AI for coding: reading, debugging and reviewing real code
AI chat earns its place on comprehension and diagnosis, not bulk generation: attach the failing file plus the exact error, ask for the three most likely causes ranked, and test them yourself. For review, paste the diff and ask what breaks rather than whether it looks fine.
Autocomplete already handles the keystrokes. What a chat model adds is a place to interrogate code you did not write, with the actual files and the actual stack trace in front of it.
ChatOTL takes source files as attachments, streams replies so you can stop a wrong answer early, and lets you branch when a debugging session forks into two hypotheses.
Tasks worth handing over
- Comprehension: explain what an unfamiliar module does and which parts carry risk.
- Debugging: rank likely causes of a specific error given the real file and trace.
- Review: ask what a diff breaks, what it leaves untested and which edge cases it misses.
- Refactoring: propose a smaller interface, then apply it one function at a time.
- Translation: move a snippet between languages or frameworks and explain the differences.
Attach the code, not a description
Source files, plain text, Markdown, CSV and JSON are extracted and passed inline to the model, up to 5 files per message at 15 MB each. A paraphrased bug report produces a paraphrased answer; the actual file and the actual error produce something testable.
Branch competing hypotheses
Fork a reply when a debugging thread splits — one branch chasing the config theory, one chasing the race condition — so neither line of reasoning gets overwritten by the other.
Switch models mid-session
Switch between OpenAI, Anthropic and Google models, or your own OpenAI-compatible endpoint, without leaving the conversation. When one model keeps repeating the same wrong fix, a different one often breaks the loop.
Where it fits
Legacy onboarding
Attach the module, ask for a map of responsibilities and risky spots.
Stuck bug
Give the file and the trace, ask for ranked causes and the cheapest test for each.
Pre-review
Paste the diff and ask what it breaks before a colleague has to.
Example prompts
- Here is the file and the stack trace. List the three most likely causes, ranked, with a test for each.
- Explain this module to someone who joined today. Flag anything that looks load-bearing.
- Review this diff for edge cases and missing tests. Do not rewrite it.
Limitations
- It cannot run your code, execute tests or touch your repository — nothing is executed on your behalf.
- No web browsing, so it cannot check current library docs, changelogs or issue trackers.
- Suggested fixes can be plausible and wrong; treat every one as a hypothesis to test.
Frequently asked questions
Can it run or test my code?
No. ChatOTL has no execution environment and no autonomous access to external systems. It reads what you attach and reasons about it; you run the tests.
Is it safe to paste proprietary code?
Every conversation, message and uploaded file is scoped by row-level security to your account. If policy requires that code never reach a third-party provider, point ChatOTL at your own OpenAI-compatible endpoint.
Which model is best for code?
It varies by task and changes often, which is why models are switchable mid-conversation — run the same question against a second model rather than guessing up front.
Can it check the latest library documentation?
No, there is no web access. Paste the relevant docs or types into the thread when the API is newer than the model's training data.
Take it to your current bug
Attach the file and the trace, and see if the ranked causes hold.
Open ChatOTL