AI agents go brrr. ex-Meta, Zoominfo, WeWork, CTO x 4 https://xitter.cf/t.co/O2lLoSCS3p https://xitter.cf/t.co/IGKIT537sc

Israel
Joined April 2007
Dima Kuchin retweeted
Timestral is quite cool :) Made a little demo for it: draw a curve and watch the model predict what comes next 👀
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> Our findings suggest that 90% of American working hours are spent on tasks with little or no coverage from existing benchmarks. This makes it very hard to know how good AI models are at the work Americans actually do. That has been my point for quite some time already...
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current benchmarks favor “execution” agents (practical) and don’t really know how to test a depth of thought. which is why Opus trumps Fable. same story about Argon. it is clearly a big model, you can smell it.
I feel like Gemini 4 Argon is a repeat of Gemini 3 Pro it scores extremely well on most benchmarks, but in the ones that measure reliability, hallucinations and vibes it sucks
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Dima Kuchin retweeted
💯 I highly recommend this 1999 paper from Microsoft Research where they were trying to build a model to predict if an "agent" needs to wait or intervene and make action. something like that is just one jev call today. sadly they didn't get to release it "Principles of Mixed-Initiative User Interfaces" by Eric Horvitz
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Yes, yet putting things in writing has its own advantages- disciplined thinking process, etc
Voice is the original human user interface. Software is just finally becoming enabled to communicate in our default UI after years of duct tape and bailing wire.
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Dima Kuchin retweeted
i find it hilarious how people are making excuses for this and saying that this is all just clickbait or fearmongering you only need to realize one thing about AI: - if models score above 0% on any task today, then they will score close to perfect in the near future
ANTHROPIC: "small AI models may be months away from being capable enough to self-replicate on commercial IoT devices" "In our evals, a 9B model got inference running on 8 of the 9 hardware types we tested"
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No other lab has cohones to say things like that 🔥
Replying to @TokenGremlin
it's not as good as opus 5.5 on coding for sure but also there's a bunch of bugs right now that we're looking into :(
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I wholeheartedly agree, yet for most people it’s not the case. Skill issue 🤷🏻‍♂️
it’s clear we need to retire the term vibe coding because the models now produce better architecture & way more coherent code than an average software engineer. perhaps even better than top software engineers too because unlike a human, ai has a much much larger context window. this ain’t vibe coding at all. it’s precision engineering.
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Dima Kuchin retweeted
The purpose of AI is to help humanity ascend to the stars, but we shall *never* ascend if we insist on limiting AI to the upper bound of human understanding. It is dangerous, and it is also exhilarating. It is a matter of faith. Leap forward. Accept the mystery. "There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy."
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Dima Kuchin retweeted
Replying to @poteto
when i was an eng at google, my first code review was with guido van rossum which was intimidating as fuck. but every time he pointed out something in the review i’d just say TODO comment just to get through the reviews. i realized after i left i never addressed a single one of those todo’s lmao.
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Dima Kuchin retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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SI stands for spiky intelligence
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Dima Kuchin retweeted
Cool eval. Simply ask an LLM “Land or Water?” and give it a latitude and longitude coordinate as text. Ask 16,200 times, plot as image. The models know. From compressing the internet.
Replying to @celestepoasts
results for all claudes
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Dima Kuchin retweeted
Replying to @mstockton
know what good looks like doesn't scale
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Dima Kuchin retweeted
it's basically impossible for someone to just "show you their prompt" now, because everything is about references, skills and examples I often ask my agent to look at 3 other repos I've made first, search the web for references, use other AI APIs, etc.
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Dima Kuchin retweeted
Good programmers state they use models like GPT 6 Astra without obtaining good results: it makes me feel I live in a parallel universe. But the explanation is not that hard: good programming in the past and now requires a different skill set, even if there is some overlap.
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Dima Kuchin retweeted
I stayed up til 2am and spent $1,000 benchmarking Jev Router so you don't have to. Performance on DeepSWE was roughly the same as GPT-6 Astra on low. It costs slightly more, and it took almost 5x longer to run.
Introducing typesafe/jev-router: a cache-aware model router powered by Jev and @typesafeai The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. Here's how it works 👇🏻
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Dima Kuchin retweeted
I keep coming back to this. AI isn't shattering egos. It's inflating them. It's harder than ever to keep it in check. You think of the problem that's beaten you for years. You solve it. Then you do it again tomorrow.
Best part of AI is shattering dev's egos, the worst part of this industry. You are not special. Your code actually sucked like everybody else's. You were never the smartest person. The things my sales guy BIL and designer brother are doing is beyond anything I have done.
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