The reason Claude code is so popular is because it’s really good at taking super vague human prose “Claude build me a million dollar SaaS”-type prompts and spitting out thousands of lines of code which cover tons of surface-level edge cases, build in tons of functionality, etc
The smaller/open models are less good at that. But that’s not how software development is done. You don’t prompt a whole app and be done with it. If you’re using it as an aid to traditional software dev, iterating on small, targeted functions, GLM works as good if not better than Claude. Anthropic expects low quality prompts. If you rubber duck GLM, you get absolutely pristine output in most cases.
This is what I've found and would likely be the consenses of the HN community.
This has lowered the barrier of entry for many non-SWEs. I'm primarily a data scientist myself in the environmental sector with limited front end development.
My partner proposed an idea to help manage her horses and over the course of several weeks we fleshed out an android app that would enable/assist her with horse care.
I spent a few days in planning mode pointing claude to the services we want to utilise and the functionality.
We now have a fully developed app that we are testing but found several features missing. For example, Claude implement X but without a level of verbosity it failed to implement the edit/deletion of X.
The app is highly tailored to her use case and I had already built the main parts as a POC but she had no user-friendly method to interact with the information she required. Claude made this possible in such a quick time frame that just seems insane. As someone time poor (as most horse people are), it would have taken me a year+ to produce something unpolished when compared to what Claude produced.
Personally I've found Deepseek v4 Flash to be as useful to me as Opus. But I don't do these silly one shot tech demos. I have the technical understanding to ask for exactly the change I want with the right terminology. I loaded up some credit on openrouter and it took me ages to hit $1 in spend.
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 can see why Anthropic is freaking out right now. China has undermined their whole business. AI models will be basic commodities where hosting providers earn a tiny margin over the raw costs rather than the predicted fortunes from being the gatekeepers to the technology.
Their business model always seemed to be "become IBM, gods of the mainframe and server-side-computing" and that always seemed a bit unlikely to last forever considering how thoroughly the PC disrupted that by being "good enough" without the lock-in.
IMO because they speed-ran it so much (raising orders of magnitudes more, pushing out new stuff orders of magnitudes faster) it will also lead to the rest of the world catching up faster and shrinking the pool of people internal to them who continually benefit compared to what actually went on with IBM. There's only so much upside when you join a company with hundreds-of-billions valuation.
I guess the other part of their business plan bet was "become literal god of AGI". Maybe it'll still happen!
the writing should have been on the wall when meta did it with llama. it was very apparent that one or two entities could release a "good enough" product into the wild commoditizing the frontier of yesterday.
Frontier models like Opus 5 are also extremely good a "research" in an academic sense, including combining elements from different fields or literatures and creating something quite novel sometimes, publishable even, from vague/speculative prompts. This is on top of implementing known methods in about 1/100th of the time it would take by hand, allowing for fast exploration of ideas. They can also roll things out from scratch, removing essentially all third party dependencies from your code base, other than Numpy or Scipy. This really buys you a lot of clarity and/or performance sometimes.
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.
To be honest, I am surprised by how good DeepSeek V4 Flash is. I use Claude Code and Codex on Claude 5 Opus and GPT-5.6 Sol most of the time, but when I use DS V4 Flash I don't feel like it's really that bad. And with oh-my-pi and just plain pi it's pretty good. To be honest the frontier models are much better at tool calling so in an assistant flow they're better but I did the dumb thing and optimized the harness for the model instead, rewriting the tools so they match what it guesses at, and DS V4 Flash does just fine.
The TTFT and tok/s are much higher on the small model so that makes it competitive for a bunch of things. It feels like what old Sonnet used to by the end of last year which is honestly damned good.
I wrote my own OpenClaw one weekend and I am running it as my assistant through Matrix with DeepSeek v4 Flash (and Qwen). It probably costs me about 2 dollars a month and is even more useful than ChatGPT would be due to me having full control on what tools it has access to.
I can do things like take a photo of a doctor's note among add the appointment to my calendar, send a PDF to my archive tagged, OCR'd etc, search info from internet, look data from Google Maps.
I can even integrate this to home assistant and talk to my agent with my open source Alexa-like system.
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]
Privacy is the main benefit of running it yourself. Sending all of your personal info to random providers on openrouter would worry me. I don't mind it for code but if it's connected to my calendar and personal data, I want it running on my machine.
I can't wait until good enough models can run on affordable hardware.
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.
This is only a thinly veiled ad. It's fine, I was curious about this exact setup, anyways.
What would be useful is a cost metric. I'm curious how much I'd be willing to spend as a premium to not have those companies piping my conversations directly to the NSA. Maybe only some conversations? Claude and OpenAI are heavily subsidized, by all accounts, so Kimi K3 on a private endpoint might end up costing more or less - that's what I want to know.
With OpenRouter this is pretty easy to see - each model page has various providers listed along with an icon marking providers with zero data retention, and the price. You can also set up an account-wide "guardrail" to prevent routing to non-ZDR providers.
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.
I love OpenCode and the blankness of it too. Clean, light, and manual. It's a good change of pace from what we are used to on the internet (very cool but slow). Also, can you hmu with that modal plan XD
I’ve enjoyed using Kimi K3. The biggest issue for me so far is finding a good harness. Claude Code is excellent for tasks that require backgrounding processes/agents, or fanning out. Codex and OpenCode I’ve found to be less capable.
Actually, if you break tasks down into small enough units, there's not much difference between open models and closed models. The only reason to use agents is the hope that they'll work with natural language input—but that's where the difficulty lies.
For example, if you modify things at the level of small functions, open models seem to perform just as wel
Yup. Been using big expensive models like opus, gpt, or glm for planning, then switch to something like gpt-oss-120B on cerebras and watch it fly. Total cost will be less than $5 for even very large tasks
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.
It surprises me that this concept took as long as it did to gain traction in… hacker news. 15 years ago folks here were compiling kernels and gentoo distros. Lately it’s been “you should just pay the man, it’s cheaper than running these things yourself”
Rolling your own Linux is nearly free and could be done on any computer you had lying around. Dropping >$5k on a computer to run a local model (badly) doesn't really scratch my "hacker" itch. The author of this article works for an AI infrastructure company and ran Kimi K3 on their infrastructure - this post reads like marketing.
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.
I don't want to use a crappy model that can't do any of the things I want it to.
I'm not coding with Claude cause I enjoy the inherent novelty of LLMs. I'm doing it cause it's enabling me to quickly solve problems without getting stuck on the hitches that have deterred me from bothering with dozens of side projects my entire life.
The reason Claude code is so popular is because it’s really good at taking super vague human prose “Claude build me a million dollar SaaS”-type prompts and spitting out thousands of lines of code which cover tons of surface-level edge cases, build in tons of functionality, etc
The smaller/open models are less good at that. But that’s not how software development is done. You don’t prompt a whole app and be done with it. If you’re using it as an aid to traditional software dev, iterating on small, targeted functions, GLM works as good if not better than Claude. Anthropic expects low quality prompts. If you rubber duck GLM, you get absolutely pristine output in most cases.
This is what I've found and would likely be the consenses of the HN community.
This has lowered the barrier of entry for many non-SWEs. I'm primarily a data scientist myself in the environmental sector with limited front end development.
My partner proposed an idea to help manage her horses and over the course of several weeks we fleshed out an android app that would enable/assist her with horse care.
I spent a few days in planning mode pointing claude to the services we want to utilise and the functionality.
We now have a fully developed app that we are testing but found several features missing. For example, Claude implement X but without a level of verbosity it failed to implement the edit/deletion of X.
The app is highly tailored to her use case and I had already built the main parts as a POC but she had no user-friendly method to interact with the information she required. Claude made this possible in such a quick time frame that just seems insane. As someone time poor (as most horse people are), it would have taken me a year+ to produce something unpolished when compared to what Claude produced.
Personally I've found Deepseek v4 Flash to be as useful to me as Opus. But I don't do these silly one shot tech demos. I have the technical understanding to ask for exactly the change I want with the right terminology. I loaded up some credit on openrouter and it took me ages to hit $1 in spend.
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 can see why Anthropic is freaking out right now. China has undermined their whole business. AI models will be basic commodities where hosting providers earn a tiny margin over the raw costs rather than the predicted fortunes from being the gatekeepers to the technology.
Their business model always seemed to be "become IBM, gods of the mainframe and server-side-computing" and that always seemed a bit unlikely to last forever considering how thoroughly the PC disrupted that by being "good enough" without the lock-in.
IMO because they speed-ran it so much (raising orders of magnitudes more, pushing out new stuff orders of magnitudes faster) it will also lead to the rest of the world catching up faster and shrinking the pool of people internal to them who continually benefit compared to what actually went on with IBM. There's only so much upside when you join a company with hundreds-of-billions valuation.
I guess the other part of their business plan bet was "become literal god of AGI". Maybe it'll still happen!
the writing should have been on the wall when meta did it with llama. it was very apparent that one or two entities could release a "good enough" product into the wild commoditizing the frontier of yesterday.
Frontier models like Opus 5 are also extremely good a "research" in an academic sense, including combining elements from different fields or literatures and creating something quite novel sometimes, publishable even, from vague/speculative prompts. This is on top of implementing known methods in about 1/100th of the time it would take by hand, allowing for fast exploration of ideas. They can also roll things out from scratch, removing essentially all third party dependencies from your code base, other than Numpy or Scipy. This really buys you a lot of clarity and/or performance sometimes.
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.
To be honest, I am surprised by how good DeepSeek V4 Flash is. I use Claude Code and Codex on Claude 5 Opus and GPT-5.6 Sol most of the time, but when I use DS V4 Flash I don't feel like it's really that bad. And with oh-my-pi and just plain pi it's pretty good. To be honest the frontier models are much better at tool calling so in an assistant flow they're better but I did the dumb thing and optimized the harness for the model instead, rewriting the tools so they match what it guesses at, and DS V4 Flash does just fine.
The TTFT and tok/s are much higher on the small model so that makes it competitive for a bunch of things. It feels like what old Sonnet used to by the end of last year which is honestly damned good.
I wrote my own OpenClaw one weekend and I am running it as my assistant through Matrix with DeepSeek v4 Flash (and Qwen). It probably costs me about 2 dollars a month and is even more useful than ChatGPT would be due to me having full control on what tools it has access to.
I can do things like take a photo of a doctor's note among add the appointment to my calendar, send a PDF to my archive tagged, OCR'd etc, search info from internet, look data from Google Maps.
I can even integrate this to home assistant and talk to my agent with my open source Alexa-like system.
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
Privacy is the main benefit of running it yourself. Sending all of your personal info to random providers on openrouter would worry me. I don't mind it for code but if it's connected to my calendar and personal data, I want it running on my machine.
I can't wait until good enough models can run on affordable hardware.
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.
This is only a thinly veiled ad. It's fine, I was curious about this exact setup, anyways.
What would be useful is a cost metric. I'm curious how much I'd be willing to spend as a premium to not have those companies piping my conversations directly to the NSA. Maybe only some conversations? Claude and OpenAI are heavily subsidized, by all accounts, so Kimi K3 on a private endpoint might end up costing more or less - that's what I want to know.
With OpenRouter this is pretty easy to see - each model page has various providers listed along with an icon marking providers with zero data retention, and the price. You can also set up an account-wide "guardrail" to prevent routing to non-ZDR providers.
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.
I was curious about the cost angle too, i.e. how much "free" coding agent I can get for what cost. Here's the research by Fable if you're interested: https://claude.ai/public/artifacts/2c9a5001-0b7e-4944-beb1-9...
GLM 5.2 feels better than Opus and K3 is as good as Fable.
Now I can't wait for someone to distill K3 into a Qwen 3.6 27b or Poolside S 2.1 sized models for a proper fast local Composer 2.5 replacement.
I love OpenCode and the blankness of it too. Clean, light, and manual. It's a good change of pace from what we are used to on the internet (very cool but slow). Also, can you hmu with that modal plan XD
Pi Agent is even leaner feeling, it's worth a try.
Will do! Heard a lot of great things about it from my friends.
I’ve enjoyed using Kimi K3. The biggest issue for me so far is finding a good harness. Claude Code is excellent for tasks that require backgrounding processes/agents, or fanning out. Codex and OpenCode I’ve found to be less capable.
> The best way I can describe it is like opening vim after spinning a bunch of time in a big fancy editor.
Harness-aside, I get this feeling sometimes when I swap from a big frontier model to something more nimble like Composer.
I can get into a better thinking and q&a loop with fast models, similar to how I can flow through a file more easily with vim.
this seems to be an advertisement for Modal. is it really your own infrastructure if the hardware is leased?
Something I've been thinking about a lot is that this new technology's primary UI is natural language. Human's are REALLY primed for natural language.
If it sounds good it must be good. The code this new model writes is incredible! It told me so!
It did say it was an unusually clean codebase.
This is such a poor quality post, reads more like a diary entry than a substantive technical post.
https://en.wikipedia.org/wiki/Blog
??
Edit: I see, had typo last word of sentence was meant to be “blog”
No, my point is that it's a post on a personal blog not a peer reviewed paper.
lol
I will take 5000 "diary entry" blog posts over LLM generated walls of text that say nothing.
People need to learn to just post the prompt rather than the LLM output which just fluffs the prompt.
I think that's what it's meant to be...
Actually, if you break tasks down into small enough units, there's not much difference between open models and closed models. The only reason to use agents is the hope that they'll work with natural language input—but that's where the difficulty lies.
For example, if you modify things at the level of small functions, open models seem to perform just as wel
Yup. Been using big expensive models like opus, gpt, or glm for planning, then switch to something like gpt-oss-120B on cerebras and watch it fly. Total cost will be less than $5 for even very large tasks
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.
I think this is where they say "loop engineering" / orchestration comes in, though I would also include context/harness engineering in the toolbox
It surprises me that this concept took as long as it did to gain traction in… hacker news. 15 years ago folks here were compiling kernels and gentoo distros. Lately it’s been “you should just pay the man, it’s cheaper than running these things yourself”
Rolling your own Linux is nearly free and could be done on any computer you had lying around. Dropping >$5k on a computer to run a local model (badly) doesn't really scratch my "hacker" itch. The author of this article works for an AI infrastructure company and ran Kimi K3 on their infrastructure - this post reads like marketing.
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.
I don't want to use a crappy model that can't do any of the things I want it to.
I'm not coding with Claude cause I enjoy the inherent novelty of LLMs. I'm doing it cause it's enabling me to quickly solve problems without getting stuck on the hitches that have deterred me from bothering with dozens of side projects my entire life.
> you can run a small model
but why?
… to hack. For fun. To play around. Why do anything?
It's fairly easy (and cheap!) to run your own email server, but most people don't bother and just use gmail. And rightly so!
Rsync the dropbox, dude
Still working on the fields medal, I’ll give that a shot next.