Tag: #workflow

A rigid, repeatable checklist to define scope before you build

Strategy in the ChatGPT Mac app → One‑shot execution in VS Code

A call to developers to align vision, planning, and AI for graceful, consistent delivery

How to structure issues and boards so AI helps with both code generation and project management

A historical and future-facing exploration of how humans and machines have shared responsibility in computation

How to move between projects and clients without burning mental energy, and even make it a strength.

Practical, real-world strategies I use to move between projects, clients, and research without losing momentum.

What I tell clients, colleagues, and friends when they ask about security, privacy, and data handling with AI tools.

Not the theory—this is what my workflow with ChatGPT looks like in practice, across code, strategy, and client work.

How I move ideas from AI conversations into working code, using VS Code and GitHub Copilot Pro ($14/month with metered usage).

A personal take on why I trust GPT-5 and OpenAI as the backbone for my daily work, from agency projects to code and content.

There are a lot of good models out there. Here’s why I still run my workflow on GPT-5 and OpenAI.

How I use GitHub Copilot to generate clearer, more useful commit messages instead of relying on vague one-liners.

Why I’m building a course platform inside the StrongStart ecosystem, what it’s for, and how it ties into Labs.

How I take 10–15 minutes of raw voice transcription, run it through GPT-5, and turn chaos into themes, domains, and actionable tasks.

Where I put my AI-processed transcripts, the formats I rely on, and how I turn them into forward momentum.

Where I see this workflow heading next: using MCP and AI agents to handle the handoff from transcription to action without me in the middle.

How I think about when agents should act automatically, when they should pause, and how to design workflows that balance speed with trust.

Tracing the journey from raw voice transcripts to AI-structured outputs, storage systems, agent orchestration, and confidence thresholds.

A deep-dive on building a local-first hub to manage AI agents, rules, and confidence thresholds—so voice unloads flow from capture → storage → action without manual shuffling.

Why most tools fail when bolted on top of old habits—and how I’m reshaping my own workflows so agents become part of the operating system, not another inbox.

A workflow for transforming shorthand or casual notes into professional communication, while capturing the technical reasoning underneath.

A Lab documenting the top use cases where ChatGPT accelerates daily work, turning raw ideas, context, and code into clear, actionable outputs.

Shifting from app-centered workflows to systems where AI agents act as operators, not accessories.

Why tools alone don’t create change, and how small, repeatable rituals turn experiments into operating systems.

How to build feeds that keep users informed and empowered, not exhausted—and what the future state of activity streams might look like.

Balancing automation with user approval—when agents should act, when they should pause, and how confidence thresholds shape trust.

Exploring the design principle that executional intelligence delivers more value than repetitive insight, and how to structure systems so AI becomes an active operator rather than a passive commentator.

A continuation of my reflection on underused experiments—this time exploring how to fold them back into habit and make them feel alive again.

The real patterns from my own history with GPT-5 — not generic tips, but the exact ways I’ve bent it to fit my workflow.

How I wired AI prompts into my repos and issues, and why using it consistently is still a goal I’m working toward.

How I bounce between conversational problem-solving in ChatGPT and in-editor flow with GitHub Copilot, plus what’s working and what I’d like to improve.

Reflections on experiments that worked technically, but stalled because I didn’t fold them into habit.

A simple but powerful way to flatten complex systems into context files for AI tools.

A look at how I can tighten up my workflow with issues, goals, and projects so I ship more and manage less chaos.

Notes on how I get the most from GPT-5 — what works, what to watch out for, and the tangible ways it improves my daily workflow.