Scott Havird

Your Cloudflare Pages Redirect Is Probably Backwards​

Here's a scenario that's more common than it should be: you set up a site on Cloudflare Pages, configure a redirect so that www points to your apex domain, and everything looks fine. Then six months later you check Google Search Console and your impressions are split across four different URL variants. Half your SEO equity is leaking because both www.yourdomain.com and yourdomain.com are being indexed as separate pages.

The culprit isn't your code. It's Cloudflare Pages silently redirecting your apex domain to www — the exact opposite of what you intended.

I ran into this on my own site and spent longer than I'd like to admit tracing it down. Here's exactly what happens and how to fix it permanently.

AI Coding Tool Comparison 2026: Claude Code vs Cursor vs GitHub Copilot vs Windsurf​

I use multiple AI coding tools every day. Not because I'm indecisive — because different tools genuinely excel at different tasks. After a year of tracking my usage through PromptConduit and monitoring releases through Havoptic, I have a data-informed perspective on where each tool shines and where it falls short.

This isn't a surface-level feature checklist. It's an honest assessment from someone who ships production code with these tools daily, tracks their release velocity, and measures their impact on productivity.

Claude Code Hooks: A Complete Guide to Automating Your AI Coding Workflow​

If you've been using Claude Code for more than a week, you've probably noticed a pattern: you keep telling it the same things. "Run prettier after editing." "Don't touch the .env file." "Run the tests before you stop." These aren't complex instructions — they're rules. And rules shouldn't depend on an LLM remembering to follow them.

That's exactly what Claude Code hooks solve. They're deterministic automation that runs at specific points in Claude Code's lifecycle, executed by the harness itself — not by Claude. If you configure a hook to format code after every edit, it will format code after every edit. No exceptions. No "I forgot."

How to Measure AI Coding Assistant Productivity: A Framework for Engineering Teams​

Here's a question I get asked constantly: "How do you know if AI coding tools are actually making your team more productive?"

It's a fair question. Engineering leaders are investing real budget in Claude Code, Cursor, and GitHub Copilot seats. Developers are restructuring their workflows around these tools. But when someone asks for data — actual numbers on impact — most teams have nothing to show.

I've been working on this problem for over a year, first as an engineering leader trying to justify AI tooling investments at Georgia-Pacific, and then by building PromptConduit to close the analytics gap. Here's the framework I've developed for measuring what actually matters.

Havoptic: I Built a Visual Release Tracker Because I Couldn't Keep Up​

Here's a confession: I can't keep up with release notes. Claude Code, Cursor, Windsurf, Gemini CLI, Copilot CLI, Codex CLI, Kiro – they're all shipping at breakneck speed, and every week there's a new version with features that could change how I work. But reading through changelogs? My eyes glaze over by paragraph two. I'm a visual person. I need to see what changed, not read a wall of text about it.

That frustration is why I built Havoptic.

PromptConduit: Building Analytics for AI Coding Assistants​

Every day, I spend hours having conversations with AI coding assistants. Claude Code helps me debug issues, Cursor generates components, and Gemini CLI answers quick questions. But here's the thing: I had no idea what I was actually asking them. What patterns emerged from my prompts? Which tools got invoked most frequently? Was I getting better at prompting over time?

These questions led me to build PromptConduit.

Building Scalable Video Generation with Remotion and Docker: A Developer's Complete Guide​

If you've ever wanted to programmatically generate videos at scale, you've probably discovered that traditional video editing tools don't cut it for automated workflows. Enter Remotion—a React-based video generation framework that lets you create videos using familiar web technologies. But here's the kicker: when you combine Remotion with Docker and GitHub Actions, you get a production-ready video generation pipeline that can scale from your laptop to the cloud.

Claude Code Template: Accelerating AI-Assisted Development​

The future of software development is here, and it's conversational. With AI coding assistants becoming increasingly sophisticated, the way we structure and approach development projects is evolving rapidly. Today, I'm excited to share the Claude Code Template – a comprehensive starter template designed to maximize productivity in AI-assisted development workflows.

AI Agents and the Future of Development: Lessons from a Hackathon​

What happens when you give a small team of developers one week, a pile of AI tools, and the audacity to think we could build something meaningful? This is our story from the KOLO AI Hackathon – a journey into what agent-led development might actually look like.

Building AI Teams with CrewAI​

Have you ever wished you could assemble a team of AI experts to tackle your projects? Imagine having a researcher who never sleeps, an analyst who processes data in seconds, and a writer who crafts perfect content – all working together seamlessly. This isn't science fiction; it's possible today with CrewAI.

AI Agents as Enterprise UI​

In the evolving landscape of enterprise architecture, we're witnessing a paradigm shift that could fundamentally transform how businesses interact with their applications. I've observed a compelling trend: the emergence of AI agents as direct intermediaries between users and data layers, effectively replacing traditional UI components. This architectural evolution promises to streamline enterprise applications while significantly reducing infrastructure complexity.

Building an AI-Powered Sizzle Reel Extraction Engine: Technical Architecture and Business Rules​

A deep dive into the technical implementation of automated video content curation using multi-cloud AI services

Advertisers want to ensure their ads are present in video content that is relevant to their message. There are intelligent video services that 3rd party vendors provide to classify videos using the industry IAB standard.

What is IAB? The IAB develops technical standards and best practices for targeted advertising. The IAB also works to educate agencies, brands, and businesses on the importance of digital advertising consent while standardizing how companies run advertisements to comply with GDPR consent guidelines.

To meet our goal, we created a workflow that includes two extractors. The first extractor calls Comscore's Media API to classify videos. The second extractor takes the results from the first extractor and maps the IAB Taxonomy provided by Comscore to WarnerMedia's taxonomy.

Goal​

Given the rising interest in short-form content, hyper-personalization, and meme-making, we wanted to explore some new ideas for using AI with video-making and matching. For Stitchy, we wanted to learn if we could match videos based on the words and specifically the timing of the words spoken. With this achieved, the next objective was to subjectively test the entertaining factors these stitched videos. We believe there may be latent commercial and/or marketing applications for Stitchy including physical locations (AT&T Stores, HP Store, WMIL, etc) as well as inside other applications & online experiences.

Say you have been tasked with building an application that requires you to know when celebrities are on the screen. Where do I get started with ContentAI? Do I need to bring my own video? What if I don't have a video? Do you have some existing data I can look at to start researching/exploring? The answer to that is yes, we do!

Motivation​

I work with very talented data scientists and engineers. A lot of the models they are building are used to identify objects and actions in a video. Their models produce raw results in a CSV, JSON format. It is difficult to validate their results just from looking at the raw data. We have tried to feed the data into 3rd party tools to visualize the data in graphs, and there are a lot of python projects that can draw bounding boxes on images or videos .. but there doesn't seem to be a good solution to visualize bounding boxes on a video on a website.

Goal​

At the WMIL (WarnerMedia Innovation Lab) an interesting project was being considered contemplating a dynamic in-person experience. The idea originated from a “how might we create immersive experience for Lab visitors with our content” type of brainstorm.

As the ideation continued, the WMIL folks and the ContentAI folks talked about different ideas around matching users with celebrities in our content. As we explored various extractors that could be relevant, we collectively settled on a first lightweight proof of concept “can we match a person to a scene or moment in one of our movies or shows?” Thus was born… FaceMatcher3000.

Overview​

Popcorn (details below) is a new app concept from WarnerMedia that is being tested in Q2 2020. The current version involves humans curating “channels” of content, which is sourced through a combination of automated services, crowdsourcing, and editorially selected clips. Project Redenbacher takes the basic Popcorn concept, but fully automates the content/channel selection process through an AI engine.

Popcorn Overview​

Popcorn is a new experience, most easily described as “TikTok meets HBOMax”. WarnerMedia premium content is “microsliced” into small clips (“kernels”), and organized into classes (“channels”). Users can easily browse through channels by swiping left/right, and watch and browse a channel by swiping up/down. Kernels are short (under a minute), and can be Favorited or Shared directly from the app. Channels are organized thematically, for example “Cool Car Chases” or “Huge Explosions” or “Underwater Action Scenes”. Channels comprise content generated by the HBO editorial team, popularity/most watched, and from AI/algorithms.

Demo​

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Redenbacher Variant​

Basic Logic​

In Redenbacher, instead of selecting a specific channel, the user starts with a random clip. The AI selects a Tag that’s associated with the clip, and automatically queues up a next piece of content that has that same Tag in common. This continues until the user interacts with the stream.

My family and I were on our annual beach vacation, during covid-19, and I had a few ideas on how to help out the condo/hotel industry.

Goal​

We began this project as an exploration around up-levelling the capabilities of “assistants” (AI, chatbot, virtual, etc) with regards to the specific field of media & entertainment. As the overall platform of voice (and other) assistants increase in capabilities, we believe that they will focus on more “generic” oriented features, which gives us the opportunity to specialize in the vertical of entertainment. For this phase of work, we are exploring the concept of what an “Entertainment Assistant” might do, and how it might function.

One such function would be, for example, a voice-driven search where the user doesn’t exactly know what they are looking for: “Show me the first time we see Jon Snow in Game of Thrones” or “Show me dance scenes from classic movies” or “Show me that scene from Friends where they say ‘we were on a break!”.

In other words: respond to a voice-based command, filter out the relevant keywords, and deliver to the user all the matching scene-based results.

Why​

Closed captions have been around for awhile its time to try to innovate on them.

Video​

Goal​

As someone who loves exploring new technologies, I was thrilled to try out Apple's ARKit at the 2017 WWDC conference. I drove to the nearest Apple store to purchase the latest iPhone, just so I could use the latest ARKit technology.

Video​

"Get off my lawn moment" setup a camera to capture how fast folks are speeding in my neighborhood. 25mph limit. pic.twitter.com/rtQZs6QFbG

— Scott Havird (@scotthavird) January 6, 2016

Motivation​

I live in a neighborhood with a 25mph speed limit. I've noticed that many people drive much faster than the speed limit. I wanted to capture how fast people were driving in my neighborhood. I set up a camera to capture the speed of cars driving by. I used a Hikvision CCTV to capture the video and a Python script with OpenCV to detect the speed of the cars.

Video​

Goal​

Hey there! I'm super excited to announce that my fun and quirky physics-based game, Tumbling Timmy, is now available on the App Store! If you've been following my journey as a solo entrepreneur since the game's initial launch

Welcome! Thank you for stopping by. This is my first post for my first blog.

I want to use this blog to highlight quick ideas and prototypes with potential use cases.