Summarizer

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.

Architects went from drawing everything on paper, to using CAD products over a generation. That's a lot of years! They're still called architects. Our tooling just had a refresh in less than 3 years and it leaves heads spinning. People are confused, fighting for or against it. Torn even between 2025 to 2026. I know I was. People need a way to describe it from 'agentic coding' to 'vibe coding' to 'modern AI assisted stack'. We don't call architects 'vibe architects' even though they copy-paste 4/5th of your next house and use a library of things in their work! We don't call builders 'vibe builders' for using earth-moving machines instead of a shovel... When was the last time you reviewed the machine code produced by a compiler? ... The real issue this industry is facing, is the phenomenal speed of change. But what are we really doing? That's right, programming.

You're correct, and I believe this is only a matter of time. Over time it has been getting better and will keep doing so.

Maybe. But it's been 3 years and it still isn't good enough to actually trust. That doesn't raise confidence that it will ever get there.

You need to put this revolution in scale with other revolutions. How long did it take for horses to be super-seeded by cars? How long did powertool take to become the norm for tradesmen? This has gone unbelievably fast.

I think things can only be called revolutions in hindsight - while they are going on it's hard to tell if they are a true revolution, an evolution or a dead-end. So I think it's a little premature to call Generative AI a revolution. AI will get there and replace humans at many tasks, machine learning already has, I'm not completely sure that generative AI will be the route we take, it is certainly superficially convincing, but those three years have not in fact seen huge progress IMO - huge amounts of churn and marketing versions yes, but not huge amounts of concrete progress or upheaval. Lots of money has been spent for sure! It is telling for me that many of the real founders at OpenAI stepped away - and I don't think that's just Altman, they're skeptical of the current approach. PS Superseded.

> Compilers will produce working output given working input literally 100% of my time in my career. I've never personally found a compiler bug. First compilers were created in the fifties. I doubt those were bug-free. Give LLMs some fifty or so years, then let's see how (un)reliable they are.

Your sentiment resonates with me a lot. I wonder what we’ll consider the inflection point 10 years from now. It seemed like the zeitgeist was screaming about scaling limits and running out of training data, then we got Claude code, sonnet 4.5, then Opus 4.5 and no ones looked back since.

I wonder too. It might be that progress on the underlying models is going to plateau, or it might be that we haven't yet reached what in retrospect will be the biggest inflection point. Technological developments can seem to make sense in hindsight as a story of continuous progress when the dust has settled and we can write and tell the history, but when you go back and look at the full range of voices in the historical sources you realize just how deeply nothing was clear to anyone at all at the time it was happening because everyone was hurtling into the unknown future with a fog of war in front of them. In 1910 I'd say it would have been perfectly reasonable to predict airplanes would remain a terrifying curiosity reserved for daredevils only (and people did); or conversely, in the 1960s a lot of commentators thought that the future of passenger air travel in the 70s and 80s would be supersonic jets. I keep this in mind and don't really pay too much attention to over-confident predictions about the technological future.

Isn’t there something off about calling predictions about the future, that aren’t possible with current tech, hype? Like people predicted AI agents would be this huge change, they were called hype since earlier models were so unreliable, and now they are mostly right as ai agents work like a mid level engineer. And clearly super human in some areas.

> ai agents work like a mid level engineer They do not. > And clearly super human in some areas. Sure, if you think calculators or bicycles are "superhuman technology". Lay off the hype pills.

Is there any reason to use Claude Code specifically over Codex or Gemini? I’ve found the both Codex and Gemini similar in results, but I never tried Claude because of I keep hearing usage runs out so fast on pro plans and there’s no free trial for the CLI.

I mostly mentioned Claude Code because it's what Mitchell first tried according to his post, and it's what I personally use. From what I hear Codex is pretty comparable; it has a lot of fans. There are definitely some differences and strengths and weaknesses of both the CLIs and the underlying LLMs that others who use more than one tool might want to weight in on, but they're all fairly comparable. (Although, we'll see how the new models released from Anthropic and OpenAI today stack up.) Codex and Gemini CLI are basically Claude Code clones with different LLMs behind them, after all.

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.

I think for a lot of people the turn off is the constant churn and the hype cycle. For a lot of people, they just want to get things done and not have to constantly keep on top of what's new or SOTA. Are we still using MCPs or are we using Skills now? Not long ago you had to know MCP or you'd be left behind and you definitely need to know MCP UI or you'll be left behind. I think. It just becomes really tiring, especially with all the FUD. I'm embracing LLMs but I think I've had to just pick a happy medium and stick with Claude Code with MCPs until somebody figures out a legitimate way to use the Claude subscription with open source tools like OpenCode, then I'll move over to that. Or if a company provides a model that's as good value that can be used with OpenCode.

It reminds me a lot of 3D Printing tbh. Watching all these cool DIY 3d printing kits evolve over years, I remember a few times I'd checked on costs to build a DIY one. They kept coming down, and down, and then around the same time as "Build a 3d printer for $200 (some assembly required)!" The Bambu X1C was announced/released, for a bit over a grand iirc? And its whole selling point was that it was fast and worked, out of the box. And so I bought one and made a bunch of random one-off-things that solved _my_ specific problem, the way I wanted it solved. Mostly in the form of very specific adapter plates that I could quickly iterate on and random house 'wouldn't it be nice if' things. That's kind of where AI-agent-coding is now too, though... software is more flexible.

> For a lot of people, they just want to get things done and not have to constantly keep on top of what's new or SOTA That hasn’t been tech for a long time. Frontend has been changing forever. React and friends have new releases all the time. Node has new package managers and even Deno and Bun. AWS keeps changing things.

You really shouldn't use the absolute hellscape of churn that is web dev as an example of broader industry trends. No other sub-field of tech is foolish enough to chase hype and new tools the way web dev is.

There's a point at which these things become Good Enough though, and don't bottleneck your capacity to get things done. To your point, React, while it has new updates, hasn't changed the fundamentals since 16.8.0 (introduction of hooks) and that was 7 years ago. Yes there are new hooks, but they typically build on older concepts. AWS hasn't deprecated any of our existing services at work (besides maybe a MySQL version becoming EOL) in the last 4 years that I've worked at my current company. While I prefer pnpm (to not take up my MacBook's inadequate SSD space), you can still use npm and get things done. I don't need to keep obsessing over whether Codex or Claude have a 1 point lead in a gamed benchmark test so long as I'm still able to ship features without a lot of churn.

Something besides AI tooling. This isn't Amway.

And lately, the sweet spot has been moving upwards every 6-8 weeks with the model release cycle.

This is what I experienced as well. these are some ticks I use now. 1. Write a generic prompts about the project and software versions and keep it in the folder. (I think this getting pushed as SKIILS.md now) 2. In the prompt add instructions to add comments on changes, since our main job is to validate and fix any issues, it makes it easier. 3. Find the best model for the specific workflow. For example, these days I find that Gemini Pro is good for HTML UI stuff, while Claude Sonnet is good for python code. (This is why subagents are getting popluar)

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.

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! .

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.

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.

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.

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.

> I've been building systems like what the OP is using since pgt3 came out. OP is also a founder of Hashicorp, so.. lol. > This is the honeymoon phase. No offense but you come across as if you didn’t read the article.

You come across as if you didn't read my post. I'll wait for OP to move their workflow to Claude 7.0 and see if they still feel as bullish on AI tools. People who are learning a new AI tool for the first time don't realzie that they are just learning quirks of the tool and underlying and not skills that generalize. It's not until you've done it a few times that you realzie you've wasted more than 80% of your time on a model that is completely useless and will be sunset in 6 months.

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.

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!"

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?

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).

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".