Edit or Die (an experimental "text editor")

Hacker News

I’ve been thinking a lot recently about what it means to write with AI, but really about what it means to edit with AI, since from my experience that’s really where I’ve been using LLMs. Before LLMs, there were apps like The Most Dangerous Writing App and Write or Die, which helped combat writer’s block by deleting your progress if you stopped typing. Even then, the amount of words written was not the point, it was how the process led to better writing. This is an exploration of that, for LLMs. Given a starting prompt, and with an LLM constantly writing, can you edit (reason?) fast enough to stay ahead of what it spits out? What does it mean to be done when it’s possible for the machine to never stop?

Visit product View on Hacker NewsSep 11, 2026

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

87%87% 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: io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

31%31% 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

20%20% 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

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

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