🚀 Project Ideas
Generating project ideas…
Summary
- Tracks prompt and completion token counts for every LLM call made from a FastAPI app, alerting when session or budget limits are approached.
- Provides developers with instant visibility and control over token consumption, eliminating surprise overages and session cuts.
Details
| Key |
Value |
| Target Audience |
Python/FastAPI developers using LLM APIs (OpenAI, Anthropic, etc.) |
| Core Feature |
Middleware that logs token usage per request, exposes metrics via Prometheus/Grafana, and sends webhook alerts on thresholds |
| Tech Stack |
Python, FastAPI, Starlette middleware, Prometheus client, optional Redis for aggregation |
| - Difficulty |
Low |
| Monetization |
Hobby |
| #### Notes |
|
| - HN users complained about hitting session limits after 150k tokens and burning 1M tokens for nothing; TokenMeter would give them early warnings. ([jesse_dot_id] "hit my session limit", [drchaim] "threw 1M tokens for nothing") |
|
| - Could spark discussion on optimal token budgeting and become a useful utility in AI‑powered backends. |
|
Summary
- Parses a codebase (Python, JS, etc.) and estimates the number of tokens required to feed the entire project or selected modules to an LLM for tasks like explanation, refactoring, or test generation.
- Helps teams gauge whether a medium‑sized codebase (≈50 k LOC) will exceed token limits before sending costly prompts.
Details
| Key |
Value |
| Target Audience |
Engineers, tech leads, and AI‑tool integrators who need to scope LLM workloads on large codebases |
| Core Feature |
CLI / VS Code extension that walks the AST, counts tokens using a tokenizer (e.g., tiktoken), and outputs a heatmap of high‑token files |
| Tech Stack |
Python (or Node), tree-sitter or libclang, tiktoken, optional React for web UI |
| Difficulty |
Medium |
| Monetization |
Hobby |
| #### Notes |
|
| - Commenters asked “how much is medium codebase, like 50kloc including docs?”; Tokenscope answers that directly, giving concrete token estimates. ([TZubiri] question) |
|
| - Provides actionable data for deciding when to chunk code or use retrieval‑augmented generation, prompting useful HN discussion. |
|
Summary
- Sits between your application and any LLM API, transparently buffering requests and continuing conversations when the server returns a token‑limit or session‑limit error.
- Eliminates manual restarting and lost context, letting developers keep working without hitting the “session limit” wall.
Details
| Key |
Value |
| Target Audience |
Developers using chat‑style LLM endpoints who hit session or token limits (e.g., FastAPI apps, notebooks) |
| Core Feature |
Intercepts 429/limit responses, stores conversation state locally, splits large prompts into chunks, and resumes seamlessly |
| Tech Stack |
Go or Python (FastAPI/Flask wrapper), Redis for session store, OpenAI compatible API spec |
| Difficulty |
Medium |
| Monetization |
Revenue-ready: Subscription tiered by monthly token volume (e.g., $9/1M tokens) |
| #### Notes |
|
| - HN users explicitly mentioned hitting session limits after large token usage; LlamaContinue would let them continue without manual intervention. ([jesse_dot_id] "hit my session limit. Continuing in a few hours.") |
|
| - Could become a popular middleware for LLM‑heavy services, fostering debate on best practices for long‑running AI sessions. |
|
