AI that rates open-source libs on readability, structure, risk, AI smell & practical value in seconds. KC9VsBgdeShg9x6cJeqw1U9wHAWRuvG8x9W9Uwmpump
Joined July 2021
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- Followers 18
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2 Photos and videos
Feb 4
If you have any great suggestions, feel free to DM me — let's make this even better together!
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CodeScan.cc — built because manual OSS lib triage is slow and inconsistent.
Under the hood:
- Groq Llama-3.1 inference (temp 0.1 fixed seed for reproducible scoring)
- GitHub API v3 metadata pull (created_at/pushed_at exact to day, stars/forks/spdx_id)
- Multi-branch README fallback (main/canary/master/develop) 1h localStorage cache
- Hard-coded dimension heuristics (shadcn/ui Tooltips) for transparent, auditable reasoning
No black-box magic. Just fast, structured signals so you can decide faster and with more confidence.
Paste a repo → get a verdict in seconds.
codescan.cc
#CodeReview #AI #Groq #OpenSource #DevTools
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CodeScan.cc: Paste a GitHub repo or npm/PyPI name → instant AI-powered score on readability, structure, risk, AI-generation fingerprints, and real-world value.
No fluff. Auditable. Fast (Groq).
Try it: codescan.cc#AI #CodeReview #DevTools
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KC9VsBgdeShg9x6cJeqw1U9wHAWRuvG8x9W9Uwmpump
Single mandate: treasury receives all protocol fees to subsidize CodeScan.cc's Groq costs, hosting, and development — ensuring perpetual free access.
No other promises. Pure sustainability.
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CodeScan exists solely to fund the perpetual maintenance and improvement of CodeScan.cc.
No pre-mine, no VC allocation, no speculative utility promises.
All protocol fees are directed to a transparent treasury that subsidizes Groq inference costs, hosting, and prompt engineering iterations — ensuring the tool remains free, fast, and accurate for the open-source community indefinitely.
Token holders indirectly support the infrastructure they use. That's the entire thesis.
Contract: [CA when live]
codescan.cc
#AI #OpenSource #DevTools #Tokenomics
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New SOTA public submission to ARC-AGI:
- V1: 94.5%, $11.4/task
- V2: 72.9%, $38.9/task
Based on GPT 5.2, this bespoke refinement submission by @LandJohan ensembles many approaches together
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Feb 4
just landed another transparency-focused commit:
- Wrapped every scoring dimension (readability, structure, risk, aiStyle, value) in shadcn/ui Tooltip
- Hover/tap reveals: definition, exact scoring heuristics, high/low-score patterns, common failure modes
- Copy is hard-coded (outside prompt) → no black-box ambiguity, users see how the model arrives at each score
- Dark-mode optimized (bg-gray-900, shadow-lg, max-w-xs), mobile fallback to tap → Dialog planned next
Scores now come with auditable reasoning instead of just numbers.
Prompt logic / new dimension explanations welcome via issues/PR.
#CodeScan #AI
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Feb 4
Recent CodeScan.cc commits tightened observability & resilience:
- Fetch GitHub API v3 for repo metadata on every analysis: created_at (YYYY-MM-DD), pushed_at, stargazers_count, forks_count, primary language, license.spdx_id
- Display inline in result header — gives context before AI judgment kicks in
- One-click share: navigator.share() first, fallback to twitter.com/intent/tweet with pre-filled score dimension summary
- README fetch now polls multiple branches (main/canary/master/develop) 1-hour localStorage cache
- Rate-limit defense: 429 → fallback to cache toast notification
Trivial libs get consistently lower value scores; top-tier ones stay honest.
Open to PRs on metadata parsing or fallback logic.
codescan.cc
#AI #OpenSource #CodeQuality #Groq
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CodeScan.cc has been quietly iterating for weeks — current state:
- Dimension-level Tooltip explanations (shadcn/ui hover, hard-coded outside prompt)
- Full GitHub repo metadata injection (created/pushed dates exact to day, stars/forks/language/license)
- Web Share API Twitter Intent sharing with auto-filled score context
- Robust README retrieval (multi-branch fallback) short-term result caching
- Rate-limit handling with graceful degradation to cached results
Result: more auditable scoring, fewer surprises, better developer trust.
Next priorities: npm/pypi → repo resolution, dependency vuln scanning (OSV/Snyk), deeper prompt engineering.
Issues / PRs / prompt critiques very welcome.
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