LLMs really are pretty good at summarizing text. The average person I interact with summarizes my tweets in all kinds of bizarre, misleading ways (and then gaslights me about how that's my fault) but look at how Grok here successfully gets the two key pieces of the argument. LLMs are either far more honest or far more capable summarizers than the average X user.

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.

LLMs now beat humans on math accuracy. But does that mean they're actually building understanding on top of the right foundations? Title: Do LLMs Exhibit Coherent Knowledge Structures in Mathematical Reasoning? A Perspective from Knowledge Space Theory URL: ❓ Do LLMs actually build on prerequisite knowledge to get answers right? 💡 Accuracy favors LLMs (92.5% for the best model, Qwen3-80B, vs. 79.6% for humans), but "perfect prerequisite satisfaction" tells a different story: 72.7% for humans vs. only 48.16% for the best LLM. Lots of correct answers rest on shaky foundations. ❓ Does giving them prerequisite hints help? 💡 Surprisingly, prerequisite-grounded context barely outperformed unrelated examples. That points to surface-level pattern matching rather than genuine structured reasoning over prerequisites. ❓ Do strong models at least share a consistent knowledge structure with each other? 💡 Human learner groups overlap at 0.9+ in their knowledge structure, but LLM pairs only overlap 0.38-0.6 — and stronger models diverge even further from humans. ❓ So what's the takeaway? 💡 Accuracy alone hides how differently LLMs "understand" math. Knowledge Space Theory offers a lens that exposes the fragmented structure lurking behind impressive scores. #LLMEval# #MathReasoning#

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#