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Boris Cherny

Boris Cherny

@boris-cherny · 35 positions · On ppll ↗

Creator of Claude Code at Anthropic.

Boris Cherny did not write this page.

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  1. AI and science

    When there’s new data, you have to update your priors.

    ↗I am often wrong — Boris Cherny's Blogborischerny.com

  2. ambition

    I love being wrong. It is my favorite, because it helps me more clearly define the problem, find the right solution, learn more quickly, and solve the problem.

    ↗I am often wrong — Boris Cherny's Blogborischerny.com

  3. 8 weeks earlier
  4. prompt injection

    the model does not seem to be prompt injectable anymore.
    spoken · machine transcript hear it at 2:01 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 1st of 20 in this recording

  5. prompt injectionneural networks

    it's it's literally we're looking at neurons in the model's brain that light up when prompt injection happens. So the model won't even tell you but we can actually see those neurons and we can figure out and diagnose that it's happening. And then you combine that with the auto mode classifier and with these three layers we just cannot demonstrate prompt injection anymore.
    spoken · machine transcript hear it at 3:01 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 2nd of 20 in this recording

  6. LLMs

    And the reason is every model is very different. So, something that you did for one model maybe 3 months ago, it just might not translate at all to the next model.
    spoken · machine transcript hear it at 4:01 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 3rd of 20 in this recording

  7. LLMs

    And what's interesting is that the model is actually a little bit more intelligent without these prompts.
    spoken · machine transcript hear it at 5:02 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 4th of 20 in this recording

  8. coding agentsAnthropicsimplicity

    for people that aren't building agentic products, but you're using Claude code, every 6 months delete your Claude MD. Delete your skills. Delete your hooks.
    spoken · machine transcript hear it at 6:55 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 5th of 20 in this recording

  9. LLMs

    And you don't want to guess what's the instruction that the model needs because you might not predict it correctly. The thing that you want to do is you want to run it.
    spoken · machine transcript hear it at 7:49 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 6th of 20 in this recording

  10. designsoftware quality

    I I think this is sort of the crazy thing about building on models. It's just so different than all the engineering that I've ever done. Like in the past when you built on systems, you built these like big beautiful systems and you really think about the system design up front. You have like a big suite of unit tests. You think about everything and you know, like a re-architecture is a big project.
    spoken · machine transcript hear it at 8:27 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 7th of 20 in this recording

  11. LLMs

    And the model is not like that. It's um the way to think about it is almost like a like a living creature, like it's something more organic.
    spoken · machine transcript hear it at 8:54 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 8th of 20 in this recording

  12. benchmarks

    I think evals, they outlive the harness a little bit, but not by that much. Like an eval might live for maybe one, two, three model generations, but nowadays the you know, we're on the exponential. The model is improving so quickly, very often we just saturate the eval, and then we have to throw it away, and we have to come up with a new eval.
    spoken · machine transcript hear it at 9:59 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 9th of 20 in this recording

  13. LLMs

    Because the model can do this at every given model generation, but there is often not a product that lets the model do this and lets it express this kind of ability to do this.
    spoken · machine transcript hear it at 11:41 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 10th of 20 in this recording

  14. LLMs

    One is you should give the model slightly harder tasks than what you think it can do.
    spoken · machine transcript hear it at 14:50 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 11th of 20 in this recording

  15. code generation

    one example is the model can now rewrite essentially any code base from one language to a different language.
    spoken · machine transcript hear it at 15:44 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 12th of 20 in this recording

  16. LLMs

    And my hypothesis is there's probably dozens, hundreds of opportunities like this with the models of today that no one has yet realized.
    spoken · machine transcript hear it at 19:21 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 13th of 20 in this recording

  17. LLMsAnthropicchat interfaces

    I think the skill nowadays is less about prompt engineering and more about figuring out how do you give Claude a hard task that seems a little bit too hard. And then how do you make it possible for Claude to verify its work along the way? And the verification I think is probably the single most important thing that people do not get right
    spoken · machine transcript hear it at 20:13 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 14th of 20 in this recording

  18. AI agents

    You don't need slash goal, you don't need slash loop. These help, but really all you need is give the model the task, give it a way to verify the output of its work so it doesn't get stuck, and it will just go.
    spoken · machine transcript hear it at 22:35 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 15th of 20 in this recording

  19. coding agents

    So when I look at engineers that have been, you know, coding for a long for a long time, you know, like for for years or for decades, this is a really really common failure mode is trying to over specify and it's trying to be overly specific and then, you know, get the model to do the to do the task exactly the way that you would have done it. And that that's just not the way the model works.
    spoken · machine transcript hear it at 24:08 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 16th of 20 in this recording

  20. scaling lawsAI agents

    Like this is actually like a new form of test time compute. Like when we talk about the scaling laws and kind of we talk about the model getting more intelligent over time, historically, it's been a function of the size of the neural net, the amount of training data, and the number of flops that you put in to the training. And then recently, we also added test time compute. So this is essentially a fancy way a researcher way of saying how many tokens does it generate. And now dynamic workflows are essentially a new way to orchestrate test time compute.
    spoken · machine transcript hear it at 26:55 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 17th of 20 in this recording

  21. AI and jobsAI agents

    we're on the path to fully automating the maintenance of our apps by doing this. And this is again hundreds of agents running every day, sometimes thousands of agents every day. It's doing the work of you know, dozens or hundreds of engineers.
    spoken · machine transcript hear it at 29:47 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 18th of 20 in this recording

  22. design

    So coding is solved for the kind of coding that I do. It's not solved for everyone. You know, there's still code bases that are like super deep systems code bases where quad still struggles.
    spoken · machine transcript hear it at 30:38 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 19th of 20 in this recording

  23. LLMs

    And it's really about being empirical. So forget all of the things that you learned about past models. Forget everything that you've learned about computer science theory in class. Look at the model, try to do a task, see where it struggles, and then based on that adjust. So it's just like very much become it's not a theoretical science, it's become an empirical science.
    spoken · machine transcript hear it at 31:41 · all korrents from this recording

    ↗Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com 20th of 20 in this recording

  24. 5 months earlier
  25. AI agents

    It’s not so much about deep work, it’s about how good I am at context switching and jumping across multiple different contexts very quickly.
    spoken · official transcript

    ↗Building Claude Code with Boris Cherny — The Pragmatic Engineernewsletter.pragmaticengineer.com

  26. coding agents

    once there is a good plan, it will one-shot the implementation almost every time.
    spoken · official transcript

    ↗Building Claude Code with Boris Cherny — The Pragmatic Engineernewsletter.pragmaticengineer.com

  27. design

    There’s just no way we could have shipped this if we started with static mocks and Figma or if we started with a PRD.
    spoken · official transcript

    ↗Building Claude Code with Boris Cherny — The Pragmatic Engineernewsletter.pragmaticengineer.com

  28. LLMs

    always make sure that when you start a migration, you finish the migration.
    spoken · official transcript

    ↗Building Claude Code with Boris Cherny — The Pragmatic Engineernewsletter.pragmaticengineer.com

  29. 7 days earlier
  30. LLMsAnthropicstartups

    At Anthropic, we don't build for the model of today, we build for the model of six months from now. And that's still my advice to founders that are building on LLMs.
    spoken · official transcript

    ↗Y Combinator, quoting Boris Cherny on the Lightcone Podcastx.com

  31. 20 months earlier
  32. open sourceJavaScriptdeveloper tools

    NPM and NodeJS should do more to make ES Modules easy to use

    ↗NPM and NodeJS should do more to make ES Modules easy to useborischerny.com 1st of 3 in this piece

  33. JavaScriptsoftware performance

    CommonJS can be challenging to statically analyze, and uses an inefficient, synchronous module loading algorithm at runtime.

    ↗NPM and NodeJS should do more to make ES Modules easy to useborischerny.com 2nd of 3 in this piece

  34. JavaScript

    We should make type=module the default for new package.json files for the npm init, yarn init, and pnpm init commands.

    ↗NPM and NodeJS should do more to make ES Modules easy to useborischerny.com 3rd of 3 in this piece