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.
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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.
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LLM Context Window Limits
LLMs lose context, ignore instructions, and waste tokens on long prompts, causing inconsistent outputs and high API costs.
In their words
I iterate 10-15 times until I get a deck to match my specifications and speed matters to me a lot.
is there anything that can analyze and read documents only once, without having to reload or reprocess them with every single interaction?
the problem that we are facing right now is training the model on answers.. That is the part we are trying to figure out..
one PROMPT can eat up, almost 20$ of API requests... it's making it basicly unusable
it keeps forgetting context and conversation history
When the AI makes a proposal that I like, how do I efficiently get the AI to do that and stick to it from beginning to end instead of continuously subverting itself?
Codex badly needs a cache timeout indicator, like Claude recently added.
Not a single time when used in context larger than around 50k+ it wont stuck into loop for me in nvfp4.
ask for a number that was at token 40k out of 500k and the model just makes something up that sounds plausible
it produces endless word salads and that if I don't tweak the inclinations a lot, it gets pronouns wrong and loses track of where it is in space
Prompts of 40–80K tokens take 7–13 minutes to prefill → practically unusable
Once it gets past 350k it starts to have contextual drift and begin to forget specific commands or rules
for a $200/mo product aimed at professionals, that isn't something anyone should have to janitor
I run long Opus sessions... and I almost always forget to compact them, so I either hit the wall or lose a pile of good context
AI developers and users
Where they talk
- r/LocalLLaMA
- r/LLMDevs
- r/AI_Agents
- r/ClaudeCode
- r/LocalLLM
- r/ClaudeAI
- r/LangChain
- r/ChatGPT
Tools they use today
- Claude
- Qwen
- ChatGPT
- Claude Code
- Codex
- DeepSeek
- Hermes
- llama.cpp
Why those fall short
- no in-app cache timeout indicator; logs are clunky to dig through
- reasoning levels are suggestions; up to 50% context used
- All quants from different providers have the same issue
- No tool keeps the developer mentally engaged with the project while waiting
- harness pushes 300k tokens into 55k model, causing errors and constant compactions
Map of ideas
Drawing the map…