Ai on Corey Daley

Your HTML Is Lying to AI Agents

Sat, 25 Apr 2026 01:13:00 -0400

Your site has two versions. The first is the rendered page: sidebar on the left, article on the right, everything in its place. The second is what machines read — the raw HTML, in source order, before CSS gets involved. On most personal blogs, that second version leads with navigation, tag clouds, and category lists before it reaches a word of the article. That’s the version every AI agent, crawler, and screen reader encounters first.

This post argues that HTML is no longer just presentation scaffolding — it’s a machine-facing interface, and most of us are still designing it as if only browsers matter. The fix is three practical changes: reordering the DOM so content leads, generating llms.txt so agents can orient to your site, and publishing plain text versions of every post so there’s nothing to strip. None require a new framework. All take an afternoon.

If an agent read your site top to bottom in raw HTML, what would it think matters most?

A 12-Month AI/ML Roadmap for Engineers Who Feel Behind

Thu, 16 Apr 2026 18:35:00 -0400

Every senior engineer I know has a version of the same conversation with themselves: “I should really learn more about ML.” It comes up during a planning meeting when someone mentions embeddings. It comes up when a job description at an interesting company lists MLOps as a requirement. Then the sprint board calls it back.

I’ve published a 12-month AI/ML learning roadmap designed specifically for experienced software engineers — not a beginner tutorial, but a structured path from ML foundations through LLMs and generative AI, ML engineering at scale, and a capstone that turns a year of steady work into visible career leverage. The core idea: AI/ML becomes career-changing when it compounds through one sustained body of work, not when it’s consumed as scattered content.

If you’ve been sitting on the feeling that you should be doing something about this — most of the resources are free, the rest are investments worth making, and the plan is already written.

Before the First Commit: What Multi-Agent Sprint Planning Actually Catches

Sat, 11 Apr 2026 18:05:00 -0400

What does a multi-agent sprint planning workflow actually produce? Not just a cleaner document — it finds real bugs in a plan before implementation begins. When the /sprint-plan command ran against a “simple” Go REST API project, the security review phase returned three critical findings: a logical contradiction that made the stated auth behavior impossible, a schema constraint that would silently break token revocation, and a SQLite pragma applied to only one connection in a pool.

The post walks through the entire planning session for org-api — from seed prompt to approved sprint document — showing what each phase of the review pipeline produced and what changed as a result. The security findings came from reading the plan carefully, not from running any code. That’s the point.

What step in your planning process is explicitly there to prove the plan wrong before implementation begins?

From Config Hub to Competing Voices: How agent-config Became My AI Collaboration Stack

Sat, 11 Apr 2026 14:50:00 -0400

I started agent-config as a shared configuration hub: one repository to rule Claude, Codex, Copilot, and Gemini. That lasted about two iterations before the cracks showed. Forcing every AI agent to share the same configuration format was the wrong abstraction — different tools, different philosophies, different file formats. The solution wasn’t more uniformity. It was a different model of collaboration entirely.

Today agent-config is Claude-specific, but Codex is still central to how I work. The difference: Codex is no longer a configuration target. It’s a competitive collaborator. Sprint plans, blog posts, security audits — every significant output runs through a workflow where Claude and Codex produce independent drafts, critique each other’s work, and force synthesis from the tension. Two AI voices with different instincts produce better output than either would alone — just like a team of people with different backgrounds does.

Is your multi-agent workflow built for sharing configuration, or for generating the productive disagreement that makes output actually better?

Free Doesn't Mean Open: How AI Is Unbundling the Open Source Bargain

Thu, 19 Mar 2026 14:15:00 -0400

Open source was never really about the license. It was about economics — no single developer could build everything alone, so you shared the source and let the community help carry the load. AI is making that trade less necessary.

With Claude Code and Codex, a solo developer can now ship and maintain classes of software that once required a contributor community. A new model is taking shape: users file issues, maintainers decide what’s worth building, AI does the implementation. No PRs to review, no design debates in GitHub comments. Meanwhile, tools like Obsidian prove free software doesn’t require open source — and have for years. The question is no longer whether to open source, but which parts of the open source bargain still matter to you.

But here’s the counterintuitive part: AI also makes some open source values more important, not less. When software can be shipped and abandoned faster than ever, forkability, auditability, and portability become user protections that matter more. The future isn’t open vs. closed — it’s deliberate vs. reflexive.

The Blog That Builds Itself: AI Automation Behind the Scenes

Tue, 10 Mar 2026 15:40:00 -0400

What happens when you stop treating AI as a writing assistant and start treating it as a co-publisher? This blog has accumulated a full automation stack: a /create-blog-post command that pits Claude and Codex against each other in a competitive draft workflow, a Python script that generates hero images using DALL-E 3 and Claude vision, pre-commit hooks that block commits with unoptimized images, and a Bash script that handles WebP conversion and thumbnail generation automatically.

The design principle behind all of it: treat each stage as a contract, not a prompt. The meta-detail: this post was written by the same pipeline it describes.

What would your content workflow look like if you designed it the same way you’d design a software system — and which parts would you never automate at all?

When Your First Version Fails: Iterating on agent-config with AI

Sat, 07 Mar 2026 16:00:00 -0500

I built agent-config v1 to centralize AI agent configurations across Claude, Codex, Copilot, and Gemini — and it failed. Not dramatically, but fundamentally: I tried to force every agent to follow the same rules in the same format, because that’s what my human instincts said made sense. The problem is those agents have completely different requirements. Gemini needs TOML. The others use Markdown. You can’t just symlink your way to consistency.

v2 fixes this by letting AI handle the translation — automated merging of global and per-agent configs, format conversion per tool, and intelligent symlink setup.

The real lesson isn’t about config management, though. It’s about failing fast, iterating faster with AI than you ever could alone, and trusting the tools to solve problems your instincts would have you paper over. Are you letting your human instincts slow down your AI iteration cycles?

Centralizing AI Agent Configurations with the agent-config Repository

Thu, 19 Feb 2026 20:26:46 -0500

If you’re juggling Claude Code, Codex, and GitHub Copilot, you know the pain of keeping each one’s configuration files in sync. My agent-config repo solves that with a single source of truth: agent instructions, reusable skills, custom commands, subagents, and prompts all live in one place, and a single make symlinks command wires them up across every tool.

Each directory is purpose-built—skills for reusable capabilities, commands for CLI tools, subagents for delegation, prompts for task-specific guidance. The setup even backs up any files it would overwrite, so you never lose existing config.

Whether you’re just starting to extend your AI tools or already deep into custom workflows, having everything version-controlled and centralized is a game changer. Are you managing your AI agent configurations in a single repository, or do you keep them scattered across tools?

Beyond LeetCode: How AI is Transforming Technical Interviews

Sun, 15 Feb 2026 17:46:25 -0500

The coding interview landscape is shifting dramatically as AI tools become standard in software development. Rather than memorizing algorithms, candidates may soon demonstrate their ability to work effectively with AI agents—breaking down projects, creating tasks in Jira, and collaborating to solve real-world problems.

This shift raises important questions about what skills truly matter and how we evaluate engineering talent in an AI-augmented world.

What does this mean for the future of technical hiring?

The AI Divide: When Innovation Amplifies Inequality

Fri, 13 Feb 2026 15:45:00 -0500

We’re witnessing something unprecedented: AI tools that can generate content, build applications, and automate creative work at scales previously unimaginable. But there’s a catch—the most powerful capabilities often come with price tags that not everyone can afford.

From individual creators competing for attention to startups facing AI-augmented giants, the ability to pay for advanced AI is becoming a new axis of inequality.

Is this just another chapter in technological progress, or are we creating a permanent divide between the AI haves and have-nots? What do you think should be done to ensure AI benefits everyone, not just those who can afford it?

The Ethics of AI-Generated Code in Open Source: A Balanced Perspective

Fri, 13 Feb 2026 11:57:51 -0500

Here’s a question that’s been keeping me up at night: When does using AI coding assistants cross the line from productivity tool to ethical problem? I’ve been using tools like GitHub Copilot and Claude Code extensively, and I started wondering—if someone submits AI-generated code to open source projects and builds their reputation on it, is that fundamentally different from using Stack Overflow or IDE autocomplete?

In my latest blog post, I explore both sides of this debate. On one hand, AI democratizes contributions and amplifies what we can accomplish. On the other, it raises serious questions about authenticity, trust, and what it means to truly ‘know’ the code you’re responsible for. The middle ground is messy and context-dependent.

Where do you draw the line? Should contributors be required to disclose AI usage? What do you think?

Finding Each AI's Place in My Workflow

Thu, 12 Feb 2026 19:28:28 -0500

I’ve stopped trying to pick the ‘best’ AI tool—instead, I’m letting each one find its place in my workflow. Here’s what’s emerged: Codex, Claude Code, and GitHub Copilot CLI handle my command-line coding from simple to complex. ChatGPT web is my go-to for image creation (oddly, ChatGPT Desktop lacks this). GitHub Copilot in VSCode crushes code completion. Claude Code and Claude Desktop excel at blog writing with Notion integration.

Each tool has found its niche, and I’m more productive because of it. I’m still exploring how to use AI as a peer for bouncing ideas off, especially in planning mode.

The future isn’t about one AI to rule them all—it’s about orchestrating multiple specialists. How are you integrating AI tools into your workflow? Have you found similar specialization patterns, or are you using a different approach?

Why AI Interviews Can Feel Fairer (And What Humans Still Do Better)

Sat, 07 Feb 2026 17:31:33 -0500

We’ve all been there: two candidates, two different interviewers, completely different experiences. One gets warm small talk and easy questions. Another faces a colder room and tougher grilling. The outcome often depends on who you got and what kind of day they were having. That’s where AI interviewers start to feel like a real upgrade.

AI brings consistency—same questions, same order, every time. It reduces unconscious bias by sticking to job-relevant criteria and avoiding the silent ‘do I like this person’ filter that favors similar backgrounds. Mood doesn’t swing the outcome because AI doesn’t have bad days. But humans still excel at reading body language, adapting on the fly, and making nuanced judgment calls.

The future probably isn’t AI replacing humans—it’s AI handling structure while humans bring empathy. What’s your take on AI interviews? Would you prefer them over traditional ones?

The Art of Iterative Cycles with AI: Why Your First Prompt is Never Your Best

Wed, 04 Feb 2026 19:50:00 -0500

Here’s something I’ve learned about AI coding assistants: the first response is rarely perfect—and that’s actually a good thing. When I started using GitHub Copilot and Claude, I expected instant perfect code. Reality? AI interprets your instructions based on patterns and context, so the first attempt is often close but not quite right.

The key is treating AI like a junior developer: start with clear instructions, review the result, provide feedback, and iterate. Each cycle gets closer to what you need. This isn’t a limitation—it’s how effective collaboration works.

The developers who succeed with AI aren’t the ones with perfect prompts; they’re the ones who embrace refinement. Have you experienced this iterative dance with AI tools? How many rounds does it usually take you to get the result you want?

Managing Blog Posts with GitHub Copilot

Wed, 04 Feb 2026 16:25:00 -0500

Managing a blog used to mean juggling multiple tools—notes apps for ideas, editors for drafting, task trackers for progress. It was fragmented and exhausting. Then I discovered GitHub Copilot can work directly with GitHub Issues and Projects, creating a seamless workflow.

Here’s how it works: I create issues for blog post ideas, GitHub Copilot reads the issue, generates the complete post with proper frontmatter and content, and automatically closes the issue when I publish. No context switching, no lost ideas, just a smooth pipeline from concept to publication. The best part? Everything lives in one place alongside my code.

If you’re blogging and using GitHub, this workflow is a game-changer. How are you managing your content pipeline? Have you tried integrating your blog workflow with your code repository?

Building with AI: Copilot and Claude

Tue, 03 Feb 2026 21:02:25 -0500

Building websites has changed dramatically—I’m no longer staring at code for hours. Instead, I’m collaborating with Claude and GitHub Copilot to build this Hugo blog. Here’s what I’ve learned: Claude is my architect. When I need structural changes or new layouts, Claude generates complete solutions and explains every decision.

GitHub Copilot is my coding assistant, finishing my thoughts as I type and handling routine tasks. Together, they create a powerful workflow where Claude handles the big picture and Copilot speeds up execution. The result? I spend less time debugging and more time creating.

If you’re curious about AI-assisted development or wondering which tool does what, this post breaks down how they complement each other. Are you using AI tools in your workflow? How do you divide the work?