Aidevcon

Defensive review of AI-agent skills, plugins, and tools using Liran Tal's security principles. Use when assessing provenance, permissions, data exposure, sandboxing, or approval boundaries before adoption.

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.

Explains the Product Brain talk and helps design curated product-memory systems for AI-assisted product work: knowledge structure, provenance, synthesis cadence, ownership, and agent-ready context packets. Use when the user asks about product context for AI, product knowledge management, product documentation for LLMs, or building a maintained product brain.

Use when the user asks about Shachar Azriel's AI Native DevCon talk on executable specs, verification layers for agentic coding, planner/verifier separation, requirement mapping, product-gap detection, and safe review architecture.

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.

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.

Summarizes, explains, and applies Lars Trieloff's AI Native DevCon talk on browser-native agents. Use for browser agents, running AI in the browser, browser-as-runtime architecture, agent containment, local-versus-cloud tradeoffs, safe AI product integration, documented APIs, user consent, credential isolation, and reviewable agent actions.

Answers questions about, retrieves safe excerpts 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, model-task fit, LLM statelessness, context window and attention trade-offs, coding harnesses, harness engineering/context engineering, skills/MCP/sub-agents/plugins/hooks, guide-and-sensor feedback loops, background agents and swarms, review bottlenecks, AI coding costs, cognitive surrender, or risk-based supervision of coding agents.

Summarizes Simon Maple's AI Native DevCon welcome and explains the conference framing: context window, latent space, tool pool, hallway track, attendee goals, and practical learning themes. Use when the user asks about the event opening, conference themes, AI-native development framing, or how to orient work around the talks.

Explains James Moss's team-skills workflow and helps design skill governance: decomposition, ownership, versioning, eval scenarios, quality review, and lifecycle maintenance. Use when the user asks about moving from solo skill hacks to team workflow, avoiding skill sprawl, or treating skills like software.

Explains Jack Wotherspoon's Humans vs Slop talk and helps create quality gates for AI-heavy software work: review-cost analysis, slop detection heuristics, durable-value metrics, and human-judgment checkpoints. Use when the user asks about AI-generated maintenance burden, review economics, or preserving taste in agentic development.

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.

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 Ryan Lopopolo's AI Native DevCon talk on harness engineering, steering coding agents with goals, constraints, context, tool scope, eval loops, and verification systems.

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 Steve Ruiz's AI Native DevCon talk on tldraw, Make Real, annotations as prompt input, canvas workflows, tldraw computer, and agents collaborating on an infinite canvas.

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.

Answers questions about, applies frameworks from, and generates artifacts based on Tammuz Dubnov's talk "When Our PM Started Writing Code: What Merge Rate Taught Us About AI Adoption." Grounds every response in the bundled transcript and outline files. Use when a user asks about AI-native org design, merge rate measurement, non-technical contributors shipping PRs, harness engineering, zero-dev-touch rate, AI adoption ROI, or wants to audit their team against Tammuz's framework, apply his concepts to their situation, draft a measurement dashboard or checklist, or understand terms like Calamarous Coding and the PM-to-engineer authority collapse.

Explains Kevin Groetzinger's Skills Everywhere talk and helps teams operationalize reusable skills: trigger design, ownership, discoverability, maintenance, quality review, and adoption loops. Use when the user asks about skill design, skill rollout, skill sprawl, shared agent instructions, or making skills reliable across a team.

Answers questions about, summarises key insights from, and applies the security guidance of Joseph Katsioloudes's talk 'Code Security Reinvented: Navigating the era of AI'. Use when the user asks about AI-assisted secure coding, MCP servers, skills, agentic workflows, the 1-to-100 security-to-developer gap, start left vs shift left, task flows, LLM-as-judge, supply-chain decisions, AI-assisted fuzzing, hallucinations and non-determinism in AI security review, GitHub Security Lab resources, or applying the talk's security framework to AI-assisted development.

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.

Explains Robert Overweg's One Brain, No Filtering talk and helps design safe knowledge-memory systems: context maps, retrieval rules, provenance labels, local knowledge-store structure, and review checkpoints. Use when the user asks about agent memory, unified knowledge bases, reducing context loss, or designing inspectable knowledge workflows.

Use when the user asks about Amit Kushwaha's AI Native DevCon talk on benchmarking agent-era systems, measuring performance beyond single LLM calls, inference, workflow complexity, tool use, and real-world workloads.

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, safe excerpts from the talk, or applying his framing to current AI work.

Explains Oleg Selajev's Docker Sandboxes talk and helps design safe, conceptual agent-isolation policies: file-sharing boundaries, network policy, secret isolation, audit expectations, and team rollout questions. Use when the user asks about sandboxed agents, hard isolation, local agent risk, or how to reason about agent safety without setup instructions.

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.

Summarizes, explains, and answers questions about Dave Farley's talk 'Vibe Coding — Is this really the best we can do?', including key arguments, frameworks, and recommendations. Provides verbatim-cited explanations of Farley's three properties of programming languages, audits AI-coding setups against his three-problems framework, drafts BDD-style executable specifications, and outlines his continuous-delivery approach to AI-driven development. Use when the user asks about Dave Farley's talk on vibe coding, agentic programming, AI-generated tests, BDD-style executable specifications, 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 approach to AI coding agents.

Explains, summarizes, and turns Matthias Luebken's talk on embedding Pi-style coding agents into safe product-design artifacts: tool-contract sketches, guardrail checklists, session-record models, and malleable-software review plans. Use when the user asks about OpenClaw, Pi-style product agents, lifecycle guardrails, agent sessions, or design-level application of these primitives.

Use when the user asks about Justin Cormack's AI Native DevCon talk on tests, observability, AI-generated behavior, evidence, instrumentation, and keeping AI systems honest when test signals are incomplete.

Answers questions about Lamis's (Anthropic) AI Native DevCon talk on context engineering, agent memory systems, and dreaming — an asynchronous, out-of-band memory-curation process. Supports factual Q&A, framework application, system auditing, artifact drafting, and concept explanation based on the talk's content. Use when the user asks about context engineering, CLAUDE.md files, agent memory persistence, skills, multi-session memory, the dreaming process, hashing-based concurrency, or wants to apply, audit against, or draft artifacts from the frameworks described in this talk.

Answers questions about, summarises key insights from, and helps apply concepts from Simon Martinelli's talk "Lessons from Spec-driven Development" — providing verbatim-grounded explanations, audits, and artifact drafts. Use when the user asks 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 Don Syme's AI Native DevCon talk on Continuous AI, GitHub agentic workflows, repository automation, and safe developer-controlled automation loops.

Answers questions about, summarizes, and applies May Walter's AI Native DevCon talk "From Blind Spots to Merged PRs" on runtime intelligence for coding agents. Use when the user asks about production telemetry for agents, prod-to-code mapping, performance fixes from runtime data, why automated PRs need provenance, Hud's runtime code sensor, weekly performance reports, AI-generated fixes with production context, or applying Walter's evidence-first agent workflow to engineering teams.

Use when the user asks about Peter Wilson and Davide Eynard's AI Native DevCon talk on cq, a Stack Overflow-like knowledge commons for agents, local/team/public knowledge sharing, and lessons from Mozilla.ai.

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 Daniel Jones (Deejay) and Tomasz's talk "More software, faster — Odevo's AI Native transformation" — including questions about how Odevo (Sweden's third-largest private tech company, residential property management) rolled out agentic coding to its developers, the discovery → workshops → pilot → training → train-the-trainer playbook, prerequisites for adopting agentic coding (CI/CD, platform, tests, coding standards), liberating structures and TRIZ workshop techniques, context window management, the 94% AI adoption metric, the 8-years-to-3-weeks platform rewrite, shifting bottlenecks to product, the "everyone a builder" vision, or applying re-cinq's AI-native transformation approach to their own organisation.

Use when the user asks about Simon Obstbaum and Rob Willoughby's AI Native DevCon talk on measuring AI agents, output evals versus trajectory evals, instrumentation, compliance, and skill activation metrics.

Summarizes Rob Sloan's harness-engineering talk and creates safe design artifacts for agent context beyond code: product-intent packets, design constraints, acceptance criteria, context ownership, and review gates. Use when the user asks about making non-code context agent-ready or improving AI work with product/design intent.

Explains Paul Stack's architecture-first AI workflow and helps create safe design artifacts: intent documents, architecture constraints, planner/reviewer loops, UAT criteria, and agent-output review gates. Use when the user asks about humans owning architecture while agents implement, why vibes do not scale, or applying the talk to team workflow design.

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.

Use when the user asks about Dave Kerr's AI Native DevCon talk on bipolar disorder, dysregulation, and AI, including responsible interpretation of personal and clinical themes from the transcript.

Use when the user asks about Shaun Smith's AI Native DevCon talk on MCP transports, remote Streamable HTTP servers, stateless protocol direction, Hugging Face MCP adoption, and future context transports.

Use when the user asks about Macey Baker and Baruch Sadogursky's workshop-style AI Native DevCon session on turning repeated agent work into skills, rules, scripts, hooks, and evals.