11 ms·

My AI Adoption Journey

• sho_hn 7mo ago
  Much more pragmatic and less performative than other posts hitting frontpage. Good article.
  • alterom 7mo ago
    Finally, a step-by-step guide for even the skeptics to try to see what spot the LLM tools have in their workflows, without hype or magic like I vibe-coded an entire OS, and you can too!.
• therein 7mo ago
  [flagged]
  • stronglikedan 7mo ago
    most AI adoption journeys are
  • alterom 7mo ago
    >Underwhelming Which is why I like this article. It's realistic in terms of describing the value-propositio of LLM-based coding assist tools (aka, AI agents). The fact that it's underwhelming compared to the hype we see every day is a very, very good sign that it's practical.
  • dang 7mo ago
    Ok, but please don't post unsubstantive comments to Hacker News.
• vonneumannstan 7mo ago
  For the AI skeptics reading this, there is an overwhelming probability that Mitchell is a better developer than you. If he gets value out of these tools you should think about why you can't.
  • dakiol 7mo ago
    Don't get it. What's the relation between Mitchell being a "better" developer than most of us (and better is always relative, but that's another story) and getting value out of AI? That's like saying Bezos is a way better businessman than you, so you should really hear his tips about becoming a billionaire. No sense (because what works for him probably doesn't work for you) Tons of respect for Mitchell. I think you are doing him a disservice with these kinds of comments.
    • tux1968 7mo ago
      Maybe you disagree with it, but it seems like a pretty straightforward argument: A lot of us dismiss AI because "it can't be trusted to do as good a job as me". The OP is arguing that someone, who can do better than most of us, disagrees with this line of thinking. And if we have respect for his abilities, and recognize them as better than our own, we should perhaps re-assess our own rationale in dismissing the utility of AI assistance. If he can get value out of it, surely we can too if we don't argue ourselves out of giving it a fair shake. The flip side of that argument might be that you have to be a much better programmer than most of us are, to properly extract value out of the AI... maybe it's only useful in the hands of a real expert.
      • jplusequalt 7mo ago
        >A lot of us dismiss AI because "it can't be trusted to do as good a job as me" Some of us enjoy learning how systems work, and derive satisfaction from the feeling of doing something hard, and feel that AI removes that satisfaction. If I wanted to have something else write the code, I would focus on becoming a product manager, or a technical lead. But as is, this is a craft, and I very much enjoy the autonomy that comes with being able to use this skill and grow it.
        • fizx 7mo ago
          I enjoy Japanese joinery, but for some reason the housing market doesn't.
• mwigdahl 7mo ago
  Good article! I especially liked the approach to replicate manual commits with the agent. I did not do that when learning but I suspect I'd have been much better off if I had.
• mjr00 7mo ago
  > Break down sessions into separate clear, actionable tasks. Don't try to "draw the owl" in one mega session. This is the key one I think. At one extreme you can tell an agent "write a for loop that iterates over the variable `numbers` and computes the sum" and they'll do this successfully, but the scope is so small there's not much point in using an LLM. On the other extreme you can tell an agent "make me an app that's Facebook for dogs" and it'll make so many assumptions about the architecture, code and product that there's no chance it produces anything useful beyond a cool prototype to show mom and dad. A lot of successful LLM adoption for code is finding this sweet spot. Overly specific instructions don't make you feel productive, and overly broad instructions you end up redoing too much of the work.
  • sho_hn 7mo ago
    This is actually an aspect of using AI tools I really enjoy: Forming an educated intuition about what the tool is good at, and tastefully framing and scoping the tasks I give it to get better results. It cognitively feels very similar to other classic programming activities, like modularization at any level from architecture to code units/functions, thoughtfully choosing how to lay out and chunk things. It's always been one of the things that make programming pleasurable for me, and some of that feeling returns when slicing up tasks for agents.
    • allenu 7mo ago
      I agree that framing and scoping tasks is becoming a real joy. The great thing about this strategy is there's a point at which you can scope something small enough that it's hard for the AI to get it wrong and it's easy enough for you as a human to comprehend what it's done and verify that it's correct. I'm starting to think of projects now as a tree structure where the overall architecture of the system is the main trunk and from there you have the sub-modules, and eventually you get to implementations of functions and classes. The goal of the human in working with the coding agent is to have full editorial control of the main trunk and main sub-modules and delegate as much of the smaller branches as possible. Sometimes you're still working out the higher-level architecture, too, and you can use the agent to prototype the smaller bits and pieces which will inform the decisions you make about how the higher-level stuff should operate.
• raphinou 7mo ago
  I recently also reflected on the evolution of my use of ai in programming. Same evolution, other path. If anyone is interested: https://www.asfaload.com/blog/ai_use/ https://www.asfaload.com/blog/ai_use/
• fix4fun 7mo ago
  Thanks for sharing your experiences :) You mentioned "harness engineering". How do you approach building "actual programmed tools" (like screenshot scripts) specifically for an LLM's consumption rather than a human's? Are there specific output formats or constraints you’ve found most effective?
• butler14 7mo ago
  I'd be interested to know what agents you're using. You mentioned Claude and GPT in passing, but don't actually talk about which you're using or for which tasks.
• EastLondonCoder 7mo ago
  This matches my experience, especially "don’t draw the owl" and the harness-engineering idea. The failure mode I kept hitting wasn’t just "it makes mistakes", it was drift: it can stay locally plausible while slowly walking away from the real constraints of the repo. The output still sounds confident, so you don’t notice until you run into reality (tests, runtime behaviour, perf, ops, UX). What ended up working for me was treating chat as where I shape the plan (tradeoffs, invariants, failure modes) and treating the agent as something that does narrow, reviewable diffs against that plan. The human job stays very boring: run it, verify it, and decide what’s actually acceptable. That separation is what made it click for me. Once I got that loop stable, it stopped being a toy and started being a lever. I’ve shipped real features this way across a few projects (a git like tool for heavy media projects, a ticketing/payment flow with real users, a local-first genealogy tool, and a small CMS/publishing pipeline). The common thread is the same: small diffs, fast verification, and continuously tightening the harness so the agent can’t drift unnoticed.
  • bdangubic 7mo ago
    This is the most common answer from people that are rocking and rolling with AI tools but I cannot help but wonder how is this different from how we should have built software all along. I know I have been (after 10+ years…)
    • EastLondonCoder 7mo ago
      I think you are right, the secret is that there is no secret. The projects I have been involved with thats been most successful was using these techniques. I also think experience helps because you develop a sense that very quickly knows if the model wants to go in a wonky direction and how a good spec looks like. With where the models are right now you still need a human in the loop to make sure you end up with code you (and your organisation) actually understands. The bottle neck has gone from writing code to reading code.
      • sksisksbbs 7mo ago
        > The bottle neck has gone from writing code to reading code. This has always been the bottleneck. Reviewing code is much harder and gets worse results than writing it, which is why reviewing AI code is not very efficient. The time required to understand code far outstrips the time to type it. Most devs don’t do thorough reviews. Check the variable names seem ok, make sure there’s no obvious typos, ask for a comment and call it good. For a trusted teammate this is actually ok and why they’re so valuable! For an AI, it’s a slot machine and trusting it is equivalent to letting your coworkers/users do your job so you can personally move faster.
• xyst 7mo ago
  [flagged]
  • dang 7mo ago
    "Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something." "Don't be snarky." "Don't be curmudgeonly. Thoughtful criticism is fine, but please don't be rigidly or generically negative." https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
• underdeserver 7mo ago
  > At a bare minimum, the agent must have the ability to: read files, execute programs, and make HTTP requests. That's one very short step removed from Simon Willison's lethal trifecta.
  • recursive 7mo ago
    I'm definitely not running that on my machine.
    • margalabargala 7mo ago
      The way this is generally implemented is that agents have the ability to request a tool use. Then you confirm "yes, you may run this grep".
    • brandonpaiz 7mo ago
      Same, but I felt okay sticking my code base in a VM and then letting an agent run there. I’d say it worked well
  • smj-edison 7mo ago
    I will say one thing Claude does is it doesn't run a command until you approve it, and you can choose between a one-time approval and always allowing a command's pattern. I usually approve the simple commands like `zig build test`, since I'm not particularly worried about the test harness. I believe it also scopes file reading by default to the current directory.
    • tehlike 7mo ago
      A lot of people run the claude with --dangerously-skip-permissions
  • glitchcrab 7mo ago
    This is why I won't run Claude without additional sandboxing. I'm currently using (and quite pleased with) https://github.com/strongdm/leash https://github.com/strongdm/leash
• jeffrallen 7mo ago
  > babysitting my kind of stupid and yet mysteriously productive robot friend LOL, been there, done that. It is much less frustrating and demoralizing than babysitting your kind of stupid colleague though. (Thankfully, I don't have any of those anymore. But at previous big companies? Oh man, if only their commits were ONLY as bad as a bad AI commit.)
• davidw 7mo ago
  This seems like a pretty reasonable approach that charts a course between skepticism and "it's a miracle". I wonder how much all this costs on a monthly basis?
  • tptacek 7mo ago
    As long as we're on the same page that what he's describing is itself a miracle.
    • 12_throw_away 7mo ago
      Take your religion somewhere else please.
    • latexr 7mo ago
      It’s not. A miracle is “an event that is inexplicable by natural or scientific laws and accordingly gets attributed to some supernatural or preternatural cause”. Could we please stop trivialising and ignoring the meaning of words?
      • mbesto 7mo ago
        The word miracle itself is hyperbolic in nature...it's meant to enchant and not to be used literal or concretely. No need to be pedantic here, there is a large cohort of the population that seemingly never thought a robot would be able to write usable code ("inexplicable by natural or scientific laws") and now here we are seeing that happen ("hey this must be preternatural! there is no other explanation")
  • probably_wrong 7mo ago
    The comment by user senko [1] links to a post from this same author with an example for a specific coding session that costs $15.98 for 8 hours of work. The example in this post talks about leaving agents running overnight, in which case I'd guess "twice that amount" would be a reasonable approximation. Or if we assume that the OP can only do 4 hours per sitting (mentioned in the other post) and 8 hours of overnight agents then it would come down to $15.98 * 1.5 * 20 = $497,40 a month (without weekends). [1] https://news.ycombinator.com/item?id=46905872 https://news.ycombinator.com/item?id=46905872
• jonathanstrange 7mo ago
  There are so many stories about how people use agentic AI but they rarely post how much they spend. Before I can even consider it, I need to know how it will cost me per month. I'm currently using one pro subscription and it's already quite expensive for me. What are people doing, burning hundreds of dollars per month? Do they also evaluate how much value they get out of it?
  • JoshuaDavid 7mo ago
    Low hundreds ($190 for me) but yes.
  • latchkey 7mo ago
    I quickly run out of the JetBrains AI 35 monthly credits for $300/yr and spending an additional $5-10/day on top of that, mostly for Claude. I just recently added in Codex, since it comes with my $20/mo subscription to GPT and that's lowering my Claude credit usage significantly... until I hit those limits at some point. 2012 + 300 + 5~200... so about $1500-$1600/year. It is 100% worth it for what I'm building right now, but my fear is that I'll take a break from coding and then I'm paying for something I'm not using with the subscriptions. I'd prefer to move to a model where I'm paying for compute time as I use it, instead of worrying about tokens/credits.
    • throwdbaaway 7mo ago
      Not using Hot Aisle for inference?
      • latchkey 7mo ago
        We're literally full. Just a few 1x GPUs available right now. So far, I haven't been happy with any of the smaller coding models, they just don't compare to claude/codex.
• senko 7mo ago
  For those wondering how that looks in practice, here's one of OP's past blog posts describing a coding session to implement a non-trivial feature: https://mitchellh.com/writing/non-trivial-vibing https://mitchellh.com/writing/non-trivial-vibing (covered on HN here: https://news.ycombinator.com/item?id=45549434 https://news.ycombinator.com/item?id=45549434)
• apercu 7mo ago
  I find it interesting that this thread is full of pragmatic posts that seem to honestly reflect the real limits of current Gen-Ai. Versus other threads (here on HN, and especially on places like LinkedIn) where it's "I set up a pipeline and some agents and now I type two sentences and amazing technology comes out in 5 minutes that would have taken 3 devs 6 months to do".
  • munksbeer 7mo ago
    I never see those type of posts. Maybe I'm immune and ignoring them.
    • apercu 7mo ago
      I want that immunity. Gen-AI evangelism is the new virtue signalling.
• polyrand 7mo ago
  > a period of inefficiency I think this is something people ignore, and is significant. The only way to get good at coding with LLMs is actually trying to do it. Even if it's inefficient or slower at first. It's just another skill to develop [0]. And it's not really about using all the plugins and features available. In fact, many plugins and features are counter-productive. Just learn how to prompt and steer the LLM better. [0]: https://ricardoanderegg.com/posts/getting-better-coding-llms-agents/ https://ricardoanderegg.com/posts/getting-better-coding-llms...
• 0xbadcafebee 7mo ago
  > I'm not [yet?] running multiple agents, and currently don't really want to This is the main reason to use AI agents, though: multitasking. If I'm working on some Terraform changes and I fire off an agent loop, I know it's going to take a while for it to produce something working. In the meantime I'm waiting for it to come back and pretend it's finished (really I'll have to fix it), so I start another agent on something else. I flip back and forth between the finished runs as they notify me. At the end of the day I have 5 things finished rather than two. The "agent" doesn't have to be anything special either. Anything you can run in a VM or container (vscode w/copilot chat, any cli tool, etc) so you can enable YOLO mode.
• deleted 7mo ago
  [deleted]
• keyle 7mo ago
  It's amusing how everyone seems to be going through the same journey. I do run multiple models at once now. On different parts of the code base. I focus solely on the less boring tasks for myself and outsource all of the slam dunk and then review. Often use another model to validate the previous models work while doing so myself. I do git reset still quite often but I find more ways to not get to that point by knowing the tools better and better. Autocompleting our brains! What a crazy time.
• libraryofbabel 7mo ago
  This is such a lovely balanced thoughtful refreshingly hype-free post to read. 2025 really was the year when things shifted and many first-rate developers (often previously AI skeptics, as Mitchell was) found the tools had actually got good enough that they could incorporate AI agents into their workflows. It's a shame that AI coding tools have become such a polarizing issue among developers. I understand the reasons, but I wish there had been a smoother path to this future. The early LLMs like GPT-3 could sort of code enough for it to look like there was a lot of potential, and so there was a lot of hype to drum up investment and a lot of promises made that weren't really viable with the tech as it was then. This created a large number of AI skeptics (of whom I was one, for a while) and a whole bunch of cynicism and suspicion and resistance amongst a large swathe of developers. But could it have been different? It seems a lot of transformative new tech is fated to evolve this way. Early aircraft were extremely unreliable and dangerous and not yet worthy of the promises being made about them, but eventually with enough evolution and lessons learned we got the Douglas DC-3, and then in the end the 747. If you're a developer who still doesn't believe that AI tools are useful, I would recommend you go read Mitchell's post, and give Claude Code a trial run like he did. Try and forget about the annoying hype and the vibe-coding influencers and the noise and just treat it like any new tool you might put through its paces. There are many important conversations about AI to be had, it has plenty of downsides, but a proper discussion begins with close engagement with the tools.
  • whatifnomoney 7mo ago
    [dead]
  • arcxi 7mo ago
    but annoying hype is exactly the issue with AI in my eyes. I get it's a useful tool in moderation and all, but I also experience that management values speed and quantity of delivery above all else, and hype-driven as they are I fear they will run this industry to the ground and we as users and customers will have to deal with the world where software is permanently broken as a giant pile of unmaintainable vibe code and no experienced junior developers to boot.
    • 7mo ago
• cal_dent 7mo ago
  Just wanted to say that was a nice and very grounded write up; and as a result very informative. Thank you. More stuff like this is a breath of fresh air in a landscape that has veered into hyperbole territory both in the for and against ai sides
• whatifnomoney 7mo ago
  [dead]
• zubspace 7mo ago
  It's so sad that we're the ones who have to tell the agent how to improve by extending agent.md or whatever. I constantly have to tell it what I don't like or what can be improved or need to request clarifications or alternative solutions. This is what's so annoying about it. It's like a child that does the same errors again and again. But couldn't it adjust itself with the goal of reducing the error bit by bit? Wouldn't this lead to the ultimate agent who can read your mind? That would be awesome.
  • cactusplant7374 7mo ago
    It is not a mind reader. I enjoy giving it feedback because it shows I am in charge of the engineering. I also love using it for research for upcoming features. Research + pick a solution + implement. It happens so fast.
  • audience_mem 7mo ago
    > It's so sad that we're the ones who have to tell the agent how to improve by extending agent.md or whatever. Your improvement is someone else's code smell. There's no absolute right or wrong way to write code, and that's coming from someone who definitely thinks there's a right way. But it's my right way. Anyway, I don't know why you'd expect it to write code the way you like after it's been trained on the whole of the Internet & the the RLHF labelers' preferences and the reward model. Putting some words in AGENTS.md hardly seems like the most annoying thing. tip: Add a /fix command that tells it to fix $1 and then update AGENTS.md with the text that'd stop it from making that mistake in the future. Use your nearest LLM to tweak that prompt. It's a good timesaver.
  • pixl97 7mo ago
    While this may be the end goal, I do think humanity needs to take the trip along with AI to this point. A mind reading ultimate agent sounds more like a deity, and there are more than enough fables warning one not to create gods because things tend to go bad. Pumping out ASI too quickly will cause massive destabilization and horrific war. Not sure who against really either. Could be us humans against the ASI, could be the rich humans with ASI against us. Anyway about it, it would represent a massive change in the world order.
• dudewhocodes 7mo ago
  Refreshing to read a balanced opinion, from a person who has significant experience and grounding in the real world.
• taikahessu 7mo ago
  Do you have any ideas on how to harness AI to only change specific parts of a system or workpiece? Like "I consider this part 80/100 done and only make 'meaningful' or 'new contributions' here" ...?
• josh-sematic 7mo ago
  This is yet one more indication to me that the winds have shifted with regards to the utility of the “agent” paradigm of coding with an LLM. With all the talk around Opus 4.5 I decided to finally make the jump there myself and haven’t yet been disappointed (though admittedly I’m starting it on some pretty straightforward stuff).
• henry_bone 7mo ago
  LLMs are not for me. My position is that the advantage we humans have over the rest of the natural world, is our minds. Our ability to think, create and express ideas is what separates us from the rest of the animal kingdom. Once we give that over to "thinking" machines, we weaken ourselves, both individually and as a species. That said, I've given it a go. I used zed, which I think is a pretty great tool. I bought a pro subscription and used the built in agent with Claude Sonnet 4.x and Opus. I'm a Rails developer in my day job, and, like MitchellH and many others, found out fairly quickly that tasks for the LLM need to be quite specific and discrete. The agent is great a renames and minor refactors, but my preferred use of the agent was to get it to write RSpec tests once I'd written something like a controller or service object. And generally, the LLM agent does a pretty great job of this. But here's the rub: I found that I was losing the ability to write rspec. I went to do it manually and found myself trying to remember API calls and approaches required to write some specs. The feeling of skill leaving me was quite sobering and marked my abandonment of LLMs and Zed, and my return to neovim, agent-free. The thing is, this is a common experience generally. If you don't use it, you lose it. It applies to all things: fitness, language (natural or otherwise), skills of all kinds. Why should it not apply to thinking itself. Now you may write me and my experience off as that of a lesser mind, and that you won't have such a problem. You've been doing it so long that it's "hard-wired in" by now. Perhaps. It's in our nature to take the path of least resistance, to seek ease and convenience at every turn. We've certainly given away our privacy and anonymity so that we can pay for things with our phones and send email for "free". LLMs are the ultimate convenience. A peer or slave mind that we can use to do our thinking and our work for us. Some believe that the LLM represents a local maxima, that the approach can't get much better. I dunno, but as AI improves, we will hand over more and more thinking and work to it. To do otherwise would be to go against our very nature and every other choice we've made so far. But it's not for me. I'm no MitchellH, and I'm probably better off performing the mundane activities of my work, as well as the creative ones, so as to preserve my hard-won knowledge and skills. YMMV I'll leave off with the quote that resonates the most with me as I contemplate AI:- "I say your civilization, because as soon as we started thinking for you, it really became our civilization, which is, of course, what this is all about." -- Agent Smith "The Matrix"
  • FeteCommuniste 7mo ago
• scarrilho 7mo ago
  With so much noise in the AI world and constant model updates (just today GPT-5.3-Codex and Claude Opus 4.6 were announced), this was a really refreshing read. It’s easy to relate to his phased approach to finding real value in tooling and not just hype. There are solid insights and practical tips here. I’m increasingly convinced that the best way not to get overwhelmed is to set clear expectations for what you want to achieve with AI and tailor how you use it to work for you, rather than trying to chase every new headline. Very refreshing.
• e40 7mo ago
  For those of working on large proprietary, in fringe languages as well, what can we do? Upload all the source code to the cloud model? I am really wary of giving it a million lines of code it’s never seen.
  • swordsith 7mo ago
    I've found mostly for context reasons its better to just have a grand overview of the systems and how they work together and feed that to the agent as context, it will use the additional files it touches to expand its understanding if you prompt well.
    • e40 7mo ago
      Does this essentially give the companies controlling these models access to our source code? That is, it goes into training future versions of the model?
      • swordsith 7mo ago
        Depends on the privacy practices of the people hosting the models, or are providing access to them. Most have an 'opt-out' of helping to train models.
• bthornbury 7mo ago
  AI is getting to the game-changing point. We need more hand-written reflections on how individuals are managing to get productivity gains for real (not a vibe coded app) software engineering.
• rthak 7mo ago
  Now that the Nasdaq crashes, people switch from the stick to the carrot: "Please let us sit down and have a reasonable conversation! I was a skeptic, too, but if all skeptics did what I did, they would come to Jesus as well! Oh, and pay the monthly Anthropic tithe!"
• seemaze 7mo ago
  What a lovely read. Thank you for sharing your experience. The human-agent relationship described in the article made me wonder: are natural, or experienced, managers having more success with AI as subordinates than people without managerial skill? Are AI agents enormously different than arbitrary contractors half a world away where the only communication is daily text exchanges?
• energy123 7mo ago
  > Immediately cease trying to perform meaningful work via a chatbot. That depends on your budget. To work within my pro plan's codex limits, I attach the codebase as a single file to various chat windows (GPT 5.2 Thinking - Heavy) and ask it to find bugs/plan a feature/etc. Then I copy the dense tasklist from chat to codex for implementation. This reduces the tokens that codex burns. Also don't sleep on GPT 5.2 Pro. That model is a beast for planning.
• noosphr 7mo ago
  I've been building systems like what the OP is using since gpt3 came out. This is the honeymoon phase. You're learning the ins and outs of the specific model you're using and becoming more productive. It's magical. Nothing can stop you. Then you might not be improving as fast as you did at the start, but things are getting better every day. Or maybe every week. But it's heaps better than doing it by hand because you have so much mental capacity left. Then a new release comes up. An arbitrary fraction of your hard earned intuition is not only useless but actively harmful to getting good results with the new models. Worse you will never know which part it is without unlearning everything you learned and starting over again. I've had to learn the quirks of three generations of frontier families now. It's not worth the hassle. I've gone back to managing the context window in Emacs because I can't be bothered to learn how to deal with another model family that will be thrown out in six months. Copy and paste is the universal interface and being able to do surgery on the chat history is still better than whatever tooling is out there. Unironically learning vim or Emacs and the standard Unix code tools is still the best thing you can do to level up your llm usage.
  • tudelo 7mo ago
    First off, appreciate you sharing your perspective. I just have a few questions. > I've gone back to managing the context window in Emacs because I can't be bothered to learn how to deal with another model family that will be thrown out in six months. Can you expand more on what you mean by that? I'm a bit of a noob on llm enabled dev work. Do you mean that you will kick off new sessions and provide a context that you manage yourself instead of relying on a longer running session to keep relevant information? > Unironically learning vim or Emacs and the standard Unix code tools is still the best thing you can do to level up your llm usage. I appreciate your insight but I'm failing to understand how exactly knowing these tools increases performance of llms. Is it because you can more precisely direct them via prompts?
    • noosphr 7mo ago
      LLMs work on text and nothing else. There isn't any magic there. Just a limited context window on which the model will keep predicting the next token until it decides that it's predicted enough and stop. All the tooling is there to manage that context for you. It works, to a degree, then stops working. Your intuition is there to decide when it stops working. This intuition gets outdated with each new release of the frontier model and changes in the tooling. The stateless API with a human deciding what to feed it is much more efficient in both cost and time as long as you're only running a single agent. I've yet to see anyone use multiple agents to generate code successfully (but I have used agent swarms for unstructured knowledge retrieval). The Unix tools are there for you to progra-manually search and edit the code base copy/paste into the context that you will send. Outside of Emacs (and possibly vim) with the ability to have dozens of ephemeral buffers open to modify their output I don't imagine they will be very useful. Or to quote the SICP lectures: The magic is that there is no magic.
• deleted 7mo ago
  [deleted]
• tppts 7mo ago
  So does everyone just run with giving full permissions on Claude code these days? It seems like I’m constantly coming back to CC to validate that it’s not running some bash that’s going to nuke my system. I would love to be able to fully step away but it feels like I can’t.
  • apetresc 7mo ago
    Honest question, when was the last time you caught it trying to use a command that was going to "nuke your system"?
    • tppts 7mo ago
      “Nuke” is maybe too strong of a word, but it has not been uncommon for me to see it trying to install specific versions of languages on my machine, or services I intentionally don’t have configured, or sometimes trying to force npm when I’m using bun, etc.
    • dymk 7mo ago
      Maybe once a month
  • apitman 7mo ago
    I run my agents with full permissions in containers. Feels like a reasonable tradeoff. Bonus is I can set up each container with exactly the stack needed.
  • glitchcrab 7mo ago
    I sandbox everything inside https://github.com/strongdm/leash https://github.com/strongdm/leash That way the blast radius is vastly reduced.
• kaffekaka 7mo ago
  > Context switching is very expensive. In order to remain efficient, I found that it was my job as a human to be in control of when I interrupt the agent, not the other way around. Don't let the agent notify you. This I have found to be important too.
• rhubarbtree 7mo ago
  If the author is here, please could you also confirm you’ve never been paid by any AI company, marketing representative, community programme, in any shape or form?
  • simianwords 7mo ago
    Bit strange that you are skeptical by default.
    • emil-lp 7mo ago
      Isn't skeptical by default quite reasonable?
      • simianwords 7mo ago
        Probably exhausting to be that way. The author is well respected and well known and has a good track record. My immediate reaction wasn’t to question that he spoke in good faith.
        • rhubarbtree 7mo ago
          I don’t know the author, and am suspicious of the amount of astroturfing that has gone on with AI. This article seems reasonable so I looked for a disclaimed and found it oddly worded, hence the request for clarification.
    • asdfaslkj353 7mo ago
      [dead]
    • evsimonisretar 7mo ago
      [dead]
  • fergie 7mo ago
    He explicitly said "I don't work for, invest in, or advise any AI companies." in the article. But yes, Hashimoto is a high profile CEO/CTO who may well have an indirect, or near-future interest in talking up AI. HN articles extoling the productivity gains of Claude on HN do generally tend to be from older, managerial types (make of that what you will).
• simgt 7mo ago
  Very nice. As a consequence of this new way of working I'm using `git worktree` and diffview all the time. For more on the "harness engineering", see what Armin Ronacher and Mario Zechner are doing with pi: https://lucumr.pocoo.org/2026/1/31/pi/ https://lucumr.pocoo.org/2026/1/31/pi/ https://mariozechner.at/posts/2025-11-30-pi-coding-agent/ https://mariozechner.at/posts/2025-11-30-pi-coding-agent/ > I really don't care one way or the other if AI is here to stay3, I'm a software craftsman that just wants to build stuff for the love of the game. I suspect having three comma on one's bank account helps being very relaxed about the outcome ;)
• tpoacher 7mo ago
  > This blog post was fully written by hand, in my own words. This reminded me of back when wysiwyg web editors started becoming a thing, and coders started adding those "Created in notepad" stickers to their webpages, to point out they were 'real' web developers. Fun times.
• tigerlily 7mo ago
  OT but, the style. The journey. What is it? What does this remind me of? Flowers for Algernon. Or at least the first half. I don't wanna see what it looks like when AI capabilities start going in reverse. But I want to know.
• anupamchugh 7mo ago
  [flagged]
  • svilen_dobrev 7mo ago
    > level 2 - becomes an immune system i'd bet that above some number there will be contradictions. Things that apply to different semantic contexts, but look same on syntax level (and maybe with various levels of "syntax" and "semantic"). And debugging those is going to be nightmare - same as debugging requirements spec / verification of that
• sublimefire 7mo ago
  Very much the same experience. But it does not talk much about the project setup and the influence of it on the session success. In the narrow scoped projects it works really well, especially when tests are easy to execute. I found that this approach melts down when facing enterprise software with large repositories and unconventional layouts. Then you need to do a bunch of context management upfront, and verbose instructions for evaluations. But we know what it needs is a refactor thats all. And the post touches on a next type of a problem, how to plan far ahead of time to utilise agents when you are away. It is a difficult problem but IMO we’re going in a direction of having some sort of shared “templated plans”/workflows and budgeted/throttled task execution to achieve that. It is like you want to give a little world to explore so that it does not stop early, like a little game to play, then you come back in the morning and check how far it went.
• MORPHOICES 7mo ago
  [dead]
• awesan 7mo ago
  I'm kind of on the same journey, a bit less far along. One thing I have observed is that I am constantly running out of tokens in claude. I guess this is not an issue for a wealthy person like Mitchell but it does significantly hamper my ability to experiment.
• deleted 7mo ago
  [deleted]
• rldjbpin 7mo ago
  not quite as technically rich as i came to expect from previous posts from op, but very insightful regardless. not ashamed to say that i am between steps 2 and 3 in my personal workflow. >Adopting a tool feels like work, and I do not want to put in the effort all the different approaches floating online feel ephemeral to me. this, just like for different tools for the op, seem like a chore to adopt. i like the fomo mongering from the community does not help here, but in the end it is a matter of personal discovery to stick with what works for you.
• hollowturtle 7mo ago
  I don't understand how Agents make you feel productive. Single/Multiple agents reading specs, specs often produced with agents itself and iterated over time with human in the loop, a lot of reviewing of giant gibberish specs. Never had a clear spec in my life. Then all the dancing for this apperantly new paradigm, of not reviewing code but verifying behaviour, and so many other things. All of this to me is a total UNproductive mess. I use Cursor autocomplete from day one till to this day, I was super productive before LLMs, I'm more productive now, I'm capable, I have experience, product is hard to maintain but customers are happy, management is happy. So I can't really relate anymore to many of the programmers out there, that's sad, I can count on my hands devs that I can talk to that have hard skills and know-how to share instead of astroturfing about AI Agents
  • elAhmo 7mo ago
    > Never had a clear spec in my life. Just because you haven't or you work in a particular way, doesn't mean everyone does things the same way. Likewise, on your last point, just because someone is using AI in their work, doesn't mean they don't have hard skills and know-how. Author of this article Mitchell is a great example of that - someone who proved to be able to produce great software and, when talking about individuals who made a dent in the industry, definitely had/has an impactful career.
    • hollowturtle 7mo ago
      Never mentioned Mitchell I'm generally speaking, 95% of industry is not Mitchell
      • elAhmo 7mo ago
        Well, you are commenting on a post he wrote.
        • hollowturtle 7mo ago
          Well, this site exist to discuss between people
• jvillasante 7mo ago
  This are all valid points and a hype-free pragmatic take, I've been wondering about the same things even when I'm still in the skeptics side. I think there are other things that should be added since Mitchell's reality won't apply to everyone: - What about non opensource work that's not on Github? - Costs! I would think "an agent always running" would add up quickly - In open source work, how does it amplify others. Are you seeing AI Slop as PRs? Can you tell the difference?
• simianparrot 7mo ago
  > If an agent isn't running, I ask myself "is there something an agent could be doing for me right now?" Solution-looking-for-a-problem mentality is a curse.
• butterNaN 7mo ago
  > having an agent running at all times This gave me a physical flinch. Perhaps this is unfounded, but all this makes me think of is this becoming the norm, millions of people doing this, and us cooking our planet out much faster than predicted.
• noisy_boy 7mo ago
  I still use the chatbot but like to do it outside-in. Provide what I need, and instruct it to not write any code except the api (signatures of classes, interfaces, hierarchy, essential methods etc). We keep iterating about this until it looks good - still no real code. Then I ask it to do a fresh review of the broad outline, any issues it foresees etc. Then I ask it to write some demonstrator test cases to see how ergonomic and testable the code is - we fine tune the apis but nothing is fleshed out yet. Once this is done, we are done with the most time consuming phase. After that is basically just asking it to flesh out the layers starting from zero dependencies to arriving at the top of the castle. Even if we have any complexities within the pieces or the implementation is not exactly as per my liking, the issues are localised - I can dive in and handle it myself (most of the time, I don't need to). I feel like this approach works very well for me having a mental model of how things are connected because the most of the time I spent was spent on that model.
• i_love_retros 7mo ago
  How much does it cost per day to have all these agents running on your computer? Is your company paying for it or you? What is your process of the agent writes a piece of code, let's say a really complex recursive function, and you aren't confident you could have come up with the same solution? Do you still submit it?
  • paracyst 7mo ago
    The guy who wrote the post is a billionaire
    • gh0stcat 7mo ago
      I thought this was a joke ie you need to be a billionaire to be able to use agents like this, but you are correct. I think we need to stop listening to billionaires. The article is well thought out and well written, but his perspective is entirely biased by never having to think about money at all... all of this stuff is incredibly expensive.
      • i_love_retros 7mo ago
        Billionaires also tend to have a vested interest in the tech being hyped and adopted, after all one doesn't become a billionaire without investments.
        • jjice 7mo ago
          Define investment in this case. He's the cofounder of HashiCorp. I guess you could refer to his equity as an investment here, but I don't really think it tracks the same in this context. He may have a vested interest, but he did cofound HashiCorp as an engineer that actually developed the products, so I find his insight at least somewhat valuable.
          • i_love_retros 7mo ago
            Well did he become a billionaire from hashicorp alone or did he invest e.g. millions in stocks (like perhaps ai stocks) to become a billionaire
• bullshitsite 7mo ago
  The Death of the "Stare": Why AI’s "Confident Stupidity" is a Threat to Human Genius OPINION | THE REALITY CHECK In the gleaming offices of Silicon Valley and the boardrooms of the Fortune 500, a new religion has taken hold. Its deity is the Large Language Model, and its disciples—the AI Evangelists—speak in a dialect of "disruption," "optimization," and "seamless integration." But outside the vacuum of the digital world, a dangerous friction is building between AI’s statistical hallucinations and the unyielding laws of physics. The danger of Artificial Intelligence isn't that it will become our overlord; the danger is that it is fundamentally, confidently, and authoritatively stupid. The Paradox of the Wind-Powered Car The divide between AI hype and reality is best illustrated by a recent technical "solution" suggested by a popular AI model: an electric vehicle equipped with wind generators on the front to recharge the battery while driving. To the AI, this was a brilliant synergy. It even claimed the added weight and wind resistance amounted to "zero." To any human who has ever held a wrench or understood the First Law of Thermodynamics, this is a joke—a perpetual motion fallacy that ignores the reality of drag and energy loss. But to the AI, it was just a series of words that sounded "correct" based on patterns. The machine doesn't know what wind is; it only knows how to predict the next syllable. The Erosion of the "Human Spark" The true threat lies in what we are sacrificing to adopt this "shortcut" culture. There is a specific human process—call it The Stare. It is that thirty-minute window where a person looks at a broken machine, a flawed blueprint, or a complex problem and simply observes. In that half-hour, the human brain runs millions of mental simulations. It feels the tension of the metal, the heat of the circuit, and the logic of the physical universe. It is a "Black Box" of consciousness that develops solutions from absolutely nothing—no forums, no books, and no Google. However, the new generation of AI-dependent thinkers views this "Stare" as an inefficiency. By outsourcing our thinking to models that cannot feel the consequences of being wrong, we are witnessing a form of evolutionary regression. We are trading hard-earned competence for a "Yes-Man" in a box. The Gaslighting of the Realist Perhaps most chilling is the social cost. Those who still rely on their intuition and physical experience are increasingly being marginalized. In a world where the screen is king, the person pointing out that "the Emperor has no clothes" is labeled as erratic, uneducated, or naive. When a master craftsman or a practical thinker challenges an AI’s "hallucination," they aren't met with logic; they are met with a robotic refusal to acknowledge reality. The "AI Evangelists" have begun to walk, talk, and act like the models they worship—confidently wrong, devoid of nuance, and completely detached from the ground beneath their feet. The High Cost of Being "Authoritatively Wrong" We are building a world on a foundation of digital sand. If we continue to trust AI to design our structures and manage our logic, we will eventually hit a wall that no "prompt" can fix. The human brain runs on 20 watts and can solve a problem by looking at it. The AI runs on megawatts and can’t understand why a wind-powered car won't run forever. If we lose the ability to tell the difference, we aren't just losing our jobs—we're losing our grip on reality itself.
• jon_north 7mo ago
  How could the author write all of that and not talk about actual time savings versus the prior method? I mean, what is the point of change if not to improve? I don't mean "I felt I was more efficient." Feelings aren't measurements. Numbers!
• wackget 7mo ago
  How much electricity (and associated materials like water) must this use? It makes me profoundly sad to think of the huge number of AI agents running endlessly to produce vibe-coded slop. The environmental impact must be massive.
  • jjice 7mo ago
    If you'd like an estimate, I like this from Simon Willison: https://simonwillison.net/2025/Nov/29/ https://simonwillison.net/2025/Nov/29/ Keep in mind that these are estimates, but you could attempt to extrapolate from here. Programming prompts probably take more because I assume the average context is a good bit higher than the average ChatGPT question, plus additional agents. All in, I'm not sure if the energy usage long term is going to be overblown by media or if it'll be accurate. I'm personally not sure yet.
• kyoji 7mo ago
  This was a great post, one of the best I've seen on this topic at HN. But why is the cost never discussed or disclosed in these conversations? I feel like I'm going crazy, there is so much written extolling the virtues of these tools but with no mention of what it costs to run them now. It will surely only get more expensive from here!
  • lysace 7mo ago
    The current realistic lower bound for actual work is the $100/€90/month Claude Max ("5x") plan. It allows roughly enough usage for a typical working month (4.25 x 40-50h). "Single-threaded", interactive usage with normal human breaks, sort of. There are two usage quota windows to be aware of: 5h and 7d. I use https://github.com/richhickson/claudecodeusage https://github.com/richhickson/claudecodeusage (Mac) to keep track of the status. It shows green/yellow/red and a percentage in the menu bar.
    • mi_lk 7mo ago
      is there a guidance on when an API v.s. a subscription is a better deal?
  • quarkz14 7mo ago
    Indeed, most of us are probably limited with what our companies let us use and also not to mention not everyone can afford to use AI tooling in their own time without thinking about the cost assuming you want to build something your company doesn't claim as their own IP.
  • mbesto 7mo ago
    Honestly, the costs are so minimal and vary wildly relative to the cost of a developer that it's frankly not worth the discussion...yet. The reality is the standard deviation of cost is going to oscillate until there is a common agreed upon way to use these tools.
    • lysace 7mo ago
      Yes, but the lack of clear pricing probably makes people think it's more expensive than it actually is. (It did so to me.) There is nothing quantifiable here: https://claude.com/pricing https://claude.com/pricing Pro: "Everything in Free, plus: More usage" Max: "Choose 5x or 20x more usage than Pro" Wow, 5x or 20x more of "more". That's some masterful communication right there.
• claytongulick 7mo ago
  I know I'm in the minority here, but I've been finding AI to be increasingly useless. I'd already abandoned it for generating code, for all the reasons everyone knows, that don't need to be rehashed. I was still in the camp of "It's a better google" and can save me time with research. The issue it, at this point in my career (30+ years) the questions I have are a bit more nuanced and complex. They aren't things like "how do I make a form with React". I'm working on developing a very high performance peer server that will need to scale up to hundreds of thousands to a million concurrent web socket connections to work as a signaling server for WebRTC connection negotiation. I wanted to start as simple as possible, so peerjs is attractive. I asked the AI if peerjs peer-server would work with NodeJS's cluster server. It enthusiastically told me it would work just fine and was, in fact, designed for that. I took a look at the source code, and it looked to me like that was dead wrong. The AI kept arguing with me before finally admitting it was completely wrong. A total waste of time. Same results asking it how to remove Sophos from a Mac. Same with legal questions about HOA laws, it just totally hallucinates things thay don't exist. My wife and I used to use it to try to settle disagreements (i.e a better google) but amusingly we've both reached a place where we distrust anything it says so much, we're back to sending each other web articles :-) I'm still pretty excited about the potential use of AI in elementary education, maybe through high school in some cases, but for my personal use, I've been reaching for it less and less.
  • HarHarVeryFunny 7mo ago
    I can relate as far as asking AI for advice on complex design tasks. The fundamental problem is that it is still basically a pattern matching technology that "speaks before thinking". For shallow problems this is fine, but where it fails is when it a useful response would require it to have analyzed the consequences of what it is suggesting, although (not that it helps) many people might respond in the same way - with whatever "comes to mind". I used to joke that programming is not a career - it's a disease - since practiced long enough it fundamentally changes the way you think and talk, always thinking multiple steps ahead and the implications of what you, or anyone else, is saying. Asking advice from another seasoned developer you'll get advice that has also been "pre-analyzed", but not from an LLM.
• erelong 7mo ago
  I think the sweet spot is ai-assisted chat with manual review: readily available, not as costly agents jump ahead to the point of the user and project being out of control and more expensive I think a lot of us still hesitate to make that jump; or at least I am not sure of a cost-effective agent approach (I guess I could manually review their output, but I could see it going off track quickly) I guess I'd like to see more of an exact breakdown of what prompts and tools and AI are used to get ideas on if I'd use that for myself more
  • Havoc 7mo ago
    Suspect the sweet spot also depends on the objective. If it’s a personal tool where you are the primary user then vibe coding all the way. You can describe requirements precisely and if it breaks there are no angry customers. Something with actual users needs a bit more care
• i_love_retros 7mo ago
  AI chat for research is great and really helps me. I just don't need the AI writing code for me, don't see the point. Once I know from the ai chat research what my solution is I can code it myself with the benefit I then understand more what I am doing. And yes I've tried the latest models! Tried agent mode in copilot! Don't need it!
• causal 7mo ago
  > Always Have an Agent Running I'm a huge believer in AI agent use and even I think this is wrong. It's like saying "always have something compiling" or "make sure your Internet is always downloading something". The most important work happens when an agent is not running, and if you spend most of your time looking for ways to run more agents you're going to streetlight-effect your way into solving the wrong problems https://en.wikipedia.org/wiki/Streetlight_effect https://en.wikipedia.org/wiki/Streetlight_effect
• randusername 7mo ago
  > I'm a software craftsman This perspective is why I think this article is so refreshing. Craftsmen approach tools differently. They don't expect tools to work for them out-of-the-box. They customize the tool to their liking and reexamine their workflow in light of the tool. Either that or they have such idiosyncratic workflows they have to build their own tools. They know their tools are custom to _them_. It would be silly to impose that everyone else use their tools-- they build different things!
• vazma 7mo ago
  I respect Hashimoto for his contributions in the field, but to be honest, I am fed up with posts talking about using AI in ways that are impossible for most people due to high costs. I want to see more posts on cost-effective techniques, rather than just another guy showing off how he turned a creative 'burning-time' hobby into a 'burning-money' one.
• 4corners4sides 7mo ago
  The author makes a point that you should redo every manual commit with AI to align you mental model of actions with how models work. This is something that I’m going to need to try. It’s related to my desire to reduce things like “discovery tax” (the phenomenon whereby a 5 minute agent task is 4 minutes of environment exploration and 1 minute of execution) and makes sure that models get things right the first time around, however, my AI improvement plan didn’t really account for how to improve the model in cases where I ended up manually resolving issues or implementing features. Some arguments are made about retaining focus and single-mindedness while working on AI. I think these points are important. It’s related to the article on cutting out over-eager orchestration and focusing on validation work (https://sibylline.dev/articles/2026-01-27-stop-orchestrating-and-start-validating/ https://sibylline.dev/articles/2026-01-27-stop-orchestrating...). There are a few sides to this covered in the article. You should always have high value task to switch to when the agent is working (instead of scrolling tiktok, instagram,X, youtube, facebook, hackernews .etc). In my case I might try start to read some books that I have on the backburner like Ghost in the Wires. You should disable agent notifications and take control of when you return to check the model context to be less ADHD ridden when programming with agents and actually make meaningful progress on the side task since you only context switch when you are satisfied. The final one is to always have at least one agent and preferably only one agent running in the background. The idea is that always having an agent results in a slow burn of productivity improvements and a process where you can slowly improve the background agent performance. Generally, always having some agent running is a good way to stay on top of what current model capabilities are. I also really liked the idea of overnight agents for library research, redevelopment of projects to test out new skills, tests and AGENTS.md modifications.
• amterp 7mo ago
  Really appreciated this perspective. Much more tempered and less hype than a lot of other articles I see. I thought the "dont let agents finishing interrupt you" workflow was an interesting point. I've set up chime hooks to basically do the opposite, and this gives me pause to wonder if I'm underestimating the cost of context switching. I'll give it a go.
• kajolshah_bt 7mo ago
  I’ve gone through a similar journey, not in big tech, but in practical business work. We started with quick experiments: generative prompts in internal tooling, a couple of proof-of-concept bots, and integration of recommendations in mobile apps. What shifted for us was when we stopped experimenting for novelty and started embedding AI where routine work slowed people down. For example, we built an intake assistant for hospitals: guided questions that organize structured history before a doctor sees the patient. At first it felt promising, but adoption only happened when clinic staff saw that it saved them time and didn’t replace their judgment. That forced us to rethink how we framed the feature. It became about support, not replacement. The real adoption turning point came when non-technical team members began using the tools without hesitation. That’s when it stopped being AI and just became part of workflow.
• novatrope 7mo ago
  Very much like my experience with Claude. First, gave it some simple tasks (Getting this error on my site, etc) The results were surprisingly good. Then I started giving Claude broader tasks, and learning how to write the prompts. Now I have come to the point where I haven't written a line of code in several weeks (quite a change for someone who learned to program on a Burroughs b5500 back in the 70's) So I guess I am a convert.