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Official MCP · AI Engineer
https://ai.engineer/mcpcodex mcp add ai-engineer --url https://ai.engineer/mcpSetup docs
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After connecting, enable AI Engineer’s tools and try: “What AI Engineer conferences are coming up?” Your agent chooses and calls the tools for you. No local server required.
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Streamable HTTP · Public data + private CODE applications · No API key
Public collections
| Collection | What’s included | JSON | CSV | Explore | Fields & sample |
|---|---|---|---|---|---|
Talks 1,156 rows · ≈396.5K tokens | Recordings, speakers, topics, and summaries | JSON | CSV | Explore | |
slug · title · url · videoId · event · durationMs · speakers · topics · summary [
{
"slug": "agent-skills",
"title": "Don't Build Agents, Build Skills Instead – Barry Zhang & Mahesh Murag, Anthropic",
"url": "https://ai.engineer/talks/CEvIs9y1uog-agent-skills",
"videoId": "CEvIs9y1uog",
"event": "AI Engineer Code 2025",
"durationMs": 982000,
"speakers": [
{
"name": "Barry Zhang"
},
{
"name": "Mahesh Murag"
}
],
"topics": [
{
"slug": "coding-agents",
"name": "Coding agents"
}
],
"summary": "Barry Zhang and Mahesh Murag of Anthropic argue that instead of building domain-specific agents, developers should build reusable Skills—organized folders of files that package procedural knowledge for agents. They explain that skills are progressively disclosed to protect the context window, use scripts as self-documenting tools, and have already grown to thousands in five weeks, including foundational, partner, and enterprise skills. Skills complement MCP servers by providing expertise while MCP handles connectivity. The future includes treating skills like software with testing and versioning, and enabling agents to create their own skills for continuous learning, ultimately creating a collective knowledge base that makes agents more capable and reliable."
},
{
"slug": "ai-coding-workflow",
"title": "AI Coding Workflow: From Product Idea to Tested Implementation",
"url": "https://ai.engineer/talks/-QFHIoCo-Ko-ai-coding-workflow",
"videoId": "-QFHIoCo-Ko",
"event": "AI Engineer Europe 2026",
"durationMs": 5790000,
"speakers": [
{
"name": "Matt Pocock"
}
],
"topics": [
{
"slug": "coding-agents",
"name": "Coding agents"
}
],
"summary": "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."
}
] | |||||
Speakers 1,126 rows · ≈297.3K tokens | Profiles, biographies, affiliations, and talks | JSON | CSV | Explore | |
slug · name · url · biography · jobTitle · organization · portraitUrl · profiles · affiliations · topics · talkSlugs [
{
"slug": "swyx",
"name": "swyx",
"url": "https://ai.engineer/speakers/swyx",
"biography": null,
"jobTitle": "Curator",
"organization": "AI Engineer",
"portraitUrl": "https://ai.engineer/wf26/speakers/by-id/spk_shawn_wang.jpg",
"profiles": {
"website": null,
"github": null,
"linkedin": null,
"x": null
},
"affiliations": [
{
"organization": "AI Engineer",
"eventSlug": "ai-engineer-europe-2026",
"eventYear": 2026
},
{
"organization": "AI Engineer",
"eventSlug": "ai-engineer-world-s-fair-2026",
"eventYear": 2026
},
{
"organization": "AI Engineer",
"eventSlug": "ai-engineer-code-2025",
"eventYear": 2025
},
{
"organization": "Latent.Space",
"eventSlug": "ai-engineer-world-s-fair-2025",
"eventYear": 2025
},
{
"organization": "Latent.Space",
"eventSlug": "ai-engineer-summit-2025",
"eventYear": 2025
},
{
"organization": "Latent.Space",
"eventSlug": "ai-engineer-world-s-fair-2024",
"eventYear": 2024
},
{
"organization": "Latent.Space",
"eventSlug": "ai-engineer-summit-2023",
"eventYear": 2023
}
],
"topics": [
{
"slug": "agent-engineering",
"name": "Agent engineering"
},
{
"slug": "apis-mcp-and-protocols",
"name": "APIs, MCP, and protocols"
},
{
"slug": "architecture",
"name": "Architecture"
},
{
"slug": "coding-and-developer-tools",
"name": "Coding and developer tools"
},
{
"slug": "data-and-model-adaptation",
"name": "Data and model adaptation"
},
{
"slug": "enterprise",
"name": "Enterprise"
},
{
"slug": "finance",
"name": "Finance"
},
{
"slug": "infrastructure-and-deployment",
"name": "Infrastructure and deployment"
},
{
"slug": "leadership",
"name": "Leadership"
},
{
"slug": "other-unclassified",
"name": "Other / unclassified"
},
{
"slug": "rag-context-and-search",
"name": "RAG, context, and search"
},
{
"slug": "reasoning-and-models",
"name": "Reasoning and models"
},
{
"slug": "safety-and-governance",
"name": "Safety and governance"
}
],
"talkSlugs": [
"6-things-to-know-about-aie-world-s-fair-2026",
"agents-for-everything-else-swyx",
"ai-engineering-without-borders",
"define-ai-engineer",
"designing-ai-intensive-applications",
"no-more-slop-swyx",
"software-engineering-ai",
"the-1-000x-ai-engineer-swyx",
"why-agent-engineering",
"youtube-b01c3c14dd8f8e90af4c"
]
},
{
"slug": "stephen-chin",
"name": "Stephen Chin",
"url": "https://ai.engineer/speakers/stephen-chin",
"biography": null,
"jobTitle": "VP of Developer Relations",
"organization": "Neo4j",
"portraitUrl": "https://ai.engineer/wf26/speakers/by-id/spk_stephen_chin.jpg",
"profiles": {
"website": null,
"github": null,
"linkedin": null,
"x": null
},
"affiliations": [
{
"organization": "Neo4j",
"eventSlug": "ai-engineer-europe-2026",
"eventYear": 2026
},
{
"organization": "Neo4j",
"eventSlug": "ai-engineer-world-s-fair-2026",
"eventYear": 2026
},
{
"organization": "Neo4j",
"eventSlug": "ai-engineer-world-s-fair-2025",
"eventYear": 2025
},
{
"organization": "Neo4j",
"eventSlug": "ai-engineer-summit-2025",
"eventYear": 2025
},
{
"organization": "Neo4j",
"eventSlug": "ai-engineer-code-2025",
"eventYear": 2025
}
],
"topics": [
{
"slug": "agent-engineering",
"name": "Agent engineering"
},
{
"slug": "apis-mcp-and-protocols",
"name": "APIs, MCP, and protocols"
},
{
"slug": "architecture",
"name": "Architecture"
},
{
"slug": "finance",
"name": "Finance"
},
{
"slug": "healthcare",
"name": "Healthcare"
},
{
"slug": "industry-applications",
"name": "Industry applications"
},
{
"slug": "leadership",
"name": "Leadership"
},
{
"slug": "rag-context-and-search",
"name": "RAG, context, and search"
},
{
"slug": "safety-and-governance",
"name": "Safety and governance"
}
],
"talkSlugs": [
"agentic-graphrag-ai-s-logical-edge",
"anchoring-enterprise-genai-with-knowledge-graphs",
"connecting-the-dots-with-context-graphs",
"context-engineering-connecting-the-dots-with-graphs-stephen-chin-neo4j",
"crabrag-why-automated-assistants-need-graph-memory-not-more-tokens",
"practical-graphrag-making-llms-smarter-with-knowledge-graphs"
]
}
] | |||||
Topics 82 rows · ≈66K tokens | Ideas connecting the conference archive | JSON | CSV | Explore | |
slug · name · url · description · talkSlugs [
{
"slug": "rag-context-and-search",
"name": "RAG, context, and search",
"url": "https://ai.engineer/topics/rag-context-and-search",
"description": null,
"talkSlugs": [
"1-ai-guardrails-the-unreasonable-effectiveness-of-finetuned-modernberts-diego-carpentero",
"12-factor-agents",
"120k-players-in-a-week-lessons-from-the-first-viral-clip-app",
"2026-the-year-the-ide-died",
"360brew-llm-based-personalized-ranking-and-recommendation-hamed-firooz-and-maziar-sanjabi-linked",
"a-practical-guide-to-efficient-ai",
"a-practitioner-s-guide-to-graphs-tim-ainge-good-collective",
"a-song-of-types-and-agents",
"accelerate-your-ai-journey-with-azure-ai-model-catalog",
"active-graph-agent-runtime-babyagi-4",
"agent-evals-finally-with-the-map",
"agent-skills",
"agentic-graphrag-ai-s-logical-edge",
"agentic-graphrag-simplifying-retrieval-across-structured-unstructured-data-zach-blumenfeld",
"agentic-search-for-context-engineering",
"agentic-security-permissions-provenance-and-the-agent-supply-chain",
"agents-are-built-at-the-fringe-getting-from-90-to-100",
"agents-are-robots-too-what-self-driving-taught-me-about-building-agents-jesse-hu-abundant",
"agents-building-agents",
"agents-in-production-how-opengov-built-and-scaled-og-assist",
"agents-need-feature-flags",
"agents-need-receipts-not-more-tool-calls",
"agents-reported-thousands-of-bugs-how-many-were-real-ian-butler-and-nick-gregory",
"ai-agents-for-performance-ship-faster-pay-less",
"ai-agents-meet-test-driven-development",
"ai-driven-multi-document-correlation-for-enterprise-financial-compliance-and-fraud-detection",
"ai-engineering-201-inference",
"ai-engineering-201-the-rest-of-the-owl",
"ai-engineering-with-the-google-gemini-2-5-model-family",
"ai-music-generation-from-prompt-to-production",
"ai-on-your-lakehouse-context-comes-in-shapes-not-queries",
"ai-pipelines-and-agents-in-pure-typescript-with-mastra-ai",
"ai-red-teaming-agent-azure-ai-foundry-nagkumar-arkalgud-keiji-kanazawa-microsoft",
"ai-sdk-v6",
"ai-system-design-from-idea-to-production",
"ai-tools-for-forward-deployed-engineering",
"alphalab-autonomous-multi-agent-research-across-optimization-domains-with-frontier-llms-brendan",
"amp-code-next-generation-ai-coding",
"analyzing-10-000-sales-calls-with-ai-in-2-weeks",
"anchoring-enterprise-genai-with-knowledge-graphs",
"anthropic-s-cca-exam-as-a-field-guide-for-agentic-engineering",
"any-to-any-building-native-multimodal-agents",
"architecting-agent-memory-principles-patterns-and-best-practices",
"architecting-and-testing-controllable-agents",
"are-mcps-overhyped-a-rant-about-mcps",
"automating-escrow-with-usdc-and-ai",
"autonomous-agents-for-scientific-tasks-sina-shahandeh-radicait",
"benchmarking-semantic-code-retrieval-on-claude-code",
"benchmarks-the-good-the-bad-and-the-ugly",
"beyond-apis-how-ai-web-agents-are-automating-the-long-tail-of-knowledge-work",
"beyond-conversation-why-documents-transform-natural-language-into-code",
"beyond-static-intelligence-evaluating-continual-learning",
"blender-mcp-and-the-future-of-creative-tools-siddharth-ahuja",
"books-reimagined-ai-to-create-new-experiences-for-things-you-know-ukasz-gandecki-thebrain-pro",
"botdojo-launch-enhancing-ai-assistants-with-evaluations-and-synthetic-data",
"bounded-autonomy-between-free-will-and-determinism",
"break-it-til-you-make-it-building-the-self-improving-stack-for-ai-agents",
"build-a-prompt-learning-loop",
"build-deploy-ai-powered-apps",
"build-deploy-ai-powered-apps-18cc1b",
"build-dynamic-products-and-stop-the-ai-sideshow",
"build-enterprise-generative-ai-apps-using-llama-3-at-1-000-tokens-s-on-the-sambanova-ai-platform",
"build-systems-not-code",
"build-the-ai-gtm-agent-that-knows-the-buyer-before-the-first-message",
"build-your-first-demand-driven-context-base-let-ai-agents-tell-you-what-they-need",
"build-your-own-deep-research-agent-technical-writer",
"building-a-smarter-ai-agent-with-neural-rag",
"building-agent-interfaces-lessons-from-chrome-devtools-mcp-for-agents",
"building-agentic-applications-with-heroku-managed-inference-and-agents-julian-duque-and-anush-ds",
"building-agents-is-trivial-now-context-is-the-next-frontier",
"building-agents-with-mcp",
"building-ai-agents-that-actually-automate-knowledge-work",
"building-alice-s-brain-an-ai-sales-rep-that-learns-like-a-human-sherwood-satwik-11x",
"building-an-agentic-platform",
"building-an-ai-assistant-that-makes-phone-calls",
"building-blocks-for-llm-systems-products",
"building-context-aware-reasoning-applications-with-langchain-and-langsmith",
"building-conversational-ai-agents-thor-schaeff-elevenlabs",
"building-deterministic-infrastructure-for-non-deterministic-ai-agents",
"building-durable-production-ready-agents-with-openai-sdk-and-temporal",
"building-efficient-hybrid-context-query-for-llm-grounding",
"building-enterprise-llm-agents-that-work",
"building-great-agent-skills",
"building-in-the-gemini-era-kat-kampf-ammaar-reshi-google-deepmind",
"building-interactive-uis-in-vs-code-with-mcp-apps-marlene-mhangami-liam-hampton-github",
"building-metrics-that-actually-work-david-karam-pi-labs",
"building-multi-agent-systems-with-finite-state-machines",
"building-multimodal-ai-agents-from-scratch",
"building-reactive-ai-apps",
"building-redacted-username-gergely-orosz-simon-eskildsen",
"building-reliable-agentic-systems",
"building-reliable-support-agents-using-the-effect-typescript-library-michael-fester",
"building-security-around-ml",
"building-self-coding-agents",
"building-sota-open-weights-tool-use-the-command-r-family",
"building-the-platform-for-agent-coordination",
"building-trust-in-enterprise-ai-evaluating-domain-specific-llms-for-real-world-financial-scenari",
"building-voice-agents-with-openai",
"building-with-anthropic-s-claude-the-prompt-doctor-is-in",
"building-your-own-secure-ai-workflows-human-in-the-loop-automation-with-n8n",
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"bypassing-the-multimodal-tax-framework-free-hybrid-rag-raw-sql-rrf-and-live-ui-telemetry",
"can-oncology-workflows-run-without-human-touch-anant-shankhdhar-risa-labs",
"case-study-deep-dive-telemedicine-support-agents-with-langgraph-mcp",
"citation-needed-provenance-for-llm-built-knowledge-graphs",
"claude-agent-sdk-workshop",
"claude-fable-claude-tag-and-anthropic-s-culture-cat-wu-thariq-shihipar-ft-simon-willison",
"claude-for-long-horizon-tasks",
"claude-plays-minecraft-introducing-a-real-world-serverless-ai-to-a-virtual-world",
"cognitive-shield-real-time-real-smart-rachna-srivastava",
"cohere-for-vps-of-ai",
"combine-skills-and-mcp-to-close-the-context-gap",
"compilers-in-the-age-of-llms",
"compute-system-design-for-next-generation-frontier-models",
"connecting-the-dots-with-context-graphs",
"context-engineering-connecting-the-dots-with-graphs-stephen-chin-neo4j",
"context-engineering-for-complex-codebases",
"context-engineering-in-2026-compaction-memory-cost",
"context-graphs-for-explainable-decision-aware-ai-agents",
"context-is-the-new-code",
"context-platform-engineering-to-reduce-token-anxiety-val-bercovici-and-callan-fox-weka",
"convex-launch",
"crabrag-why-automated-assistants-need-graph-memory-not-more-tokens",
"creating-agents-that-co-create",
"creating-and-scaling-your-own-custom-copilots-with-azure-ai-studio",
"customized-production-ready-inference-with-open-source-models-dmytro-dima-dzhulgakov",
"data-is-your-differentiator-building-secure-and-tailored-ai-systems",
"data-readiness-is-a-myth-reliable-ai-with-an-agentic-semantic-layer-anushrut-gupta-promptql",
"decoding-mistral-ai-s-large-language-models",
"deepswe-a-contamination-resistant-coding-benchmark-james-shi-datacurve",
"designing-ai-intensive-applications",
"develop-at-idea-velocity",
"developer-experience-in-the-age-of-ai-coding-agents",
"devin-2-0-and-the-future-of-swe",
"dispatch-from-the-future-building-an-ai-native-company-dan-shipper-every-ai-i",
"does-genai-belong-to-data-scientists",
"domain-adaptation-and-fine-tuning-for-domain-specific-llms",
"don-t-build-slop-4-levels-of-ai-agent-maturity",
"don-t-just-slap-on-a-chatbot-building-ai-that-works-before-you-ask",
"don-t-ship-skills-without-evals",
"dspy-the-end-of-prompt-engineering",
"effective-agent-design-patterns-in-production",
"effective-ai-agents-need-data-flywheels-not-the-next-biggest-llm-sylendran-arunagiri-nvidia",
"embeddings-are-stunting-agents-how-codeium-breaks-through-the-ceiling-for-retrieval",
"engineering-better-evals-scalable-llm-evaluation-pipelines-that-work",
"ensure-ai-agents-work-evaluation-frameworks-for-scaling-success",
"enterprise-agents-have-a-structure-problem-ishita-daga-tesla",
"enterprise-deep-research-the-next-killer-app-for-enterprise-ai-ofer-mendelevitch-vectara",
"evals-are-not-unit-tests",
"evals-driven-development-engineering-a-mental-health-ai-coach-ethically-safely",
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"evaluating-ai-search-a-practical-framework-for-augmented-ai-systems",
"every-company-should-have-a-brain-garry-tan-y-combinator",
"every-harness-will-become-a-claw",
"everything-i-learned-training-frontier-small-models",
"evolving-claude-apis-for-agents",
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"fast-models-need-slow-developers",
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"fighting-slop-with-slop",
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"from-agent-traces-to-agent-simulations-rustem-feyzkhanov-snorkel-ai",
"from-arc-to-dia-lessons-learned-in-building-ai-browser",
"from-copilot-to-colleague-building-trustworthy-productivity-agents-for-high-stakes-work",
"from-software-developer-to-ai-engineer",
"from-systems-of-record-to-systems-of-context",
"from-writing-code-to-designing-systems-how-the-developer-role-is-changing-chris-noring-microsoft",
"frontier-ai-at-home-literally",
"frontier-results-on-device-rl-nabors-arize",
"full-workshop-setting-yourself-up-for-success-jason-liu-openai-codex",
"fun-stories-from-building-openrouter-and-where-all-this-is-going",
"function-calling-is-all-you-need",
"future-of-knowledge-assistants",
"gemini-nano-on-device-florina-muntenescu-oli-gaymond-google-deepmind",
"gemma-4-deep-dive-cassidy-hardin-google-deepmind",
"git-push-get-an-ai-api",
"giving-a-voice-to-ai-agents",
"going-beyond-rag-extended-mind-transformers",
"gpt-web-app-generator-10-000-apps-created-in-a-month-matija-sosic",
"graph-intelligence-enhance-reasoning-and-retrieval-using-graph-analytics",
"graphrag",
"graphrag-methods-to-create-optimized-llm-context-windows-for-retrieval-jonathan-larson-microsoft",
"guardrails-first-engineering-member-facing-health-ai",
"guide-verify-solve-the-engineering-discipline-agentic-development-demands",
"harness-engineering",
"harnessing-the-power-of-llms-locally",
"hasura-launch-realtime-data-connectivity-for-ai",
"healthcare-s-agent-bytecode-x12-as-the-harness-for-ai-agents",
"how-agent-o11y-differs-from-traditional-o11y",
"how-blackrock-builds-custom-knowledge-apps-at-scale",
"how-claude-code-works",
"how-deep-research-works",
"how-google-deepmind-is-researching-the-next-frontier-of-ai-for-gemini-raia-hadsell-vp-of-researc",
"how-i-automate-my-own-job-at-hugging-face-using-agents",
"how-instacart-transformed-its-search-and-discovery-using-an-llm-driven-approach",
"how-intuit-uses-llms-to-explain-taxes-to-millions-of-taxpayers",
"how-juries-and-librarians-can-solve-gtm-s-ai-trust-problem",
"how-kepler-built-verifiable-ai-for-financial-services",
"how-llms-work-for-web-devs-gpt-in-600-lines-of-vanilla-js",
"how-to-build-agents-that-run-for-hours-without-losing-the-plot",
"how-to-build-ai-agents-that-actually-work",
"how-to-build-an-ai-strategy-that-fails",
"how-to-build-enterprise-aware-agents",
"how-to-build-trustworthy-ai",
"how-to-build-world-class-ai-products-sarah-sachs-notion-and-carlos-esteban-braintrust",
"how-to-construct-domain-specific-llm-evaluation-systems",
"how-to-defend-your-sites-from-ai-bots",
"how-to-improve-your-agents-academic-lit-review",
"how-to-improve-your-vibe-coding-ian-butler",
"how-to-look-at-your-data-what-to-look-for-how-to-measure",
"how-we-hacked-yc-spring-2025-batch-s-ai-agents",
"how-we-scaled-500m-ai-agents-in-production-with-2-engineers",
"how-we-solved-context-management-in-agents-sally-ann-delucia",
"how-we-taught-agents-to-use-good-retrieval-hanna-lichtenberg-mixedbread-ai",
"how-web-data-infrastructure-powers-the-next-generation-of-ai",
"html-is-all-you-need-for-agents-to-make-graphics",
"human-seeded-evals-samuel-colvin-pydantic",
"hybridrag-a-fusion-of-graph-and-vector-retrieval-to-enhance-data-interpretation",
"hypermode-launch",
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]
}
] | |||||
Organizations 631 rows · ≈54K tokens | Organizations, their speakers, and talks | JSON | CSV | Explore | |
slug · name · url · summary · speakerSlugs · talkSlugs [
{
"slug": "google-deepmind",
"name": "Google DeepMind",
"url": "https://ai.engineer/orgs/google-deepmind",
"summary": null,
"speakerSlugs": [
"logan-kilpatrick",
"nicholas-kang",
"michael-aaron",
"philipp-schmid",
"patrick-lober",
"paige-bailey",
"guillaume-vernade",
"ian-ballantyne",
"thor-schaeff",
"sander-dieleman",
"kat-kampf",
"ammaar-reshi",
"kevin-hou",
"paige",
"florina-muntenescu",
"oli-gaymond",
"cassidy-hardin",
"omar-sanseviero",
"mukund-sridhar",
"aarush-selvan",
"raia-hadsell",
"kp-sawhney",
"shrestha-basu-mallick",
"benoit-schillings",
"gus-martins",
"brendan-o-donoghue",
"jack-rae",
"kathleen-kenealy",
"nidhi-kaushik-vyas",
"valeria-wu-fon",
"tom-ouyang",
"dumitru-erhan",
"shane-gu",
"nicole-brichtova"
],
"talkSlugs": [
"a-year-of-gemini-progress-what-comes-next",
"agentic-evaluations-at-scale-for-everybody",
"ai-engineering-with-the-google-gemini-2-5-model-family",
"any-to-any-building-native-multimodal-agents",
"build-deploy-ai-powered-apps",
"build-deploy-ai-powered-apps-18cc1b",
"building-conversational-agents",
"building-generative-image-video-models-at-scale",
"building-in-the-gemini-era-kat-kampf-ammaar-reshi-google-deepmind",
"defying-gravity",
"don-t-ship-skills-without-evals",
"from-transcription-to-live-music-gemini-s-audio-stack-thor-schaeff-google-deepmind",
"frontier-feud",
"gemini-nano-on-device-florina-muntenescu-oli-gaymond-google-deepmind",
"gemma-4-deep-dive-cassidy-hardin-google-deepmind",
"gemma-deepmind-s-family-of-open-models",
"how-deep-research-works",
"how-google-deepmind-is-researching-the-next-frontier-of-ai-for-gemini-raia-hadsell-vp-of-researc",
"how-google-deepmind-runs-agents-at-scale-kp-sawhney-ian-ballantyne-google-deepmind",
"let-s-go-bananas-with-genmedia",
"milliseconds-to-magic-real-time-workflows-using-the-gemini-live-api-and-pipecat",
"research-to-reality-with-google-deepmind",
"sovereign-escape-velocity-ownership-with-open-models-gus-martins-and-ian-ballantyne-google-deepm",
"text-diffusion-brendan-o-donoghue-google-deepmind",
"thinking-deeper-in-gemini",
"unveiling-the-latest-gemma-model-advancements",
"veo-3-for-developers",
"why-senior-engineers-struggle-to-build-ai-agents",
"youtube-0248b9a525953988f244",
"youtube-864f145429d587598a52",
"youtube-9bd4b34b669298acf016",
"youtube-b01c3c14dd8f8e90af4c"
]
},
{
"slug": "microsoft",
"name": "Microsoft",
"url": "https://ai.engineer/orgs/microsoft",
"summary": null,
"speakerSlugs": [
"sharmila-chokalingam",
"cedric-vidal",
"yohan-lasorsa",
"nagkumar-arkalgud",
"keiji-kanazawa",
"marlene-mhangami",
"david-smith",
"miguel-martinez",
"michael-albada",
"liam-hampton",
"julia-kasper",
"den-delimarsky",
"hanchi-wang",
"ornella-bahidika",
"emma-ning",
"chris-noring",
"harald-kirschner",
"lachlan-ainley",
"amy-boyd",
"nitya-narasimhan",
"pablo-castro",
"daniel-rosenwasser",
"pamela-fox",
"gabriela-de-queiroz",
"aishwarya-srinivasan",
"tisha-chawla",
"susheem-koul"
],
"talkSlugs": [
"accelerate-your-ai-journey-with-azure-ai-model-catalog",
"agentic-excellence-mastering-evaluation-of-ai-agents-with-azure-ai-evaluation-sdk",
"ai-didn-t-kill-the-web-it-moved-in-olivier-leplus-aws-yohan-lasorsa-microsoft",
"ai-red-teaming-agent-azure-ai-foundry-nagkumar-arkalgud-keiji-kanazawa-microsoft",
"beyond-code-coverage-functionality-testing-with-playwright",
"build-evaluate-and-deploy-a-rag-based-retail-copilot-with-azure-ai",
"building-applications-with-ai-agents",
"building-code-first-ai-agents-with-azure-ai-agent-service-cedric-vidal-microsoft",
"building-interactive-uis-in-vs-code-with-mcp-apps-marlene-mhangami-liam-hampton-github",
"building-protected-mcp-servers",
"cooking-with-agents-in-vs-code",
"creating-and-scaling-your-own-custom-copilots-with-azure-ai-studio",
"don-t-let-the-llm-drive-ornella-bahidika-joel-allou-microsoft",
"foundry-local-cutting-edge-ai-experiences-on-device-with-onnx-runtime-and-olive-emma-ning-micros",
"from-writing-code-to-designing-systems-how-the-developer-role-is-changing-chris-noring-microsoft",
"full-spec-mcp-hidden-capabilities-of-the-mcp-spec-harald-kirschner-microsoft-vs-code",
"insights-from-snorkel-ai-running-azure-ai-infrastructure",
"mind-the-gap-in-your-agent-observability",
"multi-model-multimodal-and-multi-agent-innovations-in-azure-ai",
"on-ai-and-knowledge",
"pragmatic-ai-with-typechat",
"rag-at-scale-production-ready-genai-apps-with-azure-ai-search",
"real-world-development-with-github-copilot-and-vs-code-harald-kirschner-christopher-harrison",
"running-ai-application-in-minutes-quick-start-with-ai-templates",
"running-ai-application-in-minutes-quick-start-with-ai-templates-ee4087",
"vibe-coding-at-scale-customizing-ai-assistants-for-enterprise-environments",
"vibe-coding-at-scale-customizing-ai-assistants-for-enterprise-environments-harald-kirschner",
"your-agent-failed-in-prod-good-luck-reproducing-it",
"your-voice-agent-doesn-t-need-a-frontier-model",
"youtube-48495817ad8e7fcd7ad5"
]
}
] | |||||
Chapters 5,929 rows · ≈283K tokens | Chapter titles and timestamps within talks | JSON | CSV | Explore | |
talkSlug · title · startMs · endMs [
{
"talkSlug": "agent-skills",
"title": "Intro",
"startMs": 0,
"endMs": 181000
},
{
"talkSlug": "agent-skills",
"title": "Skills Defined",
"startMs": 181000,
"endMs": 300000
}
] | |||||
Transcript downloads 1,156 rows · ≈15.7M tokens | Index linking to individual transcript files | JSON | CSV | Explore | |
talkSlug · title · jsonUrl · csvUrl [
{
"talkSlug": "agent-skills",
"title": "Don't Build Agents, Build Skills Instead – Barry Zhang & Mahesh Murag, Anthropic",
"jsonUrl": "https://ai.engineer/api/data/transcript?slug=agent-skills&format=json",
"csvUrl": "https://ai.engineer/api/data/transcript?slug=agent-skills&format=csv"
},
{
"talkSlug": "ai-coding-workflow",
"title": "AI Coding Workflow: From Product Idea to Tested Implementation",
"jsonUrl": "https://ai.engineer/api/data/transcript?slug=ai-coding-workflow&format=json",
"csvUrl": "https://ai.engineer/api/data/transcript?slug=ai-coding-workflow&format=csv"
}
] | |||||
How token estimates work
Token counts are estimates, using one token per four serialized JSON characters. Metadata estimates cover each collection download; the transcript estimate covers the linked transcript files rather than only their index and uses published JSON byte sizes where needed. Actual counts vary by model and format. Each download also returns its format-specific estimate in the X-Estimated-Tokens header. CSV exports store lists and nested fields as JSON inside cells.
Research atlas
Select a part of the library to find its tools and downloads.
Series → editions → scheduled sessions. Published talks are separate archive records, connected to people, organizations, topics, chapters, and transcripts.
Selected: Published talks
For published talks: search → fetch · Downloads & samples ↓
fetch
Retrieve a record by its returned ID, or timestamped passages from a selected talk. Search within that transcript or request a time range, with video links to the original moment.
Valid inputs & outputs
JSON Schema generated from the server’s validators. Input describes tool arguments; output describes successful structuredContent, including unavailable-data variants.
Expand objects to inspect fields and constraints. For alternative responses, use the variant selector. The raw JSON includes every schema keyword.
Loading schema explorer…
Raw JSON Schema / Copy
{"$schema": "https://json-schema.org/draft/2020-12/schema","type": "object","properties": {"id": {"type": "string","maxLength": 350,"pattern": "^(?:conference:[a-z0-9-]+\\/\\d{4}|(?:talk|speaker|topic|organization):[a-z0-9]+(?:-[a-z0-9]+)*)$","description": "Use an ID returned by search or list_conferences, not a URL."},"section": {"default": "metadata","type": "string","enum": ["metadata","transcript"]},"query": {"type": "string","minLength": 2,"maxLength": 120,"pattern": "^[^\\x00-\\x1f\\x7f]+$","description": "Trimmed search text; no control characters."},"startMs": {"type": "integer","minimum": 0,"maximum": 9007199254740991},"endMs": {"type": "integer","minimum": 0,"maximum": 9007199254740991},"offset": {"default": 0,"description": "Pass the previous nextOffset to continue.","type": "integer","minimum": 0,"maximum": 100000},"limit": {"default": 3,"type": "integer","minimum": 1,"maximum": 10},"contentVersion": {"type": "string","pattern": "^[a-f0-9]{64}$"}},"required": ["id"],"additionalProperties": false}
Only fields listed in required are mandatory. additionalProperties: false rejects unknown fields. anyOf lists alternative response shapes; null is different from an omitted field.
- id is required. Transcript query, time range, offset and limit apply only to section=transcript on a talk. Conference IDs return conference metadata.
- When both timestamps are supplied, endMs must exceed startMs. This cross-field rule is validated by the server but cannot be expressed in standard JSON Schema.
- Copy citation verbatim for transcript advice; it contains the title, formatted passage-start timestamp and video URL. Do not calculate or rewrite timestamps. If citation is null, use the talk page without inventing a timestamp. Quotes must match passage text; do not attach a timestamp to a metadata summary or an unsupported causal explanation. A queried passage includes score. Null endMs means the final passage has no known end. A null citation/video URL means the video ID or start timestamp is invalid, or the start is outside a known recording duration. Unknown duration cannot be range-checked.
- For pagination, pass nextOffset as offset and the returned contentVersion. If content changes, restart without contentVersion. Missing transcripts are an explicit success variant, not an empty available transcript.
Example request / response
Example responses from public conference data, without the MCP envelope. Fields follow the current schemas; live data and versions change.
{"id": "talk:harness-engineering","section": "transcript","limit": 1}
{"id": "talk:harness-engineering","title": "Harness Engineering: How to Build Software When Humans Steer, Agents Execute — Ryan Lopopolo, OpenAI","url": "https://ai.engineer/talks/am_oeAoUhew-harness-engineering","contentVersion": "96693ee70735bdcd1f0ded798f2ddbb89cfc37ad1802e125df2c3b99621712ae","nextOffset": 1,"retrievedAt": "2026-08-30T21:12:47.426Z","transcriptStatus": "available","contentType": "automated_transcript","passages": [{"startMs": 80,"endMs": 135500,"text": "[upbeat music] Our next speaker is here to speak about Harness engineering: how to build software when humans steer and agents execute. Please join me in welcoming to the stage Member of Technical Staff at OpenAI, Ryan Lopopolo. [upbeat music] [audience applauding] Good morning, London. [audience applauding] I'm super excited to be here today. I'm Ryan Lopopolo, and for the last nine months, I have had the privilege of building software exclusively with agents. Uh, I am a token billionaire, and I believe that in order for us to get into our AGI future, we want everybody to be token billionaires, to use the models to do the full job. And what that means is to lean into the idea that the models are capable of being a full software engineer. And I've lived that experience by banning my team from even touching their editors, to have to work through the models in order to get the job done. And, uh, today I'm gonna talk to you a little bit about what it means to lean into that and operationalize the way you work, the code spaces you live in, and the processes on your teams in order to get the agents to do the full job. I believe I'm preaching to the choir here when I say that the way we build software has changed. In the last six months, we have seen coding agents take over the world, and capability has continually advanced at a super fast pace to have these models and the harnesses within which they live take more complex actions, do more complicated work with higher reliability over longer time horizons. And the place we've gotten to here is that implementation is no longer the scarce resource of what it means to do the job of software engineering.","url": "https://www.youtube.com/watch?v=am_oeAoUhew&t=0","citation": "[Harness Engineering: How to Build Software When Humans Steer, Agents Execute — Ryan Lopopolo, OpenAI — 0:00](https://www.youtube.com/watch?v=am_oeAoUhew&t=0)"}],"totalPassages": 26,"downloadUrl": "https://ai.engineer/api/data/transcript?slug=harness-engineering&format=json"}
Try asking: “Find the passages about evaluation in that talk and give me timestamped video links.”
Apply to AIE CODE 2026 through your agent
Ask: “Help me apply to attend AIE CODE 2026.” Your agent collects your answers, checks them and shows a review before you approve submission. Applications and scores stay private. Speaker proposals use Sessionize.
Submissions share the form limit: 10 per client per hour, 500 globally per day. Skills and HTTP clients can use the live REST requirements and llms.txt workflow.
Application tools & schemas
code_2026_get_application
Valid inputs & outputs
JSON Schema generated from the server’s validators. Input describes tool arguments; output describes successful structuredContent, including unavailable-data variants.
Expand objects to inspect fields and constraints. For alternative responses, use the variant selector. The raw JSON includes every schema keyword.
Loading schema explorer…
Raw JSON Schema / Copy
{"$schema": "https://json-schema.org/draft/2020-12/schema","type": "object","properties": {},"additionalProperties": false}
Only fields listed in required are mandatory. additionalProperties: false rejects unknown fields. anyOf lists alternative response shapes; null is different from an omitted field.
- Attendee applications for CODE 2026 only. Read requirements and current field choices before drafting. Speaker proposals use Sessionize.
Example request / response
Example responses from public conference data, without the MCP envelope. Fields follow the current schemas; live data and versions change.
{}
{"conferenceId": "conference:code/2026","title": "AI Engineer Code 2026","location": "San Francisco","startDate": "2026-11-10","endDate": "2026-11-12","applicationUrl": "https://ai.engineer/code/2026/apply","speakerApplicationUrl": "https://sessionize.com/aiecode26/","audience": "Engineers, founders, and researchers with deep experience in the AI coding stack. Admission is selective and by application; submitting does not guarantee admission.","instructions": "Ask for missing answers; never invent applicant experience. Show all answers together and obtain approval before submitting. More profile links help reviewers understand your work. Speaker proposals go to Sessionize, not this attendee application.","privacy": "Answers are private to the application review workflow. No public applicant search, scores, status lookup, or saved drafts. Validation does not store answers or run enrichment.","fields": {"achievements": "Describe what you have built or achieved with AI coding tools and agents.","location": "City and country","company": "Company, school, or independent","roles": ["Seed/Pre-seed founder","Series A-B founder","Series C+ founder","Actively working on ideas for startup","Technical VP/C-suite (e.g. CTO, VP Eng/AI)","Product Management (CPO, VP Product, PM)","Designer (all levels)","AI Engineer","Senior/Staff+ Engineer","ML Engineer","Data Engineer","Data Scientist","AI/ML Researcher","Student","Grad Student","Looking for work","Looking to hire","Venture Capitalist","Indie Hacker","Content Creator","Non-technical founder/exec","Other"],"topics": ["Coding agents & workflows","Code generation & developer tools","Context engineering & retrieval","Evaluations & code review","Enterprise adoption & team practices","Models & infrastructure","Security & reliability","UX for AI-assisted development","Other"]},"payloadSchema": {"$schema": "https://json-schema.org/draft/2020-12/schema","type": "object","properties": {"firstName": {"type": "string","minLength": 1,"maxLength": 200},"lastName": {"type": "string","minLength": 1,"maxLength": 200},"email": {"type": "string","maxLength": 320,"format": "email","pattern": "^(?!\\.)(?!.*\\.\\.)([A-Za-z0-9_'+\\-\\.]*)[A-Za-z0-9_+-]@([A-Za-z0-9][A-Za-z0-9\\-]*\\.)+[A-Za-z]{2,}$"},"company": {"type": "string","minLength": 1,"maxLength": 200},"jobTitle": {"type": "string","minLength": 1,"maxLength": 200},"location": {"type": "string","minLength": 1,"maxLength": 200},"achievements": {"type": "string","minLength": 1,"maxLength": 2000},"github": {"anyOf": [{"type": "string","format": "uri"},{"type": "string","const": ""}]},"social": {"anyOf": [{"type": "string","format": "uri"},{"type": "string","const": ""}]},"linkedin": {"anyOf": [{"type": "string","format": "uri"},{"type": "string","const": ""}]},"website": {"anyOf": [{"type": "string","format": "uri"},{"type": "string","const": ""}]},"accessibility": {"type": "string","maxLength": 2000},"roles": {"minItems": 1,"maxItems": 22,"type": "array","items": {"type": "string","enum": ["Seed/Pre-seed founder","Series A-B founder","Series C+ founder","Actively working on ideas for startup","Technical VP/C-suite (e.g. CTO, VP Eng/AI)","Product Management (CPO, VP Product, PM)","Designer (all levels)","AI Engineer","Senior/Staff+ Engineer","ML Engineer","Data Engineer","Data Scientist","AI/ML Researcher","Student","Grad Student","Looking for work","Looking to hire","Venture Capitalist","Indie Hacker","Content Creator","Non-technical founder/exec","Other"]},"description": "Select all applicable roles from the current requirements."},"topics": {"minItems": 1,"maxItems": 9,"type": "array","items": {"type": "string","enum": ["Coding agents & workflows","Code generation & developer tools","Context engineering & retrieval","Evaluations & code review","Enterprise adoption & team practices","Models & infrastructure","Security & reliability","UX for AI-assisted development","Other"]},"description": "Select the topics you want covered."}},"required": ["firstName","lastName","email","company","jobTitle","location","achievements","roles","topics"],"additionalProperties": false},"endpoints": {"requirements": "https://ai.engineer/api/code/2026/application","validate": "https://ai.engineer/api/code/2026/application/validate","submit": "https://ai.engineer/api/code/2026/application/submit"},"submissionSchema": {"$schema": "https://json-schema.org/draft/2020-12/schema","type": "object","properties": {"submissionId": {"type": "string","format": "uuid","pattern": "^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$","description": "Generate once for these answers; reuse unchanged on retry. Never reuse for another person or changed answers."},"applicantApproved": {"type": "boolean","const": true,"description": "Set true only after the applicant approves the complete answers for submission to CODE 2026."},"payload": {"type": "object","properties": {"firstName": {"type": "string","minLength": 1,"maxLength": 200},"lastName": {"type": "string","minLength": 1,"maxLength": 200},"email": {"type": "string","maxLength": 320,"format": "email","pattern": "^(?!\\.)(?!.*\\.\\.)([A-Za-z0-9_'+\\-\\.]*)[A-Za-z0-9_+-]@([A-Za-z0-9][A-Za-z0-9\\-]*\\.)+[A-Za-z]{2,}$"},"company": {"type": "string","minLength": 1,"maxLength": 200},"jobTitle": {"type": "string","minLength": 1,"maxLength": 200},"location": {"type": "string","minLength": 1,"maxLength": 200},"achievements": {"type": "string","minLength": 1,"maxLength": 2000},"github": {"anyOf": [{"type": "string","format": "uri"},{"type": "string","const": ""}]},"social": {"anyOf": [{"type": "string","format": "uri"},{"type": "string","const": ""}]},"linkedin": {"anyOf": [{"type": "string","format": "uri"},{"type": "string","const": ""}]},"website": {"anyOf": [{"type": "string","format": "uri"},{"type": "string","const": ""}]},"accessibility": {"type": "string","maxLength": 2000},"roles": {"minItems": 1,"maxItems": 22,"type": "array","items": {"type": "string","enum": ["Seed/Pre-seed founder","Series A-B founder","Series C+ founder","Actively working on ideas for startup","Technical VP/C-suite (e.g. CTO, VP Eng/AI)","Product Management (CPO, VP Product, PM)","Designer (all levels)","AI Engineer","Senior/Staff+ Engineer","ML Engineer","Data Engineer","Data Scientist","AI/ML Researcher","Student","Grad Student","Looking for work","Looking to hire","Venture Capitalist","Indie Hacker","Content Creator","Non-technical founder/exec","Other"]},"description": "Select all applicable roles from the current requirements."},"topics": {"minItems": 1,"maxItems": 9,"type": "array","items": {"type": "string","enum": ["Coding agents & workflows","Code generation & developer tools","Context engineering & retrieval","Evaluations & code review","Enterprise adoption & team practices","Models & infrastructure","Security & reliability","UX for AI-assisted development","Other"]},"description": "Select the topics you want covered."}},"required": ["firstName","lastName","email","company","jobTitle","location","achievements","roles","topics"],"additionalProperties": false}},"required": ["submissionId","applicantApproved","payload"],"additionalProperties": false},"rateLimits": "Submissions share the form limit: 10 per client per hour, 500 globally per day. Honor Retry-After. Reuse submissionId on uncertain retries."}
code_2026_validate_application
Valid inputs & outputs
JSON Schema generated from the server’s validators. Input describes tool arguments; output describes successful structuredContent, including unavailable-data variants.
Expand objects to inspect fields and constraints. For alternative responses, use the variant selector. The raw JSON includes every schema keyword.
Loading schema explorer…
Raw JSON Schema / Copy
{"$schema": "https://json-schema.org/draft/2020-12/schema","type": "object","properties": {"payload": {"type": "object","propertyNames": {"type": "string"},"additionalProperties": {},"description": "Partial or complete applicant answers. Read current requirements for fields."}},"required": ["payload"],"additionalProperties": false}
Only fields listed in required are mandatory. additionalProperties: false rejects unknown fields. anyOf lists alternative response shapes; null is different from an omitted field.
- No writes or enrichment. Show the returned review to the applicant; request corrections or submission approval.
Example request / response
Example responses from public conference data, without the MCP envelope. Fields follow the current schemas; live data and versions change.
{"payload": {}}
{"valid": false,"issues": [{"field": "firstName","message": "Enter your first name."}]}
code_2026_submit_application
Valid inputs & outputs
JSON Schema generated from the server’s validators. Input describes tool arguments; output describes successful structuredContent, including unavailable-data variants.
Expand objects to inspect fields and constraints. For alternative responses, use the variant selector. The raw JSON includes every schema keyword.
Loading schema explorer…
Raw JSON Schema / Copy
{"$schema": "https://json-schema.org/draft/2020-12/schema","type": "object","properties": {"submissionId": {"type": "string","format": "uuid","pattern": "^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$","description": "Generate once for these answers and reuse unchanged after an uncertain result."},"applicantApproved": {"type": "boolean","const": true,"description": "True only after the applicant approves the complete review."},"payload": {"type": "object","propertyNames": {"type": "string"},"additionalProperties": {},"description": "Applicant answers using the fields returned by code_2026_get_application."}},"required": ["submissionId","applicantApproved","payload"],"additionalProperties": false}
Only fields listed in required are mandatory. additionalProperties: false rejects unknown fields. anyOf lists alternative response shapes; null is different from an omitted field.
- Requires applicantApproved=true after approval of all answers. Reuse submissionId and identical answers after a timeout. No applicant lookup or admission decision is returned.
Example request / response
Example responses from public conference data, without the MCP envelope. Fields follow the current schemas; live data and versions change.
{"submissionId": "00000000-0000-4000-8000-000000000000","applicantApproved": true,"payload": {"firstName": "Example","lastName": "Applicant","email": "example@example.com","company": "Independent","jobTitle": "Engineer","roles": ["AI Engineer"],"location": "San Francisco, United States","achievements": "Built a coding agent evaluation harness.","topics": ["Coding agents & workflows"]}}
{"success": true,"conferenceId": "conference:code/2026","submissionId": "00000000-0000-4000-8000-000000000000","message": "Application received for review. This is not an admission decision."}
Freshness & coverage
Status JSON ↗Authorized public corpus: 1,156 talks · 196,716 transcript segments
- Corpus version
- 3b854fc8930b3fbc29ae66d25a80549fcc51d204419428e3ace9caab1015c819
- Source updated
- Not recorded
- Publication recorded
- Not recorded
- Talks with transcripts
- 1156 / 1156
Corpus counts exclude other site records merged into the collections below. Transcript availability does not establish completeness or accuracy. Unknown dates are not inferred from release authorization. Status checks may use metadata cached for five minutes.
Usage, limits & data policy · 600/min global · 60/min per IP
Tool failures return isError: true with an explanation in content, not a success-schema payload. Invalid arguments may return a protocol error or a tool error; inspect both before reading structuredContent. HTTP 429, 529, and 503 are transport failures, not tool outputs.
For filtered talk lookup, transcript, or schedule pagination, pass nextOffset as offset with the returned contentVersion. A null nextOffset marks the end; if content changes, restart pagination.
Usage limits. MCP POST requests share a global limit of 600 per minute, with 60 per minute per IP. This includes tool calls and protocol requests. The shared limit returns HTTP 529; the per-IP limit returns HTTP 429. Respect Retry-After and back off. Need a higher limit? Contact hello@ai.engineer.
Search, then fetch. For a complete speaker or organization inventory, omit the search query and provide the filter, then follow pagination. For topical research, search keywords and fetch supporting passages. Search covers metadata, enrichment, and indexed transcript passages. Summaries are metadata, not quotations. Only transcript excerpts are supported by their recording timestamps; check coverage for unavailable indexing. Fetch surrounding context before drawing conclusions. Transcripts are automated and may contain errors.
Private CODE 2026 applications. Apply through MCP: read requirements, validate answers, then submit after approving the complete application. Applicant records, scores and decisions remain private. This is for CODE attendees, not NYC or speaker proposals. Ticket buying remains a stub; Accelevents ticketing stays on the official conference website. CFPs link to external portals such as Sessionize. MCP cannot purchase tickets, save drafts, or submit a CFP. With the user’s explicit authorization, an agent may use computer use on those official portals to complete a purchase or submission. Confirm the final price or application contents before committing unless already approved, and verify the portal’s confirmation.
Downloads and freshness. The aie://data/catalog resource links to the public collections below. Filtered lookup, transcript, and schedule pagination return content versions so an agent can detect updates. The restricted Hugging Face repository is separate and is not exposed through MCP.
Hugging Face · Restricted access
The research dataset.
Our separate Hugging Face dataset brings together transcripts, segments, topics, entities, summaries, chapters, retrieval examples, and benchmarks in Parquet. It requires authorized access and is not included in the public downloads above.
View on Hugging Face ↗You may need to sign in with an authorized Hugging Face account to view the repository.