待翻譯：If you havent recently used Claude Code, you might not understand where AI is at

AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。

David P. Reichert Aug 03, 2026 (*) or equivalent agentic coding offerings. Usual disclosure: I work at an AI Lab (Google DeepMind). This post describes work I’ve done in my personal time, and reflects my own opinions. If my ulterior motive is promoting my employer, I’m doing a bad job, because I’ll be using a competitor’s product as the example. So, how big of a deal will AI really be? The future is hard to predict. But what should be easier to predict is the present: where AI is at now. Whether it’s Reddit, news stories, or academic papers, you can still find often-repeated opinions about how (LLM-based) AI can’t reason, should not be called intelligent, cannot flexibly adapt to changing circumstances, and can only regurgitate what it has seen in training and not solve novel problems. And fundamentally, current AI isn’t like what we know – and fear – from sci-fi: it can’t act in the world, it just provides you with information, like a more interactive version of Wikipedia or Google. And half of the time it doesn’t even work! Like others, I get the sense that such views are based on interactions with the chatbots of about a year ago, not very-current AI agents. Yes, one can get bogged down in the semantics of “reasoning” or “understanding”. But here I just mean: for practical purposes. At this point, AI agents are solving real problems for me, and do so in a way that sure looks functionally like reasoning or understanding. I believe AI will have a massive impact on the world, good and bad. For much of that impact (though not all1), what matters is that it can do these sorts of things, not whether it can “really” reason. I think much of the world isn’t taking AI seriously enough, and we can’t deal with the challenges if we can’t see them coming. So my goal here is to help convey where AI is currently at, and hint at where it might be going. As a case study, I’ll be reporting on a series of small machine learning experiments, executed with various degrees of autonomy by Anthropic’s Claude. If you’re sceptical of claims around AI, but also haven’t really engaged with recent developments in agentic coding… continue on! (If not, also continue on). Much of what I’ll be showing is produced by Claude, other than this post itself, which is written by yours truly (and yes, I like em dashes, and won’t let the machines take them away from me!). As a teaser, check out the resulting interactive demos shown in the screenshots above: neural cellular automata, mazes. What this is Here’s what the situation looks like to me: Many people have the wrong intuitions about what kind of thing AI actually is, as of this moment, and what it can do. At the core, this is about agents vs. chatbots. Therefore, working with agentic coding can qualitatively shift those intuitions, at least for technically minded folks. And what can AI do? The short version, from a researcher’s perspective: for digital work, AI agents are, at a minimum, at the level of a junior researcher or grad student, with a few superpowers like being extremely knowledgeable and fast. They do make mistakes, including weird ones; the primary limitation though is lack of longer-term continual learning and executing longer projects (say, more than a week in human time) – for now. Below I’m presenting some evidence to back up 3. Let’s see if this changes intuitions as per 1. and 2. Concretely, over a few weekends, I had Claude (first Opus 4.8, then Fable 5) undertake a number of toy machine learning explorations; toy meaning, simplified but still indicative of the bigger picture. These were simple enough to be digestible in a Substack post and each completable in an afternoon on a home PC (though corresponding to several days of work in human time). I’m sharing all the code and result write-ups, all authored by Claude. I also asked Claude to analyse and report on the process itself, centred on the underlying conversation transcripts between me and Claude. The landing page for everything is here. I should emphasise that these results aren’t some major machine learning breakthroughs – not the equivalent of yet another century-old math problem solved. But big results aren’t the point here. Instead I want to convince the reader that the foundations for general-purpose agents are absolutely there. That said, those math breakthroughs exist, too, which is why I wrote “at a minimum” in point 3 above. With this goal in mind, I also have not reviewed the results in all detail, but have done spot checks (how to deal with all the AI output is of course another challenge we are going to face…). Who this is for Readers with an interest in machine learning will naturally have an easier time interpreting the results. But much of this essay is just about what working with Claude Code looks like, and many of the lessons aren’t particular to programming either. So hopefully there’s something to take away even if you gloss over the ML parts. If you are the type that’s already in the weeds with the recent developments, much of this won’t be surprising. Some of it might still be interesting though. I also recognise that some readers won’t be fans of anthropomorphisation throughout this piece, e.g. describing agents as making decisions or having ideas. I’ll return to that at the very end, once we have all the rest for context. “Urgh more AI hype I’m not going to read this” Fine! The whole point of this is to get more people to gather their own evidence, and not take my word for it. If you are technically minded but haven’t recently used agentic coding, you can just skip to that step. So do this instead: Install Claude Code. Pick a little project that is genuinely useful to you, even if it’s just for fun (my first vibe coding project a while ago was a Satisfactory savegame analyser…). Tell Claude to make it happen. A few tips: If you don’t know how to go about something – just ask Claude (I knew little about coding on Windows, for example). Talk to it like you would to a human collaborator (see below). Be smart and apply common sense. Don’t have it install arbitrary packages or access arbitrary websites, don’t let it loose on your bank account, etc. Why is it useful to think of it like a human collaborator? Not because it is a human, or human-equivalent! But because it’s not literal magic – it can’t read your mind or tell what you know or want, unless you tell it. It needs context, just like a human would. And because it is trained to emulate how humans interact from vast amounts of data on the internet. You want it to infer that this is a situation where smart humans are getting things done together, and emulate that; not people shouting at each other on X or whatever. For a similar reason, if you deploy AI as just another boring old tool like Excel, you’re unlikely to surface its full capabilities. That said, if you want to see what all of this can look like... Just having a chat This all started with me coming across two papers that claimed to show various limitations of LLMs. I had a few ideas for improving the LLMs’ performance, so I sat down to have a chat with Claude, discuss the papers, and then talk it through implementing the ideas for me. Then I asked Claude to come up with a few toy projects of its own, and execute them mostly autonomously. At no point did I write any code, and I looked at the generated code only very rarely. I’ve also been approaching this fairly naively as far as agentic coding is concerned, I don’t have any special setup, and barely used Claude Code’s bells and whistles. The fact that this just works with no special tricks is part of the point. Of course I’m reasonably careful in terms of safety as I cautioned above, and I bring some expertise to the table that allows me to judge whether outputs look sensible and to ask the right kind of questions. A personal army of research assistants I ended up with seven mini-projects (which I’ll also call runs or experiments) and several layers of analysis on top. I won’t go through all the details, as the aim is to convey that this sort of thing is possible at all and to build intuitions around AI agents. Here I’ll only highlight a few key things. First to make sense of what happened, I should clarify the role of different (sub-)agents here, as shown in the below diagram. What makes these agents separate is essentially that they are based on different conversation contexts (though many shared some high-level notes, e.g. about how my machine is set up). We ended up with: One main conversation where we managed the whole project (call this the meta-agent). Individual researcher agents that undertook the ML explorations and wrote up individual reports. Analyst agents, which I asked the meta-agent to set up. These were tasked to look at the ML runs after the fact, including the conversation transcripts for each, and write a report about the process itself (“retellings”). Various helper agents that agents would occasionally spawn to solve sub-problems. As a special case of the latter, I advised the researcher agents to spawn ad hoc reviewer agents to check their own work. Finally, some of the runs were experiments about LLM capabilities, so involved agents as the test subjects as well. Now if this looks complicated, the key is this: All of this came about organically as the project developed. In particular, at no point did I do anything but chat to Claude. Making “new agents” simply involved: Me opening a new chat window if I wanted fresh context. Me asking Claude to delegate work however it wanted, or to come up with a system to do so in the case of the analysts (meaning, Claude wrote up instructions for the analysts to follow, which I gave some brief feedback on). Agents deciding on their own to delegate pieces of their work. Out of these agents, I worked closely with the meta-agent and two of the researcher agents for the collaborative runs. The other researcher agents got only very light feedback for the more autonomous runs (examples below). The rest I did not directly interact with. The meta-report I asked the meta-agent to make the aforementioned landing page that ties everything together. Just as an impression, the screenshot below shows the table summarising all the runs. Across the project, when talking to the agents I often used a framing along the lines of, “how far along are we with automating machine learning research?” and “do agents show the right kind of reasoning in principle?”. You’ll see this angle pop up a lot in the reports. Next I’ll look at three instructive example runs in more detail, and discuss some of the take-aways. First example: A mostly-autonomous exploration of neural nets solving mazes Let’s start with experiment 6 out of 7; it’s one of the cleaner runs, and for the last two experiments, I asked the researcher agents to come up with something that lends itself to nice visualisations, so that it’s easier to get a quick impression of the work, and of whether something reasonable came from it. In terms of machine learning, the setup is as follows. Simple neural networks are (supervisedly) trained to solve 2D mazes by outputting the solution path. Different architectures are compared, and the question is whether a recurrent net can perform better when given more iterations of state updates (or “thinking”(*) steps) at test time, and whether that helps generalisation to larger mazes than seen in training. ((*) re anthropomorphisation, as mentioned, to be discussed at the end!) Basically, it’s a toy project where the agent plays with a few smaller ideas in familiar territory – the kind of project you’d give to a student, which with a bit of work could have turned into a paper (in the past, given the topic). Nothing groundbreaking, but the essence of a machine learning experiment is there. So, what did executing this actually look like in practice? Here is the ful [truncated for AI cost control]