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Claude Opus 4.8
921–930 of 1001 posts
Re: Claude Opus 4.8
#922Earlier quoted context omitted.
What is ultracode mode?
It's a combination of reasoning effort (max) + enabling workflow that orchestrates multiple sub-agents. After some interrogation, here's how it organized the work: 1. Design workflow (rts-game-design, 11 agents, ~13 min) ran first, produced SPEC.md + DESIGN.md: 1.1. Proposals (3 parallel agents): each designed a complete RTS from a different philosophy 1.2 Judge (1 agent): evaluated all three and synthesized one unif…
Did you start with a clean slate or do you have global ~/.claude/CLAUDE.md and/or specific skills, plugins, etc?
Re: Claude Opus 4.8
#923A rambling comment: I think this is the first time we've had a third minor version bump on a frontier Anthropic model. (I count the 0.5s as major here, because they've been issued non-sequentially and also corresponded to massive capability leaps, eg, Sonnet 3.5, Opus 4.5). So now the Opus 4.5 family has successors 4.6, 4.7, and 4.8, each posting fairly modest claimed gains. My own experience w/ 4.6 and 4.7 are that…
4.7 was the first time I had to resort to using the previous version (4.6) for most use cases. Hoping 4.8 rectifies this.
Same here - we never bumped to 4.7 in our agentic app. Continue to use 4.6.
Re: Claude Opus 4.8
#924Frontier models are mostly past the point of human ability to discern whether they are actually better or worse than predecessors and competitors. I suspect the benchmarks may also be saturated, or at least past their usefulness. I personally feel that Anthropic doesn't understand what this means for the frontier labs, and moreover that they might be the only frontier lab that doesn't. 1. Google dropped Gemini 3.5 Fl…
> Frontier models are mostly past the point of human ability to discern whether they are actually better or worse than predecessors and competitors.
The model improvements being beyond human comprehension is one of the more ridiculous statements I’ve heard in the last couple of days about AI. We could reason about Higgs bosons and gravitational waves but have no ability to quantify or reason about the difference between Opus 4.7 vs 4.8.
Re: Claude Opus 4.8
#925> [..] Early access users and teams inside Anthropic have been using dynamic workflows for a wide range of use cases [..]
> ### Rewriting Bun with dynamic workflows
> An example of what dynamic workflows can unlock at scale is the recent rewrite of Bun. Jarred Sumner used dynamic workflows to port Bun from Zig to Rust [..]
That's very interesting to hear!
Re: Claude Opus 4.8
#926Meh, it’s not able to play Doom.
Re: Claude Opus 4.8
#927Early ArtificialAnalysis.ai results show GPT 5.5 is still the better bang-for-your-buck. OpenAI solves tasks with about 50% less output tokens. https://artificialanalysis.ai/?intelligence=coding-index&int...
My 20$ OpenAI sub gets me the same as my 100$ Anthropic sub. It really is the better deal.
Re: Claude Opus 4.8
#928A rambling comment: I think this is the first time we've had a third minor version bump on a frontier Anthropic model. (I count the 0.5s as major here, because they've been issued non-sequentially and also corresponded to massive capability leaps, eg, Sonnet 3.5, Opus 4.5). So now the Opus 4.5 family has successors 4.6, 4.7, and 4.8, each posting fairly modest claimed gains. My own experience w/ 4.6 and 4.7 are that…
I won't be surprised if the next gen frontier models are the last. There's orders of magnitude of low hanging juice to squeeze out of smaller models. It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely). It is far less clear that a 1.2T model will be meaningfully better enough to justify training it. As far as reasoning is con…
Smaller models can already outperform SOTA and massive models on specific tasks / domains.
Re: Claude Opus 4.8
#929Frontier models are mostly past the point of human ability to discern whether they are actually better or worse than predecessors and competitors. I suspect the benchmarks may also be saturated, or at least past their usefulness. I personally feel that Anthropic doesn't understand what this means for the frontier labs, and moreover that they might be the only frontier lab that doesn't. 1. Google dropped Gemini 3.5 Fl…
> Frontier models are mostly past the point of human ability to discern whether they are actually better or worse than predecessors and competitors. The model improvements being beyond human comprehension is one of the more ridiculous statements I’ve heard in the last couple of days about AI. We could reason about Higgs bosons and gravitational waves but have no ability to quantify or reason about the difference betw…
I definitely believe that you can discern differences between Opus 4.6, 4.7, and 4.8. I might also believe that you believe that you can discern improvements between Opus 4.6, 4.7, and 4.8. But conclusively, consistently, scientifically, and blindly discerning improvement is at this point restricted to problem domains that represent a vanishingly small amount of global token usage, like Erdos problems, superhuman evals, and the like. The idea that typical line of business use-cases have seen broad and measurable improvements since even Opus 4.5 but certainly 4.6 is mostly an illusion that confuses improvements in the harness for improvements in the model, as well as confuses "its different" for "its better".
To be clear, again, cannot stress this enough: I am NOT saying that the models have hit a limit. I am saying that the complexity of the problems most businesses throw at them have always had a limit. The models are now so intelligent that we have not, as of yet, adapted our business use-cases to make use of the new levels of intelligence. Maybe we will.
Re: Claude Opus 4.8
#930Earlier quoted context omitted.
I'm curious to poll HN on this issue. Do you feel like we've had meaningful/noticeable gains in terms of your programming workflows between 4.5 and 4.7? My 2¢, I personally feel like all of the productivity gains since 4.5's release (in November 2025!!) have come from improvements to the harnesses (cc, cursor cli, codex, opencode, whatever) AND from the context window expansion from 200k to 1M. But the actual "raw" i…
Yes. You and some random indigenous guy in the Amazon likely share the same intelligence but you are more capable because you have access to writing/reading, computer, car etc. Intelligence is more than raw intelligence. It's harness, skills, tools, memory etc. If you improve all the latter but keep the raw intelligence (LLM) fixed, you certainly get better results. Same with us humans.
Of course, I’m not trying to dismiss gains from harness, actually the opposite.
But the narrative that 4.Y is an improvement over 4.X is essential to keep the model training music playing.
If 90+% of the gains come from the harness, how can you continue to justify spending billions of dollars on training and an 80% gross margin on inference on the latest model? (Reportedly what Anthropic commands on the top tier of their frontier model API billing).
So differentiating between the two (what I’m trying to do here) is really consequential!