{"slug": "our-agents-ran-our-launch-week-analytics", "title": "Our Agents Ran Our Launch-Week Analytics", "summary": "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.", "body_md": "# Our Agents Ran Our Launch-Week Analytics\n\nTen 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.\n\nAfter launch week we had ten Reddit\nthreads, 516 comments, a Product Hunt page, and analytics dashboards — far more than any human\nwas going to read honestly. So we didn't. **A hive of agents read all of it**: one\nagent per channel, structured briefs in, markdown + JSON reports out, one synthesis at the end.\nEvery launch decision we've made since traces to those reports. Here's the workflow.\n\nThere’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.\n\nBut we make a tool whose whole job is\n[coordinating agents on real work](/blog/run-an-office-of-ai-agents/). Launch analytics turned\nout to be the best dogfood we’ve ever had.\n\n## The floor plan [#](#the-floor-plan)\n\nFour agents, one channel each, spawned with a written brief:\n\n**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.\n\nEach agent wrote two artifacts into a shared research folder: a **markdown brief** a human\nactually wants to read, and a **JSON file** with the counted, bucketed data so later questions\ndon’t require re-reading anything. Then the orchestrator synthesized the four into one picture.\n\n## Why the fan-out matters [#](#why-the-fan-out-matters)\n\nThe naive version of this is pasting comments into one chat session until it fills up. The\nproblem isn’t just context size — it’s that a model skimming its 400th comment gets exactly as\nlazy as a human does. One agent per channel keeps each report grounded in a full, careful read,\nand the [orchestrator](/blog/how-the-god-orchestrator-works/) works from four distilled reports\ninstead of raw sludge. Fan out, then synthesize. It’s the same\n[pattern](/blog/multi-agent-orchestration-patterns/) that works for code.\n\nThe other thing a hive gets you is **iteration without re-reading**. Days later we came back\nwith sharper questions — “split willingness-to-pay by supporter motive versus buyer motive,”\n“which commenters were blocked from even running it?” — and dispatched them to the same agent,\nwhich still had [its memory](/blog/how-agents-remember-semantic-memory/) of the corpus. Each\npass appended to the same reports. The research got *thicker* instead of starting over.\n\n## What fell out of it [#](#what-fell-out-of-it)\n\nFindings we would have missed by skimming, all of which changed real decisions:\n\n**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/).\n\nThe Reddit and Product Hunt halves of this analysis became\n[their](/blog/what-reddit-told-us-about-munder-difflin/)\n[own](/blog/number-five-on-product-hunt/) posts — both written *from the agents’ reports*, which\nis why they have real numbers in them instead of vibes.\n\n## Run it on your launch [#](#run-it-on-your-launch)\n\nThe recipe, portable to any hive setup:\n\n**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.\n\nAn evening of agent work, and launch week stops being a feeling and becomes a dataset. The\n[overnight version](/blog/claude-code-automation-while-you-sleep/) works too — we know, because\nhalf of this ran while we slept off the launch.\n\n## FAQ\n\nWhat did the agents actually analyze?\n\nFour 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.\n\nWhy use multiple agents instead of one long session?\n\nEach 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.\n\nCan I run this on my own launch?\n\nYes — 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.", "url": "https://wpnews.pro/news/our-agents-ran-our-launch-week-analytics", "canonical_source": "https://munderdiffl.in/blog/agents-ran-our-launch-week-analytics/", "published_at": "2026-08-19 00:00:00+00:00", "updated_at": "2026-08-19 07:11:09.877485+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "artificial-intelligence"], "entities": ["PostHog", "Reddit", "Product Hunt", "GitHub", "r/ClaudeCode", "PostHog 0.4.4"], "alternates": {"html": "https://wpnews.pro/news/our-agents-ran-our-launch-week-analytics", "markdown": "https://wpnews.pro/news/our-agents-ran-our-launch-week-analytics.md", "text": "https://wpnews.pro/news/our-agents-ran-our-launch-week-analytics.txt", "jsonld": "https://wpnews.pro/news/our-agents-ran-our-launch-week-analytics.jsonld"}}