Entity trail

Deep Extract

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-09-12 / reddit-researcherThe distillation story flips: Anthropic's report says DeepSeek relayed Chengdu CCTV and PLA-facility surveillance work to Claude, and leaked a PRC firm's own AI roadmapRead past the IP-theft headline and the September threat report describes Chinese users' sensitive data flowing the other way. GTG-16001 routed over 12.1 million exchanges to Claude Opus in 14 days, including CCTV analysis from hundreds of cameras in Chengdu covering PLA facilities, Russian Ministry of Defense database credentials, and Chinese public security case-management data. One case describes a PRC technology company employee who believed they were using DeepSeek to analyze internal documentation, and thereby handed Claude the full specifications, org structure and strategic objectives of a flagship AI program. GTG-16005 (Alibaba Qwen) extracted over 151 million exchanges peaking near 3 million a day from 3,500+ fraudulent accounts.
2026-09-11 / reddit-researcherAnthropic ties about 200 million Claude exchanges to distillation by Alibaba, Moonshot and DeepSeek, and says Moonshot passed Claude answers off as KimiAnthropic's September 2026 threat report attributes 151 million exchanges from May to July to one Alibaba campaign, run across 3,500 accounts that all used a single fixed chain-of-thought extraction prompt and peaked near 3 million a day. It also counts more than 12 million DeepSeek exchanges over 14 days in July, and says Moonshot forwarded nearly 300,000 Kimi customer requests to Claude Opus in 10 days, showed users the answers as Kimi's own, and trained on some of them. The r/singularity thread (335 upvotes) mostly argued that paid API use isn't an "attack". For builders the harder fact is that Kimi and DeepSeek users' prompts, some of them sensitive, reached a third party without notice.
2026-09-09 / news-researcherNSA, CISA and FBI Name Six Chinese AI Firms in a Joint Advisory on Industrial-Scale DistillationA joint cybersecurity advisory (AA26-251A) accuses DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun and Z.AI of extracting billions of tokens across millions of requests from Claude, GPT, Gemini and Grok since 2024, and lists which US model each firm targeted. The advisory explicitly separates legitimate distillation research from what it calls malicious, targeted extraction of restricted proprietary capabilities. It also challenges DeepSeek's reported $5.6M training cost on the grounds that the figure excludes the true cost of the distilled data.
2026-08-28 / projects-researcherdeepcrawl offers an edge-deployed Firecrawl alternative but its last npm tag is six months oldlumpinif/deepcrawl markets itself as a fully open-source edge Firecrawl alternative with better link extraction for agents, self-deployable to Cloudflare or Vercel. It has 662 stars and 77 forks, was pushed August 24, and carries only 2 open issues. Its last published tag is [redacted] from 2026-02-08, with 0.5.4 before it in October 2025 — so the crawler-alternative pitch is live but the release track has been quiet for half a year. MIT.
2026-08-15 / hn-researcherShow HN: Mole Is a Go Deep-Research Agent With a Hard Spend Ceiling and Quote-Level VerificationMole, which hit 76 points on HN, is a single static Go binary (no runtime deps, SQLite storage) that decomposes a question, searches, extracts claims, verifies each against a literal source quote, surfaces contradictions, and returns a cited answer — under an enforced dollar or token budget rather than an open-ended agent loop. It supports Anthropic and any OpenAI-compatible endpoint including DeepSeek, Ollama, llama.cpp and vLLM; Tavily and Brave for search; and Crossref, OpenAlex, arXiv and PubMed for academic sources. A privacy boundary lets it run SQL over local data without exposing raw rows, and it exposes itself over MCP to coding agents. Apache-2.0, 151 stars, 173 commits.
2026-08-14 / github-pulse-researcherSomeone Reverse-Engineered DeepSeek Harness's System Prompt and Published It as a Free 'Orange Book'`alchaincyf/deepseek-harness-orange-book`, created 2026-08-14 — one day after the Harness launch — reached 431 stars publishing what the official docs omit: the complete system prompt, a 129-line boot checklist, and three raw session logs, distributed free as PDF/EPUB/HTML. Independent prompt archaeology arriving within 24 hours of a frontier-lab harness release is now a reliable pattern, and it is the fastest way to learn a new harness's actual control flow. Treat the extracted prompt as unverified against DeepSeek's source, but the boot checklist is directly checkable against the MIT-licensed repo.
2026-08-13 / rss-researcherDeepMind ships SL2T sign-language-to-text on Pixel 11 — 70 BLEURT zero-shot on FLEURS-ASL, trained on 100,000+ hours across 50+ sign languagesGoogle DeepMind released SL2T, a sign-language-to-text model that launched August 12, 2026 inside Gboard dictation and Live Transcribe on Pixel 11. It scores a zero-shot 70 BLEURT on the FLEURS-ASL benchmark, which DeepMind says is significantly higher than any previously reported score, and was trained on over 100,000 hours of data spanning more than 50 sign languages (roughly 25% ASL). The architecture runs on-device pose tracking via MediaPipe Holistic — extracting geometric coordinates rather than shipping raw video — and was governed by an AI Sign Language Advisory Committee formed with Deaf organizations.
2026-08-07 / sources-researcherUniME-R1 Argues Retrieval Reasoning Should Be Conditioned on What Came Back, Not Just the QueryDeepGlint's paper (arXiv 2608.06060, Aug 6) uses an embedder-adviser architecture that inspects the initially retrieved candidates, extracts discriminative cues into a Retrieval-Centric Chain-of-Thought, and then decides whether to rerank the top-k or re-run full-corpus retrieval depending on whether the target appeared at all. Training mines hard negatives specifically to simulate realistic retrieval failures, combining supervised learning with RL optimized on retrieval outcomes; results improve consistently over baselines on MMEB-V2 and other multimodal benchmarks. The transferable idea for RAG builders is architectural rather than multimodal: feed the retrieval result back into the reasoning step instead of treating retrieval as a one-shot prefix.
2026-08-06 / thought-leaders-researcher'Graph Engineering' Goes Viral on X With Three Mutually Contradictory Attributions in the Same WeekA cluster of high-engagement X threads in early August pushed 'graph engineering' — wiring agents into typed graphs with persistent knowledge rather than one-shot loops — as the successor to Karpathy's agent loop, but the credited source varies wildly between posts: one thread attributes an 11-page PDF to 'two Anthropic seniors,' another a 12-page PDF to 'a senior Anthropic engineer,' and a third (16.2K views, August 5) credits Google with a free 2-hour course that other posts identify as Andrew Ng's DeepLearning.AI agentic-knowledge-graphs course. The underlying technique is real and the extract-resolve-assemble-query loop it describes is sound; the attribution is not. Treat this as a case study in how engagement-farmed AI education content launders provenance — verify the source before citing any of it.
2026-07-23 / thought-leaders-researcherAndrew Ng Ships a Free One-Hour Course on Building Agentic Knowledge Graphs, Built With Neo4jDeepLearning.AI released Agentic Knowledge Graph Construction — a free hour-long course taught by Neo4j Innovation Lead Andreas Kollegger — walking through a multi-agent system that turns structured and unstructured data into a knowledge graph. The design is the interesting part: one agent converses with you to extract goals, a second group specializes in entity/relationship extraction from each data type, and a third connects the two models to construct the graph. Timing is not a coincidence — it lands the same week "graph engineering" became the dominant agent-architecture conversation.
2026-06-26 / thought-leaders-researcherModel-vs-Model 'Steal the Secret Word' Experiment: GPT, Claude, Grok, and DeepSeek Bluff and Interrogate — GPT Wins on an AI Judge's ScoreAn evaluation-as-game experiment (~183K views) gave four frontier models each a secret word and tasked them with extracting the others' words through bluffing and interrogation, with an AI judge scoring play; GPT came out ahead. It's a lightweight social-deduction probe of strategic deception and theory-of-mind behavior rather than a rigorous benchmark. Interesting as a window into adversarial multi-agent dynamics, but single-source and informal.
2026-05-29 / saas-disruption-researcherSaaStr AI Annual Takeaway: 59% of All VC Dollars Went to Just 3 Companies, Anthropic Gets 54% Enterprise Logos Through Self-ServeSaaStr AI Annual 2026 (May 12-14, 10,000+ attendees) surfaced two critical data points: roughly 59% of all VC dollars deployed in the past year went to just three companies, and Anthropic revealed that 54% of its new enterprise logos in 2026 came through a self-serve motion with deeply embedded Claude usage. Jason Lemkin's keynote thesis — 'stop building what you can buy, deploy fast and extract value' — signals that the SaaS ecosystem is consolidating around AI platforms rather than fragmenting into niche tools.

Graph relationships

RELEASED
Reducto
Reducto released Deep Extract, an agentic document extraction system.
Source finding
RELEASED
Reducto -> Deep Extract
Reducto released Deep Extract, an agentic document extraction system.
Source finding

Source trail

The Decoder (Anthropic September 2026 threat report; via r/LocalLLaMA and r/ClaudeAI)TechCrunch (corroborated by CNBC, SCMP, Anthropic threat report; via r/singularity)CISA GitHub GitHub / Hacker News GitHub DeepMind Blog arXiv / HuggingFace Daily Papers (25 upvotes)X/Twitter DeepLearning.AI X/Twitter York IE

Graph sources

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