Raunaq will share how a pipeline of agents shrinks JavaScript across a large monolith and its micro-frontends, with zero functional or visual difference as the rule. He’ll cover the bundle bloat the pipeline found and how it hands changes it can’t prove safe to a human.

What if you could get the performance benefits of Static Site Generation without rewriting your SPA or migrating to a framework like Next.js or Nuxt?

Eduardo will explore how to wrap an existing SPA with SSG, preserve your current development and delivery model, and use edge delivery to improve loading performance and reduce infrastructure costs.

RL Nabors spent years on the React core team at Meta and on browser teams before that. Now they're building an omni-client combining X, LinkedIn, Bluesky and Mastodon content streams and publications in one private app, with no algorithm. Mima (mima.social) launched in San Francisco last month.

RL will demo it live, then take questions about local models and the future of the web in the agentic era.

A model I was working with scored R squared 0.998 on public US data. One of the input columns had been calculated from the target by the same agency that published both, so the model was rediscovering a formula it had already been handed. Tests don't catch this. Nothing throws, nothing fails, the numbers look great. So I built a linter for it. You write a manifest of what derives from what, the tool walks the graph, and CI goes red when a feature sits on a derivation path to or from your target. It returns four verdicts, including one for columns whose provenance nobody has traced yet. The code is Python. The idea applies anywhere you have a dependency graph and a CI job. I'll show the leak, the manifest, the linter, and a run going red live.
Repo: https://github.com/Adeniyikayodee/derives-from-tutorial

What happens when you stop treating AI as a chatbot and start building systems around it?

At 15, I’m using AI to build businesses, create content and manage my everyday work. In this talk, I’ll share how I’ve gone from experimenting with different AI tools to developing practical workflows across my business, Vajra Works, my personal brand and my productivity systems.

I’ll explore how I choose the right tools, structure workflows, decide what to automate and keep human judgment involved. Through real examples, demonstrations and lessons from things that haven’t worked, I’ll show how AI can move beyond prompts into systems that help get meaningful work done.

The goal is to make this a practical and valuable session for the developer audience.