Twitch plays Codebuff – a natural language coding experiment

Hacker News

Hi HN! We launched Codebuff last week, and it gave us a fun idea to try: Twitch plays Codebuff. We rigged up Twitch chat to control an old laptop of ours by sending Codebuff commands. Any chat message prefixed with ‘>’ is interpreted as a command for Codebuff. We’re starting with a blank web app running on localhost. So if you say “> Make the header spin”, then Codebuff will modify the css to make it happen. Codebuff can also run any terminal command (I hope you don’t brick our laptop haha). Yesterday we tested this out with our community and they successfully got Codebuff to add a map of the US (via installing some package that it found), add all the state capitals as red dots, and draw the shortest path through the capitals. They also built a little synthesizer together. Maybe this could create a new category of open source, where a bunch of people synchronously create whole apps together by guiding an AI with natural language input? It’s exciting to combine the creativity of a crowd of humans with the raw ability of a coding agent. It’s also just fun to create with strangers and take an app in wildly different directions! We’ll be here for a couple hours, feel free to pop on by and add some fun stuff to our website! Or maybe troll everyone, iono.

Visit product View on Hacker NewsNov 15, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, apps, new · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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