@ 67b83190:ae0a1d72

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@ c558c7cc:b0b7b89e

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@ 381dbcc7:ffc8b38f

https://youtu.be/NdUe_rzrNng?si=j-iTiyMNXuSrX3Xh

@ 7d4a4e87:c853bba8

New OP_RETURN 0xafd67f6dd79bc5dd082dd40c598fc5f77e740637e61c4505cd1300c5d5c27a3f https://mempool.space/tx/a45ab36ee6eb46cda77e70f2f63b318ce7ce13b4bb073945738d34b81731540d

@ a96adcfb:504609e0

Interpretability Will Not Reliably Find Deceptive AI Published on May 4, 2025 4:32 PM GMT(Disclaimer: Post written in a personal capacity. These are personal hot takes and do not in any way represent my employer's views.) TL;DR: I do not think we will produce high reliability methods to evaluate or monitor the safety of superintelligent systems via current research paradigms, with interpretability or otherwise. Interpretability seems a valuable tool here and remains worth investing in, as it will hopefully increase the reliability we can achieve. However, interpretability should be viewed as part of an overall portfolio of defences: a layer in a defence-in-depth strategy. It is not the one thing that will save us, and it still won’t be enough for high reliability. Introduction There’s a common, often implicit, argument made in AI safety discussions: interpretability is presented as the only reliable path forward for detecting deception in advanced AI - among many other sources it was argued for in Dario Amodei’s recent “https://www.darioamodei.com/post/the-urgency-of-interpretability ”. I disagree with this claim. The conceptual reasoning is simple and compelling: a sufficiently sophisticated deceptive AI can say whatever we want to hear, perfectly mimicking aligned behavior externally. But faking its internal cognitive processes – its "thoughts" – seems much harder. Therefore, goes the argument, we must rely on interpretability to truly know if an AI is aligned. I am concerned this line of reasoning represents an https://slatestarcodex.com/2014/08/14/beware-isolated-demands-for-rigor/ Should we give up on interpretability? No! I still think it has the potential to add a lot of value, and we will have better safeguards with interpretability as part of our portfolio. Even if it adds no value for making superintelligence safer[2], if it can add value for pre-superintelligence transformative systems that would be enough to justify investment. I just think that we should be more pragmatic about interpretability’s likely impact, and accept that while we can generally improve our safeguards we will likely not reach high reliability. High Reliability Seems Unattainable Based on the current state and foreseeable trajectory of the field without major paradigm shifts, I think that neither interpretability nor black box methods offer a high reliability[3] path to safeguards for superintelligence, in terms of evaluation or monitoring. This is due to fairly fundamental limitations of both methods, unless there are substantial breakthroughs, e.g. via using pre-superintelligence systems. This also stems from a general prior that being highly confident in any complex property of a complex system in a range of unpredictable situations is fairly implausible. However, I am pretty pessimistic in general about reliable safeguards against superintelligence with any methods, given how exceptionally hard it is to reason about how a system far smarter than me could evade my plans. As I see it we must either not create superintelligence, rely on pre-superintelligent automated researchers to find better methods, or deploy without fully reliable safeguards and roll the dice, and do as much as we can now to improve our odds.[4] This doesn't mean we should give up! It means we need a pragmatic perspective. We should aim to build the best possible monitoring and evaluation portfolio we can, using all available tools, while accepting that high reliability might be out of reach. Interpretability can add a valuable source of de-correlated signal, or augment black box methods. The goal shifts from achieving near-certainty to maximizing the chances of catching misalignment, making deception harder and riskier for the AI, even if we can't guarantee detection. Further, methods that add significant safety to pre-superintelligent transformative systems still add significant value even if they don’t scale to superintelligence - one of the key insights behind https://arxiv.org/abs/2312.06942 . Early transformative systems seem likely to be far easier to evaluate and control, but may still be very dangerous if unchecked, and extremely useful if under control. And when evaluating methods according to how much they help with early transformative systems, interpretability is just one among several reasonable methods (and probably not my first choice - things like chain of thought monitoring seem like lower hanging fruit) Why Won’t Interpretability be Reliable? It’s hard to be confident here - while it’s obvious that current techniques are unreliable, this is a question of forecasting future research progress, a highly questionable endeavour. It’s very hard to say what might be possible with a paradigm shift in how LLMs are trained, or how we do interpretability. But here are some of the issues that I predict are not going to go away, even as the field progresses: We miss things: We're still figuring out the basics. Concepts like superposition mean important features can be smeared across many neurons in non-obvious ways. Our tools like SAEs or transcoders have error and often miss things. We can misinterpret the features found, or only explain a fraction of the model's behavior. It’s hard to measure progress: We don’t have a ground truth for what’s actually going on, and must inevitably use a range of proxies of varying quality. This means it’s hard to ever be confident you really understand what’s happening. The Long Tail Problem: Models seem to have both some clean, interpretable structure and a massive, messy "long tail" of weird heuristics, special case handlers, and half-learned, noisy behaviours. I’d personally guess that important capabilities, including deception, are in the clean part, but can’t say for sure! How to prove a negative: We can find evidence for or against a hypothesis, but rigorously proving the absence of deception circuits seems incredibly hard. How do you know you didn't just miss it? How much of the model do you need to understand? 90%? 99%? 99.99%? The required level of certainty for high-stakes deployment feels far beyond our current capabilities. I’m more optimistic about interpretability as a way to potentially demonstrate misalignment, but failure to do so tells us little. Scalability Questions: Most of our best results are on toy models or yesterday's LLMs. I think that scaling is a tractable problem, but likely involves a bunch of LLM labour assisting, another source of error and uncertainty. Potential for Obfuscation: While maybe harder than faking outputs, a smart AI could potentially learn to structure its internal computations to be opaque to our specific interpretability tools. The Potential of Black-Box Methods Black-box methods, i.e. things that engage with the model’s inputs and outputs, have clear issues: https://www.alignmentforum.org/posts/E3daBewppAiECN3Ao/claude-sonnet-3-7-often-knows-when-it-s-in-alignment than my areas of expertise, but some thoughts on directions I find promising: Reading the chain of thought (with another LLM) and looking for sketchy intent[5] Editing the model’s chain of thought so it e.g. thinks it concluded that it’s not in an evaluation Asking questions to many copies of the same model and look for inconsistencies while they can’t coordinate. Finetuning the model to e.g. be more truthful or forthcoming, or https://alignment.anthropic.com/2025/modifying-beliefs-via-sdf/ . Creating honeypots that try to trick the model into revealing its intent The Role of Interpretability So, where does interpretability fit into this pragmatic portfolio? I see several key roles, viewing it not as a standalone solution, but as a valuable component. Firstly, safeguards can be categorised as follows: Evaluation: We take a system and perform extensive tests on it for undesired properties, like a tendency towards misalignment or sandbagging Monitoring: When we deploy a system we are always running some checks at inference, which try to catch and mitigate bad behaviour Importantly, this needs to be cheap to be run in production, while evaluations can be more expensive and slow A Layer of Swiss Cheese: I think the right monitoring schemes have several layers of defence (a la the https://en.wikipedia.org/wiki/Swiss_cheese_model ), with different cost to accuracy trade-offs. Interpretability techniques like probes for known dangerous features are extremely cheap, https://www.alignmentforum.org/posts/4uXCAJNuPKtKBsi28/negative-results-for-saes-on-downstream-tasks#Using_SAEs_for_OOD_Probing , and can serve as a good first layer of defence, which call more expensive tests (e.g. an LLM reading the chain of thought) if they fire. Interpretability techniques like SAEs or cross-layer transcoders may have the potential to be more expensive but accurate ways to audit what’s going on and look for anything unexpected. Even if interpretability is not more accurate, so long as it has some signal and is de-correlated, then it adds value, even if it’ll miss many things. Enhancing Black-Box Evaluations: Interpretability might make our external checks more effective. For example: Could we use steering vectors identified via interpretability to manipulate whether the model thinks it’s in an eval? Maybe we can test it more effectively if we can trick it into thinking it's not in a honeypot or evaluation setting. Can we use interpretability to better elicit information from token forcing? We can make the model say “my secret plan is”, and even if it tries to internally suppress awareness or obfuscate the output perhaps interpretability can prevent that? Interpretability may provide a sanity check that our black box methods are not missing something, e.g. whether a model is passing an eval only because it knows it’s in one and is deliberately underperforming. Debugging mysterious behaviour: One particularly interesting form of evaluation (or offline monitoring) is taking particularly weird or unexpected examples of model behaviour, e.g. it seeming to put vulnerabilities in critical code, and trying to debug what’s going on and see if it’s real alignment or not. I think interpretability methods may prove very good at raising unexpected hypotheses, and piecing together what’s happening (e.g. https://transformer-circuits.pub/2025/attribution-graphs/biology.html ) I expect these kinds of potential warning shots to happen, and anything that can add more signal here would be great. Even if we don’t trust the interpretability method fully, the hypotheses generated can be verified by other means. These applications aren't guaranteed to work or be ungameable, but they represent concrete ways internal understanding could augment external validation, creating a more robust overall system than either alone. Conclusion Let's stop framing interpretability as the one true path to reliably detecting AI deception, a view often born from an isolated demand for rigor against flawed black-box methods. Neither approach offers high reliability against superintelligence without fundamental breakthroughs, but flawed methods can still be useful. For the powerful pre-SI systems that concern us now, let's focus on building the best pragmatic portfolio of imperfect defenses we can. Interpretability is a useful part of that portfolio – offering unique angles of analysis and potential synergies with other methods – but it's one tool among many, not a substitute for a comprehensive, defense-in-depth strategy. We need to leverage every imperfect signal we can get, from both inside and outside the box. Thanks to my co-author Gemini 2.5 Pro More specifically, I basically think the only way we might get high reliability is by using pre-superintelligent systems that are sufficiently aligned/controlled to either do a ton of automated research and find some breakthroughs, or by using them as a core part of a monitoring or scalable oversight scheme. But the human research trajectory is hard. ↩︎ Defining “safety” is kinda messy, for the purposes of this post you can interpret it as evaluating ‘is the model thinking misaligned thoughts’ or monitoring for ‘is the model currently knowingly taking high-stakes actions against what we want’ ↩︎ I’m not sure what the best precise definition of highly reliable is here, maybe 99.9% chance of stopping a catastrophic outcome? It’s not key to my argument. ↩︎ I’ll avoid taking a position on the best plan… ↩︎ Obviously, https://arxiv.org/abs/2503.08679 https://www.lesswrong.com/posts/PwnadG4BFjaER3MGf/interpretability-will-not-reliably-find-deceptive-ai#comments https://www.lesswrong.com/posts/PwnadG4BFjaER3MGf/interpretability-will-not-reliably-find-deceptive-ai

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@ 274fe9ae:565805d2

Buffett Hands His Successor a Giant Cash Pile and Many Questions https://financialpost.com/pmn/business-pmn/buffett-hands-his-successor-a-giant-cash-pile-and-many-questions

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Russia Will Continue to Wage War - Zelenskyy https://benborges.xyz/2025/05/04/russia-will-continue-to-wage.html

@ 9358c676:9f2912fc

To avoid inflation, monkey buys #bitcoin 🦍🍌🔊 https://blossom.primal.net/835cb812af3b1ee15bb989f5d7524bf2a814290624fbacac74ac7041faabf19d.mp4

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Ukrainian Air Force strikes Russian command post in Donetsk region https://benborges.xyz/2025/05/04/ukrainian-air-force-strikes-russian.html

@ 3dda4500:5776f2db

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@ 7d4a4e87:c853bba8

New OP_RETURN eSg:to:TRX:TBWUC9M1HBxojnrY9rZji1domKwPVRRgmL https://mempool.space/tx/321f65f5868b26b3be94cbc9d35d586831cae9d935e6d824c00a626cb5bed628

@ ba18b654:48c19046

Many thanks. 💜😆

@ 274fe9ae:565805d2

Merrill Lynch, Morgan Stanley, Wells Fargo And UBS Will Offer Bitcoin ETFs To Clients By Year-End, Bitwise CIO Says, Predicting Record Inflows https://finance.yahoo.com/news/merrill-lynch-morgan-stanley-wells-151412178.html

@ ba18b654:48c19046

Yes! It wouldn't have happened if he was still in prison school. He'll probably call the first couple of days and then forget about us 😂. It's not the longest he's been gone, but it's the farthest he's been.

@ c558c7cc:b0b7b89e

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@ 8aedc871:924a7f05

No one, and we will all die of a science deficiency

@ 508f2865:a64b2960

Happy shake ur booty Sunday 😂

@ 1ea4ae84:b59a2ae8

📇⬇️ policy: yes: i remind you about the last policy paper i wrote you about vocational ai #emmanuelmacron - https://youtu.be/yprVButn894?si=GwXc5ZOx-CWACK1e

@ e83b66a8:b0526c2b

Ok, let me know if you have any further difficulties.

@ 7d4a4e87:c853bba8

New OP_RETURN MRB:to:USDT(ERC20):0xb165554c40bbad90be57f01b9954ab0a37071a11 https://mempool.space/tx/80503257e141bddec66601717e22524b280a993917a8a5e990c41534faa692b2

@ a619eb76:11d75935

There is a new vintage consignment store in town. Picked up this watch, couldn't resist :) https://image.nostr.build/2575349cd4534fc02fa3092cadfa86cea55cbc560501dd571b952c288d0597bd.jpg

@ ba18b654:48c19046

Amen! For a day or two until I start really missing him 🤣

@ 1bf88cbd:8e4c9e3f

There's a new nostrich in town: nostr:nprofile1qqswll5mnywht5tg7vvwglypgq8tfdmm6mqn5ewzant8jldwm7v58lqpz4mhxue69uhkummnw3ezummcw3ezuer9wchs6eanxv - expect posts of her lovely art, cats and possibly some bike stuff! You might have seen her before in my posts, she's the one with fluffy ears on her helmet. #introductions nostr:nevent1qqsghy3qz7f008cz3nd6qhdk09f69g2wpsre7f5l0yh5d0v5gl7wwycpz4mhxue69uhkummnw3ezummcw3ezuer9wchsyg80l6dej8t46950xx8y0jq5qr45kaaadsf6vhpwe4ne0khdlx2rlspsgqqqqqqs4vh306

@ 274fe9ae:565805d2

Bitcoin's Bullish Structure Remains Intact: Continuation Could Lead To Higher Prices Ahead https://finance.yahoo.com/news/bitcoins-bullish-structure-remains-intact-160227825.html

@ 0b817937:d4d97df8

i have been here 1.5 months now, i'm curious to know your thoughts. what is your #1 reason to run your own relay (public or private) ? whether you already have one or want to someday. #asknostr p.s. what i post here is 100% of little importance, however, i value the interactions i have with all of you. the data being stored in relays can disappear tomorrow for all i care.

@ b0fdd723:06f0ffd4

Wow top 😍

@ 1d80e558:91844d59

This book is written by a Japanese author and won a mystery award in Japan. It tells the story of a 200-year-old human bone excavated from a lake in India that matches perfectly with the DNA of a sister who disappeared four years ago. An older brother solves the mystery. https://image.nostr.build/b5f10f0901d2e36b91563ec958cad24afae0fbc7f3eafe8ba442cac05ae7399d.jpg

@ a3718378:fa6ae16e

Fiatucation.

@ 1a5cff51:01c75f78

Toroid shaped. 😌 nostr:nevent1qqsgtf488u250jx8ml4lhhquy2na85tdwg4de245pkg7msqe8y94f8cpzamhxue69uhhyetvv9ujumn0wd68ytnzv9hxgtczyqqp3dlwx0aj2wzrvwwx9c5jlmrspf56jwcgacm5ck76juwfkw2kgqcyqqqqqqgvayg77

@ 87031bc0:f20ab9a7

Feliz domingo para ti también 💕

@ 274fe9ae:565805d2

33 Of The Absolute Most Enraging "Boomer Complaints" That Most Generations Agree On https://www.yahoo.com/lifestyle/33-absolute-most-enraging-boomer-153804531.html

@ 7d4a4e87:c853bba8

New OP_RETURN  https://mempool.space/tx/5890ec6f2973a91ddc1f948ddcb4134e764720c7c2d31bcdc32a39c7e51aad0a

@ 2aa531ee:69ecc700

🇧🇷😎 BOLSONARO RECEBE ALTA HOSPITALAR Ex-presidente Jair Bolsonaro recebeu alta após cirurgia abdominal em Brasília. https://files.catbox.moe/nnynvr.mp4

@ 46ea6cf0:4bbf3185

‹ 从相亲圈到资本市场，留学生面临贬值困境 - 哔哩哔哩 › • Source > https://www.bilibili.com/opus/1063092027433943059 ———— • Ghostarchive > https://ghostarchive.org/archive/UmEZx • Telegraph > https://telegra.ph/从相亲圈到资本市场留学生面临贬值困境---哔哩哔哩-05-04 • archive.today > archive.today is unavailable. • Internet Archive > https://web.archive.org/web/20250504163109/https://www.bilibili.com/opus/1063092027433943059

@ 274fe9ae:565805d2

Experts start complex recovery of a superyacht that sank off Sicily, killing 7 https://www.yahoo.com/news/experts-start-complex-recovery-superyacht-155729379.html

@ a2b85453:4f66fd11

这是中国的法律吗？🤣🤣🤣求心理阴影面积 https://link.storjshare.io/raw/jxcx5jvaflyg4cq5j3t6gq5cidya/freefrom-prod/44ae3990dc93e68d7cc9a4f274b423e1fe81c815438d5c55d3047543d1e07c6a.MP4

@ 40359cd2:cdb2a492

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@ 7d4a4e87:c853bba8

New OP_RETURN OUT:61A09220C093292795B53D0870E1CF271A39A7D55DD4A00CD9CC84E0FEDE59E7 https://mempool.space/tx/5bb0dce403132d75667f935f62702cb92aeed7132d904b723097e7198d7fb51a

@ b133bfc5:49d5789d

It’s not hard when you’ve always been able pay a miner directly to place your transactions on chain & bypass the mempool…

@ a012dc82:6458a70d

From relativity to decentralization, #Bitcoin unites all equations. http://res.cloudinary.com/dsixpui68/image/upload/v1737764834/twk3meyluwds64yqf2c4.jpg

@ 8158c389:7ab23039

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@ 67b83190:ae0a1d72

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Woman missing since 1962 found 'alive and well' https://www.bbc.com/news/articles/cvgpjgwxlxwo

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https://cdn.midjourney.com/f7a80055-2753-42c9-80e5-519e0ab5f550/0_2.png

@ fc8cdfb4:9cacf88d

of a silence deficiency.

@ b0fdd723:06f0ffd4

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@ f8e6c643:03328ca9

Makes sense.

@ f03df3d4:a4d4f676

Block 895217 2 - high priority 1 - medium priority 1 - low priority 1 - no priority 1 - purging #bitcoinfees #mempool

@ e7bf8dad:839ef3db

Block 895217 2 - high priority 1 - medium priority 1 - low priority 1 - no priority 1 - purging #bitcoinfees #mempool

@ cbab7074:f9f0bd61

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@ 87031bc0:f20ab9a7

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@ 7d4a4e87:c853bba8

New OP_RETURN from:1500USDT(TRON):TAnhRpwwrdPQ9P9ZJXr1wLJkTLDLSpi4ca https://mempool.space/tx/dafb750bdedeb0911b745fb41a86010354c45f98a6a0df854106ede1f6769a19

@ db8bb06a:aeae5a6c

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@ 7d4a4e87:c853bba8

New OP_RETURN =:GAIA.ATOM:cosmos1hpv0k6drrgf22rxq4573wce8zgkl2tl2ahruqq:223591305:wr:0 https://mempool.space/tx/ad19868b44c9f24e49a8e6b38158970e0a2bd21455cc89b0d2472b34117cc4d2

@ 7ce33088:780eef48

Dating App ‘Raw’ Accidentally Rawdogs Users’ Location Data, Personal Info App users' data was left completely and utterly unprotected. https://gizmodo.com/dating-app-raw-accidentally-rawdogs-users-location-data-personal-info-2000597766

@ cfb6da66:b9055726

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@ a8d40cc9:26666deb

GM ☕🫂🧡

@ 7d4a4e87:c853bba8

New OP_RETURN PJW:to:USDT(BSC):0x3Fe049AfdDFEE35A013740DF4B1c4A31F48Fd359 https://mempool.space/tx/b495f37273f27d5cd5c9dce896df599cd3193a6b0bccddf630f0ad7002819f14

@ 7a78fbfe:48e76288

Flower Fields of Carlsbad. #grownostr #flowerstr #flowers https://v.nostr.build/Qt4sq5RvZhqTNgAR.mp4

@ 1d80e558:91844d59

Thank you😊🍌

@ 6596d001:44e1e5ed

NEW: EL SALVADOR TRAVEL AGENCY CONFIRMS 1st FLIGHT PAID 100% IN #BITCOIN LOVE TO SEE IT 🧡 https://image.nostr.build/bc16d23765829932043036119817da8e2c061f2536994a064725c2858bcccc8a.png

@ 460c25e6:ef85065c

And I am not exactly sure they should show... Marketplace is only a needed function when you already know what you want to buy or when you constantly browse for new finds around you. Things means that clients that only do marketplace can easily be better than any social media first app.

@ 3fdf8b43:27eaaad8

#Bitcoin Block Art by Blockstr! Height: 895217 Weight: 3993784 https://thebitcoinblockclock.com/blockstr/000000000000000000025c23f946c0c9a8f1506a074645dcfe4d19f5ff9d687d.png

@ 7d4a4e87:c853bba8

New OP_RETURN TRADE+:thor1qvlul0ujfrq27ja7uxrp8r7my9juegz0ug3nsg https://mempool.space/tx/23d473e60e41a4c860fc456827b8b6e2e044e9791cfc3a5d9d4df163d221d7a3

@ 7ed7d5c3:6927e200

That's some next level shit 😂🤣

@ c3b8daa4:ee116d19

Best part of having a home gym is working out without shoes. Barefoot workouts are supreme.

@ 63fe6318:330504ed

Its good...

@ f6e5e424:f8e1722b

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@ fdfefa96:3736b8f9

👋 A new block was found on the #Bitcoin network. We're at block height 895217, current #bitcoin price is $95505.37 and there are 113992810190011 #sats left to mine.

@ 63fe6318:330504ed

Sombody tuned llama with nostr already check huggingface

@ f839fb67:5c930939

GM Erik! Pv fren 🫂

@ 7d4a4e87:c853bba8

New OP_RETURN w2B:to:XRP:rNghRztjzFgp3jGW5S7oGGDPvKyzAyboZh https://mempool.space/tx/13d00e513c2d825b8dd5f653969ca5dfba7fe0764c161303a48cc1c17e83362e

@ 2f29aa33:38ac6f13

then suddenly

@ 63fe6318:330504ed

Does it actually handle dead links though?

@ 63fe6318:330504ed

s p i c y

@ ba18b654:48c19046

😂 I'm a clutter queen. I'm trying to pare it all down bit by bit. The boy is more of a trash hoarder, broken toys and junk toys from school parties and such.

@ 98aa4d34:ea7079d8

GM Sergio!

@ 7ed7d5c3:6927e200

That's neat, thanks Have you had these before?

@ 06b7819d:d1d8327c

It's fun having a wallet that is social by default. #nostr #safebox https://image.nostr.build/c949281bf50ae4ef371520bc253078703f46f0d6b4f3987061c7028ca4b6e411.jpg

@ 8a719d42:cf1f0fe5

Definitely a man in the making. Well done

@ 63fe6318:330504ed

Well ofc, here is the commit for this feature https://github.com/LNVPS/api/commit/7e10e0dd6eecc2bb3286945a281165a949fe7c90

@ 55e86c3f:9a262d53

'Banks must be trusted to hold our money and transfer it electronically, but they lend it out in waves of credit bubbles with barely a fraction in reserve. We have to trust them with our privacy, trust them not to let identity thieves drain our accounts. Their massive overhead costs make micropayments impossible.' Satoshi Nakamoto https://cdn.nostr.build/i/a4b623d82f3e50aad827b2a1b45b242f28a7275ff230d864f512ef6d2dfed46f.jpg #wisdom #philosophy #psychology #spirituality #poetry

@ 19f9afb8:ce50e4d3

Welcome to homeschooling. Lol. He's going to have a blast and you are going to miss him so much more than he is going to miss you.

@ 03742c20:2df9aa5d

Good Night

@ 63fe6318:330504ed

Yea they can just make a new account and set the lightning address to the vm

@ fc8cdfb4:9cacf88d

https://image.nostr.build/062862d226550d751eb9ced53b567683f960ec83256e02491e528ebf65fa0a58.jpg nostr:note12xr2rg2mq4e7gknzrpvttqpdp4nrh3rdk7lay9v5v9dv5fmvk3ysnyt3mk

@ 00000000:f729d700

No. I meant soothe, as it’s written in the cartoon.

@ 73b90bdd:f2daee42

How’s the surf today?

@ 63fe6318:330504ed

try 101@api.lnvps.net

@ d4338b7c:f1d23a5b

My hummingbirds are back. I missed the little buggers.

@ 508f2865:a64b2960

Sorry don’t mean to laugh at you. Just if you only knew how hard and how much energy it took to try..

@ 63fe6318:330504ed

They uses colored coin like omni layer

@ 81b26cb9:35970aaa

AI is reshaping human roles, emphasizing the need to focus on areas where AI struggles, such as creativity, ethics, and emotion, highlighting the continued importance of uniquely human skills.

@ fb67d7e6:6cdbee56

In code we trust. In #Bitcoin we build. http://res.cloudinary.com/dizsud5n6/image/upload/v1742614245/bfbxvs6jnhzyt60nrrvt.jpg

@ 8aedc871:924a7f05

Fuck them 99.9% of fed gov left to abolish nevent1qqsr3md23y2lm33dl4gkvphmu8xjka0pkaf9lvgwfzkzc23zt0mgsuspr9mhxue69uhkxmmzwfskvatdvyhxxmmd9aex2mrp0ykvkjrv

@ 7ed7d5c3:6927e200

Gm bruh

@ 63fe6318:330504ed

Ok it works, you can zap @api.lnvps.net to renew via LNURL

@ ba18b654:48c19046

Cute!!