TEI tai rai text embeding interface, biski idomesnis u ollama ir performina geriau
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Depth-aware light injection in TypeGPU I got a 448x448 monocular depth model down to ~8 ms on my M4 Pro across ~250 dispatches, which is fast enough to use in realtime :D Since the inference is… | Konrad Reczko | 270 comments
Depth-aware light injection in TypeGPU I got a 448x448 monocular depth model down to ~8 ms on my M4 Pro across ~250 dispatches, which is fast enough to use in realtime :D Since the inference is written directly in TypeGPU, I can just feed the depth buffer straight into the lighting pass. It never has to leave the GPU or go through any extra synchronization/interop step Inference, lighting and draw all go through the same command encoder | 270 comments on LinkedIn
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Elon’s SpaceX just dropped a GitHub competitor: Cursor launched Origin, their own code hosting platform built for AI agents 😳 The craziest part? It went live while GitHub is currently experiencing… | Linas Beliūnas | 155 comments
Elon’s SpaceX just dropped a GitHub competitor: Cursor launched Origin, their own code hosting platform built for AI agents 😳 The craziest part? It went live while GitHub is currently experiencing a global outage 😭 No more human-speed GitHub bottlenecks. No more agents waiting on old infrastructure. Just sync your repos and let them run at full machine speed. ↳ Code, PRs, and agents live in one place ↳ Real-time sync with GitHub (it becomes the backup) ↳ Parallel agents can commit, review, and merge nonstop Because the real bottleneck was never writing code. It was coordinating what machines produce at scale. And Cursor just removed it. GitHub is now the compatibility layer, while the actual work happens inside Cursor. Cursor is building the next generation of AI software & defining how it gets built. P.S. also check out Y Combinator's QM: The Complete Guide to YC's Open-Source AI Agent Harness 🤖: https://lnkd.in/dqMh63bi | 155 comments on LinkedIn
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AI aprašymas Vaizdas rodo klaidos pranešimo langelį, kuriame nurodoma, kad dabar šį failą naudoja kita programa.
Sugeneravo dirbtinio intelekto modelis qwen3.8-27b-dk:latest (vienu ėjimu, be samprotavimo biudžeto). Žmogus netikrino.
Self-hosted hardening: nine defects found by running it, plus wings, anchors, recall stats and an eval by Ryouku · Pull Request #17 · atvirokodosprendimai/agentsmemory
Ran agentsmemory as the actual memory for six repositories for a day, then fixed everything that broke. Nine defects, seven of which only surface once the thing is genuinely in use, plus the capabi...
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