AI & LLM Engineering · By rlaope

[omh] Context window or token budget at risk: plan compact context, token/cost budgets, summarization checkpoints, and overflow recovery before long agent work.

rlaope/oh-my-hermes

[omh] Self-hosted LLM serving on GPUs: choose the serving engine and quantization from decision tables, prepare deployment as an idempotent runbook with observed-only verification, and measure the…

rlaope/oh-my-hermes

[omh] LLM-powered feature to build: LLM app development: prepare a build handoff for an LLM-powered feature with a pinned provider boundary, schema-first outputs, versioned prompt files, grounded…

rlaope/oh-my-hermes

[omh] Fine-tuning a model on your own data -- SFT, DPO, RLVR or a LoRA adapter: decide first whether prompting or retrieval already closes the gap, choose the method from the data you have, and…

rlaope/oh-my-hermes

[omh] Missed route or run lessons to record: classify and review self-improvement store routes as an auxiliary review lane before durable writes, then record workflow attempts as metadata-only…

rlaope/oh-my-hermes

[omh] Explaining OMH itself: current-source-first documentation for OMH itself: product identity, public capability catalog, model routing, local state, and long-term memory.

rlaope/oh-my-hermes