I’m the exact same way. $15 on openrouter lasted me so long it would’ve got me fired at FAANG. Despite this, my number of commits is dramatically higher. Small, beautifully scoped changes is just good software development, and good for the wallet as well.

I think the issue is that no one is content with incremental progress. We all know one shots are mostly possible, so the age of the personal project is kind of over. There’s no motive to invest dozens of hours getting a working prototype when Claude can give you something right now. So you can’t make small changes until you have that codebase in place already. You’re forced to make sweeping changes if you use AI from the beginning. And it’s not like it matters, there’s no personal attachment to any one part of the code, it’s not even seen!

I found Mimo 2.5 Pro to be the right balance between cost and intelligence. It costs the same as DeepSeek v4 Pro, but it's better overall by a small margin.

Do you use DeepSeek for both planning and building or do you have a preferred builder? I've been looking into moving from Cursor to OpenCode, or at least have Opencode for when my allocated usage for the month is done.

I'm genuinely curious how you can do this - with limited context tokens, I find the output is often inconsistent with the project, or recreates functionality it missed, if I forget to point it at every pertinent object/library/etc it will need.

I use Claude at work; provided by my employer. I pay DeepSeek on a personal capacity.

I honestly have a better experience using DeepSeek, in part because since the cost is lower I feel more free to experiment with it. But in part because the model is excellent, and can handle everything I throw at it in a collaborative manner.

I am really excited for when V4 is out of preview and they are done with all the post-training bit.

Which harness? I tried Kimi on Opencode Go and after an hour I hit my 5 hr limit and went back to Codex.

Yeah, the top-end models still feel better at synthesizing and especially simplifying design. Any of the current GPT-5.6 models can overengineer their way around edge cases. Figuring out how to get the same thing without the overengineering and the often-performance-overhead is harder and something that seems to be still more limited to the bigger models. But even the top-end models tend to overdo it first, then only note that it could've been a fifth the code with twice the perf if you push on it to make it realize that some of those edge cases or situations aren't actually relevant to this particular code.

But the situations where you need that are narrowing every release. The "Composer 1" era of low-end models is pretty far away now.

Question if you don't mind. Would you have considered outsourcing the application you mentioned? As in, paying someone else to do it?

To me it is great that LLMs are allowing more people to use computers and software the way they were meant to. But the people that are using them this way, in my opinion, wouldn't have commissioned anybody to do it anyway. They'd just live with whatever process/pain they have. So jumping from "I can now produce a prototype in a weekend" to "software development is dead" has always felt strange to me. Just something I've been thinking about lately.

> We now have a fully developed app that we are testing but found several features missing.

This is my experience with using AI to generate tailored apps too. The first 80% makes me really excited but then it either only kind of works, has bugs, or has missing features. Even when I do eventually get it working I feel dirty using it because I just know it's badly implemented. Of the dozens I've created I don't think I still use any of them. I have a few glue scripts still in use.

> We now have a fully developed app that we are testing but found several features missing.

So not fully developed then.

this is why i think LLMs is a foundational technology like the Internet. there is no other technology on the horizon that have this much impact.

It could just make an interface and have the buttons trigger prompts. (This would ofc be worse than the MS version)

Claude code is a (pretty good) harness. Any other harness I try, I realized at some point, I’m just wishing it worked as well as CC does

Haha! Love to hear it. That's exactly what I have too: a claw-like[0] system which I originally used Sonnet for and now use my DeepSeek v4 Flash with.

In my case, I was foolish enough to run it all on my hardware which is pretty damned fast but has a duty cycle of 5% and runs idle most of the time. It's definitely better done via API.

Tell me more about the Alexa-like system! I have mine at home on a custom OpenWakeWord model trained on the word 'Aurora'. And I, too, have it hooked up to Home Assistant. I have a bunch of Eufy E21 baby cameras mounted on the walls[1] so it has vision through the house. The vision model uses GPT-5.5 on the subscription because I haven't yet set up a Qwen or Gemma multimodal that can see.

With Frigate on my home server I can even watch for events like my daughter waking up! And at night the agent sends out the vacuum if we've cleared the floor of baby toys. I feel this close to the dream of sci-fi AI. Because I auto-forward a bunch of my email to it etc. it knows about what's going on with me.

My wife will sometimes ask the agent information about me etc. and it's way easier to get a fast response rather than waiting for me to see the message etc.[2]

0: https://news.ycombinator.com/item?id=47538158

1: https://wiki.roshangeorge.dev/w/Blog/2025-12-01/Grounding_Yo...

2: https://imgur.com/a/tP12lfj

Oddly enough, I did the exact same thing this past weekend, for a couple hundred usd in Fable overage.

It was truly remarkably easy to build and package it exactly to my whims, in my case as a single docker container with a process reaper that runs llama, my Go code, tts, chat harness, browser in xvfb, and even a mailer daemon. With Gemma, it even runs on a RPi 5.

What’s absolutely wild to me is that over a couple hours, I could probably have it import parts of Home Assistant for my devices directly, and do other wacky stuff in what is essentially software for one.

OpenAI and Anthropic also claim they don't retain data. I'm not sure what it would take for me to trust any of these inference companies, and why I should trust them more than the big ones.

The author works for a company that provides Kimi. It’s different.

It doesn’t mean the post doesn’t accurately reflect the author’s views - it probably does. And the conflict of interest is well disclosed in the brief post. In fact, it’s quite natural for somebody who likes open models to work somewhere associated with them.

So personally I don’t find any impropriety going on here - but yes, it’s an ad.

I don't have a problem with that if "truly good" is measured by something other than "feels good to me".

Yes, but are self promotion posts not required to use "Show HN"/"Tell HN"? Or at least a disclosure on the comments...

Will do! Heard a lot of great things about it from my friends.

In the fullness of time, LLMs will eventually become commodities and their price will be a race to the bottom. As the competitive advantage of one LLM over another shrinks - particularly as open source models expand - the value proposition will turn to the neuro/symbolic harnesses that use those models, structures that encompass agents, agent orchestration, tooling, and component plugins. Even those from frontier AI organizations will fall to open source frameworks. The last thing standing will be those who command the data centers on which these things run, and even then, that will become a commodity, with the number of players shrinking to just a few survivors. They too will find pressure: as computational power rises, more and more such systems will be run locally. - Grady Booch

Is there any resource or benchmark actually comparing how different harnesses perform with a given model?

Really just feels like endless FOMO with how fast the iteration cycle is for harness and model development.

I am not sure -- Anthropic probably does benefit from the vertical integration here. They can train their models to work better with CC, which isn't really an option for third party harnesses.

With that said I think Codex/Kimi code are all behind CC as well, so maybe it's a question of effort and not the model.

People tend to think alike. I work in much the same way.First, I write the draft myself, then let AI handle the revisions, and finally run it through an open model in small units before I check the results.

The inflation-adjusted cost for getting one of the early personal computers to do some BASIC programming was probably a lot higher than $5k today, and you could still go cheaper. Local AI is still (expensive) hobby territory. A capable local AI machine runs for about as much as a good mountainbike.

You can run a small model on almost any hardware, for free, and just play around. It doesn’t need to be some “frontier” (whatever the hell that means) model by any means. Grab some new tech and slap it on whatever hardware you have laying around, see what you think.

Making it about the hardware costs is not the play, they’re not an actual barrier.

Still working on the fields medal, I’ll give that a shot next.

??

Edit: I see, had typo last word of sentence was meant to be “blog”

lol