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atrium – a resumable, tiling workspace manager for Claude Code, Codex

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atrium – a resumable, tiling workspace manager for Claude Code, Codex

atrium is a macOS desktop app that provides a fully resumable workspace manager with a variety of heterogenous panes including terminals, browsers, tasks, notes, and more. Over the last couple of months, I've been building an AI development tool that treats CLI AI dev harnesses as first class citizens. With Claude's SDK pricing changes on the horizon, I wanted to avoid getting locked in to paying API rates for something I already had a subscription for. It started after my computer crashed and iTerm didn't restore my sessions, so I set out to solve that problem initially. I liked it and added functionality till it became my daily driver. I ended up with atrium and I'm enjoying it so much I thought maybe others would, too. A little about the principles I started following as I built: Organization - everything fits into a hierarchy of workspaces, rooms, panes (agents, terminals, browser, notes, source control, etc), and stacks within a highly customizable mosaic. Persistence - everything, including AI sessions, long-lived processes, rooms, and panes, is saved in real time and restores (and auto-resumes!) in its entirety whether you restart or crash. State is file-based and git backed for extra reliability. Visibility - sidebars surface detailed agent cards, changes, long-lived processes, and activity. In-app and OS notifications keep you on top of everything. Control - atrium ships a comprehensive CLI that allows you and agents to drive anything in atrium: open browser panes, spawn other agents, create tasks or notes, or dream up pretty much anything. It's in early access right now (and I make no guarantees you won't notice) and free to download. All I ask is for feedback :) If you have other solutions to the problems above, I'd love to hear em in the comments! Download: https://getatrium.dev Full walkthrough (~22 min): https://www.youtube.com/watch?v=3H7rbn2KFm8

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: cursor, model, apple
96%96% 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: started, including · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
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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