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OpenHelm – OpenClaw but free (using your Claude Code subscription)

Hacker News

OpenHelm – OpenClaw but free (using your Claude Code subscription)

I tried OpenClaw. It is awesome, but token use killed me, and like with Claude Code scheduling, I ended up spending most of my time managing failed executions and tweaking prompts. I built OpenHelm to fix this. It’s a local macOS app that turns your high-level goals into a self-running job queue, built directly on top of your existing Claude Code subscription. Use it free - Download it now: https://openhelm.ai/ - GitHub: https://github.com/maxbeech/openhelm - Video: https://youtu.be/FfEBw1SCl7w How it works 1. Set a goal, eg. "cold reach out to new leads", "grow my SEO", "audit my app weekly", "keep tests green") 2. OpenHelm puts together a plan combining one-off and recurring jobs to make it happen 3. Whenever a job fails, it will spot this, adjust, and try again 4. It will keep on top of the goal, auto-adjusting what jobs are run to actually achieve it Key benefits - Fair Source, on GitHub (free for teams under four people) - Free to use (all LLM calls use your Claude Code subscription) - Fully local - Secure (any tokens/passwords stay in macOS Keychain unless you specify otherwise) Happy to answer any questions!

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Actual performance

3points
4comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, claude · Missing: agents, agent, cursor
90%90% 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
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
28%28% 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: recurring, subscription · Missing: arr, mrr, revenue
28%28% 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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