New language for teaching kids to code

Hacker News

Pickcode is a new language and editor for getting kids started with coding. The code editing experience is totally structured, where you select choices from menus rather than typing. I made Pickcode after experiences teaching kids both block coding (Scratch, App Inventor) and Python. To me, block coding is too far removed from regular coding for kids to make the connection. Pickcode provides a much clearer transition path for students to Python/JS/Java. Our target market is middle/early high school kids, and that’s who we’ve tested the product with during development. On the site, you can do tutorials to make chatbots, animated drawings, and 2D games. We have a full Intro to Pickcode course, as well as an Intro to Python course where you make regular console programs with a regular text editor. There are 30 or so free lessons accessible with an account, and the rest are paywalled for $5/month. For professional programmers, the editor is probably pretty frustrating to use (no vim keybindings!), but I hope it’s at least interesting to play with from a UI perspective. If you have kids aged 10-14, I’d love any feedback you have from trying it out with them. I love talking to users, reach out at charlie@pickcode.io!

Visit product View on Hacker NewsNov 15, 2023

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

89%89% 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: ide, io · Missing: https docs, excited, just released

77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, coding · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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