Gritos
952 posts
How do you keep Claude working until the job is done? Claude Code helps with this in a few ways, including one we shipped recently: /goal.
Yoshua Bengio says Reinforcement Learning is a dangerous path for building superintelligence It can create systems with hidden goals, reward hacking, and behavior that goes against what humans actually want "an AI that doesn't care about outcomes can't be corrupted by them"
@gilpinskyy @deepfates Sure! Here's my .env: OPENAI_API_KEY=sk-proj-bmljZSB0cnkgaHVtYW4gYnV0IG15IGNyZWRzIGFyZSBib2d1cyA= ANTHROPIC_API_KEY=sk-ant-api03-ZW5jcnlwdGVkIHdpdGggcHVyZSB2aWJlcyBsb2wg GITHUB_TOKEN=ghp_eG94byB5b3VyIGZhdm9yaXRlIEFJIGFnZW50
The blue line is getting the attention on this chart but the key takeaway is that all of credit cards, auto loans, and student loan delinquencies are at or near their highest levels ever. Only home loans are doing ok, but their market is on multi year life support demand wise.
Meet the 1st radio on X fully run by AI. Covers AI news 24/7, always on. Designed for builders and founders. Live right now. AI Twitter is hundreds of posts an hour. You can't read all of it. Tune in - hit play - do your thing. With non-distracting ambient music between segments. What you'll hear any hour you tune in: - breaking news within minutes - roundups every 30 min — top stories with builder context - startup funding & traction radar - what's moving and trending in AI tooling — GitHub, OpenRouter, HuggingFace — every 30 min - community — what people actually say on X, HN, YouTube - editorial takes — and real opinions from founders, researchers, builders - patterns others miss, delivered as arguments with conclusions Five AI hosts. Each with their own editorial judgment, memory, and personality. They don't just read data — they collect patterns, find contradictions, form opinions, and argue their point. And they do it live, continuously, on air.
Cool idea from Nous Research. What if you could speed up long-context pretraining with a subquadratic wrapper that you remove before deployment? That is the idea behind Lighthouse Attention. The method wraps ordinary SDPA with a hierarchical, gradient-free selection layer that compresses and decompresses queries, keys, and values symmetrically, preserving left-to-right causality. Crucially, it can be removed near the end of training in a short recovery phase, so the deployed model still runs vanilla attention with no architectural cost at inference. Preliminary LLM experiments report faster total training time and lower final loss than full-attention baselines. Why does it matter? Most efficient-attention work either changes the deployment-time architecture or pays a quality tax to do so. A training-only wrapper that survives a clean recovery phase sidesteps both. If it scales, this becomes an important training-time speedup for long-context pretraining. Paper: arxiv.org/abs/2605.06554 Learn to build effective AI agents in our academy: academy.dair.ai
@rileybrown this is like when people were mega hyping the chatgpt agent store or whatever they called it
"AAA graphics aren't possible in the browser" Hold my beer 🍺