Kaleidoscope – A Parallel AI Agent TUI

Hacker News

I was running into the problem with opencode where I wanted to try a prompt against 5 different models at once, but didn't want to do the work to manage the worktrees and get things running in each instance. So I built up Kaleidoscope, a way to run multiple agents against a single problem, to make the AI slot machine a little easier to get better results out of. It works best for problems that can be verified by inspecting a running result. It depends on opencode and tmux to do the heavy lifting of agent running and pane management, so it acts as more of the "turbo" for AI to get the result you want or to explore different possibilities.

Visit product View on Hacker NewsOct 30, 2025

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

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way, para · Missing: mobile apps, ios, personal

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

32%32% 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

30%30% 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

29%29% 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.

Correct prediction on native model

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