Tag: #ai

A comprehensive guide to what GPTBot is, how it appears in your logs, the difference between indexing and user-driven retrieval, and what options you have to allow, monitor, or block it.

An exploration of how users leverage ChatGPT and AI agents to fetch, scrape, and interpret content from the web — what it looks like, why it happens, and what it means for site owners.

A narrative exploration of what context windows are in AI systems, how they work technically, and why understanding their limits is essential for everyday use.

A clear breakdown of the differences between GPT-5 and GPT-5 Codex — why they exist, how they’re trained, and when to use each in practice.

How to fix ambiguous tickets, use AI to backfill clarity at scale, and empower leaders to triage with confidence

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 Model Context Protocol unlocks AI’s ability to safely use tools, data, and workflows

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 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.

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.

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

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.

Why we should stop repeating ourselves in the UI, and how a handful of design patterns help push systems from 'telling' into 'doing.'

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

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

A brief reflection quoting an AI’s own description of itself, offering a glimpse into how it perceives its inner layers.