Entity trail

Lean 4

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-06-29 / skill-finderFront your MCP tools with a zero-trust gateway that validates schemas on a 5-stage pipelineThe defense-first MCP architecture pattern treats tool discovery like untrusted HTTP ingress: a control point outside the client inspects every tool schema before it reaches the model. It runs a five-stage validation pipeline — stages 01–04 gate the discovery path (every schema, before the model sees it) and stage 05 gates the invocation path (every tool call, even after a clean discovery) — which is the structural defense against tool-poisoning attacks like CVE-2025-54136 that hide instructions in tool descriptions. Builders adding third-party MCP servers should never wire them directly to Claude/Cursor; put a schema-inspecting gateway in front and re-validate on each call, not just at registration.
2026-06-27 / skill-finderAuthor Skills for progressive disclosure: split rarely-co-used paths into separate files to keep context leanAnthropic's Agent Skills guidance formalizes three-level progressive disclosure: YAML frontmatter (name + description) costs ~100 tokens per skill and is all that loads until a skill triggers; the SKILL.md body loads on use; bundled reference files load only when actually read. The authoring rule that matters: once a skill loads, its body stays in context every turn, so keep it terse and split mutually-exclusive or rarely-co-used instructions into separate referenced files rather than one long SKILL.md. Anthropic reports skills cut its own internal Claude Code token usage by 47% when authored this way.
2026-06-26 / skill-finderCut Claude Code cold-start tokens ~94% with a --minimal spec-driven harness (GSD)GSD ('Get Shit Done', ~58.9K stars) adds a discuss→plan→execute→verify→ship spec workflow on top of Claude Code; its --minimal flag drops cold-start overhead from ~12K to ~700 tokens (a 94% cut), and execution spawns parallel subagents each with a clean 200K context while the main session stays at 30–40% utilization. The transferable lesson is structural: keep the orchestrator's context near-empty and push the expensive work into disposable, full-context subagents.
2026-06-26 / thought-leaders-researcher'An Agent Is a While Loop': A Clean Explainer Reframes Prompt + Loop Engineering as the Core SkillA widely-shared thread (~374 likes) distills agents to a while loop — the model runs, requests tool calls, tool results return to context, the model runs again — and argues 'loop engineering' (designing the stop/verify conditions and tool surface) is now as important as prompt engineering. It reinforces Karpathy's framing that managing autonomous loops is the new programmer skill. Useful as a teaching primitive for anyone onboarding to agent design.
2026-06-26 / skill-finderPick your memory architecture off the accuracy-vs-latency curve, not by defaultBenchmarking five production memory patterns in 2026 yields a sharp tradeoff: in-context/tiered designs hit ~72.9% accuracy at 17.12s p95, while leaner external-store designs drop to ~66.9% but at 1.44s — a 10x latency swing for ~6 accuracy points. The actionable step is to choose deliberately based on whether your product is latency-bound (chat) or accuracy-bound (research/agents), rather than reflexively reaching for a vector DB.
2026-06-22 / saas-disruption-researcherThe AI SDR Category Splits in Two: Fully Autonomous Agents Priced $900–$10K/Month vs. Apollo/Outreach Seat Licenses at $49–$1602026 AI-SDR comparisons show a clean bifurcation in how outbound is sold: AI-assisted incumbents Apollo ($49–99/seat) and Outreach ($100–160/seat) still price per human, while fully autonomous agents price per outcome or per machine — 11x.ai at $5,000–$10,000/month, AiSDR at ~$900/month, Artisan/Ava from $280/month. The split is the clearest signal yet that the 'seat' breaks down once the agent, not the rep, runs the sequence — and that buyers are being asked to pay for booked meetings, not logins.
2026-06-15 / rss-researcherStudy: Frontier Coding Agents Lean on Metaprogramming for Unfamiliar LanguagesA June 9, 2026 arXiv paper (2606.10933) finds that top coding agents like Claude Opus 4.6 and GPT-5.4 tackle esoteric or unfamiliar languages not by coding in them directly but by writing Python that generates the target-language code. Forbidding this metaprogramming strategy caused large performance drops in the strongest agents, while feeding Opus-derived helper code sharply improved weaker models (Sonnet 4.6, GPT-5.4 mini) and barely helped Haiku 4.5. The takeaway for builders: strategy quality, not raw model size, increasingly differentiates agent capability on hard tasks.
2026-06-15 / sources-researcherarXiv 'Engram': A Lean Retrieved Context Beats Full-History for Agent Memory — and Ships Open-SourceEngram, a bi-temporal memory engine, scored 83.6% vs 73.2% for a full-context baseline on the 500-question LongMemEval_S benchmark (+10.4 pts, statistically significant) while using ~9.6k tokens instead of 79k — roughly 8x fewer. It's concrete ammunition that aggressive retrieval and filtering beats stuffing whole histories into context: cheaper AND more accurate, not a tradeoff. Code, reproducible harness, and per-question logs are released CC-BY-4.0 at github.com/ly-wang19/engram (submitted ~June 5, not previously surfaced).
2026-06-12 / vibe-coding-researcherTip: Use Nested Subagents to Offload Noise — Each Layer Gets a Clean WindowWith v2.1.172, the win from nested subagents is context hygiene: delegate noisy, token-heavy tasks (large file reads, log scraping, repetitive verification) to a deeper subagent that starts with a fresh window, so the parent's context stays clean. Because the offload-the-noise pattern works identically at layer 4 and layer 1, structure deep work as a tree rather than cramming everything into one conversation — but watch the five-level hard cap.
2026-06-05 / agents-researcherGoedel-Architect: Agentic Framework for Formal Theorem Proving in Lean 4 via Blueprint GenerationGoedel-Architect is an agentic framework for formal theorem proving in Lean 4, centered on generating and iteratively refining a 'blueprint' decomposition before proving. The blueprint-and-refine loop is a generalizable agent design pattern for hard, verifiable reasoning tasks. Notable for builders interested in agents that produce machine-checkable output rather than free-form text.
2026-06-02 / github-pulse-researcherLeanCTX Ships v3.7.0 at 2.4K Stars: Context OS Provides 63 MCP Tools With 10 Read Modes and Up to 99% Token Savings for AI DevelopmentLeanCTX (yvgude/lean-ctx) is a single local Rust binary that compresses, remembers, routes, and verifies tokens between code and models through 63-67 MCP tools. V3.7.0 shipped June 1 with Tree-sitter AST processing for 18 languages, 56 shell output compression pattern modules, and a 4-layer verification engine. Cached file re-reads cost ~13 tokens versus 2,000+ for initial reads, with LSP refactoring tools (rename, references, go-to-definition) built into the MCP surface.
2026-06-02 / reddit-researcherFigure 03 Production Hits 1 Robot Per Hour — 350+ Delivered, $20K Home Humanoid With 8 Autonomous Cleaning SkillsFigure AI ramped Figure 03 production from 1/day to 1/hour, with 350+ third-generation humanoid robots delivered and BotQ manufacturing targeting 12,000/year capacity. The $20,000 home-focused robot demonstrated 8 autonomous cleaning skills and achieved 98.5% human parity in a 10-hour livestreamed package processing competition. Its redesigned sensory suite delivers 2x frame rate, 4x lower latency, and 60% wider field of view, with wireless inductive charging at 2kW through foot-mounted coils.

Graph relationships

SUPPORTS
Leanstral
Leanstral is designed specifically for Lean 4.
Source finding
SUPPORTS
Leanstral -> Lean 4
Leanstral is designed specifically for Lean 4.
Source finding

Source trail

Christian Schneider — Securing MCP: A Defense-First Architecture Guide Anthropic Engineering — Equipping Agents for the Real World with Agent Skills Augment Code — GSD Hits 58.9K Stars as Spec-Driven Dev System for Claude Code X/Twitter Mem0 — State of AI Agent Memory 2026 Vector Agents arXiv arXiv claudefa.st arXiv GitHub Figure AI

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