Launch week: every idea card is free.Get Pro

SaaS ideas, mined from what people actually complain about.

Every idea comes from real Reddit posts, was researched against today's competitors, and survived an independent skeptic before it made the list.

Silent LLM Model Drift

Hosted model updates and deprecations silently change behavior, degrading production AI systems without alerts or changelogs.

In their words

how are you catching regressions before your users do?

r/AI_Agents on Reddit

how are you catching regressions before your users do?

r/AI_Agents on Reddit

The thing that keeps biting us isn't losing prompts, it's not knowing whether a change quietly made things worse.

r/Startup_Ideas on Reddit

Would you be interested enough in paying for this service?

r/ClaudeCode on Reddit

keeping datasets and scoring useful as the product changed was a different story

r/AIQuality on Reddit

i'm currently stuck in a ML project that has grown outside of my knowledge level, with little available public research

r/ClaudeCode on Reddit

Why is it so hard for stakeholders to understand that the best models humankind ever created are expected to make wrong calls once in a while?

r/datascience on Reddit

There's no signal between "deployed" and "user says it's bad" except prod logs.

r/LLMDevs on Reddit

how are people pruning eval sets without losing coverage?

r/aiagents on Reddit

langsmith dataset eval has been useless for catching our actual prod regressions

r/LangChain on Reddit

we don't have a way to measure whether their errors are the kind that break transfer

r/reinforcementlearning on Reddit

Half the investigation is just figuring out what changed.

r/PromptEngineering on Reddit

In my work with LLM-included software, I built a tool that evaluates text output relative to a baseline of what's expected. It helps to ensure things don't drift over time.

Hacker News

every other model upgrade has a measurably different adherence to the existing system prompts

Hacker News

AI/ML engineers

Where they talk

  • r/AI_Agents
  • r/mlops
  • r/LLMDevs
  • r/ClaudeCode
  • r/PromptEngineering
  • r/LangChain
  • r/LocalLLaMA
  • r/datascience

Tools they use today

  • Claude
  • Braintrust
  • ChatGPT
  • Claude Code
  • Codex
  • LangSmith
  • git
  • Langfuse

Why those fall short

  • no proactive regression detection; provider knob tuning changes quality without warning
  • no systematic way to explain individual model decisions or set error expectations
  • point-in-time checks miss cumulative drift; no alert trips
  • offline datasets are static and biased; tools treat datasets as static
  • PRs show prompt diff, not behavior diff; no prompt history