. Caso algo aconteça com seu assinador, essas palavras permitirão o acesso aos seus fundos novamente. *(Aqui entra seu método pessoal de backup.)*  --- ## **Módulo 2 - Configurando um dispositivo para visualizar seus fundos e assinando transações com o assinador.** 1. Criar uma carteira **somente leitura** em outro dispositivo, como seu celular ou computador pessoal, é uma etapa bastante simples. Para este tutorial, usaremos outro smartphone Android com Electrum. Instale o Electrum a partir da aba de downloads em <https://electrum.org/> ou da própria Play Store. *(ATENÇÃO: O Electrum não existe oficialmente para iPhone. Desconfie se encontrar algum.)* 2. Após instalar o Electrum, crie uma carteira padrão, mas desta vez escolha a opção **Usar uma chave mestra**.  3. Agora, no assinador que criamos no primeiro módulo, exporte sua chave pública: vá em **Carteira &gt; Detalhes da carteira &gt; Compartilhar chave mestra pública**.  4. Escaneie o QR gerado da chave pública com o dispositivo de consulta. Assim, ele poderá acompanhar seus fundos, mas sem permissão para movimentá-los. 5. Para receber fundos, envie Bitcoin para um dos endereços gerados pela sua carteira: **Carteira &gt; Addresses/Coins**. 6. Para movimentar fundos, crie uma transação no dispositivo de consulta. Como ele não possui a chave privada, será necessário assiná-la com o dispositivo assinador.  7. No assinador, escaneie a transação não assinada, confirme os detalhes, assine e compartilhe. Será gerado outro QR, desta vez com a transação já assinada.  8. No dispositivo de consulta, escaneie o QR da transação assinada e transmita-a para a rede. --- ## **Conclusão** **Pontos positivos do setup:** - **Simplicidade:** Basta um dispositivo Android antigo. - **Flexibilidade:** Funciona como uma ótima carteira fria, ideal para holders. **Pontos negativos do setup:** - **Padronização:** Não utiliza seeds no padrão BIP-39, você sempre precisará usar o electrum. - **Interface:** A aparência do Electrum pode parecer antiquada para alguns usuários. Nesse ponto, temos uma carteira fria que também serve para assinar transações. O fluxo de assinar uma transação se torna: ***Gerar uma transação não assinada &gt; Escanear o QR da transação não assinada &gt; Conferir e assinar essa transação com o assinador &gt; Gerar QR da transação assinada &gt; Escanear a transação assinada com qualquer outro dispositivo que possa transmiti-la para a rede.*** Como alguns devem saber, uma transação assinada de Bitcoin é praticamente impossível de ser fraudada. Em um cenário catastrófico, você pode mesmo que sem internet, repassar essa transação assinada para alguém que tenha acesso à rede por qualquer meio de comunicação. Mesmo que não queiramos que isso aconteça um dia, esse setup acaba por tornar essa prática possível. ---

@ 207ad2a0:e7cca7b0

*Quick context: I wanted to check out Nostr's longform posts and this blog post seemed like a good one to try and mirror. It's originally from my [free to read/share attempt to write a novel](https://untitlednovel.dns7.top/contents/), but this post here is completely standalone - just describing how I used AI image generation to make a small piece of the work.* Hold on, put your pitchforks down - outside of using Grammerly & Emacs for grammatical corrections - not a single character was generated or modified by computers; a non-insignificant portion of my first draft originating on pen & paper. No AI is ~~weird and crazy~~ imaginative enough to write like I do. The only successful AI contribution you'll find is a single image, the map, which I heavily edited. This post will go over how I generated and modified an image using AI, which I believe brought some value to the work, and cover a few quick thoughts about AI towards the end. Let's be clear, I can't draw, but I wanted a map which I believed would improve the story I was working on. After getting abysmal results by prompting AI with text only I decided to use "Diffuse the Rest," a Stable Diffusion tool that allows you to provide a reference image + description to fine tune what you're looking for. I gave it this Microsoft Paint looking drawing:  and after a number of outputs, selected this one to work on:  The image is way better than the one I provided, but had I used it as is, I still feel it would have decreased the quality of my work instead of increasing it. After firing up Gimp I cropped out the top and bottom, expanded the ocean and separated the landmasses, then copied the top right corner of the large landmass to replace the bottom left that got cut off. Now we've got something that looks like concept art: not horrible, and gets the basic idea across, but it's still due for a lot more detail.  The next thing I did was add some texture to make it look more map like. I duplicated the layer in Gimp and applied the "Cartoon" filter to both for some texture. The top layer had a much lower effect strength to give it a more textured look, while the lower layer had a higher effect strength that looked a lot like mountains or other terrain features. Creating a layer mask allowed me to brush over spots to display the lower layer in certain areas, giving it some much needed features.  At this point I'd made it to where I felt it may improve the work instead of detracting from it - at least after labels and borders were added, but the colors seemed artificial and out of place. Luckily, however, this is when PhotoFunia could step in and apply a sketch effect to the image.  At this point I was pretty happy with how it was looking, it was close to what I envisioned and looked very visually appealing while still being a good way to portray information. All that was left was to make the white background transparent, add some minor details, and add the labels and borders. Below is the exact image I wound up using:  Overall, I'm very satisfied with how it turned out, and if you're working on a creative project, I'd recommend attempting something like this. It's not a central part of the work, but it improved the chapter a fair bit, and was doable despite lacking the talent and not intending to allocate a budget to my making of a free to read and share story. #### The AI Generated Elephant in the Room If you've read my non-fiction writing before, you'll know that I think AI will find its place around the skill floor as opposed to the skill ceiling. As you saw with my input, I have absolutely zero drawing talent, but with some elbow grease and an existing creative direction before and after generating an image I was able to get something well above what I could have otherwise accomplished. Outside of the lowest common denominators like stock photos for the sole purpose of a link preview being eye catching, however, I doubt AI will be wholesale replacing most creative works anytime soon. I can assure you that I tried numerous times to describe the map without providing a reference image, and if I used one of those outputs (or even just the unedited output after providing the reference image) it would have decreased the quality of my work instead of improving it. I'm going to go out on a limb and expect that AI image, text, and video is all going to find its place in slop & generic content (such as AI generated slop replacing article spinners and stock photos respectively) and otherwise be used in a supporting role for various creative endeavors. For people working on projects like I'm working on (e.g. intended budget $0) it's helpful to have an AI capable of doing legwork - enabling projects to exist or be improved in ways they otherwise wouldn't have. I'm also guessing it'll find its way into more professional settings for grunt work - think a picture frame or fake TV show that would exist in the background of an animated project - likely a detail most people probably wouldn't notice, but that would save the creators time and money and/or allow them to focus more on the essential aspects of said work. Beyond that, as I've predicted before: I expect plenty of emails will be generated from a short list of bullet points, only to be summarized by the recipient's AI back into bullet points. I will also make a prediction counter to what seems mainstream: AI is about to peak for a while. The start of AI image generation was with Google's DeepDream in 2015 - image recognition software that could be run in reverse to "recognize" patterns where there were none, effectively generating an image from digital noise or an unrelated image. While I'm not an expert by any means, I don't think we're too far off from that a decade later, just using very fine tuned tools that develop more coherent images. I guess that we're close to maxing out how efficiently we're able to generate images and video in that manner, and the hard caps on how much creative direction we can have when using AI - as well as the limits to how long we can keep it coherent (e.g. long videos or a chronologically consistent set of images) - will prevent AI from progressing too far beyond what it is currently unless/until another breakthrough occurs.

@ e6817453:b0ac3c39

## The Rise of Graph RAGs and the Quest for Data Quality As we enter a new year, it’s impossible to ignore the boom of retrieval-augmented generation (RAG) systems, particularly those leveraging graph-based approaches. The previous year saw a surge in advancements and discussions about Graph RAGs, driven by their potential to enhance large language models (LLMs), reduce hallucinations, and deliver more reliable outputs. Let’s dive into the trends, challenges, and strategies for making the most of Graph RAGs in artificial intelligence. ## Booming Interest in Graph RAGs Graph RAGs have dominated the conversation in AI circles. With new research papers and innovations emerging weekly, it’s clear that this approach is reshaping the landscape. These systems, especially those developed by tech giants like Microsoft, demonstrate how graphs can: * **Enhance LLM Outputs:** By grounding responses in structured knowledge, graphs significantly reduce hallucinations. * **Support Complex Queries:** Graphs excel at managing linked and connected data, making them ideal for intricate problem-solving. Conferences on linked and connected data have increasingly focused on Graph RAGs, underscoring their central role in modern AI systems. However, the excitement around this technology has brought critical questions to the forefront: How do we ensure the quality of the graphs we’re building, and are they genuinely aligned with our needs? ## Data Quality: The Foundation of Effective Graphs A high-quality graph is the backbone of any successful RAG system. Constructing these graphs from unstructured data requires attention to detail and rigorous processes. Here’s why: * **Richness of Entities:** Effective retrieval depends on graphs populated with rich, detailed entities. * **Freedom from Hallucinations:** Poorly constructed graphs amplify inaccuracies rather than mitigating them. Without robust data quality, even the most sophisticated Graph RAGs become ineffective. As a result, the focus must shift to refining the graph construction process. Improving data strategy and ensuring meticulous data preparation is essential to unlock the full potential of Graph RAGs. ## Hybrid Graph RAGs and Variations While standard Graph RAGs are already transformative, hybrid models offer additional flexibility and power. Hybrid RAGs combine structured graph data with other retrieval mechanisms, creating systems that: * Handle diverse data sources with ease. * Offer improved adaptability to complex queries. Exploring these variations can open new avenues for AI systems, particularly in domains requiring structured and unstructured data processing. ## Ontology: The Key to Graph Construction Quality Ontology — defining how concepts relate within a knowledge domain — is critical for building effective graphs. While this might sound abstract, it’s a well-established field blending philosophy, engineering, and art. Ontology engineering provides the framework for: * **Defining Relationships:** Clarifying how concepts connect within a domain. * **Validating Graph Structures:** Ensuring constructed graphs are logically sound and align with domain-specific realities. Traditionally, ontologists — experts in this discipline — have been integral to large enterprises and research teams. However, not every team has access to dedicated ontologists, leading to a significant challenge: How can teams without such expertise ensure the quality of their graphs? ## How to Build Ontology Expertise in a Startup Team For startups and smaller teams, developing ontology expertise may seem daunting, but it is achievable with the right approach: 1. **Assign a Knowledge Champion:** Identify a team member with a strong analytical mindset and give them time and resources to learn ontology engineering. 2. **Provide Training:** Invest in courses, workshops, or certifications in knowledge graph and ontology creation. 3. **Leverage Partnerships:** Collaborate with academic institutions, domain experts, or consultants to build initial frameworks. 4. **Utilize Tools:** Introduce ontology development tools like Protégé, OWL, or SHACL to simplify the creation and validation process. 5. **Iterate with Feedback:** Continuously refine ontologies through collaboration with domain experts and iterative testing. So, it is not always affordable for a startup to have a dedicated oncologist or knowledge engineer in a team, but you could involve consulters or build barefoot experts. You could read about barefoot experts in my article : Even startups can achieve robust and domain-specific ontology frameworks by fostering in-house expertise. ## How to Find or Create Ontologies For teams venturing into Graph RAGs, several strategies can help address the ontology gap: 1. **Leverage Existing Ontologies:** Many industries and domains already have open ontologies. For instance: * **Public Knowledge Graphs:** Resources like Wikipedia’s graph offer a wealth of structured knowledge. * **Industry Standards:** Enterprises such as Siemens have invested in creating and sharing ontologies specific to their fields. * **Business Framework Ontology (BFO):** A valuable resource for enterprises looking to define business processes and structures. 1. **Build In-House Expertise:** If budgets allow, consider hiring knowledge engineers or providing team members with the resources and time to develop expertise in ontology creation. 2. **Utilize LLMs for Ontology Construction:** Interestingly, LLMs themselves can act as a starting point for ontology development: * **Prompt-Based Extraction:** LLMs can generate draft ontologies by leveraging their extensive training on graph data. * **Domain Expert Refinement:** Combine LLM-generated structures with insights from domain experts to create tailored ontologies. ## Parallel Ontology and Graph Extraction An emerging approach involves extracting ontologies and graphs in parallel. While this can streamline the process, it presents challenges such as: * **Detecting Hallucinations:** Differentiating between genuine insights and AI-generated inaccuracies. * **Ensuring Completeness:** Ensuring no critical concepts are overlooked during extraction. Teams must carefully validate outputs to ensure reliability and accuracy when employing this parallel method. ## LLMs as Ontologists While traditionally dependent on human expertise, ontology creation is increasingly supported by LLMs. These models, trained on vast amounts of data, possess inherent knowledge of many open ontologies and taxonomies. Teams can use LLMs to: * **Generate Skeleton Ontologies:** Prompt LLMs with domain-specific information to draft initial ontology structures. * **Validate and Refine Ontologies:** Collaborate with domain experts to refine these drafts, ensuring accuracy and relevance. However, for validation and graph construction, formal tools such as OWL, SHACL, and RDF should be prioritized over LLMs to minimize hallucinations and ensure robust outcomes. ## Final Thoughts: Unlocking the Power of Graph RAGs The rise of Graph RAGs underscores a simple but crucial correlation: improving graph construction and data quality directly enhances retrieval systems. To truly harness this power, teams must invest in understanding ontologies, building quality graphs, and leveraging both human expertise and advanced AI tools. As we move forward, the interplay between Graph RAGs and ontology engineering will continue to shape the future of AI. Whether through adopting existing frameworks or exploring innovative uses of LLMs, the path to success lies in a deep commitment to data quality and domain understanding. Have you explored these technologies in your work? Share your experiences and insights — and stay tuned for more discussions on ontology extraction and its role in AI advancements. Cheers to a year of innovation!

@ a4a6b584:1e05b95b

## The Four-Layer Framework ### Layer 1: Zoom Out  Start by looking at the big picture. What’s the subject about, and why does it matter? Focus on the overarching ideas and how they fit together. Think of this as the 30,000-foot view—it’s about understanding the "why" and "how" before diving into the "what." **Example**: If you’re learning programming, start by understanding that it’s about giving logical instructions to computers to solve problems. - **Tip**: Keep it simple. Summarize the subject in one or two sentences and avoid getting bogged down in specifics at this stage. _Once you have the big picture in mind, it’s time to start breaking it down._ --- ### Layer 2: Categorize and Connect  Now it’s time to break the subject into categories—like creating branches on a tree. This helps your brain organize information logically and see connections between ideas. **Example**: Studying biology? Group concepts into categories like cells, genetics, and ecosystems. - **Tip**: Use headings or labels to group similar ideas. Jot these down in a list or simple diagram to keep track. _With your categories in place, you’re ready to dive into the details that bring them to life._ --- ### Layer 3: Master the Details  Once you’ve mapped out the main categories, you’re ready to dive deeper. This is where you learn the nuts and bolts—like formulas, specific techniques, or key terminology. These details make the subject practical and actionable. **Example**: In programming, this might mean learning the syntax for loops, conditionals, or functions in your chosen language. - **Tip**: Focus on details that clarify the categories from Layer 2. Skip anything that doesn’t add to your understanding. _Now that you’ve mastered the essentials, you can expand your knowledge to include extra material._ --- ### Layer 4: Expand Your Horizons  Finally, move on to the extra material—less critical facts, trivia, or edge cases. While these aren’t essential to mastering the subject, they can be useful in specialized discussions or exams. **Example**: Learn about rare programming quirks or historical trivia about a language’s development. - **Tip**: Spend minimal time here unless it’s necessary for your goals. It’s okay to skim if you’re short on time. --- ## Pro Tips for Better Learning ### 1. Use Active Recall and Spaced Repetition Test yourself without looking at notes. Review what you’ve learned at increasing intervals—like after a day, a week, and a month. This strengthens memory by forcing your brain to actively retrieve information. ### 2. Map It Out Create visual aids like [diagrams or concept maps](https://excalidraw.com/) to clarify relationships between ideas. These are particularly helpful for organizing categories in Layer 2. ### 3. Teach What You Learn Explain the subject to someone else as if they’re hearing it for the first time. Teaching **exposes any gaps** in your understanding and **helps reinforce** the material. ### 4. Engage with LLMs and Discuss Concepts Take advantage of tools like ChatGPT or similar large language models to **explore your topic** in greater depth. Use these tools to: - Ask specific questions to clarify confusing points. - Engage in discussions to simulate real-world applications of the subject. - Generate examples or analogies that deepen your understanding. **Tip**: Use LLMs as a study partner, but don’t rely solely on them. Combine these insights with your own critical thinking to develop a well-rounded perspective. --- ## Get Started Ready to try the Four-Layer Method? Take 15 minutes today to map out the big picture of a topic you’re curious about—what’s it all about, and why does it matter? By building your understanding step by step, you’ll master the subject with less stress and more confidence.

@ fe32298e:20516265

Today I learned how to install [NVapi](https://github.com/sammcj/NVApi) to monitor my GPUs in Home Assistant.  **NVApi** is a lightweight API designed for monitoring NVIDIA GPU utilization and enabling automated power management. It provides real-time GPU metrics, supports integration with tools like Home Assistant, and offers flexible power management and PCIe link speed management based on workload and thermal conditions. - **GPU Utilization Monitoring**: Utilization, memory usage, temperature, fan speed, and power consumption. - **Automated Power Limiting**: Adjusts power limits dynamically based on temperature thresholds and total power caps, configurable per GPU or globally. - **Cross-GPU Coordination**: Total power budget applies across multiple GPUs in the same system. - **PCIe Link Speed Management**: Controls minimum and maximum PCIe link speeds with idle thresholds for power optimization. - **Home Assistant Integration**: Uses the built-in RESTful platform and template sensors. ## Getting the Data ``` sudo apt install golang-go git clone https://github.com/sammcj/NVApi.git cd NVapi go run main.go -port 9999 -rate 1 curl http://localhost:9999/gpu ``` Response for a single GPU: ``` [ { "index": 0, "name": "NVIDIA GeForce RTX 4090", "gpu_utilisation": 0, "memory_utilisation": 0, "power_watts": 16, "power_limit_watts": 450, "memory_total_gb": 23.99, "memory_used_gb": 0.46, "memory_free_gb": 23.52, "memory_usage_percent": 2, "temperature": 38, "processes": [], "pcie_link_state": "not managed" } ] ``` Response for multiple GPUs: ``` [ { "index": 0, "name": "NVIDIA GeForce RTX 3090", "gpu_utilisation": 0, "memory_utilisation": 0, "power_watts": 14, "power_limit_watts": 350, "memory_total_gb": 24, "memory_used_gb": 0.43, "memory_free_gb": 23.57, "memory_usage_percent": 2, "temperature": 36, "processes": [], "pcie_link_state": "not managed" }, { "index": 1, "name": "NVIDIA RTX A4000", "gpu_utilisation": 0, "memory_utilisation": 0, "power_watts": 10, "power_limit_watts": 140, "memory_total_gb": 15.99, "memory_used_gb": 0.56, "memory_free_gb": 15.43, "memory_usage_percent": 3, "temperature": 41, "processes": [], "pcie_link_state": "not managed" } ] ``` # Start at Boot Create `/etc/systemd/system/nvapi.service`: ``` [Unit] Description=Run NVapi After=network.target [Service] Type=simple Environment="GOPATH=/home/ansible/go" WorkingDirectory=/home/ansible/NVapi ExecStart=/usr/bin/go run main.go -port 9999 -rate 1 Restart=always User=ansible # Environment="GPU_TEMP_CHECK_INTERVAL=5" # Environment="GPU_TOTAL_POWER_CAP=400" # Environment="GPU_0_LOW_TEMP=40" # Environment="GPU_0_MEDIUM_TEMP=70" # Environment="GPU_0_LOW_TEMP_LIMIT=135" # Environment="GPU_0_MEDIUM_TEMP_LIMIT=120" # Environment="GPU_0_HIGH_TEMP_LIMIT=100" # Environment="GPU_1_LOW_TEMP=45" # Environment="GPU_1_MEDIUM_TEMP=75" # Environment="GPU_1_LOW_TEMP_LIMIT=140" # Environment="GPU_1_MEDIUM_TEMP_LIMIT=125" # Environment="GPU_1_HIGH_TEMP_LIMIT=110" [Install] WantedBy=multi-user.target ``` ## Home Assistant Add to Home Assistant `configuration.yaml` and restart HA (completely). For a single GPU, this works: ``` sensor: - platform: rest name: MYPC GPU Information resource: http://mypc:9999 method: GET headers: Content-Type: application/json value_template: "{{ value_json[0].index }}" json_attributes: - name - gpu_utilisation - memory_utilisation - power_watts - power_limit_watts - memory_total_gb - memory_used_gb - memory_free_gb - memory_usage_percent - temperature scan_interval: 1 # seconds - platform: template sensors: mypc_gpu_0_gpu: friendly_name: "MYPC {{ state_attr('sensor.mypc_gpu_information', 'name') }} GPU" value_template: "{{ state_attr('sensor.mypc_gpu_information', 'gpu_utilisation') }}" unit_of_measurement: "%" mypc_gpu_0_memory: friendly_name: "MYPC {{ state_attr('sensor.mypc_gpu_information', 'name') }} Memory" value_template: "{{ state_attr('sensor.mypc_gpu_information', 'memory_utilisation') }}" unit_of_measurement: "%" mypc_gpu_0_power: friendly_name: "MYPC {{ state_attr('sensor.mypc_gpu_information', 'name') }} Power" value_template: "{{ state_attr('sensor.mypc_gpu_information', 'power_watts') }}" unit_of_measurement: "W" mypc_gpu_0_power_limit: friendly_name: "MYPC {{ state_attr('sensor.mypc_gpu_information', 'name') }} Power Limit" value_template: "{{ state_attr('sensor.mypc_gpu_information', 'power_limit_watts') }}" unit_of_measurement: "W" mypc_gpu_0_temperature: friendly_name: "MYPC {{ state_attr('sensor.mypc_gpu_information', 'name') }} Temperature" value_template: "{{ state_attr('sensor.mypc_gpu_information', 'temperature') }}" unit_of_measurement: "°C" ``` For multiple GPUs: ``` rest: scan_interval: 1 resource: http://mypc:9999 sensor: - name: "MYPC GPU0 Information" value_template: "{{ value_json[0].index }}" json_attributes_path: "$.0" json_attributes: - name - gpu_utilisation - memory_utilisation - power_watts - power_limit_watts - memory_total_gb - memory_used_gb - memory_free_gb - memory_usage_percent - temperature - name: "MYPC GPU1 Information" value_template: "{{ value_json[1].index }}" json_attributes_path: "$.1" json_attributes: - name - gpu_utilisation - memory_utilisation - power_watts - power_limit_watts - memory_total_gb - memory_used_gb - memory_free_gb - memory_usage_percent - temperature - platform: template sensors: mypc_gpu_0_gpu: friendly_name: "MYPC GPU0 GPU" value_template: "{{ state_attr('sensor.mypc_gpu0_information', 'gpu_utilisation') }}" unit_of_measurement: "%" mypc_gpu_0_memory: friendly_name: "MYPC GPU0 Memory" value_template: "{{ state_attr('sensor.mypc_gpu0_information', 'memory_utilisation') }}" unit_of_measurement: "%" mypc_gpu_0_power: friendly_name: "MYPC GPU0 Power" value_template: "{{ state_attr('sensor.mypc_gpu0_information', 'power_watts') }}" unit_of_measurement: "W" mypc_gpu_0_power_limit: friendly_name: "MYPC GPU0 Power Limit" value_template: "{{ state_attr('sensor.mypc_gpu0_information', 'power_limit_watts') }}" unit_of_measurement: "W" mypc_gpu_0_temperature: friendly_name: "MYPC GPU0 Temperature" value_template: "{{ state_attr('sensor.mypc_gpu0_information', 'temperature') }}" unit_of_measurement: "C" - platform: template sensors: mypc_gpu_1_gpu: friendly_name: "MYPC GPU1 GPU" value_template: "{{ state_attr('sensor.mypc_gpu1_information', 'gpu_utilisation') }}" unit_of_measurement: "%" mypc_gpu_1_memory: friendly_name: "MYPC GPU1 Memory" value_template: "{{ state_attr('sensor.mypc_gpu1_information', 'memory_utilisation') }}" unit_of_measurement: "%" mypc_gpu_1_power: friendly_name: "MYPC GPU1 Power" value_template: "{{ state_attr('sensor.mypc_gpu1_information', 'power_watts') }}" unit_of_measurement: "W" mypc_gpu_1_power_limit: friendly_name: "MYPC GPU1 Power Limit" value_template: "{{ state_attr('sensor.mypc_gpu1_information', 'power_limit_watts') }}" unit_of_measurement: "W" mypc_gpu_1_temperature: friendly_name: "MYPC GPU1 Temperature" value_template: "{{ state_attr('sensor.mypc_gpu1_information', 'temperature') }}" unit_of_measurement: "C" ``` Basic entity card: ``` type: entities entities: - entity: sensor.mypc_gpu_0_gpu secondary_info: last-updated - entity: sensor.mypc_gpu_0_memory secondary_info: last-updated - entity: sensor.mypc_gpu_0_power secondary_info: last-updated - entity: sensor.mypc_gpu_0_power_limit secondary_info: last-updated - entity: sensor.mypc_gpu_0_temperature secondary_info: last-updated ``` # Ansible Role ``` --- - name: install go become: true package: name: golang-go state: present - name: git clone git: repo: "https://github.com/sammcj/NVApi.git" dest: "/home/ansible/NVapi" update: yes force: true # go run main.go -port 9999 -rate 1 - name: install systemd service become: true copy: src: nvapi.service dest: /etc/systemd/system/nvapi.service - name: Reload systemd daemons, enable, and restart nvapi become: true systemd: name: nvapi daemon_reload: yes enabled: yes state: restarted ```

@ 6f6b50bb:a848e5a1

Che cosa significherebbe trattare l'IA come uno strumento invece che come una persona? Dall’avvio di ChatGPT, le esplorazioni in due direzioni hanno preso velocità. La prima direzione riguarda le capacità tecniche. Quanto grande possiamo addestrare un modello? Quanto bene può rispondere alle domande del SAT? Con quanta efficienza possiamo distribuirlo? La seconda direzione riguarda il design dell’interazione. Come comunichiamo con un modello? Come possiamo usarlo per un lavoro utile? Quale metafora usiamo per ragionare su di esso? La prima direzione è ampiamente seguita e enormemente finanziata, e per una buona ragione: i progressi nelle capacità tecniche sono alla base di ogni possibile applicazione. Ma la seconda è altrettanto cruciale per il campo e ha enormi incognite. Siamo solo a pochi anni dall’inizio dell’era dei grandi modelli. Quali sono le probabilità che abbiamo già capito i modi migliori per usarli? Propongo una nuova modalità di interazione, in cui i modelli svolgano il ruolo di applicazioni informatiche (ad esempio app per telefoni): fornendo un’interfaccia grafica, interpretando gli input degli utenti e aggiornando il loro stato. In questa modalità, invece di essere un “agente” che utilizza un computer per conto dell’essere umano, l’IA può fornire un ambiente informatico più ricco e potente che possiamo utilizzare. ### Metafore per l’interazione Al centro di un’interazione c’è una metafora che guida le aspettative di un utente su un sistema. I primi giorni dell’informatica hanno preso metafore come “scrivanie”, “macchine da scrivere”, “fogli di calcolo” e “lettere” e le hanno trasformate in equivalenti digitali, permettendo all’utente di ragionare sul loro comportamento. Puoi lasciare qualcosa sulla tua scrivania e tornare a prenderlo; hai bisogno di un indirizzo per inviare una lettera. Man mano che abbiamo sviluppato una conoscenza culturale di questi dispositivi, la necessità di queste particolari metafore è scomparsa, e con esse i design di interfaccia skeumorfici che le rafforzavano. Come un cestino o una matita, un computer è ora una metafora di se stesso. La metafora dominante per i grandi modelli oggi è modello-come-persona. Questa è una metafora efficace perché le persone hanno capacità estese che conosciamo intuitivamente. Implica che possiamo avere una conversazione con un modello e porgli domande; che il modello possa collaborare con noi su un documento o un pezzo di codice; che possiamo assegnargli un compito da svolgere da solo e che tornerà quando sarà finito. Tuttavia, trattare un modello come una persona limita profondamente il nostro modo di pensare all’interazione con esso. Le interazioni umane sono intrinsecamente lente e lineari, limitate dalla larghezza di banda e dalla natura a turni della comunicazione verbale. Come abbiamo tutti sperimentato, comunicare idee complesse in una conversazione è difficile e dispersivo. Quando vogliamo precisione, ci rivolgiamo invece a strumenti, utilizzando manipolazioni dirette e interfacce visive ad alta larghezza di banda per creare diagrammi, scrivere codice e progettare modelli CAD. Poiché concepiamo i modelli come persone, li utilizziamo attraverso conversazioni lente, anche se sono perfettamente in grado di accettare input diretti e rapidi e di produrre risultati visivi. Le metafore che utilizziamo limitano le esperienze che costruiamo, e la metafora modello-come-persona ci impedisce di esplorare il pieno potenziale dei grandi modelli. Per molti casi d’uso, e specialmente per il lavoro produttivo, credo che il futuro risieda in un’altra metafora: modello-come-computer. ### Usare un’IA come un computer Sotto la metafora modello-come-computer, interagiremo con i grandi modelli seguendo le intuizioni che abbiamo sulle applicazioni informatiche (sia su desktop, tablet o telefono). Nota che ciò non significa che il modello sarà un’app tradizionale più di quanto il desktop di Windows fosse una scrivania letterale. “Applicazione informatica” sarà un modo per un modello di rappresentarsi a noi. Invece di agire come una persona, il modello agirà come un computer. Agire come un computer significa produrre un’interfaccia grafica. Al posto del flusso lineare di testo in stile telescrivente fornito da ChatGPT, un sistema modello-come-computer genererà qualcosa che somiglia all’interfaccia di un’applicazione moderna: pulsanti, cursori, schede, immagini, grafici e tutto il resto. Questo affronta limitazioni chiave dell’interfaccia di chat standard modello-come-persona: - **Scoperta.** Un buon strumento suggerisce i suoi usi. Quando l’unica interfaccia è una casella di testo vuota, spetta all’utente capire cosa fare e comprendere i limiti del sistema. La barra laterale Modifica in Lightroom è un ottimo modo per imparare l’editing fotografico perché non si limita a dirti cosa può fare questa applicazione con una foto, ma cosa potresti voler fare. Allo stesso modo, un’interfaccia modello-come-computer per DALL-E potrebbe mostrare nuove possibilità per le tue generazioni di immagini. - **Efficienza.** La manipolazione diretta è più rapida che scrivere una richiesta a parole. Per continuare l’esempio di Lightroom, sarebbe impensabile modificare una foto dicendo a una persona quali cursori spostare e di quanto. Ci vorrebbe un giorno intero per chiedere un’esposizione leggermente più bassa e una vibranza leggermente più alta, solo per vedere come apparirebbe. Nella metafora modello-come-computer, il modello può creare strumenti che ti permettono di comunicare ciò che vuoi più efficientemente e quindi di fare le cose più rapidamente. A differenza di un’app tradizionale, questa interfaccia grafica è generata dal modello su richiesta. Questo significa che ogni parte dell’interfaccia che vedi è rilevante per ciò che stai facendo in quel momento, inclusi i contenuti specifici del tuo lavoro. Significa anche che, se desideri un’interfaccia più ampia o diversa, puoi semplicemente richiederla. Potresti chiedere a DALL-E di produrre alcuni preset modificabili per le sue impostazioni ispirati da famosi artisti di schizzi. Quando clicchi sul preset Leonardo da Vinci, imposta i cursori per disegni prospettici altamente dettagliati in inchiostro nero. Se clicchi su Charles Schulz, seleziona fumetti tecnicolor 2D a basso dettaglio. ### Una bicicletta della mente proteiforme La metafora modello-come-persona ha una curiosa tendenza a creare distanza tra l’utente e il modello, rispecchiando il divario di comunicazione tra due persone che può essere ridotto ma mai completamente colmato. A causa della difficoltà e del costo di comunicare a parole, le persone tendono a suddividere i compiti tra loro in blocchi grandi e il più indipendenti possibile. Le interfacce modello-come-persona seguono questo schema: non vale la pena dire a un modello di aggiungere un return statement alla tua funzione quando è più veloce scriverlo da solo. Con il sovraccarico della comunicazione, i sistemi modello-come-persona sono più utili quando possono fare un intero blocco di lavoro da soli. Fanno le cose per te. Questo contrasta con il modo in cui interagiamo con i computer o altri strumenti. Gli strumenti producono feedback visivi in tempo reale e sono controllati attraverso manipolazioni dirette. Hanno un overhead comunicativo così basso che non è necessario specificare un blocco di lavoro indipendente. Ha più senso mantenere l’umano nel loop e dirigere lo strumento momento per momento. Come stivali delle sette leghe, gli strumenti ti permettono di andare più lontano a ogni passo, ma sei ancora tu a fare il lavoro. Ti permettono di fare le cose più velocemente. Considera il compito di costruire un sito web usando un grande modello. Con le interfacce di oggi, potresti trattare il modello come un appaltatore o un collaboratore. Cercheresti di scrivere a parole il più possibile su come vuoi che il sito appaia, cosa vuoi che dica e quali funzionalità vuoi che abbia. Il modello genererebbe una prima bozza, tu la eseguirai e poi fornirai un feedback. “Fai il logo un po’ più grande”, diresti, e “centra quella prima immagine principale”, e “deve esserci un pulsante di login nell’intestazione”. Per ottenere esattamente ciò che vuoi, invierai una lista molto lunga di richieste sempre più minuziose. Un’interazione alternativa modello-come-computer sarebbe diversa: invece di costruire il sito web, il modello genererebbe un’interfaccia per te per costruirlo, dove ogni input dell’utente a quell’interfaccia interroga il grande modello sotto il cofano. Forse quando descrivi le tue necessità creerebbe un’interfaccia con una barra laterale e una finestra di anteprima. All’inizio la barra laterale contiene solo alcuni schizzi di layout che puoi scegliere come punto di partenza. Puoi cliccare su ciascuno di essi, e il modello scrive l’HTML per una pagina web usando quel layout e lo visualizza nella finestra di anteprima. Ora che hai una pagina su cui lavorare, la barra laterale guadagna opzioni aggiuntive che influenzano la pagina globalmente, come accoppiamenti di font e schemi di colore. L’anteprima funge da editor WYSIWYG, permettendoti di afferrare elementi e spostarli, modificarne i contenuti, ecc. A supportare tutto ciò è il modello, che vede queste azioni dell’utente e riscrive la pagina per corrispondere ai cambiamenti effettuati. Poiché il modello può generare un’interfaccia per aiutare te e lui a comunicare più efficientemente, puoi esercitare più controllo sul prodotto finale in meno tempo. La metafora modello-come-computer ci incoraggia a pensare al modello come a uno strumento con cui interagire in tempo reale piuttosto che a un collaboratore a cui assegnare compiti. Invece di sostituire un tirocinante o un tutor, può essere una sorta di bicicletta proteiforme per la mente, una che è sempre costruita su misura esattamente per te e il terreno che intendi attraversare. ### Un nuovo paradigma per l’informatica? I modelli che possono generare interfacce su richiesta sono una frontiera completamente nuova nell’informatica. Potrebbero essere un paradigma del tutto nuovo, con il modo in cui cortocircuitano il modello di applicazione esistente. Dare agli utenti finali il potere di creare e modificare app al volo cambia fondamentalmente il modo in cui interagiamo con i computer. Al posto di una singola applicazione statica costruita da uno sviluppatore, un modello genererà un’applicazione su misura per l’utente e le sue esigenze immediate. Al posto della logica aziendale implementata nel codice, il modello interpreterà gli input dell’utente e aggiornerà l’interfaccia utente. È persino possibile che questo tipo di interfaccia generativa sostituisca completamente il sistema operativo, generando e gestendo interfacce e finestre al volo secondo necessità. All’inizio, l’interfaccia generativa sarà un giocattolo, utile solo per l’esplorazione creativa e poche altre applicazioni di nicchia. Dopotutto, nessuno vorrebbe un’app di posta elettronica che occasionalmente invia email al tuo ex e mente sulla tua casella di posta. Ma gradualmente i modelli miglioreranno. Anche mentre si spingeranno ulteriormente nello spazio di esperienze completamente nuove, diventeranno lentamente abbastanza affidabili da essere utilizzati per un lavoro reale. Piccoli pezzi di questo futuro esistono già. Anni fa Jonas Degrave ha dimostrato che ChatGPT poteva fare una buona simulazione di una riga di comando Linux. Allo stesso modo, websim.ai utilizza un LLM per generare siti web su richiesta mentre li navighi. Oasis, GameNGen e DIAMOND addestrano modelli video condizionati sull’azione su singoli videogiochi, permettendoti di giocare ad esempio a Doom dentro un grande modello. E Genie 2 genera videogiochi giocabili da prompt testuali. L’interfaccia generativa potrebbe ancora sembrare un’idea folle, ma non è così folle. Ci sono enormi domande aperte su come apparirà tutto questo. Dove sarà inizialmente utile l’interfaccia generativa? Come condivideremo e distribuiremo le esperienze che creiamo collaborando con il modello, se esistono solo come contesto di un grande modello? Vorremmo davvero farlo? Quali nuovi tipi di esperienze saranno possibili? Come funzionerà tutto questo in pratica? I modelli genereranno interfacce come codice o produrranno direttamente pixel grezzi? Non conosco ancora queste risposte. Dovremo sperimentare e scoprirlo!Che cosa significherebbe trattare l'IA come uno strumento invece che come una persona? Dall’avvio di ChatGPT, le esplorazioni in due direzioni hanno preso velocità. La prima direzione riguarda le capacità tecniche. Quanto grande possiamo addestrare un modello? Quanto bene può rispondere alle domande del SAT? Con quanta efficienza possiamo distribuirlo? La seconda direzione riguarda il design dell’interazione. Come comunichiamo con un modello? Come possiamo usarlo per un lavoro utile? Quale metafora usiamo per ragionare su di esso? La prima direzione è ampiamente seguita e enormemente finanziata, e per una buona ragione: i progressi nelle capacità tecniche sono alla base di ogni possibile applicazione. Ma la seconda è altrettanto cruciale per il campo e ha enormi incognite. Siamo solo a pochi anni dall’inizio dell’era dei grandi modelli. Quali sono le probabilità che abbiamo già capito i modi migliori per usarli? Propongo una nuova modalità di interazione, in cui i modelli svolgano il ruolo di applicazioni informatiche (ad esempio app per telefoni): fornendo un’interfaccia grafica, interpretando gli input degli utenti e aggiornando il loro stato. In questa modalità, invece di essere un “agente” che utilizza un computer per conto dell’essere umano, l’IA può fornire un ambiente informatico più ricco e potente che possiamo utilizzare. ### Metafore per l’interazione Al centro di un’interazione c’è una metafora che guida le aspettative di un utente su un sistema. I primi giorni dell’informatica hanno preso metafore come “scrivanie”, “macchine da scrivere”, “fogli di calcolo” e “lettere” e le hanno trasformate in equivalenti digitali, permettendo all’utente di ragionare sul loro comportamento. Puoi lasciare qualcosa sulla tua scrivania e tornare a prenderlo; hai bisogno di un indirizzo per inviare una lettera. Man mano che abbiamo sviluppato una conoscenza culturale di questi dispositivi, la necessità di queste particolari metafore è scomparsa, e con esse i design di interfaccia skeumorfici che le rafforzavano. Come un cestino o una matita, un computer è ora una metafora di se stesso. La metafora dominante per i grandi modelli oggi è modello-come-persona. Questa è una metafora efficace perché le persone hanno capacità estese che conosciamo intuitivamente. Implica che possiamo avere una conversazione con un modello e porgli domande; che il modello possa collaborare con noi su un documento o un pezzo di codice; che possiamo assegnargli un compito da svolgere da solo e che tornerà quando sarà finito. Tuttavia, trattare un modello come una persona limita profondamente il nostro modo di pensare all’interazione con esso. Le interazioni umane sono intrinsecamente lente e lineari, limitate dalla larghezza di banda e dalla natura a turni della comunicazione verbale. Come abbiamo tutti sperimentato, comunicare idee complesse in una conversazione è difficile e dispersivo. Quando vogliamo precisione, ci rivolgiamo invece a strumenti, utilizzando manipolazioni dirette e interfacce visive ad alta larghezza di banda per creare diagrammi, scrivere codice e progettare modelli CAD. Poiché concepiamo i modelli come persone, li utilizziamo attraverso conversazioni lente, anche se sono perfettamente in grado di accettare input diretti e rapidi e di produrre risultati visivi. Le metafore che utilizziamo limitano le esperienze che costruiamo, e la metafora modello-come-persona ci impedisce di esplorare il pieno potenziale dei grandi modelli. Per molti casi d’uso, e specialmente per il lavoro produttivo, credo che il futuro risieda in un’altra metafora: modello-come-computer. ### Usare un’IA come un computer Sotto la metafora modello-come-computer, interagiremo con i grandi modelli seguendo le intuizioni che abbiamo sulle applicazioni informatiche (sia su desktop, tablet o telefono). Nota che ciò non significa che il modello sarà un’app tradizionale più di quanto il desktop di Windows fosse una scrivania letterale. “Applicazione informatica” sarà un modo per un modello di rappresentarsi a noi. Invece di agire come una persona, il modello agirà come un computer. Agire come un computer significa produrre un’interfaccia grafica. Al posto del flusso lineare di testo in stile telescrivente fornito da ChatGPT, un sistema modello-come-computer genererà qualcosa che somiglia all’interfaccia di un’applicazione moderna: pulsanti, cursori, schede, immagini, grafici e tutto il resto. Questo affronta limitazioni chiave dell’interfaccia di chat standard modello-come-persona: Scoperta. Un buon strumento suggerisce i suoi usi. Quando l’unica interfaccia è una casella di testo vuota, spetta all’utente capire cosa fare e comprendere i limiti del sistema. La barra laterale Modifica in Lightroom è un ottimo modo per imparare l’editing fotografico perché non si limita a dirti cosa può fare questa applicazione con una foto, ma cosa potresti voler fare. Allo stesso modo, un’interfaccia modello-come-computer per DALL-E potrebbe mostrare nuove possibilità per le tue generazioni di immagini. Efficienza. La manipolazione diretta è più rapida che scrivere una richiesta a parole. Per continuare l’esempio di Lightroom, sarebbe impensabile modificare una foto dicendo a una persona quali cursori spostare e di quanto. Ci vorrebbe un giorno intero per chiedere un’esposizione leggermente più bassa e una vibranza leggermente più alta, solo per vedere come apparirebbe. Nella metafora modello-come-computer, il modello può creare strumenti che ti permettono di comunicare ciò che vuoi più efficientemente e quindi di fare le cose più rapidamente. A differenza di un’app tradizionale, questa interfaccia grafica è generata dal modello su richiesta. Questo significa che ogni parte dell’interfaccia che vedi è rilevante per ciò che stai facendo in quel momento, inclusi i contenuti specifici del tuo lavoro. Significa anche che, se desideri un’interfaccia più ampia o diversa, puoi semplicemente richiederla. Potresti chiedere a DALL-E di produrre alcuni preset modificabili per le sue impostazioni ispirati da famosi artisti di schizzi. Quando clicchi sul preset Leonardo da Vinci, imposta i cursori per disegni prospettici altamente dettagliati in inchiostro nero. Se clicchi su Charles Schulz, seleziona fumetti tecnicolor 2D a basso dettaglio. ### Una bicicletta della mente proteiforme La metafora modello-come-persona ha una curiosa tendenza a creare distanza tra l’utente e il modello, rispecchiando il divario di comunicazione tra due persone che può essere ridotto ma mai completamente colmato. A causa della difficoltà e del costo di comunicare a parole, le persone tendono a suddividere i compiti tra loro in blocchi grandi e il più indipendenti possibile. Le interfacce modello-come-persona seguono questo schema: non vale la pena dire a un modello di aggiungere un return statement alla tua funzione quando è più veloce scriverlo da solo. Con il sovraccarico della comunicazione, i sistemi modello-come-persona sono più utili quando possono fare un intero blocco di lavoro da soli. Fanno le cose per te. Questo contrasta con il modo in cui interagiamo con i computer o altri strumenti. Gli strumenti producono feedback visivi in tempo reale e sono controllati attraverso manipolazioni dirette. Hanno un overhead comunicativo così basso che non è necessario specificare un blocco di lavoro indipendente. Ha più senso mantenere l’umano nel loop e dirigere lo strumento momento per momento. Come stivali delle sette leghe, gli strumenti ti permettono di andare più lontano a ogni passo, ma sei ancora tu a fare il lavoro. Ti permettono di fare le cose più velocemente. Considera il compito di costruire un sito web usando un grande modello. Con le interfacce di oggi, potresti trattare il modello come un appaltatore o un collaboratore. Cercheresti di scrivere a parole il più possibile su come vuoi che il sito appaia, cosa vuoi che dica e quali funzionalità vuoi che abbia. Il modello genererebbe una prima bozza, tu la eseguirai e poi fornirai un feedback. “Fai il logo un po’ più grande”, diresti, e “centra quella prima immagine principale”, e “deve esserci un pulsante di login nell’intestazione”. Per ottenere esattamente ciò che vuoi, invierai una lista molto lunga di richieste sempre più minuziose. Un’interazione alternativa modello-come-computer sarebbe diversa: invece di costruire il sito web, il modello genererebbe un’interfaccia per te per costruirlo, dove ogni input dell’utente a quell’interfaccia interroga il grande modello sotto il cofano. Forse quando descrivi le tue necessità creerebbe un’interfaccia con una barra laterale e una finestra di anteprima. All’inizio la barra laterale contiene solo alcuni schizzi di layout che puoi scegliere come punto di partenza. Puoi cliccare su ciascuno di essi, e il modello scrive l’HTML per una pagina web usando quel layout e lo visualizza nella finestra di anteprima. Ora che hai una pagina su cui lavorare, la barra laterale guadagna opzioni aggiuntive che influenzano la pagina globalmente, come accoppiamenti di font e schemi di colore. L’anteprima funge da editor WYSIWYG, permettendoti di afferrare elementi e spostarli, modificarne i contenuti, ecc. A supportare tutto ciò è il modello, che vede queste azioni dell’utente e riscrive la pagina per corrispondere ai cambiamenti effettuati. Poiché il modello può generare un’interfaccia per aiutare te e lui a comunicare più efficientemente, puoi esercitare più controllo sul prodotto finale in meno tempo. La metafora modello-come-computer ci incoraggia a pensare al modello come a uno strumento con cui interagire in tempo reale piuttosto che a un collaboratore a cui assegnare compiti. Invece di sostituire un tirocinante o un tutor, può essere una sorta di bicicletta proteiforme per la mente, una che è sempre costruita su misura esattamente per te e il terreno che intendi attraversare. ### Un nuovo paradigma per l’informatica? I modelli che possono generare interfacce su richiesta sono una frontiera completamente nuova nell’informatica. Potrebbero essere un paradigma del tutto nuovo, con il modo in cui cortocircuitano il modello di applicazione esistente. Dare agli utenti finali il potere di creare e modificare app al volo cambia fondamentalmente il modo in cui interagiamo con i computer. Al posto di una singola applicazione statica costruita da uno sviluppatore, un modello genererà un’applicazione su misura per l’utente e le sue esigenze immediate. Al posto della logica aziendale implementata nel codice, il modello interpreterà gli input dell’utente e aggiornerà l’interfaccia utente. È persino possibile che questo tipo di interfaccia generativa sostituisca completamente il sistema operativo, generando e gestendo interfacce e finestre al volo secondo necessità. All’inizio, l’interfaccia generativa sarà un giocattolo, utile solo per l’esplorazione creativa e poche altre applicazioni di nicchia. Dopotutto, nessuno vorrebbe un’app di posta elettronica che occasionalmente invia email al tuo ex e mente sulla tua casella di posta. Ma gradualmente i modelli miglioreranno. Anche mentre si spingeranno ulteriormente nello spazio di esperienze completamente nuove, diventeranno lentamente abbastanza affidabili da essere utilizzati per un lavoro reale. Piccoli pezzi di questo futuro esistono già. Anni fa Jonas Degrave ha dimostrato che ChatGPT poteva fare una buona simulazione di una riga di comando Linux. Allo stesso modo, websim.ai utilizza un LLM per generare siti web su richiesta mentre li navighi. Oasis, GameNGen e DIAMOND addestrano modelli video condizionati sull’azione su singoli videogiochi, permettendoti di giocare ad esempio a Doom dentro un grande modello. E Genie 2 genera videogiochi giocabili da prompt testuali. L’interfaccia generativa potrebbe ancora sembrare un’idea folle, ma non è così folle. Ci sono enormi domande aperte su come apparirà tutto questo. Dove sarà inizialmente utile l’interfaccia generativa? Come condivideremo e distribuiremo le esperienze che creiamo collaborando con il modello, se esistono solo come contesto di un grande modello? Vorremmo davvero farlo? Quali nuovi tipi di esperienze saranno possibili? Come funzionerà tutto questo in pratica? I modelli genereranno interfacce come codice o produrranno direttamente pixel grezzi? Non conosco ancora queste risposte. Dovremo sperimentare e scoprirlo! Tradotto da:\ https://willwhitney.com/computing-inside-ai.htmlhttps://willwhitney.com/computing-inside-ai.html

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I started a long series of articles about how to model different types of knowledge graphs in the relational model, which makes on-device memory models for AI agents possible. We model-directed graphs Also, graphs of entities We even model hypergraphs Last time, we discussed why classical triple and simple knowledge graphs are insufficient for AI agents and complex memory, especially in the domain of time-aware or multi-model knowledge. So why do we need metagraphs, and what kind of challenge could they help us to solve? - complex and nested event and temporal context and temporal relations as edges - multi-mode and multilingual knowledge - human-like memory for AI agents that has multiple contexts and relations between knowledge in neuron-like networks ## MetaGraphs A meta graph is a concept that extends the idea of a graph by allowing edges to become graphs. Meta Edges connect a set of nodes, which could also be subgraphs. So, at some level, node and edge are pretty similar in properties but act in different roles in a different context. Also, in some cases, edges could be referenced as nodes. This approach enables the representation of more complex relationships and hierarchies than a traditional graph structure allows. Let’s break down each term to understand better metagraphs and how they differ from hypergraphs and graphs. ## Graph Basics - A standard **graph** has a set of **nodes** (or vertices) and **edges** (connections between nodes). - Edges are generally simple and typically represent a binary relationship between two nodes. - For instance, an edge in a social network graph might indicate a “friend” relationship between two people (nodes). ## Hypergraph - A **hypergraph** extends the concept of an edge by allowing it to connect any number of nodes, not just two. - Each connection, called a **hyperedge**, can link multiple nodes. - This feature allows hypergraphs to model more complex relationships involving multiple entities simultaneously. For example, a hyperedge in a hypergraph could represent a project team, connecting all team members in a single relation. - Despite its flexibility, a hypergraph doesn’t capture hierarchical or nested structures; it only generalizes the number of connections in an edge. ## Metagraph - A **metagraph** allows the edges to be graphs themselves. This means each edge can contain its own nodes and edges, creating nested, hierarchical structures. - In a meta graph, an edge could represent a relationship defined by a graph. For instance, a meta graph could represent a network of organizations where each organization’s structure (departments and connections) is represented by its own internal graph and treated as an edge in the larger meta graph. - This recursive structure allows metagraphs to model complex data with multiple layers of abstraction. They can capture multi-node relationships (as in hypergraphs) and detailed, structured information about each relationship. ## Named Graphs and Graph of Graphs As you can notice, the structure of a metagraph is quite complex and could be complex to model in relational and classical RDF setups. It could create a challenge of luck of tools and software solutions for your problem. If you need to model nested graphs, you could use a much simpler model of Named graphs, which could take you quite far.  The concept of the named graph came from the RDF community, which needed to group some sets of triples. In this way, you form subgraphs inside an existing graph. You could refer to the subgraph as a regular node. This setup simplifies complex graphs, introduces hierarchies, and even adds features and properties of hypergraphs while keeping a directed nature. It looks complex, but it is not so hard to model it with a slight modification of a directed graph. So, the node could host graphs inside. Let's reflect this fact with a location for a node. If a node belongs to a main graph, we could set the location to null or introduce a main node . it is up to you  Nodes could have edges to nodes in different subgraphs. This structure allows any kind of nesting graphs. Edges stay location-free ## Meta Graphs in Relational Model Let’s try to make several attempts to model different meta-graphs with some constraints. ## Directed Metagraph where edges are not used as nodes and could not contain subgraphs  In this case, the edge always points to two sets of nodes. This introduces an overhead of creating a node set for a single node. In this model, we can model empty node sets that could require application-level constraints to prevent such cases. ## Directed Metagraph where edges are not used as nodes and could contain subgraphs  Adding a node set that could model a subgraph located in an edge is easy but could be separate from in-vertex or out-vert. I also do not see a direct need to include subgraphs to a node, as we could just use a node set interchangeably, but it still could be a case. ## Directed Metagraph where edges are used as nodes and could contain subgraphs As you can notice, we operate all the time with node sets. We could simply allow the extension node set to elements set that include node and edge IDs, but in this case, we need to use uuid or any other strategy to differentiate node IDs from edge IDs. In this case, we have a collision of ephemeral edges or ephemeral nodes when we want to change the role and purpose of the node as an edge or vice versa.  A full-scale metagraph model is way too complex for a relational database. So we need a better model. Now, we have more flexibility but loose structural constraints. We cannot show that the element should have one vertex, one vertex, or both. This type of constraint has been moved to the application level. Also, the crucial question is about query and retrieval needs. Any meta-graph model should be more focused on domain and needs and should be used in raw form. We did it for a pure theoretical purpose.

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Hey folks! Today, let’s dive into the intriguing world of neurosymbolic approaches, retrieval-augmented generation (RAG), and personal knowledge graphs (PKGs). Together, these concepts hold much potential for bringing true reasoning capabilities to large language models (LLMs). So, let’s break down how symbolic logic, knowledge graphs, and modern AI can come together to empower future AI systems to reason like humans. ## The Neurosymbolic Approach: What It Means ? Neurosymbolic AI combines two historically separate streams of artificial intelligence: symbolic reasoning and neural networks. Symbolic AI uses formal logic to process knowledge, similar to how we might solve problems or deduce information. On the other hand, neural networks, like those underlying GPT-4, focus on learning patterns from vast amounts of data — they are probabilistic statistical models that excel in generating human-like language and recognizing patterns but often lack deep, explicit reasoning. While GPT-4 can produce impressive text, it’s still not very effective at reasoning in a truly logical way. Its foundation, transformers, allows it to excel in pattern recognition, but the models struggle with reasoning because, at their core, they rely on statistical probabilities rather than true symbolic logic. This is where neurosymbolic methods and knowledge graphs come in. ## Symbolic Calculations and the Early Vision of AI If we take a step back to the 1950s, the vision for artificial intelligence was very different. Early AI research was all about symbolic reasoning — where computers could perform logical calculations to derive new knowledge from a given set of rules and facts. Languages like **Lisp** emerged to support this vision, enabling programs to represent data and code as interchangeable symbols. Lisp was designed to be homoiconic, meaning it treated code as manipulatable data, making it capable of self-modification — a huge leap towards AI systems that could, in theory, understand and modify their own operations. ## Lisp: The Earlier AI-Language **Lisp**, short for “LISt Processor,” was developed by John McCarthy in 1958, and it became the cornerstone of early AI research. Lisp’s power lay in its flexibility and its use of symbolic expressions, which allowed developers to create programs that could manipulate symbols in ways that were very close to human reasoning. One of the most groundbreaking features of Lisp was its ability to treat code as data, known as homoiconicity, which meant that Lisp programs could introspect and transform themselves dynamically. This ability to adapt and modify its own structure gave Lisp an edge in tasks that required a form of self-awareness, which was key in the early days of AI when researchers were exploring what it meant for machines to “think.” Lisp was not just a programming language—it represented the vision for artificial intelligence, where machines could evolve their understanding and rewrite their own programming. This idea formed the conceptual basis for many of the self-modifying and adaptive algorithms that are still explored today in AI research. Despite its decline in mainstream programming, Lisp’s influence can still be seen in the concepts used in modern machine learning and symbolic AI approaches. ## Prolog: Formal Logic and Deductive Reasoning In the 1970s, **Prolog** was developed—a language focused on formal logic and deductive reasoning. Unlike Lisp, based on lambda calculus, Prolog operates on formal logic rules, allowing it to perform deductive reasoning and solve logical puzzles. This made Prolog an ideal candidate for expert systems that needed to follow a sequence of logical steps, such as medical diagnostics or strategic planning. Prolog, like Lisp, allowed symbols to be represented, understood, and used in calculations, creating another homoiconic language that allows reasoning. Prolog’s strength lies in its rule-based structure, which is well-suited for tasks that require logical inference and backtracking. These features made it a powerful tool for expert systems and AI research in the 1970s and 1980s. The language is declarative in nature, meaning that you define the problem, and Prolog figures out **how** to solve it. By using formal logic and setting constraints, Prolog systems can derive conclusions from known facts, making it highly effective in fields requiring explicit logical frameworks, such as legal reasoning, diagnostics, and natural language understanding. These symbolic approaches were later overshadowed during the AI winter — but the ideas never really disappeared. They just evolved. ## Solvers and Their Role in Complementing LLMs One of the most powerful features of **Prolog** and similar logic-based systems is their use of **solvers**. Solvers are mechanisms that can take a set of rules and constraints and automatically find solutions that satisfy these conditions. This capability is incredibly useful when combined with LLMs, which excel at generating human-like language but need help with logical consistency and structured reasoning. For instance, imagine a scenario where an LLM needs to answer a question involving multiple logical steps or a complex query that requires deducing facts from various pieces of information. In this case, a **solver** can derive valid conclusions based on a given set of logical rules, providing structured answers that the LLM can then articulate in natural language. This allows the LLM to retrieve information and ensure the logical integrity of its responses, leading to much more robust answers. Solvers are also ideal for handling **constraint satisfaction problems** — situations where multiple conditions must be met simultaneously. In practical applications, this could include scheduling tasks, generating optimal recommendations, or even diagnosing issues where a set of symptoms must match possible diagnoses. Prolog’s solver capabilities and LLM’s natural language processing power can make these systems highly effective at providing intelligent, rule-compliant responses that traditional LLMs would struggle to produce alone. By integrating **neurosymbolic methods** that utilize solvers, we can provide LLMs with a form of deductive reasoning that is missing from pure deep-learning approaches. This combination has the potential to significantly improve the quality of outputs for use-cases that require explicit, structured problem-solving, from legal queries to scientific research and beyond. Solvers give LLMs the backbone they need to not just generate answers but to do so in a way that respects logical rigor and complex constraints. ## Graph of Rules for Enhanced Reasoning Another powerful concept that complements LLMs is using a **graph of rules**. A graph of rules is essentially a structured collection of logical rules that interconnect in a network-like structure, defining how various entities and their relationships interact. This structured network allows for complex reasoning and information retrieval, as well as the ability to model intricate relationships between different pieces of knowledge. In a **graph of rules**, each node represents a rule, and the edges define relationships between those rules — such as dependencies or causal links. This structure can be used to enhance LLM capabilities by providing them with a formal set of rules and relationships to follow, which improves logical consistency and reasoning depth. When an LLM encounters a problem or a question that requires multiple logical steps, it can traverse this graph of rules to generate an answer that is not only linguistically fluent but also logically robust. For example, in a healthcare application, a graph of rules might include nodes for medical symptoms, possible diagnoses, and recommended treatments. When an LLM receives a query regarding a patient’s symptoms, it can use the graph to traverse from symptoms to potential diagnoses and then to treatment options, ensuring that the response is coherent and medically sound. The graph of rules guides reasoning, enabling LLMs to handle complex, multi-step questions that involve chains of reasoning, rather than merely generating surface-level responses. Graphs of rules also enable **modular reasoning**, where different sets of rules can be activated based on the context or the type of question being asked. This modularity is crucial for creating adaptive AI systems that can apply specific sets of logical frameworks to distinct problem domains, thereby greatly enhancing their versatility. The combination of **neural fluency** with **rule-based structure** gives LLMs the ability to conduct more advanced reasoning, ultimately making them more reliable and effective in domains where accuracy and logical consistency are critical. By implementing a graph of rules, LLMs are empowered to perform **deductive reasoning** alongside their generative capabilities, creating responses that are not only compelling but also logically aligned with the structured knowledge available in the system. This further enhances their potential applications in fields such as law, engineering, finance, and scientific research — domains where logical consistency is as important as linguistic coherence. ## Enhancing LLMs with Symbolic Reasoning Now, with LLMs like GPT-4 being mainstream, there is an emerging need to add real reasoning capabilities to them. This is where **neurosymbolic approaches** shine. Instead of pitting neural networks against symbolic reasoning, these methods combine the best of both worlds. The neural aspect provides language fluency and recognition of complex patterns, while the symbolic side offers real reasoning power through formal logic and rule-based frameworks. **Personal Knowledge Graphs (PKGs)** come into play here as well. Knowledge graphs are data structures that encode entities and their relationships — they’re essentially semantic networks that allow for structured information retrieval. When integrated with neurosymbolic approaches, LLMs can use these graphs to answer questions in a far more contextual and precise way. By retrieving relevant information from a knowledge graph, they can ground their responses in well-defined relationships, thus improving both the relevance and the logical consistency of their answers. Imagine combining an LLM with a **graph of rules** that allow it to reason through the relationships encoded in a personal knowledge graph. This could involve using **deductive databases** to form a sophisticated way to represent and reason with symbolic data — essentially constructing a powerful hybrid system that uses LLM capabilities for language fluency and rule-based logic for structured problem-solving. ## My Research on Deductive Databases and Knowledge Graphs I recently did some research on modeling **knowledge graphs using deductive databases**, such as DataLog — which can be thought of as a limited, data-oriented version of Prolog. What I’ve found is that it’s possible to use formal logic to model knowledge graphs, ontologies, and complex relationships elegantly as rules in a deductive system. Unlike classical RDF or traditional ontology-based models, which sometimes struggle with complex or evolving relationships, a deductive approach is more flexible and can easily support dynamic rules and reasoning. **Prolog** and similar logic-driven frameworks can complement LLMs by handling the parts of reasoning where explicit rule-following is required. LLMs can benefit from these rule-based systems for tasks like entity recognition, logical inferences, and constructing or traversing knowledge graphs. We can even create a **graph of rules** that governs how relationships are formed or how logical deductions can be performed. The future is really about creating an AI that is capable of both deep contextual understanding (using the powerful generative capacity of LLMs) and true reasoning (through symbolic systems and knowledge graphs). With the neurosymbolic approach, these AIs could be equipped not just to generate information but to explain their reasoning, form logical conclusions, and even improve their own understanding over time — getting us a step closer to true artificial general intelligence. ## Why It Matters for LLM Employment Using **neurosymbolic RAG (retrieval-augmented generation)** in conjunction with personal knowledge graphs could revolutionize how LLMs work in real-world applications. Imagine an LLM that understands not just language but also the relationships between different concepts — one that can navigate, reason, and explain complex knowledge domains by actively engaging with a personalized set of facts and rules. This could lead to practical applications in areas like healthcare, finance, legal reasoning, or even personal productivity — where LLMs can help users solve complex problems logically, providing relevant information and well-justified reasoning paths. The combination of **neural fluency** with **symbolic accuracy and deductive power** is precisely the bridge we need to move beyond purely predictive AI to truly intelligent systems. Let's explore these ideas further if you’re as fascinated by this as I am. Feel free to reach out, follow my YouTube channel, or check out some articles I’ll link below. And if you’re working on anything in this field, I’d love to collaborate! Until next time, folks. Stay curious, and keep pushing the boundaries of AI!

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## Introduction: Personal Knowledge Graphs and Linked Data We will explore the world of personal knowledge graphs and discuss how they can be used to model complex information structures. Personal knowledge graphs aren’t just abstract collections of nodes and edges—they encode meaningful relationships, contextualizing data in ways that enrich our understanding of it. While the core structure might be a directed graph, we layer semantic meaning on top, enabling nuanced connections between data points. The origin of knowledge graphs is deeply tied to concepts from linked data and the semantic web, ideas that emerged to better link scattered pieces of information across the web. This approach created an infrastructure where data islands could connect — facilitating everything from more insightful AI to improved personal data management. In this article, we will explore how these ideas have evolved into tools for modeling AI’s semantic memory and look at how knowledge graphs can serve as a flexible foundation for encoding rich data contexts. We’ll specifically discuss three major paradigms: RDF (Resource Description Framework), property graphs, and a third way of modeling entities as graphs of graphs. Let’s get started. ## Intro to RDF The Resource Description Framework (RDF) has been one of the fundamental standards for linked data and knowledge graphs. RDF allows data to be modeled as triples: subject, predicate, and object. Essentially, you can think of it as a structured way to describe relationships: “X has a Y called Z.” For instance, “Berlin has a population of 3.5 million.” This modeling approach is quite flexible because RDF uses unique identifiers — usually URIs — to point to data entities, making linking straightforward and coherent. RDFS, or RDF Schema, extends RDF to provide a basic vocabulary to structure the data even more. This lets us describe not only individual nodes but also relationships among types of data entities, like defining a class hierarchy or setting properties. For example, you could say that “Berlin” is an instance of a “City” and that cities are types of “Geographical Entities.” This kind of organization helps establish semantic meaning within the graph. ## RDF and Advanced Topics ## Lists and Sets in RDF RDF also provides tools to model more complex data structures such as lists and sets, enabling the grouping of nodes. This extension makes it easier to model more natural, human-like knowledge, for example, describing attributes of an entity that may have multiple values. By adding RDF Schema and OWL (Web Ontology Language), you gain even more expressive power — being able to define logical rules or even derive new relationships from existing data. ## Graph of Graphs A significant feature of RDF is the ability to form complex nested structures, often referred to as graphs of graphs. This allows you to create “named graphs,” essentially subgraphs that can be independently referenced. For example, you could create a named graph for a particular dataset describing Berlin and another for a different geographical area. Then, you could connect them, allowing for more modular and reusable knowledge modeling. ## Property Graphs While RDF provides a robust framework, it’s not always the easiest to work with due to its heavy reliance on linking everything explicitly. This is where property graphs come into play. Property graphs are less focused on linking everything through triples and allow more expressive properties directly within nodes and edges. For example, instead of using triples to represent each detail, a property graph might let you store all properties about an entity (e.g., “Berlin”) directly in a single node. This makes property graphs more intuitive for many developers and engineers because they more closely resemble object-oriented structures: you have entities (nodes) that possess attributes (properties) and are connected to other entities through relationships (edges). The significant benefit here is a condensed representation, which speeds up traversal and queries in some scenarios. However, this also introduces a trade-off: while property graphs are more straightforward to query and maintain, they lack some complex relationship modeling features RDF offers, particularly when connecting properties to each other. ## Graph of Graphs and Subgraphs for Entity Modeling A third approach — which takes elements from RDF and property graphs — involves modeling entities using subgraphs or nested graphs. In this model, each entity can be represented as a graph. This allows for a detailed and flexible description of attributes without exploding every detail into individual triples or lump them all together into properties. For instance, consider a person entity with a complex employment history. Instead of representing every employment detail in one node (as in a property graph), or as several linked nodes (as in RDF), you can treat the employment history as a subgraph. This subgraph could then contain nodes for different jobs, each linked with specific properties and connections. This approach keeps the complexity where it belongs and provides better flexibility when new attributes or entities need to be added. ## Hypergraphs and Metagraphs When discussing more advanced forms of graphs, we encounter hypergraphs and metagraphs. These take the idea of relationships to a new level. A hypergraph allows an edge to connect more than two nodes, which is extremely useful when modeling scenarios where relationships aren’t just pairwise. For example, a “Project” could connect multiple “People,” “Resources,” and “Outcomes,” all in a single edge. This way, hypergraphs help in reducing the complexity of modeling high-order relationships. Metagraphs, on the other hand, enable nodes and edges to themselves be represented as graphs. This is an extremely powerful feature when we consider the needs of artificial intelligence, as it allows for the modeling of relationships between relationships, an essential aspect for any system that needs to capture not just facts, but their interdependencies and contexts. ## Balancing Structure and Properties One of the recurring challenges when modeling knowledge is finding the balance between structure and properties. With RDF, you get high flexibility and standardization, but complexity can quickly escalate as you decompose everything into triples. Property graphs simplify the representation by using attributes but lose out on the depth of connection modeling. Meanwhile, the graph-of-graphs approach and hypergraphs offer advanced modeling capabilities at the cost of increased computational complexity. So, how do you decide which model to use? It comes down to your use case. RDF and nested graphs are strong contenders if you need deep linkage and are working with highly variable data. For more straightforward, engineer-friendly modeling, property graphs shine. And when dealing with very complex multi-way relationships or meta-level knowledge, hypergraphs and metagraphs provide the necessary tools. The key takeaway is that only some approaches are perfect. Instead, it’s all about the modeling goals: how do you want to query the graph, what relationships are meaningful, and how much complexity are you willing to manage? ## Conclusion Modeling AI semantic memory using knowledge graphs is a challenging but rewarding process. The different approaches — RDF, property graphs, and advanced graph modeling techniques like nested graphs and hypergraphs — each offer unique strengths and weaknesses. Whether you are building a personal knowledge graph or scaling up to AI that integrates multiple streams of linked data, it’s essential to understand the trade-offs each approach brings. In the end, the choice of representation comes down to the nature of your data and your specific needs for querying and maintaining semantic relationships. The world of knowledge graphs is vast, with many tools and frameworks to explore. Stay connected and keep experimenting to find the balance that works for your projects.

@ e6817453:b0ac3c39

The temporal semantics and **temporal and time-aware knowledge graphs. We have different memory models for artificial intelligence agents. We all try to mimic somehow how the brain works, or at least how the declarative memory of the brain works. We have the split of episodic memory** and **semantic memory**. And we also have a lot of theories, right? ## Declarative Memory of the Human Brain How is the semantic memory formed? We all know that our brain stores semantic memory quite close to the concept we have with the personal knowledge graphs, that it’s connected entities. They form a connection with each other and all those things. So far, so good. And actually, then we have a lot of concepts, how the episodic memory and our experiences gets transmitted to the semantic: - hippocampus indexing and retrieval - sanitization of episodic memories - episodic-semantic shift theory They all give a different perspective on how different parts of declarative memory cooperate. We know that episodic memories get semanticized over time. You have semantic knowledge without the notion of time, and probably, your episodic memory is just decayed. But, you know, it’s still an open question: > do we want to mimic an AI agent’s memory as a human brain memory, or do we want to create something different? It’s an open question to which we have no good answer. And if you go to the theory of neuroscience and check how episodic and semantic memory interfere, you will still find a lot of theories, yeah? Some of them say that you have the hippocampus that keeps the indexes of the memory. Some others will say that you semantic the episodic memory. Some others say that you have some separate process that digests the episodic and experience to the semantics. But all of them agree on the plan that it’s operationally two separate areas of memories and even two separate regions of brain, and the semantic, it’s more, let’s say, protected. So it’s harder to forget the semantical facts than the episodes and everything. And what I’m thinking about for a long time, it’s this, you know, the semantic memory. ## Temporal Semantics It’s memory about the facts, but you somehow mix the time information with the semantics. I already described a lot of things, including how we could combine time with knowledge graphs and how people do it. There are multiple ways we could persist such information, but we all hit the wall because the complexity of time and the semantics of time are highly complex concepts. ## Time in a Semantic context is not a timestamp. What I mean is that when you have a fact, and you just mentioned that I was there at this particular moment, like, I don’t know, 15:40 on Monday, it’s already awake because we don’t know which Monday, right? So you need to give the exact date, but usually, you do not have experiences like that. You do not record your memories like that, except you do the journaling and all of the things. So, usually, you have no direct time references. What I mean is that you could say that I was there and it was some event, blah, blah, blah. Somehow, we form a chain of events that connect with each other and maybe will be connected to some period of time if we are lucky enough. This means that we could not easily represent temporal-aware information as just a timestamp or validity and all of the things. For sure, the validity of the knowledge graphs (simple quintuple with start and end dates)is a big topic, and it could solve a lot of things. It could solve a lot of the time cases. It’s super simple because you give the end and start dates, and you are done, but it does not answer facts that have a relative time or time information in facts . It could solve many use cases but struggle with facts in an indirect temporal context. I like the simplicity of this idea. But the problem of this approach that in most cases, we simply don’t have these timestamps. We don’t have the timestamp where this information starts and ends. And it’s not modeling many events in our life, especially if you have the processes or ongoing activities or recurrent events. I’m more about thinking about the time of semantics, where you have a time model as a **hybrid clock** or some **global clock** that does the partial ordering of the events. It’s mean that you have the chain of the experiences and you have the chain of the facts that have the different time contexts. We could deduct the time from this chain of the events. But it’s a big, big topic for the research. But what I want to achieve, actually, it’s not separation on episodic and semantic memory. It’s having something in between. ## Blockchain of connected events and facts I call it temporal-aware semantics or time-aware knowledge graphs, where we could encode the semantic fact together with the time component.I doubt that time should be the simple timestamp or the region of the two timestamps. For me, it is more a chain for facts that have a partial order and form a blockchain like a database or a partially ordered Acyclic graph of facts that are temporally connected. We could have some notion of time that is understandable to the agent and a model that allows us to order the events and focus on what the agent knows and how to order this time knowledge and create the chains of the events. ## Time anchors We may have a particular time in the chain that allows us to arrange a more concrete time for the rest of the events. But it’s still an open topic for research. The temporal semantics gets split into a couple of domains. One domain is how to add time to the knowledge graphs. We already have many different solutions. I described them in my previous articles. Another domain is the agent's memory and how the memory of the artificial intelligence treats the time. This one, it’s much more complex. Because here, we could not operate with the simple timestamps. We need to have the representation of time that are understandable by model and understandable by the agent that will work with this model. And this one, it’s way bigger topic for the research.”

@ 3bf0c63f:aefa459d

# início > "Vocês vêem? Vêem a história? Vêem alguma coisa? Me parece que estou tentando lhes contar um sonho -- fazendo uma tentativa inútil, porque nenhum relato de sonho pode transmitir a sensação de sonho, aquela mistura de absurdo, surpresa e espanto numa excitação de revolta tentando se impôr, aquela noção de ser tomado pelo incompreensível que é da própria essência dos sonhos..." > Ele ficou em silêncio por alguns instantes. > "... Não, é impossível; é impossível transmitir a sensação viva de qualquer época determinada de nossa existência -- aquela que constitui a sua verdade, o seu significado, a sua essência sutil e contundente. É impossível. Vivemos, como sonhamos -- sozinhos..." * [Livros mencionados por Olavo de Carvalho](https://fiatjaf.com/livros-olavo.html) * [Antiga _homepage_ Olavo de Carvalho](https://site.olavo.fiatjaf.com "Sapientiam autem non vincit malitia") * [Bitcoin explicado de um jeito correto e inteligível](nostr:naddr1qqrky6t5vdhkjmspz9mhxue69uhkv6tpw34xze3wvdhk6q3q80cvv07tjdrrgpa0j7j7tmnyl2yr6yr7l8j4s3evf6u64th6gkwsxpqqqp65wp3k3fu) * [Reclamações](nostr:naddr1qqyrgwf4vseryvmxqyghwumn8ghj7enfv96x5ctx9e3k7mgzyqalp33lewf5vdq847t6te0wvnags0gs0mu72kz8938tn24wlfze6qcyqqq823c9f9u03) --- * [Nostr](-/tags/nostr) * [Bitcoin](nostr:naddr1qqyryveexumnyd3kqyghwumn8ghj7enfv96x5ctx9e3k7mgzyqalp33lewf5vdq847t6te0wvnags0gs0mu72kz8938tn24wlfze6qcyqqq823c7nywz4) * [How IPFS is broken](nostr:naddr1qqyxgdfsxvck2dtzqyghwumn8ghj7enfv96x5ctx9e3k7mgzyqalp33lewf5vdq847t6te0wvnags0gs0mu72kz8938tn24wlfze6qcyqqq823c8y87ll) * [Programming quibbles](nostr:naddr1qqyrjvehxq6ngvpkqyghwumn8ghj7enfv96x5ctx9e3k7mgzyqalp33lewf5vdq847t6te0wvnags0gs0mu72kz8938tn24wlfze6qcyqqq823cu05y0j) * [Economics](nostr:naddr1qqyk2cm0dehk66trwvq3zamnwvaz7tmxd9shg6npvchxxmmdqgsrhuxx8l9ex335q7he0f09aej04zpazpl0ne2cgukyawd24mayt8grqsqqqa28clr866) * [Open-source software](nostr:naddr1qqy8xmmxw3mkzun9qyghwumn8ghj7enfv96x5ctx9e3k7mgzyqalp33lewf5vdq847t6te0wvnags0gs0mu72kz8938tn24wlfze6qcyqqq823cmyvl8h) --- [Nostr](nostr:nprofile1qqsrhuxx8l9ex335q7he0f09aej04zpazpl0ne2cgukyawd24mayt8gpyfmhxue69uhkummnw3ez6an9wf5kv6t9vsh8wetvd3hhyer9wghxuet5fmsq8j) [GitHub](https://github.com/fiatjaf) [Telegram](https://t.me/fiatjaf) [code](https://git.fiatjaf.com)

@ 77110427:f621e11c

> All credit to Guns Magazine. Read the full issue here ⬇️ [February 1970 PDF ](https://gunsmagazine.com/wp-content/uploads/2020/03/G0270.pdf) ---                     --- ### 📰 Past Magazine Mondays 📰 [001: May 1963](nostr:note1r5ve5en9tyv38hathy2twhm9h4dn7tq7fgradzkazskxyxtckysqeqxyzm) [002: August 1969](nostr:note1zkeur68w9h8ljswp4a4xc45exfv725v6vudqdhyukqz6kz37vdaq097f9z) --- ### ⬇️ Follow 1776 HODL ⬇️ [Website](https://1776.npub.pro) [Nostr](nostr:npub1wugsgfcs7edz70qtc56khmxv7js90mp2hwrfu46vkk4fda3puywq3xaz5a)

@ a367f9eb:0633efea

Last week, an investigation by Reuters revealed that Chinese researchers have been using open-source AI tools to build nefarious-sounding models that may have some military application. The [reporting](https://www.reuters.com/technology/artificial-intelligence/chinese-researchers-develop-ai-model-military-use-back-metas-llama-2024-11-01/) purports that adversaries in the Chinese Communist Party and its military wing are taking advantage of the liberal software licensing of American innovations in the AI space, which could someday have capabilities to presumably harm the United States. > In a June paper reviewed by Reuters, six Chinese researchers from three institutions, including two under the People’s Liberation Army’s (PLA) leading research body, the Academy of Military Science (AMS), detailed how they had used an early version of Meta’s Llama as a base for what it calls “ChatBIT”. > > The researchers used an earlier Llama 13B large language model (LLM) from Meta, incorporating their own parameters to construct a military-focused AI tool to gather and process intelligence, and offer accurate and reliable information for operational decision-making. While I’m doubtful that today’s existing chatbot-like tools will be the ultimate battlefield for a new geopolitical war (queue up the computer-simulated war from the Star Trek episode “A Taste of Armageddon“), this recent exposé requires us to revisit why large language models are released as open-source code in the first place. Added to that, should it matter that an adversary is having a poke around and may ultimately use them for some purpose we may not like, whether that be China, Russia, North Korea, or Iran? The number of open-source AI LLMs continues to grow each day, with projects like Vicuna, LLaMA, BLOOMB, Falcon, and Mistral available for download. In fact, there are over one million open-source LLMs available as of writing this post. With some decent hardware, every global citizen can download these codebases and run them on their computer. With regard to this specific story, we could assume it to be a selective leak by a competitor of Meta which created the LLaMA model, intended to harm its reputation among those with cybersecurity and national security credentials. There are potentially trillions of dollars on the line. Or it could be the revelation of something more sinister happening in the military-sponsored labs of Chinese hackers who have already been caught attacking American infrastructure, data, and yes, your credit history? As consumer advocates who believe in the necessity of liberal democracies to safeguard our liberties against authoritarianism, we should absolutely remain skeptical when it comes to the communist regime in Beijing. We’ve written as much many times. At the same time, however, we should not subrogate our own critical thinking and principles because it suits a convenient narrative. Consumers of all stripes deserve technological freedom, and innovators should be free to provide that to us. And open-source software has provided the very foundations for all of this. Open-source matters When we discuss open-source software and code, what we’re really talking about is the ability for people other than the creators to use it. The various licensing schemes – ranging from GNU General Public License (GPL) to the MIT License and various public domain classifications – determine whether other people can use the code, edit it to their liking, and run it on their machine. Some licenses even allow you to monetize the modifications you’ve made. While many different types of software will be fully licensed and made proprietary, restricting or even penalizing those who attempt to use it on their own, many developers have created software intended to be released to the public. This allows multiple contributors to add to the codebase and to make changes to improve it for public benefit. Open-source software matters because anyone, anywhere can download and run the code on their own. They can also modify it, edit it, and tailor it to their specific need. The code is intended to be shared and built upon not because of some altruistic belief, but rather to make it accessible for everyone and create a broad base. This is how we create standards for technologies that provide the ground floor for further tinkering to deliver value to consumers. Open-source libraries create the building blocks that decrease the hassle and cost of building a new web platform, smartphone, or even a computer language. They distribute common code that can be built upon, assuring interoperability and setting standards for all of our devices and technologies to talk to each other. I am myself a proponent of open-source software. The server I run in my home has dozens of dockerized applications sourced directly from open-source contributors on GitHub and DockerHub. When there are versions or adaptations that I don’t like, I can pick and choose which I prefer. I can even make comments or add edits if I’ve found a better way for them to run. Whether you know it or not, many of you run the Linux operating system as the base for your Macbook or any other computer and use all kinds of web tools that have active repositories forked or modified by open-source contributors online. This code is auditable by everyone and can be scrutinized or reviewed by whoever wants to (even AI bots). This is the same software that runs your airlines, powers the farms that deliver your food, and supports the entire global monetary system. The code of the first decentralized cryptocurrency Bitcoin is also open-source, which has allowed thousands of copycat protocols that have revolutionized how we view money. You know what else is open-source and available for everyone to use, modify, and build upon? PHP, Mozilla Firefox, LibreOffice, MySQL, Python, Git, Docker, and WordPress. All protocols and languages that power the web. Friend or foe alike, anyone can download these pieces of software and run them how they see fit. Open-source code is speech, and it is knowledge. We build upon it to make information and technology accessible. Attempts to curb open-source, therefore, amount to restricting speech and knowledge. Open-source is for your friends, and enemies In the context of Artificial Intelligence, many different developers and companies have chosen to take their large language models and make them available via an open-source license. At this very moment, you can click on over to Hugging Face, download an AI model, and build a chatbot or scripting machine suited to your needs. All for free (as long as you have the power and bandwidth). Thousands of companies in the AI sector are doing this at this very moment, discovering ways of building on top of open-source models to develop new apps, tools, and services to offer to companies and individuals. It’s how many different applications are coming to life and thousands more jobs are being created. We know this can be useful to friends, but what about enemies? As the AI wars heat up between liberal democracies like the US, the UK, and (sluggishly) the European Union, we know that authoritarian adversaries like the CCP and Russia are building their own applications. The fear that China will use open-source US models to create some kind of military application is a clear and present danger for many political and national security researchers, as well as politicians. A bipartisan group of US House lawmakers want to put export controls on AI models, as well as block foreign access to US cloud servers that may be hosting AI software. If this seems familiar, we should also remember that the US government once classified cryptography and encryption as “munitions” that could not be exported to other countries (see The Crypto Wars). Many of the arguments we hear today were invoked by some of the same people as back then. Now, encryption protocols are the gold standard for many different banking and web services, messaging, and all kinds of electronic communication. We expect our friends to use it, and our foes as well. Because code is knowledge and speech, we know how to evaluate it and respond if we need to. Regardless of who uses open-source AI, this is how we should view it today. These are merely tools that people will use for good or ill. It’s up to governments to determine how best to stop illiberal or nefarious uses that harm us, rather than try to outlaw or restrict building of free and open software in the first place. Limiting open-source threatens our own advancement If we set out to restrict and limit our ability to create and share open-source code, no matter who uses it, that would be tantamount to imposing censorship. There must be another way. If there is a “Hundred Year Marathon” between the United States and liberal democracies on one side and autocracies like the Chinese Communist Party on the other, this is not something that will be won or lost based on software licenses. We need as much competition as possible. The Chinese military has been building up its capabilities with trillions of dollars’ worth of investments that span far beyond AI chatbots and skip logic protocols. The theft of intellectual property at factories in Shenzhen, or in US courts by third-party litigation funding coming from China, is very real and will have serious economic consequences. It may even change the balance of power if our economies and countries turn to war footing. But these are separate issues from the ability of free people to create and share open-source code which we can all benefit from. In fact, if we want to continue our way our life and continue to add to global productivity and growth, it’s demanded that we defend open-source. If liberal democracies want to compete with our global adversaries, it will not be done by reducing the freedoms of citizens in our own countries. Last week, an investigation by Reuters revealed that Chinese researchers have been using open-source AI tools to build nefarious-sounding models that may have some military application. The reporting purports that adversaries in the Chinese Communist Party and its military wing are taking advantage of the liberal software licensing of American innovations in the AI space, which could someday have capabilities to presumably harm the United States. > In a June paper reviewed by[ Reuters](https://www.reuters.com/technology/artificial-intelligence/chinese-researchers-develop-ai-model-military-use-back-metas-llama-2024-11-01/), six Chinese researchers from three institutions, including two under the People’s Liberation Army’s (PLA) leading research body, the Academy of Military Science (AMS), detailed how they had used an early version of Meta’s Llama as a base for what it calls “ChatBIT”. > > The researchers used an earlier Llama 13B large language model (LLM) from Meta, incorporating their own parameters to construct a military-focused AI tool to gather and process intelligence, and offer accurate and reliable information for operational decision-making. While I’m doubtful that today’s existing chatbot-like tools will be the ultimate battlefield for a new geopolitical war (queue up the computer-simulated war from the *Star Trek* episode “[A Taste of Armageddon](https://en.wikipedia.org/wiki/A_Taste_of_Armageddon)“), this recent exposé requires us to revisit why large language models are released as open-source code in the first place. Added to that, should it matter that an adversary is having a poke around and may ultimately use them for some purpose we may not like, whether that be China, Russia, North Korea, or Iran? The number of open-source AI LLMs continues to grow each day, with projects like Vicuna, LLaMA, BLOOMB, Falcon, and Mistral available for download. In fact, there are over [one million open-source LLMs](https://huggingface.co/models) available as of writing this post. With some decent hardware, every global citizen can download these codebases and run them on their computer. With regard to this specific story, we could assume it to be a selective leak by a competitor of Meta which created the LLaMA model, intended to harm its reputation among those with cybersecurity and national security credentials. There are [potentially](https://bigthink.com/business/the-trillion-dollar-ai-race-to-create-digital-god/) trillions of dollars on the line. Or it could be the revelation of something more sinister happening in the military-sponsored labs of Chinese hackers who have already been caught attacking American[ infrastructure](https://www.nbcnews.com/tech/security/chinese-hackers-cisa-cyber-5-years-us-infrastructure-attack-rcna137706),[ data](https://www.cnn.com/2024/10/05/politics/chinese-hackers-us-telecoms/index.html), and yes, [your credit history](https://thespectator.com/topic/chinese-communist-party-credit-history-equifax/)? **As consumer advocates who believe in the necessity of liberal democracies to safeguard our liberties against authoritarianism, we should absolutely remain skeptical when it comes to the communist regime in Beijing. We’ve written as much[ many times](https://consumerchoicecenter.org/made-in-china-sold-in-china/).** At the same time, however, we should not subrogate our own critical thinking and principles because it suits a convenient narrative. Consumers of all stripes deserve technological freedom, and innovators should be free to provide that to us. And open-source software has provided the very foundations for all of this. ## **Open-source matters** When we discuss open-source software and code, what we’re really talking about is the ability for people other than the creators to use it. The various [licensing schemes](https://opensource.org/licenses) – ranging from GNU General Public License (GPL) to the MIT License and various public domain classifications – determine whether other people can use the code, edit it to their liking, and run it on their machine. Some licenses even allow you to monetize the modifications you’ve made. While many different types of software will be fully licensed and made proprietary, restricting or even penalizing those who attempt to use it on their own, many developers have created software intended to be released to the public. This allows multiple contributors to add to the codebase and to make changes to improve it for public benefit. Open-source software matters because anyone, anywhere can download and run the code on their own. They can also modify it, edit it, and tailor it to their specific need. The code is intended to be shared and built upon not because of some altruistic belief, but rather to make it accessible for everyone and create a broad base. This is how we create standards for technologies that provide the ground floor for further tinkering to deliver value to consumers. Open-source libraries create the building blocks that decrease the hassle and cost of building a new web platform, smartphone, or even a computer language. They distribute common code that can be built upon, assuring interoperability and setting standards for all of our devices and technologies to talk to each other. I am myself a proponent of open-source software. The server I run in my home has dozens of dockerized applications sourced directly from open-source contributors on GitHub and DockerHub. When there are versions or adaptations that I don’t like, I can pick and choose which I prefer. I can even make comments or add edits if I’ve found a better way for them to run. Whether you know it or not, many of you run the Linux operating system as the base for your Macbook or any other computer and use all kinds of web tools that have active repositories forked or modified by open-source contributors online. This code is auditable by everyone and can be scrutinized or reviewed by whoever wants to (even AI bots). This is the same software that runs your airlines, powers the farms that deliver your food, and supports the entire global monetary system. The code of the first decentralized cryptocurrency Bitcoin is also [open-source](https://github.com/bitcoin), which has allowed [thousands](https://bitcoinmagazine.com/business/bitcoin-is-money-for-enemies) of copycat protocols that have revolutionized how we view money. You know what else is open-source and available for everyone to use, modify, and build upon? PHP, Mozilla Firefox, LibreOffice, MySQL, Python, Git, Docker, and WordPress. All protocols and languages that power the web. Friend or foe alike, anyone can download these pieces of software and run them how they see fit. Open-source code is speech, and it is knowledge. We build upon it to make information and technology accessible. Attempts to curb open-source, therefore, amount to restricting speech and knowledge. ## **Open-source is for your friends, and enemies** In the context of Artificial Intelligence, many different developers and companies have chosen to take their large language models and make them available via an open-source license. At this very moment, you can click on over to[ Hugging Face](https://huggingface.co/), download an AI model, and build a chatbot or scripting machine suited to your needs. All for free (as long as you have the power and bandwidth). Thousands of companies in the AI sector are doing this at this very moment, discovering ways of building on top of open-source models to develop new apps, tools, and services to offer to companies and individuals. It’s how many different applications are coming to life and thousands more jobs are being created. We know this can be useful to friends, but what about enemies? As the AI wars heat up between liberal democracies like the US, the UK, and (sluggishly) the European Union, we know that authoritarian adversaries like the CCP and Russia are building their own applications. The fear that China will use open-source US models to create some kind of military application is a clear and present danger for many political and national security researchers, as well as politicians. A bipartisan group of US House lawmakers want to put [export controls](https://www.reuters.com/technology/us-lawmakers-unveil-bill-make-it-easier-restrict-exports-ai-models-2024-05-10/) on AI models, as well as block foreign access to US cloud servers that may be hosting AI software. If this seems familiar, we should also remember that the US government once classified cryptography and encryption as “munitions” that could not be exported to other countries (see[ The Crypto Wars](https://en.wikipedia.org/wiki/Export_of_cryptography_from_the_United_States)). Many of the arguments we hear today were invoked by some of the same people as back then. Now, encryption protocols are the gold standard for many different banking and web services, messaging, and all kinds of electronic communication. We expect our friends to use it, and our foes as well. Because code is knowledge and speech, we know how to evaluate it and respond if we need to. Regardless of who uses open-source AI, this is how we should view it today. These are merely tools that people will use for good or ill. It’s up to governments to determine how best to stop illiberal or nefarious uses that harm us, rather than try to outlaw or restrict building of free and open software in the first place. ## **Limiting open-source threatens our own advancement** If we set out to restrict and limit our ability to create and share open-source code, no matter who uses it, that would be tantamount to imposing censorship. There must be another way. If there is a “[Hundred Year Marathon](https://www.amazon.com/Hundred-Year-Marathon-Strategy-Replace-Superpower/dp/1250081343)” between the United States and liberal democracies on one side and autocracies like the Chinese Communist Party on the other, this is not something that will be won or lost based on software licenses. We need as much competition as possible. The Chinese military has been building up its capabilities with [trillions of dollars’](https://www.economist.com/china/2024/11/04/in-some-areas-of-military-strength-china-has-surpassed-america) worth of investments that span far beyond AI chatbots and skip logic protocols. The [theft](https://www.technologyreview.com/2023/06/20/1075088/chinese-amazon-seller-counterfeit-lawsuit/) of intellectual property at factories in Shenzhen, or in US courts by [third-party litigation funding](https://nationalinterest.org/blog/techland/litigation-finance-exposes-our-judicial-system-foreign-exploitation-210207) coming from China, is very real and will have serious economic consequences. It may even change the balance of power if our economies and countries turn to war footing. But these are separate issues from the ability of free people to create and share open-source code which we can all benefit from. In fact, if we want to continue our way our life and continue to add to global productivity and growth, it’s demanded that we defend open-source. If liberal democracies want to compete with our global adversaries, it will not be done by reducing the freedoms of citizens in our own countries. *Originally published on the website of the [Consumer Choice Center](https://consumerchoicecenter.org/open-source-is-for-everyone-even-your-adversaries/).*

@ 09fbf8f3:fa3d60f0

> ### 第三方API合集： --- 免责申明: 在此推荐的 OpenAI API Key 由第三方代理商提供，所以我们不对 API Key 的 有效性 和 安全性 负责，请你自行承担购买和使用 API Key 的风险。 | 服务商 | 特性说明 | Proxy 代理地址 | 链接 | | --- | --- | --- | --- | | AiHubMix | 使用 OpenAI 企业接口，全站模型价格为官方 86 折（含 GPT-4 ）| https://aihubmix.com/v1 | [官网](https://aihubmix.com?aff=mPS7) | | OpenAI-HK | OpenAI的API官方计费模式为，按每次API请求内容和返回内容tokens长度来定价。每个模型具有不同的计价方式，以每1,000个tokens消耗为单位定价。其中1,000个tokens约为750个英文单词（约400汉字）| https://api.openai-hk.com/ | [官网](https://openai-hk.com/?i=45878) | | CloseAI | CloseAI是国内规模最大的商用级OpenAI代理平台，也是国内第一家专业OpenAI中转服务，定位于企业级商用需求，面向企业客户的线上服务提供高质量稳定的官方OpenAI API 中转代理，是百余家企业和多家科研机构的专用合作平台。 | https://api.openai-proxy.org | [官网](https://www.closeai-asia.com/) | | OpenAI-SB | 需要配合Telegram 获取api key | https://api.openai-sb.com | [官网](https://www.openai-sb.com/) | ` 持续更新。。。` --- ### 推广： 访问不了openai，去`低调云`购买VPN。 官网：https://didiaocloud.xyz 邀请码：`w9AjVJit` 价格低至1元。

@ 3bf0c63f:aefa459d

# Anglicismos estúpidos no português contemporâneo Palavras e expressões que ninguém deveria usar porque não têm o sentido que as pessoas acham que têm, são apenas aportuguesamentos de palavras inglesas que por nuances da história têm um sentido ligeiramente diferente em inglês. Cada erro é acompanhado também de uma sugestão de como corrigi-lo. ### Palavras que existem em português com sentido diferente - _submissão_ (de trabalhos): **envio**, **apresentação** - _disrupção_: **perturbação** - _assumir_: **considerar**, **pressupor**, **presumir** - _realizar_: **perceber** - _endereçar_: **tratar de** - _suporte_ (ao cliente): **atendimento** - _suportar_ (uma idéia, um projeto): **apoiar**, **financiar** - _suportar_ (uma função, recurso, característica): **oferecer**, **ser compatível com** - _literacia_: **instrução**, **alfabetização** - _convoluto_: **complicado**. - _acurácia_: **precisão**. - _resiliência_: **resistência**. ### Aportuguesamentos desnecessários - _estartar_: **iniciar**, **começar** - _treidar_: **negociar**, **especular** ### Expressões - _"não é sobre..."_: **"não se trata de..."** ---  ## Ver também - [Algumas expressões e ditados excelentes da língua portuguesa, e outras não tão excelentes assim](https://fiatjaf.alhur.es/expressões-e-ditados.txt)

@ 4c48cf05:07f52b80

> I believe that five years from now, access to artificial intelligence will be akin to what access to the Internet represents today. It will be the greatest differentiator between the haves and have nots. Unequal access to artificial intelligence will exacerbate societal inequalities and limit opportunities for those without access to it. Back in April, the AI Index Steering Committee at the Institute for Human-Centered AI from Stanford University released [The AI Index 2024 Annual Report](https://aiindex.stanford.edu/report/). Out of the extensive report (502 pages), I chose to focus on the chapter dedicated to Public Opinion. People involved with AI live in a bubble. We all know and understand AI and therefore assume that everyone else does. But, is that really the case once you step out of your regular circles in Seattle or Silicon Valley and hit Main Street? # Two thirds of global respondents have a good understanding of what AI is The exact number is 67%. My gut feeling is that this number is way too high to be realistic. At the same time, 63% of respondents are aware of ChatGPT so maybe people are confounding AI with ChatGPT? If so, there is so much more that they won't see coming. This number is important because you need to see every other questions and response of the survey through the lens of a respondent who believes to have a good understanding of what AI is. # A majority are nervous about AI products and services 52% of global respondents are nervous about products and services that use AI. Leading the pack are Australians at 69% and the least worried are Japanise at 23%. U.S.A. is up there at the top at 63%. Japan is truly an outlier, with most countries moving between 40% and 60%. # Personal data is the clear victim Exaclty half of the respondents believe that AI companies will protect their personal data. And the other half believes they won't. # Expected benefits Again a majority of people (57%) think that it will change how they do their jobs. As for impact on your life, top hitters are getting things done faster (54%) and more entertainment options (51%). The last one is a head scratcher for me. Are people looking forward to AI generated movies?  # Concerns Remember the 57% that thought that AI will change how they do their jobs? Well, it looks like 37% of them expect to lose it. Whether or not this is what will happen, that is a very high number of people who have a direct incentive to oppose AI. Other key concerns include: - Misuse for nefarious purposes: 49% - Violation of citizens' privacy: 45% # Conclusion This is the first time I come across this report and I wil make sure to follow future annual reports to see how these trends evolve. **Overall, people are worried about AI. There are many things that could go wrong and people perceive that both jobs and privacy are on the line.** --- Full citation: *Nestor Maslej, Loredana Fattorini, Raymond Perrault, Vanessa Parli, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, and Jack Clark, “The AI Index 2024 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2024.* The AI Index 2024 Annual Report by Stanford University is licensed under [Attribution-NoDerivatives 4.0 International](https://creativecommons.org/licenses/by-nd/4.0/?ref=chooser-v1).

@ 8947a945:9bfcf626

[](https://stock.adobe.com/stock-photo/id/1010191703) **สวัสดีทุกคนบน Nostr ครับ** รวมไปถึง **watchers**และ **ผู้ติดตาม**ของผมจาก Deviantart และ platform งานศิลปะอื่นๆนะครับ ตั้งแต่ต้นปี 2024 ผมใช้ AI เจนรูปงานตัวละครสาวๆจากอนิเมะ และเปิด exclusive content ให้สำหรับผู้ที่ชื่นชอบผลงานของผมเป็นพิเศษ ผมโพสผลงานผมทั้งหมดไว้ที่เวบ Deviantart และค่อยๆสร้างฐานผู้ติดตามมาเรื่อยๆอย่างค่อยเป็นค่อยไปมาตลอดครับ ทุกอย่างเติบโตไปเรื่อยๆของมัน ส่วนตัวผมมองว่ามันเป็นพิร์ตธุรกิจออนไลน์ ของผมพอร์ตนึงได้เลย **เมื่อวันที่ 16 กย.2024** มีผู้ติดตามคนหนึ่งส่งข้อความส่วนตัวมาหาผม บอกว่าชื่นชอบผลงานของผมมาก ต้องการจะขอซื้อผลงาน แต่ขอซื้อเป็น NFT นะ เสนอราคาซื้อขายต่อชิ้นที่สูงมาก หลังจากนั้นผมกับผู้ซื้อคนนี้พูดคุยกันในเมล์ครับ  ### นี่คือข้อสรุปสั่นๆจากการต่อรองซื้อขายครับ (หลังจากนี้ผมขอเรียกผู้ซื้อว่า scammer นะครับ เพราะไพ่มันหงายมาแล้ว ว่าเขาคือมิจฉาชีพ)  - Scammer รายแรก เลือกผลงานที่จะซื้อ เสนอราคาซื้อที่สูงมาก แต่ต้องเป็นเวบไซต์ NFTmarket place ที่เขากำหนดเท่านั้น มันทำงานอยู่บน ERC20 ผมเข้าไปดูเวบไซต์ที่ว่านี้แล้วรู้สึกว่ามันดูแปลกๆครับ คนที่จะลงขายผลงานจะต้องใช้ email ในการสมัครบัญชีซะก่อน ถึงจะผูก wallet อย่างเช่น metamask ได้ เมื่อผูก wallet แล้วไม่สามารถเปลี่ยนได้ด้วย ตอนนั้นผมใช้ wallet ที่ไม่ได้ link กับ HW wallet ไว้ ทดลองสลับ wallet ไปๆมาๆ มันทำไม่ได้ แถมลอง log out แล้ว เลข wallet ก็ยังคาอยู่อันเดิม อันนี้มันดูแปลกๆแล้วหนึ่งอย่าง เวบนี้ค่า ETH ในการ mint **0.15 - 0.2 ETH** … ตีเป็นเงินบาทนี่แพงบรรลัยอยู่นะครับ  - Scammer รายแรกพยายามชักจูงผม หว่านล้อมผมว่า แหม เดี๋ยวเขาก็มารับซื้องานผมน่า mint งานเสร็จ รีบบอกเขานะ เดี๋ยวเขารีบกดซื้อเลย พอขายได้กำไร ผมก็ได้ค่า gas คืนได้ แถมยังได้กำไรอีก ไม่มีอะไรต้องเสีนจริงมั้ย แต่มันเป้นความโชคดีครับ เพราะตอนนั้นผมไม่เหลือทุนสำรองที่จะมาซื้อ ETH ได้ ผมเลยต่อรองกับเขาตามนี้ครับ : 1. ผมเสนอว่า เอางี้มั้ย ผมส่งผลงานของผมแบบ low resolution ให้ก่อน แลกกับให้เขาช่วยโอน ETH ที่เป็นค่า mint งานมาให้หน่อย พอผมได้ ETH แล้ว ผมจะ upscale งานของผม แล้วเมล์ไปให้ ใจแลกใจกันไปเลย ... เขาไม่เอา 2. ผมเสนอให้ไปซื้อที่ร้านค้าออนไลน์ buymeacoffee ของผมมั้ย จ่ายเป็น USD ... เขาไม่เอา 3. ผมเสนอให้ซื้อขายผ่าน PPV lightning invoice ที่ผมมีสิทธิ์เข้าถึง เพราะเป็น creator ของ Creatr ... เขาไม่เอา 4. ผมยอกเขาว่างั้นก็รอนะ รอเงินเดือนออก เขาบอก ok สัปดาห์ถัดมา มี scammer คนที่สองติดต่อผมเข้ามา ใช้วิธีการใกล้เคียงกัน แต่ใช้คนละเวบ แถมเสนอราคาซื้อที่สูงกว่าคนแรกมาก เวบที่สองนี้เลวร้ายค่าเวบแรกอีกครับ คือต้องใช้เมล์สมัครบัญชี ไม่สามารถผูก metamask ได้ พอสมัครเสร็จจะได้ wallet เปล่าๆมาหนึ่งอัน ผมต้องโอน ETH เข้าไปใน wallet นั้นก่อน เพื่อเอาไปเป็นค่า mint NFT **0.2 ETH** ผมบอก scammer รายที่สองว่า ต้องรอนะ เพราะตอนนี้กำลังติดต่อซื้อขายอยู่กับผู้ซื้อรายแรกอยู่ ผมกำลังรอเงินเพื่อมาซื้อ ETH เป็นต้นทุนดำเนินงานอยู่ คนคนนี้ขอให้ผมส่งเวบแรกไปให้เขาดูหน่อย หลังจากนั้นไม่นานเขาเตือนผมมาว่าเวบแรกมันคือ scam นะ ไม่สามารถถอนเงินออกมาได้ เขายังส่งรูป cap หน้าจอที่คุยกับผู้เสียหายจากเวบแรกมาให้ดูว่าเจอปัญหาถอนเงินไม่ได้ ไม่พอ เขายังบลัฟ opensea ด้วยว่าลูกค้าขายงานได้ แต่ถอนเงินไม่ได้ **Opensea ถอนเงินไม่ได้ ตรงนี้แหละครับคือตัวกระตุกต่อมเอ๊ะของผมดังมาก** เพราะ opensea อ่ะ ผู้ใช้ connect wallet เข้ากับ marketplace โดยตรง ซื้อขายกันเกิดขึ้น เงินวิ่งเข้าวิ่งออก wallet ของแต่ละคนโดยตรงเลย opensea เก็บแค่ค่า fee ในการใช้ platform ไม่เก็บเงินลูกค้าไว้ แถมปีนี้ค่า gas fee ก็ถูกกว่า bull run cycle 2020 มาก ตอนนี้ค่า gas fee ประมาณ 0.0001 ETH (แต่มันก็แพงกว่า BTC อยู่ดีอ่ะครับ) ผมเลยเอาเรื่องนี้ไปปรึกษาพี่บิท แต่แอดมินมาคุยกับผมแทน ทางแอดมินแจ้งว่ายังไม่เคยมีเพื่อนๆมาปรึกษาเรื่องนี้ กรณีที่ผมทักมาถามนี่เป็นรายแรกเลย แต่แอดมินให้ความเห็นไปในทางเดียวกับสมมุติฐานของผมว่าน่าจะ scam ในเวลาเดียวกับผมเอาเรื่องนี้ไปถามในเพจ NFT community คนไทนด้วย ได้รับการ confirm ชัดเจนว่า scam และมีคนไม่น้อยโดนหลอก หลังจากที่ผมรู้ที่มาแล้ว ผมเลยเล่นสงครามปั่นประสาท scammer ทั้งสองคนนี้ครับ เพื่อดูว่าหลอกหลวงมิจฉาชีพจริงมั้ย โดยวันที่ 30 กย. ผมเลยปั่นประสาน scammer ทั้งสองรายนี้ โดยการ mint ผลงานที่เขาเสนอซื้อนั่นแหละ ขึ้น opensea แล้วส่งข้อความไปบอกว่า mint ให้แล้วนะ แต่เงินไม่พอจริงๆว่ะโทษที เลย mint ขึ้น opensea แทน พอดีบ้านจน ทำได้แค่นี้ไปถึงแค่ opensea รีบไปซื้อล่ะ มีคนจ้องจะคว้างานผมเยอะอยู่ ผมไม่คิด royalty fee ด้วยนะเฮ้ย เอาไปขายต่อไม่ต้องแบ่งกำไรกับผม เท่านั้นแหละครับ สงครามจิตวิทยาก็เริ่มขึ้น แต่เขาจนมุม กลืนน้ำลายตัวเอง ช็อตเด็ดคือ เขา : เนี่ยอุส่ารอ บอกเพื่อนในทีมว่าวันจันทร์ที่ 30 กย. ได้ของแน่ๆ เพื่อนๆในทีมเห็นงานผมแล้วมันสวยจริง เลยใส่เงินเต็มที่ 9.3ETH (+ capture screen ส่งตัวเลขยอดเงินมาให้ดู)ไว้รอโดยเฉพาะเลยนะ ผม : เหรอ ... งั้น ขอดู wallet address ที่มี transaction มาให้ดูหน่อยสิ เขา : 2ETH นี่มัน 5000$ เลยนะ ผม : แล้วไง ขอดู wallet address ที่มีการเอายอดเงิน 9.3ETH มาให้ดูหน่อย ไหนบอกว่าเตรียมเงินไว้มากแล้วนี่ ขอดูหน่อย ว่าใส่ไว้เมื่อไหร่ ... เอามาแค่ adrress นะเว้ย ไม่ต้องทะลึ่งส่ง seed มาให้ เขา : ส่งรูปเดิม 9.3 ETH มาให้ดู ผม : รูป screenshot อ่ะ มันไม่มีความหมายหรอกเว้ย ตัดต่อเอาก็ได้ง่ายจะตาย เอา transaction hash มาดู ไหนว่าเตรียมเงินไว้รอ 9.3ETH แล้วอยากซื้องานผมจนตัวสั่นเลยไม่ใช่เหรอ ถ้าจะส่ง wallet address มาให้ดู หรือจะช่วยส่ง 0.15ETH มาให้ยืม mint งานก่อน แล้วมากดซื้อ 2ETH ไป แล้วผมใช้ 0.15ETH คืนให้ก็ได้ จะซื้อหรือไม่ซื้อเนี่ย เขา : จะเอา address เขาไปทำไม ผม : ตัดจบ รำคาญ ไม่ขายให้ละ เขา : 2ETH = 5000 USD เลยนะ ผม : แล้วไง ผมเลยเขียนบทความนี้มาเตือนเพื่อนๆพี่ๆทุกคนครับ เผื่อใครกำลังเปิดพอร์ตทำธุรกิจขาย digital art online แล้วจะโชคดี เจอของดีแบบผม ----------- ### ทำไมผมถึงมั่นใจว่ามันคือการหลอกหลวง แล้วคนโกงจะได้อะไร [](https://stock.adobe.com/stock-photo/id/1010196295) อันดับแรกไปพิจารณาดู opensea ครับ เป็นเวบ NFTmarketplace ที่ volume การซื้อขายสูงที่สุด เขาไม่เก็บเงินของคนจะซื้อจะขายกันไว้กับตัวเอง เงินวิ่งเข้าวิ่งออก wallet ผู้ซื้อผู้ขายเลย ส่วนทางเวบเก็บค่าธรรมเนียมเท่านั้น แถมค่าธรรมเนียมก็ถูกกว่าเมื่อปี 2020 เยอะ ดังนั้นการที่จะไปลงขายงานบนเวบ NFT อื่นที่ค่า fee สูงกว่ากันเป็นร้อยเท่า ... จะทำไปทำไม ผมเชื่อว่า scammer โกงเงินเจ้าของผลงานโดยการเล่นกับความโลภและความอ่อนประสบการณ์ของเจ้าของผลงานครับ เมื่อไหร่ก็ตามที่เจ้าของผลงานโอน ETH เข้าไปใน wallet เวบนั้นเมื่อไหร่ หรือเมื่อไหร่ก็ตามที่จ่ายค่า fee ในการ mint งาน เงินเหล่านั้นสิ่งเข้ากระเป๋า scammer ทันที แล้วก็จะมีการเล่นตุกติกต่อแน่นอนครับ เช่นถอนไม่ได้ หรือซื้อไม่ได้ ต้องโอนเงินมาเพิ่มเพื่อปลดล็อค smart contract อะไรก็ว่าไป แล้วคนนิสัยไม่ดีพวกเนี้ย ก็จะเล่นกับความโลภของคน เอาราคาเสนอซื้อที่สูงโคตรๆมาล่อ ... อันนี้ไม่ว่ากัน เพราะบนโลก NFT รูปภาพบางรูปที่ไม่ได้มีความเป็นศิลปะอะไรเลย มันดันขายกันได้ 100 - 150 ETH ศิลปินที่พยายามสร้างตัวก็อาจจะมองว่า ผลงานเรามีคนรับซื้อ 2 - 4 ETH ต่องานมันก็มากพอแล้ว (จริงๆมากเกินจนน่าตกใจด้วยซ้ำครับ) บนโลกของ BTC ไม่ต้องเชื่อใจกัน โอนเงินไปหากันได้ ปิดสมุดบัญชีได้โดยไม่ต้องเชื่อใจกัน บบโลกของ ETH **"code is law"** smart contract มีเขียนอยู่แล้ว ไปอ่าน มันไม่ได้ยากมากในการทำความเข้าใจ ดังนั้น การจะมาเชื่อคำสัญญาจากคนด้วยกัน เป็นอะไรที่ไม่มีเหตุผล ผมไปเล่าเรื่องเหล่านี้ให้กับ community งานศิลปะ ก็มีทั้งเสียงตอบรับที่ดี และไม่ดีปนกันไป มีบางคนยืนยันเสียงแข็งไปในทำนองว่า ไอ้เรื่องแบบเนี้ยไม่ได้กินเขาหรอก เพราะเขาตั้งใจแน่วแน่ว่างานศิลป์ของเขา เขาไม่เอาเข้ามายุ่งในโลก digital currency เด็ดขาด ซึ่งผมก็เคารพมุมมองเขาครับ แต่มันจะดีกว่ามั้ย ถ้าเราเปิดหูเปิดตาให้ทันเทคโนโลยี โดยเฉพาะเรื่อง digital currency , blockchain โดนโกงทีนึงนี่คือหมดตัวกันง่ายกว่าเงิน fiat อีก อยากจะมาเล่าให้ฟังครับ และอยากให้ช่วยแชร์ไปให้คนรู้จักด้วย จะได้ระวังตัวกัน ## Note - ภาพประกอบ cyber security ทั้งสองนี่ของผมเองครับ ทำเอง วางขายบน AdobeStock - อีกบัญชีนึงของผม "HikariHarmony" npub1exdtszhpw3ep643p9z8pahkw8zw00xa9pesf0u4txyyfqvthwapqwh48sw กำลังค่อยๆเอาผลงานจากโลกข้างนอกเข้ามา nostr ครับ ตั้งใจจะมาสร้างงานศิลปะในนี้ เพื่อนๆที่ชอบงาน จะได้ไม่ต้องออกไปหาที่ไหน ผลงานของผมครับ - Anime girl fanarts : [HikariHarmony](https://linktr.ee/hikariharmonypatreon) - [HikariHarmony on Nostr](https://shorturl.at/I8Nu4) - General art : [KeshikiRakuen](https://linktr.ee/keshikirakuen) - KeshikiRakuen อาจจะเป็นบัญชี nostr ที่สามของผม ถ้าไหวครับ

@ 8947a945:9bfcf626

[](https://stock.adobe.com/stock-photo/id/1010191703) **Hello everyone on Nostr** and all my **watchers**and **followers**from DeviantArt, as well as those from other art platforms I have been creating and sharing AI-generated anime girl fanart since the beginning of 2024 and have been running member-exclusive content on Patreon. I also publish showcases of my artworks to Deviantart. I organically build up my audience from time to time. I consider it as one of my online businesses of art. Everything is slowly growing **On September 16**, I received a DM from someone expressing interest in purchasing my art in NFT format and offering a very high price for each piece. We later continued the conversation via email.  ### Here’s a brief overview of what happened  - The first scammer selected the art they wanted to buy and offered a high price for each piece. They provided a URL to an NFT marketplace site running on the Ethereum (ETH) mainnet or ERC20. The site appeared suspicious, requiring email sign-up and linking a MetaMask wallet. However, I couldn't change the wallet address later. The minting gas fees were quite expensive, ranging from **0.15 to 0.2 ETH**  - The scammers tried to convince me that the high profits would easily cover the minting gas fees, so I had nothing to lose. Luckily, I didn’t have spare funds to purchase ETH for the gas fees at the time, so I tried negotiating with them as follows: 1. I offered to send them a lower-quality version of my art via email in exchange for the minting gas fees, but they refused. 2. I offered them the option to pay in USD through Buy Me a Coffee shop here, but they refused. 3. I offered them the option to pay via Bitcoin using the Lightning Network invoice , but they refused. 4. I asked them to wait until I could secure the funds, and they agreed to wait. The following week, a second scammer approached me with a similar offer, this time at an even higher price and through a different NFT marketplace website. This second site also required email registration, and after navigating to the dashboard, it asked for a minting fee of **0.2 ETH**. However, the site provided a wallet address for me instead of connecting a MetaMask wallet. I told the second scammer that I was waiting to make a profit from the first sale, and they asked me to show them the first marketplace. They then warned me that the first site was a scam and even sent screenshots of victims, including one from OpenSea saying that Opensea is not paying. **This raised a red flag**, and I began suspecting I might be getting scammed. On OpenSea, funds go directly to users' wallets after transactions, and OpenSea charges a much lower platform fee compared to the previous crypto bull run in 2020. Minting fees on OpenSea are also significantly cheaper, around 0.0001 ETH per transaction. I also consulted with Thai NFT artist communities and the ex-chairman of the Thai Digital Asset Association. According to them, no one had reported similar issues, but they agreed it seemed like a scam. After confirming my suspicions with my own research and consulting with the Thai crypto community, I decided to test the scammers’ intentions by doing the following I minted the artwork they were interested in, set the price they offered, and listed it for sale on OpenSea. I then messaged them, letting them know the art was available and ready to purchase, with no royalty fees if they wanted to resell it. They became upset and angry, insisting I mint the art on their chosen platform, claiming they had already funded their wallet to support me. When I asked for proof of their wallet address and transactions, they couldn't provide any evidence that they had enough funds. Here’s what I want to warn all artists in the DeviantArt community or other platforms If you find yourself in a similar situation, be aware that scammers may be targeting you. ----------- ### My Perspective why I Believe This is a Scam and What the Scammers Gain [](https://stock.adobe.com/stock-photo/id/1010196295) From my experience with BTC and crypto since 2017, here's why I believe this situation is a scam, and what the scammers aim to achieve First, looking at OpenSea, the largest NFT marketplace on the ERC20 network, they do not hold users' funds. Instead, funds from transactions go directly to users’ wallets. OpenSea’s platform fees are also much lower now compared to the crypto bull run in 2020. This alone raises suspicion about the legitimacy of other marketplaces requiring significantly higher fees. I believe the scammers' tactic is to lure artists into paying these exorbitant minting fees, which go directly into the scammers' wallets. They convince the artists by promising to purchase the art at a higher price, making it seem like there's no risk involved. In reality, the artist has already lost by paying the minting fee, and no purchase is ever made. In the world of Bitcoin (BTC), the principle is "Trust no one" and “Trustless finality of transactions” In other words, transactions are secure and final without needing trust in a third party. In the world of Ethereum (ETH), the philosophy is "Code is law" where everything is governed by smart contracts deployed on the blockchain. These contracts are transparent, and even basic code can be read and understood. Promises made by people don’t override what the code says. I also discuss this issue with art communities. Some people have strongly expressed to me that they want nothing to do with crypto as part of their art process. I completely respect that stance. However, I believe it's wise to keep your eyes open, have some skin in the game, and not fall into scammers’ traps. Understanding the basics of crypto and NFTs can help protect you from these kinds of schemes. If you found this article helpful, please share it with your fellow artists. Until next time Take care ## Note - Both cyber security images are mine , I created and approved by AdobeStock to put on sale - I'm working very hard to bring all my digital arts into Nostr to build my Sats business here to my another npub "HikariHarmony" npub1exdtszhpw3ep643p9z8pahkw8zw00xa9pesf0u4txyyfqvthwapqwh48sw Link to my full gallery - Anime girl fanarts : [HikariHarmony](https://linktr.ee/hikariharmonypatreon) - [HikariHarmony on Nostr](https://shorturl.at/I8Nu4) - General art : [KeshikiRakuen](https://linktr.ee/keshikirakuen)

@ e6817453:b0ac3c39

Hey folks, today we're diving into an exciting and emerging topic: personal artificial intelligence (PAI) and its connection to sovereignty, privacy, and ethics. With the rapid advancements in AI, there's a growing interest in the development of personal AI agents that can work on behalf of the user, acting autonomously and providing tailored services. However, as with any new technology, there are several critical factors that shape the future of PAI. Today, we'll explore three key pillars: privacy and ownership, explainability, and bias. <iframe width="560" height="315" src="https://www.youtube.com/embed/fehgwnSUcqQ?si=nPK7UOFr19BT5ifm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> ### 1. Privacy and Ownership: Foundations of Personal AI At the heart of personal AI, much like self-sovereign identity (SSI), is the concept of ownership. For personal AI to be truly effective and valuable, users must own not only their data but also the computational power that drives these systems. This autonomy is essential for creating systems that respect the user's privacy and operate independently of large corporations. In this context, privacy is more than just a feature—it's a fundamental right. Users should feel safe discussing sensitive topics with their AI, knowing that their data won’t be repurposed or misused by big tech companies. This level of control and data ownership ensures that users remain the sole beneficiaries of their information and computational resources, making privacy one of the core pillars of PAI. ### 2. Bias and Fairness: The Ethical Dilemma of LLMs Most of today’s AI systems, including personal AI, rely heavily on large language models (LLMs). These models are trained on vast datasets that represent snapshots of the internet, but this introduces a critical ethical challenge: bias. The datasets used for training LLMs can be full of biases, misinformation, and viewpoints that may not align with a user’s personal values. This leads to one of the major issues in AI ethics for personal AI—how do we ensure fairness and minimize bias in these systems? The training data that LLMs use can introduce perspectives that are not only unrepresentative but potentially harmful or unfair. As users of personal AI, we need systems that are free from such biases and can be tailored to our individual needs and ethical frameworks. Unfortunately, training models that are truly unbiased and fair requires vast computational resources and significant investment. While large tech companies have the financial means to develop and train these models, individual users or smaller organizations typically do not. This limitation means that users often have to rely on pre-trained models, which may not fully align with their personal ethics or preferences. While fine-tuning models with personalized datasets can help, it's not a perfect solution, and bias remains a significant challenge. ### 3. Explainability: The Need for Transparency One of the most frustrating aspects of modern AI is the lack of explainability. Many LLMs operate as "black boxes," meaning that while they provide answers or make decisions, it's often unclear how they arrived at those conclusions. For personal AI to be effective and trustworthy, it must be transparent. Users need to understand how the AI processes information, what data it relies on, and the reasoning behind its conclusions. Explainability becomes even more critical when AI is used for complex decision-making, especially in areas that impact other people. If an AI is making recommendations, judgments, or decisions, it’s crucial for users to be able to trace the reasoning process behind those actions. Without this transparency, users may end up relying on AI systems that provide flawed or biased outcomes, potentially causing harm. This lack of transparency is a major hurdle for personal AI development. Current LLMs, as mentioned earlier, are often opaque, making it difficult for users to trust their outputs fully. The explainability of AI systems will need to be improved significantly to ensure that personal AI can be trusted for important tasks. ### Addressing the Ethical Landscape of Personal AI As personal AI systems evolve, they will increasingly shape the ethical landscape of AI. We’ve already touched on the three core pillars—privacy and ownership, bias and fairness, and explainability. But there's more to consider, especially when looking at the broader implications of personal AI development. Most current AI models, particularly those from big tech companies like Facebook, Google, or OpenAI, are closed systems. This means they are aligned with the goals and ethical frameworks of those companies, which may not always serve the best interests of individual users. Open models, such as Meta's LLaMA, offer more flexibility and control, allowing users to customize and refine the AI to better meet their personal needs. However, the challenge remains in training these models without significant financial and technical resources. There’s also the temptation to use uncensored models that aren’t aligned with the values of large corporations, as they provide more freedom and flexibility. But in reality, models that are entirely unfiltered may introduce harmful or unethical content. It’s often better to work with aligned models that have had some of the more problematic biases removed, even if this limits some aspects of the system’s freedom. The future of personal AI will undoubtedly involve a deeper exploration of these ethical questions. As AI becomes more integrated into our daily lives, the need for privacy, fairness, and transparency will only grow. And while we may not yet be able to train personal AI models from scratch, we can continue to shape and refine these systems through curated datasets and ongoing development. ### Conclusion In conclusion, personal AI represents an exciting new frontier, but one that must be navigated with care. Privacy, ownership, bias, and explainability are all essential pillars that will define the future of these systems. As we continue to develop personal AI, we must remain vigilant about the ethical challenges they pose, ensuring that they serve the best interests of users while remaining transparent, fair, and aligned with individual values. If you have any thoughts or questions on this topic, feel free to reach out—I’d love to continue the conversation!

@ e6817453:b0ac3c39

In the modern world of AI, managing vast amounts of data while keeping it relevant and accessible is a significant challenge, mainly when dealing with large language models (LLMs) and vector databases. One approach that has gained prominence in recent years is integrating vector search with metadata, especially in retrieval-augmented generation (RAG) pipelines. Vector search and metadata enable faster and more accurate data retrieval. However, the process of pre- and post-search filtering results plays a crucial role in ensuring data relevance. <iframe width="560" height="315" src="https://www.youtube.com/embed/BkNqu51et9U?si=lne0jWxdrZPxSgd1" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> ## The Vector Search and Metadata Challenge In a typical vector search, you create embeddings from chunks of text, such as a PDF document. These embeddings allow the system to search for similar items and retrieve them based on relevance. The challenge, however, arises when you need to combine vector search results with structured metadata. For example, you may have timestamped text-based content and want to retrieve the most relevant content within a specific date range. This is where metadata becomes critical in refining search results. Unfortunately, most vector databases treat metadata as a secondary feature, isolating it from the primary vector search process. As a result, handling queries that combine vectors and metadata can become a challenge, particularly when the search needs to account for a dynamic range of filters, such as dates or other structured data. ## LibSQL and vector search metadata LibSQL is a more general-purpose SQLite-based database that adds vector capabilities to regular data. Vectors are presented as blob columns of regular tables. It makes vector embeddings and metadata a first-class citizen that naturally builds deep integration of these data points. ``` create table if not exists conversation ( id varchar(36) primary key not null, startDate real, endDate real, summary text, vectorSummary F32_BLOB(512) ); ``` It solves the challenge of metadata and vector search and eliminates impedance between vector data and regular structured data points in the same storage. As you can see, you can access vector-like data and start date in the same query. ``` select c.id ,c.startDate, c.endDate, c.summary, vector_distance_cos(c.vectorSummary, vector(${vector})) distance from conversation where ${startDate ? `and c.startDate >= ${startDate.getTime()}` : ''} ${endDate ? `and c.endDate <= ${endDate.getTime()}` : ''} ${distance ? `and distance <= ${distance}` : ''} order by distance limit ${top}; ``` **vector\_distance\_cos** calculated as distance allows us to make a primitive vector search that does a full scan and calculates distances on rows. We could optimize it with CTE and limit search and distance calculations to a much smaller subset of data. This approach could be calculation intensive and fail on large amounts of data. Libsql offers a way more effective vector search based on FlashDiskANN vector indexed. ``` vector_top_k('idx_conversation_vectorSummary', ${vector} , ${top}) i ``` **vector\_top\_k** is a table function that searches for the top of the newly created vector search index. As you can see, we could use only vector as a function parameter, and other columns could be used outside of the table function. So, to use a vector index together with different columns, we need to apply some strategies. Now we get a classical problem of integration vector search results with metadata queries. ## Post-Filtering: A Common Approach The most widely adopted method in these pipelines is **post-filtering**. In this approach, the system first retrieves data based on vector similarities and then applies metadata filters. For example, imagine you’re conducting a vector search to retrieve conversations relevant to a specific question. Still, you also want to ensure these conversations occurred in the past week.  Post-filtering allows the system to retrieve the most relevant vector-based results and subsequently filter out any that don’t meet the metadata criteria, such as date range. This method is efficient when vector similarity is the primary factor driving the search, and metadata is only applied as a secondary filter. ``` const sqlQuery = ` select c.id ,c.startDate, c.endDate, c.summary, vector_distance_cos(c.vectorSummary, vector(${vector})) distance from vector_top_k('idx_conversation_vectorSummary', ${vector} , ${top}) i inner join conversation c on i.id = c.rowid where ${startDate ? `and c.startDate >= ${startDate.getTime()}` : ''} ${endDate ? `and c.endDate <= ${endDate.getTime()}` : ''} ${distance ? `and distance <= ${distance}` : ''} order by distance limit ${top}; ``` However, there are some limitations. For example, the initial vector search may yield fewer results or omit some relevant data before applying the metadata filter. If the search window is narrow enough, this can lead to complete results. One working strategy is to make the top value in vector\_top\_K much bigger. Be careful, though, as the function's default max number of results is around 200 rows. ## Pre-Filtering: A More Complex Approach Pre-filtering is a more intricate approach but can be more effective in some instances. In pre-filtering, metadata is used as the primary filter before vector search takes place. This means that only data that meets the metadata criteria is passed into the vector search process, limiting the scope of the search right from the beginning. While this approach can significantly reduce the amount of irrelevant data in the final results, it comes with its own challenges. For example, pre-filtering requires a deeper understanding of the data structure and may necessitate denormalizing the data or creating separate pre-filtered tables. This can be resource-intensive and, in some cases, impractical for dynamic metadata like date ranges.  In certain use cases, pre-filtering might outperform post-filtering. For instance, when the metadata (e.g., specific date ranges) is the most important filter, pre-filtering ensures the search is conducted only on the most relevant data. ## Pre-filtering with distance-based filtering So, we are getting back to an old concept. We do prefiltering instead of using a vector index. ``` WITH FilteredDates AS ( SELECT c.id, c.startDate, c.endDate, c.summary, c.vectorSummary FROM YourTable c WHERE ${startDate ? `AND c.startDate >= ${startDate.getTime()}` : ''} ${endDate ? `AND c.endDate <= ${endDate.getTime()}` : ''} ), DistanceCalculation AS ( SELECT fd.id, fd.startDate, fd.endDate, fd.summary, fd.vectorSummary, vector_distance_cos(fd.vectorSummary, vector(${vector})) AS distance FROM FilteredDates fd ) SELECT dc.id, dc.startDate, dc.endDate, dc.summary, dc.distance FROM DistanceCalculation dc WHERE 1=1 ${distance ? `AND dc.distance <= ${distance}` : ''} ORDER BY dc.distance LIMIT ${top}; ``` It makes sense if the filter produces small data and distance calculation happens on the smaller data set.  As a pro of this approach, you have full control over the data and get all results without omitting some typical values for extensive index searches. ## Choosing Between Pre and Post-Filtering Both pre-filtering and post-filtering have their advantages and disadvantages. Post-filtering is more accessible to implement, especially when vector similarity is the primary search factor, but it can lead to incomplete results. Pre-filtering, on the other hand, can yield more accurate results but requires more complex data handling and optimization. In practice, many systems combine both strategies, depending on the query. For example, they might start with a broad pre-filtering based on metadata (like date ranges) and then apply a more targeted vector search with post-filtering to refine the results further. ## **Conclusion** Vector search with metadata filtering offers a powerful approach for handling large-scale data retrieval in LLMs and RAG pipelines. Whether you choose pre-filtering or post-filtering—or a combination of both—depends on your application's specific requirements. As vector databases continue to evolve, future innovations that combine these two approaches more seamlessly will help improve data relevance and retrieval efficiency further.

@ 3bf0c63f:aefa459d

# How to do curation and businesses on Nostr Suppose you want to start a Nostr business. You might be tempted to make a closed platform that reuses Nostr identities and grabs (some) content from the external Nostr network, only to imprison it inside your thing -- and then you're going to run an amazing AI-powered algorithm on that content and "surface" only the best stuff and people will flock to your app. This will be specially good if you're going after one of the many unexplored niches of Nostr in which reading immediately from people you know doesn't work as you generally want to discover new things from the outer world, such as: - food recipe sharing; - sharing of long articles about varying topics; - markets for used goods; - freelancer work and job offers; - specific in-game lobbies and matchmaking; - directories of accredited professionals; - sharing of original music, drawings and other artistic creations; - restaurant recommendations - and so on. But that is not the correct approach and damages the freedom and interoperability of Nostr, posing a centralization threat to the protocol. Even if it "works" and your business is incredibly successful it will just enshrine you as the head of a _platform_ that controls users and thus is prone to all the bad things that happen to all these platforms. Your company will start to display ads and shape the public discourse, you'll need a big legal team, the FBI will talk to you, advertisers will play a big role and so on. If you are interested in Nostr today that must be because you appreciate the fact that it is not owned by any companies, so it's safe to assume you don't want to be that company that owns it. **So what should you do instead?** Here's an idea in two steps: 1. **Write a Nostr client tailored to the niche you want to cover** If it's a music sharing thing, then the client will have a way to play the audio and so on; if it's a restaurant sharing it will have maps with the locations of the restaurants or whatever, you get the idea. Hopefully there will be a NIP or a NUD specifying how to create and interact with events relating to this niche, or you will write or contribute with the creation of one, because without interoperability this can't be Nostr. The client should work independently of any special backend requirements and ideally be open-source. It should have a way for users to configure to which relays they want to connect to see "global" content -- i.e., they might want to connect to `wss://nostr.chrysalisrecords.com/` to see only the latest music releases accredited by that label or to `wss://nostr.indiemusic.com/` to get music from independent producers from that community. 2. **Run a relay that does all the magic** This is where your value-adding capabilities come into play: if you have that magic sauce you should be able to apply it here. Your service -- let's call it `wss://magicsaucemusic.com/` -- will charge people or do some KYM (know your music) validation or use some very advanced AI sorcery to filter out the spam and the garbage and display the best content to your users who will request the global feed from it (`["REQ", "_", {}]`), and this will cause people to want to publish to your relay while others will want to read from it. You set your relay as the default option in the client and let things happen. Your relay is like your "website" and people are free to connect to it or not. You don't own the network, you're just competing against other websites on a leveled playing field, so you're not responsible for it. Users get seamless browsing across multiple websites, unified identities, a unified interface (that could be different in a different client) and social interaction capabilities that work in the same way for all, and **they do not depend on you, therefore they're more likely to trust you**. --- Does this centralize the network still? But this a simple and easy way to go about the matter and scales well in all aspects. Besides allowing users to connect to specific relays for getting a feed of curated content, such clients should also do all kinds of "social" (i.e. following, commenting etc) activities (if they choose to do that) using the outbox model -- i.e. if I find a musician I like under `wss://magicsaucemusic.com` and I decide to follow them I should keep getting updates from them even if they get banned from that relay and start publishing on `wss://nos.lol` or `wss://relay.damus.io` or whatever relay that doesn't even know anything about music. The hardcoded defaults and manual typing of relay URLs can be annoying. But I think it works well at the current stage of Nostr development. Soon, though, we can create events that recommend other relays or share relay lists specific to each kind of activity so users can get in-app suggestions of relays their friends are using to get their music from and so on. That kind of stuff can go a long way.

@ 3bf0c63f:aefa459d

# Nostr: a quick introduction, attempt #2 Nostr doesn't subscribe to any ideals of "free speech" as these belong to the realm of politics and assume a big powerful government that enforces a common ruleupon everybody else. Nostr instead is much simpler, it simply says that servers are private property and establishes a generalized framework for people to connect to all these servers, creating a true free market in the process. In other words, Nostr is the public road that each market participant can use to build their own store or visit others and use their services. (Of course a road is never truly public, in normal cases it's ran by the government, in this case it relies upon the previous existence of the internet with all its quirks and chaos plus a hand of government control, but none of that matters for this explanation). More concretely speaking, Nostr is just a set of definitions of the formats of the data that can be passed between participants and their expected order, i.e. messages between _clients_ (i.e. the program that runs on a user computer) and _relays_ (i.e. the program that runs on a publicly accessible computer, a "server", generally with a domain-name associated) over a type of TCP connection (WebSocket) with cryptographic signatures. This is what is called a "protocol" in this context, and upon that simple base multiple kinds of sub-protocols can be added, like a protocol for "public-square style microblogging", "semi-closed group chat" or, I don't know, "recipe sharing and feedback".