Someone dropped a new pipeline into an MIT pain-mining repo this week. The real story isn’t the pipeline. It’s what broke when they pointed the old one at professionals.
What shipped
The unfairgaps-os repo already had 4 pipelines extracting business pain from court filings, regulatory fines, and enforcement data. The new 5th pipeline, profession-scan, targets individual professionals instead of B2B industries. The original 4 pipelines are built around finding where companies get caught: OSHA violations, EPA enforcement actions, SEC filings, FTC consent decrees. Genuinely useful signal for B2B compliance tools. But completely useless for solo practitioners, because nobody drags an auto detailer to court over forgetting to track mileage for three months. The profession-scan pipeline was built specifically for freelancers, licensed tradespeople, and self-employed professionals that enforcement data misses entirely.
The twist
The first attempt was simple: narrow the existing court-records approach. Type “lawyers in US” instead of “construction in US.” Didn’t work. Professionals don’t file lawsuits over the tedious daily compliance tasks that actually kill their time. The pain isn’t in enforcement events. It’s in the regulation forcing them to do tedious work, plus the grind of doing it every day. An auto detailer doesn’t go to court because they forgot to track mileage. They just spend 45 minutes on Sunday night doing it manually and resent every minute of it.
The insight here is worth sitting with. Courts and regulators capture catastrophic failures. They’re completely blind to the daily friction that drains hours from millions of solo operators. Shifting the data source from lawsuits to government websites and professional association resources means mining a completely different layer of pain: the recurring, low-stakes, regulation-driven tasks that never make headlines but consume real time every single week. That layer is largely untouched, and it maps directly to tools people would actually pay for.
So the data source flipped entirely. From lawsuits to .gov sites, BLS, law.cornell.edu, and professional associations.
How the pipeline works
🔍 Stage 1: Build the profession profile
- 7 WebSearch queries per profession across
.gov, BLS, law.cornell.edu, and professional association sites - Covers: daily routine, regulations, tools, jargon, fears, communities, market size
The goal is a dense reference document the LLM can reason from in stage 2. A solid auto-detailer profile includes IRS mileage tracking requirements, state licensing boards, EPA-regulated chemicals, and hourly rate data pulled from professional detailing forums. Generic profiles produce generic outputs. Specific profiles give the model something real to anchor to: actual compliance dates, actual dollar figures, actual professional vocabulary that shows up in the communities where these people spend time.
🤖 Stage 2: Infer the pain
- Profile goes to Opus 4.7 with a deductive prompt
- Output: 8 to 15 specific painful tasks plus an AI tool spec for each one
- Specs include calculator inputs, compliance citations, template variables, the works
The deductive prompt doesn’t ask the model to brainstorm ideas. It asks the model to reason forward from a specific regulation to the task that regulation creates to the tool that eliminates that task. That’s why the output references an actual IRS rate instead of a vague “expense tracker.”
Running stage 2 on auto detailers produced 13 buildable tool ideas. Not vague “AI for detailers” concepts. Actual specs. The pricing calculator has the 2026 IRS mileage rate ($0.67/mile) and 15.3% self-employment tax baked in. That level of specificity is the whole point. It’s the difference between an idea and a prototype. Someone could take that spec, open a no-code builder, and have a working tool ready by the weekend. Most brainstorming sessions don’t get you anywhere close to that level of precision.
Pro tips
Each stage 2 run takes about 5 minutes of LLM time. No API key needed if you have a Claude Code subscription. Batch your runs: pick 10 professions from the repo’s existing profiles, queue them up, and let them run while you handle other work. A rough discovery pass across 10 niches in under an hour is genuinely doable.
130 US profession profiles are already in the repo. 25 have full pain bundles. The other 105 are sitting there waiting for someone to run stage 2 on them. That’s 105 potential discovery passes nobody has done yet. If one niche out of every 13 is worth building on, running the full set in a weekend afternoon is a reasonable bet.
When reviewing results, filter for tools where the pain is recurring and the underlying regulation is stable. A pricing calculator tied to the IRS mileage rate updates once a year at most. Durable pain, durable tool. Pain tied to a one-time policy change is a much weaker foundation.
Honest framing from the author: this is a discovery funnel, not validated pain. Sift 130 professions in an afternoon, find 5 to 10 candidates worth building, then spend a real week doing customer interviews. Beats staring at a blank page trying to brainstorm.
Get started 🎯
MIT repo: github.com/AyanbekDos/unfairgaps-os
Best place to start: the auto-detailer pain bundle JSON, linked in the repo.
Extended my MIT pain-mining repo with a 5th pipeline – shipped 130 profession profiles + 25 ready-made pain bundles
by u/Ogretape in PromptEngineering