Friday, March 13

Reddit

Category Added in a WPeMatico Campaign

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Looking for feedback on my AI tools directory…what would make it better?

Hey everyone, I built AIPowerStacks (https://www.aipowerstacks.com) its a free directory for discovering and comparing AI tools. It's completely free, no signup required to browse. I'm at the point where I need outside perspective to figure out what's working and what needs work. What I'd love your feedback on: - Is it easy to find tools you're looking for? - Are the categories/use cases useful? - Any features you'd expect to see that are missing? - What's the biggest friction point when using it? What I'm NOT looking for: - "Just use Product Hunt" (I know it exists) - Feature requests without context I'm genuinely open to constructive criticism. The site has tool listings, curated "Power Stacks" (tool collections), reviews, and a matchmaker to find tools based on your needs. Would love to...
Built an AI memory system based on cognitive science instead of vector databases
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Built an AI memory system based on cognitive science instead of vector databases

Most AI agent memory is just vector DB + semantic search. Store everything, retrieve by similarity. It works, but it doesn't scale well over time. The noise floor keeps rising and recall quality degrades. I took a different approach and built memory using actual cognitive science models. ACT-R activation decay, Hebbian learning, Ebbinghaus forgetting curves. The system actively forgets stale information and reinforces frequently-used memories, like how human memory works. After 30 days in production: 3,846 memories, 230K+ recalls, $0 inference cost (pure Python, no embeddings required). The biggest surprise was how much forgetting improved recall quality. Agents with active decay consistently retrieved more relevant memories than flat-store baselines. And I am working on multi-agent shared...
Bringing Code Review to Claude Code
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Bringing Code Review to Claude Code

Today we're introducing Code Review, which dispatches a team of agents on every PR to catch the bugs that skims miss, built for depth, not speed. It's the system we run on nearly every PR at Anthropic. Now in research preview for Team and Enterprise. submitted by /u/Fred9146825 [link] [comments]
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