An AI-powered tool that generates humorous and witty roasts of any public GitHub profile. It analyzes a user's repositories, contribution history, and coding languages to create personalized, funny commentary in multiple languages. Perfect for developers with a sense of humor.
An AI-powered tool that analyzes your GitHub profile and starred repositories to generate insightful summaries and witty roasts. Discover what your coding interests truly reveal about you in a fun, shareable format.
An AI-powered tool that generates humorous and witty roasts of any public GitHub profile. It analyzes a user's repositories, contribution history, and coding languages to create personalized, funny commentary in multiple languages. Perfect for developers with a sense of humor.
An AI-powered tool that analyzes your GitHub profile and starred repositories to generate insightful summaries and witty roasts. Discover what your coding interests truly reveal about you in a fun, shareable format.
| Feature | github_roast | StarLens |
|---|---|---|
| Primary category | Profile Analysis | Code Analysis |
| Added | 2025-08-04 | 2025-08-11 |
| Pricing | Free | Free |
| Official website | github-roast.pages.dev | starlens.aisprint.dev |
| Product type | Website | Website |
| Performance data | ||
| User rating | Not verified | Not verified |
| Comments | 0 | 0 |
| Monthly visits | 1.1K | 3.4K |
| Monthly growth | 2.4% | Not verified |
| Favorites | 129 | 76 |
| Details | View details | View details |
Compare github_roast and StarLens by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
In the github_roast vs StarLens monthly traffic comparison, github_roast currently shows 1.1K visits and StarLens shows 3.4K; StarLens has about 3 times the visible traffic of github_roast, an absolute difference of about 2.2K visits. This reflects visible reach, not feature quality or paid users.
Only github_roast has complete third-party traffic details; StarLens uses visits recorded inside ToolMage. These scopes cannot estimate market share directly, and on-site views should not be treated as the product’s total website traffic.
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 40.96% | 463 |
| 🇨🇦Canada | 18.49% | 209 |
| 🇮🇳India | 14.85% | 168 |
| 🇮🇩Indonesia | 14.8% | 167 |
| 🇧🇷Brazil | 10.9% | 123 |
This in-depth github_roast vs StarLens comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. github_roast is primarily listed under “Profile Analysis”, while StarLens is primarily listed under “Code Analysis”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Primary category (github_roast: Profile Analysis; StarLens: Code Analysis); Monthly visits (github_roast: 1.1K; StarLens: 3.4K); Favorites (github_roast: 129; StarLens: 76); Website (github_roast: github-roast.pages.dev; StarLens: starlens.aisprint.dev); Added (github_roast: 2025-08-04; StarLens: 2025-08-11). These facts are more useful for selection than brand visibility alone.
In the github_roast vs StarLens monthly traffic comparison, github_roast currently shows 1.1K visits and StarLens shows 3.4K; StarLens has about 3 times the visible traffic of github_roast, an absolute difference of about 2.2K visits. This reflects visible reach, not feature quality or paid users.
Only github_roast has complete third-party traffic details; StarLens uses visits recorded inside ToolMage. These scopes cannot estimate market share directly, and on-site views should not be treated as the product’s total website traffic.
The current traffic scope is not sufficient for a reliable product ranking. Treat monthly visits as a market-interest signal, then decide using taxonomy, use cases, pricing, and a like-for-like trial rather than reading exposure as product capability.
github_roast and StarLens currently overlap in shared tags: AI, code analysis, developer, github, open source, and roast. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
github_roast's unique categories/tags are Profile Analysis, Humor, funny, generator, humor, profile review, and programming; StarLens's are Code Analysis, Personalized Content, Analytics, Profile Enhancement, developer tools, llm, n8n, and profile analysis. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.
github_roast has no verified rating, 0 comments, 129 favorites, and 104 likes；StarLens has no verified rating, 0 comments, 76 favorites, and 89 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Put github_roast on the priority trial list when the task aligns with “Profile Analysis” and especially Profile Analysis, Humor, funny, generator, humor, and profile review. This follows recorded positioning and does not imply unlisted capabilities are absent.
github_roast also currently records: pricing is free, product type is website, 1.1K verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
Put StarLens on the priority trial list when the task aligns with “Code Analysis” and especially Code Analysis, Personalized Content, Analytics, Profile Enhancement, developer tools, and llm. This follows recorded positioning and does not imply unlisted capabilities are absent.
StarLens also currently records: pricing is free, product type is website, 3.4K on-site monthly views, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
The available data describes positioning, public visibility, and community signals, but it cannot prove output quality, speed, integration effort, privacy, or long-term cost in your workflow. Before deciding, run the same representative tasks in github_roast and StarLens, then record completion time, accuracy, manual corrections, and the real paid threshold. A like-for-like trial turns this comparison into a defensible adoption decision.
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