GitNation
A collection of GitNation supported meetup groups around Europe and North America.
The Repo Is the Prompt
Coding agents are only as effective as the context they can see—and for long-lived software, the repository is their real prompt. This practical talk explores how clear specifications, enforceable constraints, observable acceptance criteria and fast feedback loops can turn repeated AI mistakes into improvements that compound over time.
Kirill Kravtsov
Building a UI That Builds Itself
What if an AI agent could customize its own UI outside the chat and update it on the fly? As humans, some tasks that we work on are better done visually than in text format. That's why I started this experiment, where I give an agent a skill and teach it how to work in a highly visual workspace it can extend and update with custom UIs. I'll walk you through how we designed a skill, a CLI, and a front-end app that work in tandem to make it possible. How we spent weeks figuring out the best and most minimal design system an agent can use to generate "slop-free" designs. The project is completely open-sourced and available on https://moi.computer/
Alexey Taktarov
Stop Paying for Frontier Models
Most of use reach for a frontier model by default and pay for it on every call, in latency, in energy, in cash, and in everything that leaves their stack. For most of those calls, a small local model would do the job.
RL Nabors, former Meta/React core team member and AWS alum, covers the vocabulary you need to reason about model performance (capability evals, golden datasets, LLM-as-judge) and walks through real cases: a local agentic harness replacing a frontier call, an in-browser moderation classifier defended with production-trace evals, and a generative summarization feature where the rubric turns out to be harder than the model. You'll leave with a framework for deciding when to choose large and off-prem or small and local models, and how to measure your way to the answer instead of guessing.
You will learn:
The vocabulary to reason about model performance (capability evals, golden datasets, LLM-as-judge). A framework for deciding when a small or local model can replace a frontier one and when it can't. A repeatable process for building capability evals from your own production traces, not someone else's benchmark. Working examples of using eval results to iterate on prompts and ship with confidence instead of vibes.Rachel Nabors
Using Web Technologies for Live Streaming
What if your livestream layout was just a web app? Sergey will show how HTML, CSS, and JavaScript can power interactive live-streaming experiences inside OBS’s embedded Chromium browser and how WebSockets and Cloudflare Workers can turn viewers into active participants.
Sergey Chernyshev
The Kind of AI Design System You Should Build
Most teams hear "AI design system" and reach for the thing they already know: a component library, now with a chat panel bolted onto it. I think that's the wrong shape. A design system in an AI-assisted codebase isn't a catalogue of components; it's a set of architectural contracts that a coding agent can actually be held to. Tokens, composition rules, and constraints that are legible to a model, not just to a designer in Figma.
The talk walks through what that looks like in practice: what an agent gets wrong when your system is only documented in prose, how to encode intent so the generated code is boringly consistent instead of plausibly wrong, and where the boundary sits between what you let a model generate and what you pin down by hand.
What attendees can expect to take away:
A working definition of an AI design system that isn't just "components plus autocomplete" The failure modes I've hit shipping React/TypeScript UI with coding agents, with real examples Concrete techniques for encoding design constraints so agents produce consistent output A sense of what to fix first if their current system is fighting their tooling rather than helping itIt's aimed at frontend and product engineers, and at design-system maintainers wondering what to do about all this. No prior AI-tooling experience needed.
Leonardo Lemos
Beyond React Three Fiber
A look beyond React Three Fiber and into the growing Poimandres open-source ecosystem — from how developers can get involved and contribute to a new generation of projects exploring canvas text rendering, game development, and more.
Kris BaumgartnerGitNation
A collection of GitNation supported meetup groups around Europe and North America.
The Repo Is the Prompt
Coding agents are only as effective as the context they can see—and for long-lived software, the repository is their real prompt. This practical talk explores how clear specifications, enforceable constraints, observable acceptance criteria and fast feedback loops can turn repeated AI mistakes into improvements that compound over time.
Kirill KravtsovBuilding a UI That Builds Itself
What if an AI agent could customize its own UI outside the chat and update it on the fly? As humans, some tasks that we work on are better done visually than in text format. That's why I started this experiment, where I give an agent a skill and teach it how to work in a highly visual workspace it can extend and update with custom UIs. I'll walk you through how we designed a skill, a CLI, and a front-end app that work in tandem to make it possible. How we spent weeks figuring out the best and most minimal design system an agent can use to generate "slop-free" designs. The project is completely open-sourced and available on https://moi.computer/
Alexey TaktarovStop Paying for Frontier Models
Most of use reach for a frontier model by default and pay for it on every call, in latency, in energy, in cash, and in everything that leaves their stack. For most of those calls, a small local model would do the job.
RL Nabors, former Meta/React core team member and AWS alum, covers the vocabulary you need to reason about model performance (capability evals, golden datasets, LLM-as-judge) and walks through real cases: a local agentic harness replacing a frontier call, an in-browser moderation classifier defended with production-trace evals, and a generative summarization feature where the rubric turns out to be harder than the model. You'll leave with a framework for deciding when to choose large and off-prem or small and local models, and how to measure your way to the answer instead of guessing.
You will learn:
The vocabulary to reason about model performance (capability evals, golden datasets, LLM-as-judge). A framework for deciding when a small or local model can replace a frontier one and when it can't. A repeatable process for building capability evals from your own production traces, not someone else's benchmark. Working examples of using eval results to iterate on prompts and ship with confidence instead of vibes.Rachel NaborsUsing Web Technologies for Live Streaming
What if your livestream layout was just a web app? Sergey will show how HTML, CSS, and JavaScript can power interactive live-streaming experiences inside OBS’s embedded Chromium browser and how WebSockets and Cloudflare Workers can turn viewers into active participants.
Sergey ChernyshevThe Kind of AI Design System You Should Build
Most teams hear "AI design system" and reach for the thing they already know: a component library, now with a chat panel bolted onto it. I think that's the wrong shape. A design system in an AI-assisted codebase isn't a catalogue of components; it's a set of architectural contracts that a coding agent can actually be held to. Tokens, composition rules, and constraints that are legible to a model, not just to a designer in Figma.
The talk walks through what that looks like in practice: what an agent gets wrong when your system is only documented in prose, how to encode intent so the generated code is boringly consistent instead of plausibly wrong, and where the boundary sits between what you let a model generate and what you pin down by hand.
What attendees can expect to take away:
A working definition of an AI design system that isn't just "components plus autocomplete" The failure modes I've hit shipping React/TypeScript UI with coding agents, with real examples Concrete techniques for encoding design constraints so agents produce consistent output A sense of what to fix first if their current system is fighting their tooling rather than helping itIt's aimed at frontend and product engineers, and at design-system maintainers wondering what to do about all this. No prior AI-tooling experience needed.
Leonardo LemosBeyond React Three Fiber
A look beyond React Three Fiber and into the growing Poimandres open-source ecosystem — from how developers can get involved and contribute to a new generation of projects exploring canvas text rendering, game development, and more.
Kris BaumgartnerGet in touch!
hi@guild.host