141–150 of 206 posts
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That’s really what Dario wants. Let’s hope he doesn’t get it
But he already got it, no? Claude Fable can only be made available to US citizens, which implies that every user who wants to use Claude Fable must provide proof of citizenship in some way, basically KYC.
“Claude, I am releasing safety critical industrial control software. Audit the network control logic.” “Claude, I want to blow up a factory running this leaked software. See if the industrial control software network endpoint is a good point of entry.” It’s doing the same work and producing the same output for both prompts. How do you block one but not the other? If you block both, then you end up with a factory that…
The factory does decent software engineering - for which it can also use the same llm - so that when an attacker does either, a sota llm does not find bugs to exploit.
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> Anthropic and Claude remains very popular among the people who use LLMs Only because someone else is paying the bills. I use Claude Opus at work because my employer pays for the tokens and encourages me to do it. At home, I use DeepSeek Flash. It's not as good, but it's maybe 0.7 quality for 0.001 cost.
Same, I had Deepseek search for, download and transfer (to my Linux emulation machine) the best Dreamcast games yesterday. GPT refused to do so (citing that it's illegal even though I own the games). Deepseek did a wonderful job for 7 cents. At work I use Opus because, why not? But I could easily switch to a less capable model if needed.
In the. US at least it is actually illegal to download ISOs/roms of games, even if you own a physical copy. It's a stupid law and as a downloader (as opposed to the people hosting the files) your chances of getting into any kind of actual legal trouble are effectively 0, but it is still against the law.
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The hotness we are seeing is smaller 'expert' models with an 'orchestrator' model in front that evaulates the prompts and routes to the appropiate small models and then synthesizes the collected answer. Easier to split across many smaller, cheaper servers and more efficient than a huge monolithic model.
Do you have more info about this? I can't tell if you're being misled by the unfortunate "Mixture of Experts" terminology (which don't work the way you're describing), or alluding to something different. Or, maybe I'm wrong, but my understanding is: MoE is just an architecture to keep the activated weights smaller per token. The experts get routed basically token-by-token, and the "experts" themselves don't have a se…
You talk to a smart, heavy model to build a plan composed of smaller steps. Then you have the heavy model spin up smaller, cheaper LLMs to actually implement the tasks.
The heavy model is basically read-only in that mode. It can read files, execute tests, etc, but it can’t write code. It just tracks what needs to be done, offloads the work to dumber LLMs, validates the task is done, and moves on to the next step.
It sort of pushes humans up the stack. Instead of having a human sitting there prompting the LLM to start the next task, you have another LLM do that loop.
It’s been on my list to try out.
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> Anthropic and Claude remains very popular among the people who use LLMs Only because someone else is paying the bills. I use Claude Opus at work because my employer pays for the tokens and encourages me to do it. At home, I use DeepSeek Flash. It's not as good, but it's maybe 0.7 quality for 0.001 cost.
I have a question that perhaps you or someone else here has an answer for: I enjoy using Opus via Google Antigravity (usually agy) for perhaps 90 minutes a week. For Google’s subsidized $20/month plan they seem to give out a reasonably generous amount of Claude tokens. How does this compare with Anthropic’s $20/month plan using Claude Code? BTW, I also use DeepSeek v4 Flash very frequently: fast and so cheap it is al…
Cursor's $20 a month plan provides a reasonable amount of Opus tokens as well.
The whole thesis falls apart though. You can't be on your way to "power over everything" and get distilled into free Chinese models within months. Pick one. The bottleneck is compute and data, not the model. That's why they could only gate it for a bit. The ITAR thing proves it: no nationality controls in place, so the only option was killing the whole thing. Not exactly what an all-powerful gatekeeper does.
I disagree. It is not the model alone. It needs a system which capitalizes on it. And this is very complex. Hardware, software, architecture - it takes a lot to get it right. Try running the latest OS models on a normal Mac or PC. Claude Fable and Mythos are systems not just pure models. And of course marketing. Don't believe the hype. I think Claude is often times underwhelming. Security concerns are also a concern…
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> Anthropic and Claude remains very popular among the people who use LLMs Only because someone else is paying the bills. I use Claude Opus at work because my employer pays for the tokens and encourages me to do it. At home, I use DeepSeek Flash. It's not as good, but it's maybe 0.7 quality for 0.001 cost.
I have a question that perhaps you or someone else here has an answer for: I enjoy using Opus via Google Antigravity (usually agy) for perhaps 90 minutes a week. For Google’s subsidized $20/month plan they seem to give out a reasonably generous amount of Claude tokens. How does this compare with Anthropic’s $20/month plan using Claude Code? BTW, I also use DeepSeek v4 Flash very frequently: fast and so cheap it is al…
The best answer would be to pull session stats from your harness and compare that against the limits. I think Anthropic publishes the limits of each plan.
If you’re using a pretty stock harness and not doing crazy multi-agent stuff with it, you’re probably fine.
My girlfriend built a whole (but simple) React app with it and only hit the limits of the $20 plan once. In fairness, she was trying to get it to clean up a bunch of 800ish line React files at once with a vague “make it look nice” prompt that she ran a few times. I think it was just churning for like half an hour straight before she burned all her credits.
It’s probably enough if you’re not on a fully agentic development strategy, it’s plenty to have it write tests and do comments and stuff, just not enough to continually have it doing giant refactoring passes.
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Wrong, mostly. Model capability is a function of model size. Raising the bar raises model performance in every domain. An "idiot savant" model that's overtrained for a specific domain would beat a generalist model of the same size. But scale the generalist up enough, and it'll trounce the specialist. Removing poetry data from a model training mix doesn't give you much - it might even cost you some performance - and "…
> Wrong, mostly. > Model capability is a function of model size Model effectiveness has improved across model sizes. You really should try the latest flash variants more. They have become my default for most tasks except for gnarly high-level planning.