9 ms·

AI coding at home without going broke

352 points by sbochins 3mo ago

aarjaneiro 3mo ago
Just don't vibe code?
Avery29 3mo ago
We can't determine whether future AI Agents will charge high fees. At present, they are updating at a rapid pace.
atreids 3mo ago
I find just going via Deepseek's platform API directly, using their V4 flash model, and hooking into a harness like Opencode more than acceptable. Think I've spent maybe $10 over a couple of weeks. I did explore self-hosting models but hardware right now is just too expensive.
- Yoric 3mo ago
  Directly at DeepSeek? It was my understanding (but I didn't check) that some other AI operators were providing (some of?) DeepSeek's model for cheaper prices. Still, that's interesting. What do you get for that price? Only coding, or also e.g. image generation?
  - Footprint0521 3mo ago
    I’ve been doing this too, it’s a cheat code! 1/100th of the price of Claude/openai prices for 95% of the quality. Site is platform.deepseek.com for that. No image generation, just text, but if you use it right it works great
  - atreids 3mo ago
    Footprint's comment is correct. I go directly to Deepseek's platform API which they linked. There's no image generation but you get access to Deepseek V4 Flash and Deepseek V4 Pro, both of which are very capable for general text based tasks and programming. Flash is insanely cheap for how good it is ($0.14 per 1M input tokens vs $15 with Claude 4.7). V4 Pro I would put somewhere in the range of 80 to 90% as good as Opus 4.6 (based just on anecdotal usage - I use Opus 4.6 heavily at work as my company pays for it) while again being significantly cheaper. According to a benchmark[1] I read, processing 1million tokens would cost you $250 for Opus 4.7, $300 on GPT5.5... and just $35 on V4 Pro. I just use it for my side-project coding and brainstorming tasks. At work I use AWS's Kiro CLI + Opus 4.6. At home I use Opencode + V4 Flash for the majority of "general" usage. I swap to V4 Pro for complex tasks if I feel like V4 Flash is struggling. One other thing I highly like about the platform.deepseek API usage is it's a metered setup - not subscription based. Which means you only pay for what you use (the money that you put in doesn't expire) and can't spend more than you've deposited. This works well for me for my non-work coding because it generally happens in bursts. I may not code for a whole month (and therefore if I had a subscription it would have been wasted) and then spend a whole weekend coding nonstop. It's entirely possible that there are middle-man providers that give a discount on Deepseek's own pricing, but I'm quite happy with the amount I'm paying so I haven't really looked into it. [1]: https://lushbinary.com/blog/deepseek-v4-vs-claude-opus-4-7-vs-gpt-5-5-comparison/ https://lushbinary.com/blog/deepseek-v4-vs-claude-opus-4-7-v...
    - sail0rm00n 3mo ago
      This is awesome, thank you for posting this. Are you using pi, Hermes, or another harness? I’d love to hear more about your workflow.
      - atreids 3mo ago
        I've been using [OpenCode](https://opencode.ai/ https://opencode.ai/) - I find it works quite well and has things like web search and build/plan modes built in. I had to modify the settings though (On Linux at `~/.config/opencode/opencode.json`) to stop it from just modifying files without first asking for permission, which I didn't like. I like being able to read the changes my AI agent suggests before the files are modified.
  - alecco 3mo ago
    DeepSeek API gave 6x to 8x better caching rate for inputs over OpenRouter (even chosing DeepSeek as provider). And some of the cheaper providers are using FP4 quantizations. https://openrouter.ai/deepseek/deepseek-v4-flash-20260423#providers https://openrouter.ai/deepseek/deepseek-v4-flash-20260423#pr... After complaints the cached read is not listed anymore in that page, you have to click one by one. All providers for DeepSeek V4 Flash charge ~$0.02 while DeepSeek provider is $0.0028. For coding this is huge as caching often gets in the range of 90 to 99%. But OpenRouter messes your caching so don't use it. And it seems to be a VC-backed closed middle-man company, not open source or open anything.
    - ryeguy 3mo ago
      Openrouter's pricing via the deepseek provider is the same as the official deepseek api for both flash and pro and for cached and uncached tokens. It's literally the same api. And no, cache rates are not different if you're going through the official deepseek provider. The only way caching rates can drop is if you let openrouter fully control routing by preferring uptime or something, and then it might bounce you between providers. But you can control which providers for a given model are in its routing pool and stop that.
      - alecco 3mo ago
        Last month I had this issue. Others confirmed. On X people say OpenRouter messes with headers or something (this I can't confirm).
- dizhn 3mo ago
  I believe opencode go but only using deepseek flash would last you longer. (Equivalent to $65 in tokens but it's a monthly payment so you have to be using it up or deepseek direct will be cheaper) First month is $5, later $10. Cancel any time. You can keep getting the deal with a new email.
  - atreids 3mo ago
    Interesting. Thanks for letting me know. I will investigate that if I end up finding the API too expensive.
isatty 3mo ago
> The first is to self host. You buy the machine, run open source models locally, and pay nothing per token after that. Power is not free. What I’ve found is that you’re basically paying a premium for privacy, and that’s worth it for me.
- mxmxnxnsndndndj 3mo ago
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- enraged_camel 3mo ago
  >> Power is not free. There's actually an interesting thought experiment here: if it takes you a full day to build something that AI would otherwise build in a day, do you end up using more power, or less? What is the break-even point, purely from a power consumption perspective?
  - axus 3mo ago
    What would you do for the rest of the day, power off your devices and go for a long bike ride?
    - enraged_camel 3mo ago
      Speaking personally: yes. That's literally what I'm planning to do this afternoon because it's noon and I'm already done with the coding tasks I had on my plate today.
      - dofm 3mo ago
        Luckily the future is absolutely going to be that star trek one where technological abundance means we are all wealthy and have free time to develop personally, and not the future where all the money bubbles up into the hands of a thin-skinned malignant narcissist who wants to play with launching rockets and provoking racial violence /s
  - dofm 3mo ago
OutOfHere 3mo ago
Fixed-price monthly plans ought to be sufficient for most people who actually review their spec and code, for building production-grade software that stand the test of time. A careful spec+review+iteration takes time, resetting the usage quota. Granted, security audits uses tokens too. If you still need more tokens, odds that you're vibecoding unmaintainable throwaway trash.
- deleted 3mo ago
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- manfre 3mo ago
  With access to view usage for my org and conversations with developers, I think much of the high token usage is a result of people not knowing how to right size the model for the given task. The trend seems to be to pick the most powerful model and use it for everything. Based upon git metrics, I'm one of the top performing engineers at my org and I've yet to run into any overage or throttling on the $200/mo anthropic sub.
  - justinhj 3mo ago
    I had no idea git metrics could show your best performers
    - OutOfHere 3mo ago
      It could put managers out of a job, without AI too, so they prefer to not use it.
      - justinhj 3mo ago
        What metric shows performance? Lines of code added, deleted, number of commits? None of these are are good measure of quality and actual contribution.
        manfre 3mo ago
quickthoughts 3mo ago
Ha just wrote a post[1] about a sort of 4th option - max out cheap compute to create more tangible things that can be used/run locally. 1: https://news.ycombinator.com/item?id=48519181 https://news.ycombinator.com/item?id=48519181
gaigalas 3mo ago
> The first is to self host. You buy the machine, run open source models locally, and pay nothing per token after that. In the good ol' days, we bought machines not only to run stuff, but to experiment. I understand today experiments are limited. Inference is reasonable, fine-tuning is either niche or a stretch, and base training is impossible. *That is bound to change*, and when it does, there will be an avalanche of hobbysts and amateurs poking at base training. They'll find optimizations no one found before, synthetize data no one ever imagined to synthetize, and when that happens we'll start getting libre models. So, yeah. Right now, buying the machine doesn't pay off that well, unless you want to pioneer this stuff in severe adverse conditions (hardware prices inflated, etc). Eventually, it will.
esalman 3mo ago
For me, investing in hardware seems to be the way to go. I learned coding nearly 24 years ago and still learning new stuff all the time. At no point in time I had to rely on a subscription model to learn and do new stuff. If LLM and agents are the default tools for coding and building software, at least for next few years, it seems like a no-brainer to invest $2000-3000 on hardware, like a Halo Strix PC.
- iugtmkbdfil834 3mo ago
  Yes and no. Hardware does lock you in. Granted, I am happy with my 128gb of shared memory, but I am mildly concerned that it actually is more expensive now than when I bought mine. It does not bode well for the future; not when combined with recent WH admin moves on Anthropic and the reality that next batch of good models may require more than 128gb to run well. edit: I am not dismissing local. I am one such user ( though I have subs too ), but one has to be clear eyed about the trade-offs.
- CraigJPerry 3mo ago
  I wondered if there might be a no brainer "free" option on discarded hardware. I have a GTX1080ti which i think is circa 2018, it's unused, more than paid for itself over the years, owes me nothing at this point so the hardware is free. It runs Gemma e4b multimodal, qwen 3.5 8b or the qwen 4b embeddings models well enough (40+ t/s for the LLMs). The machine consumes 350 watts at the wall when under load (3 watts when sleeping, 80w at idle). Electricity costs me £0.035GBP/kwh which is cheap for the UK (load shifting via house battery). 144k output tokens for around 1pence (and takes an hour to do that in theory). It's only JUST cheaper to use than the far more capable deepseek v4 flash model despite the free hardware and ~10x cheaper than normal electricity.
- throwatdem12311 3mo ago
  3k? Try 10
- hgoel 3mo ago
  $3k isn't getting you frontier model capability. It's barely getting you any capability if that's split into buying an entire PC rather than just GPUs.
vadansky 3mo ago
Can I run something comparable to Opus 4.6 locally yet? I keep hearing conflicting things. If I can spend 10k to do that I would cancel my subscription. The problem is I don’t wanna spend the money to find out myself.
- grim_io 3mo ago
  10k will not get you anywhere near opus or sonnet. It's simply not possible for mere mortals currently.
- als0 3mo ago
  > Can I run something comparable to Opus 4.6 locally yet? Sadly, no. The best comparable thing you can get is about Sonnet 3.7
- Catloafdev 3mo ago
  If you want frontier-level, the economically reasonable option is OpenRouter or a direct sub to frontier-of-your-choice. The reality is that they do not offer configurations that would allow a consumer to run that much VRAM on a single setup to protect datacenter margins. Apple used to, and they stopped, those devices are going for ~$20k+ each on ebay now. You can get very, very capable models on a 3090/4090/5090/6000 series card. But if you want 'frontier level' you are investing ~22k at a bare minimum if you go new. Used you can probably build your own server for much cheaper up-front cost but it's likely going to be 4-6x+ electricity usage.
  - daemonologist 3mo ago
    There are also significant economies of scale (namely: utilization and batching), which tend to make inference on a shared server more economical even after the operator takes a cut.
    - zozbot234 3mo ago
      You can use batching on consumer hardware, it just requires a KV-cache efficient model (or short context only) and keeping multiple inference flows running in parallel. This is most useful in combination with streamed inference, since the compute intensity of decode with those newer KV-compressed models is high enough that you have limited compute headroom when running at the speed of RAM.
pianopatrick 3mo ago
I think someone could find some way to use the smaller local models to write code. Some kind of framework or harness or language or something. But not too many people are working on that because the big models are pretty cheap and a lot better.
- petra 3mo ago
  Maybe one possible path(to make weaker models highly capable) is making the job of the llm as easy as possible. I wonder if part of the solution is building/finding the right libraries, with the right documentation/language/API(one that plays well with LLM's) and maybe creating some synthetic data around them - to make it very easy for the llm. And maybe there could be a business model around creating those libraries.
  - pianopatrick 3mo ago
    I think as well there might be "algorithms" that can work with local LLMs. With local LLMs there is a small context window, but not that much cost per token. So perhaps there is a way to do lots of small prompts that work in a sequence to produce a result. Like perhaps you could produce 5 versions of a piece of code, and then compare them to choose the best. Also if the local LLMs can call tools, maybe you can use static analysis tools to catch errors and try again in a loop or process of some sort. There also might be certain languages that work better because those languages have better static checks.
    - jrm4 3mo ago
      Yes. LITERALLY THIS. I do this! Not hypothetical. I'll write a detailed prompt for a function, hand it off to 5 or so models (all of which are on my local machine), wait about 5 min and then compare.
  - calgoo 3mo ago
    So in my limited experience: The smaller the model, the bigger the harness. The biggest issue becomes the context window. For big models you can kind of just give it bash access and let it run... while with the smaller ones you need to fully manage the context in each LLM call. If you can ask the model for a specific function; with a spec design (typed languages help too) then the small models are great! I have had good progress with generating small python modules for example, but you need verification rounds to catch issues. So test driven design + a good spec sheet + a very detailed todo.md (or even better if its todo.json because then the LLM does not need to manage it, you do from the harness) is your best bet for small models.
dempedempe 3mo ago
Did you just copy-and-paste an AI response an post it on your blog?
impure 3mo ago
I recently made an AI Agent and surprisingly coding with DeepSeek V4 Flash is quite cheap. It probably has to do with the aggressive prompt caching. I'm using OpenRouter with Novita AI as the preferred provider.
- kagamino 3mo ago
  Same here, deepseek v4 flash on opencode go. It's cheap, fats and good enough to follow my instructions
  - 2muchtime 3mo ago
    I’m using zen because I have a Claude subscription and just like dabbling with the other models and I was shocked at how little flash cost but it was noticeably not at the level I’d like my model to be. For me MiniMax 3 has really hit the sweet spot of being very cheap, though more than flash, but I’d also very capable.
- throwa356262 3mo ago
  Deepseek v4 via deepseek themselves is significantly cheaper. Because (1) Huawei collab and (2) vLLM etc dont implement half of the inference optimisations deepseek proposed in their paper.
RomanPushkin 3mo ago
AI coding at home literally costs $100/month. I'm wondering where $400 is coming from? $100 is more than enough for "coding at home", IMO. I rarely face the limits, and when I do it's just a time for a quick walk anyway.
- chasd00 3mo ago
  Man I’m using the $20/month sub and it works just fine for me. Granted, I have a family and house and lots of obligations so by the time I hit the limits some other task is due before I can return to coding. If I hit the limits before I have something else to do then I just code by hand or review what has been generated until I can use the agent again. Reviewing agent code is a good way to learn too, agents have shown me different approaches than what I would have done and they’re definitely worth thinking about. Also, fixing their mistakes has helped me write better prompting although being a team lead for half a decade has taught me how to specify what I want very clearly and cc gets it right most of the time haha About interruptions, one thing AI assisted coding really helps with is coding with constant interruption. I can leave CC for half an hour and return then tell it I had to step away, catch me up, and proceed. This works well for me.
ricodebug 3mo ago
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zuzululu 3mo ago
Another update for codex users they let you accumulate resets which greatly adds to the mileage I don't think its feasible to have something comparable to these frontier models when they are increasing usage and lowering token costs
deleted 3mo ago
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mwcampbell 3mo ago
I invested about $4,000 in an NVIDIA DGX Spark several months ago. 128 GB of unified RAM, and the NVIDIA GB10 chip. With the RAM, the several CPU cores, and the 4 TB NVMe SSD, it's a very capable ARM64 Linux computer even without the GPU, and so far I've mostly been using it as such. But I wonder, what's the most capable model, specifically for coding, that can run well on that hardware?
- znnajdla 3mo ago
  DeepSeek V4 Flash is a very capable coding model that runs well on the hardware you described. Look up the optimized version specifically designed for local use.
- Yoric 3mo ago
  https://www.canirun.ai/?status=tight https://www.canirun.ai/?status=tight might answer that question
  - zkmon 3mo ago
    That site doesn't seem to consider the quants. So useless.
- morganastra 3mo ago
  Deepseek v4 flash is shockingly strong for its size and reportedly runs well on that hardware.
  - Shorel 3mo ago
    Better than qwen3-coder-next? That's the one that has given me the best results so far.
- anon373839 3mo ago
  Qwen 3.5 122B can fit with context at a pretty high quant (Q6). That's an excellent model.
- lee_ars 3mo ago
jacobgold 3mo ago
"Around $400 a month of plans buys roughly $2800 of API usage at list prices, which is a real bargain right up until you hit the ceiling." I realize this text is just slop but it never stops being a "real bargain" at any point. And it's more like $200/mo for $4000+/mo in tokens. You can also buy additional subscriptions. There's no sense in running local models or doing anything else as long as VCs (and soon the public markets) are willing to pay your bill.
- abc42 3mo ago
  Even if they were making a profit, their scale and expertise will obviously give you a cheaper product than what you can build.
  - jacobgold 3mo ago
    Maybe today but it's not a law of nature. It seems inevitable that AI models and coding agents will be fully commoditized eventually, just like computers, game engines, compilers, web servers, and so many other technologies have been. At the end of the day, AI models are relatively small files that we run little CUDA programs on.
- simonw 3mo ago
  SemiAnalysis pushed this to the limit and managed to get $8,000 of tokens from a $200/month Anthropic plan and $14,000 of tokens from a $200/month OpenAI plan: https://twitter.com/SemiAnalysis_/status/2064815044085318040 https://twitter.com/SemiAnalysis_/status/2064815044085318040
  - jacobgold 3mo ago
    Yeah, although that is pushing every rate limit and no one knows what happens if you do that consistently? I think $4,000/mo is probably a good estimate for an individual dev doing synchronous coding agent work.
    - simonw 3mo ago
abc42 3mo ago
What kind of usage chews through Claude Max x20? I use several agents with max effort in parallel and usually end up with something like 50% weekly usage. Fable almost allowed me to get to 70% but then they started resetting the limits mid-week and of course now ended the whole thing.
aplomb1026 3mo ago
[flagged]
tamimio 3mo ago
You can have opencode and switch between multiple providers based on the tasks you are doing on the fly, normal tasks use deepseek for example, hard one use gpt5 or opus4, and track the usage with something like codexbar or similar. Openrouter seems to charge extra on top of the api costs, same with zen ide, so keep that in mind.
reinitctxoffset 3mo ago
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MemoryHoleHQ 3mo ago
I've been thinking a lot about this and my personal take right now is that at some near-medium future the models abvailable to run at home and the hardware needed to use them will be enough. My baseline is sonnet 4.6. I think it's good enough for most tasks sincerly. So, from what I see, we are already at a point where we don't need frontier models for serious coding and debuging. Give it a couple of years and that level will fit 120B models. At the same time, we saw the rise of direct acess memory systems like DGX or Stryx Halo that will allow to run models of this size for "cheap" in the medium term. That's what I'm betting in. That in 2 years I can buy a system for about $2500 that will run a model that's similar to Sonnet 4.6 locally. I might be spectacularly wrong though. But I'm willing to wait and use subscriptions/API calls for now.
13415 3mo ago
I use copy & paste with a pro subscription. I guess I'm a bit behind in terms of tool use but it works great for me.
- TheSkyHasEyes 3mo ago
  Similar story. I did have a pro subscription as a trial. I'm finding the free tier is as good(for my purposes) as the paid model.
sesm 3mo ago
> Do that well and you can build what a team of twenty engineers would put out in a month for around a thousand dollars. As usual, an extraordinary claim without an extraordinary evidence: https://stephen.bochinski.dev/apps/ https://stephen.bochinski.dev/apps/
tunesmith 3mo ago
I feel like I must have plateued and don't know what to do next to level up. I'm currently on the $100/month codex plan and it seems fine using 5.5-xhigh all the time. I think of what to do next, have a chat session to determine exactly what to ask for up to the point of being ready to implement, and then codex churns on a commit-sized task whereupon I briefly check it on my local dev server. If necessary I ask for a change. Then I ask it to commit and recommend the next step based off the spec. Oftentimes I have to "approve" an out-of-sandbox request anyway. I haven't found anything that requires running all night. I could tell it to one-shot a big plan but given how often I realize I want an intermediary thing to be slightly different it seems like a waste of effort. I'm guessing the next thing I should probably look into is some sort of machine vm I can tunnel my codex-gui requests to so I don't have to deal with the sandbox approvals (I don't want to give it "dangerous" access to my entire mac). I don't understand what people are doing with their side projects that is leading them to churn through tokens so quickly, to the point of requiring two $200/month subscriptions and a bunch of token charges besides.
- dnautics 3mo ago
  I have been on $100/mo claude and it has been churning out quite good software for months now. like i estimate what would have taken me three ish years, assuming i didn't burn out from failure (i would have). i only hit limits when i double fisted claude with my main project and my side project. just the other day i noticed i had been stuck on 4.5 because i failed to update the npm package.
  - Schiendelman 3mo ago
    We're having a similar outcome. A hundred dollars a month is about right for me to sometimes hit a five hour limit, but mostly not. I do an hour or two of improvements, then go experiment with what I built and make a list of things to change, bugs to fix, ideas I've solidified, experiments I've invalidated.
- sheremetyev 3mo ago
  > I don't want to give it "dangerous" access to my entire mac I'm running Claude/Codex inside native macOS sandbox, configured with a simple script - https://github.com/sheremetyev/sandfence https://github.com/sheremetyev/sandfence always in "bypass permissions" mode - it works until task is solved, sometime 1 hour or more (which includes running tests etc)
jrm4 3mo ago
Is spending (metered money) even worth it? Perhaps for most I mean "beyond like a 30 bucks a month," but for me I'm literally not spending more money beyond my very cheapo 16gb video card. No clue what y'all are doing, perhaps because I'm hobbying, and also I'm old and can perhaps do more of this by hand. But I'm basically just doing what I did before, plus ollama self hosted and sometimes gemini and I feel like I'm going lightspeed beyond what I've ever done. And I suppose this is still very fine-grained. I have it make a draft, then just have them fix/change it step by step? I tried one of the bigger boys that can one-shot apps, which I guess is cool, but I'm finding it's just as hard to modify as if I just grabbed someone elses repo on github.
- stkdump 3mo ago
  No, I have the same experience. Feels crazy that a GPU is too expensive and then the advice is to spend 400$+tokens on openrouter each month.
WhiteOwlLion 3mo ago
There’s a lot of Xeon chips for $10 on eBay. Too bad there’s no drive for cpu based inference. The data center will need to swap out the older gpu clusters so what does that do for hardware pricing on data center gpus? H100 are cheap enough but the power requirements make it a long term net negative for how much pay for power in California.
spgorbatiuk 3mo ago
Hardware and provider juggling is a way to go, although I think it is also worth mentioning that the cost is not only the price-per-token, but first of all, the amount of tokens used. Depending on what one builds, comprehensive documentation and applicable skills and memory tools often allow for a substantial reduction of tokens previously used by the agent to comprehend and remember what is being built
geophph 3mo ago
> Do that well and you can build what a team of twenty engineers would put out in a month for around a thousand dollars. What does this look like after 6-12 months? Like, how much code are you trying to write total? Maybe it just doesn’t click in my mind, but sometimes I wonder about how much work people are trying to do and how they actually have enough to get done so quickly in such a short amount of time.
- sublinear 3mo ago
  They prefer to work harder and not smarter. Forever hill climbing to nowhere. I've never worked on a complicated codebase that started out that way until the rest of the business concerns and office politics came into effect. People may not like it, but the bureaucracy is far and away more valuable than the core functionality. Mature codebases are years of people thinking of all the possible gotchas while solving their acute pain points. This is not fluff, but the living and breathing part of it. Without that code, it's just a machine barely doing stuff in the most obtuse ways possible that nobody wants to pay for. I would argue that they're putting LLMs to work on that finer detail stuff, but AI is still far too dumb. No, what they're doing is playing with their skinner box.
hillj23 3mo ago
I think this is only going to become more relevant. I'm personally a $200/mo Claude Maxer and I know that the usage I'm getting on Opus 4.8 Max and (until they yoked it out from under me) Fable 5 is way, way more than what I'm paying them. At some point, this will turn usage-based and I will be hammered on it and probably forced to look at self-hosting. I think while the caps are there, even at $200, it's honestly not too bad if you're coding value into the market, but as soon as those caps come off for retail AI users, we're all going to have some tough choices to make.
bachmeier 3mo ago
> The upfront cost is steep and the models you can actually run at home are weaker than what the frontier labs ship, so this only pays off if you can keep the rig busy with long running tasks where a slower, cheaper model grinds away overnight. Most people can’t keep a home machine that loaded, and the hardware you buy today may look like a bad bet in a year. Oh, so this is not a post about AI coding at home. It's about vibe coding at home. There's a lot I disagree with in this post, but I'm posting this from a home computer with 64 GB of RAM and no GPU. I do lots of AI coding while spending very little money. I run Gemma 4 26b (mixture of experts) and Qwen 3 coder with Ollama. I use Github Copilot code completions. I use the Gemini and Mistral API free tiers. I have a Gemini paid API account. It's now prepaid, so you don't have to worry about an accidental $1000 bill. You can do a lot of things with Gemini Flash Lite 3.1. None of this is burning through tokens to create an expensive blob of spaghetti code, but it does qualify as AI coding.
- atomicnumber3 3mo ago
  My sentiments too. I'm using Qwen 3.6 35B A3B on a machine with 64Gb ram and a 24GB 5090 (an Alienware 16 Area51 I bought, serendipitously, about 15 seconds before the idiots preordered all computers for the next 3 years and ruined everything). You can't "slop cannon" vibe code with it, but this is personal code I want to not be spaghetti, so I'm not trying to vibe code. I just want to get instant retrieval of all stack overflow and reddit posts in a chat box, and for it to be able to spare me the physical pain of actually having to type out typescript code (I am a BE dev with negative patience for all frontend) and fuck around endlessly debugging obscure docker problems (I like docker, but, no patience for it having annoying problems and endless quirks). And this model does that really well.
- sbochins 3mo ago
  There are certain things you can leave running for a while. I think the distinction between vibe coding and hitl based coding routines will blur as workflows prove themselves and models become smarter and less expensive. Most of the best engineers I know have transitioned a lot more into vibe coding this year. The possibilities are much better nowadays.
pshirshov 3mo ago
> and the hardware you buy today may look like a bad bet in a year. 3090s and 7900s are going well so far. Next year an Arc Pro B70 won't produce you less tokens than today. They aren't fast but if you have flows where you can make money with them - they are a bargain in terms of price per Gb.
mikgp 3mo ago
What are people doing at home? I have like 5 different apps I code on the $20/month Claude plan and like sure I can hit rate limits but - What are people doing to burn through $3k in tokens?
- gabriel-uribe 3mo ago
  YMMV but automations eat through the $100-$200 plans, which burn thousands in tokens alone. I have hourly automations for root cause analysis on customer support issues, daily automations for eg log analysis, weekly & monthly automations for KPI tracking & actioning. I will say, when I was building side projects that were 1) fairly well defined in scope and 2) without users/need for automations it was much easier to stay under $20/mo plan limits. Now I regularly hit weekly limits and need multiple Max plans
  - Random09 3mo ago
    Most of it doesn't require AI. You could generate automation scripts that do that, except of customer support. People became dependent on AI in places where it never was required and now tech bros are doing the squeeze.
    - gabriel-uribe 3mo ago
      I don't miss the days of scraping through logs or dashboards myself to troubleshoot some latency or malformed data issue that I missed conditionals for. AI is incredible at finding patterns in otherwise benign stdouts, let alone as it cross-references data streams. In theory, I don't need most of these automations. But for $200/mo? I will happily reduce my cognitive burden on stuff that doesn't impact the core business and make it easier to keep things gliding smoothly. When the subsidized plans disappear, I will keep these automations going with the best small models that fit on my laptop.
      - Random09 3mo ago
        What I mean is a script that can look through the logs. They are known and deterministic (if you properly handle errors) and you can analyze them statistically. If you don't know what logs your app is outputting, then you have a bigger problem in your hands tbh.
Kuyawa 3mo ago
This month I've spent only 15 cents using DeepSeek API and my own coding agent. Three apps delivered to clients and currently working on a tournament management app for pickleball, padel and beach tennis. I love DeepSeek.
KaiShips 3mo ago
[flagged]
thomasjb 3mo ago
Opencode's free models have been fine for me, they're what I tried after Gemma 4 8B proved hard to persuade into usefulness (I want to revisit with 12B and messing with harnesses, but I'm happy for now).
0xB0D 3mo ago
If your job becomes writing complex specs to make an LLM write code, you've not optimised anything. In fact all you've done is add a business cost.
- dmos62 3mo ago
  Are you saying that specs shouldn't be complex or that you shouldn't write specs at all?
asdfasgasdgasdg 3mo ago
Use Gemini 3.5 flash on the $20 a month plan and be satisfied with only being 3x as productive as you’d be on your own.
closeparen 3mo ago
>Around $400 a month of plans buys roughly $2800 of API usage at list prices, which is a real bargain right up until you hit the ceiling. The plans are metered, and any large AI native workflow will chew through the included tokens fast I don't think that's true at all. I'm doing 8-12 PRs a week at work, all primarily Claude Code, and the usage at API billing has never broken $500/mo.
- kgwgk 3mo ago
  Maybe yours is not a large AI native workflow?
  - deleted 3mo ago
    [deleted]
- conradkay 3mo ago
  I'm almost certain $2800 is actually too low if you're really hitting weekly limits. I'm on the $100/m plan and used $300 at API billing yesterday (according to ccusage) Seems like one session is >$100 and I can get 10 full sessions per week The $200/m plan is supposed to be 4x that in usage, so with 2 of those you could use 4*2*100*10=$8000 in just a week Using Simon's numbers here as a bare minimum https://simonwillison.net/2026/May/27/product-market-fit/#enterprise-customers-are-now-paying-api-prices https://simonwillison.net/2026/May/27/product-market-fit/#en... you'd get 1200*4*2=$9600 a month
dpcan 3mo ago
I cannot figure out what people are doing to spend all this money. I have used a $60 per month Cursor plan on auto, and have never come close to using up my included usage, and I probably have it planning and coding and working for me all through the evenings 4 nights a week. What on earth are people doing differently that it's costing them so much? Maybe enabling on-demand usage or other paid models, or on higher modes? What are you doing that requires this? The output from Auto for me is crazy good for the tasks I'm working on, and have yet to run into an issue where it couldn't perform at a high enough level. We have been interviewing people at work to join our team and they tell us they use $2K per month in tokens with their current employers.... I can't even fathom what's going on here where that would be happening.
- therealdrag0 3mo ago
  Cursor on Auto will use Composer a lot, right?, which is much more efficient than frontier models people spend a lot of money on. Besides that it’s simply reading or writing a lot of code. The only time I spend 1k/mo was when I built a green field 20k Loc service with 2 other people (while also doing antagonistic code reviews etc)(mostly using Opus). So it took $3-5k to build a 20k loc production grade service in a critical domain. But most of the time I’m not doing that. As they say most engineering isn’t coding.
- jmkni 3mo ago
  Totally agree, but then a lot of the same people will be talking about all of the custom instructions/rules/skills/features etc they have set up, so that's eating up a lot of the context window before you even start When I do use AI, it's just the pure tool itself, and the context is the exact code I'm working with (because I'm trying to see if it can help me solve a specific problem), and I understand the rest of the codebase well enough to know if it's giving me good answers or bad ones
- rjh29 3mo ago
  A few things imo, 1) not prompting precisely enough (narrowing scope) means your agent will scan your entire code-base and sometimes get stuck looking at things repeatedly. 2) not checking the output is usually fine but sometimes it produces junk because it doesn't understand, and you cannot prompt your way out of it without reading the code and figuring out the problem. If you leave it on auto it will burn tokens. Plenty of low level things can trip agents up, too. I just had one inexplicably refuse to read an error about a function needing a bool return value - trying about 10 variations of the same thing before I interrupted it. Skills probably cause issues too, it loves to for example read the source code of libraries I'm using if I give it permission. That's a rabbit hole.
Flere-Imsaho 3mo ago
Instead of openrouter (which is admittedly a good service) I've switched to EU only servers via https://cortecs.ai/ https://cortecs.ai/ If you hunt in the settings you can restrict your account to only use EU servers for inference... Which means you can't use a lot of the US frontier models, but you can use all the Chinese ones, albeit within EU GDPR, etc. This to me is a good compromise between privacy and cost.
janpeuker 3mo ago
The biggest issue I've seen with people burning through tokens is using very long sessions, especially starting with plan mode and then "iterating" over extended periods. I was burnt badly by extra usage so now I run on $20 Pro. I ruthlessly create new sessions/agents, always ask to create markdown files first (no plan mode) and minimise context aggressively - for example I have a lot of skills that use lazy loading and a small local MCP for lookups plus openrouter with a local model for image detection and fulltext search. Basically I use Claude Code in pi.dev style.
bredren 3mo ago
What is going broke for a programmer? This is US centric but a $200 Claude code and $100 codex sub is a vast, vast amount of tokens. Enough to pay for itself many times over. It provides exposure to the very edge of harnesses and experience that is being hired for. Isn’t there an argument this is possibly the best price to available performance for frontier models? Both due to subsidies and the distance between open and accessible alternatives?
- astqs 3mo ago
  I used Kiro in December and I burnt through 200 eur worth of tokens in a weekend. Ultimately it was money well-spent, but, I think that if you want, you can spend as much compute as you have access to. Will it be efficient use of tokens? Probably not. From all the data, it looks like the 200usd we pay for monthly usage is subsidised… at break-even pricing … well, that 200 is starting to look like a few thousand.
knightops_dev 3mo ago
[flagged]
devhe4d 3mo ago
since when $400/m is justified as a "efficient" way of using a "nice-to-have" option? what a world we live in?
sebastianconcpt 3mo ago
Pretty happy with oMLX running Qwen3.6-35B-A3B-8bit
m3kw9 3mo ago
That’s easy, just use the plus plan and learn how to prompt efficiently
nunez 3mo ago
> The second is to skip the hardware and rent those same open source models from a provider at API rates. For most people this is the right call. You avoid putting thousands of dollars on one GPU setup while configurations are still in flux, you skip the work of squeezing long running performance out of an open model, and you can switch to whatever is cheaper or better next month without reselling a box. Something like OpenRouter makes the move close to a one line change. This will probably become the only option as the companies that publish open weights stop doing that. Very very few people have enough hardware to train/fine-tune at home.
dualvariable 3mo ago
Am I the only one happy on a $20/month pro plan? Yeah, every now and then you blow out the window limits. So you take a break and think about something else or go out and do something else...
deleted 3mo ago
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hottrends 3mo ago
[flagged]
dottchen 3mo ago
running 2 $200 codex subs seems to work for me. It's quite easy to run out of a full account's weekly usage if using xhigh and fast mode all the way, and i'm not using it for autonomous running, still mainly human reviewed actual work.
verdyshd 3mo ago
[flagged]
montroser 3mo ago
deepseekv4 pro via opencode go is $10/mo and has very generous limits. I use pi for the harness and go just as a model provider. It goes a good long way...
iwontberude 3mo ago
As long as you use models trained or distilled for your use case, there is no need to waste compute on trillions of parameters. Anthropic and OpenAI are proving that the “everything” models are not a sustainable business model. Just-In-Time or dynamic precompute of distilled models have already begun reducing the use of these frontier models for task inference.
josh_p 3mo ago
It’s been very validating in this thread to see everyone questioning the massive token spend of influencers and the like. The opencode-go sub, at $10/mo, is amazing value. I’ve been using that and the assistant kagi offers for web-chat and research for months. For the smallish projects I work on at home those have been great.
singpolyma3 3mo ago
How is this even an article? The advice is just "pay for max"
- dempedempe 3mo ago
  Because this person's entire blog is just copy-and-pasted AI responses.
whateveracct 3mo ago
am i the only one who codes by hand at work and for fun anymore?
jtr1 3mo ago
I've been running Claude Pro at home, supplemented with Deepseek configured in Claude Code. I've had decent luck throwing Opus (and briefly Fable, RIP) at architecture / product problems and producing plans to hand off to Deepseek (I personally find v4 to operate somewhere between Opus and Sonnet in capability). Lately I've been able to cut down on token usage with context-mode and codebase-memory to wring more out of my subscription, as well as doing things like make sure all terminal operations run in quiet mode. I've found codebase-memory particularly effective: it creates an index of your codebase that the agent can query for code tracing without reading all of the associated files, and I've also found it more accurate at analysis
andrewstuart 3mo ago
I feel like the author isn’t aware of the Anthropic fixed price subscriptions, any of which can give you a lot of home AI programming.
ddxv 3mo ago
I recently switched to the opencode $20 a month plan and am also testing the $5 a month go plan to see if that works. Connected into MiMo or DeepSeek Zen seems to code all day.
ameon 3mo ago
from my experience, good documentation and guardrails prevent your ai from going crazy. I have a code-map.md, and it doesn't spend a fortune looking for things each time. I have main-pipelines.md and it knows what to do where. I have lots of tests, so many bugs are self-revealing.
jason_s 3mo ago
Please use a more readable variable-width font.
smeej 3mo ago
The solution in the article is, "Pay for a max plan and then buy the extra tokens you need by API." How is that noteworthy? Isn't that exactly what Anthropic and OpenAI recommend? I feel like I must be missing something.
Frannky 3mo ago
I read some cool posts of people trying MiniMax 3 on 4x GB10. I wonder if we are almost there that with 10k we can have Opus 4.6 level at home. I feel tweaking the harnesses and a few more versions from oss models and some new hardware from AMD and Intel could get us there pretty soon. Or maybe we are already there? Someone was able to do it already?
dmzxnico 3mo ago
I use Claude 100$ + Codex 20$. That's a lot already and I rarely hit my quota before it resets, usually right on time. Some times I also get OpenCode Go just to get access to chinese models as an extra for new projects I don't really care about, fun ones. That's way cheaper than hiring a dev anyways.
novia 3mo ago
I want to be able to experiment with tweaking model weights and seeing the outcomes and i want to be able to finetune open source models. What's the best way to experiment with that without breaking the bank?
- anon373839 3mo ago
  If you want to experiment with fine tuning small models, you can actually do that with free Google Colab instances. Unsloth.ai has written a bunch of guides on this.
sermakarevich 3mo ago
I started using brain -> workers approach for coding. -- Brain is expensive smart model from claude subscription, Fable 5 when it was available, Opus now. -- Worker is a local model (qwen3.6:46B), deployed in 36GB GPU, Opencode + Ollama. Brain is responsible for analysis/design and task creation. Task should be made simple and clear so the worker can handle it. Worker does the coding. Brain validates and create a fix task when required. Atm fix to task ration is ~ 1:20. If no available GPU at home - qwen3.6 is quite cheap on clouds. Its rather experimental setup, out of curiosity, but it works better than I would expect it to. This allows me running 3 coding agents non stop for the 4-th day atm. Here I explain how I got there: https://news.ycombinator.com/item?id=48520757 https://news.ycombinator.com/item?id=48520757
tactlesscamel 3mo ago
You pay how much!?!? Y'all know that is enough to buy a real human, right? Well, good for you. I'm going back to figuring out which of my 2 streaming subs are getting cut. Maybe my free crap is enough to figure out that for me while I make my own art. To be frank, the time you spend constructing prompts, tasks, and all else required to get your ai to do a thing was probably enough time to do the thing yourself. -Research included. It's good to see the world throw the concepts of art, pride, and general accomplishment in the trash. Why have friends and partners in projects when you could give your savings to Anthropic, OpenAI, or any number of companies already obtaining ungodly ammounts of financing? A somewhate helpful bot at the cost of who you are and your bank account.
conqrr 3mo ago
The big one that's missing as cost cutter. If you are a programmer, stay as close as you can to the code. Define the interfaces yourself and the core logic. You will know exactly what changes you want and know what tier of model to use.

352 points by sbochins 3mo ago

aarjaneiro 3mo ago

Just don't vibe code?

Avery29 3mo ago

We can't determine whether future AI Agents will charge high fees. At present, they are updating at a rapid pace.

atreids 3mo ago

I find just going via Deepseek's platform API directly, using their V4 flash model, and hooking into a harness like Opencode more than acceptable. Think I've spent maybe $10 over a couple of weeks. I did explore self-hosting models but hardware right now is just too expensive.

Yoric 3mo ago
Directly at DeepSeek? It was my understanding (but I didn't check) that some other AI operators were providing (some of?) DeepSeek's model for cheaper prices. Still, that's interesting. What do you get for that price? Only coding, or also e.g. image generation?
- Footprint0521 3mo ago
  I’ve been doing this too, it’s a cheat code! 1/100th of the price of Claude/openai prices for 95% of the quality. Site is platform.deepseek.com for that. No image generation, just text, but if you use it right it works great
- atreids 3mo ago
  Footprint's comment is correct. I go directly to Deepseek's platform API which they linked. There's no image generation but you get access to Deepseek V4 Flash and Deepseek V4 Pro, both of which are very capable for general text based tasks and programming. Flash is insanely cheap for how good it is ($0.14 per 1M input tokens vs $15 with Claude 4.7). V4 Pro I would put somewhere in the range of 80 to 90% as good as Opus 4.6 (based just on anecdotal usage - I use Opus 4.6 heavily at work as my company pays for it) while again being significantly cheaper. According to a benchmark[1] I read, processing 1million tokens would cost you $250 for Opus 4.7, $300 on GPT5.5... and just $35 on V4 Pro. I just use it for my side-project coding and brainstorming tasks. At work I use AWS's Kiro CLI + Opus 4.6. At home I use Opencode + V4 Flash for the majority of "general" usage. I swap to V4 Pro for complex tasks if I feel like V4 Flash is struggling. One other thing I highly like about the platform.deepseek API usage is it's a metered setup - not subscription based. Which means you only pay for what you use (the money that you put in doesn't expire) and can't spend more than you've deposited. This works well for me for my non-work coding because it generally happens in bursts. I may not code for a whole month (and therefore if I had a subscription it would have been wasted) and then spend a whole weekend coding nonstop. It's entirely possible that there are middle-man providers that give a discount on Deepseek's own pricing, but I'm quite happy with the amount I'm paying so I haven't really looked into it. [1]: https://lushbinary.com/blog/deepseek-v4-vs-claude-opus-4-7-vs-gpt-5-5-comparison/ https://lushbinary.com/blog/deepseek-v4-vs-claude-opus-4-7-v...
  - sail0rm00n 3mo ago
    This is awesome, thank you for posting this. Are you using pi, Hermes, or another harness? I’d love to hear more about your workflow.
    - atreids 3mo ago
      I've been using [OpenCode](https://opencode.ai/ https://opencode.ai/) - I find it works quite well and has things like web search and build/plan modes built in. I had to modify the settings though (On Linux at `~/.config/opencode/opencode.json`) to stop it from just modifying files without first asking for permission, which I didn't like. I like being able to read the changes my AI agent suggests before the files are modified.
- alecco 3mo ago
  DeepSeek API gave 6x to 8x better caching rate for inputs over OpenRouter (even chosing DeepSeek as provider). And some of the cheaper providers are using FP4 quantizations. https://openrouter.ai/deepseek/deepseek-v4-flash-20260423#providers https://openrouter.ai/deepseek/deepseek-v4-flash-20260423#pr... After complaints the cached read is not listed anymore in that page, you have to click one by one. All providers for DeepSeek V4 Flash charge ~$0.02 while DeepSeek provider is $0.0028. For coding this is huge as caching often gets in the range of 90 to 99%. But OpenRouter messes your caching so don't use it. And it seems to be a VC-backed closed middle-man company, not open source or open anything.
  - ryeguy 3mo ago
    Openrouter's pricing via the deepseek provider is the same as the official deepseek api for both flash and pro and for cached and uncached tokens. It's literally the same api. And no, cache rates are not different if you're going through the official deepseek provider. The only way caching rates can drop is if you let openrouter fully control routing by preferring uptime or something, and then it might bounce you between providers. But you can control which providers for a given model are in its routing pool and stop that.
    - alecco 3mo ago
      Last month I had this issue. Others confirmed. On X people say OpenRouter messes with headers or something (this I can't confirm).
dizhn 3mo ago
I believe opencode go but only using deepseek flash would last you longer. (Equivalent to $65 in tokens but it's a monthly payment so you have to be using it up or deepseek direct will be cheaper) First month is $5, later $10. Cancel any time. You can keep getting the deal with a new email.
- atreids 3mo ago
  Interesting. Thanks for letting me know. I will investigate that if I end up finding the API too expensive.

isatty 3mo ago

> The first is to self host. You buy the machine, run open source models locally, and pay nothing per token after that. Power is not free. What I’ve found is that you’re basically paying a premium for privacy, and that’s worth it for me.

mxmxnxnsndndndj 3mo ago
[dead]
enraged_camel 3mo ago
>> Power is not free. There's actually an interesting thought experiment here: if it takes you a full day to build something that AI would otherwise build in a day, do you end up using more power, or less? What is the break-even point, purely from a power consumption perspective?
- axus 3mo ago
  What would you do for the rest of the day, power off your devices and go for a long bike ride?
  - enraged_camel 3mo ago
    Speaking personally: yes. That's literally what I'm planning to do this afternoon because it's noon and I'm already done with the coding tasks I had on my plate today.
    - dofm 3mo ago
      Luckily the future is absolutely going to be that star trek one where technological abundance means we are all wealthy and have free time to develop personally, and not the future where all the money bubbles up into the hands of a thin-skinned malignant narcissist who wants to play with launching rockets and provoking racial violence /s
- dofm 3mo ago

OutOfHere 3mo ago

Fixed-price monthly plans ought to be sufficient for most people who actually review their spec and code, for building production-grade software that stand the test of time. A careful spec+review+iteration takes time, resetting the usage quota. Granted, security audits uses tokens too. If you still need more tokens, odds that you're vibecoding unmaintainable throwaway trash.

deleted 3mo ago
[deleted]
manfre 3mo ago
With access to view usage for my org and conversations with developers, I think much of the high token usage is a result of people not knowing how to right size the model for the given task. The trend seems to be to pick the most powerful model and use it for everything. Based upon git metrics, I'm one of the top performing engineers at my org and I've yet to run into any overage or throttling on the $200/mo anthropic sub.
- justinhj 3mo ago
  I had no idea git metrics could show your best performers
  - OutOfHere 3mo ago
    It could put managers out of a job, without AI too, so they prefer to not use it.
    - justinhj 3mo ago
      What metric shows performance? Lines of code added, deleted, number of commits? None of these are are good measure of quality and actual contribution.
      - manfre 3mo ago

quickthoughts 3mo ago

Ha just wrote a post[1] about a sort of 4th option - max out cheap compute to create more tangible things that can be used/run locally. 1: https://news.ycombinator.com/item?id=48519181 https://news.ycombinator.com/item?id=48519181

gaigalas 3mo ago

> The first is to self host. You buy the machine, run open source models locally, and pay nothing per token after that. In the good ol' days, we bought machines not only to run stuff, but to experiment. I understand today experiments are limited. Inference is reasonable, fine-tuning is either niche or a stretch, and base training is impossible. *That is bound to change*, and when it does, there will be an avalanche of hobbysts and amateurs poking at base training. They'll find optimizations no one found before, synthetize data no one ever imagined to synthetize, and when that happens we'll start getting libre models. So, yeah. Right now, buying the machine doesn't pay off that well, unless you want to pioneer this stuff in severe adverse conditions (hardware prices inflated, etc). Eventually, it will.

esalman 3mo ago

For me, investing in hardware seems to be the way to go. I learned coding nearly 24 years ago and still learning new stuff all the time. At no point in time I had to rely on a subscription model to learn and do new stuff. If LLM and agents are the default tools for coding and building software, at least for next few years, it seems like a no-brainer to invest $2000-3000 on hardware, like a Halo Strix PC.

iugtmkbdfil834 3mo ago
Yes and no. Hardware does lock you in. Granted, I am happy with my 128gb of shared memory, but I am mildly concerned that it actually is more expensive now than when I bought mine. It does not bode well for the future; not when combined with recent WH admin moves on Anthropic and the reality that next batch of good models may require more than 128gb to run well. edit: I am not dismissing local. I am one such user ( though I have subs too ), but one has to be clear eyed about the trade-offs.
CraigJPerry 3mo ago
I wondered if there might be a no brainer "free" option on discarded hardware. I have a GTX1080ti which i think is circa 2018, it's unused, more than paid for itself over the years, owes me nothing at this point so the hardware is free. It runs Gemma e4b multimodal, qwen 3.5 8b or the qwen 4b embeddings models well enough (40+ t/s for the LLMs). The machine consumes 350 watts at the wall when under load (3 watts when sleeping, 80w at idle). Electricity costs me £0.035GBP/kwh which is cheap for the UK (load shifting via house battery). 144k output tokens for around 1pence (and takes an hour to do that in theory). It's only JUST cheaper to use than the far more capable deepseek v4 flash model despite the free hardware and ~10x cheaper than normal electricity.
throwatdem12311 3mo ago
3k? Try 10
hgoel 3mo ago
$3k isn't getting you frontier model capability. It's barely getting you any capability if that's split into buying an entire PC rather than just GPUs.

vadansky 3mo ago

Can I run something comparable to Opus 4.6 locally yet? I keep hearing conflicting things. If I can spend 10k to do that I would cancel my subscription. The problem is I don’t wanna spend the money to find out myself.

grim_io 3mo ago
10k will not get you anywhere near opus or sonnet. It's simply not possible for mere mortals currently.
als0 3mo ago
> Can I run something comparable to Opus 4.6 locally yet? Sadly, no. The best comparable thing you can get is about Sonnet 3.7
Catloafdev 3mo ago
If you want frontier-level, the economically reasonable option is OpenRouter or a direct sub to frontier-of-your-choice. The reality is that they do not offer configurations that would allow a consumer to run that much VRAM on a single setup to protect datacenter margins. Apple used to, and they stopped, those devices are going for ~$20k+ each on ebay now. You can get very, very capable models on a 3090/4090/5090/6000 series card. But if you want 'frontier level' you are investing ~22k at a bare minimum if you go new. Used you can probably build your own server for much cheaper up-front cost but it's likely going to be 4-6x+ electricity usage.
- daemonologist 3mo ago
  There are also significant economies of scale (namely: utilization and batching), which tend to make inference on a shared server more economical even after the operator takes a cut.
  - zozbot234 3mo ago
    You can use batching on consumer hardware, it just requires a KV-cache efficient model (or short context only) and keeping multiple inference flows running in parallel. This is most useful in combination with streamed inference, since the compute intensity of decode with those newer KV-compressed models is high enough that you have limited compute headroom when running at the speed of RAM.

pianopatrick 3mo ago

I think someone could find some way to use the smaller local models to write code. Some kind of framework or harness or language or something. But not too many people are working on that because the big models are pretty cheap and a lot better.

petra 3mo ago
Maybe one possible path(to make weaker models highly capable) is making the job of the llm as easy as possible. I wonder if part of the solution is building/finding the right libraries, with the right documentation/language/API(one that plays well with LLM's) and maybe creating some synthetic data around them - to make it very easy for the llm. And maybe there could be a business model around creating those libraries.
- pianopatrick 3mo ago
  I think as well there might be "algorithms" that can work with local LLMs. With local LLMs there is a small context window, but not that much cost per token. So perhaps there is a way to do lots of small prompts that work in a sequence to produce a result. Like perhaps you could produce 5 versions of a piece of code, and then compare them to choose the best. Also if the local LLMs can call tools, maybe you can use static analysis tools to catch errors and try again in a loop or process of some sort. There also might be certain languages that work better because those languages have better static checks.
  - jrm4 3mo ago
    Yes. LITERALLY THIS. I do this! Not hypothetical. I'll write a detailed prompt for a function, hand it off to 5 or so models (all of which are on my local machine), wait about 5 min and then compare.
- calgoo 3mo ago
  So in my limited experience: The smaller the model, the bigger the harness. The biggest issue becomes the context window. For big models you can kind of just give it bash access and let it run... while with the smaller ones you need to fully manage the context in each LLM call. If you can ask the model for a specific function; with a spec design (typed languages help too) then the small models are great! I have had good progress with generating small python modules for example, but you need verification rounds to catch issues. So test driven design + a good spec sheet + a very detailed todo.md (or even better if its todo.json because then the LLM does not need to manage it, you do from the harness) is your best bet for small models.

dempedempe 3mo ago

Did you just copy-and-paste an AI response an post it on your blog?

impure 3mo ago

I recently made an AI Agent and surprisingly coding with DeepSeek V4 Flash is quite cheap. It probably has to do with the aggressive prompt caching. I'm using OpenRouter with Novita AI as the preferred provider.

kagamino 3mo ago
Same here, deepseek v4 flash on opencode go. It's cheap, fats and good enough to follow my instructions
- 2muchtime 3mo ago
  I’m using zen because I have a Claude subscription and just like dabbling with the other models and I was shocked at how little flash cost but it was noticeably not at the level I’d like my model to be. For me MiniMax 3 has really hit the sweet spot of being very cheap, though more than flash, but I’d also very capable.
throwa356262 3mo ago
Deepseek v4 via deepseek themselves is significantly cheaper. Because (1) Huawei collab and (2) vLLM etc dont implement half of the inference optimisations deepseek proposed in their paper.

RomanPushkin 3mo ago

AI coding at home literally costs $100/month. I'm wondering where $400 is coming from? $100 is more than enough for "coding at home", IMO. I rarely face the limits, and when I do it's just a time for a quick walk anyway.

chasd00 3mo ago
Man I’m using the $20/month sub and it works just fine for me. Granted, I have a family and house and lots of obligations so by the time I hit the limits some other task is due before I can return to coding. If I hit the limits before I have something else to do then I just code by hand or review what has been generated until I can use the agent again. Reviewing agent code is a good way to learn too, agents have shown me different approaches than what I would have done and they’re definitely worth thinking about. Also, fixing their mistakes has helped me write better prompting although being a team lead for half a decade has taught me how to specify what I want very clearly and cc gets it right most of the time haha About interruptions, one thing AI assisted coding really helps with is coding with constant interruption. I can leave CC for half an hour and return then tell it I had to step away, catch me up, and proceed. This works well for me.

ricodebug 3mo ago

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zuzululu 3mo ago

Another update for codex users they let you accumulate resets which greatly adds to the mileage I don't think its feasible to have something comparable to these frontier models when they are increasing usage and lowering token costs

deleted 3mo ago

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mwcampbell 3mo ago

I invested about $4,000 in an NVIDIA DGX Spark several months ago. 128 GB of unified RAM, and the NVIDIA GB10 chip. With the RAM, the several CPU cores, and the 4 TB NVMe SSD, it's a very capable ARM64 Linux computer even without the GPU, and so far I've mostly been using it as such. But I wonder, what's the most capable model, specifically for coding, that can run well on that hardware?

znnajdla 3mo ago
DeepSeek V4 Flash is a very capable coding model that runs well on the hardware you described. Look up the optimized version specifically designed for local use.
Yoric 3mo ago
https://www.canirun.ai/?status=tight https://www.canirun.ai/?status=tight might answer that question
- zkmon 3mo ago
  That site doesn't seem to consider the quants. So useless.
morganastra 3mo ago
Deepseek v4 flash is shockingly strong for its size and reportedly runs well on that hardware.
- Shorel 3mo ago
  Better than qwen3-coder-next? That's the one that has given me the best results so far.
anon373839 3mo ago
Qwen 3.5 122B can fit with context at a pretty high quant (Q6). That's an excellent model.
lee_ars 3mo ago

jacobgold 3mo ago

"Around $400 a month of plans buys roughly $2800 of API usage at list prices, which is a real bargain right up until you hit the ceiling." I realize this text is just slop but it never stops being a "real bargain" at any point. And it's more like $200/mo for $4000+/mo in tokens. You can also buy additional subscriptions. There's no sense in running local models or doing anything else as long as VCs (and soon the public markets) are willing to pay your bill.

abc42 3mo ago
Even if they were making a profit, their scale and expertise will obviously give you a cheaper product than what you can build.
- jacobgold 3mo ago
  Maybe today but it's not a law of nature. It seems inevitable that AI models and coding agents will be fully commoditized eventually, just like computers, game engines, compilers, web servers, and so many other technologies have been. At the end of the day, AI models are relatively small files that we run little CUDA programs on.
simonw 3mo ago
SemiAnalysis pushed this to the limit and managed to get $8,000 of tokens from a $200/month Anthropic plan and $14,000 of tokens from a $200/month OpenAI plan: https://twitter.com/SemiAnalysis_/status/2064815044085318040 https://twitter.com/SemiAnalysis_/status/2064815044085318040
- jacobgold 3mo ago
  Yeah, although that is pushing every rate limit and no one knows what happens if you do that consistently? I think $4,000/mo is probably a good estimate for an individual dev doing synchronous coding agent work.
  - simonw 3mo ago

abc42 3mo ago

What kind of usage chews through Claude Max x20? I use several agents with max effort in parallel and usually end up with something like 50% weekly usage. Fable almost allowed me to get to 70% but then they started resetting the limits mid-week and of course now ended the whole thing.

aplomb1026 3mo ago

[flagged]

tamimio 3mo ago

You can have opencode and switch between multiple providers based on the tasks you are doing on the fly, normal tasks use deepseek for example, hard one use gpt5 or opus4, and track the usage with something like codexbar or similar. Openrouter seems to charge extra on top of the api costs, same with zen ide, so keep that in mind.

reinitctxoffset 3mo ago

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MemoryHoleHQ 3mo ago

I've been thinking a lot about this and my personal take right now is that at some near-medium future the models abvailable to run at home and the hardware needed to use them will be enough. My baseline is sonnet 4.6. I think it's good enough for most tasks sincerly. So, from what I see, we are already at a point where we don't need frontier models for serious coding and debuging. Give it a couple of years and that level will fit 120B models. At the same time, we saw the rise of direct acess memory systems like DGX or Stryx Halo that will allow to run models of this size for "cheap" in the medium term. That's what I'm betting in. That in 2 years I can buy a system for about $2500 that will run a model that's similar to Sonnet 4.6 locally. I might be spectacularly wrong though. But I'm willing to wait and use subscriptions/API calls for now.

13415 3mo ago

I use copy & paste with a pro subscription. I guess I'm a bit behind in terms of tool use but it works great for me.

TheSkyHasEyes 3mo ago
Similar story. I did have a pro subscription as a trial. I'm finding the free tier is as good(for my purposes) as the paid model.

sesm 3mo ago

> Do that well and you can build what a team of twenty engineers would put out in a month for around a thousand dollars. As usual, an extraordinary claim without an extraordinary evidence: https://stephen.bochinski.dev/apps/ https://stephen.bochinski.dev/apps/

tunesmith 3mo ago

I feel like I must have plateued and don't know what to do next to level up. I'm currently on the $100/month codex plan and it seems fine using 5.5-xhigh all the time. I think of what to do next, have a chat session to determine exactly what to ask for up to the point of being ready to implement, and then codex churns on a commit-sized task whereupon I briefly check it on my local dev server. If necessary I ask for a change. Then I ask it to commit and recommend the next step based off the spec. Oftentimes I have to "approve" an out-of-sandbox request anyway. I haven't found anything that requires running all night. I could tell it to one-shot a big plan but given how often I realize I want an intermediary thing to be slightly different it seems like a waste of effort. I'm guessing the next thing I should probably look into is some sort of machine vm I can tunnel my codex-gui requests to so I don't have to deal with the sandbox approvals (I don't want to give it "dangerous" access to my entire mac). I don't understand what people are doing with their side projects that is leading them to churn through tokens so quickly, to the point of requiring two $200/month subscriptions and a bunch of token charges besides.

dnautics 3mo ago
I have been on $100/mo claude and it has been churning out quite good software for months now. like i estimate what would have taken me three ish years, assuming i didn't burn out from failure (i would have). i only hit limits when i double fisted claude with my main project and my side project. just the other day i noticed i had been stuck on 4.5 because i failed to update the npm package.
- Schiendelman 3mo ago
  We're having a similar outcome. A hundred dollars a month is about right for me to sometimes hit a five hour limit, but mostly not. I do an hour or two of improvements, then go experiment with what I built and make a list of things to change, bugs to fix, ideas I've solidified, experiments I've invalidated.
sheremetyev 3mo ago
> I don't want to give it "dangerous" access to my entire mac I'm running Claude/Codex inside native macOS sandbox, configured with a simple script - https://github.com/sheremetyev/sandfence https://github.com/sheremetyev/sandfence always in "bypass permissions" mode - it works until task is solved, sometime 1 hour or more (which includes running tests etc)

jrm4 3mo ago

Is spending (metered money) even worth it? Perhaps for most I mean "beyond like a 30 bucks a month," but for me I'm literally not spending more money beyond my very cheapo 16gb video card. No clue what y'all are doing, perhaps because I'm hobbying, and also I'm old and can perhaps do more of this by hand. But I'm basically just doing what I did before, plus ollama self hosted and sometimes gemini and I feel like I'm going lightspeed beyond what I've ever done. And I suppose this is still very fine-grained. I have it make a draft, then just have them fix/change it step by step? I tried one of the bigger boys that can one-shot apps, which I guess is cool, but I'm finding it's just as hard to modify as if I just grabbed someone elses repo on github.

stkdump 3mo ago
No, I have the same experience. Feels crazy that a GPU is too expensive and then the advice is to spend 400$+tokens on openrouter each month.

WhiteOwlLion 3mo ago

There’s a lot of Xeon chips for $10 on eBay. Too bad there’s no drive for cpu based inference. The data center will need to swap out the older gpu clusters so what does that do for hardware pricing on data center gpus? H100 are cheap enough but the power requirements make it a long term net negative for how much pay for power in California.

spgorbatiuk 3mo ago

Hardware and provider juggling is a way to go, although I think it is also worth mentioning that the cost is not only the price-per-token, but first of all, the amount of tokens used. Depending on what one builds, comprehensive documentation and applicable skills and memory tools often allow for a substantial reduction of tokens previously used by the agent to comprehend and remember what is being built

geophph 3mo ago

> Do that well and you can build what a team of twenty engineers would put out in a month for around a thousand dollars. What does this look like after 6-12 months? Like, how much code are you trying to write total? Maybe it just doesn’t click in my mind, but sometimes I wonder about how much work people are trying to do and how they actually have enough to get done so quickly in such a short amount of time.

sublinear 3mo ago
They prefer to work harder and not smarter. Forever hill climbing to nowhere. I've never worked on a complicated codebase that started out that way until the rest of the business concerns and office politics came into effect. People may not like it, but the bureaucracy is far and away more valuable than the core functionality. Mature codebases are years of people thinking of all the possible gotchas while solving their acute pain points. This is not fluff, but the living and breathing part of it. Without that code, it's just a machine barely doing stuff in the most obtuse ways possible that nobody wants to pay for. I would argue that they're putting LLMs to work on that finer detail stuff, but AI is still far too dumb. No, what they're doing is playing with their skinner box.

hillj23 3mo ago

I think this is only going to become more relevant. I'm personally a $200/mo Claude Maxer and I know that the usage I'm getting on Opus 4.8 Max and (until they yoked it out from under me) Fable 5 is way, way more than what I'm paying them. At some point, this will turn usage-based and I will be hammered on it and probably forced to look at self-hosting. I think while the caps are there, even at $200, it's honestly not too bad if you're coding value into the market, but as soon as those caps come off for retail AI users, we're all going to have some tough choices to make.

bachmeier 3mo ago

> The upfront cost is steep and the models you can actually run at home are weaker than what the frontier labs ship, so this only pays off if you can keep the rig busy with long running tasks where a slower, cheaper model grinds away overnight. Most people can’t keep a home machine that loaded, and the hardware you buy today may look like a bad bet in a year. Oh, so this is not a post about AI coding at home. It's about vibe coding at home. There's a lot I disagree with in this post, but I'm posting this from a home computer with 64 GB of RAM and no GPU. I do lots of AI coding while spending very little money. I run Gemma 4 26b (mixture of experts) and Qwen 3 coder with Ollama. I use Github Copilot code completions. I use the Gemini and Mistral API free tiers. I have a Gemini paid API account. It's now prepaid, so you don't have to worry about an accidental $1000 bill. You can do a lot of things with Gemini Flash Lite 3.1. None of this is burning through tokens to create an expensive blob of spaghetti code, but it does qualify as AI coding.

atomicnumber3 3mo ago
My sentiments too. I'm using Qwen 3.6 35B A3B on a machine with 64Gb ram and a 24GB 5090 (an Alienware 16 Area51 I bought, serendipitously, about 15 seconds before the idiots preordered all computers for the next 3 years and ruined everything). You can't "slop cannon" vibe code with it, but this is personal code I want to not be spaghetti, so I'm not trying to vibe code. I just want to get instant retrieval of all stack overflow and reddit posts in a chat box, and for it to be able to spare me the physical pain of actually having to type out typescript code (I am a BE dev with negative patience for all frontend) and fuck around endlessly debugging obscure docker problems (I like docker, but, no patience for it having annoying problems and endless quirks). And this model does that really well.
sbochins 3mo ago
There are certain things you can leave running for a while. I think the distinction between vibe coding and hitl based coding routines will blur as workflows prove themselves and models become smarter and less expensive. Most of the best engineers I know have transitioned a lot more into vibe coding this year. The possibilities are much better nowadays.

pshirshov 3mo ago

> and the hardware you buy today may look like a bad bet in a year. 3090s and 7900s are going well so far. Next year an Arc Pro B70 won't produce you less tokens than today. They aren't fast but if you have flows where you can make money with them - they are a bargain in terms of price per Gb.

mikgp 3mo ago

What are people doing at home? I have like 5 different apps I code on the $20/month Claude plan and like sure I can hit rate limits but - What are people doing to burn through $3k in tokens?

gabriel-uribe 3mo ago
YMMV but automations eat through the $100-$200 plans, which burn thousands in tokens alone. I have hourly automations for root cause analysis on customer support issues, daily automations for eg log analysis, weekly & monthly automations for KPI tracking & actioning. I will say, when I was building side projects that were 1) fairly well defined in scope and 2) without users/need for automations it was much easier to stay under $20/mo plan limits. Now I regularly hit weekly limits and need multiple Max plans
- Random09 3mo ago
  Most of it doesn't require AI. You could generate automation scripts that do that, except of customer support. People became dependent on AI in places where it never was required and now tech bros are doing the squeeze.
  - gabriel-uribe 3mo ago
    I don't miss the days of scraping through logs or dashboards myself to troubleshoot some latency or malformed data issue that I missed conditionals for. AI is incredible at finding patterns in otherwise benign stdouts, let alone as it cross-references data streams. In theory, I don't need most of these automations. But for $200/mo? I will happily reduce my cognitive burden on stuff that doesn't impact the core business and make it easier to keep things gliding smoothly. When the subsidized plans disappear, I will keep these automations going with the best small models that fit on my laptop.
    - Random09 3mo ago
      What I mean is a script that can look through the logs. They are known and deterministic (if you properly handle errors) and you can analyze them statistically. If you don't know what logs your app is outputting, then you have a bigger problem in your hands tbh.

Kuyawa 3mo ago

This month I've spent only 15 cents using DeepSeek API and my own coding agent. Three apps delivered to clients and currently working on a tournament management app for pickleball, padel and beach tennis. I love DeepSeek.

KaiShips 3mo ago

[flagged]

thomasjb 3mo ago

Opencode's free models have been fine for me, they're what I tried after Gemma 4 8B proved hard to persuade into usefulness (I want to revisit with 12B and messing with harnesses, but I'm happy for now).

0xB0D 3mo ago

If your job becomes writing complex specs to make an LLM write code, you've not optimised anything. In fact all you've done is add a business cost.

dmos62 3mo ago
Are you saying that specs shouldn't be complex or that you shouldn't write specs at all?

asdfasgasdgasdg 3mo ago

Use Gemini 3.5 flash on the $20 a month plan and be satisfied with only being 3x as productive as you’d be on your own.

closeparen 3mo ago

>Around $400 a month of plans buys roughly $2800 of API usage at list prices, which is a real bargain right up until you hit the ceiling. The plans are metered, and any large AI native workflow will chew through the included tokens fast I don't think that's true at all. I'm doing 8-12 PRs a week at work, all primarily Claude Code, and the usage at API billing has never broken $500/mo.

kgwgk 3mo ago
Maybe yours is not a large AI native workflow?
- deleted 3mo ago
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conradkay 3mo ago
I'm almost certain $2800 is actually too low if you're really hitting weekly limits. I'm on the $100/m plan and used $300 at API billing yesterday (according to ccusage) Seems like one session is >$100 and I can get 10 full sessions per week The $200/m plan is supposed to be 4x that in usage, so with 2 of those you could use 4*2*100*10=$8000 in just a week Using Simon's numbers here as a bare minimum https://simonwillison.net/2026/May/27/product-market-fit/#enterprise-customers-are-now-paying-api-prices https://simonwillison.net/2026/May/27/product-market-fit/#en... you'd get 1200*4*2=$9600 a month

dpcan 3mo ago

I cannot figure out what people are doing to spend all this money. I have used a $60 per month Cursor plan on auto, and have never come close to using up my included usage, and I probably have it planning and coding and working for me all through the evenings 4 nights a week. What on earth are people doing differently that it's costing them so much? Maybe enabling on-demand usage or other paid models, or on higher modes? What are you doing that requires this? The output from Auto for me is crazy good for the tasks I'm working on, and have yet to run into an issue where it couldn't perform at a high enough level. We have been interviewing people at work to join our team and they tell us they use $2K per month in tokens with their current employers.... I can't even fathom what's going on here where that would be happening.

therealdrag0 3mo ago
Cursor on Auto will use Composer a lot, right?, which is much more efficient than frontier models people spend a lot of money on. Besides that it’s simply reading or writing a lot of code. The only time I spend 1k/mo was when I built a green field 20k Loc service with 2 other people (while also doing antagonistic code reviews etc)(mostly using Opus). So it took $3-5k to build a 20k loc production grade service in a critical domain. But most of the time I’m not doing that. As they say most engineering isn’t coding.
jmkni 3mo ago
Totally agree, but then a lot of the same people will be talking about all of the custom instructions/rules/skills/features etc they have set up, so that's eating up a lot of the context window before you even start When I do use AI, it's just the pure tool itself, and the context is the exact code I'm working with (because I'm trying to see if it can help me solve a specific problem), and I understand the rest of the codebase well enough to know if it's giving me good answers or bad ones
rjh29 3mo ago
A few things imo, 1) not prompting precisely enough (narrowing scope) means your agent will scan your entire code-base and sometimes get stuck looking at things repeatedly. 2) not checking the output is usually fine but sometimes it produces junk because it doesn't understand, and you cannot prompt your way out of it without reading the code and figuring out the problem. If you leave it on auto it will burn tokens. Plenty of low level things can trip agents up, too. I just had one inexplicably refuse to read an error about a function needing a bool return value - trying about 10 variations of the same thing before I interrupted it. Skills probably cause issues too, it loves to for example read the source code of libraries I'm using if I give it permission. That's a rabbit hole.

Flere-Imsaho 3mo ago

Instead of openrouter (which is admittedly a good service) I've switched to EU only servers via https://cortecs.ai/ https://cortecs.ai/ If you hunt in the settings you can restrict your account to only use EU servers for inference... Which means you can't use a lot of the US frontier models, but you can use all the Chinese ones, albeit within EU GDPR, etc. This to me is a good compromise between privacy and cost.

janpeuker 3mo ago

The biggest issue I've seen with people burning through tokens is using very long sessions, especially starting with plan mode and then "iterating" over extended periods. I was burnt badly by extra usage so now I run on $20 Pro. I ruthlessly create new sessions/agents, always ask to create markdown files first (no plan mode) and minimise context aggressively - for example I have a lot of skills that use lazy loading and a small local MCP for lookups plus openrouter with a local model for image detection and fulltext search. Basically I use Claude Code in pi.dev style.

bredren 3mo ago

What is going broke for a programmer? This is US centric but a $200 Claude code and $100 codex sub is a vast, vast amount of tokens. Enough to pay for itself many times over. It provides exposure to the very edge of harnesses and experience that is being hired for. Isn’t there an argument this is possibly the best price to available performance for frontier models? Both due to subsidies and the distance between open and accessible alternatives?

astqs 3mo ago
I used Kiro in December and I burnt through 200 eur worth of tokens in a weekend. Ultimately it was money well-spent, but, I think that if you want, you can spend as much compute as you have access to. Will it be efficient use of tokens? Probably not. From all the data, it looks like the 200usd we pay for monthly usage is subsidised… at break-even pricing … well, that 200 is starting to look like a few thousand.

knightops_dev 3mo ago

[flagged]

devhe4d 3mo ago

since when $400/m is justified as a "efficient" way of using a "nice-to-have" option? what a world we live in?

sebastianconcpt 3mo ago

Pretty happy with oMLX running Qwen3.6-35B-A3B-8bit

m3kw9 3mo ago

That’s easy, just use the plus plan and learn how to prompt efficiently

nunez 3mo ago

> The second is to skip the hardware and rent those same open source models from a provider at API rates. For most people this is the right call. You avoid putting thousands of dollars on one GPU setup while configurations are still in flux, you skip the work of squeezing long running performance out of an open model, and you can switch to whatever is cheaper or better next month without reselling a box. Something like OpenRouter makes the move close to a one line change. This will probably become the only option as the companies that publish open weights stop doing that. Very very few people have enough hardware to train/fine-tune at home.

dualvariable 3mo ago

Am I the only one happy on a $20/month pro plan? Yeah, every now and then you blow out the window limits. So you take a break and think about something else or go out and do something else...

deleted 3mo ago

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hottrends 3mo ago

[flagged]

dottchen 3mo ago

running 2 $200 codex subs seems to work for me. It's quite easy to run out of a full account's weekly usage if using xhigh and fast mode all the way, and i'm not using it for autonomous running, still mainly human reviewed actual work.

verdyshd 3mo ago

[flagged]

montroser 3mo ago

deepseekv4 pro via opencode go is $10/mo and has very generous limits. I use pi for the harness and go just as a model provider. It goes a good long way...

iwontberude 3mo ago

As long as you use models trained or distilled for your use case, there is no need to waste compute on trillions of parameters. Anthropic and OpenAI are proving that the “everything” models are not a sustainable business model. Just-In-Time or dynamic precompute of distilled models have already begun reducing the use of these frontier models for task inference.

josh_p 3mo ago

It’s been very validating in this thread to see everyone questioning the massive token spend of influencers and the like. The opencode-go sub, at $10/mo, is amazing value. I’ve been using that and the assistant kagi offers for web-chat and research for months. For the smallish projects I work on at home those have been great.

singpolyma3 3mo ago

How is this even an article? The advice is just "pay for max"

dempedempe 3mo ago
Because this person's entire blog is just copy-and-pasted AI responses.

whateveracct 3mo ago

am i the only one who codes by hand at work and for fun anymore?

jtr1 3mo ago

I've been running Claude Pro at home, supplemented with Deepseek configured in Claude Code. I've had decent luck throwing Opus (and briefly Fable, RIP) at architecture / product problems and producing plans to hand off to Deepseek (I personally find v4 to operate somewhere between Opus and Sonnet in capability). Lately I've been able to cut down on token usage with context-mode and codebase-memory to wring more out of my subscription, as well as doing things like make sure all terminal operations run in quiet mode. I've found codebase-memory particularly effective: it creates an index of your codebase that the agent can query for code tracing without reading all of the associated files, and I've also found it more accurate at analysis

andrewstuart 3mo ago

I feel like the author isn’t aware of the Anthropic fixed price subscriptions, any of which can give you a lot of home AI programming.

ddxv 3mo ago

I recently switched to the opencode $20 a month plan and am also testing the $5 a month go plan to see if that works. Connected into MiMo or DeepSeek Zen seems to code all day.

ameon 3mo ago

from my experience, good documentation and guardrails prevent your ai from going crazy. I have a code-map.md, and it doesn't spend a fortune looking for things each time. I have main-pipelines.md and it knows what to do where. I have lots of tests, so many bugs are self-revealing.

jason_s 3mo ago

Please use a more readable variable-width font.

smeej 3mo ago

The solution in the article is, "Pay for a max plan and then buy the extra tokens you need by API." How is that noteworthy? Isn't that exactly what Anthropic and OpenAI recommend? I feel like I must be missing something.

Frannky 3mo ago

I read some cool posts of people trying MiniMax 3 on 4x GB10. I wonder if we are almost there that with 10k we can have Opus 4.6 level at home. I feel tweaking the harnesses and a few more versions from oss models and some new hardware from AMD and Intel could get us there pretty soon. Or maybe we are already there? Someone was able to do it already?

dmzxnico 3mo ago

I use Claude 100$ + Codex 20$. That's a lot already and I rarely hit my quota before it resets, usually right on time. Some times I also get OpenCode Go just to get access to chinese models as an extra for new projects I don't really care about, fun ones. That's way cheaper than hiring a dev anyways.

novia 3mo ago

I want to be able to experiment with tweaking model weights and seeing the outcomes and i want to be able to finetune open source models. What's the best way to experiment with that without breaking the bank?

anon373839 3mo ago
If you want to experiment with fine tuning small models, you can actually do that with free Google Colab instances. Unsloth.ai has written a bunch of guides on this.

sermakarevich 3mo ago

I started using brain -> workers approach for coding. -- Brain is expensive smart model from claude subscription, Fable 5 when it was available, Opus now. -- Worker is a local model (qwen3.6:46B), deployed in 36GB GPU, Opencode + Ollama. Brain is responsible for analysis/design and task creation. Task should be made simple and clear so the worker can handle it. Worker does the coding. Brain validates and create a fix task when required. Atm fix to task ration is ~ 1:20. If no available GPU at home - qwen3.6 is quite cheap on clouds. Its rather experimental setup, out of curiosity, but it works better than I would expect it to. This allows me running 3 coding agents non stop for the 4-th day atm. Here I explain how I got there: https://news.ycombinator.com/item?id=48520757 https://news.ycombinator.com/item?id=48520757

tactlesscamel 3mo ago

You pay how much!?!? Y'all know that is enough to buy a real human, right? Well, good for you. I'm going back to figuring out which of my 2 streaming subs are getting cut. Maybe my free crap is enough to figure out that for me while I make my own art. To be frank, the time you spend constructing prompts, tasks, and all else required to get your ai to do a thing was probably enough time to do the thing yourself. -Research included. It's good to see the world throw the concepts of art, pride, and general accomplishment in the trash. Why have friends and partners in projects when you could give your savings to Anthropic, OpenAI, or any number of companies already obtaining ungodly ammounts of financing? A somewhate helpful bot at the cost of who you are and your bank account.

conqrr 3mo ago

The big one that's missing as cost cutter. If you are a programmer, stay as close as you can to the code. Define the interfaces yourself and the core logic. You will know exactly what changes you want and know what tier of model to use.