Igor Soarez @igorsoarez
Dad x2. Engineer building on my own terms, or failing at it, mostly. Consulting by day, indie projects by night. AI agent infra. London, England Joined March 2009-
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we wanted intelligence, instead we got next-word prediction we didn't get what we wanted, but we sure use it as if we did
i keep thinking about this one regardless of how good the model is, to get the best out of it you need some form of filtered iteration easier if you have an automated evaluator (or oracle), but that's rarely the case model leaderboards are converging, so this gap between the
i keep thinking about this one regardless of how good the model is, to get the best out of it you need some form of filtered iteration easier if you have an automated evaluator (or oracle), but that's rarely the case model leaderboards are converging, so this gap between the center and the edge is where the value is the implication: "what model are you using" is a tired question, it's mostly a matter of cost now. hello kimi, glm, minimax. the right question: "what system are you using"?
on using llms - green: what you're expecting if you call it intelligence. result is disappointment and frustration - yellow: what you can get out of it with iteration and careful prompting. usage requires intense work. - red: what you're actually getting with general use.
kool-aid has turned sour and the big churches are just catching up to this many indy devs and small teams have already switched a good while ago i've been on kimi for months, switched over from claude, feels like selling a 911 carrera to buy a mx-5 miata 😂
DeepSeek open source model price/quality is so high that even Microsoft is shifting to it after investing hundreds of billions in the OpenAI cash incinerator failure Again, all developing as I told you it would
@iamtrask Is there anything really preventing these companies from bundling open weight models or even proprietary models behind what you think is a single model?
@rcbregman Noise, complexity, misaligned incentives - is it really marvelous?
Great read. We went from “doing” to “managing” to “directing” insanely fast. The part I struggle with most with is letting go of understanding the details. Years of trauma learning that details bite. Finding tight abstractions you can trust, like in this case, is key.
on using llms - green: what you're expecting if you call it intelligence. result is disappointment and frustration - yellow: what you can get out of it with iteration and careful prompting. usage requires intense work. - red: what you're actually getting with general use. outside your domain it feels impressive because you can't tell the difference. useful to level up to the average, but its important to remember there's a whole slope beyond it.
An overdue cold-plunge
More AI-generated code doesn't make your team faster. It might actually slow you down.
Chinese, or open weight? Americans have dropped the ball on the open weight model race. Platform-wise you also get a lot more bang for buck with going east.
This is a pretty striking shift toward Chinese models by American AI startups since the start of the year. substack.com/@profgmarkets/…
A developer posted this on Reddit, and I haven't stopped thinking about it.
We still don't grasp LLMs. That's evident in our choice of words to anthropomorphize what's really just a statistical model. We don't understand what the existence of a large statistical language model implies. It does not imply intelligence, but it’s something easily confound-able with it. The training corpus was human written, with thought and intelligence, so the output is a sort of mirror of humans, of how we write and reason to some extent. the mirror is confusing because people have never seen that kind of mirror before. Once you identify these models as mirrors or the text they are given, problems like "sycophancy" and "hallucinations", described in these anthropomorphized terms sound silly. Once you identify these models as mirrors or the text they are given, problems like "sycophancy" and "hallucinations", described in these anthropomorphized terms sound silly.
“never expected” 😆 🙉
Inference will never be free or even negligibly cheap. Everyone will get a token budget. ➤ Productivity gains from tokens are hard to attribute. ➤ Consumption is easy to scale. ➤ Waste is nearly invisible. You don't know where to cut. That's all you need for a price floor.
In a previous era, I often made a big deal about deleting unnecessary bits in a project repository. “What’s the big deal? It’s not harmful” — but it is, like clutter on a work table. I’m glad there’s now a bigger imperative: it costs tokens and increases perplexity!
This won’t last. They’ll need adjustable, per division and per employee budgets
*UBER SETS $1,500 MONTHLY CAP ON SOME AI CODING TOOLS FOR STAFF $UBER officially reeling in the Claude budget after blowing their AI budget earlier this year. Undoubtedly more companies to follow
dealers know better than getting high on their own supply
I can now probably say this: Two months ago, inside Anthropic someone suggested building a token leaderboard. A heated internal debate followed and the decision was made to *never* ever do it… because several people inside Anthropic simply thought ahead of the consequences
@mitchellh How’s this different from a ceo or director or anyone who hires or employs subject matter experts outside their own domain?
This what collapse looks like. Societal culture collapse. Every thought, trend, what you assume is mutually assumed gets pushed and compressed toward the statistical model. A worldwide echo chamber worthy of a black mirror episode. Alpha comes from the ability to disconnect.
This is such a good post. orchidfiles.com/im-tired-of-ai…
engineers are realizing that any serious work requires LLM use to work *slower* orgs are still scrambling to answer “what are we getting back from all this AI spend?” unproven but generally accepted answer is “faster” or “more” but the direction to look is “higher quality”
Good post! “Using AI to write better code more slowly”: nolanlawson.com/2026/05/25/usi… This is what I've been doing with my smalloc project. Wrote all the (core) code myself while asking AIs to teach me about stuff, and now I'm repeatedly asking AIs to “Find more bugs in this.”.
Inference will never be free or even negligibly cheap. Everyone will get a token budget. ➤ Productivity gains from tokens are hard to attribute. ➤ Consumption is easy to scale. ➤ Waste is nearly invisible. You don't know where to cut. That's all you need for a price floor. Tokens will forever be a scarce resource you need to allocate consciously, much like old problems: headcount for managers, capital for investments, ad budget for marketers. Tokens won’t be different. You need a performance-adjusted budget.
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