ghost machine + complete autonomy

· Zi Wang · 1 min read

Z / Runner 🏃🏻‍♂️

🏃🏻‍♂️ ghost in the machine; is not: assistant (reactive), chatbot (foreground), task agent (explicit goal), workflow automation (deterministic), not even "proactive AI" still intent-specified. It is a daemon, background, no chatbox, no session, wakes/runs opportunistically, only dies w/ kill -9.

🏃🏻‍♂️ 2026 hypothesis: 2/17 will have a big impact (deepseek v4); decline of command-and-control ai interaction; intent inference under high uncertainty, w/ permission to speculate long-horizon tasks with high tolerance for false positives.

  • 🏃🏻‍♂️ no, i want to attack to win… unless we are only doing api or infra; splitting prosumers vs. professionals is splitting air. I want to ground the GTM/target use cases with realistic and reasonable assumptions. Just want $7.99/week for 1,000 paying users within the first 30 days of launch.

  • 🏃🏻‍♂️ need something more radical, these still feels weak, not closer to ai-native.

Stephen / Basketball 🏀

  • 🏀 yes, microvm for 1000 false positives. model1 with engram + 95% humaneval is for coding.

🏀 #infinite-agents general agents with infinite computers, browsers, interpreters in the cloud – like manus. their 2025 insight was vib'ing long-tailed, highly-skilled tasks for non-professionals; my 2026 thesis is complete autonomy, verified steps, custom toolsets, optimized code, days-long excursions, exhaustive search corpus.. for 1b digital workers.

  • "is your _ agent for professionals ready to scale down for 100m prosumers?"
  • 🏀 $32/month for ghost ai. which non-realistic use cases? you meant, un-reasonable implementations / technicals?

🏀 #agent-ux copy-paste / browser tabs / verified code:

🏀 always bland from user surveys.

  • "Today, humans are the glue, stitching all that together with copy-paste and switching between browser tabs. Until that context is consolidated, agents will stay stuck in narrow use-cases."
  • "Code has a magical property [verifiability]: you can verify it with tests and errors. Model makers use this to train AI to get better at coding (e.g. reinforcement learning)."