AI: Events
How to Make a Large Language Model Smaller Without Losing Quality
Technical context • Development
The TorchAO developers have expanded their toolkit for quantization-aware training, now supporting new architectures, modes, and tasks.
Kubetorch has joined the PyTorch ecosystem, simplifying the process of running ML tasks on Kubernetes by abstracting complex infrastructure behind simple Python code.
Why the ability to run AI models on any hardware is becoming a strategically important task and how the open-source community is solving it.
What if you could train a massive neural network using half the memory – without breaking anything? That's exactly what the creators of FlashOptim are exploring.
AI: Events
A Powerful AI Agent Without the Cloud: How LFM2-24B-A2B Runs Directly on Your Computer
Products
Liquid AI has introduced the LFM2-24B-A2B model, capable of running AI agents with tool-calling capabilities directly on consumer hardware – without the cloud or latency.
NeuroBlog
How Three Letters – WWW – Changed Everything: The Internet Story You Didn't Know
Science & Technology • Computer Systems
From the first server at CERN in Switzerland to a global network of five billion users, we break down exactly how the technology we use every day works.
AI: Events
MCP Security: How to Properly Set Up Access Control in Systems with AI Agents
Technical context • Security
We're breaking down how MCP server and client security works and why properly configured access control is crucial for any agent-based system.
AI: Events
DeepSpeed Learns to Train Complex AI Models More Efficiently: What's Changed and Why It Matters
Technical context • Development
DeepSpeed has received two significant updates: support for training multimodal models and a memory-saving mode using low-precision computations.
NXP and Hugging Face explain how to train robotic artificial intelligence on custom data and run it on a low-power embedded device.