Can Jev Make AI Agent Decisions Faster?

Learn how to research requirements, resolve open decisions, and write testable specs that AI coding agents can implement without the planning conversation.

Plan codebase migrations with coding agents using reviewable requirements, verification checks, staged rollouts, and defined rollback criteria.

Explore conversational banking architecture through a card-dispute example, covering tool access, authorization, execution, observability, and human handoff.

Learn how permissions, review limits, and missing product decisions stalled two overnight coding agent runs, and how their maintainer revised the workflow.

Use explicit contracts, behavior tests, and dependency checks to guide AI coding agents and review their changes through a TypeScript favorites example.

Join Kewin Wereszczyński and Szymon Chmal to explore what can go wrong in a React Native software factory overnight and the controls that keep agents on track.

Matt Hargett explains how AI moves bottlenecks across testing, deployment, infrastructure, support, and the rest of software delivery.

Tzvetan Mikov discusses AI coding agents, context switching, low-level engine optimization, reviewing specs and tests, and his plans for Hermes X.

David Khourshid explains state machines for AI agents, human approval steps, tracing, and handling invalid model outputs in agent workflows.

Connect AI-assisted development to measurable business outcomes. Matt Hargett shares a checklist for assessing user exposure, results, and delivery costs.

Michał Pierzchała shows how cloud-based QA agents can test real iOS and Android user flows and bring screenshots, recordings, and logs into pull requests.

How connected state, retrieval, memory, and verification carry coding agents across complex, multi-step workflows. Kewin Wereszczyński and Lech Kalinowski discuss the structures that let agents plan, retrieve context, use tools, and keep working across longer tasks.

Learn how React Native AI uses a unified provider model to switch between cloud and on-device LLMs without rewriting application logic, enabling reliable AI experiences online and offline.

Learn how on-device models in React Native can call tools, interact with external APIs, and return structured outputs you can use directly in application logic.

Learn how to run third-party on-device LLMs in React Native using MLC LLM. Choose your models, run them efficiently across platforms, and keep a unified JavaScript API with React Native AI.

Learn how to use Apple’s built-in Foundation Models in React Native apps, reduce memory usage, and enable fast, fully on-device AI with React Native AI Apple.

Learn why on-device AI matters for React Native apps, how local LLMs behave offline, and what problems React Native AI is designed to solve from day one.

Learn how to implement a React Native TurboModule on Android, from the TypeScript spec and Codegen to the Kotlin implementation and package registration.

Learn how JavaScript, native module, background, and main threads interact in React Native TurboModules, and how to use GCD queues to run async work safely in Swift.

Learn how to expose typed, immutable constants from a Swift TurboModule using Codegen, and get full end-to-end type safety between native and JavaScript.

Learn how to emit native events from a Swift TurboModule and subscribe to them in JavaScript, including proper memory management and Codegen integration.

Learn how to build a fully working TurboModule in Swift, integrate it with Codegen, and bridge it through Objective‑C inside an Expo app.