Tag

#second-brain

Articles tagged "second-brain" — 6 entries.

Article №17 agentic NIM ~90 minutes — 30 min to design the tool surface, 30 min to wire FastMCP + pgvector, 15 min to register with Claude Code, 15 min for the demo and trace
Second Brain

Second Brain as a Tool — Wrapping the RAG Stack in MCP for Claude Code

Closing the Second Brain arc. Four MCP tools wrap the RAG chain — embed, retrieve, optionally rerank, generate — and any Claude Code session anywhere on the box becomes a grounded research client. 200 lines of Python, one launcher, one .mcp.json entry.
Article №15 observability NeMo Evaluator ~60 minutes end-to-end — 40 s to ingest the blog into pgvector, 2 min for retrieval, 4 min for generation across three 8B variants, 90 s for the LoRA variant, 9 min for grading
Second Brain

Ragas, Reranked — What 44 Held-Out Questions Say About the Second Brain Stack

A Ragas-style harness written in 200 lines of stdlib Python, run locally on the DGX Spark, against four variants of the Second Brain RAG chain. Naive RAG scores 3.30 / 5. Rerank RAG scores 4.27. LoRA+RAG is a surprise — it does not beat naive. Retrieval is where the points come from.

uses fieldkit.eval

Article №14 fine-tuning Hugging Face PEFT + Qwen2.5-3B-Instruct ~45 minutes end-to-end — 5 min corpus via NIM 8B, 69 s training, 3 min benchmark, plus a 6 GB base-model download
Second Brain

LoRA on Your Own Q&A — What 231 Pairs Actually Teach a 3B Model

231 own-voice Q&A pairs, a rank-16 LoRA, 69 s of training on a GB10 Spark. The adapter won't memorize your exact numbers, but it will take a model that refuses 61% of questions about your work and turn it into one that answers all of them in your voice. For facts you still need RAG.

uses fieldkit.eval

Article №13 deployment TensorRT-LLM + Triton Inference Server ~4 hours including two container pulls and three engine builds
Second Brain

TensorRT-LLM on the Spark — FP8 Isn't the Reason to Drop NIM. NVFP4 Is.

Dropping below NIM to raw TensorRT-LLM on a GB10 Spark. FP8 beats NIM's vLLM by 10-15% — barely worth the rebuild. NVFP4 beats it by 76% on decode, 43% on TTFT, and ships a 34%-smaller engine. The reason to drop NIM is the Blackwell-native 4-bit kernel, not FP8.
Article №12 foundations Foundation 10-minute read; no hands-on
Foundations

One Substrate, Three Apps — Where the Foundation Forks

Seven articles installed one stack on the Spark — NIM, Embed, pgvector, RAG glue, reranker, generator A/B, Guardrails. This bridge retells that install as three different answers to one question — corpus plus 128 GB — and walks readers to the top of three tracks.
Article №11 inference NeMo Guardrails ~90 minutes on top of the rerank-fusion / bigger-generator chain
Foundations

One Rail, Three Policies — NeMo Guardrails on the Retrieval Path

NeMo Guardrails drops a policy gate between retrieval and generation. One install, three per-arc configs — PII for Second Brain, style for LLM Wiki, code-safety for Autoresearch — and a 15-query benchmark: 100% block recall, 100% clean pass. Rails are scaffolding; detectors are the content.

uses fieldkit.rag