Swap chat model to Nemotron FP8 with KV cache quantisation
Takeaway: Next time I'd pin the huggingface-hub and vllm/transformers versions up front instead of discovering the conflict at build time.
Stack · 6 sessions
vllm sessions with coding agents
6 sessions on vllm with a coding agent: the agent failed at least once in 2, and 4 finished without a human stepping in.
Swap storyboard chat model to Nemotron-3-Nano-4B-FP8 on vLLM
Takeaway: Next time I'd flag the huggingface_hub/vllm version compatibility upfront so the agent pins a working version before the first deploy attempt, since that's what actually blew up the build.
Survey ML datasets, models, and deployment options via Q&A
Takeaway: This wasn't really a build — I used the assistant purely as a research reference to survey datasets, models, fine-tuning approaches, and deployment options before deciding on my own stack.
Survey ML infrastructure options for an LLM-powered product
Takeaway: Next time I'd scope this to one decision at a time instead of firing off 260+ open-ended questions in one sitting — I never actually got to building anything, just collecting options.
Survey ML/AI tooling, models and datasets across 600+ Q&A turns
Takeaway: I'd start by telling the assistant up front which of these were meant to touch the actual repo versus general ML knowledge, since it kept flagging that ambiguity itself.
Survey LLM/ML tooling, datasets, and deployment options via open Q&A
Takeaway: I'd start by scoping this to the actual decisions I needed to make for my project instead of asking the assistant to cover the entire ML ecosystem in one sitting — most of these answers never got applied to code.