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GuzmanPintos/README.md

Hi, I'm Guzman πŸ‘‹

Co-founder and product builder working at the intersection of hashrate and agents.

I helped build Luxor, infrastructure and markets for Bitcoin mining, and I'm now building Tenki, cloud infrastructure for agents.

Tenki

tenki.cloud: cloud infrastructure for your AI agents

  • 🧰 Sandbox: secure computers for AI agents
  • πŸ” Code Reviewer: AI code review that catches real bugs
  • ⚑ Runners: faster, lower-cost GitHub Actions runners
  • πŸ–₯️ Sandbox ADE: native desktop app for orchestrating sandbox sessions (macOS, Linux .deb, Linux .AppImage)

Luxor

luxor.tech: the full-stack compute company (bitcoin mining and AI)

  • ⛏️ Mining Pool: U.S.-based, SOC 1 & 2 certified, with FPPS, Fixed, and Upfront payouts
  • πŸ’» LuxOS: ASIC firmware for Bitmain and MicroBT miners
  • πŸŽ›οΈ Commander: fleet monitoring and remote control at scale
  • πŸ“¦ Hardware: ASIC and GPU brokerage, logistics, and financing
  • πŸ“ˆ Derivatives: OTC contracts for hedging hashprice
  • πŸ”Œ Energy: retail power, grid participation, and demand response
  • πŸ“Š Hashrate Index: mining market data and research

What I'm thinking about

  • Compute as a market, from Bitcoin hashrate to AI workloads. I've spent years building infrastructure and markets for Bitcoin hashrate. Now I'm working out how those lessons carry over to AI: how compute gets priced, scheduled, verified, and delivered.

  • Software in a world where agents are the primary builders. How development changes when agents can write, run, and review their own code, and what infrastructure makes that safe and reliable. What happens when we solve the taste problem?

  • Energy markets as an optimization problem. Flexible compute can turn variable energy into economic output, and software can coordinate supply, demand, and physical hardware in real time.

Elsewhere

Website Β· X

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  1. GuzmanPintos GuzmanPintos Public

  2. evo evo Public

    Forked from evo-hq/evo

    turns your codebase into an autoresearch loop β€” discovers what to measure, instruments the benchmark, then runs tree search with parallel subagents.

    Python