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Adrafinil – keep a lid-closed Mac awake only while agents work

Hacker News

Adrafinil – keep a lid-closed Mac awake only while agents work

A month ago there was a wave of posts and tweets about engineers walking around cafes and parks with their MacBooks propped half-open, as fully closing the lid forces sleep that stops their AI agents. Some people made snarky comments about using tmux or Amphetamine, and some defended their choice with “but I only need it sometimes, and forgetting to disable Amphetamine and finding my laptop discharged in my bag is worse.” This is a solution to this problem. Unlike caffeinate, it will prevent your MacBook from sleeping even with the lid closed, with no external power or display, using pmset disablesleep 1. Unlike other sleep-preventing apps, Adrafinil only activates when there’s an agent actively doing something. It detects agent activity through hooks it installs into Claude Code, Codex, and others. To reassure you it’s working, the app shows the active status in the menu bar, and it plays a chime when you close the lid. Once the agent is done, Adrafinil detects it and lets the laptop go to sleep by setting pmset disablesleep back to 0. It will also let it sleep in case of overheating. And if you want to manually toggle it, you can install an optional MCP and tell your agent to keep the MacBook awake for a specific time. It has four binaries, one of which is a root helper exposing a single setSleepBlocked call. All the logic and policy live in the unprivileged parts. They’re all notarized, and the app is fully open source (MIT).

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, model
98%98% 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 · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, month · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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