DevSwat
Turn codebases into interactive maps, graphs, and governance
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DevSwat - Turn codebases into interactive maps, graphs, and governance

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DevSwat Code Analysis is a code intelligence platform that turns large codebases into interactive maps, dependency graphs, and governance reports. Unlike traditional static analyzers, it combines scan, compare, trace, and agent workflows so teams can understand architecture, review changes, and act on issues in one place. It also supports GitHub scans, uploads, saved analyses, and AI-assisted governance, making it useful for both local exploration and team-scale code review.

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So while using AI for coding I relays that the context window are getting up so fast if I have big code base which cost burn of the tokens and hallucination so I create the GPS navigation for AI Agent to get fast acess to the code base and navigate through which eliminate hallucination, token consuming and code drift.

one thing that would be super helpful is a built-in dashboard for tracking model drift and confidence scores over time, so teams can see at a glance when something is going sideways in production without digging through logs

Love the idea of making complex codebases easier to understand visually. Interactive maps and governance can save teams a lot of time when onboarding or maintaining large systems, especially as AI-generated code becomes more common.

Curious...does DevSwat integrate with GitHub and support tracking architectural changes over time?

Have you considered adding a built-in cost-tracking dashboard that breaks down token spend and latency per request? Would make it way easier to justify the bill to stakeholders and spot which workflows are quietly eating budget.

Have you considered adding a visual debugging dashboard that shows model decision paths in real time? It would be super useful for spotting exactly where the AI drifts or fails in production without digging through logs.

Finally got around to poking at DevSwat, and the observability hooks feel genuinely useful for catching flaky model behavior. Nice to see someone focused on making AI dependable instead of just flashy.