The 8 loops and the 5-layer architecture needed to replace fragile funnels with a resilient GTM engine.
Heaps do lie: debugging a memory leak in vLLM. | Mistral AI
The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.
The Future of Meta Superintelligence: A 1 Year Progress Update
A top tier RL environment startup spawns out of thin air, the most aggressive compute ramp we've ever seen, 2000km+ scale-across, and some advice for Google DeepMind
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.
TurboQuant: Redefining AI efficiency with extreme compression
Amir Zandieh, Research Scientist, and Vahab Mirrokni, VP and Google Fellow, Google Research
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?”
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.
Pace Layers and AI Integration - The Phoenix Architecture
Automated Architecture Synthesis via Targeted Evolution | Liquid AI
Today, we report advances in automated neural network architecture design and customization. We developed algorithms for the synthesis of tailored architectures (STAR), based on evolutionary algorithms applied to a numerical representation for model architectures derived from a new design theory. STAR automates the process of architecture discovery and optimization, turning it into an end-to-end process. With these methods, we have been able to tailor architectures to custom tasks, metrics, and hardware. We used STAR to synthesize hundreds of different designs that outperform strong Transformer and hybrid architectures in quality, with smaller caches and number of parameters.
LukeW | The Evolution of AI Products
At this point, the use of artificial intelligence and machine learning models in software has a long history. But the past three years really accelerated the ev...
pguso/ai-agents-from-scratch
Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.
Novita AI – Model Libraries & GPU Cloud - Deploy, Scale & Innovate
Novita AI provides 200+ Model APIs, custom deployment, GPU Instances, and Serverless GPUs. Scale AI, optimize performance, and innovate with ease and efficiency.
Where AI Startups Scale to Production
Discover the most efficient way to build, tune and run your AI models and applications on top-notch NVIDIA® GPUs.
Supermemory
The memory layer for AI agents. Context engineering platform powering enterprise APIs, developer plugins, and a personal app that remembers everything.
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
Georgi Gerganov on Twitter / X
gpt-oss is a great modelIMO OpenAI showed us the blueprint for winning local AI:- Interleaved SWA- Small head sizes in the attention- Attention sinks- Mixture of Experts FFN- 4-bit trainingAll of these parts combined together result in the best architecture suitable for…— Georgi Gerganov (@ggerganov) August 28, 2025
The Phoenix Architecture
Generative AI coding demands what we've always known: modularity, clear boundaries, disposable components. Principles that scaled human teams are now table stakes. Here, we make the implicit explicit