Research Lead, Protocols @anchorage Digital. Strong Cryptography Maximalist. RTs and likes are not endorsements. Newsletter https://xitter.cf/t.co/DS2oeC3HtK
California
Joined June 2008
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Foursquare officially ending kind of hurts at a weirdly personal level. They put NYC tech scene on the map. We used to check in on foursquare all the time back in 2012-2014s. Don’t think I interacted directly with @dens but we used to crib their tech notes. I’m pretty sure learned scala in college because 4sq used it.
So long and thanks for all the mayor appointments!
Foursquare was acquired last month. The outcome was disappointing. It's taken me a while to write this, but I think I'm finally done grieving it. Here are some thoughts on our final years, an effort to honor the team who helped build & maintain FSQ's legacy, and my attempt to finally get some closure. teendrama.substack.com/p/som…
prasincs ⚓️🇺🇸🇳🇵 retweeted
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
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prasincs ⚓️🇺🇸🇳🇵 retweeted
PSA: Do NOT use public WiFi or shady DNS servers for the next few weeks.
SSL is practically broken and you can be attcked with an MITM attack. Your ISP and other intermediaries can also spy on you. Use a trusted VPN.
It will take a few days at least to flush out this crap CA.
Instead of using 384 bit random private keys, they used private keys like the super secret, unguessable number 4.
They are a cryptography company. They had one job. They're selling Post-Quantum Cryptography while doing shit like this lmao.
Readers added context they thought people might want to know
SSL/TLS is not broken. The vulnerability involved eight eMudhra intermediate certificates, which major browsers and operating systems (Chrome, Firefox, Apple, and Microsoft) had already blocked by October 20. Updated devices are protected against these MITM attacks.
arstechnica.com/security/2026/…
emudhra.we-have-a-problem.com
Tested @antirez’s Dwarfstar on my 128GB M5 Max Mac Studio with DeepSeek Flash 4.1 and I can’t believe I’m getting 16 tok/s output with ssd offload. That’s 3x more than what’d fit in memory.
prasincs ⚓️🇺🇸🇳🇵 retweeted
When I was writing Unreal Engine 1, my time was divided as follows:
- Around 30% went to seriously deep coding on hard problems: assembly language texture mappers, C++ BSP trees, data persistence, object oriented systems design, and networking.
- Around 70% was just typing and refactoring large bodies of code needed for a complete engine feature set. This was not hard, and while I was doing it my mind was mostly on the deeper coding topics, thinking and planning for what's next. It didn't feel much different from vibe coding today, except that it was just me typing code on an IBM Model M keyboard rather than prompting it and reviewing the results.
So, it has always seemed perfectly logical that we would rely on AI for that 70%, once the AI could do it with a similar quality standard as we could, which has never been perfection. It came as no surprise that, after a few years of AI model evolution, it was able to take the reigns on that 70% and free up programmers' time for more valuable tasks.
What's more of a surprise is the emergence of AI as a sort of insta-Carmack able to solve hard technical problems quite cleverly and often cleanly, and that it's able to reason about and work in complex 3D scenes both by operating the tools and by directly understanding the raw data files.
I abandoned assembly language in 1998 when out-of-order CPUs made much simpler C/C++ code faster, and never I missed it. If prompting through architectural documents, concept art, and design plans can transcend typing code and tweaking vertices, that will enable developers to spend their time on higher-level, higher value technical and creative tasks.
What matters is the end product, whether it's a game or an engine. Anything developers can do to try out more ideas and make better software and games faster is a win for everyone.
TLDR but I agree with the sentiment
People seem confused about why Tesla is getting so much backlash over its new "flying Roadster." For those of us who were once genuinely enthusiastic about Tesla, this isn't about hating innovation. It's about a long history of broken promises, misleading expectations, and increasingly absurd marketing.
Tesla unveiled the second-generation Roadster in 2017, took customer deposits, and promised deliveries in 2020. Six years past that deadline, it still hasn't delivered the car. Instead, it's demonstrating an even more outrageous version with features that will likely never make it into production. The original 2nd gen Roadster would still be an amazing car today. Why not just deliver what was promised?
Then there's Full Self-Driving. Tesla marketed the idea that Model 3 owners could turn their cars into income-generating robotaxis. That never materialized. Even if the technology eventually gets there, the opportunity has already passed for many early buyers. Timing matters. Delivering something a decade late isn't fulfilling the promise you sold to customers.
And then there's Solar Roof. Unveiled in 2016 as a revolutionary approach to residential solar, it struggled with costs, installation challenges, and adoption far below expectations. After nearly a decade, Tesla discontinued it entirely in August 2026. Another grand vision abandoned, leaving existing customers with a discontinued product to maintain.
Then came the Cybertruck, marketed as a nearly indestructible, revolutionary truck. Instead, we got a vehicle with questionable build quality, numerous recalls, expensive repairs, disappointing results in several real-world off-road tests, and specifications that fell short of the original promises. It literally looks like a dumpster, but aesthetics aside, the collapsing demand speaks volumes about the reality of what Tesla delivered. Another heavily hyped product that fell dramatically short of expectations.
This is how consumer trust erodes. Not through one disappointment, but through years of accumulating them. And this is part of the reason why many people who were once enthusiastic about Tesla have become critical and cynical about it.
But there's something even more fundamental here: Tesla has completely departed from the grounded, practical vision that made so many of us enthusiastic about the company in the first place.
Electric cars were ambitious, but the idea was grounded in reality. People could charge at home, infrastructure could gradually expand, technology could improve, and millions of ordinary people could eventually benefit. There was a realistic path toward mass adoption.
What the hell does a flying Roadster powered by compressed-air thrusters have to do with any of that?
It's an expensive, impractical engineering stunt with enormous packaging, safety, and regulatory challenges, and no credible near-term path toward mass-market usefulness. Modern electric cars already achieve ridiculous acceleration without this nonsense. What practical problem does it solve? Who actually needs it? When will ordinary consumers benefit?
And watching Tesla fans compare this experimental prototype in a Roadster-shaped shell to actual production vehicles people can buy today is ridiculous.
What's especially infuriating is the cult-like mentality surrounding all of this. You can't point out the history of failed promises, question the practicality, or demand realistic timelines without being accused of being a "hater" or having "Elon derangement syndrome."
Tesla deserves recognition for helping make electric cars a reality, but that success doesn't give it a blank check to keep marketing increasingly fantastical promises while failing to deliver on previous commitments.
This isn't hatred. It's disappointment from people who once believed in the company, watched it accomplish extraordinary things, and have since watched it increasingly substitute practical innovation with spectacle and promises.
Elon fanboys are also ignoring a bigger pattern that goes beyond Tesla.
What made many of us enthusiastic about Elon and Tesla and in the first place was a grounded vision of technological progress that could genuinely improve millions of lives. Electric cars were ambitious, but practical, achievable, and useful to ordinary people. Now we're supposed to get excited about flying cars with compressed-air thrusters that have no credible path to mass adoption and solve no meaningful problem for the average consumer.
It's the same kind of thinking behind marketing the colonization of Mars as some urgent necessity for humanity's survival. There's nothing wrong with space exploration, but Earth isn't about to become uninhabitable because of the Sun. We're talking about geological timescales, while humanity faces enormous problems RIGHT NOW: poverty, hunger, access to education, corruption, abuse of power, wars, and a lack of government accountability. We have plenty of very real problems to solve before worrying about humanity's survival on another planet.
And then there's DOGE. I was genuinely excited about the original idea of making government more efficient, reducing waste, and increasing accountability. Instead, we got questionable savings figures, inexperienced people making sweeping decisions, serious concerns over access to sensitive personal information, and an initiative that ultimately fizzled out without delivering its grand promises. Another enormous narrative that fell apart when confronted with reality.
That's what makes all of this so frustrating. It's not that we stopped believing in technological progress. It's that we've watched someone who once pursued grounded, transformative ideas increasingly embrace fantastical promises, political spectacle, and questionable narratives disconnected from practical reality.
And somehow, pointing out this history of disappointment gets you labeled a "hater" or accused of having "Elon derangement syndrome."
The problem isn't dreaming big. It's repeatedly selling people those dreams, failing to deliver, and then expecting everyone to applaud the next, even more ridiculous promise without asking what happened to the last one.
The world doesn't need more spectacular promises. It needs practical solutions to real problems, honest expectations, and accountability for what was promised. That's what made Tesla exciting in the first place, and that's what so many of us feel has been betrayed.
This is very good explanation of why you shouldn’t cook Costco steaks medium rare. I prefer local butchers if I’m planning to do that.
Costco blade tenderizes its steaks. The label says so, and it's the reason a Costco steak comes with a cooking temperature printed on it.
Before the steak hits the case it goes through a machine that pushes rows of thin blades into the meat to cut the muscle fibers, so a Choice steak eats more tender than it would on its own. You can't see the cuts once it's done, except sometimes in the fat on a ribeye.
The catch is what rides in on the blades. E. coli lives on the outside of a cut of beef, which is why a rare steak is normally safe. The sear kills what's on the surface and the inside was never exposed. Push that surface into the middle with a blade and rare isn't safe anymore. That's why USDA says a mechanically tenderized steak gets cooked to 145F with a 3 minute rest, the same number as a pork chop, where a steak that wasn't tenderized carries no cooking instruction at all.
USDA figures about 11% of the beef sold in the US is mechanically tenderized, and since 2016 the words have to be on the label. Costco started printing them in 2012, after an E. coli outbreak in Canada was traced to its blade tenderized steaks. The ground beef isn't tenderized. If the package in your cart says blade tenderized or mechanically tenderized, cook it through.
prasincs ⚓️🇺🇸🇳🇵 retweeted
Okay, so it's just compressed air.
Easier.
But crappy specific impulse (less than 50 seconds).
Even assuming that 50% of the mass of the car is the compressed air, that gives you less than 10s of useful thrust (with thrust-to-weight ratio larger than 1.0). Probably more like 5 seconds, actually.
So the car must be an empty, lightweight shell containing big compressed air tanks.
Cute demo, but totally impractical.
But of course, you knew that.
prasincs ⚓️🇺🇸🇳🇵 retweeted
Chill, it’s only been 3 hours. He’s 5 years old so it takes time.
Anyways, we started with bijections and cardinality, moved on to countable versus uncountable infinities, Cantor’s diagonal argument, Hilbert’s Hotel, ordinal arithmetic and transfinite induction. I’m trying to get to the aleph hierarchy before bedtime, but he’s really struggling with the axiom of choice and Zorn’s lemma.
I think I’ll wait until the morning to cover the continuum hypothesis—don’t want to give him bad dreams, ya know.
prasincs ⚓️🇺🇸🇳🇵 retweeted
I cannot let a steam engine analogy go by without bringing up John Wilkinson, perhaps the most underappreciated engineer of the Industrial Revolution.
Watt had figured out how to dramatically improve the steam engine, but he spent years struggling to actually manufacture one. The problem was embarrassingly simple: nobody could make a sufficiently round cylinder.
The casting and machining tolerances were atrocious. Watt complained that one 18-inch cylinder was out of round by as much as THREE-EIGHTHS OF AN INCH. Steam leaked past the piston. He tried packing the gaps with cork, oiled rags, paper, and apparently even old hats. Nothing worked particularly well.
Enter John Wilkinson, a British ironmaster whose immediate concern was manufacturing better cannons.
At the time, iron cannons were commonly cast around a core, introducing defects and misalignment that could make the barrels inaccurate or dangerously weak. Wilkinson developed a process for boring cannons from solid castings, and in 1774 patented a vastly improved boring machine.
The key innovation was almost comically mechanical: a massive, rigid boring bar, supported at both ends, that could cut a genuinely straight, circular hole instead of following the irregularities of the casting.
He adapted this technology to Watt's cylinders. By 1776, Matthew Boulton was boasting that Wilkinson had bored a FIFTY-INCH cylinder accurate to within the thickness of an old shilling.
Suddenly, the steam engine was a commercially viable machine.
And the story gets better. Wilkinson became one of Watt's first customers, buying an engine to drive the blast machinery at his own ironworks. The steam engine was now helping manufacture the iron that made more steam engines possible.
I find this whole episode absolutely extraordinary. The Industrial Revolution was, for a critical period, bottlenecked on the ability to make a sufficiently round hole.
We remember Watt, and rightly so. We remember the invention of the steam engine and the subsequent development of thermodynamics. But the man who made the machinery that made the whole thing possible is practically a footnote.
I think we consistently underestimate how much of scientific progress is actually downstream of advances in tooling, measurement, and manufacturing precision. It's not just that technology precedes theory. Sometimes the decisive breakthrough is that someone finally figures out how to build the thing the theorists have been talking about for twenty years.
Anyway, I think John Wilkinson deserves considerably more credit for modern civilization than he gets.
a few thoughts:
1. pretraining science is kind of like how people built quite good steam engines before they understood thermodynamics. “Science owes more to the steam engine than the steam engine owes to science.”-(L.J. Henderson). if nick is right, which i am pretty sure he is, progress points in the direction of more interpretability, and pretraining was "slouching towards monosemanticity". my guess would be that major breakthroughs in interp also advance capabilities
2. nick is a good friend of mine, safe to say my origin story at openai wouldn't have happened if i hadn't met him, and it's fascinating watching him operate and seeing the early drafts evolve into this. he's the most profoundly happy guy I know- not driven by guilt, activism, or religious terror, but by fascination. he of course understands the stakes of understanding neural networks, but it doesn't seem like the core reason he does it: he just loves the neural nets. afaict he's posting this from some beautiful hideaway in tokyo. there's a lesson in there for all the +EV maxers. i feel very grateful to have met him
3. i do think it's crazy how few people across the world were staring at neural nets growing: most have moved to a different level of behaviorist analysis, thinking about reward functions and test scores and whatnot. it does not bode that well for the major model companies, oai included, that fundamental breakthroughs like this are coming from outside. we've become too much of a pressure cooker focusing on local incentives, studying narrow tracks under old paradigms, mostly doing engineering, rather than fundamental research, and implicitly hoping the next model will figure it out
prasincs ⚓️🇺🇸🇳🇵 retweeted
US Ambassador Marquita Maytag invites PM Tulsi Giri to an orgy, goes braless wearing a t-shirt that says 'keep those tips up', hosts parties with ganja and cookies, refuses to vacate the embassy when fired, and illegally takes Nepali artifacts when she leaves the country.
This is, unreservedly, my favorite account of an ambassador behaving badly, from a diplomatic oral history site. I read this years ago and cannot forget it, and you won’t either. (If they made this a movie, it’d be R rated.)
adst.org/2015/06/this-woman-…
prasincs ⚓️🇺🇸🇳🇵 retweeted
Today we're open-sourcing pg_vectorsearch, a new vector search extension for PostgreSQL.
The performance improvements over HNSW are pretty remarkable. At 100 million vectors:
◆︎ Faster queries, 4× less RAM: 6 ms vs. 8 ms at 95% recall, using 128 GB vs. 512 GB RAM. HNSW couldn't even build on 128 GB.
◆︎ 7× smaller indexes: 25 GB vs. 180 GB at 100M vectors.
◆︎ 50× faster index builds: 49 minutes vs. 42 hours on identical hardware.
And at 1 million vectors, PRISM delivers 15 ms queries at 99% recall on just 2 GB RAM, versus HNSW's 392 ms on 4 GB.
What leads to these improvements? Rather than traversing an HNSW graph with random memory accesses, our new PRISM index partitions vectors based on distance, compresses them using RaBitQ, and scans them efficiently using SIMD vectorization techniques. This takes better advantage of modern CPUs and memory locality.
This is actually our second open-source vector index. We learned a lot from building DiskANN-based pgvectorscale, including some important limitations of graph-based approaches. Those lessons convinced us there was a better architectural foundation, so we started over.
pg_vectorsearch is still in alpha, with more engineering ahead. But we believe fast, scalable vector search should live alongside your relational data, without requiring a separate vector database.
Released under the permissive PostgreSQL License, just like our pg_textsearch extension for BM25 keyword search. Together, they provide the building blocks for hybrid vector + keyword search in PostgreSQL.
Feedback and contributions welcome!
Code and blog post below.
⚡️⚡️⚡️
At the first event for @nealstephenson’s new book in the Bomb Light series at @ThirdPlaceBooks
It’d be funnier if they had to use Chinese model to analyze the data. Because guardrails.
I jest, espionage is nothing to mess with.
We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work.
At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated.
We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.
Back in late 90s and early 2000s, we fought against proprietary software like windows and in many ways Linux has won. It’s evident by the fact that one can sign up to dot, grokbot or muse and get fully working machines for people. I’ve long believed that only really techies care about the operating systems and minutae. However all this means now we’re on the battle again for service-ization and loss of control because all your private information is stored in one of these clouds. It’s easier in some ways to setup local servers now but also more expensive to justify given these vendors are giving you pretty powerful VMs for free as your assistant.
prasincs ⚓️🇺🇸🇳🇵 retweeted
Anyone comparing privacy solutions should check out Vitalik's new checklist.
Here's where Prividium stands:
✓ Hash-based proofs. Prividium uses Airbender’s entirely hash-based STARK proofs, with no elliptic-curve or lattice dependency in the proof system.
✓ No FHE. Prividium’s privacy does not require fully homomorphic encryption, so it has no dependency on lattice-based FHE.
✓ No public encrypted data. Prividium does not publish encrypted transaction data for outsiders to collect now and try to decrypt later.
✓ Private signatures, including multisig. For transactions and multisigs operating inside Prividium, signatures stay within the private environment.
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…
prasincs ⚓️🇺🇸🇳🇵 retweeted
SIMD-0525 mission accomplished: 200ms slots are now live on mainnet-beta.
Blocks land twice as often as they did at genesis. An epoch now takes about a day instead of two.
IBRL
Final slot reduction step: 200ms slots activation now pending on mainnet-beta.
400 → 350 → 300 → 250 → 200ms
Reminder, SIMD-0525 features take effect one epoch after they activate. Thus, will go live at the epoch 1053 boundary ~15:00 UTC, Friday Oct 9th.
- epoch 1051: pending activation
- epoch 1052: feature active
- epoch 1053: 200ms slots live
prasincs ⚓️🇺🇸🇳🇵 retweeted
To secure open-source software, we’re launching OSS Scanner.
We’ll use our frontier models to periodically scan opted-in open-source projects for vulnerabilities, at no cost. Our reports will provide a proof-of-concept, explanation, and suggested fix.
anthropic.com/research/launc…
