Comment by libraryofbabel

18 hours ago

Strongly recommend this blog post too which is a much more detailed and persuasive version of the same point. The author actually goes and builds a coding agent from zero: https://ampcode.com/how-to-build-an-agent

It is indeed astonishing how well a loop with an LLM that can call tools works for all kinds of tasks now. Yes, sometimes they go off the rails, there is the problem of getting that last 10% of reliability, etc. etc., but if you're not at least a little bit amazed then I urge you go to and hack together something like this yourself, which will take you about 30 minutes. It's possible to have a sense of wonder about these things without giving up your healthy skepticism of whether AI is actually going to be effective for this or that use case.

This "unreasonable effectiveness" of putting the LLM in a loop also accounts for the enormous proliferation of coding agents out there now: Claude Code, Windsurf, Cursor, Cline, Copilot, Aider, Codex... and a ton of also-rans; as one HN poster put it the other day, it seems like everyone and their mother is writing one. The reason is that there is no secret sauce and 95% of the magic is in the LLM itself and how it's been fine-tuned to do tool calls. One of the lead developers of Claude Code candidly admits this in a recent interview.[0] Of course, a ton of work goes into making these tools work well, but ultimately they all have the same simple core.

[0] https://www.youtube.com/watch?v=zDmW5hJPsvQ

54 comments

libraryofbabel

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xnx  1 hour ago

> The reason is that there is no secret sauce and 95% of the magic is in the LLM itself

Makes that "$3 billion" valuation for Windsurf very suspect

  • rrrx3  1 hour ago

    Indeed. But keep in mind they weren't just buying the tooling - they get the team, the brand, and the positional authority as well. OpenAI could have spun up a team to build an agent code IDE, and they would have been starting on the back foot with users, would have been compared to Cursor/Windsurf...

    The price tag is hefty but I figure it'll work out for them on the backside because they won't have to fight so hard to capture TAM.

  • TonyEx  1 hour ago

    The value in the windsurf acquisition isn't the code they've written, it's the ability to see what people are coding and use that information to build better LLMs. -- Product development.

datpuz  14 hours ago

Can't think of anything an LLM is good enough at to let them do on their own in a loop for more than a few iterations before I need to reign it back in.

  • vidarh  4 hours ago

    They've written most of the recent iterations of X11 bindings for Ruby, including a complete, working example of a systray for me.

    They also added the first pass of multi-monitor support for my WM while I was using it (restarted it repeatedly while Claude Code worked, in the same X session the terminal it was working in was running).

    You do need to reign them back in, sure, but they can often go multiple iterations before they're ready to make changes to your files once you've approved safe tool uses etc.

  • CuriouslyC  14 hours ago

    The main problem with agents is that they aren't reflecting on their own performance and pausing their own execution to ask a human for help aggressively enough. Agents can run on for 20+ iterations in many cases successfully, but also will need hand holding after every iteration in some cases.

    They're a lot like a human in that regard, but we haven't been building that reflection and self awareness into them so far, so it's like a junior that doesn't realize when they're over their depth and should get help.

    • vendiddy  6 hours ago

      I think they are capable of doing it, but it requires prompting.

      I constantly have to instruct them: - Go step by step, don't skip ahead until we're done with a step - Don't make assumptions, if you're unsure ask questions to clarify

      And they mostly do this.

      But this needs to be default behavior!

      I'm surprised that, unless prompted, LLMs never seem to ask follow-up questions as a smart coworker might.

  • eru  10 hours ago

    The hope is that the ground truth from calling out to tools (like compilers or test runs) will eventually be enough keep them on track.

    Just like humans and human organisations also tend to experience drift, unless anchored in reality.

  • mkagenius  14 hours ago

    I built android-use[1] using LLM. It is pretty good at self healing due to the "loop", it constantly checks if the current step is actually a progress or regress and then determines next step. And the thing is nothing is explicitly coded, just a nudge in the prompts.

    1. clickclickclick - A framework to let local LLMs control your android phone (https://github.com/BandarLabs/clickclickclick)

  • loa_in_  8 hours ago

    You don't have to. Most of the appeal is automatically applying fixes like "touch file; make" after spotting a trivial mistake. Just let it at it.

  • JeremyNT  1 hour ago

    Definitely true currently, which is why there's so much focus on using them to write real code that humans have to actually commit and put their names on.

    Longer term, I don't think this holds due to the nature of capitalism.

    If given a choice between paying for an LLM to do something that's mostly correct versus paying for a human developer, businesses are going to choose the former, even if it results in accelerated enshittification. It's all in service of reducing headcount and taking control of the means of production away from workers.

wepple  16 hours ago

Ah, it’s Thorsten Ball!

I thoroughly enjoyed his “writing an interpreter”. I guess I’m going to build an agent now.

orange_puff  9 hours ago

I have been trying to find such an article for so long, thank you! I think a common reaction to Agents is “well, it probably cannot solve a really complex problem very well”. But to me, that isn’t the point of an agent. LLMs function really well with a lot of context, and agent allows the LLM to discover more context and improve its ability to answer questions.

deadbabe  4 hours ago

Generally when LLM’s are effective like this, it means a more efficient non-LLM based solution to the problem exists using the tools you have provided. The LLM helps you find the series of steps and synthesis of inputs and outputs to make it happen.

It is expensive and slow to have an LLM use tools all the time for solving the problem. The next step is to convert frequent patterns of tool calls into a single pure function, performing whatever transformation of inputs and outputs are needed along the way (an LLM can help you build these functions), and then perhaps train a simple cheap classifier to always send incoming data to this new function, bypassing LLMs all together.

In time, this will mean you will use LLMs less and less, limiting their use to new problems that are unable to be classified. This is basically like a “cache” for LLM based problem solving, where the keys are shapes of problems.

The idea of LLMs running 24/7 solving the same problems in the same way over and over again should become a distant memory, though not one that an AI company with vested interest in selling as many API calls as possible will want people to envision. Ideally LLMs are only needed to be employed once or a few times per novel problem before being replaced with cheaper code.