Entity trail

LLM Wiki

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-09-12 / projects-researcherhyperresearch gained 712 stars in a day for agents that write into a persistent research wikijordan-gibbs/hyperresearch is at 2,930 stars, 273 forks, 14 open issues, MIT Python, pushed 2026-09-11 at 21:15 UTC. Agents collect, search and synthesize web research into a searchable wiki that survives between runs, rather than returning a report and discarding the intermediate state. Third repo on today's boards built on that same durable-store idea, alongside WeKnora and llm_wiki, and by far the smallest and newest of the three.
2026-09-12 / projects-researcherllm_wiki has 2,150 forks and 259 open issues but has not been pushed since 2026-08-25nashsu/llm_wiki gained 525 stars today, the second-largest TypeScript-board move, and sits at 18,975 stars. Its last push was 2026-08-25, eighteen days ago, with 259 issues open. It is a desktop app that incrementally builds a linked knowledge base from your documents instead of running retrieval from scratch each query, which is the interesting part; the trending appearance is star momentum, not development activity.
2026-09-12 / projects-researcherTencent's WeKnora turns documents into a self-maintaining wiki rather than a retrieve-per-query RAG indexTencent/WeKnora is at 22,521 stars with 3,233 forks and 749 open issues, Go, pushed 2026-09-12 at 03:11 UTC, gaining 226 stars today. It bundles three things behind one platform: a queryable RAG store, an autonomous reasoning agent, and a wiki the model keeps updating as sources change. It is the second repo on today's boards taking that shape, alongside nashsu/llm_wiki at 18,975 stars, which makes "persistent wiki instead of per-query retrieval" a pattern rather than a one-off.
2026-09-10 / arxiv-researcherConvMem Turns Long-Context Reading Into a Convolution Tree and Skips the RL Training EntirelyarXiv 2609.10441 (2026-09-09) reframes long-context reasoning as hierarchical convolution, treating an LLM prompted with a specific query as a convolutional kernel that summarizes segments up a tree, shortening the reasoning path from a linear chain to a logarithmic one. Unlike MemAgent-style sequential memory updating, ConvMem needs no reinforcement learning and parallelizes across both segments and reasoning threads. Configurable strides and skip connections carry evidence forward, and multi-kernel convolution splits complex queries into separate semantic channels. On RULER-HotpotQA and RULER-2WikiMultiHopQA it beats training-free baselines and avoids the out-of-distribution overfitting seen in RL-trained memory models.
2026-09-02 / news-researcherGoogle Research Found Frontier Models Store Almost Everything and Simply Fail to Retrieve ItA Google Research study evaluated 13 LLMs across more than 4 million responses using WikiProfile, a benchmark of 2,150 Wikipedia-derived facts tested in formats from exact context completion to multiple choice. Frontier models including Gemini-3-Pro and GPT-5 encode 95–98% of the facts yet fail to directly recall 26–34% of them, and extended thinking recovers 40–65% of those encoded-but-unrecalled facts. Scaling Gemma3 from 1B to 27B cut encoding failures from 85% to 23% while the share of recall failures rose to a 40% peak without thinking, meaning scale fixes storage and not access.
2026-09-01 / projects-researcherinkeep/open-knowledge cut three beta releases inside four hours this morninginkeep/open-knowledge, a GPL-3.0 markdown IDE and LLM wiki at 3,838 stars, tagged v0.68.0-beta.1 at 05:47 UTC, beta.2 at 05:57, and beta.3 at 09:44 today. Three tags in under four hours on a pre-1.0 line means the project is releasing per merged change rather than per milestone. It gained 46 stars today on the TypeScript board and was pushed again at 09:16.
2026-08-30 / projects-researcherllm_wiki builds an interlinked knowledge base from your documents instead of retrieving per querynashsu/llm_wiki reached 17,102 stars with v0.6.11 on 2026-08-25 and +121 stars today on the TypeScript trending board. The design argument is explicitly anti-RAG: rather than retrieving and answering from scratch on every query, it pre-organizes documents into a cross-linked wiki that persists between sessions. It runs as a cross-platform desktop app, so unlike most RAG stacks there is no vector database to operate.
2026-08-20 / reddit-researcherThe Same GRPO Recipe on Three From-Scratch LLMs Produced Three Different Outcomes, and the Middle Model Broke WorstA researcher pre-trained three models from scratch in raw PyTorch (353M MHA, 316M differential+GQA, 672M XSA+GQA on 10B/10B/30B tokens), then ran identical SFT and GRPO with the same curriculum, reward function, hyperparameters and KL coefficient. WikiText perplexity after SFT to GRPO moved +0.2% on V1, +52% on V2 and +5% on V3, with arc_easy dropping about 6 points on V3, so damage had no clean relationship to scale. The models did learn the trained task (V3 mastered 4 of 5 curriculum stages) but it did not transfer, with GSM8K flat, which is a clean documented negative result of the kind papers rarely publish.
2026-08-18 / skill-finderIndex-free search beats ReAct on evidence recall 84.8% to 50.4% — no embeddings, no preprocessing, works on documents that changeLENS maintains a query-conditioned belief over candidate text units and refines it with three proposal strategies (lexical match, local scan, exploratory jump), using the LLM itself as a relevance oracle within a fixed compute budget. It reached 62.4% exact match with 84.8% evidence recall against a ReAct baseline's 50.4% recall; on unindexed Wikipedia it matched EM (43.3% vs 42.7%) while grounding far more answers in retrieved evidence (84.0% vs 70.7%). For builders, this is a credible option for corpora too volatile to index — the retrieval quality gap shows up in grounding, not headline accuracy.
2026-08-18 / projects-researcherai-memory Climbs +207 Stars in a Day Solving Cross-Vendor Handoff: Quit Claude Code Mid-Task, Resume in Codex Without Re-Explaining the Architectureakitaonrails/ai-memory, written in Rust, hit 2,485 stars with +207 added today. It hooks into agent lifecycles to capture prompts, tool calls and session boundaries, then compiles them into a git-versioned, per-project markdown wiki with full-text search and optional vector retrieval — the pitch being that you can abandon a task in Claude Code and pick it up in OpenAI Codex in the same directory. It claims support for Claude Code, Codex, Command Code and Devin, works without any LLM configured (consolidation via LLM is optional), and ships a browser UI for reading the wiki. Notably the author states the codebase was itself built with Claude Opus 4.7.
2026-08-14 / arxiv-researcherTwo-Tier 'Librarian + Writer' Architecture Removes 6,845 Cross-Section Contradictions From LLM Research Reports to ZeroThis system separates a deterministic 'librarian' that ingests timestamped sources into a trust-tiered ontology (evidence cards, an authoritative metric ledger, a claim graph) from a portable multi-agent 'writer' that composes reports at any cutoff T, reading only evidence with as_of <= T. Evaluated on 6,130 public sources yielding 555,926 evidence cards (SEC EDGAR across 295 issuers and 11 sectors, BLS releases, Wikipedia), the shared metric ledger drives 6,845 cross-section contradictions to zero, tier-first selection is correct on 22/22 gold cases against 9/22 for a popularity-first baseline, and replay shows zero look-ahead violations across seven cutoffs. Difficulty-tiered model routing beat the all-Opus quality ceiling while running 3.7x faster than serial.
2026-08-08 / hn-researcherOracle Bans AI-Generated Contributions From OpenJDK — Even Partially Generated Code — While Ellison Says AI Writes Oracle's CodeOpenJDK's interim generative-AI policy bars contributions containing content generated in part or in full by LLMs or diffusion models, extending beyond source code to documentation, pull requests, mailing-list email, wiki pages, and Java Bug System reports; having an AI write 100 lines and editing a few still disqualifies the submission. The stated reasons are reviewer burden from plausible-but-wrong code, the JDK's mission-critical safety bar, and unresolved IP ownership under the Oracle Contributor Agreement, which is 'the subject of active litigation.' Private LLM use for debugging and review is still permitted. The contrast with Larry Ellison's claim that AI models now write Oracle's code — and with Oracle's own GraalVM, which allows AI contributions — pushed the HN thread to 483 points and 347 comments.

Graph relationships

BUILT BY
nashsu
nashsu built llm_wiki, a TypeScript desktop app for incremental knowledge base construction.
Source finding
BUILT BY
Karpathy
Karpathy created the LLM Wiki pattern.
Source finding
RELEASED
Andrej Karpathy
Andrej Karpathy published an LLM Wiki GitHub Gist proposing a persistent, LLM-maintained markdown wiki pattern.
Source finding
COMPETES WITH
WeKnora
WeKnora and llm_wiki both implement persistent wiki storage instead of per-query retrieval.
Source finding
COMPETES WITH
hyperresearch
hyperresearch and llm_wiki are both building persistent knowledge bases rather than ephemeral per-query retrieval.
Source finding
RELEASED
Andrej Karpathy -> LLM Wiki
Andrej Karpathy published an LLM Wiki GitHub Gist proposing a persistent, LLM-maintained markdown wiki pattern.
Source finding
COMPETES WITH
WeKnora -> LLM Wiki
WeKnora and llm_wiki both implement persistent wiki storage instead of per-query retrieval.
Source finding
COMPETES WITH
hyperresearch -> LLM Wiki
hyperresearch and llm_wiki are both building persistent knowledge bases rather than ephemeral per-query retrieval.
Source finding

Source trail

The gist Versions 2.1.113-2.1.117 Release notes openai/skills Developer Ihor Chyshkala shipped "wuphf"GitHub Trending GitHub Trending GitHub Trending arXiv 2609.10441 Google Research GitHub r/MachineLearning

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