SaaS ideas your next customers are already looking for
100 SaaS startup ideas for the people who said they'd pay, from agency owners to dentists. Micro SaaS ideas, AI SaaS ideas and B2B SaaS ideas: find yours, build it, sign them up.
Popular:
| # | Idea |
|---|
No idea matches this filter.
Pain points in business: what people struggle with
Search the problems to solve, as people described them on Reddit and other platforms, in their own words.
Popular:
| # | Pain point |
|---|
No pain point matches this filter.
AI Agent Context Loss
AI agents lose, bloat, or leak context across long or multi-session workflows, causing forgotten rules, repeated work, and inflated token costs.
In their words
the chief of staff would become hard to steer as it kept summarizing analyzing and discussing but that actually stopped the project itself from moving forward
Is there native handoff, or am I still manually copy-pasting specs into Markdown files?
My openclaw status shows the active session footprint hovering at a massive 327k tokens
the summary can miss small decisions and I end up revisiting the same issues
this might save us from the weekly "why did it do that" panic
For any long running session there a risk of context being eradicated or the dreaded, "I've reached the end of my useful context" message.
a lot of waste was not from the actual coding — it was from Codex repeatedly rereading too much context
I stopped making the LLM collect all the information by itself.
I just can't get it to write the thing reliably as the day goes on.
It shouldn't be on the user to setup good workflows for codex to use in software developing.
The server cleared a prefix of the conversation as it grew too large.
Three months later those docs are out of date. Nobody updated them. The agent starts producing bad output, the team stops trusting it, and it just quietly dies.
Getting the correct knowledge into its context window without overpopulating it is the actual engineering effort for most agents.
the hardest part has never been connecting an AI model, it's keeping every app's context consistent afterward.
AI agent developers and users
Where they talk
- r/AI_Agents
- r/ClaudeCode
- r/ClaudeAI
- r/hermesagent
- r/codex
- r/LLMDevs
- r/LangChain
- r/openclaw
Tools they use today
- Claude Code
- Claude
- Codex
- Hermes
- Cursor
- MCP
- OpenCode
- Slack
Why those fall short
- instructions in markdown can be ignored; agent rereads same files
- no universal CI rule; some boundaries only protocol discipline
- agents confused purposes or missing context; chief of staff kept summarizing instead of moving project forward
- after compaction re-answers settled questions and may contradict earlier decisions
- snapshots are fixed and read-only, cannot answer novel questions
Map of ideas
Drawing the map…