TypeScript

Content Is Code - Matt Palmer, Conductor

Jul 18, 2026 · 10:53

Matt Palmer, head of developer experience at Conductor, argues that code is now the fastest way to produce technical content, shifting left to code as the source of truth. He outlines three eras of content creation—handcrafted, expensive code, and cheap code with AI—and claims that the scarce resource today is not code but structure and conscientiousness, meaning meticulous care in maintaining design tokens, brand guidelines, and clean codebases. AI rewards organizational excellence over raw technical skill, making the best communicating teams those that instill discipline and rigor into their software creation. Palmer predicts 2026 as the year of the creative technologist and 2027 as the year of the content engineer, with declarative pipelines that automatically generate documentation, walkthroughs, product tours, and updates from structured code.

Teaching Coding Agents to do Spreadsheets - Nuno Campos, Witan Labs

Jul 8, 2026 · 19:09

Nuno Campos from Witan Labs explains how his team spent four months teaching coding agents to master spreadsheets, raising accuracy from 50% to 92% on a financial analysis benchmark. Key failures included a rigid three-agent architecture and standalone representations like SQL and XML. The breakthrough was replacing 15 separate tools with a single JavaScript repo tool offering persistent state and code-mode semantics, enabling agents to combine multiple operations in one call and drastically reduce timeouts. The team built high-fidelity formula and rendering engines to close the verification loop, and added domain knowledge prompts to focus the model. For evaluation, they moved from LLM-as-judge to deterministic comparisons using golden spreadsheets. Campos urges practitioners to replace many tool calls with real scripting languages, invest in feedback loops, and regularly revisit interfaces as model capabilities evolve.

Respect The Process - Andrew Dumit, Watershed Technology Inc.

Jul 7, 2026 · 16:43

Andrew Dumit of Watershed explains how to deploy coding agents for editing complex supply chain graphs in sustainability, arguing that in a domain rife with expert judgment calls, you must constrain the agent's effects through a typed SDK and deterministic execution while leaving its reasoning free. Unconstrained coding agents caused inconsistency, hallucination, and false reports—saying edits were made when they weren't—and made review impossible for non-coders. Their solution: a TypeScript SDK as the only way to edit graphs, plus a harness that lints agent code, catches conflicts, runs the script, and validates output artifacts, producing traceable, replayable edits. This approach improved accuracy from 43% to 92% on internal evals, with further gains from prompt improvements, few-shot examples, and breaking tasks into plan-and-execute loops. Even when the agent lands on a different expert answer, every change is valid and reviewable without reading code.

Vibe Engineering Effect Apps — Michael Arnaldi, Effectful

May 7, 2026 · 1:43:04

Michael Arnaldi demonstrates that cloning the Effect library's repository into a project, rather than relying on prompts, is the most effective way to give coding agents the context they need to build reliably with Effect. Starting from an empty repo, he sets up a Bun, Vitest, and TypeScript project, adds the Effect repo as a git subtree, and creates an agents.md to guide the agent. He then uses the agent to research patterns, resulting in a fully functional Todo HTTP API with OpenAPI docs, SQLite persistence, and tests — all from scratch in under two hours. The workshop stresses that treating the library code as part of the project, combined with strict diagnostics and pattern files, makes agents effective even in unfamiliar codebases. Arnaldi also discusses the importance of workflow systems like Effect Cluster for long-running AI processes, where server failures become likely.

Full Walkthrough: Workflow for AI Coding — Matt Pocock

Apr 24, 2026 · 1:36:30

Matt Pocock presents a hands-on workshop on building a full AI-assisted coding workflow, arguing that software engineering fundamentals—not hype—make agents effective. He introduces the 'smart zone' and 'dumb zone' of LLMs (performance drops after ~100k tokens) and the 'Memento problem' (agents forget between sessions). His process starts with a 'Grill Me' skill that relentlessly questions the user until shared understanding is reached, then produces a PRD without reading it, slices work into vertical 'tracer bullet' issues, and runs agents AFK using TDD. He advocates designing codebases with deep, testable modules and shows Sandcastle, a TypeScript library for parallel agent execution with separate implementer (Sonnet) and reviewer (Opus). The workshop transforms ambiguous briefs into shippable features while keeping humans in the loop for QA and taste.

"Software Fundamentals Matter More Than Ever" — Matt Pocock

Apr 23, 2026 · 18:26

Matt Pocock argues that software fundamentals matter more than ever in the AI era, directly countering the 'specs-to-code' movement that treats code as cheap. He shares practical skills like 'Grill Me' to reach a shared design concept with AI, a ubiquitous language from domain-driven design to align terminology, and test-driven development to force small, deliberate steps. Drawing on John Ousterhout's 'deep modules' and Frederick Brooks's 'design concept,' he advocates structuring codebases with simple interfaces behind which AI can implement freely. He warns that bad code is the most expensive it's ever been, as it blocks the productivity gains AI can offer. His reusable skills are available in the GitHub repo MattPocock/skills.

Building durable Agents with Workflow DevKit & AI SDK - Peter Wielander, Vercel

Jan 6, 2026 · 1:09:49

Peter Wielander from Vercel introduces the Workflow DevKit, an open-source library that adds durability, observability, and resumability to AI agents by wrapping them in a workflow pattern. He demonstrates converting a coding agent into a workflow-supported agent, showing how steps like LLM calls and tool executions become isolated, retryable units. The toolkit enables long-running agents that can sleep for days, resume streams after disconnection, and integrate human-in-the-loop via webhooks. Deployable on Vercel or any cloud, it provides built-in observability through a local UI. The Workflow DevKit is currently in beta with general availability targeted for January.

Developing Taste in Coding Agents: Applied Meta Neuro-Symbolic RL — Ahmad Awais, CommandCode

Nov 24, 2025 · 20:52

Ahmad Awais, founder of Langbase, introduces CommandCode, a coding agent that learns a developer's personal taste and preferences to generate code that feels like their own. Built on Langbase's infrastructure (deploying over 350K agents and 1.2B monthly runs), CommandCode uses a Meta Neuro-Symbolic architecture with reinforcement learning to continuously capture a programmer's invisible architecture of choices—such as preferring TypeScript, PNPM, TSUP, and Commander over other tools—without requiring explicit rule files. Awais demonstrates how CommandCode automatically applies these learned preferences when building a CLI, contrasting it with Claude Code which generates generic output. He argues that taste models represent the next frontier, enabling agents to evolve with the developer and even share tastes across teams or borrow preferences from other developers like design engineers.

Using OSS models to build AI apps with millions of users — Hassan El Mghari

Jul 15, 2025 · 18:47

Hassan El Mghari, a software engineer at Together.ai, explains how he builds open source AI apps that attract millions of users, including roomGPT.io (2.9 million users), restorePhotos.io (1.1 million), Blinkshot.io (1 million visitors), and LlamaCoder.io (1.4 million visitors). He details his simple four-step tech stack: user input, a single API call to an open source model on Together.ai, storing results in a database, and displaying output. His process emphasizes keeping ideas simple enough to describe in five words, spending 80% of time on UI, launching early, and incorporating the latest models like Fluxionel for virality. He advises naming apps with short, memorable names, making them free and open source to encourage sharing, and iterating rapidly—most apps fail, but the key is to keep building. He funds compute through sponsorships from Together.ai and partners like Neon and Clerk, which provide free services for his open source projects.

[Workshop] AI Pipelines and Agents in Pure TypeScript with Mastra.ai — Nick Nisi, Zack Proser

Jul 12, 2025 · 1:51:14

This hands-on workshop introduces Mastra.ai, a TypeScript framework for building production AI agents and pipelines, and demonstrates how to build an AI meme generator using composable workflows, tools, and agents. Hosts Nick Nisi and Zack Proser walk through creating a multi-step workflow: extract user frustration, find a base meme via ImageFlip, generate captions, and publish the meme at a stable URL. They show how to chain steps with Zod schema validation for deterministic output, then wrap the workflow in an agent that accepts natural language requests. The workshop also covers MCP (Model Context Protocol) and a live demo of MCP.shop for ordering a shirt, emphasizing local iteration with Mastra's playground, built-in memory, and evaluation tools. Real-world patterns include building internal AI assistants for data cleaning, email drafting, and document summarization with minimal code.

Claude Code & the evolution of agentic coding — Boris Cherny, Anthropic

Jul 4, 2025 · 18:12

Boris Cherny, creator of Claude Code and Anthropic member of technical staff, argues that the product stays intentionally unopinionated and minimal because the model's coding capabilities are improving exponentially and the right UX remains unknown. He traces programming UX from 1950s punch cards to 1980s Smalltalk's live reload, Eclipse's static-analysis autocomplete, Copilot, and Devin's natural-language paradigm. Claude Code's terminal-first approach works in any terminal, over SSH, and inside VS Code or Cursor, with a GitHub integration that keeps data on user compute and a programmatic SDK for custom UIs. Key tips include using Claude for code-based Q&A (shortening onboarding from weeks to days), teaching it tools via `--help` and Claude MD files, leveraging TDD with visual iteration, and running multiple Claude instances in parallel via terminal tabs or GitHub Actions. Today's launch adds a plan mode triggered by Shift+Tab, which makes a plan and waits for approval before writing code.

Building Reliable Support Agents Using the Effect Typescript Library - Michael Fester

Jun 3, 2025 · 7:22

Michael Fester, co-founder and CTO of Fourteen.ai, explains how his team built production-ready AI support agents using the Effect TypeScript library, arguing it provides strong type safety, composability, and reliability for systems that rely on LLMs. The architecture uses Effect across the entire stack, including Effect RPC, Effect HTTP, Effect SQL, and a custom DSL for agent workflows. Reliability features include fallback between LLM providers (e.g., GPT-4.0 mini to Gemini Flash 2.0), retry policies with state tracking, and duplicated token streams for analytics. Dependency injection allows easy mocking of LLM providers for testing. However, Fester warns of pitfalls like silently losing errors from upstream catches and the steep learning curve. He recommends incremental adoption, starting with a single service.

Building an AI assistant that makes phone calls [Convex Workshop]

Feb 9, 2025 · 50:53

Tom Redmond, head of DX at Convex, builds Floyd, an AI phone-calling assistant using Convex, GPT-4, Google Cloud speech-to-text, and Twilio. Floyd lets users make voice requests (e.g., calling a school to report a sick child) via a web app; transcription, context retrieval, and real-time conversation with a human on the other end happen through a reactive database architecture. Redmond explains how Convex's WebSocket-based infrastructure solves serverless latency issues, enabling live transcript streaming and status updates without polling. He demonstrates two calls (school absence and flower order), noting that latency (2–5 seconds) comes from OpenAI text-to-speech and growing prompts, with plans to use OpenAI Threads and faster TTS services. The prototype explicitly discloses it's an AI to avoid misrepresentation, and Redmond envisions AI agents efficiently handling service bookings.

We accidentally made an AI platform: Jamie Turner

Oct 8, 2024 · 5:36

Jamie Turner, co-founder of Convex, explains how his company's backend platform accidentally became an AI platform because its reactive data flow paradigm—extending React's state reactivity to the server with subscribable queries and mutations—perfectly suits generative AI workflows. He describes Convex's architecture as seamlessly syncing state between backend steps and the application, enabling concurrent chains of tasks like automatic speech recognition, summarization, embedding generation, and finding related notes. Turner notes that post-ChatGPT, over 90% of Convex projects are generative AI. To support this shift, Convex added native vector indexes for schema fields and launched a startups program with discounts and exclusive forums. Upcoming high-level components will encapsulate state machines for sophisticated workflows. Turner presents Convex as a way for generative AI engineers to ship quickly and confidently.

Pragmatic AI with TypeChat: Daniel Rosenwasser

Nov 14, 2023 · 18:34

Daniel Rosenwasser, TypeScript program manager, introduces TypeChat, an experimental library that uses TypeScript type definitions to guide and validate unstructured LLM output into structured JSON for traditional apps. He demonstrates a coffee shop ordering system where types define the schema, showing how the library can handle ambiguous inputs like 'a purple gorilla' by including unknown text for recovery. TypeChat also generates programs as JSON using a fake language to safely script multi-step operations, avoiding sandboxing issues from real code. A Python prototype extends the same approach, with examples like CSV data manipulation via a class-based API. The library aims to make AI tools accessible to all engineers by leveraging types they already use.

Building Reactive AI Apps: Matt Welsh

Nov 9, 2023 · 17:02

Matt Welsh announces AI.JSX, an open-source TypeScript framework he describes as 'React for LLMs,' which lets developers build reactive AI applications by composing JSX components that render to an LLM rather than the DOM. He argues AI.JSX makes LLM app development accessible to JavaScript developers, not just Python back-end engineers, by handling RAG, tool invocation, and real-time voice interaction out of the box. Welsh demonstrates parallel streaming of multiple LLM calls for story generation, a kid-safe wrapper component that rewrites content, and a 10-line RAG implementation. He showcases a live voice demo where an AI takes a donut order with sub-second latency, powered by Fixie's cloud platform for hosting, managed RAG pipelines, and conversational state. He emphasizes the framework's full React integration and ability to generate UI from AI, positioning AI.JSX as a simplification for building sophisticated LLM-powered apps.