Themata.AI
Themata.AI

Popular tags:

#developer-tools#ai-agents#llms#claude#ai-ethics#code-generation#openai#ai-safety#anthropic#open-source

AI is changing the world. Don't stay behind. Clear summaries, community insight, delivered without the noise. Subscribe to never miss a beat.

© 2026 Themata.AI • All Rights Reserved

Privacy

|

Cookies

|

Contact
ai-agentsdeveloper-toolscode-generationparallel-computing

Parallel coding agents with tmux and Markdown specs

How I run 4–8 parallel coding agents with tmux and Markdown specs

schipper.ai

March 2, 2026

14 min read

🔥🔥🔥🔥🔥

57/100

Summary

Parallel coding agents are run using tmux, Markdown files, bash aliases, and six slash commands. A role naming convention is applied per tmux window, designating roles such as Planner for creating specs, Worker for implementation, and PM for backlog management.

Key Takeaways

  • The setup utilizes tmux, Markdown files, bash aliases, and six slash commands to manage 4 to 8 parallel coding agents effectively.
  • Each Feature Design (FD) file includes the problem statement, considered solutions, the final solution with an implementation plan, and verification steps.
  • The FD lifecycle is managed through six slash commands, which facilitate creating, tracking, and verifying feature designs.
  • Each FD is assigned a unique identifier and tracked in an index, allowing for organized management of multiple projects and their statuses.
Read original article

Community Sentiment

Mixed

Positives

  • The use of multiple agents working in parallel can enhance productivity, as seen in browser automation projects, although context management remains a challenge.
  • The integration of shared markdown specs and a ground truth file can facilitate better coordination among agents, potentially improving their effectiveness in collaborative tasks.

Concerns

  • Despite the potential of parallel agent setups, there is skepticism about their actual productivity, as many are still waiting to see significant software outcomes from these approaches.
  • Managing context drift among agents is a significant bottleneck, indicating that current methods may not be robust enough for complex projects.

Related Articles

Prioritising Similar Functions

The long tail of LLM-assisted decompilation

Feb 16, 2026

How I Use Claude Code | Boris Tane

How I use Claude Code: Separation of planning and execution

Feb 22, 2026

The 8 Levels of Agentic Engineering — Bassim Eledath

Levels of Agentic Engineering

Mar 10, 2026

Building for an audience of one: starting and finishing side projects with AI

Building for an audience of one: starting and finishing side projects with AI

Feb 17, 2026

Building a C compiler with a team of parallel Claudes

We tasked Opus 4.6 using agent teams to build a C Compiler

Feb 5, 2026