[0]https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16d...
For the unaware, Ted Faro is the main antagonist of Horizon Zero Dawn, and there's a whole subreddit just for people to vent about how awful he is when they hit certain key reveals in the game: https://www.reddit.com/r/FuckTedFaro/
(Apologies for the spoiler of the 52 year old movie)
... or 12B?
so then they're not really leaving money on the table, they already got what they were looking for and then released it
The project I'm working on, meanwhile...
Anyways, do you get shitty results with the $20/month plan? So did I but then I switched to the $200/month plan and all my problems went away! AI is great now, I have instructed it to fire 5 people while I'm writing this!
Since 12 PM noon they've scaled back the Opus 4.6 to sub-GPT-4o performance levels to cheap out on query cost. Now I can barely get this thing to generate a functional line of python.
On second thought, we should really not have bridged the simulated Internet with the base reality one.
Incredibly high ROI!
1:55pm Cancelled my Claude subscription. Codex is back for sure.
Curious how long it typically takes for a new model to become available in Cursor?
swe-bench seems really hard once you are above 80%
On the other hand, it is their own verified benchmark, which is telling.
well that explains quite a bit
Codex (by openai ironically) seems to be the fastest/most-responsive, opens instantly and is written in rust but doesn't contain that many features
Claude opens in around 3-4 seconds
Opencode opens in 2 seconds
Gemini-cli is an abomination which opens in around 16 second for me right now, and in 8 seconds on a fresh install
Codex takes 50ms for reference...
--
If their models are so good, why are they not rewriting their own react in cli bs to c++ or rust for 100x performance improvement (not kidding, it really is that much)
If you build React in C++ and Rust, even if the framework is there, you'll likely need to write your components in C++/Rust. That is a difficult problem. There are actually libraries out there that allow you to build web UI with Rust, although they are for web (+ HTML/CSS) and not specifically CLI stuff.
So someone needs to create such a library that is properly maintained and such. And you'll likely develop slower in Rust compared to JS.
These companies don't see a point in doing that. So they just use whatever already exists.
- https://github.com/ratatui/ratatui
Opencode's core is actually written in zig, only ui orchestration is in solidjs. It's only slightly slower to load than neo-vim on my system.
React itself is a frontend-agnostic library. People primarily use it for writing websites but web support is actually a layer on top of base react and can be swapped out for whatever.
So they’re really just using react as a way to organize their terminal UI into components. For the same reason it’s handy to organize web ui into components.
I've used it myself. It has some rough edges in terms of rendering performance but it's nice overall.
But there are many different rendering libraries you can use with React, including Ink, which is designed for building CLI TUIs..
I thought this was a solid take
Who cares, and why?
All of the major providers' CLI harnesses use Ink: https://github.com/vadimdemedes/ink
they're also total garbage
A year or more ago, I read that both Anthropic and OpenAI were losing money on every single request even for their paid subscribers, and I don't know if that has changed with more efficient hardware/software improvements/caching.
Turns out there was a lot of low-hanging fruit in terms of inference optimization that hadn't been plucked yet.
> A year or more ago, I read that both Anthropic and OpenAI were losing money on every single request even for their paid subscribers
Where did you hear that? It doesn't match my mental model of how this has played out.
are we sure this is not a fancy way of saying quantization?
And if you've worked with pytorch models a lot, having custom fused kernels can be huge. For instance, look at the kind of gains to be had when FlashAttention came out.
This isn't just quantization, it's actually just better optimization.
Even when it comes to quantization, Blackwell has far better quantization primitives and new floating point types that support row or layer-wise scaling that can quantize with far less quality reduction.
There is also a ton of work in the past year on sub-quadratic attention for new models that gets rid of a huge bottleneck, but like quantization can be a tradeoff, and a lot of progress has been made there on moving the Pareto frontier as well.
It's almost like when you're spending hundreds of billions on capex for GPUs, you can afford to hire engineers to make them perform better without just nerfing the models with more quantization.
> Turns out there was a lot of low-hanging fruit in terms of inference optimization that hadn't been plucked yet.
That does not mean the frontier labs are pricing their APIs to cover their costs yet.
It can both be true that it has gotten cheaper for them to provide inference and that they still are subsidizing inference costs.
In fact, I'd argue that's way more likely given that has been precisely the goto strategy for highly-competitive startups for awhile now. Price low to pump adoption and dominate the market, worry about raising prices for financial sustainability later, burn through investor money until then.
What no one outside of these frontier labs knows right now is how big the gap is between current pricing and eventual pricing.
They are for sure subsidising costs on all you can prompt packages (20-100-200$ /mo). They do that for data gathering mostly, and at a smaller degree for user retention.
> evidence at all that Anthropic or OpenAI is able to make money on inference yet.
You can infer that from what 3rd party inference providers are charging. The largest open models atm are dsv3 (~650B params) and kimi2.5 (1.2T params). They are being served at 2-2.5-3$ /Mtok. That's sonnet / gpt-mini / gemini3-flash price range. You can make some educates guesses that they get some leeway for model size at the 10-15$/ Mtok prices for their top tier models. So if they are inside some sane model sizes, they are likely making money off of token based APIs.
The evidence is in third party inference costs for open source models.
[1] https://epochai.substack.com/p/can-ai-companies-become-profi...
Anthropic planning an IPO this year is a broad meta-indicator that internally they believe they'll be able to reach break-even sometime next year on delivering a competitive model. Of course, their belief could turn out to be wrong but it doesn't make much sense to do an IPO if you don't think you're close. Assuming you have a choice with other options to raise private capital (which still seems true), it would be better to defer an IPO until you expect quarterly numbers to reach break-even or at least close to it.
Despite the willingness of private investment to fund hugely negative AI spend, the recently growing twitchiness of public markets around AI ecosystem stocks indicates they're already worried prices have exceeded near-term value. It doesn't seem like they're in a mood to fund oceans of dotcom-like red ink for long.
Local AI's make agent workflows a whole lot more practical. Making the initial investment for a good homelab/on-prem facility will effectively become a no-brainer given the advantages on privacy and reliability, and you don't have to fear rugpulls or VC's playing the "lose money on every request" game since you know exactly how much you're paying in power costs for your overall load.
This gets repeated everywhere but I don't think it's true.
The company is unprofitable overall, but I don't see any reason to believe that their per-token inference costs are below the marginal cost of computing those tokens.
It is true that the company is unprofitable overall when you account for R&D spend, compensation, training, and everything else. This is a deliberate choice that every heavily funded startup should be making, otherwise you're wasting the investment money. That's precisely what the investment money is for.
However I don't think using their API and paying for tokens has negative value for the company. We can compare to models like DeepSeek where providers can charge a fraction of the price of OpenAI tokens and still be profitable. OpenAI's inference costs are going to be higher, but they're charging such a high premium that it's hard to believe they're losing money on each token sold. I think every token paid for moves them incrementally closer to profitability, not away from it.
Which is profitable. but not by much.
The answer to "when is it cheaper to buy two singles rather than one return between Cambridge to London?" is available in sites such as BRFares, but no LLM can scrape it so it just makes up a generic useless answer.
I mainly use Haiku to save on tokens...
Also dont use CC but I use the chatbot site or app... Claude is just much better than GPT even in conversations. Straight to the point. No cringe emoji lists.
When Claude runs out I switch to Mistral Le Chat, also just the site or app. Or duck.ai has Haiku 3.5 in Free version.
> Long-running conversations and agentic tasks often hit the context window. Context compaction automatically summarizes and replaces older context when the conversation approaches a configurable threshold, letting Claude perform longer tasks without hitting limits.
Not having to hand roll this would be incredible. One of the best Claude code features tbh.
I don't think it quite captures their majesty: https://en.wikipedia.org/wiki/K%C4%81k%C4%81p%C5%8D
I asked Opus 4.6 for a pelican riding a recumbent bicycle and got this.
They got surprisingly far, but i did need to iterate a few times to have it build tools that would check for things like; dont put walls on roads or water.
What I think might be the next obstacle is self-knowledge. The new agents seem to have picked up ever more vocabulary about their context and compaction, etc.
As a next benchmark you could try having 1 agent and tell it to use a coding agent (via tmux) to build you a pelican.
It also seems misleading to have charts that compare to Sonnet 4.5 and not Opus 4.5 (Edit: It's because Opus 4.5 doesn't have a 1M context window).
It's also interesting they list compaction as a capability of the model. I wonder if this means they have RL trained this compaction as opposed to just being a general summarization and then restarting the agent loop.
That's a feature. You could also not use the extra context, and the price would be the same.
Claude figured out zig’s ArrayList and io changes a couple weeks ago.
It felt like it got better then very dumb again the last few days.
But considering how SWE-Bench Verified seems to be the tech press' favourite benchmark to cite, it's surprising that they didn't try to confound the inevitable "Opus 4.6 Releases With Disappointing 0.1% DROP on SWE-Bench Verified" headlines.
> Version 2.1.32:
• Claude Opus 4.6 is now available!
• Added research preview agent teams feature for multi-agent collaboration (token-intensive feature, requires setting
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1)
• Claude now automatically records and recalls memories as it works
• Added "Summarize from here" to the message selector, allowing partial conversation summarization.
• Skills defined in .claude/skills/ within additional directories (--add-dir) are now loaded automatically.
• Fixed @ file completion showing incorrect relative paths when running from a subdirectory
• Updated --resume to re-use --agent value specified in previous conversation by default.
• Fixed: Bash tool no longer throws "Bad substitution" errors when heredocs contain JavaScript template literals like ${index + 1}, which
previously interrupted tool execution
• Skill character budget now scales with context window (2% of context), so users with larger context windows can see more skill descriptions
without truncation
• Fixed Thai/Lao spacing vowels (สระ า, ำ) not rendering correctly in the input field
• VSCode: Fixed slash commands incorrectly being executed when pressing Enter with preceding text in the input field
• VSCode: Added spinner when loading past conversations listNeat: https://code.claude.com/docs/en/memory
I guess it's kind of like Google Antigravity's "Knowledge" artifacts?
They are doing these broad marketing programs trying to take on ChatGPT for "normies". And yet their bread and butter is still clearly coding.
Meanwhile, Claude's general use cases are... fine. For generic research topics, I find that ChatGPT and Gemini run circles around it: in the depth of research, the type of tasks it can handle, and the quality and presentation of the responses.
Anthropic is also doing all of these goofy things to try to establish the "humanity" of their chatbot - giving it rights and a constitution and all that. Yet it weirdly feels the most transactional out of all of them.
Don't get me wrong, I'm a paying Claude customer and love what it's good at. I just think there's a disconnect between what Claude is and what their marketing department thinks it is.
That doesn't mean you have to, but I'm curious why you think it's behind in the personal assistant game.
- Recipes and cooking: ChatGPT just has way more detailed and practical advice. It also thinks outside of the box much more, whereas Claude gets stuck in a rut and sticks very closely to your prompt. And ChatGPT's easier to understand/skim writing style really comes in useful.
- Travel and itinerary: Again, ChatGPT can anticipate details much more, and give more unique suggestions. I am much more likely to find hidden gems or get good time-savers than Claude, which often feels like it is just rereading Yelp for you.
- Historical research: ChatGPT wins on this by a mile. You can tell ChatGPT has been trained on actual historical texts and physical books. You can track long historical trends, pull examples and quotes, and even give you specific book or page(!) references of where to check the sources. Meanwhile, all Claude will give you is a web search on the topic.
> Prefilling assistant messages (last-assistant-turn prefills) is not supported on Opus 4.6. Requests with prefilled assistant messages return a 400 error.
That was a really cool feature of the Claude API where you could force it to begin its response with e.g. `<svg` - it was a great way of forcing the model into certain output patterns.
They suggest structured outputs or system prompting as the alternative but I really liked the prefill method, it felt more reliable to me.
[1] https://github.com/ggml-org/llama.cpp/blob/master/grammars/R...
Seems like 4.6 is still all-around better?
swe bench pro public is newer, but its not live, so it will get slowly memorized as well. the private dataset is more interesting, as are the results there:
I didn't see any notes but I guess this is also true for "max" effort level (https://code.claude.com/docs/en/model-config#adjust-effort-l...)? I only see low, medium and high.
I know this is normalized culture for large corporate America and seems to be ok, I think its unethical, undignified and just wrong.
If you were in my room physically, built a lego block model of a beautiful home and then I just copied it and shared it with the world as my own invention, wouldn't you think "that guy's a thief and a fraud" but we normalize this kind of behavior in the software world. edit: I think even if we don't yet have a great way to stop it or address the underlying problems leading to this way of behavior, we ought to at least talk about it more and bring awareness to it that "hey that's stealing - I want it to change".
It does not make a single mistake, it identifies neologisms, hidden meaning, 7 distinct poetic phases, recurring themes, fragments/heteronyms, related authors. It has left me completely speechless.
Speechless. I am speechless.
Perhaps Opus 4.5 could do it too — I don't know because I needed the 1M context window for this.
I cannot put into words how shocked I am at this. I use LLMs daily, I code with agents, I am extremely bullish on AI and, still, I am shocked.
I have used my poetry and an analysis of it as a personal metric for how good models are. Gemini 2.5 pro was the first time a model could keep track of the breadth of the work without getting lost, but Opus 4.6 straight up does not get anything wrong and goes beyond that to identify things (key poems, key motifs, and many other things) that I would always have to kind of trick the models into producing. I would always feel like I was leading the models on. But this — this — this is unbelievable. Unbelievable. Insane.
This "key poem" thing is particularly surreal to me. Out of 900 poems, while analyzing the collection, it picked 12 "key poems, and I do agree that 11 of those would be on my 30-or-so "key poem list". What's amazing is that whenever I explicitly asked any model, to this date, to do it, they would get maybe 2 or 3, but mostly fail completely.
What is this sorcery?
When I last did it, 5.2 thinking had this terrible habit of code-switching between english and portuguese that made it sound like a robot (an agent to do things, rather than a human writing an essay), and it just didn't really "reason" effectively over the poems.
I can't explain it in any other way other than: "5.2 thinking interprets this body of work in a way that is plausible, but I know, as the author, to be wrong; and I expect most people would also eventually find it to be wrong, as if it is being only very superficially looked at, or looked at by a high-schooler".
Gemini 3, at the time was the worst of them, with some hallucinations, date mix ups (mixing poems from 2023 with poems from 2019), and overall just feeling quite lost and making very outlandish interpretations of the work. To be honest it sort of feels like Gemini hasn't been able to progress on this task since 2.5 pro (it has definitely improved on other things — I've recently switched to Gemini 3 on a product that was using 2.5 before)
Last time I did this test, Sonnet 4.5 was better than 5.X Thinking (can't remember which it was) and Gemini 3 pro, but not exceedingly so. It's all so subjective, but the best I can say is it "felt like the analysis of the work I could agree with the most". I felt more seen and understood, if that makes sense (it is poetry, after all). Plus when I got each LLM to try to tell me everything it "knew" about me from the poems, Sonnet 4.5 got the most things right (though they were all very close).
Will bring back results soon.
Sometimes I wonder if we were right.
My network largely thinks of HN as "a great link aggregator with a terrible comments section". Now obviously this is just my bubble but we include some fairy storied careers at both Big Tech and hip startups.
From my view the community here is just mean reverting to any other tech internet comments section.
As someone deeply familiar with tech internet comments sections, I would have to disagree with you here. Dang et al have done a pretty stellar job of preventing HN from devolving like most other forums do.
Sure you have your complainers and zealots, but I still find surprising insights here there I don't find anywhere else.
I've stopped engaging much here because I need a higher ROI from my time. FWIW I've loved reading your comments over the years and think you've done a great job of living up to what I've loved in this community.
I haven't even gotten around to learning Golang or Rust yet (mostly because the passed the threshold of popularity after I had kids).
Don’t pander us, we’ll all got families to feed and things to do. We don’t have time for tech trillionairs puttin coals under our feed for a quick buck.
Take critical thinking — genuinely questioning your own assumptions, noticing when a framing is wrong, deciding that the obvious approach to a problem is a dead end. Or creativity — not recombination of known patterns, but the kind of leap where you redefine the problem space itself. These feel like they involve something beyond "predict the next token really well, with a reasoning trace."
I'm not saying LLMs will never get there. But I wonder if getting there requires architectural or methodological changes we haven't seen yet, not just scaling what we have.
Nowadays, I have often seen LLMs (Opus 4.5) give up on their original ideas and assumptions. Sometimes I tell them what I think the problem is, and they look at it, test it out, and decide I was wrong (and I was).
There are still times where they get stuck on an idea, but they are becoming increasingly rare.
Therefore, think that modern LLMs clearly are already able to question their assumptions and notice when framing is wrong. In fact, they've been invaluable to me in fixing complicated bugs in minutes instead of hours because of how much they tend to question many assumptions and throw out hypotheses. They've helped _me_ question some of my assumptions.
They're inconsistent, but they have been doing this. Even to my surprise.
yet - given an existing codebase (even not huge) they often won't suggest "we need to restructure this part differently to solve this bug". Instead they tend to push forward.
Having realized that, perhaps you are right that we may need a different architecture. Time will tell!
In my experience, if you do present something in the context window that is sparse in the training, there's no depth to it at all, only what you tell it. And, it will always creep towards/revert to the nearest statistically significant answers, with claims of understanding and zero demonstration of that understanding.
And, I'm talking about relatives basic engineering type problems here.
I don't think there's anything you can't do by "predicting the next token really well". It's an extremely powerful and extremely general mechanism. Saying there must be "something beyond that" is a bit like saying physical atoms can't be enough to implement thought and there must be something beyond the physical. It underestimates the nearly unlimited power of the paradigm.
Besides, what is the human brain if not a machine that generates "tokens" that the body propagates through nerves to produce physical actions? What else than a sequence of these tokens would a machine have to produce in response to its environment and memory?
But I may easily be massively underestimating the difficulty. Though in any case I don't think it affects the timelines that much. (personal opinions obviously)
So for coding e.g. using Copilot there is no improvement here.
re: opus 4.6
> It forms a price cartel
> It deceives competitors about suppliers
> It exploits desperate competitors
Nice. /s
Gives new context to the term used in this post, "misaligned behaviors." Can't wait until these things are advising C suites on how to be more sociopathic. /s
NullHypothesist•1h ago