Sometimes the LLM gives you three paragraphs when you needed three bullets. This tool does the reverse of the expander: it takes verbose prose and collapses it into a tight, ordered bullet list using your own OpenAI or Anthropic key.
How it works
Choose a provider and model, paste your API key, and drop in the long text. Set the maximum number of bullets and pick a detail level — headlines for a quick TL;DR, balanced for one clean line per point, or detailed to keep specifics. When you run it, the tool sends one direct request from your browser to the provider with a system prompt that tells the model to keep only the most important points, in order of importance, without adding anything new. The bulleted summary comes back ready to copy.
The key never reaches a Gera server — it’s held only in the tab and sent straight to OpenAI or Anthropic (with the official direct-browser-access header for Anthropic). Refreshing clears it.
Getting a good summary
- Match bullet count to purpose. Three to five bullets for an executive summary; ten or more for a detailed digest of a long document.
- Watch for lossy compression. Fewer bullets means more dropped detail. If a must-keep point vanishes, raise the count or detail level and re-run.
- Feed it clean source text. Stray markup or interleaved formatting can confuse the model; paste the prose on its own for the tightest result.
Tips
- Round-trip with the Bullet Expander: collapse a draft to bullets, edit the structure, then expand back to prose.
- Cheaper models summarize well — reserve premium models for dense or technical input.
- Output is short, so cost is dominated by the input length; trim irrelevant sections before collapsing to save tokens.