Descriptions:
Getting better at AI coding isn’t really about better prompts or context engineering alone. After over a year of using AI coding assistants constantly every single day (especially Claude Code), the thing that actually changed my output was rebuilding the workflow around parallel coding agents. That’s the setup I’ll walk you through in this video!
I often have 3-10 Claude Code agents running in parallel on isolated worktrees. What makes it work is: planning, implementing, and validating in separate sessions, fresh-context reviews catching the bugs the implementer missed, and adopting a factory mindset, not vibe coding.
I’ll also cover the challenges nobody warns you about when you scale parallel agents (port conflicts, dependency duplication, and the silent killer: database collisions when 5 agents run migrations at once), and show Archon orchestrating the whole thing from GitHub issue to merged PR while I’m recording. Plus the honest tradeoffs most AI coding content skips over.
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– Neon (serverless Postgres with instant database branching for parallel agents):
https://get.neon.com/esWDBKR
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– Archon workflow masterclass this Friday in Dynamous: https://dynamous.ai
– Demo repo (issue triage dashboard + worktree scripts + review command):
https://github.com/coleam00/GitHubIssueTriager
– Archon (my open-source harness builder, ships most of this by default):
https://github.com/coleam00/Archon
– Codex plugin for Claude Code (used for the adversarial review in Pillar 4):
https://github.com/openai/codex-plugin-cc
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0:00 Intro to Parallel Agentic Development
3:12 Pillar 1: The Issue Spec
4:54 Pillars 2 & 3: Git Worktrees + Plan/Build/Implement
9:22 Pillar 4: Independent Code Review
13:28 Pillar 5: The Self-Healing Layer
16:18 Real-World Engineering Challenges
18:17 Database Branching and Port Management
21:23 Managing Tokens and Bottlenecks
23:16 Final Thoughts
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Join me as I push the limits of what is possible with AI. I’ll be uploading videos weekly – at least every Wednesday at 7:00 PM CDT!
