HN user
https://lucrbvi.com
Such a gem, thanks to the author for sharing it's findings :)
The only problem I have with planing in latent space is that it can be really noisy and not representative of the positions in the game (the latent are trained for semantic, so the optimizer can focus a set of specific features and can skip positions, which means it cannot know "where" to go by optimizing on the latents directly).
Anthropic theorize that middle layers in an LLM is a "J-Space" used to "think" about the future answer or about abstract concepts.
Their method is used to identify which tokens can appears in which layers of the model.
In the article the author says they are doing reinforcement learning with LLMs.
How does this differ from dottxt's Outlines[0] on the technical level? Are you using some JSON grammar to force the LM head distribution to follow it?
it's using webgpu kernels, probably a false positive
I really like QBE but I hope they will make it a true library because I don't want to launch a subprocess to compile a program.
Cool but why would anyone write an agent in C? Since the language is verbose it seems to be a painful choice to use C for this task.
They have added a lot of optimization focussing on the KV-cache, so they can have a much larger window without eating all the VRAM.
The 1M window might be usable, but it will probably underperform against a smaller window of course.
Mollie seems to only provide services to business based in European Economic Area, Switzerland and the UK [0], so yes?
[0] https://help.mollie.com/hc/en-us/articles/115002116105-Can-I...
Mollie might be a direct competitor
Is there anyone that feels that LLMs are wrong for computer use? It's like robotic, if find LLMs alone are really slow for this task
Some people are speculating that Opus 4.7 is distilled from Mythos due to the new tokenizer (it means Opus 4.7 is a new base model, not just an improved Opus 4.6)
Hi! I share my first blog post ever about the definition on AGI following the annoucement of Claude Myhtos Preview.
I hope some of you might found this interesting.
Sounds like Multi-Head Latent Attention (MLA) from DeepSeek
I wouldn't be surprised if Vercel were bought by Anthropic/OAI (but maybe it would be too expensive?)
This is a weird pattern accross OpenAI/Anthropic to buy startups building better toolings.
I don't really see the value for OAI/Anthropic, but it's nice to know that uv (+ ty and many others) and Bun will stay maintained!
I share the feeling; but people using it are mostly non-technicals (despite the 50+ config files lol) and are just runing it constantly to do random things.
But a message bot + Claude Code/Codex would be the better version
The whole article feels like an ad
The "egg" system seems really good!
It will maybe solved soon if we train yet another neural network on scanning GitHub activities; but by also adding other forges like codeberg, gitlab, self-hosted forgejo, etc... to not lock non-github users out
Still really good idea!
Why does OpenClaw have 800,000+ lines of code?? Isn't it just a connector for LLM APIs and other tools?
It's just hallucinating training data, the model is very small so it cannot be useful at all
The name of the article is misleading, probably some clickbait since it's talking about ASI
Still, it's a good reminder that AGI is not a 'superintelligent' entity but just a _general_ intelligence
Yes, in "runtime optimization" the model is just a computation graph so we can use a lot of well known tricks from compilation like dead code elimination and co..
You should know that there is already a solution for this, SafeTensors [0].
But it may be a nice tool for those who download "unsafe" models
It's just Anthropic being Anthropic, nothing new