Built a research library from 183,901 Reddit posts to find business opportunities people are already asking for

A few weeks ago, I started building a small research product around a simple idea:

People complain about broken workflows, expensive tools, confusing migrations, bad support, missing features, and painful workarounds every day.

A lot of those conversations happen in public.

The problem is not that the signals don’t exist.

The problem is that they are buried inside noise.

Reddit is full of useful market signals, but most of them are mixed with jokes, rants, one-off complaints, self-promotion, generic founder advice, and posts that sound interesting but do not point to a real business opportunity.

That is what led me to build SignalCards.

SignalCards turns public Reddit conversations into curated, source-backed Opportunity Cards for founders, agencies, consultants, and growth teams.

Not raw alerts.

Not keyword dumps.

Not “here are 500 posts, good luck.”

A SignalCard is meant to answer a more useful question:

Is there a real pain here, who feels it, what are they doing today, which tools are involved, and what could someone build, sell, or test next?

What the library includes today

The SignalCards library now includes:

• 6 reports
• 495 Opportunity Cards
• 183,901 Reddit posts reviewed

To be clear, “posts reviewed” does not mean I manually read 183,901 posts one by one.

It means those posts were processed by the research pipeline before curation.

The final product is much smaller on purpose.

The goal is not to show every possible signal.

The goal is to curate the strongest ones.

The library includes both:

• a current fresh issue based on recent conversations;
• historical archives rebuilt from older research windows.

The fresh issue shows what is happening now.

The archives help build context over time.

That distinction matters because I am not trying to build just another Reddit scraper.

I want SignalCards to become a source-backed opportunity library that gets more useful as the historical base grows.

Why I think raw scraping is not enough

A lot of tools in this space are starting to appear.

Many of them do some version of:

• scrape Reddit;
• search keywords;
• cluster complaints;
• score posts;
• send alerts;
• suggest places to reply.

That can be useful.

But I think the category becomes commoditized if the output is only raw posts or generic clusters.

The harder part is editorial judgment.

For each Opportunity Card, I care about things like:

• Is the pain repeated or specific?
• Is there buying evidence?
• Are people naming alternatives?
• Are they already paying for something?
• Are they trying to migrate away from a tool?
• Is there a workaround?
• Is there a clear persona?
• Could a founder, agency, consultant, or product team actually act on this?

This is why SignalCards is built around reports and Opportunity Cards, not just alerts.

The goal is to reduce the research burden.

You should be able to open a report and quickly understand:

• what people are struggling with;
• which tools are being replaced or complained about;
• where migration intent is appearing;
• where product gaps exist;
• what kind of action might be worth testing.

A real example: AI-native customer success

One public sample from the current issue came from a conversation around teams evaluating more AI-native customer success workflows.

At the raw post level, this could look like another generic “AI tool” discussion.

But the underlying signal is more specific:

Customer success teams are trying to move from reactive dashboards and manual account reviews toward systems that can detect risk, summarize account context, and surface what needs attention before renewal or churn becomes obvious.

On its own, that is just another thread about AI.

The SignalCard version turns it into a more structured market signal:

Persona: Customer Success lead / SaaS operations team

Pain: account health, renewal risk, support context, and customer signals are spread across too many tools

Current workaround: manual account reviews, spreadsheets, CRM notes, support tickets, and recurring internal check-ins

Buying evidence: teams are actively evaluating AI-native alternatives instead of just discussing AI in the abstract

Possible opportunity: AI account-review agent, churn-risk workflow, customer-health summarizer, implementation service, or vertical customer-success copilot for a specific SaaS segment

That is the difference I am trying to create.

Not:

“Here is another AI discussion.”

But:

“Here is a structured market signal showing where teams are trying to replace manual customer-success work with a more intelligent workflow.”

What I learned while building it

The biggest lesson so far is that raw volume is not the hard part.

The hard part is deciding what deserves to become a card.

A post can have a lot of comments and still be useless.

A small thread can contain a very specific business pain.

A complaint can sound interesting but have no buyer.

A migration story can reveal a much stronger opportunity than a generic “what should I build?” post.

That is why the product is intentionally opinionated.

It does not ask users to configure keywords or subreddits.

It does not try to show everything.

It tries to produce a smaller number of structured cards that are easier to reason from.

Fresh issues vs historical archives

One thing I had to clarify while building this is the difference between fresh issues and historical archives.

Fresh issues are based on recent monitoring and active research windows. They prioritize timeliness.

Historical archives are rebuilt after the fact from larger historical datasets. They review many more posts, but they are still curated down to the strongest cards.

So a higher post count does not mean a longer report.

It means broader coverage before curation.

The important thing is not the raw volume.

The important thing is what survives the curation process.

The next layer: temporal intelligence

The part I am most interested in now is what I call temporal intelligence.

The current issue includes a short section:

What changed since the last issue

For example, it compares the current issue to the previous one and looks at things like:

• whether buying evidence is getting stronger;
• whether product ideas or competitor complaints are becoming more visible;
• whether signals are shifting from general founder threads to more tool-specific communities;
• whether people are talking more about replacing existing stacks instead of adding more tools.

This is still early.

But I think this is where the product can become more defensible.

A scraper can copy a list of posts.

A competitor can copy a scoring system.

But if SignalCards keeps accumulating issues and archives, it becomes possible to say:

We don’t just find signals. We track how market pains evolve over time.

That is the direction I want to push.

Who this is for

SignalCards is probably useful if you are:

• looking for SaaS ideas grounded in real conversations;
• running an agency and looking for service angles;
• validating a market before building;
• researching competitor complaints;
• looking for migration pain;
• trying to understand what people are already paying for or replacing;
• tired of generic “startup idea” lists.

It is not meant to be a magic idea generator.

It will not guarantee customers.

It will not replace talking to users.

But it can help you start from better evidence.

What is live today

The public site now includes:

• a free preview of the current issue;
• a report library;
• historical archives;
• a methodology page;
• sample Opportunity Cards;
• a monthly subscription option;
• a custom scan option for a specific niche, website, competitor set, or market angle.

The current library is still small, but it is growing.

The next steps for me are:

• keep publishing fresh issues;
• keep adding historical archives;
• improve temporal comparisons;
• measure which reports and cards people actually read;
• keep answering founder questions without spamming links.

What I am looking for

I am especially interested in feedback from founders, agencies, consultants, and growth teams.

The question I am trying to answer is:

Is a curated research library more useful than another alert feed?

And more specifically:

Would you rather receive raw posts to investigate yourself, or fewer structured Opportunity Cards with source context, evidence, and suggested angles?

You can explore the free preview here:

https://getsignalcards.com/reports?utm_source=indiehackers&utm_medium=article&utm_campaign=library_launch

I would love feedback on the positioning, the report format, and whether the Opportunity Card structure feels useful enough to act on.