aidevcon-2026-ldn

Use when the user asks about Matthias Lübken's talk "A Piece of PI – Embedding The OpenClaw Coding Agent In Your Product" — including questions about embedding Pi (pi.dev) or coding agents in products, the OpenClaw after-sales prototype, Pi's "radical extensibility" and lifecycle hook extensions, tool design for agents ("don't make your agent guess"), agent sessions as event-log trees, malleable software (Ink & Switch), or applying his primitives (agent setup, tools, extensions, sessions) and patterns (workflow, chat, malleable) to current agent-building work.

Use when the user asks about Dana Lawson's talk "Built for Humans. Now Agents Are Here." (Netlify CTO, 2026) — including questions about Agent Experience (AX), the AX paradox, redesigning CLIs/build logs/deploy previews for agents, moving from APIs to capabilities, event-driven agent architectures, blueprints (skills/recipes/context/ADRs), software factories, autonomous development loops, sandbox + human-in-loop + audit/rollback trust principles, the expanded "builder persona," or applying Netlify's AX approach to the user's own platform.

Welcome to AI Native DevCon — Context Engineering, Memory Systems, and Dreaming

Assists with questions about a practitioner panel talk titled 'From Pipelines to Prompts: Surviving the Shift to AI' featuring Stephane Jourdan, Simon (Saxo Bank), and Samantha. Use when a user asks about what panelists said, argued, or disagreed on regarding AI-native transformation, harness engineering, observability, developer cognitive load, feedback loops, reflector agents, or co-driving vs. self-driving analogies. Answers factual questions with verbatim transcript quotes, applies panelist frameworks to user situations, surfaces relevant panel insights during related discussions, and explains concepts like harness engineering, self-learning production agents, and explainability tooling.

More software, faster — Odevo's AI Native transformation

Skills Everywhere: Pipelining Knowledge Your Engineers Can Read and Your Agents Can Use

Spec-Driven Development: From Prompting to Production-Ready Systems

Assists with questions about Hannah Foxwell's talk 'The Reinvention of the Dev Team'. Use when a user asks about Foxwell's arguments on agentic software development, engineering team composition, AI-driven velocity, dev-to-PM ratios, the three anchors (build something worth building, speed requires safety, people matter), the Keep/Trash/Try inventory, on-call sustainability, broken-comb skills, or wants to audit their own team against Foxwell's framework.

Use when the user asks about Maximiliano Firtman's ("Maxi") AI Native DevCon talk on Web MCP and the agentic web — including questions about how Web MCP differs from MCP, exposing frontend tools to AI agents, the imperative vs declarative Web MCP APIs, tool design guidance (name/description/input schema/execute), the Chrome 149 origin trial, using Chrome DevTools MCP or Puppeteer as a bridge today, the Doom demo, or applying Firtman's "pick one high-value page state" adoption approach to current work.

Use when the user asks about Dave Farley's talk "Vibe Coding — Is this really the best we can do?" — including questions about vibe coding, agentic programming, AI-generated tests, BDD-style executable specifications as prompts, problem-specific DSLs, why natural language is insufficient as a programming language, the three properties of programming languages (formal grammar / unambiguous intent / deterministic execution), the three problems AI programming creates (precise specification, verification, incrementalism), fifth-generation programming, AI as compiler, or applying Farley's continuous-delivery-style approach to working with AI coding agents.

Use when the user asks about Tammuz Dubnov's talk "When Our PM Started Writing Code: What Merge Rate Taught Us About AI Adoption" — including questions about what "AI-native" means, harness engineering, merge rate as an AI-adoption metric, non-technical contributors (PMs, designers) opening pull requests, PR fatigue, the ~74% merge rate / ~84% zero-dev-touch numbers from Autonomy AI, why Uber/Microsoft's AI spend isn't translating to velocity, Shopify as a positive example, Calamarous Coding, feature-flag-driven developer autonomy, or applying his framework to the user's own engineering org.

Answers questions about Brian Douglas's talk on training AI on your own code. Use when a user asks about Brian Douglas's pipeline for capturing agent sessions, extracting skills from traces, fine-tuning small local models, tapes/steros tooling, SFT vs DPO decisions, or wants to apply his agent telemetry and training data approach to their own work with Claude Code, QLoRA, or parallel agents.

Use when the user asks about Patrick Debois's talk "Coding Agents Don't Scale Themselves. Neither Do Your Teams. The Rise of Agent Enablement." — including questions about agent enablement teams, the three pillars (Enablement, Platform, Governance), the Context Development Lifecycle applied to org charts, AI product engineers, agent KPIs like turns-per-task, harnesses and shared context libraries, fixing the system vs. fixing the code, the barrel mental model, continuous learning as the next CI/CD, or how VPs / team leads / platform teams should scale AI coding agents across an org.

Answers questions about, retrieves verbatim quotes from, explains concepts from, and summarizes key arguments in Birgitta Böckeler's talk "State of Play: AI Coding Assistants" (AI Native Dev conference, 2026). Use when the user asks about the last 12 months in AI coding assistants, the Opus 4.5 moment, LLM statelessness, context window and attention trade-offs, choosing the right model for a task, the ecosystem around models, or her Thoughtworks/Martin Fowler-site writing on AI-assisted software delivery.

Use when the user asks about Christopher Batey's talk 'Building Product Teams in the Age of AI: What We Had to Relearn Every Quarter' (Latent Space, 2026) — including questions about running AI-assisted product engineering teams, his three pillars (path to production at AI speed, training/evaluating AI-enabled engineers, designing workflow for parallel change), ADR-first workflows with agents, why review becomes the bottleneck, the producer 'black box' (harness/host/model), vanity metrics vs adoption, two-to-four-person sub-streams, one-complex-task-at-a-time, 'you build it, you run it, you drive adoption', or applying his approach to current work.