@yibie: 360 PRs in 30 Days, Can't Code, Doesn't Read Plans: How Matt Van Horn Designed His Agent Loop. Last month, a person named Matt Van Horn caught my attention. • No CS degree • Can't code • Doesn't read …

360 PRs in 30 Days, Can't Code, Doesn't Read Plans: How Matt Van Horn Designed His Agent Loop Last month, a person named Matt Van Horn caught my attention. • No CS degree • Can't code • Doesn't read AI-written plans But in the past 30 days, he merged 360 PRs on GitHub across 40 repositories, averaging 12 per day. His project last30days reached 30,000+ GitHub stars and was #1 trending at one point. Printing Press's tweet got 3,447 likes. Agent Cookie has 522 stars. How did he do it? The answer is one sentence: Stop prompting the Agent, start designing a loop where the Agent prompts itself. One Sentence Judgment Matt Van Horn isn't writing code. He turned "prompting the Agent to do things" into a loop. He designed an operating system for the Agent, not instructions for the Agent. This is the same logic as Tibo making Codex support any model: instead of competing on prompt quality, compete on the quality of the Agent runtime. Three Projects, One Methodology Matt built three tools that exactly cover the three layers of an Agent Loop: | Layer | Project | Purpose | Stars | |-------|---------|---------|-------| | Perception | last30days | Multi-source social media search, 13 platforms in parallel, tells the Agent what the external discussion is | 30K+ | | Execution | Printing Press | CLI factory, turns any API into a tool interface the Agent can call efficiently | 3.4K likes | | Infrastructure | Agent Cookie | Cross-machine browser session sync, keeps the Agent always logged in | 522 | Relationship of the three layers: last30days tells the loop what to do → Printing Press gives the loop tools to execute → Agent Cookie keeps the loop running unattended. Essentially, this is the prototype of an Agent OS. How to Design the Loop? Deconstructing last30days' 1400-line SKILL.md last30days is not a "tool". It is Matt's most complete expression of "how to design an Agent Loop". Its architecture has two layers: • Python engine: handles mechanical work – parallel search of 13 platforms, sort by engagement, cross-source clustering and merging, denoising. It's the "muscle". • 1400-line SKILL.md: handles judgment work – how to interpret results, how to synthesize output, what format is correct. It's the "brain". Key Design: Constraining the LLM with 8 LAWS SKILL.md is not a document. It is a finite state machine written in natural language. The core is 8 LAWS, each with a "named failure mode": | LAW | Constraint | Corresponding Regression | |-----|-----------|-------------------------| | LAW 1 | Forbid outputting Sources section at end | Peter Steinberger disaster #3 | | LAW 2 | Forbid inventing titles | v3.0.6 0/8 public regression | | LAW 3 | Forbid em-dash | Common AI-slop tell | | LAW 4 | Forbid ## section headings | Peter Steinberger disaster #2 | | LAW 6 | Forbid outputting raw evidence directly | Hermes 2026-04-19 disaster | Every LAW has a specific, nameable failure case behind it. This follows the same logic as software team postmortems – bug found → given a name → rule added → verified. Loop Flow: From Raw Topic to Final Output Input "Kanye West" ↓ STEP 0: Engine self-check (are you reading the latest SKILL.md or a stale clone?) ↓ STEP 0.45: Query quality check ("gift for a 42-year-old man" → reframe, no one posts that) ↓ STEP 0.55: Parse entities → @kanyewest + r/Kanye + GitHub associations ↓ STEP 0.75: LLM generates JSON query plan (search strategy, weight assignment) ↓ STEP 1: Python engine parallel search of 13 sources → sort by engagement → cross-source clustering ↓ STEP 2: LLM supplements WebSearch to fill blog/news gaps ↓ Judge: LLM synthesizes final output according to 8 LAWS ↓ Feedback loop: each regression has a name → fixed in next loop The impressive thing about this flow is not any single step, but the hybrid of "code + natural language contract". Code handles mechanical search and sorting, LLM handles judgment-based synthesis. The interface between them is SKILL.md. Stop Prompting the Agent, Start Designing the Loop—What This Means Matt summarized his 20 principles in a tweet on June 3rd: 1. Have an idea → /ce-plan generates a plan (don't read it yourself, the plan is for the Agent) 2. Vague idea → first /ce-brainstorm 3. Make the plan, don't read it 4. Use voice, don't type 5. Run 4-6 tabs of parallel tasks 6. Don't set permission restrictions for the Agent (YOLO) 7. You are taste, the Agent is the hand 8. Any task done twice → write a skill 9. Laptop that never shuts down 10. Run last30days before making a plan The core of these 20 principles is one sentence: You are no longer a prompt writer. You are a loop designer. Old workflow: Person has idea → Person writes prompt → Agent executes → Person reviews results → Person writes next prompt New workflow: Person has idea → Loop reads signals → Loop generates prompt → Agent executes → Loop checks results → Loop decides next step The person shifts from operator to supervisor. Practical Implications for Content Studios Back to our scenario: the entire process of topic selection, reconnaissance, and creation can also be designed as a loop: 1. Perception layer: Scheduled task triggers → agent_search + last30days pull materials in parallel 2. Execution layer: LLM scores and recommends → generates draft → human reviews and adjusts taste 3. Infrastructure layer: All workflows written into skills, solidified after two repetitions The essence of what Matt did with last30days is turning "manually searching Reddit + X + YouTube" into a loop. The essence of running a content studio is gradually turning manual material searching, drafting, and publishing into individual loops. Today's prompt engineering is just practice for this step. The real compound interest is: every skill you write deepens the automation of the loop. #AIEngineering #AgentLoop #AgenticEngineering #OpenSource

Input "Kanye West"
  ↓
STEP 0: Engine self-check (are you reading the latest SKILL.md or a stale clone?)
  ↓
STEP 0.45: Query quality check ("gift for a 42-year-old man" → reframe, no one posts that)
  ↓
STEP 0.55: Parse entities → @kanyewest + r/Kanye + GitHub connections
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STEP 0.75: LLM generates JSON query plan (search strategy, weight allocation)
  ↓
STEP 1: Python engine searches 13 sources in parallel → sort by engagement → cross-source clustering
  ↓
STEP 2: LLM supplements with WebSearch to fill blog/news gaps
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Judge: LLM synthesizes final output according to the 8 LAWS
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Feedback loop: every regression has a name → fix in the next cycle