#RAG

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Sep 18Friday

GitHub Blog · AI & MLSep 18, 23:00

Should you read the code, is RAG dead, and did Skills kill MCP?

GitHub Podcast 最新一期拆解了五个 AI 热门观点：AI 生成的代码仍需阅读和负责，但审查力度应按风险分级；Skills 与 MCP 解决不同问题，前者是打包的团队经验，后者是连接工具与数据的标准，可组合使用；RAG 并未死亡，它为模型提供训练数据之外的相关信息，减少 token 浪费并让回答更有依据。

May 25Monday

r/LocalLLaMAMay 25, 21:50

The reason small-model agent stacks aren't the default is not whether they work

A Reddit post argues small-model agent stacks are not default for business reasons, not capability limits: Gemma 4 31B reaches 86.4% on tau2-bench, and DeepSeek V4-Flash output tokens are priced about 89x below Claude Opus 4.6. The operational risk is verification, because 7–9B models produced broken reasoning for roughly half to two-thirds of correct answers in a cited audit.

Why it matters: HKR-H/K/R all pass: the angle is contrarian, with benchmark, cost, and verifier-failure numbers. Reddit-source uncertainty keeps it in the 78–84 recommendation band, not P1.

Take

Small-model agent stacks are blocked less by task capability than by accountability around verification. The summary gives strong hooks: Gemma 4 31B hits 86.4% on tau2-bench, and DeepSeek V4-Flash output tokens are priced around 1/89 of Claude Opus 4.6. On raw inference cost, the case is obvious.

The ugly number is the audit claim: 7–9B models had broken reasoning in roughly half to two-thirds of correct answers. Enterprises do not buy benchmark wins; they buy failure modes they can audit. A big model is expensive, but splitting planner, tool-caller, and verifier creates more thresholds, logs, rollbacks, and ownership fights. The Reddit body is blocked by 403, so the audit sample and tau2-bench setup are not visible. I would not treat this post as evidence that the default stack is about to flip.

May 7Thursday

Computing Life · Share · YageMay 7, 08:00

Agent Filesystems: From Feeding Models Memory to Letting Models Browse Files

The article frames agent filesystems as a three-stage shift from raw context to memory systems to filesystem-as-context, covering design choices from Turso, Anthropic, Vercel, and Manus, and listing four overlooked blind spots.

Why it matters: HKR-H/K/R all pass, but this is design commentary rather than a product or research release. Named comparisons across Turso, Anthropic, Vercel, and Manus justify featured, not the 78+ band.

Take

Filesystem-as-context is starting to take budget away from RAG for a blunt reason: the model should not reread the whole workspace every turn. The article names Turso AgentFS, Anthropic’s filesystem-style MCP work, Vercel’s no-vector-database knowledge-base template, and Manus context engineering. All four push retrieval, organization, and pruning into a manipulable file layer before prompting.

I buy the direction, but not the “better memory” framing. Vector DBs solved finding material in 2023; agent filesystems solve repeated work without hauling the same material through the context window. The body gives the three-stage frame and four blind spots, but no benchmark, token-savings rate, or failure-recovery mechanism. Without those numbers, AgentFS is still an engineering taste, not a default architecture.

Apr 3Friday

X · @op7418Apr 3, 10:49

Karpathy shared how he builds a local AI knowledge base

Karpathy uses Obsidian and local Markdown to build a personal wiki, stores source material in a RAW folder, then has an LLM generate summaries, indexes, concept pages, links, and visualizations. The setup can answer questions over the wiki and write reports or new files, but the post also says AI-generated content can pollute the corpus and should be separated from trusted sources; the post does not disclose the model, scale, or automation details.

Why it matters: HKR-H and HKR-R land because Karpathy’s local-first wiki workflow is inherently clickable and discussable for AI practitioners. HKR-K lands on the RAW→LLM→summary/index/link mechanism, but missing model, corpus size, and automation details keep it in the mid-70s.

Take

Karpathy’s setup matters because it keeps the control plane for AI memory inside local Markdown. Obsidian, a RAW folder, backlinks, indexes, concept pages, and visualizations are old parts. The useful bit is provenance: the user can inspect source material and model-generated derivatives as files.

The contamination warning is the sharpest part. The post says AI-generated content should be separated from trusted sources, or the wiki poisons itself through recursive reuse. That is the part most cloud memory products hand-wave. OpenAI and Anthropic sell persistent memory as UX, but they rarely expose source boundaries this plainly. The post does not disclose model choice, corpus size, or automation scripts, so I would not treat this as a reproducible product blueprint. It is closer to hygiene guidance for heavy personal-knowledge users than a new RAG architecture.

Jan 19Monday

Import AI (Jack Clark)Jan 19, 22:03

Import AI 441: My agents are working. Are yours?

Jack Clark says his research agents processed thousands of papers while he hiked or slept, and Claude finished site scraping, embeddings, local vector search, and a GUI in under one hour. The post confirms multi-agent retrieval, cross-checking, and report generation; it does not disclose model versions, cost, failure rate, or benchmark data. The point to watch is workflow friction dropping enough for AI to shift from single prompts to ongoing delegated work.

Why it matters: HKR-H lands with the challenge in the headline; HKR-K lands because Clark describes a <1 hour workflow with retrieval, cross-checking, and report generation. Missing model version, cost, failure rate, and evaluation keep it in featured, not p1.

Take

Clark’s sharp signal is not the “thousands of papers” line; it is that agents crossed the laziness threshold for a serious research user. The concrete bit matters: Claude Cowork scraped jack-clark.net, installed software, created embeddings, built local vector search, and shipped a GUI in under one hour. Last year, models could do pieces of that. The annoying part was chaining them without environment failures and human babysitting.

I don’t buy the stronger “better insights” claim without samples, model version, cost, failure rate, or audit protocol. But the workflow claim lands. METR’s task-horizon framing has been abstract for many teams; Clark is giving the lived version. Agents are not first replacing scientists. They are eating the half-engineering, half-literature chores that made capable people procrastinate.