TypeScript Complexity Tracer

Hacker News

Hey HN! If you’re programming in TypeScript you know how annoying it is to wait for VSCode to infer complex types. To fix this, we created the $15,000 TSPerf Challenge [1] and we’re excited to announce there is a solution! You can install the TypeScript Complexity Tracer VSCode plugin here [2]. The plugin was created by Larry Layland and Daniel Roe (NuxtJS maintainer). The challenge was sponsored by Unkey.com, Scalar.com and Tigrisdata.com You can watch Daniel Roe showing a demo and Rhys Sullivan testing the plugin on his open source repository Shiptalkers.dev live on Algora TV [3] [1] https://algora.io/challenges/tsperf [2] https://marketplace.visualstudio.com/items?itemName=tsperf.t... [3] https://algora.tv

Visit product View on Hacker NewsJun 14, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

80%80% 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.

Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, 000 · Missing: https docs, just released, exist

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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: visual, code, open · Missing: mac, agents, macos

33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

11%11% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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