Codebases grow over time, and as they do, they tend to get worse to work with. This usually isn't anyone's particular fault. People come and go, features get shipped, dependencies get added. It feels like a death by a thousand cuts.At Sentry, we noticed this too: Long CI pipelines, hundred...

Voice interaction with AI is evolving rapidly, focusing on creating natural conversation experiences with crucial time delays. Models in Voice AI are rapidly evolving, emphasizing the importance of latency budgets. Design decisions aim to minimize latency per stage, impacted significantly by network and backend location. Winning teams treat latency budgets like profit and loss statements, with user completion detection being crucial. Understanding VAD thresholds and turn detection importance to avoid frequent interruptions. Prioritizing interruption handling and efficient function calling for user engagement. Enhancing function call responsiveness and optimizing voice agent workflows for efficient task handling. Automated evaluations with LLM judge and observability prioritization for latency and audio replay. Development in Voice AI follows an iterative approach, starting narrow and expanding with an evaluation framework.

You know how to build client apps—but where do client developers fit in the new world of ChatGPT and MCP? If you’ve used GraphQL before, it turns out your knowledge translates directly. This talk demonstrates how to build MCP apps using Apollo Client’s new open source AI apps integration along wi...

2 years ago, the engineering team's structure was fairly standardized, 2-pizza cross-functional teams and an engineering manager leading the team. These days, everyone is figuring it out as they go, making adjustments and seeing what may work and what not.

In this talk, Gregor wi...

For decades, frontend performance has been guided by the simple principle that faster is better. We optimize load times, reduce latency, and remove every unnecessary delay from the user journey.While building streaming AI interfaces with React, I discovered that traditional performance met...

Every web app needs icons, and there are surprisingly many ways to load them: img tags, icon fonts, inline SVGs, SVG sprites, and Icon components. Most teams pick one by habit or choose what's easy. The choice affects bundle size, accessibility, caching, rendering quality, and developer experienc...

Presentation on building a customer support bot with Langraff, Amazon Bedrock as base. LangChain Python library for LLMs. RAG - Retrieval Augmented Generation for accessing current information. LangChain using Bedrock and Amazon Knowledge Base. LangRaph classifies queries intelligently. Using LandGraph on top of LangChain for multiple scenarios with human intervention. Connected to Amazon Bedrock for knowledge base. No guessing with large language models; rely on a connected knowledge base for accurate responses. Chat GPT creation involves memory for context; tools like Land Graph aid in this process. Land Graph usage for flow creation; Demo with Kiro agent ID and new file uploads. Setting up AWS MCP server with agent skills; Using AWS agent toolkit for best practices in service setup; Flow for intent routing and order refund eligibility. Handling Order Refund Requests Using Agent Tools and Lookup; Case Sensitivity in Order Number Search; Validating Order Eligibility for Refund. Uploading Knowledge Base to AWS MCP Server; Verification of Email Address for Refund; Human in the Loop for Refund Approval. Human in the Loop Feature of LaneGraph; Uploading Documents to Bedrock; Utilizing Vectors for Knowledgebase Queries. Utilizing AWS Agent Toolkit for Workflow Best Practices; Connecting to KnowledgeBases for Querying; Building App with Mastering LaneGraph. Generating Design and Requirements for App Development; Utilizing Agent Skills in Spec-Driven Workflow; Ensuring Accuracy with Mastering Langraff Skill. Detailing Setup for Coding Assistant with AWS Agent Toolkit; Requesting Feedback and Interaction; Importance of Langraff in Graph-based Solutions. Enhancing Chatbot Prompt Suggestions Dynamically based on Conversation Context; Utilizing In-Memory Thread for Follow-up Questions in Langraff; Swapping Question Prompts with Tags for User-friendly Experience. Utilizing Different Models for Chatbot Tasks; Implementing Security Measures; Continuous Knowledge Base Updates for Accuracy.

Frontend teams are increasingly using AI to speed up UI development by generating code directly from designs. While the results can look impressive, they often fall short in real production environments: incorrect components, missing accessibility, and broken design-system rules are common failur...

Component tests started in real browsers with Karma: high fidelity, but slow feedback and painful config. Jest + JSDOM made tests fast and easy, but emulation can miss rendering, layout, and browser APIs.Playwright and Vitest Browser Mode bring back browser-first testing with headless spee...

Frontend apps morph at lightspeed, obliterating traditional test automation in their wake – CSS flips, components explode on re-render, and your test suite? Total carnage, half of it flaming red!Buckle up: I'm unleashing a game-changer – harnessing the browser's Accessibility Tree (the sec...