Catch fast. Fix fast.
Apply safe suggestions in one click and hand complex fixes to your coding agent with full context.
Reviews for AI-powered teams who move fast
— but don't break things.
2-click onboarding · No credit card
Built for teams shipping with the tools they already trust
Your team moves fast with AI. But fast should not mean sloppy. YCoder makes sure every line still earns its merge.
YCoder does the heavy lifting and spots the hard-to-find issues. Your team makes the final call.
Run a sample reviewApply safe suggestions in one click and hand complex fixes to your coding agent with full context.
Get a change summary, file-by-file walkthrough, risk map, and architecture diagram before you read a line.
Surface the issues humans miss and filter low-value comments so every finding deserves attention.
Ask follow-up questions, create issues, request tests, generate docstrings, or teach the reviewer.
Bring repository instructions, path-aware rules, coding standards, and workflow controls into every review.
Turn review activity into standup summaries, sprint insights, quality trends, and actionable engineering reports.
This demo sends the diff to YCoder's production `/api/review` endpoint and renders the actual structured findings.
Run the sample diff to see real API findings.
Review at the pull request, directly in your IDE, or from the command line before code leaves your machine.
Automated walkthroughs, inline findings, suggested fixes, and merge checks for GitHub and GitLab.
Catch local issues while context is fresh and make fixes without switching away from your editor.
Run focused scans, ask questions about a diff, and produce machine-readable output for automation.
$ ycoder review --staged
✓ analyzed 14 files
2 findings need attention
Codebase awareness is table stakes. YCoder pulls together the signals needed to understand the real impact of every change.
Dependency relationships and repository guidelines reveal the downstream impact of a change.
Linked issues, documentation, product requirements, and web context explain what the code should do.
Static analysis and security signals are combined and deduplicated to reduce false positives.
Set the baseline with your rules and style guides, then improve future reviews through feedback and path-aware instructions.
“For migrations, always require a rollback path and a backwards-compatible deploy.”
Learning saved for future reviewsGive the reviewer feedback in natural language and carry the instruction into future reviews.
Apply precise guidance to the code where it matters.
Pass instructions between your coding agent and reviewer.
Save the last hours of work and make sure the change is actually ready to ship.
Create your own pre-merge quality checks in natural language.
API contract remains backwards compatibleIdentify coverage gaps and generate targeted test cases.
4 edge cases proposedDocument changed behavior while the implementation context is fresh.
3 public methods documentedYour private code is isolated during processing and never used to train shared models.
TLS protects code and review data at every network boundary.
Review context is retained only as long as required to complete the requested workflow.
No credit card needed
“Review the risky code first, explain why it matters, and leave the cosmetic noise behind.”High-signal review
“Give the whole team the same careful review standard, even when everyone is shipping at AI speed.”Consistent quality
“Catch edge cases, security slips, and hidden dependencies before they become production incidents.”Confident merges
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