URGENT: 5 Remote Agent Tips To Ship Faster, Better Software

Show Notes

In this Daily video, Parker breaks down practical ways to use remote agents to ship faster and better software, with five go-to tips and a look at context, prompts, and tooling.

The five remote agent tips

• Tip 1: Pick an ID and author your flow between editors

  • Switch between VS Code and Cursor to ride the saucy outputs. If one editor spits out something solid, roll with it.

• Tip 2: Use Augment for the heavy lifting

  • Augment is the stronger context engine for big tasks. Use it for the core work; Cursor is great for quick tabbing and navigation.

• Tip 3: Manage context with a dedicated tool

  • Use Memory Bank or Augment’s context engine to keep task-relevant context in sync with outputs.

• Tip 4: Structure context with repeatable patterns

  • Build and store context in organized formats (reference files, LMS.txt, JSON, doc patterns, etc.). Don’t dump everything into one place—patterned context (and even “yoinking” from other projects) keeps outputs sane.

• Tip 5: Design and chain prompts (prompts are the sauce)

  • Prompts are central: use modular, task-specific prompts, leverage augment instructions and clipboard-driven inputs (e.g., Alt+6 Lin), and map prompts to the software development lifecycle. Start manual, then move to agentic, then full agent.

Context patterns and examples

• Use a left-hand “reference” structure with exported files from other projects to seed context.
• Keep dedicated files for different kinds of context:
  • LMS.txt and lm.txt for structured docs
  • JSON dumps from doc scrapers
  • Other project docs you’ve extracted and adapted
• Avoid dumping everything into one context; pick one pattern and stick with it to maintain predictable outputs.

SDLC alignment and agent progression

• Map prompts to each stage of the software development life cycle.
• Three-stage workflow:
  1. Manual: you do it by hand to validate the flow.
  2. Agentic: you chain tasks with agents, still watching outputs.
  3. Full agent: autonomous execution with monitoring.
• Build a master control prompt that coordinates the sub-prompts and agent actions across the lifecycle.

Tooling and best practices

• Formatting and linting

  • Use Python: Black
  • Prefer Biome over uncontrolled lints; avoid lms for basic linting
  • Prefer package.json scripts to run linting and formatting

• Type safety

  • Emphasize TypeScript (strong tooling beats guesswork)
  • Don’t be lazy with your stack choices

• Key tools mentioned

  • VS Code, Cursor
  • Augment (context engine)
  • Memory Bank
  • External context patterns (LMS.txt, lm.txt, JSON, doc scrapers)

Quick takeaways

• Start with a manual workflow to define the steps, then automate them with agents.
• Choose a consistent editor and stick to the one that yields better outputs.
• Build a structured context system and modular prompts to keep work scalable.
• Treat prompts as first-class code: design, test, and map them to your SDLC.

Links

• Augment (context engine) for prompts and context
• Memory Bank context management solution
• Claude Code tooling and integrations
• Bolt AI-powered full-stack app builder

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