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<h1>Everything worth opening</h1>
<p class="lede">114 links, each one checked. Plugins are ranked by installs from Obsidian's own community stats rather than by stars, because in this ecosystem the two disagree by an order of magnitude. Figures are from September 2026 and will drift.</p>
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<footer>Generated from the repository. <a href="https://github.com/undefined-ui/second-brain-os">github.com/undefined-ui/second-brain-os</a> · by <a href="https://x.com/undefinedKi" rel="noopener">@undefinedKi</a></footer>
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Paid"}, {"file": "tools.md", "kind": "Tool", "group": "Search and graph", "name": "ripgrep", "url": "https://github.com/BurntSushi/ripgrep", "metric": "", "desc": "Fast enough that most vaults never need an index"}, {"file": "tools.md", "kind": "Tool", "group": "Search and graph", "name": "NetworkX", "url": "https://networkx.org", "metric": "", "desc": "Graph analysis once you have exported an edge list"}, {"file": "tools.md", "kind": "Tool", "group": "Search and graph", "name": "Ladybug", "url": "https://github.com/LadybugDB/ladybug", "metric": "", "desc": "Embedded graph database, no server. The maintained continuation of Kuzu, which was archived in October 2025"}, {"file": "tools.md", "kind": "Tool", "group": "Search and graph", "name": "Neo4j", "url": "https://neo4j.com", "metric": "", "desc": "Full graph database. Overkill unless you build on top"}, {"file": "tools.md", "kind": "Tool", "group": "Search and graph", "name": "Gephi", "url": "https://gephi.org", "metric": "", "desc": "Visual graph exploration beyond Obsidian's view"}, {"file": "tools.md", "kind": "Tool", "group": "Search and graph", "name": "txtai", "url": "https://github.com/neuml/txtai", "metric": "", "desc": "12,931 stars. Semantic search and LLM workflows, if you decide you need embeddings"}, {"file": "tools.md", "kind": "Tool", "group": "Publishing", "name": "Quartz", "url": "https://github.com/jackyzha0/quartz", "metric": "13,185", "desc": "Vault to static site with backlinks, local graph and search"}, {"file": "reading.md", "kind": "Reading", "group": "The source document", "name": "Karpathy's llm-wiki gist", "url": "https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f", "metric": "", "desc": "The pattern everything in this repo builds on."}, {"file": "reading.md", "kind": "Reading", "group": "The pattern at company scale", "name": "An organizational second brain: AI that learns from experts", "url": "https://engineering.fb.com/2026/09/02/ml-applications/organizational-second-brain-ai-learns-from-experts/", "metric": "", "desc": "The same pattern this guide teaches, run inside Meta with a whole domain team as the user."}, {"file": "reading.md", "kind": "Reading", "group": "The tradition it comes from", "name": "Andy Matuschak's notes", "url": "https://notes.andymatuschak.org", "metric": "", "desc": "The Zettelkasten method. The argument for atomic notes and dense linking still holds; the manual labour it demands is the part that killed it for most people. - Building a Second Brain, Tiago Forte. Where the term comes from, and the PARA m"}, {"file": "reading.md", "kind": "Reading", "group": "New in October 2026", "name": "Anthropic donates Agent Skills to the Agentic AI Foundation", "url": "https://www.anthropic.com/news/donating-agent-skills-to-the-agentic-ai-foundation", "metric": "", "desc": "The `SKILL.md` format moves to the Linux Foundation's agent foundation, name, spec and site included. The skills in this repo are now written to a neutral standard."}, {"file": "reading.md", "kind": "Reading", "group": "New in October 2026", "name": "Building agents: when to fold subagents into your main agent loop", "url": "https://claude.com/resources/articles/building-agents-when-to-fold-subagents-into-your-main-agent-loop", "metric": "", "desc": "The case that one loop with skills and progressively disclosed tools often matches a fleet of subagents, when subagents are still worth it, and a migration plan that starts from ten golden eval cases."}, {"file": "reading.md", "kind": "Reading", "group": "New in October 2026", "name": "Building effective agent automations", "url": "https://claude.dev/blog/building-effective-agent-automations/", "metric": "", "desc": "A scheduled agent built end to end: a bookmark per source, a ledger of what was already reported, a per-run spend cap, and a failed source reported as a failure rather than a quiet day. Every pattern carries over to a nightly ingest."}, {"file": "reading.md", "kind": "Reading", "group": "New in October 2026", "name": "Automating eval design and hillclimbing with Claude", "url": "https://claude.dev/blog/automating-eval-design-and-hillclimbing/", "metric": "", "desc": "Two commands that build an eval in your codebase and then improve against it, keeping a change only when both the train split and a held-out split improve. Read it with Hamel Husain's critique."}, {"file": "reading.md", "kind": "Reading", "group": "New in October 2026", "name": "How to use decision models for evals", "url": "https://hamel.dev/blog/posts/decision-model-evals/", "metric": "", "desc": "Jev, OpenAI's Decisions API and three other decision models compared as cheap pass or fail judges on 98 human-labelled agent traces, with the advice to tune the threshold rather than keep 0.5."}, {"file": "reading.md", "kind": "Reading", "group": "New in October 2026", "name": "How the Claude Code team does code review", "url": "https://claude.dev/blog/solving-code-review-at-anthropic/", "metric": "", "desc": "Parallel finders, a separate voting verifier and a three-round cap took first-round passes from 65% to 78%. Real numbers on making a critic loop converge."}, {"file": "papers.md", "kind": "Paper", "group": "GraphRAG", "name": "From Local to Global: A Graph RAG Approach to Query-Focused\nSummarization", "url": "https://arxiv.org/abs/2404.16130", "metric": "", "desc": "Edge, Trinh, Cheng, Bradley, Chao, Mody, Truitt, Metropolitansky, Ness, Larson. Microsoft Research. Implementation at microsoft/graphrag."}, {"file": "papers.md", "kind": "Paper", "group": "HippoRAG", "name": "HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language\nModels", "url": "https://arxiv.org/abs/2405.14831", "metric": "", "desc": "Guti\u00e9rrez, Shu, Gu, Yasunaga, Su."}, {"file": "papers.md", "kind": "Paper", "group": "Lost in the Middle", "name": "Lost in the Middle: How Language Models Use Long\nContexts", "url": "https://arxiv.org/abs/2307.03172", "metric": "", "desc": "Liu, Lin, Hewitt, Paranjape, Bevilacqua, Petroni, Liang. Published in TACL."}, {"file": "papers.md", "kind": "Paper", "group": "New in October 2026", "name": "Follow the Entities: A Corpus Map for Agentic Search", "url": "https://arxiv.org/abs/2609.37226", "metric": "", "desc": "An offline pass writes one page per recurring entity that links to every document mentioning it, and agents walk that map instead of searching flat files. Across seven models and three benchmarks they found more evidence with fewer tokens. "}, {"file": "papers.md", "kind": "Paper", "group": "New in October 2026", "name": "Context as Infrastructure: Knowledge Compilation for Retrieval in Agentic Systems with Harnesses", "url": "https://arxiv.org/abs/2610.41172", "metric": "", "desc": "A harness with only grep and BM25 answered 85% of BrowseComp-Plus and 91% of FRAMES with no vector index. Compiled artifacts such as entity graphs and wikis added no accuracy, but a claim-evidence tree cut query tokens by 38% on average. Su"}, {"file": "papers.md", "kind": "Paper", "group": "New in October 2026", "name": "Keep Your Receipts: Trustworthy Cleanup of Agent-Built Knowledge Graphs", "url": "https://arxiv.org/abs/2610.17719", "metric": "", "desc": "Every merge, edge and summary sentence the model writes records the evidence behind it, so deleting a retracted source becomes set arithmetic with no model calls. Two builds of the same 300 documents differed in 29,475 artifacts, which is w"}, {"file": "papers.md", "kind": "Paper", "group": "New in October 2026", "name": "LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation", "url": "https://arxiv.org/abs/2609.10239", "metric": "", "desc": "Graph retrieval steered by algorithms at query time, with adaptive thresholds and a penalty on hub nodes, instead of an LLM on every hop. Against GraphRAG Global and DRIFT it cut latency over 100x and cost over 99% while matching quality."}, {"file": "papers.md", "kind": "Paper", "group": "New in October 2026", "name": "EvalMem: An Operation-Level Diagnostic Framework for Long-Term Memory Systems", "url": "https://arxiv.org/abs/2609.22231", "metric": "", "desc": "Splits every memory failure into not stored, not retrieved or not used, and retrieval was the most common. Adding a wiki-style layer of source pages, hub pages and versioned facts improved accuracy by 2.5 points on LoCoMo."}, {"file": "papers.md", "kind": "Paper", "group": "New in October 2026", "name": "CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents", "url": "https://arxiv.org/abs/2609.26779", "metric": "", "desc": "Compaction that only truncates or drops content and never compacts an earlier compaction. Up to 50% cheaper under a bounded context, with Terminal-Bench scores held or improved, and simple enough to copy."}, {"file": "papers.md", "kind": "Paper", "group": "New in October 2026", "name": "The Unreliable Progress Bar: Can LLM Agents Reliably Report Task Progress Throughout Execution?", "url": "https://arxiv.org/abs/2609.08589", "metric": "", "desc": "Most models lose accuracy reporting their own progress mid-task, and the newest turn conservative near the finish. The authors conclude that frameworks should not drive task flow on self-reports alone, which is the case for stop rules owned"}, {"file": "papers.md", "kind": "Paper", "group": "New in October 2026", "name": "Frozen Judges, Moving Agents: Version-Dependent LLM-Judge Error", "url": "https://arxiv.org/abs/2609.34198", "metric": "", "desc": "A fixed LLM judge makes different errors on different agent versions. On SWE-bench Verified, eight judge-only comparisons declared upgrades that execution results could not confirm. 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