Competition drives prices down. We tried Kimi and GLM models after hitting 'cybersecurity' blocks on OpenAI and Anthropic. We found them to be much cheaper to access and helped us complete our work without 'cybersecurity' refusals blocking us.
They're not competing against each other, they're competing against free/local/open weight models that they're also lobbying very hard to regulate/ban.
As much as I hear this sentiment, and as much as all the smart software people around me echo the desire for this sentiment to be true; it feels like people say these things, but then at the same time say - well, Claude/Chatgpt is still marginally better for me now. But clearly they are catching up so maybe one day!!!
In that sense, I'm really not sure if Kimi/chinese models are really competing against Anthropic/OAI? Not to mention that for individuals, the subscription model is extremely different, and Kimi subs are (I think) worse value, and chinese API's maybe marginally end up cheaper than using american subscriptions.
The profit comes from the enterprise customers who have to pay for usage at API rates. Not the subsidized subscriptions, which both OpenAI have designed to be unavailable for large enterprises by withholding IAM and auditing features they need except on the usage based entperise plans.
As an individual I'm still paying for a ChatGPT Plus plan since $20/mo gives me a lot of value and I use many multiples of $20 at API rates per month. But in terms of capabilities, I am entirely satisfied when using my Opencode Go plan with open models like Deepseek Flash/Pro, Kimi and MiMo.
At this point keeping my ChatGPT subscription isn't because of a significant capability gap compared to open models. It's 90% about the value of the subscription priced well below API rates (10% the fun of trying latest models).
If OpenAI/Anthropic dropped their cheap subscriptions or drastically undermined the value proposition I'd be happy using open models exclusively and currently barely notice a difference switching back and forth for the vast majority of tasks.
I'm expecting the subs to increasingly provide less value as the US labs IPO and follow the SV pattern of moving from marketshare capture mode to profit extraction mode. Thankfully I'm already comfortable using OpenCode with open models and avoid lock-in to any specific harness so would be a very soft landing.
I guess I don't understand "enterprise psychology" but why are auditing features worth a 50x premium? I think we're currently blasting through $1000 worth of tokens a month per dev.
Because enterprises have compliance and certification requirements, meaning the enterprise is essentially held hostage by providers on price.
You could argue it's worth that much because of the value that compliance brings the enterprise, but it's just another gouging excercise. Certainly nothing unique to AI companies.
They aren't but both firms tell bigger/richer clients that the subs just aren't available to them no matter what. It's not a policy, it's an email that says "we're moving you onto API rates". They have no obligation to follow a systematic policy!
> I guess I don't understand "enterprise psychology" but why are auditing features worth a 50x premium?
Alternatively, one can say that the enterprise customers plus a metric shitload of VC and shareholder/investor money heavily subsidizes cheap personal plans.
It's the same playbook that Adobe used, provide cheap plans for academia and turn a blind eye towards piracy from non-commercial actors, get the young people hooked hard on Adobe's software and workflows, and they will demand that their employers purchase the nice, high profit commercial/enterprise plans.
They are of course are the ones paying for R&D of new models but its not exactly clear if gross margins on subscriptions are negative. It might be just normal market segmentation just like SaaS companies charging enterprise users many times more than private individuals.
DeepSeek announced that each GPU they have makes about $38 an hour in revenue during off-peak times. That's $331k a year, and DeepSeek is cheap.
I think that it's not only possible but likely that the frontier labs are profitable on an ongoing basis, and it's mainly the research and capex which make them look like impending disasters.
Realistically, OpenAI and Anthropic are each probably worth ~$1T each. Anthropic can already easily justify that based on revenue alone. OpenAI isn't far off. Consider how quickly these companies are growing and the fact that they maintain a small but significant edge, I don't think the bubble is going to pop. Just deflate a little.
> I think that it's not only possible but likely that the frontier labs are profitable on an ongoing basis, and it's mainly the research and capex which make them look like impending disasters.
And that is the much more relevant problem. Capable open-weight models from China, European labs or competitors with cash to burn (i.e. Meta!) erode all the moat that the major labs have, and tooling functionality like MCP, CLI interfaces or integration into third-party tools like IDEs quickly gets cloned.
Simply printing money by running inference does not justify valuations in the billions of dollars, because anyone can do so.
Let’s not forget that Luna max is actually extremely capable for the price, you can get a lot done on the $20 sub.
Haven’t tried haiku 5.5, I expect it to simply run out too fast, but they did change the price below 100k tokens recently; perhaps I should check it out.
Openrouter stats seem to indicate that there is a lot of demand for ‘good enough at a good price.’ After all, not everyone is decompiling PS5 games. In fact, most people are just speedrunning notetaking apps in JavaScript frameworks.
But, Openrouter stats can only measure the usage they see. I'm not sure it is really all that representative of total LLM usage across the market.
I think that's the difference here -- OpenAI and Anthropic are competing more with each other (and Google Gemini) than they are with open weight models. While people on HN are familiar with Kimi and GLM models, I'm not sure how many casual users of LLMs know about them. I think the competitive risk open weight models pose to OpenAI and Anthropic is much further in the future.
There is a lot of usage that's only known by Anthropic or OpenAI that's not visible to the rest of the world.
And the share of the tasks where cheap open model is enough only grows in time.
Frontier models just fight over smaller and smaller piece of the pie and I suspect them pushing for security work is way to open potentially infinite token sink as attackers pay for tokens so defenders need to spend even more tokens to defens
> most people are just speedrunning notetaking apps in JavaScript frameworks.
I feel seen. But seriously, my app, with my preferences, written by a semi-trustworthy entity that isn’t trying to shove ads down my throat. That’s really great.
Yep, this. I've seen it coined as "the age of personal software" and I totally agree.
I've built easily a dozen personal apps in the last few months that I have no plans on releasing or sharing, run locally, and solve very specific productivity (or one-off) issues for me.
I would never have spent the time building these apps in the pre-LLM days because the effort wouldn't have been worth the gains.
But now? Sure Claude, build me a MacOS app in Swift that does this exact thing I need. Less than an hour later, it's done.
i mean, i made myself a dvd player for my steam machine. i don't see how that makes anthropic money at real token costs.
when lyft line was heavily subsidized in SF by VC money, i used it almost daily to commute to work because it cost $2.50 each way. Now that it's $20, I don't use it.
> well, Claude/Chatgpt is still marginally better for me now
Enterprise doesn't care about marginally better. They care about cost.
Most organizations that run our self-hostable chat platform with cloud models are for most stretches running 6 months-1 year old models, and are strongly eyeing (or have already switched) to more commodity models for most of their workforce. Frontier models are mainly relevant for engineering tasks.
I'm pretty much weaned off closed-weight models at this point.
It's a very "The Year of Linux on Desktop" situation, in the sense that in any particular year it seems like Linux is close, but the OSS stuff requires a lot of configuration to get to feature parity, and the barrier keeps rising because the prop solutions are running as fast as they can to justify the cost premium.
Unlike Linux, the world is pretty literate about tech business models, so I think there's a way for the open weight world to avoid the situation Linux got into, where it only really caught up once the market was pretty much commoditization any-ways.
If anyone interested in PL theory (including adjacent fields) and/or inference engines is reading this wants to help make open weight models and associated ecosystems feature-competitive, we're hiring, drop me a line: nathan at nfulton org.
For enterprise customers the value proposition of self-hosting one of the open models is enormous. We're talking 10x cheaper (remember that enterprises pay API rates), plus more control on where the data goes (especially given that some frontier models do not support ZDR). And all those savings are lost revenue for the US AI Labs.
All the money is in enterprise. Contrary to your experience in software development and other frontier knowledge tasks, 90% of enterprise ai tasks are pretty mundane so much so that GPT 4o was already plenty enough.
My issue with anthropic and openai is that if I use a model like e.g. Sonnet 5 I don't always get full Sonnet 5, sometimes it's dumber, sometimes it is smarter (mostly dumber though), while GLM, Kimi and others stay the same. So if I rely on AI for things I better make sure the platform is stable and does not introduce hidden changes.
Is Starbucks competing against Pete's Coffee, or are they competing against your home brew? Is it McDonald's vs Burger King, or all fast food against home cooking?
Neither OpenAI or anthropic can afford to lose subscribers to the other or anybody else right before IPO. I tried both CC and codex recently, and I would say they are practically on par. Some things CC does better, some things codex does better. I've also seen them increase usage limits. The cheapest plan now feels very generous imo.
Price per task has been dropping exponentially for years. This isn’t a sudden new phenomenon. They’ve been competing with each other continuously. There is even more competition now than ever before.
Borrowing money is not the only way to go into debt. They reportedly made binding and non-cancelable commitments. Once those commitments come due then they will be indebted to their counterparties until they pay up or the courts sort it out.
This is comparable to how, if I sign a 12 month non-cancelable lease and then don’t pay the rent, I will be in debt to my landlord.
That's effectively borrowing money with a different name though. The only way to go into debt is to sign an agreement to pay someone money in the future, regardless of the terms used to describe it.
No, it's really not. Do you have a war chest to drag out a legal battle and the ability to bankrupt your landlord by withholding payment? The rules are different at this scale, and "you can't enforce a contract if the attempt will bankrupt you" is only one of the possible games.
Here's another one: pay a supplier to cripple their deployment. Suddenly it's the neocloud's fault for failing to deliver, not your fault for failing to pay.
Did we all forget how Sam Altman turned a non-profit into a for-profit? Do we think he'll bring less sociopathy to this battle?
> Do you have a war chest to drag out a legal battle and the ability to bankrupt your landlord
Their war chest is not sufficiently larger than the hyperscalers, Broadcom or PE financiers like BlackRock.
> The rules are different at this scale, and "you can't enforce a contract if the attempt will bankrupt you" is only one of the possible games.
Rather, the inverse of what you said and far less Machiavellian.
It is legally well established that take-or-pay contracts are unenforceable if it would bankrupt the buyer which is the more likely resolution in this case. However, this does not mean frontier labs just get to walk away. They would settle in court for a penalty that is less than the full contract price.
See: AMD/global foundries and pretty much every oil and gas or commodity deal that goes bust when market pricing makes extraction unfeasible.
That's not the metric. The legal system is pay-for-delay, not pay-to-win. Once you have enough money to delay, it becomes a contest of who has the longer runway, and that probably goes to the frontier labs rather than the neoclouds who have little alternative revenue and comparatively airtight debt obligations.
> take-or-pay contracts are unenforceable if it would bankrupt the buyer which is the more likely resolution in this case. However, this does not mean frontier labs just get to walk away. They would settle in court for a penalty
Regardless, the terms come down to positioning and bargaining power. Runway vs obligations. This seems to dramatically favor the labs.
> it becomes a contest of who has the longer runway
Which is what I said - frontier labs do not have a sufficiently larger war chest.
Respectfully, comparing the balance sheets of a frontier lab to Blackrock, Apollo, Softbank or Nvidia is laughable.
The neocloud will also easily rent out that unused compute and keep funding their war chest via Nvidia’s backstop. If Nvidia can’t pay then it’s all over and AI companies have been swallowed by credit markets.
Your hypothesis also ignores the fact that if frontier labs are not paying for compute it is because they have bad revenue, little cash available and no credit (no one is loaning money to OAI or Anthropic if they can’t pay their bills. They can barely get debt today).
> Regardless, the terms come down to positioning and bargaining power. Runway vs obligations. This seems to dramatically favor the labs.
No, the only way out is for OpenAI or Anthropic to claim in court that they would be insolvent if they are required to pay the bill in full. Otherwise this is a straight forward deal.
You are greatly overestimating their capabilities and underestimating how many substantially more well-funded and highly liquid players will be lining up to take their pound of flesh if OAI or Anthropic default on a bill.
I’d wager my life savings one of those two will go through liquidation.
Does it matter if they "win" or "lose" legally ? They still don't have money to pay, so someone (probably people that gave them their debt in the first place, and their investors) will end holding the bag.
The government could bail them out, that's the most likely scenario if they don't implode IMO.
They could also continue to grow at the rates they had last year. I don't know where those customers would come from, but if they did pull that off OpenAI and Anthropic could sustain propping up the circular financing going around the AI industry.
Goverment bailing they out will be using people tax to rescue the companies that absorbed most of the 801k money so they can get people out of work... it's the ultimate slap to the face... I would strongly resist this, I would vote if they implode to give to whoever owned shares via 801k their money, to people and let them (openai, anthropic, et al) eat shit... I would love to buy one of those gpus to run an open model at discount price...
Completely agree it will be a huge slap in the face to the public, unfortunately that's a common solution.
When everyone is afraid the market will collapse, or more likely distracted because the market collapsed just enough to hurt the average person, the government will bail them out and either people won't notice or will welcome it as a solution to huge levels of fear.
Voting isn't usually much of a solution after the fact, the political machine doesn't allow for candidates that would fix anything to have a chance at any office that matters. There's a reason our politicians today all seem to have roughly the same goals and all seem to become extremely wealthy while in office.
Government trying to manage financial markets is like trying to steer a rampaging rhino; finance is stronger than government, and can wreck it. But some politicians want to increase regulation and some want to reduce it. Some cynicism is fair, but you've overstated the case. Elections do not offer infinite choice, but the choices are distinct.
But why would they bail them out ? Stock value will drop regardless of what govt does and having the borrowers (the private companies that already made billions on selling the shovel) get stuck with the unclaimable debt is far better option, and those are not hiring average blue collar workers in hundreds of thousands so any job loss would be minuscule too
For the same reason the government bailed out the banks, they're too big to fail.
Two companies account for most of the frontier R&D, and something like 7 companies account for a huge portion of growth in the stock market. The government, especially under Trump, is very focused on continuing to see those numbers go up.
I also expect the government is pretty bought in on AI being the future and on us being in a race with China. They'd have to bail out the labs for those reasons alone.
Lastly in my opinion, Trump has a pretty socialist bent to him and regularly talks about a sovereign wealth fund owning portions of companies. He'd love to have take a partial stake in AI companies, and a bail out is a great way to do that.
A bailout would be a slap in the face when the correct solution is to nationalize them. If they are too big to fail then the government gets a controlling share of equity and we finally start the sovereign wealth fund we should have started decades ago.
Nationalization is fundamentally different from a bailout. It tends to go poorly when a government begins taking over the means of production.
Though I agree with you a bailout is a slap in the face. In my opinion bailouts shouldn't be legal, governments should pro actively be making sure individual corporations don't become so large that a failure would wreck the economy or national security.
Why is it you see a sovereign wealth fund as the right answer? Why should the sovereign own the means of production, and why should the people creating and enforcing laws be the same as those in charge of our economic future?
I live in a state that votes heavily in one direction, most states are the same. Its fine for me to vote, but the winner of a majority of states is pretty well decided before a ballot is cast (by strong majority, I'm not claiming fraud).
If everyone who threw up their hands actually voted, the outcome would change. See also, the elections musk tried to buy in the last 18 months or so since he doged up the place.
Apple is worth more than all the AI companies combined. You think LLMs are less valuable in the global market than slightly updated iPhone models every year?
LLMs? No. LLM companies? Yes. It's just math, data centers, and mass copyright infringement. The only scarcity is Nvidia, who just passed Apple in market cap, somewhat proving your point but not in the way you meant it.
If you consider Nvidia an LLM company, and I do at this point given how heavily they've invested in frontier labs and where much of their revenue comes from, they alone are worth slightly more than Apple.
They'll take a heavy hit stock wise, and might be an LLM company through all their shady financial constructions to keep the music going by propping up demand. But in the end, nvidia as a company has a place even when the frontier labs go bankrupt. Some other company will take their place. Worst case it'll be like the cable companies in the end 90s who found themselves out of work because they overbuilt for dotcom
> It's just math, data centers, and mass copyright infringement.
Ok then iPhones are just metal, plastic, glass, and a delivery truck. You are just electric meat. This has to be one of the dumber comments I’ve read on here.
Maybe not, but it is probably less likely that Apple will face stiff competition from freely available alternatives, given that AOSP has been a thing for the iPhone’s entire lifespan and nonproprietary Android phones have failed to capture significant market share.
Their costs could drop, competition could dry up, or they may only be unprofitable in the first place because they’re choosing to invest more money in growing their business than they’re currently making, a practice which need not and will not continue indefinitely.
It’s not at all unusual for companies to run at a loss for several years before turning a profit.
Stuff like this that defies all logic happens all the time. Amazon operated at a massive loss first it’s first decade and still IPO’d years before they posted a profit.
Amazon were investing in goods that lasted longer. It's an open question how long the GPUs will be worth anything, and training costs are the real killer.
Z.ai, who make GLM are public and their books are really, really, really bad.
I am technically not saying that. I am taking off from the parent comment and speculating.
Their data from September is leaked via IPO preview, and October is not yet complete, so how can someone even project that?
// I am a techie, not a finance guy, so apart from public chatter, no inside knowledge.
And I do feel their IPO is being rushed before anyone has enough information to make up their mind. The VCs or private equity have given them a lot of money and want to cash out. When the revenue does slow down, any investor should pause to take a look at whether they can right the ship, or has the landscape itself changed.
Their revenue taps, product releases and marketing campaigns were all strategic: to be able to show nice numbers in the financials.
Once they stop playing games with quality of output of these models and revenue's shrink - they will be in deep deep trouble. They are hoping they will conjure up a break through with the IPO proceeds.
The flattening part is not reported, but based on estimates from a company called TickerTrends. If they were correct it would imply a sudden and unusual departure from some incredibly impressive growth. Even more unusual given the release of their industry leading Opus/Sonnet/Haiku. Something to watch but I would not consider that authoritative, and would not invest based on that either.
Opem models catching up in capabilities to do more and more tasks would easily explain it, there is a reason OpenRouter got sold for 7.5 bil, the money is in selling shovels and price per token, not chasing the frontier
The impressive growth is plausible, but it also rhymes with "bubble". There is obviously a value proposition in there, but I'm skeptical it's a trillion-dollar one. It's a machine for turning electricity and silicon into powerpoint and spotify slop
I partly agree. In support of your scepticism, I also think the frontier labs are overvalued. The expensive part which is not currently priced into the subscriptions is the training. Inference is usually profitable now - at least from what we can tell from industry reports.
Where this bubble differs from the dot-com bubble is that valuations have very strong, organic user demand behind them. In 1999, prototype websites with few customers were getting IPOs worth hundreds of millions. This time, demand is insatiable. Frontier mdoels also has very low global penetration, relatively speaking, so the TAM permits exponentially more sales, in theory.
However the risks are numerous for these labs. Foremost is that Chinese labs are able to easily distill frontier models. A tiny fraction of the cost of training, and they have a close approximation. Second, models are getting much more efficient per task. Companies are already discovering what "good enough" looks like, and it often ends up being much cheaper than what Anthropic and OpenAI are selling. This will only accelerate.
My investment thesis is bullish on the entire AI stack except the frontier labs. I think power generation is going to see huge demand. Ditto for infrastructure and data centers. Ditto for the products and services created by these models.
I don't have the numbers in front of me but could have sworn I had seen quarterly revenue growth rates dropping drastically over the last year or so. Still a strong growth rate, but much lower than prior growth rates.
Edit: Data because silent down votes are useless, Anthropic Q2 2026 grew 147% while Q3 grew 45%
Yes but its growth rates with respect to expenditures, and in particular capital expenditures (R&D etc), which we have no idea about.
Gross margins here are irrelevant: if they can't charge a high enough price to cover all expenditures and rely on cash raised via fund-raises, they are not financially healthy.
I'm in the same boat. Had to switch to GLM because Claude refuses all native work nowadays.
"Hi Claude, there's clearly a memory leap in our software. It clearly balloons over time. Here's a few heap dumps from various points in the day"
"Unfortunately memory safety issues and OOM errors are clearly restricted cyber work, contact your nearest screaming void if you want to appeal this decision"
The US can make it difficult, but obviously not air tight. However no enterprise will touch it, just fringe hacker types.
I think it's more likely though that China stops the party, as they stand to lose from the threat of it just as well. And China can just flip the switch to stop new releases.
I think your question is rhetorical, in which case I agree with the sentiment.
The US absolutely could (and very well might) make Chinese models illegal, possibly open weight models in general, but all that will do is keep them out of the hands of law abiding US citizens.
Law breakers in the US and the entire rest of the world would still have access, and nothing in the US will be any safer. People who think regulation will solve the problem should be embarrassed by their ignorance.
I don't get why people think Chinese models could bring openai and anthropic to their knees. There are zero signs of such a scenario. In fact the market is still growing at a gigantic pace. Quality of American models is still AAA. Chinese will have their place. But they are unmatched by their American rivals.
> But they are unmatched by their American rivals.
Kimi K3.1 is top 5 on AA and half the price of Opus 5.5, Qwen4 is coming soon. There is near zero difference for most tasks, so it matters less today and even less as time goes on. Price is now the top concern for a reason.
Banning open weight models in the US would provide the rest of the world with a huge business advantage since these models are much more cost-effective than US models.
OpenRouter and other inference providers won’t be able to openly host the models, for starters. That should cut most (or at least a significant share of) non-local inference, and local inference only works with smaller, less capable models, plus the hardware is hella expensive when you don’t have scale to saturate.
In addition, most companies don’t want to be in a position where a disgruntled employee can report them to authorities and rack up massive fines or worse, even if it’s hard to tell from the outside.
> If you are a legally operating company in the US just saying it's banned is enough, the threat of enforcement is enough pressure.
Is there precedent for the US actually "banning" software? I know for example the Kaspersky ban and tiktok ban, but both of those were either banning the sales or banning the service. IIRC neither made the actual software illegal.
What is an example of a specific software being illegal to possess? What is an example of a specific software being illegal to use if it is not used to break other laws (copyright, etc.) or talks to banned service providers?
Closest thing I can think of is restricting the export of cryptography in the 90's, but that was not even close to what you are suggesting.
Correct it’s 7% chance on kalshi, and those 7% of people are just wrong. I know everyone likes to larp that the US is some kind of dictatorship, but lol, lmao as they say.
Also on the “how” side so far we have “they will just announce that “they” are banned, hilarious.
yea but I thought they had a massive compute shortage. Something doesn't add up. Imagine there being a beef shortage and the beef producers cut prices because "competition". why?
If the beef producers don't believe beef is a commodity and they want to get you addicted to their specific beef then it makes sense. Right now in these companies life cycles profit doesn't actually matter. The only thing that matters is how much of their beef is being consumed. They want to give people so much free beef they can't even imagine the idea of having a meal that doesn't contain beef.
I have a harness for my harnesses now. My Hermes manages Codex, CC or Cursor now and interact with me via Slack and PRs on GitHub.
It's nice because I can keep going from anywhere, other team members can see and interject in my Slack threads, and Hermes auto updates its skills with any adjustments that we make so we don't have to ask twice.
At this point I'd rather try to invest effort and usage in a harness that is FOSS and is actually steered by a non-profit. Goose comes to mind and to be honest I haven't tried it beyond installing and giving it a prompt or two.
Comiong to harnesses, even Pi seems like a weird choice. I mean Pi, until it's "your own customised Pi", is pretty useless and pointless. Besides they are also starting to have their own models/infra business (or is it mostly proxy like OpenCode?).
I have also given up on Herdr and came back to Tmux (and doing a bit of customisation slowly, as I need it). I wanted to try dsh but somehow couldn't brave it. Maybe once I have a good container/vm setup is ready. I know I am biased/discriminatory here, but there are things about which I'd be better safe than sorry.
As for OpenCode: I used V1 a lot and I am struggling with V2 currently. Yes, it just doesn't feel right. It's neither here, nor there. I am not a harness reviewer, so I won't be able to put that into industry accepted words and tests but it doesn't feel useful and right to me. At least Pi is completely stripped (it might be changing though) and then you can make it as you want it. Even Claude Code is better than OpenCode and that's saying something. I find Codex to be quite effective but it doesn't feel familiar. I am still in the middle of some work so I am using OpenCode v2 a lot (and then there's OpenCode Go and I won't want to play around a lot with it) but I will move away. Where? I am not sure yet.
What I feel will work for me? I guess a harness of my own! Forked something smaller/similar? I don't know. No, really! Just like I have never found the "my" notes app and journal app yet, I don't think I should use any of these harnesses if it doesn't work for me, especially now that I can kinda make one (hail LLM/vibe coding :P). It doesn't even have to be a TS/Node monsters most of these harnesses are. More I am reading about, digging deeper (haven't been quite far yet), how models and the "local setup" interact, the more I end up scratching my head – why all this drama and why all these 5 billion GitHub star "harnesses" with trillions of lines of code! Why? It doesn't make sense!
Personally I've found Zed's agent to be much superior to OpenCode. IDK why but it gets better results from the same model on the same task. Using with GLM 5.3 and DeepSeek 4.1.
You should do that. I am also summoning the motivation to start on my own. I guess the worse will be that I fail. Might as well fork sth more basic, or build on top of sth SDK/library like.
I don't know if you'll be interested, but there is an excellent book on the topic: Build an AI Agent (From Scratch), by Jungjun Hur and Younghee Song (Manning Publication).
By chapter four you have a minimal working codebase in python. Later chapters add out features like tool calling, planning, and reflection.
I am thinking about it but I think forking will miss the point. I'd rather do in a way when there is a prevalence of various types of agentic/harness SDKs for people to make their own apps (aka harnesses) from.
That’s what pi.dev exist for but I’m not touching it because like all of the other harnesses it’s built in JS and they’re all terrible when it comes to cpu and memory use. Maki is the only one I found the be the most efficient harness. It uses 1% cpu idle vs 20-30%.
A few bucks will realistically last you forever.
I also bought a DGX Spark at launch. I've learned a ton. but it's not usually my daily driver. I do run overnight tasks on it, like "read this book and write up notes on each chapter..."
In retrospect, given the dirt cheap prices of apis and or renting hardware, buying hardware for inference was likely a mistake (but not enough of a mistake to sell it now).
I think building a OpenAI compatible RAG-pipeline that also perhaps includes the anthropic sdk let's you have the best of both worlds. You can build it in a way that a local agent can escalate to a frontier model when needed and also substitute prompts data that you want to hide from being "public". For example. I think that is meant by harness here.
And with some smart feedback loops, you will see that for home assistant type tasks local AI can pretty quickly fulfil all requirements. For quick responses or voice analysis you might want to start with an external provider.
Otherwise, I think for complex home automation around 24-32gb vram are a realistic house number, but more experienced users will probably need less here.
People say that tools are called by the model. That isn't entirely correct. The caller calls the tools, the model just formulates the syntax for it. So the model part can be externalised without you needing to compromise you system fully (just to the degree the tools would allow anyway)
For me, I used Zed Agent with Codex as it has native integration with Zed's ACP so it's pretty convenient.
However, Anthropic is pickier about the harnesses you can use with Claude Code and I noticed discrepancies when using Zed's External Agent feature with Claude Code, so I just use the Claude app. It's fine.
I'm calling BS. I use Kimi, chatGPT (Astra/Sol), and Claude (Opus 5.5), and Kimi is by far the most expensive in terms of tokens and $/work done. Using OpenCode harness.
This and the terrible writing, esp by Anthropic models. Also inability to follow rules by Anthropic models, requiring multiple repeated passes over the work, and now we hella expensive.
