Well, it's not like it never happened to me to "burn tokens" with some lifetime issue. :D But yeah, if you're working in Rust on something with sharp edges, LLM will get get hurt. I just don't tend to have these in my projects.

Even more basic failure mode. I told it to convert/copy a bit (1k LOC) of blocking code into a new module and convert to async. It just couldn't do a proper 1:1 logical _copy_. But when I manually `cp <src> <dst>` the file and then told it to convert that to async and fix issues, it did it 100% correct. Because fundamentally it's just non-deterministic pattern generator.

Not that I have seen, which is probably a big part of the disconnect. Mostly it's tribal knowledge. I learned through experimentation, but I've seen tips here and there. Here's my workflow (roughly)

> Create a CLAUDE.md for a c++ application that uses libraries x/y/z

[Then I edit it, adding general information about the architecture]

> Analyze the library in the xxx directory, and produce a xxx_architecture.md describing the major components and design

> /agent [let claude make the agent, but when it asks what you want it to do, explain that you want it to specialize in subsystem xxx, and refer to xxx_architecture.md

Then repeat until you have the major components covered. Then:

> Using the files named with architecture.md analyze the entire system and update CLAUDE.md to use refer to them and use the specialized agents.

Now, when you need to do something, put it in planning mode and say something like:

> There's a bug in the xxx part of the application, where when I do yyy, it does zzz, but it should do aaa. Analyze the problem and come up with a plan to fix it, and automated tests you can perform if possible.

Then, iterate on the plan with it if you need to, or just approve it.

One of the most important things you can do when dealing with something complex is let it come up with a test case so it can fix or implement something and then iterate until it's done. I had an image processing problem and I gave it some sample data, then it iterated (looking at the output image) until it fixed it. It spent at least an hour, but I didn't have to touch it while it worked.

> if you did /init in a new code base, that Claude would set itself up to maximize its own understanding of the code.

This is definitely not the case, and the reason anthropic doesnt make claude do this is because its quality degrades massively as you use up its context. So the solution is to let users manage the context themselves in order to minimize the amount that is "wasted" on prep work. Context windows have been increasing quite a bit so I suspect that by 2030 this will no longer be an issue for any but the largest codebases, but for now you need to be strategic.

Actually, 10000th

https://www.bridgemeister.com/fulllist.htm

Respectfully, it's absolutely important to "gatekeep" a title that has an established definition and certain expectations attached to the title.

OP says, "BUT YOU DON’T KNOW HOW THE CODE WORKS.. No I don’t. I have a vague idea, but you are right - I do not know how the applications are actually assembled." This is not what I would call an engineer. Or a programmer. "Prompter", at best.

And yes, this is absolutely "lesser than", just like a middleman who subcontracts his work to Fiverr (and has no understanding of the actual work) is "lesser than" an actual developer.

I think it's also that the potential is far from being realized yet we're constantly bombarded by braindead marketers trying to convince us that it's the best thing ever already. This is tiring especially when the leadership (not held back by any technical knowledge) believes them.

I'm sure AI will get there, I also think it's not very good yet.

Anecdotally, we use Opus 4.5 constantly on Zed's code base, which is almost a million lines of Rust code and has over 150K active users, and we use it for basically every task you can think of - new features, bug fixes, refactors, prototypes, you name it. The code base is a complex native GUI with no Web tech anywhere in it.

I'm not talking about "write this function" but rather like implementing the whole feature by writing only English to the agent, over the course of numerous back-and-forth interactions and exhausting multiple 200K-token context windows.

For me personally, definitely at least 99% all of the Rust code I've committed at work since Opus 4.5 came out has been from an agent running that model. I'm reading lots of Rust code (that Opus generated) but I'm essentially no longer writing any of it. If dot-autocomplete (and LLM autocomplete) disappeared from IDE existence, I would not notice.

I don't know if you've tried Chatgpt-5.2 but I find codex much better for Rust mostly due to the underlying model. You have to do planning and provide context, but 80%+ of the time it's a oneshot for small-to-medium size features in an existing codebase that's fairly complex. I honestly have to say that it's a better programmer than I am, it's just not anywhere near as good a software developer for all of the higher and lower level concerns that are the other 50% of the job.

If you have any opensource examples of your codebase, prompt, and/or output, I would happily learn from it / give advice. I think we're all still figuring it out.

Also this SIMD translation wasn't just a single function - it was multiple functions across a whole region of the codebase dealing with video and frame capture, so pretty substantial.

Is that a context issue? I wonder if LSP would help there. Though Claude Code should grep the codebase for all necessary context and LSP should in theory only save time, I think there would be a real improvement to outcomes as well.

The bigger a project gets the more context you generally need to understand any particular part. And by default Claude Code doesn't inject context, you need to use 3rd party integrations for that.

I don't hold calculators to high bars. They think 0.1 + 0.2 = 0.30000000000000004:

https://qntm.org/notpointthree

Yeah, the online Gemini app is not good for long lived conversations that build up a body of decisions. The context window gets too large and things drop.

What I’ve learned is that once you reach that point you’ve got to break that problem down into smaller pieces that the AI can work productively with.

If you’re about to start with Gemini-cli I recommend you look up https://github.com/github/spec-kit. It’s a project out of Microsoft/Github that encodes a rigorous spec-then-implement multi pass workflow. It gets the AI to produce specs, double check the specs for holes and ambiguity, plan out implementation, translate that into small tasks, then check them off as it goes. I don’t use spec-kit all the time, but it taught me that what explicit multi pass prompting can do when the context is held in files on disk, often markdown that I can go in and change as needed. I think it ask basically comes down to enforcing enough structure in the form of codified processes, self checks and/or tests for your code.

Pro tip, tell spec-kit to do TDD in your constitution and the tests will keep it on the rails as you progress. I suspect “vibe coding” can get a bad rap due to lack of testing. With AI coding I think test coverage gets more important.

They work much better off a test that must pass. That they can “see”. Without it they are just making up some other acceptance criteria.

I've found this too.

I find I'm doing more Typescript projects than Python because of the superior typing, despite the fact I prefer Python.

I think anecdotes like this may prove very relevant the next few years. AI might make bad code, but a project of bad code that's still way smaller than a bloated alternative, and has a UX tailored to your exact requirements could be compelling.

A big part of the problem with existing software is that humans seem to be pretty much incapable of deciding a project is done and stop adding to it. We treat creating code like a job or hobby instead of a tool. Nothing wrong with that, unless you're advertising it as a tool.

Have you tried VidHub? Works nicely against almost anything. Plex, jellyfin, smb/webdav folder etc

Vs code copilot extension the harness is not great, but Opus 4.5 with Copilot CLI works quite well.

oh I noticed! I've begun doing that on my laptop. I just started going down all my list of sideprojects one by one, then two by two, a Claude Code instance in a terminal window for each folder. It's a bit mental

I'm finding that branding and graphic design is the most arduous part, that I'm hoping to accelerate soon. I'm heavily AI assisted there too and I'm evaluating MCP servers to help, but so far I do actually have to focus on just that part as opposed to babysit

We were enjoying cheap second hand rack mount servers, RAM, hard drives, printers, office chairs and so on for a decade after the original dot com crash. Every company that went out of business liquidated their good shit for pennies.

I'm hoping after AI comes back down to earth there will be a new glut of cheap second hand GPUs and RAM to get snapped up.

Right. And same for railways, which had a huge bubble early on. Over-hyped on the short time horizon. Long term, they were transformative in the end, although most of the early companies and early investors didn’t reap the eventual profits.

But the dot-com bubble wasn't overhyed in retrospect. It was under-hyped.

Beads is amazing. It’s such a simple concept but elevates agentic coding to another levels

The value I'm getting from this stuff is so large that I'll take those risks, personally.

I've said this multiple times:

This is why you use this AI bubble (it IS a bubble) to use the VC-funded AI models for dirt cheap prices and CREATE tools for yourself.

Need a very specific linter? AI can do it. Need a complex Roslyn analyser? AI. Any kind of scripting or automation that you run on your own machine. AI.

None of that will go away or suddenly stop working when the bubble bursts.

Within just the last 6 months I've built so many little utilities to speed up my work (and personal life) it's completely bonkers. Most went from "hmm, might be cool to..." to a good-enough script/program in an evening while doing chores.

Even better, start getting the feel for local models. Current gen home hardware is getting good enough and the local models smart enough so you can, with the correct tooling, use them for suprisingly many things.

> 1) There exists a threshold, only identifiable in retrospect, past which it would have been faster to locate or write the code yourself than to navigate the LLM's correction loop or otherwise ensure one-shot success.

I can run multiple agents at once, across multiple code bases (or the same codebase but multiple different branches), doing the same or different things. You absolutely can't keep up with that. Maybe the one singular task you were working on, sure, but the fact that I can work on multiple different things without the same cognitive load will blow you out of the water.

> 2) The intuition and motivations of LLMs derive from a latent space that the LLM cannot actually access. I cannot get a reliable answer on why the LLM chose the approaches it did; it can only retroactively confabulate. Unlike human developers who can recall off-hand, or at least review associated tickets and meeting notes to jog their memory. The LLM prompter always documenting sufficiently to bridge this LLM provenance gap hits rub #1.

Tell the LLM to document in comments why it did things. Human developers often leave and then people with no knowledge of their codebase or their "whys" are even around to give details. Devs are notoriously terrible about documentation.

> 3) Gradually building prompt dependency where one's ability to take over from the LLM declines and one can no longer answer questions or develop at the same velocity themselves.

You can't develop at the same velocity, so drop that assumption now. There's all kinds of lower abstractions that you build on top of that you probably can't explain currently.

> 4) My development costs increasingly being determined by the AI labs and hardware vendors they partner with. Particularly when the former will need to increase prices dramatically over the coming years to break even with even 2025 economics.

You aren't keeping up with the actual economics. This shit is technically profitable, the unprofitable part is the ongoing battle between LLM providers to have the best model. They know software in the past has often been winner takes all so they're all trying to win.

I'm working mostly in a web framework that's used by me and almost nobody else (the weird little ASGI wrapper buried in Datasette) and I find the coding agents pick it up pretty fast.

One trick I use that might work for you as well:

  Clone GitHub.com/simonw/datasette to /tmp
  then look at /tmp/docs/datasette for
  documentation and search the code
  if you need to

If your framework is missing documentation tell Claude Code to write itself some documentation based on what it learns from reading the code!

Cursor's planning functionality is very similar and I have found that I can even use "cheap" models like their Composer-1 and get great results in the planning phase, and then turn on Sonnet or Opus to actually produce the plan. 90% of the stuff I need to argue about is during the planning phase, so I save a ton of tokens and rework just making a really good spec.

It turns out that Waterfall was always the correct method, it's just really slow ;)

If you're a developer at the dawn of the AI revolution, there is absolutely a gun to your head.

The human still has to manage complexity. A properly modularized and maintainable code base is much easier for the LLM to operate on — but the LLM has difficulty keeping the code base in that state without strong guidance.

Putting “Make minimal changes” in my standard prompt helped a lot with the tendency of basically all agents to make too many changes at once. With that addition it became possible to direct the LLM to make something similar to the logical progression of commits I would have made anyway, but now don’t have to work as hard at crafting.

Most of the hype merchants avoid the topic of maintainability because they’re playing to non-technical management skeptical of the importance of engineering fundamentals. But everything I’ve experienced so far working with LLMs screams that the fundamentals are more important than ever.

It usually works well for me. With very big tasks I break the plan into multiple MD files with the relevant context included and work through in individual sessions, updating remaining plans appropriately at the end of each one (usually there will be decision changes or additions during iteration).

It takes a lot of plan to use up the context and most of the time the agent doesn't need the whole plan, they just need what's relevant to the current task.

I've lived through multiple incredibly rapid changes in tech throughout my career, and the lesson always learned was there is a lot of wasted energy keeping up.

Two big examples:

- Period from early mvc JavaScript frontends (backbone.js etc) and the time of the great React/Angular wars. I completely stepped out of the webdev space during that time period.

- The rapid expansion of Deep Learning frameworks where I did try to keep up (shipped some Lua torch packages and made minor contributions to Pylearn2).

In the first case, missing 5 years of front-end wars had zero impact. After not doing webdev work at all for 5-years I was tasked with shipping a React app. It took me a week to catch up, and everything was deployed in roughly the same time as someone would have had they spent years keeping up with changes.

In the second case, where I did keep up with many of the developing deep learning frameworks, it didn't really confer any advantage. Coworkers who I worked with who started with Pytorch fresh out of school were just as proficient, if not more so, with building models. Spending energy keeping up offered no value other than feeling "current" at the time.

Can you give me a counter example of where keeping up with a rapidly changing area that's unstable has conferred a benefit to you? Most of FOMO is really just fear. Again, unless you're trying to sell your self specifically as a consultant on the bleeding edge, there's no reason to keep up with all these changes (other than finding it fun).

If everything changes every month, then stuff you learn next month would be obsolete in two months. This is a response to people saying "adapt or be left behind". There's so much thrashing that if you're not interested with the SOTA, you can just wait for everything to calm down and pick it up then.

If you've found nothing useful about AI so far then the problem is likely you

> On two occasions I have been asked, 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

-- Charles Babbage

> ... Proceeds to explain how it's crippled and all the workarounds you have to do to make it less crippled.

No - that's not what I did.

You don't need an extra-long context full of irrelevant tokens. Claude doesn't need to see the code it implemented 40 steps ago in a working method from Phase 1 if it is on Phase 3 and not using that method. It doesn't need reasoning traces for things it already "thought" through.

This other information is cluttering, not helpful. It is making signal to noise ratio worse.

If Claude needs to know something it did in Phase 1 for Phase 4 it will put a note on it in the living markdown document to simply find it again when it needs it.

> There's some magical "right context" that will fix all the problems.

All I can tell you is that in my own lived experience, I've had some fantastic results from AI, and it comes from telling it "look at this thing here, ok, i want you to chain it to that, please consider this factor, don't forget that... blah blah blah" like how I would have spelled things out to a junior developer, and then it really does stand a really solid chance of turning out what I've asked for. It helps a lot that I know what to ask for; there's no replacing that with AI yet.

So, your own situation must fall into one of these coarse buckets:

- You're doing something way too hard for AI to have a chance at yet, like real science / engineering at the frontier, not just boring software or infra development

- Your prompts aren't specific enough, you're not feeding it context, and you're expecting it to one-shot things perfectly instead of having to spend an afternoon prompting and correcting stuff

- You're not actually using and getting better at the tools, so you're just shouting criticisms from the sidelines, perhaps as sour grape because you're not allowed by policy / company can't afford to have you get into it.

IDK. I hope it's the first one and you're just doing Really Hard Things, but if you're doing normal software developer stuff and not seeing a productivity advantage, it's a fucking skill issue.

It's like working with humans:

  1) define problem
  2) split problem into small independently verifiable tasks
  3) implement tasks one by one, verify with tools

With an LLM usually 1) is just a markdown file, 2) is a markdown checklist, Github issues (which Claude can use with the `gh` cli) and every loop of 3 gets a fresh context, maybe the spec from step 1 and the relevant task information from 2

I haven't ran into context issues in a LONG time, and if I have it's usually been either intentional (it's a problem where compacting wont' hurt) or an error on my part.

I realize your experience has been frustrating. I hope you see that every generation of model and harness is converting more hold-outs. We're still a few years from hard diminishing returns assuming capital keeps flowing (and that's without any major new architectures which are likely) so you should be able to see how this is going to play out.

It's in your interest to deal with your frustration and figure out how you can leverage the new tools to stay relevant (to the degree that you want to).

Regarding the context window, Claude needs thinking turned up for long context accuracy, it's quite forgetful without thinking.

Opus 4.1 was released in August or smth.

Ah, gotcha, I am still working through the article, but its detailed focus on all the moving parts under the covers is making it hard to grok the high-level workflow.

> How is it useful to you that these companies are so valuation hungry that they are moving money into this technology in such a way that people are fearful it could cripple the entire global economy?

The creation of entire new classes of profession has always been the result of technological breakthroughs. The automobile did not cripple the economy, even as it ended the buggy-whip barons.

> How is it useful to you that this tech is so power hungry that environmental externalities are being further accelerated while regular people's utility costs are raising to cover the increased demand(whether they use the tech to "code" or "manifest art")?

There will be advantages to lower-power computing, and lower-cost electricity. Implement carbon taxes and AI companies will follow the market incentive to install their datacentres in places where sustainable power is available for cheap. We'll see China soaring to new heights with their massive solar investment, and America will eventually figure out they have to catch up and cannot do so with coal and gas.

> How is it useful to you that this tech is so compute hungry that they are seemingly ending the industry of personal compute to feed this tech's demand?

Temporary problem, the demand for personal computing is not going to die in five years, and meanwhile the lucrative markets for producing this equipment will result in many new factories, increasing capacity and eventually lowering prices again. In the meantime, many pundits are suggesting that this may thankfully begin the end of the Electron App Era where a fuckin' chat client thinks it deserves 1GB of RAM.

Consider this: why are we using Electron and needing 32GB of RAM on a desktop? Because web developers only knew how to use Javascript and couldn't write a proper desktop app. With AI, desktop frameworks can have a resurgence; why shouldn't I use Go or Rust and write a native app on all platforms now that the cost of doing so is decreasing and the number of people empowered to work with it is increasing? I wrote a nice multithreaded fractal renderer in Rust the other day; I don't know how to multithread, write Rust, and probably can't iterate complex numbers correctly on paper anymore....

> How is it useful to you that this tech is so water hungry that it is emptying drinking water acquifers?

This is only a problem in places that have poor water policy, e.g. California (who can all thank the gods that their reservoirs are all now very full from the recent rain). This problem predates datacenters and needs to be solved - for instance, by federalizing and closing down the so-called Wonderful Company and anyone else who uses underhanded tactics to buy up water rights to grow crops that shouldn't be grown there.

Come and run your datacenters up in the cold North, you won't even need evaporative cooling for them, just blow a ton of fresh air in....

> How is it useful to you that this tech is being used to manufacture consent?

Now you've actually got an argument, and I am on your side on this one.

> If at any point any of these releases were "genuine inflection points" it would be unnecessary to proselytize such. It would be self evident. Much like rain.

Agreed.

Now, I suggest reading through all of this to note that I am not a fan of tech bros, that I do want this to be a bubble. Then also note what else I'm saying despite all that.

To me, it is self-evident. The various projects I have created by simply asking for them, are so. I have looked at the source code they produce, and how this has changed over time: Last year I was describing them as "junior" coders, by which I meant "fresh hire"; now, even with the same title, I would say "someone who is just about to stop being a junior".

> "The oppressed need to acknowledge that their oppression is useful to their oppressors."

The capacity for AI to oppress you is in direct relation to its economic value.

> How is it useful to you that this tech is so power hungry that environmental externalities are being further accelerated while regular people's utility costs are raising to cover the increased demand(whether they use the tech to "code" or "manifest art")?

The power hunger is in direct proportion to the demand. Someone burning USD 20 to get Claude Code tokens has consumed approximately USD 10 of electricity in that period, with the other USD 10 having been spread between repaying the model training cost and the server construction cost.

The reason they're willing to spend USD 20 is to save at least US 20 worth of dev time. This was already the case with the initial version of ChatGPT pro back in the day, when it could justify that by saving 23 dev minutes per month. There's around a million developers in the USA, just that group increasing electricity spending by USD 10/month will put a massive dent on the USA's power grid.

Gets worse though. Based on my experience, using Claude Code optimally, when you spend USD 20 you get at least 10 junior sprints' worth of output. Hiring a junior for 10 sprints is, what, USD 30,000? The bound here is "are you able to get value from having hired 1,500 juniors for the price of one?"

One can of course also waste those tokens. Both because nobody needs slop, and because most people can't manage one junior never mind 1500 of them.

However, if the economy collectively answers "yes", then the environmental externalities expand until you can't afford to keep your fridge cold or your lights on.

This is one of the failure modes of the technological singularity that people like me have been forewarning about for years, even when there's no alignment issues within the models themselves. Which there are, because Musk's one went and called itself Mecha Hitler, while being so sycophantic about Musk himself that it called him the best at everything even when the thing was "drinking piss", which would be extremely funny if he wasn't selling this to the US military.

> How is it useful to you that this tech is so compute hungry that they are seemingly ending the industry of personal compute to feed this tech's demand?

This will pass. Either this is a bubble, it pops, the manufacturers return to their roots; or it isn't because it works as advertised, which means it leads to much higher growth rates, and we (us, personally, you and me) get personal McKendree cylinders each with more compute than currently exists… or we get turned into the raw materials for those cylinders.

I assume the former. But I say that as one who wants it to be the former.

> How is it useful to you that this tech is so water hungry that it is emptying drinking water acquifers?

Is it what's emptying drinking water acquifers?

The combined water usage of all data centers in Arizona. All of them. Together. Which is over 100 DCs. All of them combined use about double what Tesla was expecting from just the Brandenburg Gigafactory to use before Musk decided to burn his reputation with EV consumers and Europeans for political point scoring.

> How is it useful to you that this tech is being used to manufacture consent?

This is one of the objectively bad things, though it's hard to say if this is more or less competent at this than all the other stuff we had three years ago, given the observed issues with the algorithmic feeds.

The hype curve is a problem, but it's difficult to prevent. I myself have never made such a prediction. Though it now seems that the money and effort to create working coding tools is near an inflection point.

"It would be self evident." History shows the opposite at inflection points. The "self evident" stage typically comes much later.

It's employing so may people who specialize in Salesforce configuration that every year San Francisco collapses under the weight of 50,000+ of them attending Dreamforce.

And it's actually kind of amazing, because a lot of people who earn six figures programming Salesforce came to it from a non-traditional software engineering background.

And by the same token (ha) for some folks we're looking at their first hype wave. If you're a bit older, you've seen similar things like 4GLs and visual programming languages and blockchain and expert systems. They each left their mark on our profession but most of their promises were unfounded and ultimately unrealized.

Lol. In a few years when the world is awash in AI-generated slop [1] my "past skills" will not only be relevant, they will be actively sought after.

[1] Like the recent "Gas Town" and "Beads" that people keep mentioning in the comments that require extensive scripts/human intervention to purge from the system: https://news.ycombinator.com/item?id=46510121

The 80s TUI frameworks are still not beaten in developer productivity buy GUI or web frameworks. They have been beaten by GUIs in usability, but then the GUIs reverted into a worse option.

Too bad they were mostly proprietary and won't even run in modern hardware.

Open/local models are available.

Maybe not as good, but they can certainly do far far more than what was available a few years ago.

It's better because now you can automate something tedious in your life with a computer without having to first climb a six month learning curve.

Again, open models exist. These companies don't have a monopoly on the tech and they know it.

So maybe celebrate open/private/local models for empowering people rather than selfishly complain about it?

> The prompts and responses are used as training data.

They show a clear pop-up where you choose your setting about whether or not to allow data to be used for training. If you don't choose to share it, it's not used.

I mean I guess if someone blindly clicks through everything and clicks "Accept" without clicking the very obvious slider to turn it off, they could be caught off guard.

Assuming everyone who uses Claude is training their LLMs is just wrong, though.

Telemetry data isn't going to extract your codebase.

> Is 'warfighting adversaries' some convoluted code for allowing Aurornis to 'see a 1337x in productivity'?

Much as I despair at the current developments in the USA, and I say this as a sexual minority and a European, this is not "tools of the oppressor" in their own words.

Trump is extremely blunt about who he wants to oppress. So is Musk.

"Support our warfighters and maintain strategic advantage over our adversaries" is not blunt, it is the minimum baseline for any nation with assets anyone else might want to annex, which is basically anywhere except Nauru, North Sentinel Island, and Bir Tawil.

[flagged]

The one caveat with this, is that in messy code bases it will perpetuate bad things, unless you're specific about what you want. Then again, human developers will often do the same and are much harder to force to follow new conventions.

Lints are typically well suited for syntactic properties or some local semantic properties. Almost all interesting challenges in software design and evolution involve nonlocal semantic properties.

FYI Opus is available and pretty usable in claude-code on the $20/Mo plan if you are at all judicious.

I exclusively use opus for architecture / speccing, and then mostly Sonnet and occasionally Haiku to write the code. If my usage has been light and the code isn't too straightforward, I'll have Opus write code as well.