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Hello everyone So I'm just struggling a lot with the sense of meaning and ethics and stuff in the growing world of AI. I think a lot of people are - people have trained their whole lives as journalists or accountants or lawyers and will be rendered obsolete overnight. I thought I was relatively safe as like a musician but I saw a video of an AI woman playing guitar and it was basically impossible to tell that it was AI [(here is a Youtube video about it featuring clips])(https://youtu.be/L9f-hnyAhsQ?si=IxxHXEiLgfWnnBes&t=89) other than some obvious errors. But the point is the inflexions like the wrist or arm or shoulder tensing at the correct moment as someone who's played guitar for years that's literally what guitarists do. I don't know identity, meaning, purpose. Apparently we'll b...

Integrated an evolutionary database to Karpathy's autoresearch project that replaces the simple tsv file based logging in the original project. Evolutionary algorithms have shown to be a powerful tool for autonomously discovering optimal solutions to problems with large search spaces. Famously, Google DeepMind's AlphaEvolve system uses evolutionary algorithms to discover state of the art matrix multiplication algorithms. The implementation of the evolutionary database itself is based heavily on the implementation in OpenEvolve. Would love thoughts and suggestions from the community. Check it out: https://github.com/hgarud/autoresearch submitted by /u/hgarud [link] [comments]

submitted by /u/Fcking_Chuck [link] [comments]

Robert Lange, founding researcher at Sakana AI, joins Tim to discuss Shinka Evolve — a framework that combines LLMs with evolutionary algorithms to do open-ended program search. The core claim: systems like AlphaEvolve can optimize solutions to fixed problems, but real scientific progress requires co-evolving the problems themselves. In this episode: - Why AlphaEvolve gets stuck: it needs a human to hand it the right problem. Shinka Evolve tries to invent new problems automatically, drawing on ideas from POET, PowerPlay, and MAP-Elites quality-diversity search. The architecture of Shinka Evolve: an archive of programs organized as islands, LLMs used as mutation operators, and a UCB bandit that adaptively selects between frontier models (GPT-5, Sonnet 4.5, Gemini) mid-run. The credit...

JL‑Engine‑Local is a dynamic agent‑assembly engine that builds and runs AI agents entirely in RAM, wiring up their tools and behavior on the fly. Sorry in advance for the vid quality i dont like making them. JL Engine isn’t another chat UI or preset pack — it’s a full agent runtime that builds itself as it runs. You can point it at any backend you want, local or cloud, and it doesn’t blink; Google, OpenAI, your own inference server, whatever you’ve got, it just plugs in and goes. The engine loads personas, merges layers, manages behavior states, and even discovers and registers its own tools without you wiring anything manually. It’s local‑first because I wanted privacy and control, but it’s not locked to local at all — it’s backend‑agnostic by design. The whole point is that the age...

The standoff between Anthropic and the Pentagon has forced the tech industry to once again grapple with the question of how its products are used for war – and what lines it will not cross. Amid Silicon Valley’s rightward shift under Donald Trump and the signing of lucrative defense contracts, big tech’s answer is looking very different than it did even less than a decade ago. submitted by /u/PixeledPathogen [link] [comments]

Nvidia Corp. (NASDAQ:NVDA) is drawing attention after reports that TikTok parent ByteDance is planning a major overseas deployment of the company's newest AI chips, highlighting how Chinese tech firms are expanding computing capacity outside China amid export restrictions. ByteDance is reportedly preparing a large AI hardware buildout in Malaysia through a cloud partner, The Wall Street Journal reported on Friday. submitted by /u/WinOdd7962 [link] [comments]

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...