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AI Code Reviews Directly in Azure DevOps (No Context Switching)

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AI Code Reviews Directly in Azure DevOps (No Context Switching)

I built a browser extension that adds AI code reviews directly to Azure DevOps Pull Request pages. No copying code, no switching tabs. The problem: I was constantly switching between Azure DevOps and external AI tools for code reviews. Copying code blocks, losing context, breaking flow. The solution: An extension that adds an "AI Review" button directly on PR pages. It uses Azure DevOps Git APIs to fetch actual code diffs and provides instant reviews right where you're working. Works with both dev.azure.com and visualstudio.com URL formats. Key features: - Zero context switching - everything happens in-browser - Persists during SPA navigation - Uses Git APIs for accurate code analysis - Simple setup (just add PAT token) Demo: https://thinkreview.dev/media/thinkreview-azure-devops-full.... I've been using it for a few weeks and it's saved me significant time. Would love feedback from other Azure DevOps users: - Does this solve a real problem for you? - What features would make it more useful? - Any Azure DevOps-specific quirks I should be aware of? Available: https://chromewebstore.google.com/detail/thinkreview-ai-code... More details: https://thinkreview.dev/blog/i-built-an-ai-code-review-tool-...

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, context · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: reviews, users · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users · Missing: mobile apps, ios, personal
27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: code review, ide, io · Missing: https docs, excited, just released
18%18% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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