Freelancers using Linux in Kyrgyzstan
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IURII PAIMURZIN
Kyrgyzstan
AI Automation Architect for Operations, CRM & Revenue
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AI Automation Architect for Operations, CRM & Revenue
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An AI agent becomes much more useful when it has somewhere real to work. Not another disposable chat. A private Linux workspace where it can keep project context, use browser and terminal tools, run automations, and leave behind inspectable files, logs, and deployed systems. This is the operating model behind two recent builds: InterviewCoach: Android/Compose + FastAPI/LangGraph, a public APK, and 111,723 deterministic load-test requests with 0 failures. An omnichannel agent: one LangGraph-controlled brain, PostgreSQL/pgvector state, and active Instagram, Telegram, and WhatsApp adapters. I have now made the same managed environment available as a Contra product. Starter is $30/month: 2 vCPU, 4 GB RAM, 40 GB storage, automation runtime, controlled Google sign-in, and 30 minutes of expert support. The boundary is explicit: the subscription covers the workspace and its support allocation. Custom integrations, migration, and production hardening are scoped separately. https://contra.com/products/l68ei2Zz-managed-private-ai-workspace-expert-support If you are a founder or operator with one real workflow to prove before committing to a larger build, this is the entry point.
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I designed it as a premium application for shaping the emotional quality of a private interior through light. Instead of treating lighting like a smart-home settings panel, the concept treats light as an architectural material. From a single responsive surface, the user can: adjust luminous intensity tune atmosphere / bloom scrub the solar meridian across the day switch material emphasis compare states save a preset open a commission summary Prototype: https://stitch.withgoogle.com/preview/11025185860304544382?node-id=d326bee735504c7382712f3311866d8a I used Stitch inside a multi-agent workflow orchestrated through Codex CLI + MCP. Stitch handled the interface generation and iterative design passes, while the agent workflow helped pressure-test directions, validate interaction states, and keep the concept moving toward a working product surface instead of a static mockup. What I especially liked: fast concept generation strong visual exploration quick iteration on layout and interaction direction What I’d still like to improve: stronger material-response differences a more ownable room identity even richer visual transitions between light states Built for the Google Stitch Challenge on Contra.
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I made this short video in ElevenLabs to show one small part of what I’m building at Sell.Systems (http://Sell.Systems). The idea is simple: too much work still disappears in handoffs. research → planning → building → testing → delivery every step protects quality, but it also leaks context. So I’m building a different setup: a private AI workspace with a web terminal, separate execution lanes for different jobs, and one protected core that stays under control. One lane can handle web work. Another can test ideas. Another can do lead research, content, or reporting. They can connect when needed. They can stay isolated when they should. The goal is not “more AI everywhere.” The goal is better architecture: clear boundaries, controlled execution, and a system that gets more useful after every run. Sell.Systems (http://Sell.Systems) is the build layer. Automation.Sell.Systems (http://Automation.Sell.Systems) is the managed layer. Not another chatbot. More like a private operating layer for real AI work. Which workflow in your business still has too many handoffs?
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Private AI Campus: Managed Workspaces for Real AI Systems
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