Defaulting to cloud APIs is no longer the only way to ship AI in iOS apps. This hands-on workshop dives into Apple Foundation Models, moving past theory to build privacy-first AI features directly into native apps.

We will focus on practical implementation: how to actually run these models, customize them for your specific domain, and empower them by providing external tools to enrich context and build truly smart, localized experiences. Requirements: MacBook, Apple Intelligence-compatible iPhone, and basic Swift/Xcode skills..

BIO

The zero to one phase of an AI startup is where most projects quietly fail. Not because the models are weak, but because the problem is wrong, the scope is off, the team is misbuilt, and execution drifts away from real customer value.
This talk presents a practical zero to one playbook for developers and technical leaders who want to move from builder to founder in the AI era. It connects three elements that are usually treated separately: early product execution, early team design, and what investors actually fund at pre seed and seed stage.
You will learn how to choose a problem customers will pay to solve, how to turn AI capabilities into a real product wedge instead of a demo, how to run tight design partner loops, how to structure a two to four person early team, which roles to delay, and which concrete signals make investors back early technical teams. The session also highlights common early stage failure patterns, including overengineering, premature scaling, and misaligned hires.

BIO

LinkedIn | Github

The definition of “Mobile Engineering” is shifting. As we enter the Agentic Era, the bottleneck is no longer writing syntax – it’s architecture, governance, and verification.

This session is for developers and leaders ready to upgrade their mental models and their tech stacks. We will examine real-world “Agentic Patterns” that allow small mobile teams to ship with the velocity of giants.

BIO

LinkedIn

LLMs don’t fail because they “lack intelligence.” They fail because we keep asking them to play games whose rules we never wrote down. Wittgenstein’s idea of language games offers a sharper mental model: meaning doesn’t live in definitions, but in use—and use is governed by shared practices, context, and what counts as a correct move.

This talk treats prompts, tools, and RAG as the rulebook that turns fluent text into dependable behavior. Instead of “better prompts,” we focus on clearer games: intent, constraints, allowed moves, and explicit output contracts. We’ll explore why hallucinations and prompt drift are often not “bugs” in the model, but mismatches between the game we think we’re playing and the one we actually set up—and how lightweight, rule-based evaluations can catch that gap before production does. 

BIO

LinkedIn

Vibe coding has brought app development to everyone — but is it adequate for professional-level work? And what happens when you move beyond the vibe, into more structured, agentic approaches?
We’ll walk through the spectrum of AI-assisted development, from free-form generation to spec-driven workflows, with a clear goal in mind: understanding what it takes to produce enterprise-grade software with agentic tools.
Along the way we’ll cover predictability and consistency across a codebase, the current limits of today’s tools, and the gap between a working prototype and code you’d actually ship.

BIO

LinkedIn

Here’s the problem with generic AI assistants: a senior engineer asks “How do I deploy this service?” and gets the same tutorial everyone else gets. The AI doesn’t know how YOUR team actually operates – your deployment workflow, your infrastructure decisions, or the lessons you learned when production went down at 3am.

This talk demonstrates how to build AI systems that work the way your experts actually work – encoding the step-by-step processes, decision-making patterns, and operational knowledge that typically live only in senior developers’ heads.

You’ll see working examples of AI systems that mimic how experts operate across different domains – from technical workflows to content creation to cross-team processes. The key isn’t what they automate, but how they capture and reproduce expert methodology.

The Strategic Imperative:
Generic AI tools are a commodity – everyone gets the same answers from ChatGPT and Copilot. The competitive advantage comes from AI that knows how YOUR organization operates. This matters when tribal knowledge walks out the door, onboarding takes months, and “how we do things” lives in Slack threads and senior developers’ heads.

Embracing AI means making it personal – encoding your team’s operational expertise into shareable, scalable systems.

Lessons Learned:
✓ When encoding workflows adds value vs. when traditional automation wins
✓ What I over-engineered and regret
✓ Patterns for capturing operational expertise without maintenance nightmares
✓ Real constraints and trade-offs from daily use

Attendees leave with live demonstrations, GitHub repositories, and a framework for deciding when personalized AI systems make sense vs. when generic tools are the right choice.

BIO

LinkedIn | Github