Meronix (@Meronix15) profile picture

Retired CEO of my local crack house. Retarded and zero hoes

Joined January 2019
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Meronix retweeted
Here is Proton CTO's AI manifesto, that I obtained multiple copies of 404media.co/manifestos-fear-…
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Meronix retweeted
Software around me is not improving nor is it expanding. If AI drastically lower the cost of software… then we have a mystery, don’t we? The answer is that building the software was never the difficult part in these cases. Our organizations have been optimized for process, meetings, and risk avoidance instead of shipping working systems. It is institutional decay: layers of people whose main job is to prevent other people from doing the work.
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Caring is the moat, you should feel visceral pain when things are bad or broken and stop at nothing to fix them
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to all my followers who believe in this, sincerely - it's bullshit. this is an era of unprecedented technology and with it comes unknown challenges, it is like a new world map in a game where only paid promotional beta testers have been before. what is the value of discovering a counterfactual now when people have stopped reading everything? how reliable is this tech where you chat with a bot and it hallucinates 3 times a day but you never know on what? what is the cost of discovering such bugs late for your projects? what is the cost of discovering problems/bugs when entire world is wearing the same glasses and everyone has same blindspots? "there is no technical moat" - yeah, for you. ofc you would think that if you are someone who has always just wanted to get by their day and do nothing beyond what is forced on you, then yeah, sure. but if you tell a guy like me "everyone is offloading to the same machine to think, using the same overabstracted superficially verified systems, where agentic PR count is a porn to be consumed by those who think they are doing something". if you tell a guy like me that, i would say there is now an opportunity to be hercules amongst men. there is an opportunity to really use this tech around you to imitate what fabrice bellard could have pulled off in your niche if he cared as much as you. there is an insane opportunity to be unlimited, your moat is starting your morning where others have stopped caring about something.
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Meronix retweeted
How have software engineering fundamentals changed with agentic coding? Here is our AI Engineering Skills map for software engineering fundamentals.
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1. Reduce the size of the task you hand over to an agent. Don't give it large swaths of tasks unsupervised if you don't have a codebase pattern the agent cannot continually reference as a source of truth. This applies heavily to monorepos, microservices with a structure org-wide, template repos, etc. While agents can one-shot, quality reduces with time and lack of human intervention in-between huge swaths of work causes rotten output. Which leads me to: 2. Decide when you bring humans into the loop: You don't have to review every PR. Have another agent decide the blast radius of the PR. You can have a workflow that deterministically generates a blast radius and use AI to drill deeper). The blast radius should determine whether a PR review agent is good enough for a merge into prod or requires HITL. 3. Scoped agents.md files. One giant agents.md file is a recipe for disaster. Keep the files scoped. Put instructions affecting a specific part of the codebase in that folder alone. Don't dump everything in the root agents.md file. 4. Let your skills mirror your working pattern, don't blindly copy skills. Skills should evolve from the way the team works and what has been proven to be successful. Copy-pasting skills into a codebase is a highway to hell. It is either too verbose and burns tokens, focuses on the wrong metrics for success, or too minimal and wastes tokens by doing nothing to help. Let team members write the skills, feel free to read externally packaged skills and adapt what works for you. 5. Self-improve. When the agent produces slop, don't yet fix the code, fix the instructions, the skills, the agents.md file, your factory. Fix it there. Then fix your code. Your skills and agents.md file MUST evolve with your review process. It must not be static. There are a few more and I'd love to share but typing is hard. This will solve 80% of your problems. The remaining 10% is for me 😉 and the last 10% is for the black box of AI we haven't solved.
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Meronix retweeted
I am currently living off a EUR loan; I borrow EURC at 0.22% from Aave v4 on the Arc chain: pro.aave.com/explore/reserve… That rate is lower than inflation. I am shorting the Euro, which keeps falling (and will continue to fall) against BTC. I live (and earn—or rather, save) in line with my ideology: "never trust the state or state-issued money." It has been working for me in the long run. Naturally, I bypass MiCA regulations, as the entire process is anonymous and requires no KYC. EU bureaucrats don't like this and want to regulate DeFi! Of course, no one knows the exact details, since it involves DeFi. My Claude agent analyzed the smart contracts on the Arc/Ethereum blockchain and the mechanism for minting new tokens, then wrote a DeFi bridge for me from scratch that runs locally on my computer. I have no idea what European bureaucrats will do about that. In any case, I would argue that the EU is becoming the world's largest financial totalitarian regime. It is doing everything in its power to prevent you from easily escaping (including pressuring non-EU banks not to open accounts for you). cryptoslate.com/eu-faces-sep… teapawt.net/StaniKulechov/status/2…
Disappointed by the European Central Bank’s (ECB) and European Banking Authority’s (EBA) responses to the MiCA consultation. They are not only advocating for a prohibition on paying yield on stablecoins, but also for restrictions on CASPs providing access to DeFi, including protocols offering yield on non-MiCA-authorized stablecoins, without providing a clear framework for how access to DeFi should work in practice. They also suggest that CASPs offering access to DeFi should restrict it to certain groups of users through so-called “suitability tests,” alongside a potential certification regime for DeFi lending protocols. It is unclear how these requirements would work in practice. If regulators determine which protocols are deemed suitable for European users, the result risks looking very different from the open DeFi that exists today. Instead, we could end up with more walled gardens that undermine the liquidity and network effects of open financial networks, ultimately limiting Europeans’ ability to access new ways to grow and preserve wealth. These proposals are presented as measures to “protect consumers.” But excessive restrictions can instead create more friction for innovation and slow the development of open, transparent, and auditable financial infrastructure that can benefit consumers. The reality is that onchain finance, including stablecoins, DeFi, and tokenized securities, has the potential to reshape financial infrastructure by reducing friction, increasing transparency, and expanding access to financial opportunities. European consumers and businesses stand to benefit significantly from this transformation, particularly if the companies building this infrastructure can continue to build and compete from within Europe. Meanwhile, U.S. regulators, including the SEC and CFTC, have increasingly taken a more pragmatic approach toward enabling onchain financial innovation. European regulators should put users and their interests at the center of the framework. Banks and other incumbents should be able to adapt their businesses to serve users in an increasingly onchain economy, rather than relying on artificial restrictions that risk increasing costs and reducing access for Europeans as the rest of the world moves onchain. DeFi will win.
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Meronix retweeted
ok it's official... AI-written unit tests and integration tests are empirically proven to be unhelpful. you should tell your agents to stop writing tests by themselves on the deepswe eval set, banning sonnet 5.5 high from writing any tests actually resulted in slightly higher success rate (non stat-sig), with less time and token spent (stat-sig). this is pretty hard empirical evidence across the tests that were written by the "tests allowed" baseline arm, 65% of them were unit tests, 35% were integration tests, neither bucket resulted in any improvement compared to not writing any tests at all i also picked a random sample of 44 tasks subset where i completely disabled executing even existing tests - it also did not affect success rate at all i spot checked many tests written in the baseline arm, and my intuition is that most tests are simply a repetition of the implementation agent-written tests do not add any value because both the implementation and the tests were simply the agent's interpretation of our intent. the tests aren't any more accurate than the implementation itself i suspect we can still extract some value from unit tests and integration tests if we describe them ourselves when we believe we can articulate our intent better through test cases than through requirements. but i have not proven this yet also worth noting, during deepswe eval the agent wrote almost no e2e tests (only 17, compared to 3000+ unit/integration tests written). so this analysis does not prove nor disprove the value of e2e tests - i will do another eval specifically for that
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Meronix retweeted
Building a Memory Pipeline: 🦄 AI That Works #77 teapawt.net/i/broadcasts/1nxnRBQgm…
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Meronix retweeted
8 months ago I joined Reflection to build a frontier western open weight model. Today we're releasing Beam (501B total, 23B active with an Apache 2.0 license). I worked in many roles over this time, but spent a majority of the time here leading our data platform team. We started from zero and built out all of the: pipelines, storage, inference, infrastructure, and tooling needed to get this model built. Processing + storage. Beam trained on 23.8T tokens. I'm most proud of the OCR pipeline we put together generating trillions of tokens from hundreds of millions of PDFs. We scaled the data team from working with single terabytes to multiple petabytes. None of this happens without the team. The talent across research and engineering here is the best I've worked with, and it's why we could ship this on a first release. Looking forward to getting this model and future releases into everyones hands soon!
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: reflection.ai/beam
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Wanted to write a bit about the impact of AI on HFT development 01/ it’s not great Well starting with the obvious, there are not many resources out there when it comes to proper HFT. No one is going to open source their oms, risk engine, pricing modules Many times you will have to refactor completely some modules to scale properly. Your main goal as an HFT developer is to lower the probability of mistakes, mistakes cost money and they shouldn’t happen. You are sitting in between the quant that will tweak the configuration files you put at his disposal. It’s your’role to make sure things are doing what they seem to be doing. Circuit breakers should be everywhere, everything should be auditable. Focus on the hotpath and load test it: from the moment your program receives data from your provider to the moment your sending quotes to exchanges. Every single line and function should be simple and easily understandable. This is where AI development will fail you if you don’t do it properly. Minimize the lines of code not the opposite. But write as much slop to try to break it! Do not over abstract, exchanges are completely different from each others. We all tried to create the right abstractions to make it fit but well no just don’t. 02/ won’t get much better This is inherently how the LLMs are working and they have been unlucky that there is not much good code out there. Sure a good actor model design (more on this in another post) can help, sure some good skills can help too, but you’re the one loosing money at the end, you’re the one that forgot that circuit breaker or realized AI added some magic number there. 03/ seat in the drivers place Having said all that, I still think one cannot dev without it today so how ? It will be very frustrating, you will have to code with the agents as if the code you write could actually kill people. Use the agent as a rubber duck, above all keep things simple and well organized. You could get probably get better results than before AI because well you can do mu ch more stuff. But it’s not going to be an easy road there. I do not see a future where GPT 10 changes all these principles unfortunately but happy to be proven wrong!
i see people want more HFT tricks so here we go probably most of you doing any serious hft know these simple ones but I think it's always good to see what hft optimizations actually looks like 01/ every action is sent over 3 ws channels at once why? just benchmark it, you will see by yourself its not a global rule, every exchange has its own internal system and logic. TCP networks are inherently random, by multiplying the load you get rid of the tail not enough IPs ? you can always have more IPs, it's absolutely never a bottleneck 02/ think about shards exchanges have to shard their backend, not everyone will use the same sharding keys tho, again you have to benchmark it (be careful not to introduce biases there) some subaccounts could have lower latency, some markets, some api keys or a mix of everything you have to be creative and understand how the underlying system you're interacting with is shaped, there is no magic rule, there will be nothing online 03/ watch what other traders are doing, hyperliquid is public so use this, analyse patterns, find alpha, its out there, waiting for you 04/ oh great so agents can do it well, I wish but from personal experience they do lack that creativity, you can easily benchmark previous exchanges' tricks on new ones you're integrating and use them to reverse-engineer/understand better the underlying api/elb/matching engine
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Meronix retweeted
I honestly believe there's going to be more software engineering jobs in the future. This is an insight I learned from @cramforce on just how much software is being created. I don't think it's going to be all doom and gloom for our industry. The future is going to be incredible. Embrace it
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I still read the code
I am most afraid of us eating the productivity gains of agents by just becoming lazier.
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I’ve been trying to get a better sense of how GPUs work. I’ve read a lot online, but the following posts were particularly helpful: Making Deep Learning Go Brrrr From First Principles - horace.io/brrr_intro.html What Shapes Do Matrix Multiplications Like? - thonking.ai/p/what-shapes-do… How to Optimize a CUDA Matmul Kernel for cuBLAS-like Performance: a Worklog - siboehm.com/articles/22/CUDA…
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Meronix retweeted
I'm seeing a lot of euphoria about how Opus 5.5 is good at TLA+, and this means that all software will soon be formally verified. As a person who loves TLA+ so much he wrote a book on it, I want to throw a particular cold shower on people's enthusiasm by talking about the limits of what you can actually verified with it. The high level simplification is that TLA+ sees a system as a set of "behaviors", or possible sequences of states. For example, the pseudocode "pick a random number from 1-3 and decrement it to 1" has three behaviors: `{3 -> 2 -> 1, 2 -> 1, 1}`. From here, there are two basic kinds of TLA+ properties: - `[]P` means that `P` is true in *all states* of *every behavior*. - `<>P` means that `P` is true in *at least one state* of *every behavior*. `[]P` is immediately useful as an **invariant**, or something that always be true of your system. This is things like "your data is never corrupt" or "there's always at least one server online." `<>P` is a little more abstract, but for technical math reasons I won't get into here, can be stacked with `[]` to create really complex and useful properties. `<>[]P` represents things like "the algorithm eventually converges on the right answer", `[]<>P` things like "if two data stores desync, they will eventually resync", and `[](P => <>Q)` things like "If a message is put on the queue, it's eventually processed by a worker". Really cool stuff! These primitives were chosen to make a wide array of properties useful. And if we're clever, we can do all sorts of more complex properties, like bounded time constraints and history properties. But we're always constrained to 1) define a logical formula 2) over individual behaviors, and 3) check that all behaviors satisfy that formula. So some things that we *cannot* express in TLA+: - Possibility and reachability properties: that it's always possible to *make* P true, even if you don't actually decide to. Things like "I can always shut down the computer" or "A user can always change their password". These can't be expressed with `<>P` because that's "for all behaviors, P happens at least once", we actually want "for all behavior prefixes, there is at least one behavior where P happens at least once". - Hyperproperties: properties that are defined over two or more traces. These are things like "painting a car red doesn't make it faster" or "users cannot infer secret data by observing public data". We can't do these because TLA+ only looks at one behavior at a time. - Statistical properties: 95% latency is 1ms. Impossible because most of these are hyperproperties. - Properties about if a system is robust against code changes. Impossible because, uh, you have new behaviors now. Some of these are solvable in different logical formalisms. CTL can do reachability, PRISM can do statistical properties, etc. Those have their own tradeoffs and limitations, though, and no system can do everything. Others are solvable with a lot of cleverness tailored to the specific spec, like lifting a model into a hypermodel. But these are insanely inefficient and make your "clever spec" diverge significantly from the real world system, so introduce a lot more opportunity for things to go wrong. The core problem, though, is (1): properties are logical formula. If we don't know how to express a system property as a logical formula, we can't verify it. 99% of the properties we care about fall under this. The information on the site is easy for a user to find. Our LLMs behave as we expect them to. Our application can't be used to break the law. TLA+ (and Quint and Lean and Rocq) are near-useless here, no matter how clever you are. Don't get me wrong: `[]P` and `<>P` represent a huge range of useful properties and TLA+ is incredible at finding awful concurrency bugs. But there's a lot it fundamentally can't do and we shouldn't believe that it will solve all our worries about software bugs. And the same goes for all other formal verification languages, too.
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Meronix retweeted
I’ve spoken to over 50 people 1:1 over the last several months to better understand how they code with AI. It’s an absolute mess. Here are the top 8 mistakes I see. 0:00 - Intro 1:19 - Mistake 1: Talking to 2026 models like they’re 2025 models 2:43 - Mistake 2: Devising funky metaphorical factories to orchestrate your agents 3:59 - Mistake 3: Treating context as sacred 6:02 - Mistake 4: Treating previous conversations as sacred 7:59 - Mistake 5: Having a todo list 10:00 - Mistake 6: Using funky tools 13:47 - Mistake 7: Stressing over code quality 24:27 - Mistake 8: Using branches and worktrees
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Meronix retweeted
What an insane summer !!! Several of the software libraries I maintain got massively faster! Some of this software is stuff that you probably use. For weeks I have been telling people that I was preparing an article... 'A summer of AI optimization'. Here it is. I have never seen anything like it. This was well optimized code in the first place. And we just made much of it much faster, much better, in a few short months. And it wasn't planned. It just happened.
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If you’re a CS student, I’d suggest writing your thesis around SDCs (silent data corruptions) and efficient algorithmic fault tolerance. We’re quickly going to need to accept the (un)reliability of computing systems again! Every indicator (density, lower voltage gating, raw scale) is moving towards SDCs becoming a more serious issue over the next decade. And let’s not forget when all of this stuff starts to go in space and we have to deal with cosmic events on “not-super-rad-hard” CPUs+GPUs! No, you can’t solve it all in hardware. It’s prohibitively expensive; and the long tail of “potential SDCs” is just too long. One bad apple ruins your pie. One bad GPU can inject repeated corruptions into otherwise noise-tolerant workloads (*cough* AI training). If you continuously accept a corrupted value from say…a bad tensor core, that bad value can quickly spread across the whole cluster. If it happens again and again and again and no one notices; it’s like putting a little bit of spin on a bowling ball. Eventually, the overall trajectory ends up widely different! When you start to imagine this stuff being in space (cosmic bit flips), and quantized(!), each remaining bit carries more critical information. Long term we’re going to have to accept clever, low-overhead software algorithms. Accepting say, a ~3% perf loss can be *absolutely* worth it if it increases your odds of detecting a mercurial core enough!
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Meronix retweeted
I don't understand the negativity toward AI coding. Used properly, it's extremely useful. We've spent decades trying to write better code faster, and this is the single biggest improvement I've seen in my lifetime.
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