microcenter has RTX 5090 deal for $2k today if you wanna pick up one 🙂
COMPUTING
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KAT-Dev-72B-Exp: Agentic Coding Model Ranks #2 SWE-Bench
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HUGE a new Agentic coding model, fits on 4x RTX 3090s @ 4-bit, fully local KAT-Dev-72B-Exp by Kwaipilot – Claude Code setup guide included – ranks #2 on SWE-Bench Verified – excels at long-horizon coding + tool-use – multi-stage tuned: Mid-Training, SFT + RFT, Agentic RL
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China’s autonomous rail-free train revolutionizes urban mobility
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El futuro del transporte es ya una realidad en China. Un tren autónomo que circula sin raíles.
— Juan Merodio (@juanmerodio) 10 octobre 2025
Una solución innovadora que redefine la movilidad urbana y que es capaz de mover a unos 300 viajeros a una velocidad de 70 km/h pic.twitter.com/7oiFh8NoH3El futuro del transporte es ya una realidad en China. Un tren autónomo que circula sin raíles. Una solución innovadora que redefine la movilidad urbana y que es capaz de mover a unos 300 viajeros a una velocidad de 70 km/h
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LLM Infrastructure: Still Early, Much Work Ahead
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LLM infra right now is like Linux in the 90s we're still early & there is a lot of work to do
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Inference Efficiency Drives AI Production Profitability and ROI
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As AI moves from prototypes to production, inference efficiency becomes the ultimate driver of profitability. Open benchmarks like InferenceMax provide a shared standard to measure performance, TCO, and ROI, validating NVIDIA’s full-stack approach of co-designed hardware and
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NVIDIA InferenceMAX Achieves 10x Performance Efficiency Gains
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Industries are scaling AI like never before. To help organizations extract maximum value, NVIDIA systems are built to deliver unmatched performance at AI factory scale. The latest InferenceMAX v1 benchmarks confirm: 10× more performance per watt, translating to lower
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Inference Efficiency Drives AI Production Profitability and ROI
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As AI moves from prototypes to production, inference efficiency becomes the ultimate driver of profitability. Open benchmarks like InferenceMax provide a shared standard to measure performance, TCO, and ROI, validating NVIDIA’s full-stack approach of co-designed hardware and
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AI and analog computing revolutionize transformers
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Sources:
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https://research.ibm.com/blog/how-can-analog-in-memory-computing-power-transformer-models?utm_source=chatgpt.com
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https://nature.com/articles/s43588-025-00854-1?utm_source=chatgpt.com
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Smarter physics, not bigger chips, for AI
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it’s no longer “bigger chips = better AI” it’s “smarter physics = better AI.” With analog IMC, a model doesn’t waste energy copying weights back and forth. That overhead vanishes. IBM and others already show that MoE models map well to analog designs, boosting energy and
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AI Models Moving to Devices: Key Considerations
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ai models might soon live in your device, not just in giant datacenters. if you’re a founder / engineer / prompt architect, start thinking:
→ designing models aware of analog constraints
→ hybrid architectures: analog + digital fallbacks
→ compression, pruning, or