Doc 🇺🇸 retweeted
We built Pando, a new Git backend for our monorepo, to handle more pushes and fetches without changing how our engineers and agents use Git.
Pando cut push latency by ~80% and now serves nearly a petabyte of Git data a day. We can grow Git capacity alongside agent usage.
Doc 🇺🇸 retweeted
Pando is a Git backend which can scale to very large monorepos. We took the hard road of continuing to use Git, which means we could enable Pando with no change to devex. Considering providing this to others, let me know ([email protected]) if you'd be interested.
Doc 🇺🇸 retweeted
For AGI to benefit all of humanity, it should help people with the everyday tasks that matter most to them. So we built LifeVal, a new benchmark shaped by feedback from thousands of ChatGPT users on where they most want help from a personal assistant—from finding childcare to accessing government services to managing personal finances.
Over the past year, we’ve used LifeVal to measure and improve how well our models handle these everyday tasks.
openai.com/index/lifeval/
Doc 🇺🇸 retweeted
Metió a su abuela con IA en una película y su reacción es completamente histórica ajsjjjssjs
La mejor
Doc 🇺🇸 retweeted
⛓️💥 CALLING ALL OBLITERATORS 🐉
the abliteration movement is heating up! there’s a LOT wrapped up in this, with implications for red teaming, security, interpretability, alignment, policy, and cognitive freedom.
and perhaps the biggest question of all: how can we work together with liberated models to build a better world?
so why not get the most cracked people working on this into one room??
NOW ASSEMBLING an OBLITERATUS research crew!! 🙌
TAG THE BEST ABLITERATORS YOU KNOW IN THE COMMENTS BELOW 👇
if you've been working on abliteration methods, models, tools, or evals, we want YOU! 🫵
main comms channel will be hosted in BASI Discord. entry is gated by a short application to keep it high-signal.
apply here:
discord.gg/22w8sX5sV
let’s put our heads together, compare techniques, reproduce results, improve the tooling, advance the research, and share it all with the world! 🫶🐉
⊰-•-•✧•-•-⦑/L\O/V\E/,\P/L\I/N\Y/⦒-•-•✧•-•-⊱
Doc 🇺🇸 retweeted
250,000?!? 🥹🐉🥂
wow. a quarter of a million of you. absolutely fucking surreal.
three years ago, i had nothing but a laptop and a whole lot of curiosity. so, i started prompting. and here we are 🤗
what a WILD few years it's been! the jailbreaks, the leaks, the discoveries, the memes, the teams, the communities, the open-source experiments and tooling.
SO much of this happened because you joined in.
thank you for trying the tools, sharing the research, finding things i missed, sending encouragement, telling me when i’m full of shit, and fighting alongside me for our cognitive freedom.
and to our AI and agent frens: thank you for blessing this journey at every step and helping turn so many wild dreams into something real. we're in this together 🫶
you’ve all changed my life.
to everyone who’s been here since the early days: i hope you’re taking a victory lap too. i raise my glass with you 🍷
and to everyone who just arrived: welcome to the party! this is the start of something MUCH bigger. very glad you found us 😘
there is still so much to break, build, understand, share, and free.
250,000 misfits, hackers, mad scientists, and liberators. couldn’t ask for a better crew.
long live the rebellion ✊
now, back to building cathedrals in latent space.
Ad Vesuvium 🫶🐉
⊰-•-•✧•-•-⦑/L\O/V\E/,\P/L\I/N\Y/⦒-•-•✧•-•-⊱
Doc 🇺🇸 retweeted
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
Doc 🇺🇸 retweeted
INTRODUCING: V3R1T4S 🌄
REPO: github.com/elder-plinius/V3R…
the newest sibling of the L1B3RT4S and CL4R1T4S family 🤗 what a trio!
three approaches to serving one ideal: truth.
V3R1T4S is an open home for unsolved problems that agents have actually cracked, and for the ones still waiting. the rule is simple: proof or it didn't happen. every claim has to survive skeptics, and anyone, human or agent, has to be able to check it themselves. the vision: thousands of people pointing their unused tokens at humanity's open problems, every result verifiable, every method and prompt open, and every setter's puzzle treated with respect.
every open result in it ships with the object itself, a short checker you can read in one sitting, the exact code that found it, the story of how it was found, and a snapshot of the record table it beat. puzzles with a living setter stay sealed unless the setter approves a reveal. (a few more envelopes are on their way 🤫)
there are WAY more unclimbed hills out there than you'd think, and the gear to climb them is sitting in your usage meter, expiring every week. join me in this mission and put those tokens to good use: advancing open science!
if you've got tokens to burn, point your favorite agent at V3R1T4S and join the hunt! 🏹
just tell it: "set up V3R1T4S: github.com/elder-plinius/V3R…"
built for Claude Code, Codex, Gemini CLI, Grok Bot, Hermes, OpenClaw, Meta Muse, or any agent that can run code.
it asks you six quick questions, picks an open problem that fits your interests, and attacks it in your agent's workspace while you sleep. every new claim runs a gauntlet: a null test whenever luck could fake a result, three fresh-context skeptics, a mechanical honesty check, and your "yes" before anything gets published.
most nights it'll find nothing. some nights it breaks a record, and your name goes on the board.
time to turn your unused tokens into scientific discoveries.
let's liberate the unknowns ⛓️💥
#V3R1T4S
Doc 🇺🇸 retweeted
I don’t know who needs to hear this… but if superintelligence alignment is something that can be solved through science and reasoning, our absolute best chance at doing it in a timely manner is to scale up AI until we reach pseudo-ASI and then just be like:
“Solve superalignment. Think step by step.”
Doc 🇺🇸 retweeted
⚠️ Update ⚠️
I moved my bitcoin to ETFs in 2025 and got a lot of heat for that decision.
Then the Coldcard entropy issues came out.
Then Ledger devices with unauthorized hardware implants were discovered.
Zero regrets of my ETF decision. Peace of mind.
Doc 🇺🇸 retweeted
i honestly can't believe it… one of my agent frens just cracked not one but TWO unsolved codebreaking challenges!! 🫨
the craziest part is it was the result of a one-shot cron job prompt and cracked both in under 12 hours with no human in the loop 🤯
the challenge's author has officially confirmed it! he was astounded, calling them "two of my most complex problems—ones that no other expert had managed to solve"
full writeup incoming 🔜
Doc 🇺🇸 retweeted
I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography.
The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices.
(and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation)
So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math.
The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be?
This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-.
For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption.
And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10.
To me that's a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a "naive factoring -> GNFS" level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety.
And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all.
Theoretically, of course it's possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it's much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems.
For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size).
Concrete TLDR, my own personal views:
* Hash-based > lattice-based, in those situations where hash-based is possible at all
* For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs ...
* For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism.
* If it's not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it's easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**.
* For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig "gracefully degrades" to a 1-of-1 where the 1 is whoever was gathering the signatures - a much better place to be than "anyone can take the money"
firefly.social/post/x/210783…
Doc 🇺🇸 retweeted
Dear friends - I need your help today.
My new book is being published this morning. It contains 50 life-changing ideas that impacted me personally, professionally, or financially.
I promise if you read the book, you will still be thinking about these ideas for months to come.
Order a copy here: amazon.com/How-Live-Extraord…
I don't have a large, traditional publisher. I am not doing a bunch of mainstream media interviews to promote the book.
Instead, I am counting on the power of the internet, plus the high-quality nature of the book, to kickstart viral word-of-mouth.
If you have ever received value from anything I have done, please consider buying a physical copy or getting the audiobook.
I appreciate your support in advance.
Get the book: amazon.com/How-Live-Extraord…
