aidevcon-2026-ldn

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a proposal for sharing knowledge across agents locally and in a public commons, reducing repeated agent mistakes from outdated training data, and the Mozilla Manifesto principles cq draws on.

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 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.

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 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 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 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 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 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 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.

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 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.

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 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 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 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.

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 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.

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.

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.