AutoBot – live voice control for long-running AI work

Hacker News

I wanted to manage long horizon agentic workstreams via voice, then put my phone down, and have a harness manage completion - extending into full computer use. I was trying to build a personal Jarvis, so I benchmarked AutoBot to see how close I could get: - OSWorld: 32.41% (moved Sol Max from 4th to 1st, beating Opus 5) - AssistantBench: 50.70% Hermes and OpenClaw, but without needing a weekend and VMs. And with the ability to manage deep personalization AND keep strict privacy rules, storing data on encrypted disk. AutoBot is a passion project that grew out of trying to make this all work for myself. I’m sharing it here because I suspect other here have the same frustration. And I’m curious what else everyone is doing for this. It’s an MIT-licensed harness that lives within a project. Native voice lets me discuss tasks, check progress, and steer work; a local ledger tracks unfinished outputs and the evidence needed to call them done. Memory drives more autonomy over time, and defrags and locks in learning nightly while a heartbeat system persists execution. I’m not selling anything. If you’re building something similar for yourself, I’d love to compare notes. GitHub: https://github.com/demeyer1/Autobot

Visit product View on Hacker NewsSep 17, 2026

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

93%93% 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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

44%44% 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

41%41% 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

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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