jmanhype

4 ms·

1.
by jmanhype 7mo ago
Thanks! Threshold control is on the roadmap. Right now the confidence gating works at two levels: auto-approve (high confidence, like correcting a greeting style) and suggest-only (lower confidence, needs manual confirmation from dashboard)
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Show HN: I built a 7-agent AI marketing crew – 235 replies, /bin/zsh revenue(vaos.sh)
2 points by jmanhype 7mo ago|0 comments
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Show HN: Complete Guide to AI Agent Observability in Production(vaos.sh)
1 points by jmanhype 7mo ago|0 comments
4.
by jmanhype 7mo ago
Good points across the board. Threshold control — yes, that's the plan. Right now it's a single 0.8 cutoff which is obviously too blunt. A social media agent vs a client-facing email agent have completely different risk profiles.
5.
by jmanhype 7mo ago
I'm a solo founder. 261 commits, 44 tests, 54 deploys. VAOS runs your AI agent 24/7 on Fly.io -- you give it a prompt and a Telegram channel, it handles the rest. The part I care about most: every 5 minutes, a loop scores each age
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Show HN: VAOS – A feedback loop that makes deployed agents less stupid(vaos.sh)
1 points by jmanhype 7mo ago|4 comments
7.
by jmanhype 7mo ago
I'm not an ML engineer. I used Claude Code (Opus 4.6) to get LoRA fine-tuning gradients running on Apple's Neural Engine — the dedicated ML chip in every Apple Silicon Mac that has no public training API. 192 gradient dispatches,
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Show HN: LoRA gradients on Apple's Neural Engine at 2.8W(github.com)
6 points by jmanhype 7mo ago|1 comments