George Tsiolis (@gtsiolis) header
George Tsiolis (@gtsiolis) profile picture

Product Design @LocalStack. Core Team @GitLab. 0→1 software design for remote teams and agents. ● ○ ○

Athens, Greece
Joined June 2009
Photo by George Tsiolis (@gtsiolis)Photo by George Tsiolis (@gtsiolis)Photo by George Tsiolis (@gtsiolis)Photo by George Tsiolis (@gtsiolis)Photo by George Tsiolis (@gtsiolis)Photo by George Tsiolis (@gtsiolis)Photo by George Tsiolis (@gtsiolis)Photo by George Tsiolis (@gtsiolis)Photo by George Tsiolis (@gtsiolis)
#UX design explorations with iterations. I recently started doing some design explorations again, and frames started moving and grouping around in a familiar way I've been doing design iterations over the last few years.
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George Tsiolis retweeted
One habit I picked up earlier this year with coding agents is saving notes from some sessions inside each repository, but never committing them. Kinda like portable memory and context I can reuse with ANY coding agent in future sessions.
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Progress over process. Good process reduces uncertainty, makes coordination easier, and makes failures cheaper. Bad process is theater that adds overhead and holds teams back.
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One habit I picked up earlier this year with coding agents is saving notes from some sessions inside each repository, but never committing them. Kinda like portable memory and context I can reuse with ANY coding agent in future sessions.
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This week we introduced lstk v1, the new recommended @LocalStack CLI. One installation to manage the emulator, run IaC commands against it, view deployed resources across regions and accounts, put emulator configuration alongside code, and manage snapshots. A lot of my work has been in the details you notice when something goes wrong. Making setup work without an interactive terminal. Rejecting malformed input. Handling environments without internet access. Making sure stopping Terraform gives it a chance to clean up. Those details matter even more when the caller is a coding agent. To make room for other product work, I also designed a few agent loops to help maintain the CLI itself and automating recurring work with the team including... 1. Updating reference docs after each weekly release 2. Addressing PR review feedback and resolving corresponding threads 3. Posting a single review comment per PR to help authors merge with confidence 4. Rebasing pending PRs and resolving any code conflicts 5. Surfacing useful stale PRs before next release Two aha moments I had with these loops: 1. Sitting in a morning meeting thinking I need to get back to 15 review comments on a docs PR, then finding the agent had already addressed them, pushed a commit, and posted a summary. 2. A new weekly release triggering the agent to update a pending docs PR instead of opening another overlapping one. Building these loops also informed how we think about agents using the CLI. So, we’ve shipped non-interactive workflows, input validation, structured JSON output for selected commands, and updated agent skills. Meet lstk. . . . $ npm install -g @localstack/lstk $ npx skills add localstack/skills
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George Tsiolis retweeted
Everything gets singificantly harder when you have to convince the people instead of writing the code.
George Tsiolis retweeted
The remote playbook becomes the AI playbook. The patterns that reduced coordination costs in remote teams are now becoming the operating system for AI-native organizations. AI adoption is an organizational design problem, not a tooling problem. AI agents are remote collaborators. They're never in the room, never part of the side conversation, and never benefit from undocumented context. They work with artifacts your organization leaves behind: documents, issues, decisions, code. That’s why the operating principles by all-remote organizations like @GitLab matter more than ever. Documentation becomes executable context. Transparency becomes infrastructure. Decision velocity becomes the constraint. Async collaboration becomes loop design. Remote teams cut coordination cost with clear handoffs instead of standing meetings. Agents run the same way. The loop matters more than the prompt: what you assign, how you check it, where it hands back. Documentation becomes executable context. Docs, issues, and decisions are now runtime input for agents. Knowledge that isn't documented can't be retrieved. Knowledge retrieval replaces knowledge sharing. The author becomes the first reviewer. AI multiplies output, not review capacity. Self-review before requesting review becomes a required validation step. Transparency becomes infrastructure. AI can only reason over visible context. Hidden decisions become blind spots. Transparency is no longer about culture, it’s about reliable execution. Breaking the single source of truth creates execution bugs. Two conflicting docs create coordination costs, and make agents act with false confidence. Motion becomes cheap. AI makes producing work almost free. More PRs, more drafts, and more experiments don’t necessarily mean more progress. The real metric that survives is whether the work solved the problem. Bias for action becomes a permission model. Remote teams trusted people to act without waiting. Agents need the same trust, but with explicit boundaries. Reversible work should flow automatically, and irreversible work should pause for human judgment. Decision velocity becomes the competitive advantage. Every principle above reduces the coordination cost of making decisions. Organizations that optimize for decision velocity by making more informed decisions, faster and more often, will outperform those that simply produce more code, documents, or pull requests. The organizations that learned to coordinate distributed teams are best positioned to coordinate distributed intelligence. 🤖
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George Tsiolis retweeted
You'd never let a junior engineer learn Terraform on prod. Your agent is faster than the junior. In both directions. Give it a fake AWS it can break: apply, wipe, rebuild until green. Harness Week finale, with @LocalStack. pydantic.dev/articles/harnes…
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I was a bit skeptical about loop engineering. Then one morning, while away from keyboard, an agent resolved all 15 review comments on a docs PR the same agent had opened automatically after the latest CLI release. 😶
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The remote playbook becomes the AI playbook. The patterns that reduced coordination costs in remote teams are now becoming the operating system for AI-native organizations. AI adoption is an organizational design problem, not a tooling problem. AI agents are remote collaborators. They're never in the room, never part of the side conversation, and never benefit from undocumented context. They work with artifacts your organization leaves behind: documents, issues, decisions, code. That’s why the operating principles by all-remote organizations like @GitLab matter more than ever. Documentation becomes executable context. Transparency becomes infrastructure. Decision velocity becomes the constraint. Async collaboration becomes loop design. Remote teams cut coordination cost with clear handoffs instead of standing meetings. Agents run the same way. The loop matters more than the prompt: what you assign, how you check it, where it hands back. Documentation becomes executable context. Docs, issues, and decisions are now runtime input for agents. Knowledge that isn't documented can't be retrieved. Knowledge retrieval replaces knowledge sharing. The author becomes the first reviewer. AI multiplies output, not review capacity. Self-review before requesting review becomes a required validation step. Transparency becomes infrastructure. AI can only reason over visible context. Hidden decisions become blind spots. Transparency is no longer about culture, it’s about reliable execution. Breaking the single source of truth creates execution bugs. Two conflicting docs create coordination costs, and make agents act with false confidence. Motion becomes cheap. AI makes producing work almost free. More PRs, more drafts, and more experiments don’t necessarily mean more progress. The real metric that survives is whether the work solved the problem. Bias for action becomes a permission model. Remote teams trusted people to act without waiting. Agents need the same trust, but with explicit boundaries. Reversible work should flow automatically, and irreversible work should pause for human judgment. Decision velocity becomes the competitive advantage. Every principle above reduces the coordination cost of making decisions. Organizations that optimize for decision velocity by making more informed decisions, faster and more often, will outperform those that simply produce more code, documents, or pull requests. The organizations that learned to coordinate distributed teams are best positioned to coordinate distributed intelligence. 🤖
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I'd never written a line of Go, but have been shipping code daily to the new @localstack CLI. Claude Code, Codex, and Linear agents do most of the building. What's left is deciding what the CLI should be, and what it shouldn't. . . . . % npx @localstack/lstk
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What a journey, team! 🍊
We’ve reached an agreement to acquire @ona_hq. Its secure cloud execution technology will help Codex take on longer-running work, even when laptops are closed, and help more organizations deploy agents securely in production. After closing, Ona will join OpenAI’s Codex team. openai.com/index/openai-to-a…
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Welcoming @linear to the @localstack CLI contributors.
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Things we shipped last month in the @LocalStack CLI: + New snapshot command for saving and loading snapshots, with save and load top-level alias commands, and auto-starting on load. 💿 + New reset command to reset emulator state, and --persist option for enabling persistence across restarts. ♻️ + New Snowflake and Azure emulation support, and default emulator prompt selection on first run. 🏂 + New aws and az proxy commands for running the AWS CLI and Azure CLI against the emulator with zero configuration. 🚪 + Better container runtime support, help text wrapping, and a few dozen more terminal UI improvements. 🍭 . . . . % npx @localstack/lstk
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Easy reading is hard writing.
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Sometimes product clarity means deleting more than you add to stop anyone from spending cycles on terrible ideas.
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Two quality of life improvements since the initial release of lstk, the new @localstack CLI: License validation on startup. Automatic AWS CLI and SDK profile setup. % npx @localstack/lstk
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