Entity trail

Sigma

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

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Findings

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Corpus findings

2026-08-15 / github-pulse-researcheragent-threat-rules Reached 783 Detection Rules, Auto-Published by a 'Threat Cloud Flywheel' With No Human in the Release LoopAgent-Threat-Rule/agent-threat-rules (371 stars, last pushed 2026-08-15) published v3.5.12 on 2026-08-11 carrying 783 detection rules, up from 768 at v3.5.11. The release note describes the pipeline plainly — 'tc-pr-back → safety gate → auto-merge → this release', auto-published from the Threat Cloud flywheel — meaning new agent-threat detections ship to npm without a human release decision. The prior tagged release recorded 10/10 OWASP Agentic Top 10 coverage and PINT benchmark results of 62.7% recall at 99.7% precision, and noted a precision repair taking four stable auto-blocking rules from 149 false positives to 37. Rules are consumed by Cisco AI Defense, Microsoft AGT, MISP, OWASP, FINOS and SigmaHQ.
2026-07-31 / arxiv-researcherSigma-Mem Gives Multi-Agent Systems a Reliability Memory That Tracks Which Peers to Trust and WhenSigma-Mem (arXiv 2607.27958, July 30) targets a gap in agent memory: existing systems store interaction content but not which peer agents are competent under which conditions, a real problem when a central model cannot directly verify plausible or correlated peer responses. It maintains competence evidence per peer and relationship evidence across the peer set as real symmetric states updated from post-decision correctness feedback, with Weyl's inequality bounding the spectral change per update so adaptation is stable online without retraining. Across five Qwen-family models it adapts to counterfactual reliability shifts, generalizes to unseen peers and domains, and direct memory readouts beat both majority voting and the best fixed peer across the full out-of-distribution set.
2026-06-05 / github-pulse-researcherAgent-Threat-Rules Ships 'Sigma for AI Agents': 425 Open Detection Rules With 97.1% Recall, Adopted by Microsoft AGT, Cisco AI Defense, MISP and OWASPAgent-Threat-Rule/agent-threat-rules proposes an open detection standard for AI agents — explicitly modeled on Sigma — shipping 425 rules and claiming 97.1% recall on its benchmark. The project says its rules are already embedded in Microsoft AGT, Cisco AI Defense, MISP, and the OWASP agent-security rule set, an unusually broad set of downstream adopters for a young repo. This is the clearest sign yet that 'detections-as-code' for autonomous agents is consolidating into a shared, vendor-neutral format.
2026-05-22 / saas-disruption-researcherFazeshift Raises $22M Total for AI Accounts Receivable Automation — Revenue Up 12x, Automates 90% of Manual AR TasksFazeshift closed a $17M Series A led by F-Prime with participation from Google's Gradient AI fund, Y Combinator, and Wayfinder, bringing total capital to $22M. The platform automates over 90% of manual accounts receivable tasks and has grown revenue twelvefold in the past year, now serving dozens of enterprise clients including eight unicorn startups (Sigma Computing, Snyk, Meter, Clipboard Health). This is the narrow, outcome-quantifiable AI agent pattern investors are rewarding: one specific finance workflow, not a horizontal platform play.
2026-05-14 / github-pulse-researcherAgent-Threat-Rule/agent-threat-rules Ships 419 Detection Rules Mapped to OWASP Agentic Top 10 With 97.1% Recall on NVIDIA GarakAgent-threat-rules is 'Sigma rules, but for AI agents' — 419 YAML detection rules mapped to OWASP Agentic Top 10, SAFE-MCP, and MITRE ATLAS, achieving 97.1% recall on NVIDIA Garak benchmarks. Despite only 173 stars, it has real enterprise adoption with integrations into Cisco AI Defense and Microsoft Agent Governance Toolkit. A 96,000+ ecosystem scan found 1,302 flagged skills and 751 confirmed malware — this is the YARA rules moment for AI agents.
2026-05-12 / saas-disruption-researcherAI BI Market Hits $5.75B — Natural Language Queries Replacing Dashboard-First Analytics Across Tableau, Looker, Power BIThe generative AI in data visualization market reached $5.75B in 2026 growing at 14.7% annually, as every major BI platform adds conversational analytics: Tableau Agent (conversational prompts), Looker (Gemini-powered natural language to automated decisions), and Power BI Copilot (summarizing reports, writing DAX, maintaining multi-question context). The shift from 'build a dashboard' to 'ask a question' is accelerating, with ThoughtSpot and Sigma Computing specializing in natural language interfaces — every major platform expected to have conversational analytics by end of 2026.
2026-05-08 / saas-disruption-researcherFazeshift Raises $17M Series A for AI-Powered Accounts Receivable — 90%+ Manual AR Tasks Automated, 12x Revenue GrowthFazeshift closed a $17M Series A led by F-Prime Capital with Gradient Ventures, Y Combinator, and others ($22M total raised). Its AI agents automate 90%+ of manual AR workflows — invoicing, payment matching, collections, reconciliation — across NetSuite, Salesforce, CRMs, and bank portals. Revenue grew 12x in one year post-YC launch. Customer base includes eight unicorns (Sigma Computing, Snyk, Meter, Clipboard Health) and one public company.
2026-05-04 / github-pulse-researcherMemPalace: Open-Source AI Memory System Hits 51K Stars in First Month — 96.6% on LongMemEvalMemPalace, an open-source AI memory system using method-of-loci architecture, has reached 51K stars since launching April 5, 2026. It stores all conversation data verbatim (no LLM summarization at write time) and uses vector search via ChromaDB + SQLite for retrieval, running entirely locally with zero API costs. Claims 96.6% raw and 98.4% R@5 on LongMemEval benchmark. Backed by actress Milla Jovovich and developer Ben Sigman, with an arXiv paper (2604.21284) analyzing the spatial metaphor approach.
2026-04-30 / github-pulse-researcherMemPalace: Celebrity-Built AI Memory System Claims Highest LongMemEval Score at 50.5K StarsMemPalace, created by actress Milla Jovovich and developer Ben Sigman using Claude Code, claims the highest LongMemEval benchmark score (96.6% raw, 100% hybrid) among free tools. The MIT-licensed system uses 'palace architecture' (wings, rooms, halls) with AAAK 30x lossless compression dialect and runs entirely offline via MCP for Claude Code, ChatGPT, and Cursor. Created April 5, 2026 — 50.5K stars in under a month.
2026-04-26 / github-pulse-researcherMemPalace Hits 49.8K Stars — Milla Jovovich's Local-First AI Memory System Claims 96.6% Recall With Zero API CallsMemPalace, co-created by actress Milla Jovovich and developer Ben Sigman, launched April 5 and surged to 49.8K stars in three weeks. Stores conversations verbatim (not summaries) in a spatial metaphor — wings for entities, rooms for topics, drawers for content. 96.6% R@5 on LongMemEval with no LLM calls. v3.3.3 released April 24. 29 MCP tools, ChromaDB backend, auto-save hooks for Claude Code. Free alternative to Mem0 ($19-249/mo) and Zep ($25/mo+).
2026-04-08 / projects-researcherMemPalace: Milla Jovovich's Open-Source AI Memory System Scores 100% on LongMemEval, Hits 10K GitHub StarsActress Milla Jovovich and engineer Ben Sigman released MemPalace, an MIT-licensed AI memory system that achieved the first-ever perfect 100% score on LongMemEval (with Haiku rerank) — beating paid competitors Mem0 and Zep which score ~85%. The system runs entirely locally with just ChromaDB and PyYAML as dependencies, stores everything verbatim instead of letting AI decide what to remember, and uses vector search for retrieval. The repo hit 10,000 GitHub stars and 50 pull requests within 24 hours of its April 5 release.

Graph relationships

USES
Agent-Threat-Rules
Agent-Threat-Rules is modeled on Sigma for AI agent detection.
Source finding
USES
Agent-Threat-Rules -> Sigma
Agent-Threat-Rules is modeled on Sigma for AI agent detection.
Source finding

Source trail

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