nah, there are low powered tools and there’s high powered tools. If people want $20/month happy meal toys in business that business will get left behind. Ignore the consumer market, make Bugatti’s instead - https://ghuntley.com/redlining

ps - catchup for social zoom beers?

> Please don't use HN primarily for promotion. It's ok to post your own stuff part of the time, but the primary use of the site should be for curiosity.

https://news.ycombinator.com/newsguidelines.html

Agree with the other commenter. I have seen at least 2 other posts today where you plug your project.

If that's the case you should stop using it, because there's no way you see any ROI when you spend that much to just do some coding stuff.

It would be cheaper to your company to literally pay your salary while you do nothing.

Claude Max is less than a 1/2 percentage point of a Jr. Devs average salary. If you can't make that work then....

If you're using Cursor with Claude it's gonna be pretty much the same thing. Personally I use Claude Code because I hate the Cursor interface but if you like it I don't think you're missing much.

I'll add on to this: I don't really use agent modes a lot. In an existing codebase, they waste a lot of my time for mixed results. Maybe Claude Code is so much better at this that it enables a different paradigm of AI editing—but I'd need easy, cheap access to try it.

Doesn’t resonate with me because I’ve spent over $1,000 on Claude Code at this point and the return is worth it. The spend feels cheap compared to output.

In contrast - I’m not interested in using cheaper, less-than, services for my livelihood.

Have I got bad news for you.... Microsoft announced imposing limits on "premium" models from next week. You get 300 "free" requests a month. If you use agent, you consume about 3-4 requests per action easily, I estimate to burn through 300 in about 3-5 working days.

Basically anything that isnt gpt4o is premium, and I find gpt4o near useless compared to Claude and Gemini in copilot.

Whoever is paying for your time should calculate how much time you’d save between the different products. The actual product price comparison isn’t as important as the impact on output quality and time taken. Could be $1000 a month and still pay for itself in a day, if it generated >$1000 extra value.

This might mean the $10/month is the best. Depends entirely on how it works for you.

(Caps obviously impact the total benefit so I agree there.)

> The problem is that this is $100/mo with limits

Limits are a given on any plan. It would be too easy for a vibe coder to hammer away 8 hours a day for 20 days a week if there was nothing stopping them.

The real question is whether this is a better value than pay as you go for some people.

> I'm not interested in paying 10x as much and still having limits. It would have to be 10x as useful

I don't think this is the right way to look at it. If CoPilot helps you earn an extra $100 a month (or saves you $100 worth of time), and this one is ~2x better, it still justifies the $100 price tag.

I think this thinking is flawed. First, it presupposes a linear value/cost relationship. That is not always true - a bag that costs 100x as much is not 100x more useful.

Additionally, when you’re in a compact distribution, being 5% better might be 100x more valuable to you.

Basically, this assumes that the marginal value is associated with cost. I don’t think most things, economically, seem to match that pattern. I will sometimes pay 10x the cost for a good meal that has fewer calories (nutritional value)

I am glad people like you exist, but I don’t think the proposition you suggest makes sense.

Is any of the ~$20/month with unlimited usage tooling actually profitable though? It goes without saying that if all else is equal then the product sold at a greater loss will be more popular, but that only works until the vendor runs out of money to light on fire.

I've tried every variation of this very thing. Even managed to build a quick and dirty ticketing system that I could assign to the LLM of my choosing. WITH context. Talking Graph Codebase's diagrams, mappings, tree structure of every possibility, simple documentation, complex documentation, a bunch of OSS to do this very thing automatically etcetcetc.

In the codebase I've tried modularity via monorepo, or faux microservices with local apis, monoliths filled with hooks and all the other centralized tricks in the book. Down to the very very simple. Whatever I could do to bring down the context window needed.

Eventually.....your return diminish. And any time you saved is gone.

And by the time you've burned up a context window and you're ready to get out. Now you're expeciting it to output a concise artifact to carry you to the next chat so you don't have to spend more context getting that thread up to speed.

Inevitably the context window and the LLMs eagerness to touch shit that it's not supposed (the likelihood of which increases with context) always gets in the way.

Anything with any kind of complexity ends up in a game of too much bloat or the LLM removing pieces that kill other pieces that it wasn't aware about.

/VENT

Yes. For small apps, as well distributed systems.

You have to puppeteer it and build a meta context/tasking management system. I spend a lot of time setting Claude code up for success. I usually start with Gemini for creating context, development plans, and project tasking outlines (I can feed large portions of codebase to Gemini and rely on its strategy). I’ve even put entire library docsites in my repos for Claude code to use - but today they announced web search.

They also have todos built in which make the above even more powerful.

The end result is insane productivity - I think the only metric I have is something like 15-20k lines of code for a recent distributed processing system from scratch over 5 days.

Yes. It costs me a few bucks per feature, which is an absolute no-brainer.

If you don't like what it suggests, undo the changes, tweak your prompt and start over. Don't chat with it to fix problems. It gets confused.

Absolutely stellar for 0-to-1-oriented frontend-related tasks, less so but still quite useful for isolated features in backends. For larger changes or smaller changes in large/more interconnected codebases, refactors, test-run-fix-loops, and similar, it has mostly provided negative value for me unfortunately. I keep wondering if it's a me problem. It would probably do much better if I wrote very lengthy prompts to micromanage little details, but I've found that to be a surprisingly draining activity, so I prefer to give it a shot with a more generic prompt and either let it run or give up, depending on which direction it takes.

Here's a very small piece of I code I generated quickly (i.e. <5 min) for a small task (I generated some data and wanted to check the best way to compress it):

https://gist.github.com/rachtsingh/e3d2e2b495d631b736d24b56e...

Is it correct? Sort of; I don't trust the duration benchmark because benchmarking is hard, but the size should be approximately right. It gave me a pretty clear answer to the question I had and did it quickly. I could have done it myself but it would have taken me longer to type it out.

I don't use it in large codebases (all agentic tools for me choke quickly), but part of our skillset is taking large problems and breaking them into smaller testable ones, and I give that to the agents. It's not frequent (~1/wk).

Just to throw my experience in, it's been _wildly_ effective.

Example;

I'm wrapping up, right now, an updated fork of the PHP extension `phpredis` because Redis 8 recently was released with support for a new data type, Vector Set but the phpredis extension (which is far more performant that non-extension redis libraries for PHP) doesn't support the new vector-related commands. I forked the extension repo, which is in C (I'm a PHP developer, I had to install CLion for the first time just to work along with CC) and fired up claude code with the initial prompt/task of analyzing the extensions code and documenting the purpose, conventions, and anything that it (claude) felt would benefit the bootstrapping process of future sessions such that whole files wouldn't need to be read into a CLAUDE.md file.

This initially, depending on the size of the codebase, could be "expensive". Being that this is merely a PHP extension and isn't a huge codebase, I was fine letting it just rip through the whole thing however it saw fit - were this a larger codebase I'd take a more measured approach to this initial "indexing" of the codebase.

This results in a file that claude uses like we do a readme.

Next I end this session, start a new one and tell it to review that CLAUDE.md file (I specifically tell it to do this, every single new session start moving forward) and then generate a general overview/plan of what needs to be done in order to implement the new Vector Set related commands so that I can use this custom phpredis extension in my PHP environments. I indicated that I wanted to generate a suite of tests focused on ensuring each command works with all of it's various required and optional parameters and that I wanted to use docker containers for the testing rather than mess up my local dev environment.

$22 in API costs and ~6 hours spent and I have the extension, working, in my local environment with support for all of the commands I want/need to use. (there's still 5 commands that I don't intend to use that I haven't implemented)

Not only would I have certainly never embarked upon trying to extend a C PHP extension, I wouldn't have done so over the course of an evening and morning.

Another example:

Before this redis vector sets thing I used CC to build a python image and text embedding pipeline backed by Redis streams and Celery that consumes tasks pushed to the stream by my Laravel application that currently manages ~120 million unique strings and ~65 million unique images that I've been generating embeddings for. Prior to this I'd spent very little time with Python and zero with anything related to ML. Now I have a performant python service that's portable that I run from my Macbook (M2 Pro) or various GPU-having Windows machines in my home that generate the embeddings on an 'as available' basis, pushing the results back to a redis stream that my Laravel app then consumes and processes.

The results of these embeddings and the similarity-related features that they've brought to the Laravel application are honestly staggering. And while I'm sure I could have spent months stumbling through all of this on my own - I wouldn't have, I don't have that much time for side project curiosities.

Somewhat related - these similarity features have directly resulted in this side project becoming a service people now pay me to use.

On a day to do - the effectiveness is a learned skill. You really need to learn how to work with it in the same way you, as a layperson, wouldn't stroll up to a highly specialized piece of aviation technology and just infer how to use it optimally. I hate to keep parroting "skill issue" but - it's just wild to me how effective these tools are and how there's so many people who don't seem to be able to find any use.

If it's burning through cash, you're not being focused enough with it. If it's writing code that's always slightly wrong, stop it and make adjustments. Those adjustments likely/potentially need to be documented in something like I described above in a long-running document used similarly to a prompt.

From my own experience, I watch the "/settings/logs" route on anthropics website while CC is working once I know that we're getting rather heavy with the context. Once it gets into the 50-60,000 tokens range I either aim to wrap up whatever the current task is, or I understand that things are going to start getting a little wonky into the 80k+ range. It'll keep on working up into the 120-140k tokens or more - but you're likely going to end up with lots of "dumb" stuff happening. You really don't want to be here unless you're _sooooo close_ to getting done what you're trying to. When the context gets too high and you need/want to reset but you're mid task - /compact [add notes here about next steps] and it'll generate a summary that will then be used to bootstrap the next session. (Don't do this more than once, really, as it starts losing a lot of context - just reset the session fully after the first /compact)

If you're constantly running into huge contexts you're not being focused enough. If you can't even work on anything without reading files with thousands of lines - either break up those files somehow or you're going to have to be _really_ specific with the initial prompt and context - which I've done lots of. Say I have a model that belongs to a 10+ year old project that is 6000 lines long and I want to work on a specific method in that model - I'll just tell claude in the initial message/prompt which line that method starts on, ends on and what number of lines from the start of the model it should read (so it can get the namespace, class name, properties, etc) and then let it do it's thing. I'll tell it specifically not to read more than 50 lines of that file at a time when looking for something or reviewing something, or even to stop and ask me to locate a method/usages of things, etc rather than reading whole files into context.

So, again, if it's burning through money - focus your efforts. If you think you can just fire it up and give it a generic task - you're going to burn money and get either complete junk, or something that might technically work but is hideous, at least to you. But, if you're disciplined and try to set or create boundaries and systems that it can adhere to - it does, for the most part.

Sounds like they are great fans of Numberwang.

Claude Code runs in the terminal

If your employer spends $20k a month on you (salary + everything else), $200 a month breaks even at around a 1% boost in productivity.

To rescue a flailing project that I took over when a senior hire ghosted a customer in the middle of a project, I got the 200$ Pro package from OpenAI (which is much less usable than Claude for our purposes; there were other benefits related to my client's relationship w/ OpenAI)

In the end, I was able to rescue the code part, rebuilding a 3 month long 10 person project in 2 weeks, with another 2 weeks to implement a follow-up series of requirements. The sheer amount of discussion and code creation would have been impossible without AI, and I used the full limits I was afforded.

So to answer your question, I got my money's worth in that specific use case. That said, the previous failing effort also unearthed a ton of unspoken assumptions that I was able to leverage. Without providing those assumptions to the AI, I couldn't have produced the app they wanted. Extracting that information was like extracting teeth so I'm not sure if we would have really had a better situation if we started off with everyone having an OpenAPI Pro account.

* Those who work in enterprise know intuitively what happened next.