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Method and coverage

332 problems from 1,735 posts so far; 201 corroborated. 177 sources read, 10% of registry companies with at least one problem.

How a post becomes a problem

  1. 1ReadEvery day at 05:25 UTC (and a lighter pass at 17:25 UTC) the scan reads public sources: Hacker News through the Algolia API, public Reddit feeds (a few subreddits a run, slowly), Stack Overflow, the most-reacted GitHub issues of major data and AI repos, Discourse forums, Bluesky and Mastodon public posts, YouTube video titles, 1 and 2 star App Store reviews, and outages, deprecations, breaches and advisories from other fru.dev sites.
  2. 2Keep what states a painA post counts when it says something hurts: "struggling with", "doesn't work", "workaround", "is there a way", "rate limit", "nightmare" and about a hundred more phrases. Issues, unanswered questions, forum help topics and low-star reviews count by nature. On general sources the post must also be about tech.
  3. 3Name the issue and the companiesEach post is matched to a known issue (the table below) and to the companies and products it names: a product list plus every company in the companies.fru.dev registry, matched by exact name.
  4. 4GroupPosts with the same issue about the same product become one problem once two posts say it; until then they join the general problem. Posts about a product with no known issue are grouped by the title word they share (3 or more posts). Statements are written by these rules, 12 words or fewer.
  5. 5MeasurePer problem: posts per day, the last 7 days against the 7 before (rising, steady, fading, new), days active, distinct people and places, frustration from strong words, and the top three links. Persistent means 5 or more active days across two weeks and still active this week.
  6. 6Keep historyPosts, day counts and daily snapshots are append-only; every change is on the Changes page. A cluster backed by fewer than 3 posts from 2 people or places stays marked unverified.

Coverage by source

SourcePosts
GitHub issues1,068
Reddit159
App Store reviews132
fru.dev sites107
Community forums98
Hacker News96
Stack Overflow62
DEV5
Bluesky5
Mastodon2
Lobsters1

Registry: 761 companies.fru.dev companies enrolled, 77 with at least one problem (10%). The rest are watched on every run. Last run: seed, ok, 657 posts added, 125 new problems, 2 new sources.

Gaps, named

  • Reddit answers scripts slowly and blocks bursts, so each run reads a few subreddits in turn; a subreddit is read about every five days.
  • Slack: only communities that publish public archives. Private workspaces are never joined or read.
  • YouTube comments need a YouTube Data API key; until one is set only video titles are read.
  • X, LinkedIn, G2 and TrustRadius are not read: they need a login or do not allow it.
  • Bluesky is read by search, Mastodon by public tags on mastodon.social only.
  • Rules miss sarcasm and can file a post under the wrong issue; clusters with few posts are marked unverified. Suggest a fix on any problem page.

The issues the rules know

Issue
LLMs give confidently wrong answers
Prompt injection makes AI features unsafe to ship
API rate limits and usage caps block real work
Context windows run out on real workloads
Model quality silently gets worse between versions
AI agents are unreliable, loop or stall midway
Models fail to return valid structured output
RAG retrieval returns irrelevant or missing context
No reliable way to evaluate LLM app quality
Prompts break or get ignored when models change
LLM responses are too slow for users
LLM token costs are hard to predict and control
Unclear what AI vendors do with company data
Running LLMs locally hits memory and speed limits
Fine-tuning is costly and results are unpredictable
GPU access, drivers and CUDA versions are painful
Deploying and serving models is harder than training
ML experiments are hard to reproduce and track
Model and feature drift goes unnoticed in production
AI-generated code is buggy and hard to review
AI assistants lose context in large codebases
AI tool plans, limits and pricing keep changing
Upstream schema changes break downstream pipelines
Bad data slips into production unnoticed
Backfills are slow, costly and risky
Data pipelines fail silently or break often
Debugging orchestration and DAG failures is hard
Ingestion connectors and CDC are fragile
Streaming pipelines lag, drop or reorder events
Data modeling choices are unclear and hard to change
Hard to trace lineage and find trusted tables
Model, table and pipeline sprawl becomes unmanageable
Testing data pipelines and SQL is hard
Small files, skew and partitioning hurt performance
Databases hit connection, locking and scaling limits
Vector search quality and scaling are hard to get right
Queries run too slowly and are hard to tune
Teams do not trust or agree on the numbers
Dashboards are slow, broken or unused
Endless ad hoc requests swamp data teams
Critical work still runs on fragile spreadsheets
Warehouse compute costs spiral out of control
Observability and logging costs outgrow the value
Cloud bills are surprising and hard to attribute
Tool prices rise faster than the value they give
Kubernetes is too complex for most teams
CI builds are slow and tests are flaky
Infrastructure code drifts from what is really deployed
Alert noise and on-call load burn teams out
Permissions and IAM are confusing and error-prone
Secrets and API keys leak or are hard to rotate
Containers are slow to build, bloated or crash-looping
Jobs and services run out of memory
Malicious packages slip into dependencies
Vulnerability scanners flood teams with noise
Breaches expose customer and company data
Login, SSO and OAuth flows break
Service outages and slowdowns disrupt work
Deprecations force unplanned migrations
Upgrades and new releases break working setups
Migrating between platforms is slow and risky
Vendor lock-in makes switching tools costly
Documentation is outdated, thin or confusing
Vendor support is slow or unhelpful
Dependency conflicts break installs and builds
Integrations between tools are brittle glue code
Local development setups are hard to get right
Legacy systems and tech debt slow everything down
Tools are clunky and unintuitive to use
Everyday tools feel slow and laggy
Error messages are cryptic and unhelpful
Worry that AI is replacing engineering jobs
Leadership pushes AI mandates without clear value
Tech job hunting is brutal right now
Technical interviews feel broken and exhausting
Burnout and overwork across tech teams
Hard to keep up with the pace of new tools

Rising problems by email

Mondays: the problems in data, tech and AI that grew fastest that week.

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