How our agent, Möbius automated a Core ML port in ~12 h (vs. 2 weeks), hit 0.99998 parity, and made it 3.5× faster, while staying on the CPU.
Liquid AI on Twitter / X
Today, we release LFM2.5-350M. Agentic loops at 350M parameters.A 350M model trained for reliable data extraction and tool use, where models at this scale typically struggle.<500MB when quantized, built for environments where compute, memory, and latency are constrained.🧵 pic.twitter.com/zZPKzcCwH9— Liquid AI (@liquidai) March 31, 2026
The Universal Execution Layer for AI
Optimize any AI model on any engine, across all hardware. Dria’s topology-aware compiler and peer-to-peer runtime merge CPUs, GPUs, NPUs & chiplets into one fabric—maximising utilisation, cutting inference cost and ending vendor lock-in.
TheStage AI – Faster, Cheaper AI Inference
Accelerate models on NVIDIA & edge. Full guides for setup, optimization & deploy. ANNA, QLIP, Elastic Models, CLI & API. Built for AI teams & devs.
raullenchai/Rapid-MLX
The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replacement. Works with Claude Code, Cursor, Aider.
raullenchai/Rapid-MLX
The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replacement. Works with Claude Code, Cursor, Aider.
Together AI | The AI Native Cloud
Build what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research.
ZETIC | On-Device AI for Everything - for any model, on any device, in any framework
Built by ex-Qualcomm AI Engineer. Automate on-device AI deployment with full NPU optimization. Benchmark on 100+ physical devices and ship in hours with just 3 lines of code.
google-ai-edge/LiteRT
LiteRT, successor to TensorFlow Lite. is Google's On-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization
Alex Cheema on Twitter / X
This is why we need open benchmarks for local AI.Otherwise it turns into tribalism and name calling.We will be publishing the largest database of open benchmarks for local AI, tested on 1,000+ real hardware setups. Every device, every interconnect, different… https://t.co/ZsU3PCdSsZ— Alex Cheema (@alexocheema) March 9, 2026
Why I hope Apple keeps investing in on-device AI
Edge intelligence is safer, more private, and less resource-intensive than cloud-based AI services.
LLMs Can Now Write GPU Kernels That Beat torch.compile - Break AI Scaling Limits in 7 Days
We're now seeing multi-agent systems that take your PyTorch code and produce CUDA or Triton kernels with 2x to 14x speedups over torch.compile(mode='max-autotune-no-cudagraphs'). Not on toy benchmarks. On real models like Llama-3.1-8B, Whisper, and Stable Diffusion. Learn proven techniques to shift the scaling law intercept and achieve 10-50% performance gains.
Coasts — Containerized Hosts for AI Agents
Free, open source parallel runtimes for AI agents. Run multiple isolated environments on your machine — no cloud, no conflicts.
AI-Native Cloud | DigitalOcean
Run AI products in production with a unified stack for agents, inference, and cloud—built for control, performance, and economics at scale.
The Ma of a New Machine – Scott Jenson
The current Silicon Valley flex is trading notes on your favorite new AI tools over lunch. Each week brings another one to explore. Some of these tools are very impressive; I’ve been able to reply to someone with an alternative UI design in less than a minute, and they were dumbfounded: “How did you do that so fast?”
