AI for developers, without shipping your code to a shared workspace
For developers, the highest-value uses are explaining unfamiliar code, reviewing a diff you already wrote, and reasoning through a bug from real logs — with the ability to switch models mid-thread when one gets stuck.
Different models fail differently on code. ChatOTL lets you change model in the middle of a conversation, so a stuck thread gets a second opinion instead of a restart.
Everything is scoped to your account, and you can run against your own OpenAI-compatible endpoint if your codebase can't leave your infrastructure.
The loop that pays off
- Explain: attach an unfamiliar file and ask what it does and what it assumes.
- Review: paste your own diff and ask for failure modes, not praise.
- Debug: paste the stack trace plus the relevant function and ask for hypotheses ranked by likelihood.
- Branch: fork the reply when two fixes look plausible, then compare both threads.
Model switching, per task
Switch between models from OpenAI, Anthropic and Google — or your own OpenAI-compatible endpoint — in the middle of a conversation. Replies stream token by token with stop and regenerate on every turn, so a bad direction costs seconds.
Wire it into your agent clients
An OAuth-protected MCP server at /mcp lets clients such as Claude, Cursor or ChatGPT list, read, search and create your conversations as you. Row-level security still applies to every call.
Attachments that suit code
Attach up to 5 files per message (15 MB each). Source code, plain text, Markdown, CSV and JSON are extracted and passed inline, so the model sees the file rather than a summary of it.
Where it fits
Onboarding a codebase
Attach the entry point and ask for the control flow and hidden assumptions.
Pre-PR review
Paste the diff and ask for edge cases and failure modes before a human looks.
Log triage
Paste the trace and the suspect function, ask for ranked hypotheses.
Example prompts
- Here's the diff. List the ways this breaks under concurrent writes.
- Explain what this module assumes about its callers, then list what would break if those assumptions fail.
- Given this stack trace and this function, rank the three most likely causes and how I'd disprove each.
Limitations
- No web browsing, so it can't check a library's current docs or a breaking change for you — paste them in.
- It doesn't act on external systems: no repo access, no running commands, no autonomous agents.
- Generated code needs review and tests like any other; the model can be confidently wrong.
Frequently asked questions
Can I use my own API keys?
Yes. Provider keys are stored per account and never shared between users, and you can point ChatOTL at your own OpenAI-compatible endpoint instead.
Can it read my repository?
Not directly. It has no repo or filesystem access — you attach the specific files you want it to see, up to 5 per message at 15 MB each.
Does it work with Claude Desktop or Cursor?
Yes, through the OAuth-protected MCP server at /mcp. Connected clients can list, read, search and create your conversations as you, under the same row-level security.
Why switch models mid-conversation?
Because models fail differently. When one loops on a bug, switching keeps the accumulated context and gets a genuinely different attempt at it.
Bring your own keys
Point it at the provider or endpoint you already trust.
Open ChatOTL