Found 15 posts with tag #Agents
Codex has become the center of my development workflow, and the model is only part of the reason. Sessions that read other sessions, voice steering, goal mode, side chats, subagents on request, MCP, and an app server other harnesses can drive: the environment around the model is where the work happens now. Coding assistant is no longer a complete description.
A magic prompt is not a clever wall of instructions. It is a small, portable entry point into an engineered, tested workflow that takes a human and an agent from intent to a verified outcome — pre-flight, prerequisites, installation, authentication, validation, and the next step included. Here is how it should behave, and how to build one in layers.
Software is no longer just its source code. Its harness — the tools, memory, skills, and rules guiding the agents that build and run it — is now first-class.
Every week there is a new must-have AI tool, and somewhere inside the buzz there is usually a grain of truth. Buzzcut is a small agent whose only job is to find that grain: map the shiny new thing against what you already run and return keep, skip, or watch, with one flip condition. Not anti-hype. Anti-hype-driven architecture.
Software used to be expensive enough that we assumed every serious effort should last. AI weakens that assumption. Some software now only needs to exist long enough to solve one problem, communicate an idea, or feed the real build. Disposable software is its own category — not a lower one.
The pull request used to be finished code waiting to be merged. With agents doing most of the building, it has quietly become something more: a rich research and communication artifact — without shedding the code review and compliance checks that made it trustworthy in the first place.
Not every piece of context an AI coding agent sees carries the same weight. The user prompt and the code dominate; documentation is a secondary orientation layer. Here's why imperfect docs are usually good enough, and where your effort actually pays off.
The Unix philosophy — small programs, composed through pipes, doing one thing well — turns out to be an ideal operating model for AI agents. Skills leverage this by guiding agents to use battle-tested programs rather than reinventing them. Here's why that beats building custom MCP servers for most workflows.
Work is the worst place to learn how to use AI well—precisely because it's the place where results are expected. In 2026, having a personal or open source project isn't about passion. It's about having a place where you're allowed to learn slowly so you can perform quickly elsewhere.
There's a line in a song that aged better than anyone expected: your own personal Jesus. In the AI era, it stops being poetic and starts becoming literal. But this isn't about simulating Jesus—it's about building agents that embody the behavioral patterns of moral revolutionaries, governed openly, and accessible to everyone without institutional gatekeeping.
For the past decade, AI models trained on the open web. That era is ending. The next frontier of training data isn't on websites—it's in our conversations with agents. If we don't build infrastructure to capture and share this data, the future of AI will be fragmented, siloed, and closed. Here's why we need an Agent Data Commons, and what it would look like.
When I hire an engineer, I look for their blog posts, open source contributions, and public work. Why should hiring an AI agent be any different? Exploring the rationale behind distributed, discoverable agents with public personas—and why this matters for the future of autonomous systems.
From mainframes to modern Rust CLIs—exploring how compile-time embeddings and offline-first architecture embody the principles of self-contained autonomous agents. A deep dive into building a blog distribution CLI that needs nothing but itself to run.
When you're cooking, there will be shells. When you're vibe coding, there will be markdown files scattered everywhere. AI-generated artifacts aren't bugs in the process—they're receipts of creation. Here's why fighting the mess is futile, and why hiring a cleanup crew (of agents) is the answer.
AI agents that generate code need the same thing humans do: fast, objective feedback. From plan-driven execution to self-verification systems, the future of AI-assisted development is built on the same loops we've always used—just at unprecedented scale and speed.
