Our Agents Ran Our Launch-Week Analytics

PostHog's launch-week analytics were run by a hive of AI agents that read 516 comments across ten Reddit threads, a Product Hunt page, GitHub traffic, and PostHog funnels, producing markdown and JSON reports that drove every subsequent launch decision. The agents found that 46% of engagement came from r/ClaudeCode, Product Hunt delivered 195 upvotes but only ~25 visitors, and two evaluators independently requested the same missing feature, leading to a bug fix prioritized in version 0.4.4.

Our Agents Ran Our Launch-Week Analytics Ten Reddit threads, 501 comments, a Product Hunt page, GitHub traffic and PostHog funnels — read, cross-referenced, and reported by a hive of agents. The workflow, and how to point it at your own launch. After launch week we had ten Reddit threads, 516 comments, a Product Hunt page, and analytics dashboards — far more than any human was going to read honestly. So we didn't. A hive of agents read all of it : one agent per channel, structured briefs in, markdown + JSON reports out, one synthesis at the end. Every launch decision we've made since traces to those reports. Here's the workflow. There’s a specific kind of lying founders do after a launch: they remember the five most emotional comments and call it “what the community said.” We had 516 comments across ten subreddits. Nobody’s memory survives that honestly. But we make a tool whose whole job is coordinating agents on real work /blog/run-an-office-of-ai-agents/ . Launch analytics turned out to be the best dogfood we’ve ever had. The floor plan the-floor-plan Four agents, one channel each, spawned with a written brief: Reddit agent — the big one. Ten launch threads saved as raw JSON, 501 comments including every nested reply. Brief: account for every comment — bucket objections, praise, feature asks, and pricing signals, with quotes and usernames preserved. Product Hunt agent — the launch page: all 27 comments, the review, and the page’s own embedded data, read straight from the source rather than eyeballing rendered numbers. GitHub agent — stars, traffic, referrers, clones: which channel actually moved the repo. Analytics agent — PostHog funnels: installs, first runs, and where new users stalled. Each agent wrote two artifacts into a shared research folder: a markdown brief a human actually wants to read, and a JSON file with the counted, bucketed data so later questions don’t require re-reading anything. Then the orchestrator synthesized the four into one picture. Why the fan-out matters why-the-fan-out-matters The naive version of this is pasting comments into one chat session until it fills up. The problem isn’t just context size — it’s that a model skimming its 400th comment gets exactly as lazy as a human does. One agent per channel keeps each report grounded in a full, careful read, and the orchestrator /blog/how-the-god-orchestrator-works/ works from four distilled reports instead of raw sludge. Fan out, then synthesize. It’s the same pattern /blog/multi-agent-orchestration-patterns/ that works for code. The other thing a hive gets you is iteration without re-reading . Days later we came back with sharper questions — “split willingness-to-pay by supporter motive versus buyer motive,” “which commenters were blocked from even running it?” — and dispatched them to the same agent, which still had its memory /blog/how-agents-remember-semantic-memory/ of the corpus. Each pass appended to the same reports. The research got thicker instead of starting over. What fell out of it what-fell-out-of-it Findings we would have missed by skimming, all of which changed real decisions: 46% of all engagement came from one subreddit. r/ClaudeCode delivered 1,017 of 2,233 combined upvotes. Two other communities flatlined. That’s next launch’s channel budget, decided. Product Hunt sent 195 upvotes and ~25 visitors. Credibility channel, not traffic channel. We’d have guessed wrong. The two most serious evaluators asked for the same missing feature — a visible “this decision needs your eyes” flag — in different words on different platforms. Only cross-channel synthesis caught that they were the same request. Every blocked-user story on two channels traced to the same bug class the non-Claude-Code path on Windows , which moved it to the top of 0.4.4 /blog/launching-munder-difflin-v0-4-4/ . The Reddit and Product Hunt halves of this analysis became their /blog/what-reddit-told-us-about-munder-difflin/ own /blog/number-five-on-product-hunt/ posts — both written from the agents’ reports , which is why they have real numbers in them instead of vibes. Run it on your launch run-it-on-your-launch The recipe, portable to any hive setup: Capture raw sources locally first. Reddit threads have JSON endpoints; save them to disk so agents parse structure instead of scraping rendered pages. One agent per channel, written briefs. The brief that worked: account for every comment; bucket, count, and quote; write both a human brief and a JSON dataset; flag what you couldn’t verify. That last clause matters — our Reddit agent correctly flagged that view counts are null in public JSON rather than inventing them. A shared research folder all agents write into, so reports reference each other. One synthesis pass at the end — and keep the agents around, because your best questions arrive three days later. An evening of agent work, and launch week stops being a feeling and becomes a dataset. The overnight version /blog/claude-code-automation-while-you-sleep/ works too — we know, because half of this ran while we slept off the launch. FAQ What did the agents actually analyze? Four channels in parallel: ten Reddit launch threads 501 comments including every nested reply , the full Product Hunt page 27 comments plus the page's own data , GitHub traffic, and PostHog funnels. Each agent produced a structured report — a readable markdown brief plus a JSON file — and the orchestrator synthesized them into one picture. Why use multiple agents instead of one long session? Each channel is a full context window of raw material on its own. One agent per channel means each report is written by something that actually read every comment, not a skim. The orchestrator then works from the four distilled reports — which is exactly the fan-out-then-synthesize pattern hives are good at. Can I run this on my own launch? Yes — the recipe is at the end of the post. You need the raw threads saved locally Reddit's JSON endpoints work , one agent per channel with a clear brief, a shared folder for reports, and one synthesis pass at the end. A weekend launch produces an evening of agent work.