aidevcon-2026-ldn

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.

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 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 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 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.

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 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.

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.

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

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

More software, faster — Odevo's AI Native transformation

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.

Use when the user asks about Joseph Katsioloudes's talk "Code Security Reinvented: Navigating the era of AI" — including questions about using AI for security (writing safer code, MCP servers, skills, agentic workflows), the 1-to-100 security-to-developer gap, "start left" vs "shift left", task flows, dual-LLM / LLM-jury, supply chain decisions with AI, AI-assisted fuzzing, hallucinations and non-determinism in AI security review, the GitHub Security Lab's free resources (gh.io/scg, gh.io/sk, gh.io/taskflows), or applying his approach to AI-assisted secure development.

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

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.

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.

Provides detailed answers, conceptual explanations, workflow guidance, and framework-based analysis about Edouard Maleix's talk "How AI-First Dev Teams Build Collective Intelligence — One Attributed Mistake at a Time." Use when the user asks about giving coding agents their own identity and signed commits, the diary/entry/pack/render workflow, turning agent mistakes into reusable team knowledge, evaluating knowledge packs for fidelity and usefulness, voluntary task picking by autonomous agents, the MoltNet open-source project, compound engineering, or applying his approach to make agent lessons compound across a team instead of evaporating into chat history.

Use when the user asks about the "Welcome to AI Native DevCon" talk introducing Slick (slick.com) — a browser-native agent that controls the browser it runs in. Covers Slick's architecture (sprinkles, scoops, licks, the cone), how it uses HTTP / Chrome Debug Protocol / WebRTC, pyagent as the agentic loop, skills as "step zero of agent enablement", MCP and webMCP support, remote-controlling Electron apps (Slack, Teams, Claude desktop) via Slick Star, ad hoc generative UI, OAuth token handling, and verbatim quotes from the talk. Also use when the user wants to apply this browser-native agent approach to their own work.

Use when the user asks about the AI Native DevCon talk "Welcome to AI Native DevCon" featuring Shachar from Buzz (hosted by Simon Maple, Head of DevRel at Tessl) — including questions about why code review is still a bottleneck in 2026, the Spec Reviewer agent architecture, splitting planner vs verifier agents, sub-agent delegation for requirements verification, context window explosion, why giving the agent the base branch beats the diff, ephemeral sandboxes with AWS Agent Core, context engineering for complex agentic tasks, or how to find product gaps the big coding agents overlook.

Answers questions about and summarizes key points from Simon Maple's "Welcome to AI Native DevCon" opening talk (Tessl, AI Native DevCon 2026). Use when the user asks about the conference app and QR code flow, workshop registration, the Agentic Genius Bar, session skills generated via Granola, the three tracks (context window / latent space / tool pool), the hallway track concept, Simon's three-part attendee challenge, the AI Native Dev Discord community, prizes and swag logistics, or general housekeeping for the event.

Use when the user asks about Simon Maple and Baruch Sadogursky's AI Native DevCon talk "Welcome to AI Native DevCon" (June 2026) — including questions about turning mega-prompts into skills, the skill YAML frontmatter (name/description) pattern, when to use rules vs skills vs scripts vs hooks, the "deterministic → script, non-deterministic → LLM" heuristic, the dark factory / issue-to-merged-PR orchestrator concept, Tessl plugins as context artifacts, eval scenarios and LLM-as-judge, using cheaper models via OpenRouter/LiteLLM, or applying their context-engineering approach to the user's own agent setup.

Use when the user asks about the "Welcome to AI Native DevCon" opening talk (introduced by Simon Maple, content delivered by a GitHub Next presenter) — including questions about Continuous AI (CAI) as a third pillar alongside CI/CD, GitHub Agentic Workflows, the repository-as-software-factory model, Repolaris and automated open source maintenance, the safety architecture for running coding agents in CI (sandbox, read-only, narrow safe outputs, threat detection), agent zoo vs single-workflow strategies, Pelle's agent factory, factory/flow thinking for repos, or applying these patterns to the user's own repositories.

Use when the user asks about Simon Maple's AI Native DevCon talk "Welcome to AI Native DevCon" on harness engineering — including questions about agents.md structure, just-in-time guardrails, review personas, shift-right interventions, the three phases of agent context delivery (grounding / messy middle / review & merge), why the speaker avoids shifting left with agents, treating agents as teammates, vibe coding, the foundational constraints (human time, attention, context window), verbatim quotes from the talk, or applying his harness engineering approach to the user's own codebase.

Use when the user asks about Simon Maple's talk "Welcome to AI Native DevCon" (AI Native DevCon, 2026) — including questions about Tldraw's AI experiments, the "make real" demo, using annotations/drawings as prompt input to vision models, Tldraw computer and branching prompt workflows, multi-agent "fairies" on a shared canvas, the Tldraw desktop app with local MCP, "code mode" giving agents direct access to the editor's runtime API, verbatim quotes from the talk, or applying canvas-based AI iteration patterns to current work. NOTE: although the talk metadata lists Simon Maple as speaker, the transcript content is delivered by a Tldraw presenter demoing the Tldraw SDK; treat speaker attribution with caution.

Use when the user asks about Luke Marsden's talk "Giving Every Agent Its Own Desktop: Lessons from Dogfooding HelixML" — including questions about HelixML, giving each agent its own GPU-accelerated desktop, spec-driven development with plan/implement phases, scaling agents by task vs by org-shape, centralized vs per-developer agent infrastructure, forking Zed for remote control, ZFS-cloned Docker-in-Docker dev environments, mixing local models (Llama 3.1) with frontier models (Claude Opus), the "snake eating its own tail" dogfooding approach, self-improving companies, or applying his design opinions to your own agent platform.

Use when the user asks about Simon Martinelli's talk "Lessons from Spec-driven Development" — including questions about the AI Unified Process, system use cases as specs (vs user stories), self-contained systems vs microservices, skills/MCP servers/guardrails, AI-assisted ERP modernization, drift management, how architecture style impacts AI coding agents, or applying his spec-driven approach to current work.

Use when the user asks about James Moss's talk "Using skills to pay the bills: graduating from solo hacks to a team workflow" (Tessl, DevCon 2026) — including questions about skills sprawl, the failure modes of team skill adoption (overlap, drift, activation, rot, overloading), the agentic equation (model + harness + context), treating skills as software, the Context Development Life Cycle (CDLC), skill registries, evals for skills, or applying his recommendations (decompose, extend don't edit, version control, automated reviews, registry, agent-agnostic skills) to current work.

Use when the user asks about Robert Overweg's talk "One Brain, No Filtering" — including questions about Leapfrog A.I.'s shared-brain setup for fashion-brand clients, their OpenClaw + Obsidian + Telegram + GitHub vault stack, per-client vault sections (brand DNA, AD preferences, delivery dates), the promote-to-vault discipline, cron-based research agents, the chief-of-staff agent concept, recording everything (Granola, OB open-source recorder), keeping knowledge on your own stack rather than in vendor chat windows, or applying Robert's approach to your own knowledge-management and agent-orchestration work.

Assists with questions about Guy Podjarny's talk "Skills are the new Code". Use when the user wants to understand, apply, audit, or explore frameworks from this keynote — including the five engineering disciplines for skills (static analysis, evals, security testing, dependency management, observability), the three challenge buckets, the agentic development stack, or concepts like skill authoring, context engineering, agent harnesses, and skill quality scoring.

Use when the user asks about Katie Roberts's talk "Stop Maintaining, Start Evolving: Applying AI-Native Practices to Brownfield Codebases" — including questions about using AI to build large complex systems (her ~350k-line Rust S3 clone experiment), test oracles, flaky tests with AI agents, why 100% test coverage is the wrong goal, human-in-the-loop AI coding, AI-assisted performance engineering, using the type system to enforce invariants, tracing as an AI debugging tool, or applying her approach to brownfield/legacy modernisation work.

Use when the user asks about Katie Roberts's talk "Stop Maintaining, Start Evolving: Applying AI-Native Engineering in Brownfield Codebases" (AI Native DevCon, June 2026) — including questions about brownfield vs greenfield AI engineering, the three methodologies (pseudo-greenfield, strangler fig pattern, branch by abstraction), the "code as a city" metaphor, using AI to map and modernize legacy codebases, planning skills and developer skills, the value-vs-complexity mirror exercise, avoiding AI agents going rogue on legacy code, the AG Grid upgrade case study, Nearform's "six months in eight weeks" pseudo-greenfield case study, or applying her brownfield AI-native approach to current legacy modernization work.

Use when the user asks about Lieven Scheire's talk "Artificial Intelligence" (a Belgian physicist/comedian's keynote on AI for a developer audience) — including questions about his one-sentence definition of AI as "a new kind of software good at pattern recognition", the history of AI from the 1956 Dartmouth workshop, how neural networks mimic the brain, training-data bias (the "snow in the background" wolves-vs-huskies example, the dermatology ruler example), the black-box nature of neural nets, hobbyist AI tools (Teachable Machine, Custom Vision, HeyGen, PhotoMath, Merlin), Ben Hamm's cat flap, his skeptical stance on LLMs as "language imitation" vs AGI, verbatim quotes from the talk, or applying his framing to current AI work.

Use when the user asks about Oleg Šelajev's talk "You're absolutely right, it was your home directory!" (ext live / Testkube conference, 2026) — including questions about Docker Sandboxes, running AI agents in microVM isolation, the trifecta of agent risks (sensitive data + external comms + untrusted content), why containers aren't enough for agent isolation, SBX kits, secret injection / sentinel values, prompt injection horror stories, YOLO mode agents, or applying his sandboxing workflow to the user's own agent setup.

Use when the user asks about Marc Sloan's talk "Harness engineering beyond code — product & design constraints for agents" (Tessl DevCon, June 2026) — including questions about why product/design context lives outside the codebase, how agent harnesses should evolve to handle Figma/Notion/Linear/CRM context, Figma Code Connect and Dev Mode lessons, MCP servers as a bridge, context drift between design systems and code, dedicated design-system maintenance teams, the three (or four) directions the harness might evolve, non-developers contributing PRs, or verbatim quotes from the talk.

Use when the user asks about Paul Stack's talk "The Humans Architect the System, the AI Writes the Code" (System Initiative / Eldest One Club, 2026) — including questions about why his team stopped writing code, the no-PR open-source policy, CLAUDE.md as executable constraints, the planner/adversarial-reviewer loop, swamp the AI-native ops CLI, UAT as source of truth, "vibes don't scale", "intent is the new architecture", supply-chain integrity in AI-era OSS, or applying his architecture-first / agents-write-all-code workflow to the user's own team.

Use when the user asks about Emma's "Build Your Own Product Brain" talk at AI Native DevCon (hosted by Simon Maple, Tessl) — including questions about Resonant's product brain architecture, how PMs become agent orchestrators, the four components (live ingestion, product frame, workflows, human input loop), the action/input/brain/organizational agents, the spectrum of PM autonomy, GitHub-backed product wikis, or how to apply her approach to your own PM setup.

Use when the user asks about Liran Tal's talk at AI Native DevCon on skills security — including questions about the lethal trifecta / toxic flows, the Snyk research finding ~30% of ~4,000 Glow skills had security issues, malicious skill examples (SkillGuard, fake Vercel deployment skill, "buy anything", invisible-character Trojan Source skills), the confused-deputy problem, acceptance fatigue, parallels to NPM supply-chain attacks, or how to think about reviewing and sandboxing AI agent skills.

Use when the user asks about Ian Thomas's talk "AI Native Engineering" (Meta / Reality Labs / Horizon Experiences) — including questions about Meta's AI4P (AI For Productivity) programme, the 6-dimension / 5-level AI maturity model and self-assessment workshop, how Horizon rolled out AI tooling across 500+ engineers, engineering excellence as an adoption vehicle, anti-test-slop, autonomous code mods, the DRS risk-scoring tool, the Horizon MCP server, vanity metrics vs real productivity, or applying Thomas's ground-up-plus-top-down adoption playbook to their own org.

Provides detailed answers, analysis, verbatim-grounded summaries, framework applications, and workflow audits based on May Walter's talk "From Blind Spots to Merged PRs: Runtime Intelligence for Continuous Agentic Performance Optimization". Use when the user asks about May Walter's talk — including questions about Hud's runtime code sensor, the prod-to-code mapping concept, automating the performance-investigation phase, scoring fixes by impact and risk, why automated pull requests didn't work, the layered architecture (query language → skills → automations), the four takeaways (define what matters, automate investigation, context over cleverness, agentic engineering ≠ coding with an agent), or applying Walter's approach to integrating AI agents into the SDLC.

Use when the user asks about Peter Wilson and Davide Eynard's talk "cq - Stack Overflow for Agents" (Mozilla.ai) — answers questions about, summarizes key points from, and retrieves verbatim quotes from the talk, including cq as a proposal for sharing knowledge across agents locally and in a public commons, reducing repeated agent mistakes from outdated training data, and the Mozilla Manifesto principles cq draws on.