However, you'll have to gauge for yourself how closely their tasks resemble your tasks.

1/ https://cognition.com/blog/frontier-code

2/ https://cognition.com/frontiercode

Now, if the debate is really about which option is more cost effective, then we could easily run an A/B test to find out. Though TBH my instinct is that that experiment is likely to cost more than the potential cost savings.

What I will say is that my own sense from experimenting around in a non-rigorous way is that the answer depends on how you use the tool. For actual vibecoding you should always go for the SOTA model because it will need less oversight. It’s also less likely to get stuck in a vicious loop that fruitlessly wastes tokens. But for a more hands-on approach where you move in small, carefully planned increments that you review and test in human-comprehensible chunks, smaller models may be preferable. SOTA ones don’t do that much better when working that way, and the slower inference adds a detrimental amount of friction to the work cycle.

If this is a cost conscious company where I'm going to get a fairly limited amount of Sol, or a nearly unlimited amount of Luna, I'm probably choosing Luna.

Sol medium has been a nice balance between intelligence and response time. Luna xhigh can achieve similar scores on the evals, but it takes noticeably longer. My impression is that the higher reasoning effort helps compensate for the lower base intelligence.

Cost is definitely a big factor, but latency and intelligence matter too. If I had the budget, I’d take Sol medium over Luna xhigh.

From using both on real scenarios, Sol is noticeably better at navigating around issues, exploring alternatives, and being creative when the obvious approach doesn’t work. That matters quite a bit when you’re investigating live alerts, where the path to the root cause isn’t always straightforward.

Sol already lacks judgement. It will absolutely add idiotic tests and comments. Luna is that but worse so if you account for things like going down wrong paths, producing bad results, overthinking then it could easily cost you more to get less.

Yes. Categorically. Anyone who tells you otherwise and that luna is “just as good” does not know what they are talking about.

Going from sol to luna is a downgrade.

It is not a question, it is a fact.

> Is sol actually worth the extra cost?

Is a question only you can answer, because it has no generic answer.

Right now, for me, being able to use sol is worth the cost, but using it all the time is not.

I’m sure going from using it to using luna feels rubbish; but there are realities about costs you have to face sooner or later.

Maybe like… give your team credits and make them pick the right tool for the job; and if they burn their credits on sol in 20 minutes, well, tough luck buddy, looks like you're coding by hand for the rest of the month.

Team will quickly shift. People hate losing access to ai.

The way vision and language models converge into the same geometric space should be extremely alarming for the "you don't need global knowledge for local tasks" type dreams.

And to be clear I'm not saying that smaller models don't or can't work well, or that we shouldn't be heading in this direction. And it's not quite the case that broad knowledge is strictly necessary. But it never seems to be negative! And so far it is the best way we've found to do... everything. Small models are good to the extent they are like big models, not to the extent that they are small.

There are still tasks that even Fable is bad at doing. And many are just mundane things. Because of the fact that you have to steer it on those tasks, you might as well steer an 80% model that is 5x faster. And those do exist.

Naturally there’s a bit of a gap because the faster models need steering on tasks the slower models don’t so there’s no smooth transition but I find it worth it. Especially if you want to stay in flow.

Ironically this sometimes means starting a plan with a great model, planning with a worse model, iterating, then submitting it to a better model for review, and then having the better model do the implementation.

It's a bit like saying you always want the fastest and best car; Sure, you can have it if you keep paying for it. But a small car will also get you from A to B, will use less gas and will be much cheaper.

That's great, but the thing that worries me is that many companies have billions invested in the AI bubble. It's around 1.5 trillion last time I looked. It's all circular spending between the companies building out the infrastructure, and the models. None of it is profitable. They will want to recoup that 1.5 trillion from consumers, which means using online-only pay-as-you-go cloud models. They will inevitably see that people using capable local AI are "lost customers" and they will try to kill the ability to locally host AI or somehow enshitify it enough to make paying a subscription more palatable.

I'm not saying I believe that will happen, I'm just worried that it will. Is anyone else worried about that as well?

Second, when cloud models become unavailable or otherwise deteriorate, these will be all you have. May as well prepare.

Unless you must 1-shot with no harness it’s the same amount of power, maybe more because the big “good” models make too many assumptions and tend to become rigid.

Mistral 7b can do anything, and it’s basically instant even on an M3

For example in language, this is called the “curse of multilinguality”. Small models that handle a single translation direction can easily outperform big ones that try to handle them all.

https://arxiv.org/pdf/2311.09205

In any case, for most tasks the question is not “how many tasks can this model kind of do well” but “given time/cost constraints, what is the maximum level of quality we can achieve”. And for that, small models are usually very competitive.

Something that helps homeowners build anything on their property, for example https://myhomecat.com

I think there is more opportunity for consumer application of AI. Im surprised we dont see more models in game. Small models that do more purpose driven things not "this npc is just a chatbot with a personality in its system prompt." I mean we literally have systems in games called "AI" and we dont think these much more powerful AIs have a place?

I don't have to be an automotive engineer to start my car and put it in drive.

At some point there will be a surplus of fast memory and even in a crash the current generation of SLMs are bounced to be plenty to build a lot of intelligence at home.

These things would be considered magic even 4 years ago!

It will be interesting to track the improvements of these 7B model over time.

There will be a turning point in the next few years where it attract enough consumer attention to create yet another Smartphone and PC super cycle.

For code, they are great, but for creativity for NPC controllers, they leave something to be desired, but work well enough for testing, so I don't burn tokens until I'm actually playing my games.

But nothing one-shots a prototype better than Fable 5. I can have a prototype built in 30 minutes, hooked up to my local LLMs and Claude Code is very good at testing the interactions and even tuning the prompts of the NPCs for better experiences.

The infancy phase of this technology is represented by the pursuit of making wildly grand, wildly expensive, all-purpose models that somehow discern a user's full accurate intent from a lazy, underdeveloped, vague idea that they ambiguously and poorly express in a couple dozen words.

The adolescence will arrive as those outsized and ill-considered ambitions collapse and we instead see a cambrian explosion of restrained but efficient model+harness-tuples that have been distilled, finetuned, and rigged to deliver on narrowly scoped but idiosyncratically-shaped tasks with incredible efficiency and erogonomics.

However, back then I was getting the AI to write individual functions or classes or a test suite. I was decomposing the larger task into smaller tasks, delegating some of them to the AI, reviewing the results and composing the codebase from those. I was also essentially the harness.

Today the models can write and test and deploy an entire project. In terms of the code quality, I actually don't think today's frontier models would have written it much better than the 2023 models did. So in terms of raw coding capabilities i.e. converting a high-level specification into working code, I think we hit the peak way back in 2024 itself.

What has changed is the AI has learned how to do the task I was doing (besides being the "harness"!), which was the mid-to-higher level "engineering" aspects like decomposing a task, specifying it to a reasonable level, reviewing the outputs, and course correcting as needed.

I'm not sure if that is something the AI labs explicitly focused on during training (which may be why Meta is having its highly paid engineers do annotation work), or an emergent property of "better reasoning" (which I believe Dario implied in a podcast), or some mix of both.

But the fact remains that even the weaker models are more capable than we realize, and many being open weights, are here to stay.

DeepSeek v4 flash has been dirt cheap and so fast that my development loop is;

- small prompt

- review

- small prompt

- review

I build software with the same quality I normally would but it's way faster to produce and I think more about architecture and flows than I do about implementation details. The small diffs let me accept / modify / veto diffs and if the model struggles, I just write it by hand. It prevents compounding defects from leading the model astray (like you see in vibe coding).

In some cases vibe coding is useful, like when the complete specification is available (e.g. creating a JavaScript engine that implements the standard) - but anything that requires iterative development sees vibe coding break down pretty quickly (you could argue that is the case for a JavaScript engine).

I feel energised by AI assisted coding rather than drained, as it's a force multiplier for my skills and it lets me build more than I could by myself.

That said, most of my team vibe codes and reviewing their work is like pulling teeth.

AI is a utility that can abstract code to such a high level it is indiscernible from natural language.

But the idea they’d be better than a bigger model is cope, you’re pretty much always better off running the biggest one you can bring to bear within your constraints.

The word “most” is doing a lot of work here. On a percentage basis perhaps most tasks a typical SWE needs to do when they aren’t in meetings or writing docs are just glorified autocomplete. But that’s boring and that’s why people don’t usually talk about it.

People are addicted to chasing frontier models because they all have memories of spending a week on a deeply challenging algorithm problem or even have crazy complicated algorithms they cannot implement themselves and want to have the models achieve this technical breakthrough. It’s the kind of productivity boost from spending one week on a problem to spending one hour. In contrast the productivity boost from spending ten minutes to spending one minute just doesn’t occupy people’s mind.

The cost isn't just what you're billed. There are security, privacy etc. concerns.

I find that when I give an LLM my full handcrafted codebase, it does very well. It follows my conventions, sees the intent and can coherently build within its scope. It writes much better code than a 'vibe' prompt.

It is always tempting and I myself will continue pushing the boundaries, but when you keep an LLM in reasonable scope (that may be one line, function, file at a time, depending on your idea of reasonable), you, by definition, can get sound utility out of them.

I think the muscle memory of doing those tiny problems is good for our minds, but solving larger-scale issues is also challenging.

I'm on vacation right now and getting claude to build a mostly-throwaway e2e testing harness (admittedly not small-prompt-review-repeat) for a backend API to speed up our existing e2e test suites which do click-ops to set up tests 8-10 years ago, we had a team who spent 3-4 months every year maintaining our E2E suite and people would do rotations on there to spread the knowledge.

I basically want an industry standard practice implemented on my team of 4 devs who are too busy doing other things.

http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Recent comment touching on this in relation to LLM's in more depth: https://news.ycombinator.com/item?id=49322695#49323341

For a casual consumer, it is no doubt a worse chat buddy. Knows less historical facts à la Wikipedia.

But I’m not really using LLMs for that kind of entertainment and I don’t rely on them for fact-checking anyway. It’s almost worse to rely on a smoother talker for something it can’t possibly know.

For feeding in thousands of daily updates and getting it to predict the next one based on a crude list of tasks, it’s great. For completing code files in my style, also great. It can also handle most small customer service issues and refunds related to my apps on App Store, the back and forth to collect data from users.

For a booking engine I run with a partner, it is great at confirming bookings and following up. It handles cancellations which are about 40% of confirmed bookings due to the nature of the business. I used to feel like I was wasting my time with them - there is no way we can charge for it. Having basically a smart bot do it makes everyone happier (even the customer who knows they aren’t hurting anyone’s feelings).

For invoices, I prefill notes and things based on previous ones. It’s fine. I don’t need a massive model that takes 30s to reply etc. it’s way overkill. Maybe worse than overkill - off in a direction I don’t need.

Actually built a full invoicing product for that, using it too.

I use Mistral 7b and LlamaIndexTS on Node, I run it on a MacBook M3 and on a Linux server with only 8GB VRAM (old gaming PC).

Basically flawless, runs very fast and I don’t even know what paying for “tokens” is :)

I'm not sure I know very many engineers who would fall in this bucket. Or do you mean the business types who suddenly think AI can replace all the engineers?

Even a big mainstream product (like Gemini) cannot handle more than ~1k lines without missing details and making mistakes. And about every 1k lines, it seems to forget the previous 1k, doesn’t it? So you can never hold more than a file or 2 (or 3) in context at a time without losing details.

What you find is that the big models like Gemini are doing vector storage and retrieval too, and breaking prompts down into chunks for various models to handle to assemble a thorough response.

If you want that kind of control in your outputs, and be able to hold a lot in your inputs, I don’t see any other way regardless of which model you use.

Absolutely false. At least when it comes to multimodal inputs, even a simple classifier will outperform the largest LLMs who still hallucinate details or don’t describe audio and images accurately.

And there’s also the issue of cost/inference speed. Running a trillion parameter model for all tasks will be incredibly costly, require a cloud API, while a tiny CNN can be run locally or at a cost multiple orders of magnitude lower.

Commenter below gets it absolutely correct: stockfish, which runs on your 5 year old phone, is dramatically better at chess than Fable. Like, so much better that it’s not even remotely comparable. The theory of the Bitter Lesson, and it’s only a theory, is that LLMs could eventually outperform stockfish. It’s not true today and it remains to be seen whether it will ever be true. For now, specialized models are absolutely better at specialized tasks.

If you look at value as purely the LLM output, then there's a valid argument that the best frontier models will always be better than fine tuned specialists. (I'm not convinced personally, but it's a defensible claim)

But that misses two dimensions: 1. The cost of acquiring that output 2. What is actually "good enough" for that specialist domain

Not every output needs to be the best to produce value.

And as specialist models increase in cost, their cost/value proposition goes down.

At some point, there's a threshold where cheaper, fine tuned models are "good enough" at the task and also substantially cheaper than the expert models.

That's where fine tuning helps.

Personally, I became a believer in fine tuning after fine tuning a 1B Qwen model as a second pass over my local voice transcription app, achieving excellent accuracy at ~zero token cost and waaaay lower latency than if I'd invoked my Claude subscription under the hood.

I get that a lot of people don't have them. And a single one can be VERY performant. And the smaller models like a 7B can run on much smaller hardware like a mid-range [3|4|5]060.

My entire AI Dev Box cost $4500 in parts. 128GB RAM, i7-10700, 1TB and 2TB SSD, and 2x 3090s. Today's prices and inflation have definitely made that price tag seem a lot better than it was, but it was an investment in all things GPU that were happening in 2020 (crypto, blender, image gen), then LLMs exploded.

VibeThinker 3B constitutes extraordinary evidence, IMO. The first such evidence I've seen myself. Very small model, very low literacy, almost no world knowledge, but it is as good at math and logical reasoning as models a hundred times larger.

The Bitter Lesson is a valid and trenchant observation about how about we got here, but I think it's a mistake to assume it tells us very much about where we're going. Too much has changed recently and is still doing so.

GP isn’t suggesting that focused narrow model(s) will be more capable than large model, but that many small focused models can have sufficient capability while being more optimal.

Also, the bitter lesson is just wrong. The bitter lesson is about hand tuned AI vs computational general methods. However in truth today’s AI uses both. We have general compute heavy models which require narrow expert instructions (eg tools internet docs).

LLMs would not be as good without expertly written context, and expert context without LLMs aren’t as good either.

If Orang mane bans Claude, they've got their local models.

The latter has already happened too so I'd say their risk modeling is spot on.

In fact I noticed that this is the one place where people discussing AI on HN tend to talk past each other. On the one hand people are talking about supreme intelligence like designing new algorithms (on the same vein as finding counter examples for the Jacobian conjecture) and on the other hand people are just satisfied using AI to automate a few quotidian tasks that hitherto couldn’t be automated.

The models are not even really trained bitter lesson-style anymore. That concept peaked during the era of pre-train scaling, back when it was thought that making a bigger and bigger GPT-3 would automatically solve all problems through prompting. In 2026, the most important part of training is post-training, which uses vast quantities of niche, hand-curated data to fit the models for specific tasks in domains like tax law.

Is this whole thing than maybe a read vs write optimisation again? Spent more time and effort training more knowledge into the model upfront and get it out in a single question instead of training a small model and needing more steps to answer the same question?

Any similar model aimed at coding?

A >10B model for mass spawning/swarming and reporting back to a larger model

Dumb AIs are needed for customer service. Most of that industry is still at "press 1 for sales, 2 for billing..." and needs something that will run locally on a 1U server.

And perhaps at the end it all gets a single pass by a god-tier model for overall sanity and congruence, but the actual work, planning, coordination, and even user interaction was done by cheaper and faster agents of much more limited capability.

I can't find the comment you're referring to, but the latest versions of stockfish are based on neural networks trained on millions of games, so if anything the Bitter Lesson turned out true here.

You could sell those and have enough money to pay for hosted inference for years.

Even the best models available lose a ton of detail over time if you were to paste in tens of thousands of lines of code.

The only way to hold huge amounts of context with a high degree of accuracy is to store it using various mechanisms (one of which is RAG).

On “effectiveness”, I mean end use case effectiveness in the tasks at hand, not whatever benchmark the model developer or vendor themselves come up with - which may or may not be useful to the work I’m doing.

It really is absurd to ask programming questions to a model also trained about the lifecycle of a fruit fly.

Instead of building small models from scratch, we train an enormous model and use ridiculous amounts of GPU memory. In the end, the whole thing is shoved into RAM because we don’t know where the useful parts are…

We certainly would know where they were if they were just in smaller models in the first place!

The actual argument of the Bitter Lesson essay is pretty limited but people's interpretation of it has gradually drifted until it's seen as prediction that current LLM will reach AGI at a large enough scale.

Don't get me wrong, there are advantages to a fully local model in that, I can have agents looping 24/7 even when my internet is not working. But this is niche enough that if I had to price the advantages they don't seem worth it.

If I'm willing to pay the Openrouter tax, I can fire up Openrouter today and just get access to whatever model I want, and still pay a fraction for tokens as what I'm paying with the big guys.

Bitter lesson #1: don't waste time optimizing code when a faster processor is around the corner.

What countered it: Moore's law stopped working.

Bitter lesson #2 similarly relies on scaling laws that might have diminishing returns wrt model runtime vs intelligence. Runtime matters for turnaround on the problem you're solving.

I wouldn't use it for anything important without heavy supervision, as it's very weak outside its specialty. Not ideal for instruction-following tasks.

3090 pricing is something of a wild card. Since the only big-mem consume cards are the xx90s, and a 5090 is pushing $5000, resale value has gone way up. The bottom hit ~$700 last year. It's still a very good GPU, if power hungry.

Your comment is like a meta comment of "LLMs are generating everything, after a while the ouroboros will eat itself. (Which I agree with)" If people aren't hacking on this shit just because, you have completely conceded control of software to a handful of sociopaths, and open source software is dead.

Secondly, the bitter lesson is predicated on compute being cheap. There was a period where a hand-tuned algorithm informed by human expertise would outperform a raw alpha-beta search at Chess. Then compute got cheaper, and DeepBlue ascended to the top. Compute is now expensive again relative to the tasks being performed. We are absolutely still in a period where human expertise in training LLMs will outperform a naive approach with more raw compute.

You can do each of those at various hosts and own nothing. Or own a couple "over priced" cards and do it all at home on battery power for a few hours while the power is out.

Stockfish is the best chess search engine we've got, and you can learn some good heuristics for chess search policy that will make time-limited chess search a lot more powerful. That's perfectly in line with the Bitter Lesson.

In contrast, LLMs playing chess are relying solely on learned behavior. The inference harnesses surrounding them aren't designed to do chess things, they're designed to do autoregressive token decoding, which isn't a search process. Reasoning traces can resemble a search process, but they're far less efficient - the LLM would have to work out each legal move, test each one, calculate a score, and simulate minimax over all of that. Assuming the LLM is smart enough to even do all that.

A hand-crafted approach can absolutely beat data if your approach unlocks more search and/or learning than the general solution.

It’s not unfathomable that if a personal, generally intelligent local AI provides enough utility and doesn’t require you to tweak CLI flags millions of Americans would want one.

It would be better to compare models at how well they can write the code for chess engines, otherwise it's just saying that Fable is not a good CPU emulator, which is obvious.

It was a 96 core gen 4 epyc+supermicro board build with consumer NVMe drives on 1x16->4x4 "dumb" bifurcation cards. I had to get a few MCIO-> PCIe adapters as well to get the full lane coverage. Mounted in a standard EATX compatible consumer case with a consumer PSU and a lot of Noctua fans - surprisingly cool and quiet for what it is.

Motherboard+CPU I got from Ebay. Rest from the best MicroCenter/Amazon/Walmart deal of that day. Bought juuuuust before the AI pricing apocalypse, largely by pure chance.

The bitter lesson is that simply scaling training on more games—including self-play—trumps any hand-crafted human input, whether that's fine-tuning on human commentary or clever engineering tricks.

Current models are just high-dimensional interpolation engines. The denser the data sampling, the more accurate the interpolation gets. Given a choice between denser sampling and anything else, denser sampling always wins. That is the bitter lesson.

Computer chess is the canonical example of this.

The common mistake is to think “maybe if we use a blend of raw data and hand-crafted heuristics, we’ll get the best of both worlds!” But the bitter lesson says no, beyond a certain point it’s better just to use the data.

Thinking that an LLM might be able to improve on purely “big data” machine learning seems to me to be the same incorrect idea. Its “intelligence” is no more useful than human intelligence. The LLM is based on a massive data corpus, sure, but the amount of data specifically about chess in there pales in comparison to just playing billions of games of chess.

I'm not saying everyone has to run local LLMs, because the APIs are in a race to the bottom, and my $10 of OpenRouter credits I bought months ago is down to $8.94 because most models give you MILLIONS of tokens for a US Quarter.

https://mcpmarket.com/server/stockfish

https://til.simonwillison.net/llms/mcp-in-claude-and-chatgpt...

Now let's look at the bitter lesson again. It says that general methods that leverage computation are ultimately the most effective, and by a large margin.

That's different from just saying to leverage computation (which is how I would interpret "unlocks more search/learning"). If the lesson is "more computation wins, when sufficiently channeled" you're basically looking at a truism. Of course more computation beats less when it's used right. The bitter lesson is about abandoning specialization in order to get more computation, and while there's a couple ways where that helps with chess, there's a lot more ways where it's counterproductive. It looks like it's more true for Go than it is for chess, and that it's not universally true. It probably correlates with the state space.

Not really, if anything it's closer to the opposite. The Bitter Lesson essay literally has this as an example:

> These researchers wanted methods based on human input to win and were disappointed when they did not.[1]

and

> Enormous initial efforts went into avoiding search by taking advantage of human knowledge, or of the special features of the game, but all those efforts proved irrelevant, or worse, once search was applied effectively at scale[1]

The actual bitter lesson is this:

> breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.[1]

Applying to the "LLMs-for-chess" example the bitter lesson approach would be to put many, many more games into the LLM.

Does this work? People have trained fairly small LLMs that are competitive Stockfish at the ELO 1500-2000 level, eg: https://github.com/kinggongzilla/chess-bot-3000

This seems to be evidence that large LLMs probably don't have as much chess training data as Stockfish does.

[1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html

In the latter case, the chess example would tend to support the Bitter Lesson, rather than refute it.

I would also be VERY slow to claim that general-purpose models will never be competitive at chess. It wasn't so long ago that transformers couldn't add two-digit numbers reliably without resorting to tool use. They are now as good at "mental arithmetic" as any human savant. It wouldn't surprise me at all to see someone come up with a model that just happens to be really, really good at leveraging the portions of its general training data having to do with chess.

In fact you could argue that AGI demands such a model, if we are to assume that LLMs are a guidepost in that direction.

In the case of stockfish, the harness is a tree search around the neural network evaluations.

> maybe if we use a blend of raw data and hand-crafted heuristics

I don't follow. They're suggesting giving raw chess data to the LLM, no heuristics involved.

However, what most people think of as Moore's Law--CPU speed doubles every 18 months--broke somewhere between 90nm and 22nm.

And even the actual Moore's Law--2x the transistors every 18 months--doesn't hold for all types of chips anymore. Memory only gained 2x density over 10 years.

What matters is you've got the Duramax HD King Ranch TRD Big-Boy machine. Doesn't matter the cost. You can tow anything, drive anywhere, do anything, and do it all in comfort. Other than parking in a normal parking spot comfortably. Or even park it in your own garage at home.

I've seen this exact scenario many times personally.

This is tunnel vision. The percentage of people who could afford the hardware you could at the time you back it so vanishingly small. I do not know a single non-tech person who has multiple graphics cards in a single computer.

> I would also be VERY slow to claim that general-purpose models will never be competitive at chess.

This is not the claim. The claim is that for the same amount of compute, a general-purpose language model will never beat a Chess model. I'm dubious, but allow for the possibility that a language model could eventually compete at a top level against humans with enough compute. However, it will never compete with a dedicated Chess model with similar resources. Training a model for a specific task with the same amount of compute will outperform training a general-purpose model with the same amount of compute. This should be common sense, right? The bitter lesson was only about compute over human algorithms, not at throwing compute at a generalised domain over throwing compute at a specific domain.

You made arguments against two claims that I did not make (that I was trying to refute the bitter lesson or that I claimed that LLMs could never be competitive against humans at Chess), so I'd like to ask you read my statements a little more carefully this time.

Americans by and large don't do that. Much of the population engages in discretionary spending with debt instruments. Combined with mass innumeracy, they're all oblivious to the true cost of their purchases because they only think of the monthly payment.

Just kidding of course

My personal expectation is closer to 5 years than 10, which is why I wouldn't touch Anthropic or OpenAI stock with a ten-foot pole, personally, no matter how high their theoretical valuation is. Because their business model is doomed in the long run.

And in 2020 Stockfish 12 adding some NN evaluation. And then in 2023 Stockfish 16 entirely removing the classical position evaluation code.

https://stockfishchess.org/blog/2023/stockfish-16/