Ty

TypeScript Security Scanner

Hacker News

TypeScript Security Scanner

Hi HN, I’m Guillaume, the cofounder of Bearer, an Open Source SAST solution. After launching a few weeks ago here on Hacker News with support for Ruby and JavaScript stacks, I’m happy to report we’ve just released a new version (v1.2) with TypeScript support! In terms of code coverage, we use the same rules already implemented for vanilla JavaScript, but as usual, you can build your own. The rules list is here: https://docs.bearer.com/reference/rules/ It’s a first version for TS, but we believe that thanks to the pre-existing JavaScript support it should already provide good insights. If you have some TypeScript code, we would love for you to try it out and let us know in the comment below or on our Discord your experience and how we can improve the findings. You can access the repo here: https://github.com/Bearer/bearer Thank you again!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, code, open · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: just released, exist, open source · Missing: https docs, excited, lua
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: scanner · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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.

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