LLM Wiki is an open-source RAG alternative that builds a persistent knowledge graph from documents using chain-of-thought ingest and multimodal extraction.

LLM agents learn by interacting with environments, but static setups limit their growth. Today at 2:00 PM, join Zifeng Wang at the #COLM2026# Google booth (#107#) to learn about EnvHarness, a plug-in architecture that dynamically reshapes environment behaviors to improve reinforcement learning and agent adaptability.

LLM-assisted hardware hacking - for fun and profit I bet other people are already experimenting with the same thing, but I want to share my experience with my project, and how local / open-source LLMs saved the day. I have a bunch of Tuya Zigbee devices around my apartment, and while they work fine out of the box, I still want to tweak certain things. For example, the TS0046 button has an annoying 500 ms delay after each press, and I want to remove it. The task requires: 1. Dumping the FW 2. Reverse engineering (RE) 3. Writing a new FW 4. Compiling and flashing The first step was already a roadblock, since most Tuya devices use Telink's SWS protocol, which requires a costly flasher. A UART bit-banging project (from pvvx) exists, but it never worked in my case. I initially asked Claude to handle the task, as I was curious whether it would trip the safeguards. It turns out it did, at the very key steps. So I ended up running pi with GLM-5.3 (via an HF inference provider; I could host it myself, but I don't have a big server rack in my apartment). To my surprise, it worked well. About 20 minutes later, the app was running, the FW was dumped, and it was already doing RE. And as I'm writing this, it has already finished writing the new FW and flashing it. Without open-source LLMs, I would never have been able to achieve this.

LLM Compressor v0.14.0 is out, and GPTQ just got its biggest speedup since launch. A new Triton kernel makes quantization ~15x faster end to end. Batching layers that share a shape pushes that to ~30x on some MoE workloads. Even the old eager path is 1.5-2x faster. Also new: expanded MSE/iMatrix observers that beat GPTQ for NVFP4 on internal benchmarks, REAP pruning with distributed DDP, and support for GLM 5.3 and Qwen3.8. Full release notes:

llm-d flow control, chapter 3: where control starts. Throughput can plateau while latency keeps rising. The admission point is where flow control decides to keep a request in the queue or admit it to the model server pod. Using a load test, you can find that cutoff point for your model and workload.

llm-d flow control, chapter 2: shared inference under burst pressure. A GPU pool can have spare capacity on average and still run out during a traffic burst. With @_llm_d_ flow control enabled, excess requests are queued until capacity becomes available.

llm-d flow control interactive learner, chapter 1: capacity utilization. Here’s the same traffic in separate tenant-GPU reservations versus a shared pool. Sharing lets tenants use capacity that would otherwise sit idle.

LLM bills spiral when debugging sessions or multi-agent workflows run away. Omnigent warns first, then downgrades, doesn't hard-stop mid-task. 👇 🛡️ trivial_gate routes trivial requests off Opus, Fable, GPT-5 💰 Progressive budgets block expensive tiers; work continues on Sonnet/Haiku 📊 /usage breaks spend by harness, model, and session Phased rollout: visibility → routing → budgets Read more: #Omnigent# #LLMOps# #GenAI#