{ "$schema": "https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json", "basics": { "email": "mike.reese1212@gmail.com", "label": "I like inventing things. I'm building agentic systems and the experiments that show whether they work.", "location": { "city": "San Francisco, CA" }, "name": "Mike Reese", "profiles": [ { "network": "GitHub", "url": "https://github.com/mike-reese", "username": "mike-reese" }, { "network": "LinkedIn", "url": "https://www.linkedin.com/in/mike-reese-4b2129188/", "username": null } ], "summary": "Founded 2 companies, innovated in the heavy equipment industry with a physics-based universal compatibility matching engine based on a graph structure, and currently trying to find the Rosetta Stone for the context layer of LLMs that unlocks purely declarative prompting for long-running tasks in an evolving system.\n\nBig proponent of thinking from first principles, removing friction, questioning the status quo, and reducing complex systems down to quantifiable, pure-signal experiments. AKA - just cut the Gordian Knot.\n \nSo, I'm really enjoying the evolving paradigm and rapid pace at the frontier of AI :-)", "url": "https://mikereese.dev" }, "meta": { "canonical": "https://mikereese.dev/resume.json", "version": "v1.0.0" }, "projects": [ { "description": "Forked and hosted the Gas City multi-agent runtime on AWS: one EKS cluster per environment, autoscaled with Karpenter, running about 12 agent crews, one per business domain, communicating via EventBridge. Custom agent packs and role definitions, rewrote Gas City, Dolt and Beads integrations to be Kubernetes-native.", "entity": "Matchpoint Intelligence", "keywords": [ "Gas City", "Kubernetes (EKS)", "Karpenter", "AWS CDK", "Beads", "Dolt" ], "name": "Gas City on AWS", "roles": [ "Sole Developer" ], "url": "https://mikereese.dev/work/gas-city/" }, { "description": "Ideator → Author → Generator → Evaluator → Judge automated loops over 465 work units folded about 20 repos into monorepos. DAG dependency graph, Observability dashboard, Full telemetry coverage with 'Miner' agents triggered on merges to identify design pattern opportunities, operational complexity improvements, and removal candidates (bloat reduction). Account pool with automated swapping and session resume when limits were hit.", "entity": "Matchpoint Intelligence", "keywords": [ "Codex", "Claude Code", "Python", "SQLite", "git worktrees" ], "name": "Loop Harness", "roles": [ "Sole Developer" ], "url": "https://mikereese.dev/work/loop-harness/" }, { "description": "Current Focus. An application layer and context management system for agentic work to facilitate declarative, long-running tasks in an evolving system (which is every system). Purpose is to reduce Drift / Bloat / Sprawl / Regressions over long-running tasks by creating a measure for epistemic understanding with agents. Stateful Knowledgebase to derive the current 'Truth', Self-extending ontology, context / action / effect checks, telemetry on measurable system objectives and agent sessions / turns / tool use for a given declarative prompt. Three prototypes to date. Experiment harness exists solely to facilitate design decisions for this idea.", "entity": "Independent research", "keywords": [ "Python", "Rust", "Dolt", "FastAPI", "Cytoscape.js", "MCP", "Claude Code hooks", "Codex hooks", "Docker" ], "name": "Telos", "roles": [ "Sole author" ], "url": "https://mikereese.dev/work/telos/" }, { "description": "Isolated runtime experiments on agentic techniques. Goal is pure-signal hypothesis testing, prioritizing rapid iteration and validation with toy model repos. Uses Scientific Method - causal claims, control/test arms, measurable outcomes, separate validation environment combining quantitative and qualitative measurements, multiple runs to reduce variance.", "entity": "Independent research", "keywords": [ "Python", "Docker", "SQLite", "LLM judges", "z3" ], "name": "Experiment harness", "roles": [ "Sole author" ], "url": "https://mikereese.dev/work/experiment-harness/" }, { "description": "Invented a universal, physics-based compatibility engine for heavy machinery and attachments. Used inference to project machine and attachment specifications into hardware component-level subgraphs & used set theory to generate the Cartesian product of viable component combinations. Modeled attachment fit for heavy equipment as the intersection of three physical property relationships between linked hardware components: connect, power and lift. 25M-node, 100M-edge graph.", "entity": "Matchpoint Intelligence", "keywords": [ "Python", "AWS Step Functions", "Lambda", "SQS", "DynamoDB", "Neptune", "Gremlin", "OpenCypher", "CDK", "Bedrock" ], "name": "Compatibility engine", "roles": [ "Sole Developer" ], "url": "https://mikereese.dev/work/compatibility-engine/" }, { "description": "Let Attachments King shoppers filter every product by their machine, beyond what Shopify's collection pages allow, using a Bloom filter per product carried through to checkout. Online-store sales rose 99% in the first month.", "entity": "Attachments King", "keywords": [ "Shopify Liquid", "Shopify metaobjects and metafields", "JavaScript modules", "Event bus and reducer", "IndexedDB", "Checkout UI extensions" ], "name": "Storefront fitment filter", "roles": [ "Sole Developer" ], "url": "https://mikereese.dev/work/storefront-fitment-filter/" } ], "skills": [ { "keywords": [ "Claude Code", "Codex", "MCP servers", "agent hooks", "generator/evaluator loops", "LLM judges" ], "name": "Agents" }, { "keywords": [ "pre-registered design", "isolation with Docker", "LLM-as-judge rubrics", "z3" ], "name": "Experiments" }, { "keywords": [ "graph modeling", "Neptune (Gremlin, OpenCypher)", "Dolt", "SQLite", "set theory", "Bloom filters" ], "name": "Data" }, { "keywords": [ "AWS Step Functions", "Lambda", "SQS", "DynamoDB", "EKS", "Karpenter", "CDK", "Cloudflare Workers" ], "name": "Cloud" }, { "keywords": [ "Shopify themes and checkout extensions", "IndexedDB", "Next.js" ], "name": "Frontend" }, { "keywords": [ "Rust", "Python", "TypeScript" ], "name": "Languages" } ], "work": [ { "endDate": null, "name": "Telos", "position": "Independent research", "startDate": "2026-08", "summary": "A context and application layer for agentic work, prioritizing epistemic understanding in an evolving system for declarative, long-running tasks. Designing via an experiment harness that validates agent techniques with controlled experiments." }, { "endDate": "2026-09", "name": "Matchpoint Intelligence", "position": "Founder", "startDate": "2026-02", "summary": "Invented and built a physics-based, universal heavy-equipment compatibility platform, its agent data API, and the agent crews that maintained it." }, { "endDate": "2026-01", "name": "Attachments King", "position": "Founder", "startDate": "2023-12", "summary": "E-commerce company for heavy machinery attachments. Bootstrapped to $4.21M in annual revenue." } ] }