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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Most AI automation projects do not fail because the model is weak. They fail because the operating system around the model is missing.
Before I let an agent touch a live workflow, I define four things:
01 / The source of truth.
02 / The decisions the model may make.
03 / The actions a deterministic gate must approve.
04 / The evidence that proves the result.
That pattern now runs across three public builds:
BUILD 01 / Organizational AI Memory with source-backed retrieval.
BUILD 02 / Monday + Zoom + RTMP meeting automation with transcription and AI scoring.
BUILD 03 / Browser-first Telegram operations with Postgres truth, Qdrant retrieval, and deterministic gates.
I have packaged the entry point as a focused AI Workflow Audit: one process, one success metric, a risk map, and a 30-day implementation roadmap.
https://contra.com/s/XgBvUXrq-ai-workflow-audit-30-day-automation-roadmap
What is the first workflow in your business that is still held together by copying, checking, and chasing?
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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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Built this around a direction I care about a lot: moving from generic AI demos toward AI business automation systems that feel operational, controlled, and real.
Most AI visuals still stop at interfaces, prompts, or output shots.
I wanted this pack to point at a different layer:
the environment where AI business automation actually becomes usable for teams.
That means showing a world that suggests:
control
consequence
continuity
real delivery infrastructure.
I review one real workflow and show where manual work, fragmentation, and execution risk can be reduced through a managed AI automation approach.
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I just tested the new EL studio today and asked AI to make a short story about our business😃
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Private AI Campus: Managed Workspaces for Real AI Systems
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