DEV Community: DDMarketer

10 software problems people are actively complaining about right now (with the scores)

DDMarketer — Sun, 30 Aug 2026 18:14:06 +0000

I run a pipeline that reads public complaints — Reddit, Hacker News, GitHub, Stack Exchange, Trustpilot, app store reviews, product forums, X — and scores each recurring one for commercial intent. Two LLM passes, deduplication across distinct authors so one loud thread does not look like a market, then an editorial gate. 942 have cleared it.

Here are the ten highest-scoring right now, with the raw numbers. Commercial intent is a 0-100 estimate of willingness to pay; confidence is how sure the scoring is.

1. Automate 40% of property manager calls, cut after-hours costs

intent 100/100 · confidence 90/100 · Real Estate / Local Operations · surfaced from reddit

Read the problem and the evidence

2. Stop Stripe Connect fraud before platform shutdown

intent 100/100 · confidence 90/100 · Security / Compliance · surfaced from reddit

Read the problem and the evidence

3. AWS Marketplace usage fraud detection and remediation

intent 100/100 · confidence 80/100 · Security / Compliance · surfaced from reddit

Read the problem and the evidence

4. 60-second mobile-first e-signatures, no account needed

intent 90/100 · confidence 95/100 · Legal / Practice · surfaced from twitter

Read the problem and the evidence

5. Podcast download validation that filters bots reliably

intent 80/100 · confidence 80/100 · Creator Operations · surfaced from reddit

Read the problem and the evidence

6. 24/7 M365 phishing response for BYOD education

intent 80/100 · confidence 80/100 · Security / Compliance · surfaced from reddit

Read the problem and the evidence

7. Recover failed Stripe payments for Laravel apps

intent 80/100 · confidence 80/100 · Finance / Accounting · surfaced from reddit

Read the problem and the evidence

8. Stop metadata drift across Glue and other data catalogs

intent 80/100 · confidence 80/100 · Data / Analytics · surfaced from reddit

Read the problem and the evidence

9. Secure occupied home showings for property managers

intent 80/100 · confidence 90/100 · Real Estate / Local Operations · surfaced from reddit

Read the problem and the evidence

10. Stripe dunning that counts all retry attempts

intent 80/100 · confidence 90/100 · Finance / Accounting · surfaced from reddit

Read the problem and the evidence

What I notice looking at this list

Security and compliance is over-represented at the top. Three of the ten are fraud or abuse response. That was not true of the corpus as a whole — across all 942, security is 7th by volume. It is a this-week artifact, and I mention it because it is exactly the kind of pattern that looks like a trend if you only read the top of a list.

The good ones are boring. Automating after-hours property manager calls scores 100. Nobody is going to tweet about building that.

My corpus has a bias I cannot fully remove. Dev tools are 44% of everything I hold, and my biggest single source is GitHub (563 items) ahead of Reddit (299). Not because developer problems are worth more — because developers complain in public, in writing, on platforms with APIs. Anyone mining public text inherits this.

Querying this from your editor

There is a free MCP server, so your coding agent can search the corpus without leaving the editor:

claude mcp add --transport http ddmarketer https://www.ddmarketer.com/api/mcp

Three tools need no key and no account: search_gaps, get_top_gaps, and validate_idea. That last one scores an idea you already have, and it returns a weak verdict when it only finds loosely related complaints rather than pretending a near-miss is validation.

A dependency-free reference client is on GitHub if you want to see exactly what goes over the wire — about 50 lines of plain fetch, no SDK.

The caveat that matters

Complaint volume is not demand. Somebody being annoyed on Reddit does not mean they will pay to fix it. This narrows where to look. You still have to talk to people before you build.

My highest-value data category was starved because of how I ranked ingest targets

DDMarketer — Sat, 29 Aug 2026 16:03:26 +0000

I run a pipeline that mines public complaints and scores them for commercial intent. Last week I pulled approval rates by category and found something that had been quietly costing me the best data I had.

Here is the table that started it:

category                approved/total   rate   mean intent
HR / Recruiting              28/  38      74%      80.1
Data / Analytics            177/ 242      73%      73.9
Dev Tools / SaaS Infra      560/1001      56%      71.9
Finance / Accounting         86/ 206      42%      75.6

HR and recruiting has the best approval rate and the highest mean commercial intent of any category with real volume. It also has the smallest candidate pool by an order of magnitude: 38 against dev tools' 1,001.

My first assumption was that the classifier or the editorial gate was rejecting HR items. It was not. 74% of them get approved, the highest rate in the corpus. They were simply never being fetched.

The actual cause

The ingest walks a list of targets per source, and a per-source time budget cuts the tail. So target ordering decides what the corpus is made of.

The ordering was: protect the proven high-priority targets, then rotate everything else by staleness. That is a reasonable design — it stops unproven targets displacing measured ones at random, which an earlier jitter-based approach did (the newly-reached targets measured 65% in scope against 80% for the established ones).

But there were 9 protected targets against roughly 13 reachable slots per run. So about 4 rotating slots, against 105 never-fetched targets on that source. r/humanresources and r/recruiting were sitting in a queue roughly 26 weeks deep.

11 of the 13 HR targets on active sources had never been fetched once.

Why the fix is priority 8 and not 9

The obvious move is promoting them into the protected band. That is wrong: the protected band was already 9 of ~13 slots, so adding three more would consume the rotation entirely and recreate the same starvation for everything else.

The rotating pool sorts by staleness ascending, then priority descending. Every never-fetched target ties at staleness 0 — so priority is the tiebreak among them. Setting the HR targets to 8, one below the protected threshold, moves them to the front of the unfetched queue without displacing the protected head or the rotating tail.

UPDATE source_targets t
SET priority = 8
FROM sources s
WHERE t.source_id = s.id
  AND s.status = 'active'
  AND t.is_active = true
  AND t.last_fetched_at IS NULL
  AND t.priority < 8
  AND ( ... explicit target list ... )

Guarded on last_fetched_at IS NULL and priority < 8 so it is idempotent, and scoped to an explicit list of target values so it cannot touch anything else. 11 rows.

The part that generalises

If your pipeline has a budget that cuts a tail, the ordering of that queue silently decides what your data is about. Mine had produced a corpus that was 44% developer tools, and I had been reading that as a finding about where the opportunities are. It was not a finding. It was an artifact of which targets the budget happened to reach.

Two things worth checking in your own pipeline:

What fraction of your configured sources have never run? Mine was 79%. Some of that was deliberate (a source disabled because its content was a decade old), but most of it was queue depth.
Is your highest-yield segment also your smallest? That combination usually means starvation, not a real ceiling.

The corpus is at ddmarketer.com if you want to see what came out of it, and there is a free MCP server so your agent can query it directly — claude mcp add --transport http ddmarketer https://www.ddmarketer.com/api/mcp. But the pipeline lesson is the transferable part.

I scored 942 user complaints for commercial intent. The data embarrassed me.

DDMarketer — Sat, 29 Aug 2026 15:35:00 +0000

I have abandoned enough side projects to accept that my ideas were the problem. So instead of having ideas, I spent a few months reading other people's complaints: Reddit, Hacker News, GitHub, Stack Exchange, Trustpilot, app store reviews, product forums, X.

The pipeline is six stages.

Source from public complaints only. No surveys, no private data.
Prefilter the obvious noise with keywords and heuristics, before spending money on inference.
Deduplicate. This mattered far more than I expected. One thread with 200 upvotes is one person's problem amplified 200 times, not 200 people with the problem. Frequency across distinct authors and sources is the actual signal.
Two LLM passes. The first confirms it is a real, software-shaped problem. The second extracts the opportunity, the persona, and what people currently do instead.
Score for commercial intent, confidence, and willingness to pay.
Editorial gate. Nothing publishes automatically. I read every one.

942 have cleared that.

Three things in the data I did not expect

My corpus is mostly about developers, and that is a bias, not a finding.

Dev Tools and SaaS Infrastructure is 44% of everything I have. My single largest source is GitHub with 563 items, ahead of Reddit at 299. That is not because developer problems are the most valuable. It is because developers complain in public, in writing, on platforms with APIs.

Anyone mining public text has this skew. I had it for weeks before I went looking for it.

The highest-intent category is the one I have almost nothing in.

HR and recruiting averages 80.1/100 commercial intent, the highest of any category with meaningful volume. I have 28 items in it. Dev tools averages 71.9 across 560.

When I dug into why, the answer was worse than "not enough sources". The HR targets existed and were enabled. 11 of the 13 on active sources had never been fetched once, because the ingest budget kept getting consumed by the same high-priority targets every run. I had been mining where it was easy, and the ranking made that permanent.

The good ones are boring.

Top of this week:

[intent 100 / confidence 90]  Automate 40% of property manager after-hours calls
[intent 100 / confidence 90]  Stop Stripe Connect fraud before platform shutdown
[intent  90 / confidence 95]  60-second mobile-first e-signatures, no account

Nobody is going to tweet about building after-hours call automation for property managers. The ideas that sound exciting score badly, over and over.

Querying it from the editor

Your agent can already build almost anything, so the bottleneck moved to deciding what to build. I put the corpus behind an MCP server so I can ask while I am already in the editor:

claude mcp add --transport http ddmarketer https://www.ddmarketer.com/api/mcp

Or in any MCP client config:

{
  "mcpServers": {
    "ddmarketer": {
      "type": "http",
      "url": "https://www.ddmarketer.com/api/mcp"
    }
  }
}

Three tools need no key and no account: search_gaps, get_top_gaps, and validate_idea. That last one scores an idea you already have, and it will tell you when there is nothing there — it returns a weak verdict when it only finds loosely related complaints, rather than pretending a near-miss is validation.

What this does not do

Complaint volume is not demand. Somebody being annoyed on Reddit does not mean they will pay to fix it. The corpus narrows where to look. You still have to talk to people.

The scores come from a language model. They are consistent enough to rank against each other, but I would not read 80 vs 75 as meaningful.

And the editorial gate is one person, which is a quality gate, not peer review.

The question I cannot answer

How do you separate "people complain about this" from "people would pay to fix this"? I score willingness to pay as its own dimension, but I have no ground truth to check it against, and one paying customer is not a validation set.

If you have solved this, I would genuinely like to hear how.