Build update #2 for RouterCore. I ran the full Track 2 loop on AMD Developer Cloud: - synthetic DevOps routing dataset - Qwen LoRA fine-tuning - ROCm PyTorch on AMD Instinct MI300X - eval vs deterministic baseline Confirmed setup: torch 2.9.1+rocm6.4 HIP 6.4 AMD Instinct MI300X VF Results: FakeRouter: required fields 28.57% status accuracy 57.33% unsafe rejection 100% false routes 0% Safety-tuned LoRA on AMD ROCm: required fields 100% status accuracy 86.67% unsafe rejection 100% false routes 0% Fine-tuning improved structured routing quality, while policy stayed authoritative for safety. Repo: github.com/Jayteare/routerco… Space: lablab-ai-amd-developer-hack… @lablab_ai @AIatAMD #BuildInPublic #ROCm #AIAgents #FineTuning

Building RouterCore for the AMD Developer Hackathon. It’s a safe routing layer for AI agents: before a DevOps agent executes, RouterCore decides whether a request should be routed, clarified, confirmed, rejected, or escalated. Current MVP: FakeRouter baseline, schema validation, policy engine, clarification loop, Gradio demo, synthetic train/eval data, eval comparison tools, optional HF model + LoRA path for AMD ROCm Baseline: JSON validity 100% workflow accuracy 97.01% unsafe rejection 100% false route rate 0% Next: fine-tune a compact model on AMD GPUs with ROCm. Repo: github.com/Jayteare/routerco… @lablab_ai @AIatAMD #BuildInPublic #AIAgents #ROCm #FineTuning #HuggingFace

Quick question: What is the most annoying workflow problem you are dealing with right now? AI, engineering, infra, product, dashboards, ops, research, anything. Trying to understand what people actually need, not just what sounds cool to build.