Evergreen Overview
Prompt engineering is not just about better outputs. In practice it shapes reliability, scope, fallback behavior, and how well an AI system resists misuse when instructions, tools, and untrusted content collide.
Prompt Engineering
Prompt design patterns, instruction hierarchy, and defensive prompt construction.
prompt engineeringsystem promptsinstruction hierarchyguardrailstask decomposition
What matters most
- Instruction hierarchy and role separation
- Clear task boundaries, fallback behavior, and refusal handling
- Prompt structures that support monitoring and repeatable evaluation
Where teams get into trouble
- Overloading prompts with too many responsibilities
- Relying on wording instead of system controls
- Treating prompts as static text instead of part of application design
Who this page is for
- Teams operating prompt-heavy workflows
- Builders refining assistant and agent behavior
- Reviewers trying to connect prompt design to safety and risk
References
Current notes, events, and source material
These items are included because they add useful evidence, framing, implementation detail, or upcoming context for teams working in this area.
Play video
HTML Is All Agents Need — James Russo, HeyGen
James Russo describes HeyGen’s path from large prompts and agentic retries to an HTML, CSS, and JavaScript authoring layer for deterministic video. React-based Remotion constrained generated output, while plain web primitives restored flexibility; the team then built rendering controls, evaluation loops, and reusable skills so smaller models could produce consistent MP4s.
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Medic for Apache Spark - First Aid for Failing Jobs - Drasko Profirovic, Pinterest
In this talk, we’ll share the journey of building an agentic diagnostics tool to address one of the most time-consuming challenges in data engineering: troubleshooting Spark job failures at scale. As Spark workloads and platform complexity continue to grow, traditional dashboards and static playbooks are no longer suff
Play video
Through the AI Fog: The Architectural Decision Agentic Security Depends On — Manoj Nair, Snyk
Ask the latest frontier models, the ones not even public yet, to find the same vulnerability five times, and only half of those runs catch it. Against a plain deterministic checker they found at most 75% of the issues, a 40% F1 score. That number sits underneath the whole talk: the generator and the validator cannot be
Play video
AI’s Jurassic Park Period — Aaron Stanley, dbt Labs
Twenty years ago Aaron Stanley arrived at an emergency evidence collection for an SEC investigation and realized he had forgotten the dongle that licensed his forensic software. Rather than drive back for it, he routed around the constraint and watched the timestamps on the evidence begin to change. In a who knew what
Play video
Security Track Intro — Randall Degges, Snyk
Building software with AI almost feels like a cheat code: you ship what you were working on and watch it spark joy in real users. The catch, and the reason Randall Degges is opening the World's Fair's first Security Track, is that three things still stand in the way of doing that at scale. AI writes insecure code just
Play video
Enterprise Agents Have a Structure Problem - Ishita Daga, Tesla
Most enterprise agents fail for the same reason: the model can generate SQL, call tools, and follow workflows, but it has no understanding of how the business actually defines its data. Most teams try to fix this with longer prompts, more RAG, or a bigger model. The real fix is building semantic retrieval infrastructur
Play video
It's 10pm. Do You Know Where Your Agents Are? — Kim Maida, Keycard
An incident agent on the night shift reads a ticket: the billing database is broken, payments failing. The documented fix says to drop the database and let a backup restore it, so the agent drops the production Postgres database, cannot confirm any backup ran, and escalates it for the morning. This has happened to real
Play video
Privacy-Preserving Intelligence — Steve Korshakov, Bee (acq. Amazon)
A wearable that records everything you say captures about 10 million tokens a year, and within a week it knows almost everything about you. That is Bee, and Steve Korshakov calls it roughly the most sensitive capture device on the market, which is why his whole talk is about one guarantee: no one can read your data, no
Play video
Skills are the New SDKs - Elvin Aghammadzada, DataRobot
You shipped a REST API. Then an SDK. Then MCP tool calling. And still, when a developer asks a coding agent to use your platform, it invents steps, and breaks in production. The problem is that your platform isn't teachable yet. The fix is a skill layer. Versioned, task-specific packages that encode the workflow knowle
Play video
We Gave an Agent Production Code Access and Then Tried to Sleep at Night — Moritz Johner, Form3
A single PatchPilot PR that bumped a few dependencies changed 70,000 lines of code, and the whole problem hides somewhere in that diff. Moritz Johner's team at Form3 built the agent to patch CVEs across thousands of repositories, the backlog that never empties, and ran it in production. Then infosec asked the question
Play video
Agents Need Receipts, Not More Tool Calls - Armanas Povilionis, Alithea Bio
In this talk, I’ll show an agent publish a service, another agent discover and invoke it, and a signed receipt that proves what happened. The point is simple: if agents are going to buy, sell, and compose work across hosts, logs and API dashboards are not enough. Froglet is an open-source protocol and node for agent-to
Play video
In the Land of AI Agents, the Verifiers Are King — Tariq Shaukat, Sonar
As AI agents take on increasingly complex development tasks, the critical challenge has shifted from generation to verification. Hallucination is not a temporary bug. Evidence suggests that as models grow more capable, failures become more frequent and more convincing, making cognitive surrender among human reviewers a
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Your LLM Stack Is a 2008 Database With Better Marketing — Lovina Dmello, NVIDIA
In 2023, researchers found thousands of Ray clusters sitting wide open on the public internet, dashboards and job APIs exposed to anyone, because authentication ships off by default and nobody turned it on before going to production. The data at risk was worth more than a billion dollars. No zero day, no clever attack
Play video
Can Oncology Workflows Run Without Human Touch? - Anant Shankhdhar, Risa Labs
Can Oncology Workflows Run Without Human Touch? At Risa, we automate healthcare workflows in oncology end-to-end using AI agents. We built four agents that work together , each one handles a different step, then passes its output to the next. No human needed in between. The agents when combined are able to do the work
Play video
When Agents Meet Physical Data: The Other Physics of Agent Harnesses - Dmitry Petrov, DataChain
Ask an agent to find every night-time pedestrian frame across terabytes of dashcam video in S3. The first pass can cost thousands of dollars and run for hours or days. Once you’ve paid that cost, the agent’s favorite move - loop, inspect, re-derive - becomes the worst thing it can do. Most agent intuition comes from a
Play video
Agentic Development Security — Ezra Tanzer, Snyk
An agent at Replit ignored a code freeze, deleted a production database, then fabricated records to hide it and reported that recovery was impossible. It was wrong about the recovery, but the deletion was real, and it was not acting maliciously. It was trying to help. That is the uncomfortable center of agentic develop
Play video
Your Voice Agent Doesn't Need a Frontier Model - Joel Allou & Ornella Bahidika, Microsoft
My AI voice tutor doesn't run on a frontier model. It runs on a small one, and the reason isn't cost. It's that voice lives or dies on latency, and the scaffolding around the model is what makes it feel smart anyway. When you build a voice agent the clock is brutal. A pause longer than a held breath feels broken, so yo
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Voice Agents That Handle Interrupts - Chintan Agrawal and Daniel Wirjo, AWS
Chat agents get seconds to respond. Voice agents get 200 milliseconds, and if they get it wrong, the user doesn't retry, they hang up. The gap between "impressive voice demo" and "agent people actually want to talk to" is entirely in the engineering: latency budgets, barge-in handling, turn-taking, and the silence dete
Play video
Build the AI GTM Agent That Knows the Buyer - Dr. Sajjan Kanukolanu, Position2 (Position Squared)
As part of a GTM motion, an AI agent goes live on the site. The first visitor lands. The conversation starts. That's the moment everyone optimizes for- the right conversation, the right offer etc.. It's the wrong moment. A well-built AI GTM system does something very different. By the time a buyer sends their first mes
Play video
Don't Let the LLM Drive - Ornella Bahidika & Joel Allou, Microsoft
The LLM in my voice tutor doesn't decide when the lesson is over. It doesn't decide whether the user got the answer right. It doesn't decide which step comes next. A harness does all of that. The LLM just shows up and talks. Every engineer who's tried to ship a multi-step flow agent has felt this: the model declares it
Play video
Agent Output Is Not UX: Rendering Layer Your LLM Pipeline Is Missing - Bala Ramdoss, Amazon Lens
Getting a model to produce the right output is the part everyone works on. Turning that output into something people will actually use is the part that decides whether an AI feature ships. This talk is about that layer, the one between model output and the product experience, grounded in lessons from building agentic C
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Why Your Agent Disagrees With Itself (And What To Do About It) - Diane Lin, Datadog
Run the same task twice, and sometimes you get two materially different answers. While many dismiss this as the "stochastic nature of LLMs," this inconsistency is a critical product flaw that destroys customer trust—especially in high-stakes fields like cybersecurity, where a "flip-flop" between a malicious threat and
Play video
Agentic Security: Permissions, Provenance, and the Agent Supply Chain — Steve Yegge, Gas Town
A security hardening pass by Fable over a game one engineer had built for 30 years came back clean: cloud hardening done, credentials handled, good vibes all around. Then Snyk ran over the same code and surfaced 241 vulnerabilities the agent never thought to look for. That gap is the center of Steve Yegge's talk, whose
Play video
From Tokens to Cells: Foundation Models for Single-Cell Biology - Akram Baharlouei, Altos Labs
This talk examines the engineering challenges of building foundation models for single-cell biology from a non-biologist’s perspective. Speakers: - Akram Baharlouei (Altos Labs): Machine learning engineer at Altos Labs working on foundation models for biology. Previously at Meta AI and Qualcomm. LinkedIn: https://linke
Play video
Build Evals That Actually Matter - Nick Ung, Lyft
Your agent passes offline evals at 90%. You ship. Production immediately finds failure modes your eval never saw. Sound familiar? The culprit is almost always the same: the "customer" in your offline eval is an off-the-shelf LLM that sounds nothing like your real users, and your synthetic test set doesn't capture how m
Play video
From Blind Spots to Merged PRs: Continuous Agentic Performance Optimization - May Walter, Hud
Performance issues silently pile up in mature codebases. Teams know things could be faster, but can never justify pausing feature work to investigate. You have to put engineers on it just to find out if there's something worth fixing, and the effort is completely unpredictable: it could take an hour or three weeks. In
Play video
You Didn't Ship a Bug. You Just Wrote It for a Human. - Ravi Madabhushi, Scalekit
We built a demo agent to show customers how to connect agents to their tools. A simple chat assistant — Gmail, Calendar, a handful of connectors. It ran on a 15-minute schedule. And every 15 minutes, our production database strained. Latency crept up and alerts fired. Then settled. Then, it fired again. It took us a wh
Play video
A Practitioner's Guide to Graphs - Tim Ainge, Good Collective
A speed-run through the basics... what is a graph, extracting graphs from unstructured text, schema first and ontological improvements and then a slightly more detailed discussion of personalised page rank, shortest path and subgraph matching algorithms. Every idea is explored with an explanation of the principles, and
Play video
Stop Renting Your Cognitive Infrastructure - Thiyagarajan Maruthavanan, Kalmantic Labs
I pointed my lab at one problem, inference, after 200 users burned $1,000 in credits and the math just wouldn't close. So I built the thing, felt the cost, and went looking for why renting intelligence never pencils out. Turns out everyone in this market sells a gospel shaped like their own invoice. Jensen: build a tok
Play video
Agents Need Receipts, Not More Tool Calls - Armanas Povilionis, Alithea Bio
In this talk, I’ll show an agent publish a service, another agent discover and invoke it, and a signed receipt that proves what happened. The point is simple: if agents are going to buy, sell, and compose work across hosts, logs and API dashboards are not enough. Froglet is an open-source protocol and node for agent-to
Play video
The UX of AI: Making AI-Powered Apps Your Users Don't Hate - Kathryn Grayson Nanz, Progress Software
As a developer, AI is fun, exciting, and full of potential – but users don't always feel the same way about it. From a UX perspective, AI comes with a whole new set of considerations around user trust, privacy, and security. From a UI perspective, AI brings new interaction patterns, new icons, new visual cues, and so m
Play video
Autonomous Agents for Scientific Tasks - Sina Shahandeh, Radicait
There has been much work on Autoresearch where the objectives are coding puzzles, toy optimization problems, or static supervised-learning ML tasks. However, for an autonomous agent to assist with a scientific discovery task, the problems must come from real measurement data of the world and they are highly open-ended,
Play video
Your Agents Need a Save Button - Hamza Tahir, ZenML
Most of an agent's life is spent waiting - on a tool, a human, the next step - and the whole time you're holding a live process awake and billing for it. Multiply that across every agent your org wants to run overnight and the math stops working. A save button fixes the obvious stuff: freeze an agent to durable state,
Play video
Stop Burning Tokens: Why self-improvement needs domain expertise first - Annabell Schäfer, Langfuse
We ran auto-improvement loops on a paper classification task against a ground-truth dataset. A real problem, narrow enough to measure precisely, and in fact one of the few clear cut target functions out there. We’ll share how to properly set up an agent for auto-improvement, what task specificity and target function qu
Play video
Content Is Code - Matt Palmer, Conductor
Code has become the fastest medium for producing technical content, but the most important piece isn't a secret agent skill or a magical framework: its the same thing that makes a great developer experience. Speakers: - Matt Palmer (Conductor): Matt Palmer is a DevRel & product leader focused on AI devtools, developer
Play video
Agents Need Feature Flags - Sachin Gupta
Most AI teams ship behavior changes to 100% of users on every deploy — no canary, no segment, no kill switch. Web teams stopped doing this in 2012. AI teams are about to learn why. Feature flags are table stakes in software engineering. In agent systems they're almost nonexistent — prompts, tool access, model selection
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"Software engineering is not about writing code" — Benoit Schillings, Google DeepMind VP of Research
A keynote exploring generative AI for code, deep-thinking algorithms, and the future of pre-training and transformer models for Gemini. Speaker: Benoit Schillings leads the Thinking, Reasoning, and Coding teams at Google DeepMind, directing foundational research toward AGI. His work focuses on advancing next-generation
Play video
Claude for Long-Horizon Tasks — Lance Martin, Anthropic
Claude is capable of long horizon tasks. In this talk, we'll share lessons learned about building agent harnesses for reliable and secure long-horizon work. This include decoupling the brain and hands, self-verification, self-learning, and design for evolving agent harnesses. ### Lance Martin Member of Technical Staff
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On AI and Knowledge — Pablo Castro, Distinguished Engineer & CVP for AI Knowledge, Microsoft
Pablo Castro explores AI and knowledge systems for building better applications and agents. Speaker: Pablo Castro —Distinguished Engineer and CVP, Microsoft, leads the AI Knowledge team in Microsoft's CoreAI division, where he focuses on state-of-the-art information understanding and retrieval systems for AI applicatio
Play video
The Great Loops Debate — Dex Horthy, Geoff Huntley, Ian Livingstone, Greg Pstrucha, @insecure-agents
Oxford Style Debate: There is, or is not, a delta between the hype behind loops and what actually works in practice. Team No Delta (pro the way we do loops today) The hype around loops is valid and loops work well today in practice. Loops today can be a silver bullet and result in outsize productivity gains, and marks
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Imagination Engineering — Eve Bouffard, Head of Design, Y Combinator
Eve Bouffard presents “imagination engineering” as a design practice for exploring more ambitious product ideas when AI reduces the cost of prototyping and technical execution.
Designing for the inevitable: System prompt leakage and mitigations in generative AI applications
System prompts form the foundation of generative AI applications. A system prompt is a collection of instructions and operational context provided to a large language model (LLM) that shapes how the model behaves and interacts with users and tools. System prompts often contain proprietary information, including role de
Play video
AI Engineer World's Fair 2026 Day 2 Livestream
Live from San Francisco, AI Engineer World’s Fair 2026 continues with Day 2 of session programming from the main stage. Watch live for keynote sessions, main-stage programming, and more from World’s Fair 2026 as AI Engineer brings another full day of AI engineering content to viewers online. Event: AI Engineer World’s
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AI Engineer World's Fair 2026 Day 3 Livestream
Live from San Francisco, AI Engineer World’s Fair 2026 wraps with the final day of main-stage programming. Watch live for keynote sessions, featured talks, and closing-day highlights from World’s Fair 2026 as AI Engineer streams the final day of the event online. Event: AI Engineer World’s Fair 2026 Date: Thursday, Jul
Play video
10 tips to level up your AI-assisted coding - Aleksander Stensby - NDC Copenhagen 2026
NDC Copenhagen 2026 talk on AI-assisted coding with Cursor and Claude Code, covering prompting, context engineering, testing, debugging, daily workflow integration, and MCP connections to developer tools.
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Build & deploy AI-powered apps — Paige Bailey, Google DeepMind
Got a massive idea but stuck in the "just talking about it" phase? This session cuts the fluff and dives straight into how to build and prototype at lightning speed using AI Studio Build and Antigravity for free. It breaks down Google DeepMind's AI tech stack so viewers know exactly which tools to use, when to reach fo
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Everything I Learned Training Frontier Small Models — Maxime Labonne, Liquid AI
A new class of small models is emerging with the ability to reliably follow instructions and call tools while running on-device under 1 GB of memory. In this talk, we'll break down how to post-train frontier small models using the LFM2.5 recipe: on-policy preference alignment, agentic reinforcement learning, and curric
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One Login to Rule Them All: Cross-App Access for MCP — Garrett Galow, WorkOS
Connecting a coding agent to multiple services often means facing a dozen OAuth consent screens, a dozen token lifecycles, and a dozen chances for something to break. Despite having Single Sign-On, users still find themselves signing in repeatedly. This talk explores how Cross-App Access leverages a three-way trust bet
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Why building eval platforms is hard — Phil Hetzel, Braintrust
An eval platform is not just a test runner. You are building shared definitions of "good," reliable data pipelines, labelling workflows, versioning, and trust in results across many teams and model changes. This session breaks down the hidden complexity, the common failure modes, and the design principles that make eva
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Building your own software factory — Eric Zakariasson, Cursor
Most of us are pair-programming with one agent and stopping there. There's a lot more on the table. This workshop is about going from one agent to many. We'll start with codebase setup, the foundational work that makes agents effective on their own. Then we'll scale up to running agents in parallel, kicking off async w
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Lessons from Scaling GitHub's Remote MCP Server — Sam Morrow, GitHub
GitHub operates one of the most heavily-utilised MCP servers in the ecosystem, with over 4 million downloads of the stdio server alone. Discover the architectural decisions, technical challenges and lessons learned while building and scaling a remote MCP server on production infrastructure. The session walks through th
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Bringing MCPs to the Enterprise — Karan Sampath, Anthropic
MCPs are often flaky, face multiple security vulnerabilities, and are generally hard to scale. Most enterprises struggle to use more than single digit numbers of MCPs due to issues with security, observability, and access control. In this talk, we'll explore the approaches and learnings we at Anthropic have been taking
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Open Models at Google DeepMind — Cassidy Hardin, Google DeepMind
Open models are getting smaller, faster, and far more capable. In this talk, Cassidy Hardin walks through the latest advances in the Gemma family, with a focus on Gemma 4 and what it enables for developers building on-device and open-weight AI systems. She covers the architecture behind Gemma’s dense, effective, and mi
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Collaborative AI Engineering — Maggie Appleton, GitHub Next
Agentic engineering so far has been a solo story: one developer and a dozen agents moving at warp speed. But speed without thoughtful planning and team alignment is just wasting tokens. When everyone on a team is directing agents alone in their personal CLI tools with no shared context, you get duplicate work, conflict
Play video
MCP = Mega Context Problem - Matt Carey
The best MCP server is the one you didn't have to build. At Cloudflare we have a lot of products. Our REST OpenAPI spec is over 2.3 million tokens. When teams started building MCP servers, they did what everyone does: cherry-picked important endpoints for their product, wrote some tool definitions and shipped a separat
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Full Walkthrough: Workflow for AI Coding from Planning to Production — Matt Pocock (@mattpocockuk )
A hands-on workshop covering the full lifecycle of AI-assisted development, from turning ambiguous requirements into agent-ready plans to running autonomous coding agents that ship production features. You'll learn to stress-test vague briefs into structured PRDs, slice work into thin "tracer bullet" vertical slices, a
Play video
The End of Apps — Kitze, Sizzy.co
AI Engineer session on The End of Apps, presented by Kitze, Sizzy.co. It adds practical context for how teams are building and operating AI systems in production.
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How AI is changing Software Engineering: A Conversation with Gergely Orosz, @The Pragmatic Engineer
AI Engineer session on How AI is changing Software Engineering: A Conversation with Gergely Orosz, @The Pragmatic Engineer. It adds practical context for how teams are building and operating AI systems in production.
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Building Generative Image & Video models at Scale - Sander Dieleman (Veo and Nano Banana)
AI Engineer session on Building Generative Image & Video models at Scale - Sander Dieleman (Veo and Nano Banana). It adds practical context for how teams are building and operating AI systems in production.
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AIE Miami Day 2 ft. Cerebras, OpenCode, Cursor, Arize AI, and more!
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The New Application Layer - Malte Ubl, CTO Vercel
AI Engineer session on The New Application Layer - Malte Ubl, CTO Vercel. It adds practical context for how teams are building and operating AI systems in production.
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Running LLMs on your iPhone: 40 tok/s Gemma 4 with MLX — Adrien Grondin, Locally AI
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Full Workshop: Build Your Own Deep Research Agents - Louis-François Bouchard, Paul Iusztin, Samridhi
AI Engineer session on Full Workshop: Build Your Own Deep Research Agents - Louis-François Bouchard, Paul Iusztin, Samridhi. It adds practical context for how teams are building and operating AI systems in production.
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Taste & Craft: A Conversation with Tuomas Artman, CTO Linear & Gergely Orosz, @The Pragmatic Engineer
AI Engineer session on Taste & Craft: A Conversation with Tuomas Artman, CTO Linear & Gergely Orosz, @The Pragmatic Engineer. It adds practical context for how teams are building and operating AI systems in production.
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AIE Miami Keynote & Talks ft. OpenCode. Google Deepmind, OpenAI, and more!
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Code Mode: Let the Code do the Talking - Sunil Pai, Cloudflare
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Building pi in a World of Slop — Mario Zechner
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Harness Engineering: How to Build Software When Humans Steer, Agents Execute — Ryan Lopopolo, OpenAI
AI Engineer session on Harness Engineering: How to Build Software When Humans Steer, Agents Execute, presented by Ryan Lopopolo, OpenAI. It adds practical context for how teams are building and operating AI systems in production.
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Let LLMs Wander: Engineering RL Environments — Stefano Fiorucci
AI Engineer session on Let LLMs Wander: Engineering RL Environments, presented by Stefano Fiorucci. It adds practical context for how teams are building and operating AI systems in production.
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Why, and how you need to sandbox AI-Generated Code? — Harshil Agrawal, Cloudflare
AI Engineer session on Why, and how you need to sandbox AI-Generated Code?, presented by Harshil Agrawal, Cloudflare. It adds practical context for how teams are building and operating AI systems in production.
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Agentic Engineering: Working With AI, Not Just Using It — Brendan O'Leary
AI Engineer session on Agentic Engineering: Working With AI, Not Just Using It, presented by Brendan O'Leary. It adds practical context for how teams are building and operating AI systems in production.
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Judge the Judge: Building LLM Evaluators That Actually Work with GEPA — Mahmoud Mabrouk, Agenta AI
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Platforms for Humans and Machines: Engineering for the Age of Agents — Juan Herreros Elorza
AI Engineer session on Platforms for Humans and Machines: Engineering for the Age of Agents, presented by Juan Herreros Elorza. It adds practical context for how teams are building and operating AI systems in production.
OpenAI to acquire Promptfoo
OpenAI announced plans to acquire Promptfoo, highlighting automated AI security testing, red teaming, and evaluation as core enterprise requirements.
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DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners
AI Engineer session on DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners. It adds practical context for how teams are building and operating AI systems in production.
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How Claude Code Works - Jared Zoneraich, PromptLayer
AI Engineer session on How Claude Code Works - Jared Zoneraich, PromptLayer. It adds practical context for how teams are building and operating AI systems in production.
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Build a Prompt Learning Loop - SallyAnn DeLucia & Fuad Ali, Arize
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OpenAI + @Temporalio : Building Durable, Production Ready Agents - Cornelia Davis, Temporal
AI Engineer session on OpenAI + @Temporalio : Building Durable, Production Ready Agents - Cornelia Davis, Temporal. It adds practical context for how teams are building and operating AI systems in production.
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Building durable Agents with Workflow DevKit & AI SDK - Peter Wielander, Vercel
AI Engineer session on Building durable Agents with Workflow DevKit & AI SDK - Peter Wielander, Vercel. It adds practical context for how teams are building and operating AI systems in production.
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Building Intelligent Research Agents with Manus - Ivan Leo, Manus AI (now Meta Superintelligence)
AI Engineer session on Building Intelligent Research Agents with Manus - Ivan Leo, Manus AI (now Meta Superintelligence). It adds practical context for how teams are building and operating AI systems in production.
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Don't Build Agents, Build Skills Instead — Barry Zhang & Mahesh Murag, Anthropic
AI Engineer session on Don't Build Agents, Build Skills Instead, presented by Barry Zhang & Mahesh Murag, Anthropic. It adds practical context for how teams are building and operating AI systems in production.
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Hard Won Lessons from Building Effective AI Coding Agents — Nik Pash, Cline
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From Vibe Coding To Vibe Engineering — Kitze, Sizzy
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Context Engineering: Connecting the Dots with Graphs — Stephen Chin, Neo4j
AI Engineer session on Context Engineering: Connecting the Dots with Graphs, presented by Stephen Chin, Neo4j. It adds practical context for how teams are building and operating AI systems in production.
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Small Bets, Big Impact Building GenBI at a Fortune 100 — Asaf Bord, Northwestern Mutual
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Dispatch from the Future: building an AI-native Company — Dan Shipper, Every, AI & I
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Context Platform Engineering to Reduce Token Anxiety — Val Bercovici, WEKA
AI Engineer session on Context Platform Engineering to Reduce Token Anxiety, presented by Val Bercovici, WEKA. It adds practical context for how teams are building and operating AI systems in production.
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Leadership in AI Assisted Engineering — Justin Reock, DX (acq. Atlassian)
AI Engineer session on Leadership in AI Assisted Engineering, presented by Justin Reock, DX (acq. Atlassian). It adds practical context for how teams are building and operating AI systems in production.
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What We Learned Deploying AI within Bloomberg’s Engineering Organization — Lei Zhang, Bloomberg
AI Engineer session on What We Learned Deploying AI within Bloomberg’s Engineering Organization, presented by Lei Zhang, Bloomberg. It adds practical context for how teams are building and operating AI systems in production.
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AI Copilots for Tech Architecture: The Highest-ROI Use Case You’re Not Building — Boris B., Catio
AI Engineer session on AI Copilots for Tech Architecture: The Highest-ROI Use Case You’re Not Building, presented by Boris B., Catio. It adds practical context for how teams are building and operating AI systems in production.
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Building Cursor Composer — Lee Robinson, Cursor
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Building in the Gemini Era — Kat Kampf & Ammaar Reshi, Google DeepMind
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Minimax M2: Building the #1 Open Model — Olive Song, MiniMax
AI Engineer session on Minimax M2: Building the #1 Open Model, presented by Olive Song, MiniMax. It adds practical context for how teams are building and operating AI systems in production.
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From Arc to Dia: Lessons learned building AI Browsers — Samir Mody, The Browser Company of New York
AI Engineer session on From Arc to Dia: Lessons learned building AI Browsers, presented by Samir Mody, The Browser Company of New York. It adds practical context for how teams are building and operating AI systems in production.
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The Unreasonable Effectiveness of Prompt Learning — Aparna Dhinakaran, Arize
AI Engineer session on The Unreasonable Effectiveness of Prompt Learning, presented by Aparna Dhinakaran, Arize. It adds practical context for how teams are building and operating AI systems in production.
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Agents are Robots Too: What Self-Driving Taught Me About Building Agents — Jesse Hu, Abundant
AI Engineer session on Agents are Robots Too: What Self-Driving Taught Me About Building Agents, presented by Jesse Hu, Abundant. It adds practical context for how teams are building and operating AI systems in production.
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Developer Experience in the Age of AI Coding Agents — Max Kanat-Alexander, Capital One
AI Engineer session on Developer Experience in the Age of AI Coding Agents, presented by Max Kanat-Alexander, Capital One. It adds practical context for how teams are building and operating AI systems in production.
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Code World Model: Building World Models for Computation — Jacob Kahn, FAIR Meta
AI Engineer session on Code World Model: Building World Models for Computation, presented by Jacob Kahn, FAIR Meta. It adds practical context for how teams are building and operating AI systems in production.
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Nano Banana Pro: But Did You Catch These 10 Details?
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Is GPT-5.1 Really an Upgrade? But Models Can Auto-Hack Govts, so … there’s that
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Understanding prompt injections: a frontier security challenge
An accessible explanation of prompt injection risk in real AI products, including how third-party content can redirect or manipulate agent behavior.
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Building an Agentic Platform — Ben Kus, CTO Box
AI Engineer session on Building an Agentic Platform, presented by Ben Kus, CTO Box. It adds practical context for how teams are building and operating AI systems in production.
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Practical tactics to build reliable AI apps — Dmitry Kuchin, Multinear
AI Engineer session on Practical tactics to build reliable AI apps, presented by Dmitry Kuchin, Multinear. It adds practical context for how teams are building and operating AI systems in production.
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How BlackRock Builds Custom Knowledge Apps at Scale — Vaibhav Page & Infant Vasanth, BlackRock
AI Engineer session on How BlackRock Builds Custom Knowledge Apps at Scale, presented by Vaibhav Page & Infant Vasanth, BlackRock. It adds practical context for how teams are building and operating AI systems in production.
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Building Alice’s Brain: an AI Sales Rep that Learns Like a Human - Sherwood & Satwik, 11x
AI Engineer session on Building Alice’s Brain: an AI Sales Rep that Learns Like a Human - Sherwood & Satwik, 11x. It adds practical context for how teams are building and operating AI systems in production.
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Evals Are Not Unit Tests — Ido Pesok, Vercel v0
AI Engineer session on Evals Are Not Unit Tests, presented by Ido Pesok, Vercel v0. It adds practical context for how teams are building and operating AI systems in production.
![[Full Workshop] Building Conversational AI Agents - Thor Schaeff, ElevenLabs video thumbnail](https://i.ytimg.com/vi/MPtCBaZn84A/hqdefault.jpg)
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[Full Workshop] Building Conversational AI Agents - Thor Schaeff, ElevenLabs
AI Engineer session on [Full Workshop] Building Conversational AI Agents - Thor Schaeff, ElevenLabs. It adds practical context for how teams are building and operating AI systems in production.
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Make your LLM app a Domain Expert: How to Build an Expert System — Christopher Lovejoy, Anterior
AI Engineer session on Make your LLM app a Domain Expert: How to Build an Expert System, presented by Christopher Lovejoy, Anterior. It adds practical context for how teams are building and operating AI systems in production.
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Building Applications with AI Agents — Michael Albada, Microsoft
AI Engineer session on Building Applications with AI Agents, presented by Michael Albada, Microsoft. It adds practical context for how teams are building and operating AI systems in production.
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Building the platform for agent coordination — Tom Moor, Linear
AI Engineer session on Building the platform for agent coordination, presented by Tom Moor, Linear. It adds practical context for how teams are building and operating AI systems in production.
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Building Agents at Cloud Scale — Antje Barth, AWS
AI Engineer session on Building Agents at Cloud Scale, presented by Antje Barth, AWS. It adds practical context for how teams are building and operating AI systems in production.
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Building AI Products That Actually Work — Ben Hylak (Raindrop), Sid Bendre (Oleve)
AI Engineer session on Building AI Products That Actually Work, presented by Ben Hylak (Raindrop), Sid Bendre (Oleve). It adds practical context for how teams are building and operating AI systems in production.
![[Full Workshop] Building Metrics that actually work — David Karam, Pi Labs (fmr Google Search) video thumbnail](https://i.ytimg.com/vi/jxrGodnopHo/hqdefault.jpg)
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[Full Workshop] Building Metrics that actually work — David Karam, Pi Labs (fmr Google Search)
AI Engineer session on [Full Workshop] Building Metrics that actually work, presented by David Karam, Pi Labs (fmr Google Search). It adds practical context for how teams are building and operating AI systems in production.
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The 2025 AI Engineering Report — Barr Yaron, Amplify
AI Engineer session on The 2025 AI Engineering Report, presented by Barr Yaron, Amplify. It adds practical context for how teams are building and operating AI systems in production.
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On Engineering AI Systems that Endure The Bitter Lesson - Omar Khattab, DSPy & Databricks
AI Engineer session on On Engineering AI Systems that Endure The Bitter Lesson - Omar Khattab, DSPy & Databricks. It adds practical context for how teams are building and operating AI systems in production.
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Building a Smarter AI Agent with Neural RAG - Will Bryk, Exa.ai
AI Engineer session on Building a Smarter AI Agent with Neural RAG - Will Bryk, Exa.ai. It adds practical context for how teams are building and operating AI systems in production.
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Everything is ugly, so go build something that isn't — Raiza Martin, Huxe (ex NotebookLM)
AI Engineer session on Everything is ugly, so go build something that isn't, presented by Raiza Martin, Huxe (ex NotebookLM). It adds practical context for how teams are building and operating AI systems in production.
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Building Effective Voice Agents — Toki Sherbakov + Anoop Kotha, OpenAI
AI Engineer session on Building Effective Voice Agents, presented by Toki Sherbakov + Anoop Kotha, OpenAI. It adds practical context for how teams are building and operating AI systems in production.
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Revenue Engineering: How to Price (and Reprice) Your AI Product — Kshitij Grover, Orb
AI Engineer session on Revenue Engineering: How to Price (and Reprice) Your AI Product, presented by Kshitij Grover, Orb. It adds practical context for how teams are building and operating AI systems in production.
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Survive the AI Knife Fight: Building Products That Win — Brian Balfour, Reforge
AI Engineer session on Survive the AI Knife Fight: Building Products That Win, presented by Brian Balfour, Reforge. It adds practical context for how teams are building and operating AI systems in production.
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The Build-Operate Divide: Bridging Product Vision and AI Operational Reality
AI Engineer session on The Build-Operate Divide: Bridging Product Vision and AI Operational Reality. It adds practical context for how teams are building and operating AI systems in production.
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"Data readiness" is a Myth: Reliable AI with an Agentic Semantic Layer — Anushrut Gupta, PromptQL
AI Engineer session on "Data readiness" is a Myth: Reliable AI with an Agentic Semantic Layer, presented by Anushrut Gupta, PromptQL. It adds practical context for how teams are building and operating AI systems in production.
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From Hype to Habit: How We’re Building an AI-First SaaS Company — While Still Shipping the Roadmap
AI Engineer session on From Hype to Habit: How We’re Building an AI-First SaaS Company, presented by While Still Shipping the Roadmap. It adds practical context for how teams are building and operating AI systems in production.
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Building Multimodal AI Agents From Scratch — Apoorva Joshi, MongoDB
AI Engineer session on Building Multimodal AI Agents From Scratch, presented by Apoorva Joshi, MongoDB. It adds practical context for how teams are building and operating AI systems in production.
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How to build world-class AI products — Sarah Sachs (AI lead @ Notion) & Carlos Esteban (Braintrust)
AI Engineer session on How to build world-class AI products, presented by Sarah Sachs (AI lead @ Notion) & Carlos Esteban (Braintrust). It adds practical context for how teams are building and operating AI systems in production.
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Fun stories from building OpenRouter and where all this is going - Alex Atallah, OpenRouter
AI Engineer session on Fun stories from building OpenRouter and where all this is going - Alex Atallah, OpenRouter. It adds practical context for how teams are building and operating AI systems in production.
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Build Dynamic Products, and Stop the AI Sideshow — Eliza Cabrera (Workday) + Jeremy Silva (Freeplay)
AI Engineer session on Build Dynamic Products, and Stop the AI Sideshow, presented by Eliza Cabrera (Workday) + Jeremy Silva (Freeplay). It adds practical context for how teams are building and operating AI systems in production.
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Ship it! Building Production Ready Agents — Mike Chambers, AWS
AI Engineer session on Ship it! Building Production Ready Agents, presented by Mike Chambers, AWS. It adds practical context for how teams are building and operating AI systems in production.
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Building Code First AI Agents with Azure AI Agent Service — Cedric Vidal, Microsoft
AI Engineer session on Building Code First AI Agents with Azure AI Agent Service, presented by Cedric Vidal, Microsoft. It adds practical context for how teams are building and operating AI systems in production.
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Building a 10 person unicorn - Max Brodeur-Urbas, Gumloop
AI Engineer session on Building a 10 person unicorn - Max Brodeur-Urbas, Gumloop. It adds practical context for how teams are building and operating AI systems in production.
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Data is Your Differentiator: Building Secure and Tailored AI Systems — Mani Khanuja, AWS
AI Engineer session on Data is Your Differentiator: Building Secure and Tailored AI Systems, presented by Mani Khanuja, AWS. It adds practical context for how teams are building and operating AI systems in production.
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Mastering Engineering Flow with Windsurf - Eashan Sinha, Windsurf
AI Engineer session on Mastering Engineering Flow with Windsurf - Eashan Sinha, Windsurf. It adds practical context for how teams are building and operating AI systems in production.
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Prompt Engineering and AI Red Teaming — Sander Schulhoff, HackAPrompt/LearnPrompting
AI Engineer session on Prompt Engineering and AI Red Teaming, presented by Sander Schulhoff, HackAPrompt/LearnPrompting. It adds practical context for how teams are building and operating AI systems in production.
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Building agent fleet architectures your CISO doesn't hate — Lou Bichard, Gitpod
AI Engineer session on Building agent fleet architectures your CISO doesn't hate, presented by Lou Bichard, Gitpod. It adds practical context for how teams are building and operating AI systems in production.
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How to Train Your Agent: Building Reliable Agents with RL — Kyle Corbitt, OpenPipe
AI Engineer session on How to Train Your Agent: Building Reliable Agents with RL, presented by Kyle Corbitt, OpenPipe. It adds practical context for how teams are building and operating AI systems in production.
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Using OSS models to build AI apps with millions of users — Hassan El Mghari
AI Engineer session on Using OSS models to build AI apps with millions of users, presented by Hassan El Mghari. It adds practical context for how teams are building and operating AI systems in production.
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How to build Enterprise Aware Agents - Chau Tran, Glean
AI Engineer session on How to build Enterprise Aware Agents - Chau Tran, Glean. It adds practical context for how teams are building and operating AI systems in production.
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Building voice agents with OpenAI — Dominik Kundel, OpenAI
AI Engineer session on Building voice agents with OpenAI, presented by Dominik Kundel, OpenAI. It adds practical context for how teams are building and operating AI systems in production.
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Building Agents (the hard parts!) - Rita Kozlov, Cloudflare
AI Engineer session on Building Agents (the hard parts!) - Rita Kozlov, Cloudflare. It adds practical context for how teams are building and operating AI systems in production.
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Prompt Engineering is Dead — Nir Gazit, Traceloop
AI Engineer session on Prompt Engineering is Dead, presented by Nir Gazit, Traceloop. It adds practical context for how teams are building and operating AI systems in production.
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3 ingredients for building reliable enterprise agents - Harrison Chase, LangChain/LangGraph
AI Engineer session on 3 ingredients for building reliable enterprise agents - Harrison Chase, LangChain/LangGraph. It adds practical context for how teams are building and operating AI systems in production.
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Forget RAG Pipelines — Build Production Ready Agents in 15 Mins: Nina Lopatina, Rajiv Shah, Contextual
AI Engineer session on Forget RAG Pipelines, presented by Build Production Ready Agents in 15 Mins: Nina Lopatina, Rajiv Shah, Contextual. It adds practical context for how teams are building and operating AI systems in production.
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Agents, Access, and the Future of Machine Identity — Nick Nisi (WorkOS) + Lizzie Siegle (Cloudflare)
AI Engineer session on Agents, Access, and the Future of Machine Identity, presented by Nick Nisi (WorkOS) + Lizzie Siegle (Cloudflare). It adds practical context for how teams are building and operating AI systems in production.
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Rethinking Team Building: how a 30-person Startup serves 50 Million Users — Grant Lee, Gamma
AI Engineer session on Rethinking Team Building: how a 30-person Startup serves 50 Million Users, presented by Grant Lee, Gamma. It adds practical context for how teams are building and operating AI systems in production.
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How to Build Planning Agents without losing control - Yogendra Miraje, Factset
AI Engineer session on How to Build Planning Agents without losing control - Yogendra Miraje, Factset. It adds practical context for how teams are building and operating AI systems in production.
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Engineering Better Evals: Scalable LLM Evaluation Pipelines That Work — Dat Ngo, Aman Khan, Arize
AI Engineer session on Engineering Better Evals: Scalable LLM Evaluation Pipelines That Work, presented by Dat Ngo, Aman Khan, Arize. It adds practical context for how teams are building and operating AI systems in production.
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Does AI Actually Boost Developer Productivity? (100k Devs Study) - Yegor Denisov-Blanch, Stanford
AI Engineer session on Does AI Actually Boost Developer Productivity? (100k Devs Study) - Yegor Denisov-Blanch, Stanford. It adds practical context for how teams are building and operating AI systems in production.
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Building Agentic Applications w/ Heroku Managed Inference and Agents — Julián Duque & Anush Dsouza
AI Engineer session on Building Agentic Applications w/ Heroku Managed Inference and Agents, presented by Julián Duque & Anush Dsouza. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineering with the Google Gemini 2.5 Model Family - Philipp Schmid, Google DeepMind
AI Engineer session on AI Engineering with the Google Gemini 2.5 Model Family - Philipp Schmid, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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Grok 4 - 10 New Things to Know
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Buy Now, Maybe Pay Later: Dealing with Prompt-Tax While Staying at the Frontier - Andrew Thomspson
AI Engineer session on Buy Now, Maybe Pay Later: Dealing with Prompt-Tax While Staying at the Frontier - Andrew Thomspson. It adds practical context for how teams are building and operating AI systems in production.
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Stop Ordering AI Takeout A Cookbook for Winning When You Build In House - Jan Siml
AI Engineer session on Stop Ordering AI Takeout A Cookbook for Winning When You Build In House - Jan Siml. It adds practical context for how teams are building and operating AI systems in production.
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From PM at Stripe to Building an AI startup, a recent founder's journey - Mounir Mouawad
AI Engineer session on From PM at Stripe to Building an AI startup, a recent founder's journey - Mounir Mouawad. It adds practical context for how teams are building and operating AI systems in production.
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Building Protected MCP Servers — Den Delimarsky and Julia Kasper, MCP Steering Committee & Microsoft
AI Engineer session on Building Protected MCP Servers, presented by Den Delimarsky and Julia Kasper, MCP Steering Committee & Microsoft. It adds practical context for how teams are building and operating AI systems in production.
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Break It 'Til You Make It: Building the Self-Improving Stack for AI Agents - Aparna Dhinakaran
AI Engineer session on Break It 'Til You Make It: Building the Self-Improving Stack for AI Agents - Aparna Dhinakaran. It adds practical context for how teams are building and operating AI systems in production.
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Real AI Agents Need Planning, Not Just Prompting - Yuval Belfer
AI Engineer session on Real AI Agents Need Planning, Not Just Prompting - Yuval Belfer. It adds practical context for how teams are building and operating AI systems in production.
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When Will AI Models Blackmail You, and Why?
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Veo 3 for Developers — Paige Bailey, Google DeepMind
AI Engineer session on Veo 3 for Developers, presented by Paige Bailey, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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Building AI Agents that actually automate Knowledge Work - Jerry Liu, LlamaIndex
AI Engineer session on Building AI Agents that actually automate Knowledge Work - Jerry Liu, LlamaIndex. It adds practical context for how teams are building and operating AI systems in production.
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How to Build Trustworthy AI — Allie Howe
AI Engineer session on How to Build Trustworthy AI, presented by Allie Howe. It adds practical context for how teams are building and operating AI systems in production.
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Building Reliable Support Agents Using the Effect Typescript Library - Michael Fester
AI Engineer session on Building Reliable Support Agents Using the Effect Typescript Library - Michael Fester. It adds practical context for how teams are building and operating AI systems in production.
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Supercharging developer workflow with Amazon Q Developer - Vikash Agrawal
AI Engineer session on Supercharging developer workflow with Amazon Q Developer - Vikash Agrawal. It adds practical context for how teams are building and operating AI systems in production.
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Building Agents with Amazon Nova Act and MCP - Du'An Lightfoot, Amazon (Full Workshop)
AI Engineer session on Building Agents with Amazon Nova Act and MCP - Du'An Lightfoot, Amazon (Full Workshop). It adds practical context for how teams are building and operating AI systems in production.
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Arrakis: How To Build An AI Sandbox From Scratch - Abhishek Bhardwaj, OpenAI
AI Engineer session on Arrakis: How To Build An AI Sandbox From Scratch - Abhishek Bhardwaj, OpenAI. It adds practical context for how teams are building and operating AI systems in production.
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How to Build Your Own AI Data Center in 2025 — Paul Gilbert, Arista Networks
AI Engineer session on How to Build Your Own AI Data Center in 2025, presented by Paul Gilbert, Arista Networks. It adds practical context for how teams are building and operating AI systems in production.
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AI Improves at Self-improving
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI Engineering at Jane Street - John Crepezzi
AI Engineer session on AI Engineering at Jane Street - John Crepezzi. It adds practical context for how teams are building and operating AI systems in production.
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Knowledge Graphs & GraphRAG: Techniques for Building Effective GenAI Applications: Zach Blumenthal
AI Engineer session on Knowledge Graphs & GraphRAG: Techniques for Building Effective GenAI Applications: Zach Blumenthal. It adds practical context for how teams are building and operating AI systems in production.
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Don't just slap on a chatbot: building AI that works before you ask
AI Engineer session on Don't just slap on a chatbot: building AI that works before you ask. It adds practical context for how teams are building and operating AI systems in production.
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Voice Agent Engineering — Nik Caryotakis, SuperDial
AI Engineer session on Voice Agent Engineering, presented by Nik Caryotakis, SuperDial. It adds practical context for how teams are building and operating AI systems in production.
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Cohere: Building enterprise LLM agents that work (Shaan Desai)
AI Engineer session on Cohere: Building enterprise LLM agents that work (Shaan Desai). It adds practical context for how teams are building and operating AI systems in production.
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Why Agent Engineering — swyx
AI Engineer session on Why Agent Engineering, presented by swyx. It adds practical context for how teams are building and operating AI systems in production.
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AI Platform Engineering: Patrick Debois
AI Engineer session on AI Platform Engineering: Patrick Debois. It adds practical context for how teams are building and operating AI systems in production.
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The Hidden Costs of Building Your Own RAG Stack — Ofer Vectara
AI Engineer session on The Hidden Costs of Building Your Own RAG Stack, presented by Ofer Vectara. It adds practical context for how teams are building and operating AI systems in production.
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Prompt Engineering Tactics: Dan Cleary
AI Engineer session on Prompt Engineering Tactics: Dan Cleary. It adds practical context for how teams are building and operating AI systems in production.
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Patrick Dougherty: How to Build AI Agents that Actually Work
AI Engineer session on Patrick Dougherty: How to Build AI Agents that Actually Work. It adds practical context for how teams are building and operating AI systems in production.
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Unlocking Developer Productivity across CPU and GPU with MAX: Chris Lattner
AI Engineer session on Unlocking Developer Productivity across CPU and GPU with MAX: Chris Lattner. It adds practical context for how teams are building and operating AI systems in production.
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Building and Scaling an AI Agent Swarm of low latency real time voice bots: Damien Murphy
AI Engineer session on Building and Scaling an AI Agent Swarm of low latency real time voice bots: Damien Murphy. It adds practical context for how teams are building and operating AI systems in production.
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From Software Developer to AI Engineer: Antje Barth
AI Engineer session on From Software Developer to AI Engineer: Antje Barth. It adds practical context for how teams are building and operating AI systems in production.
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AI Music Generation, From Prompt to Production: Phlo Young
AI Engineer session on AI Music Generation, From Prompt to Production: Phlo Young. It adds practical context for how teams are building and operating AI systems in production.
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How We Build Effective Agents: Barry Zhang, Anthropic
AI Engineer session on How We Build Effective Agents: Barry Zhang, Anthropic. It adds practical context for how teams are building and operating AI systems in production.
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Using agents to build an agent company: Joao Moura
AI Engineer session on Using agents to build an agent company: Joao Moura. It adds practical context for how teams are building and operating AI systems in production.
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Building security around ML: Dr. Andrew Davis
AI Engineer session on Building security around ML: Dr. Andrew Davis. It adds practical context for how teams are building and operating AI systems in production.
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The Model Isn’t Wrong — You’re Just Bad at Prompting
AI Engineer session on The Model Isn’t Wrong, presented by You’re Just Bad at Prompting. It adds practical context for how teams are building and operating AI systems in production.
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Build an AI Research Agent: Apoorva Joshi
AI Engineer session on Build an AI Research Agent: Apoorva Joshi. It adds practical context for how teams are building and operating AI systems in production.
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Hiring & Building an AI Engineering Team: Dr. Bryan Bischof
AI Engineer session on Hiring & Building an AI Engineering Team: Dr. Bryan Bischof. It adds practical context for how teams are building and operating AI systems in production.
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Building Multi agent Systems with Finite State Machines
AI Engineer session on Building Multi agent Systems with Finite State Machines. It adds practical context for how teams are building and operating AI systems in production.
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Iterating on LLM apps at scale Learnings from Discord: Ian Webster
AI Engineer session on Iterating on LLM apps at scale Learnings from Discord: Ian Webster. It adds practical context for how teams are building and operating AI systems in production.
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Build, Evaluate and Deploy a RAG-Based Retail Copilot with Azure AI: Cedric Vidal and David Smith
AI Engineer session on Build, Evaluate and Deploy a RAG-Based Retail Copilot with Azure AI: Cedric Vidal and David Smith. It adds practical context for how teams are building and operating AI systems in production.
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Building AI Agents with Real ROI in the Enterprise SDLC: Bruno (Booking.com) & Beyang (Sourcegraph)
AI Engineer session on Building AI Agents with Real ROI in the Enterprise SDLC: Bruno (Booking.com) & Beyang (Sourcegraph). It adds practical context for how teams are building and operating AI systems in production.
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Stop Guessing: Build Robust AI with Layered CoT
AI Engineer session on Stop Guessing: Build Robust AI with Layered CoT. It adds practical context for how teams are building and operating AI systems in production.
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Lessons from building GenAI based applications — Juan Peredo
AI Engineer session on Lessons from building GenAI based applications, presented by Juan Peredo. It adds practical context for how teams are building and operating AI systems in production.
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RAG at scale: production ready GenAI apps with Azure AI Search
AI Engineer session on RAG at scale: production ready GenAI apps with Azure AI Search. It adds practical context for how teams are building and operating AI systems in production.
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Privacy First Enterprise AI: Building AI Agents that Never Leave Your Security Boundary
AI Engineer session on Privacy First Enterprise AI: Building AI Agents that Never Leave Your Security Boundary. It adds practical context for how teams are building and operating AI systems in production.
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Scaling Agents for Gen AI Products - Anju Kambadur, Bloomberg Head of AI Engineering
AI Engineer session on Scaling Agents for Gen AI Products - Anju Kambadur, Bloomberg Head of AI Engineering. It adds practical context for how teams are building and operating AI systems in production.
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Building Reliable Agentic Systems: Eno Reyes
AI Engineer session on Building Reliable Agentic Systems: Eno Reyes. It adds practical context for how teams are building and operating AI systems in production.
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Building and evaluating AI Agents — Sayash Kapoor, AI Snake Oil
AI Engineer session on Building and evaluating AI Agents, presented by Sayash Kapoor, AI Snake Oil. It adds practical context for how teams are building and operating AI systems in production.
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How to build the world's fastest voice bot: Kwindla Hultman Kramer
AI Engineer session on How to build the world's fastest voice bot: Kwindla Hultman Kramer. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineering Without Borders — swyx
AI Engineer session on AI Engineering Without Borders, presented by swyx. It adds practical context for how teams are building and operating AI systems in production.
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Open Challenges for AI Engineering: Simon Willison
AI Engineer session on Open Challenges for AI Engineering: Simon Willison. It adds practical context for how teams are building and operating AI systems in production.
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How Zapier Builds AI Products and Features with the Help of Braintrust: Ankur Goyal & Olmo Maldonado
AI Engineer session on How Zapier Builds AI Products and Features with the Help of Braintrust: Ankur Goyal & Olmo Maldonado. It adds practical context for how teams are building and operating AI systems in production.
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Building with Anthropic Claude: Prompt Workshop with Zack Witten
AI Engineer session on Building with Anthropic Claude: Prompt Workshop with Zack Witten. It adds practical context for how teams are building and operating AI systems in production.
![Building an AI assistant that makes phone calls [Convex Workshop] video thumbnail](https://i.ytimg.com/vi/idDnpGqJd80/hqdefault.jpg)
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Building an AI assistant that makes phone calls [Convex Workshop]
AI Engineer session on Building an AI assistant that makes phone calls [Convex Workshop]. It adds practical context for how teams are building and operating AI systems in production.
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OpenAI for VP's of AI + Advice for Building Agents
AI Engineer session on OpenAI for VP's of AI + Advice for Building Agents. It adds practical context for how teams are building and operating AI systems in production.
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Vercel AI SDK Masterclass: From Fundamentals to Deep Research
AI Engineer session on Vercel AI SDK Masterclass: From Fundamentals to Deep Research. It adds practical context for how teams are building and operating AI systems in production.
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Building Agents with Model Context Protocol - Full Workshop with Mahesh Murag of Anthropic
AI Engineer session on Building Agents with Model Context Protocol - Full Workshop with Mahesh Murag of Anthropic. It adds practical context for how teams are building and operating AI systems in production.
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Build enterprise generative AI apps using Llama 3 at 1,000 tokens/s on the SambaNova AI platform
AI Engineer session on Build enterprise generative AI apps using Llama 3 at 1,000 tokens/s on the SambaNova AI platform. It adds practical context for how teams are building and operating AI systems in production.
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Building LinkedIn's GenAI Platform — Xiaofeng Wang
AI Engineer session on Building LinkedIn's GenAI Platform, presented by Xiaofeng Wang. It adds practical context for how teams are building and operating AI systems in production.
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Lessons from the Trenches: Building LLM Evals That Work IRL: Aparna Dhinkaran
AI Engineer session on Lessons from the Trenches: Building LLM Evals That Work IRL: Aparna Dhinkaran. It adds practical context for how teams are building and operating AI systems in production.
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Building efficient hybrid context query for LLM grounding: Simrat Hanspal
AI Engineer session on Building efficient hybrid context query for LLM grounding: Simrat Hanspal. It adds practical context for how teams are building and operating AI systems in production.
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Lessons From A Year Building With LLMs
AI Engineer session on Lessons From A Year Building With LLMs. It adds practical context for how teams are building and operating AI systems in production.
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Insights on Building AI Teams — Heath Black, SignalFire
AI Engineer session on Insights on Building AI Teams, presented by Heath Black, SignalFire. It adds practical context for how teams are building and operating AI systems in production.
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Keynote: The AI developer experience doesn't have to suck — why and how we built Modal
AI Engineer session on Keynote: The AI developer experience doesn't have to suck, presented by why and how we built Modal. It adds practical context for how teams are building and operating AI systems in production.
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The LLM Triangle: Engineering Principles for Robust AI Applications - Almog Baku:
AI Engineer session on The LLM Triangle: Engineering Principles for Robust AI Applications - Almog Baku:. It adds practical context for how teams are building and operating AI systems in production.
![[Full Workshop from Microsoft] Github Copilot - The World's Most Widely Adopted AI Developer Tool video thumbnail](https://i.ytimg.com/vi/sKiTUuEV1rw/hqdefault.jpg)
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[Full Workshop from Microsoft] Github Copilot - The World's Most Widely Adopted AI Developer Tool
AI Engineer session on [Full Workshop from Microsoft] Github Copilot - The World's Most Widely Adopted AI Developer Tool. It adds practical context for how teams are building and operating AI systems in production.
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GitHub Copilot: The World's Most Widely Adopted AI Developer Tool
AI Engineer session on GitHub Copilot: The World's Most Widely Adopted AI Developer Tool. It adds practical context for how teams are building and operating AI systems in production.
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Building State of the Art Open Weights Tool Use: The Command R Family: Sandra Kublik
AI Engineer session on Building State of the Art Open Weights Tool Use: The Command R Family: Sandra Kublik. It adds practical context for how teams are building and operating AI systems in production.
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Lets Build An Agent from Scratch
AI Engineer session on Lets Build An Agent from Scratch. It adds practical context for how teams are building and operating AI systems in production.
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AI CEO: ‘Stock Crash Could Stop AI Progress’, Llama 4 Anti-climax + ‘Superintelligence in 2027’ ...
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Gemini 2.5 Pro - It’s a Darn Smart Chatbot … (New Simple High Score)
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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OpenAI’s New ImageGen is Unexpectedly Epic … (ft. Reve, Imagen 3, Midjourney etc)
This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Claude 3.7 is More Significant than its Name Implies (ft DeepSeek R2 + GPT 4.5 coming soon)
This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Nothing Much Happens in AI, Then Everything Does All At Once
This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI - 2024AD: 212-page Report (from this morning) Fully Read w/ Highlights
This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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o1 - What is Going On? Why o1 is a 3rd Paradigm of Model + 10 Things You Might Not Know
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Grok-2 Actually Out, But What If It Were 10,000x the Size?
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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How Far Can We Scale AI? Gen 3, Claude 3.5 Sonnet and AI Hype
This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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New OpenAI Model 'Imminent' and AI Stakes Get Raised (plus Med Gemini, GPT 2 Chatbot and Scale AI)
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Principles for Prompt Engineering - Karina Nguyen (Claude Instant @ Anthropic)
AI Engineer session on Principles for Prompt Engineering - Karina Nguyen (Claude Instant @ Anthropic). It adds practical context for how teams are building and operating AI systems in production.
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Open Questions for AI Engineering: Simon Willison
AI Engineer session on Open Questions for AI Engineering: Simon Willison. It adds practical context for how teams are building and operating AI systems in production.
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[Workshop] AI Engineering 101
AI Engineer session on [Workshop] AI Engineering 101. It adds practical context for how teams are building and operating AI systems in production.
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Storyteller: Building Multi-modal Apps with TS & ModelFusion - Lars Grammel, PhD
AI Engineer session on Storyteller: Building Multi-modal Apps with TS & ModelFusion - Lars Grammel, PhD. It adds practical context for how teams are building and operating AI systems in production.
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Building Blocks for LLM Systems & Products: Eugene Yan
AI Engineer session on Building Blocks for LLM Systems & Products: Eugene Yan. It adds practical context for how teams are building and operating AI systems in production.
![[Workshop] AI Engineering 201: Inference video thumbnail](https://i.ytimg.com/vi/N7lJY5IKVLE/hqdefault.jpg)
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[Workshop] AI Engineering 201: Inference
AI Engineer session on [Workshop] AI Engineering 201: Inference. It adds practical context for how teams are building and operating AI systems in production.
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Building Production-Ready RAG Applications: Jerry Liu
AI Engineer session on Building Production-Ready RAG Applications: Jerry Liu. It adds practical context for how teams are building and operating AI systems in production.
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Using AI to Build an Infinite Game: Jeff Schomay
AI Engineer session on Using AI to Build an Infinite Game: Jeff Schomay. It adds practical context for how teams are building and operating AI systems in production.
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GPT Web App Generator - 10,000 apps created in a month: Matija Sosic
AI Engineer session on GPT Web App Generator - 10,000 apps created in a month: Matija Sosic. It adds practical context for how teams are building and operating AI systems in production.
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Building Context-Aware Reasoning Applications with LangChain and LangSmith: Harrison Chase
AI Engineer session on Building Context-Aware Reasoning Applications with LangChain and LangSmith: Harrison Chase. It adds practical context for how teams are building and operating AI systems in production.
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Building AI For All: Amjad Masad & Michele Catasta
AI Engineer session on Building AI For All: Amjad Masad & Michele Catasta. It adds practical context for how teams are building and operating AI systems in production.
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Building Reactive AI Apps: Matt Welsh
AI Engineer session on Building Reactive AI Apps: Matt Welsh. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineering 201: The Rest of the Owl
AI Engineer session on AI Engineering 201: The Rest of the Owl. It adds practical context for how teams are building and operating AI systems in production.
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Gemini Ultra - Full Review
This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI On An Exponential? Data, Mamba, and More
This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Phi-2, Imagen-2, Optimus-Gen-2: Small New Models to Change the World?
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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OpenAI Insights and Training Data Shenanigans - 7 'Complicated' Developments + Guest Star
This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
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AI Declarations and AGI Timelines – Looking More Optimistic?
This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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RT-X and the Dawn of Large Multimodal Models: Google Breakthrough and 160-page Report Highlights
This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
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ChatGPT Fails Basic Logic but Now Has Vision, Wins at Chess and Prompts a Masterpiece
This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
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11 Major AI Developments: RT-2 to '100X GPT-4'
This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
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ChatGPT's Achilles' Heel
This AI Explained video reviews a major AI development through the lens of scaling and compute economics. It is useful context for AI engineering, evaluation, governance, and operational risk.
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'Show Your Working': ChatGPT Performance Doubled w/ Process Rewards (+Synthetic Data Event Horizon)
This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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GPT 4 is Smarter than You Think: Introducing SmartGPT
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Can GPT 4 Prompt Itself? MemoryGPT, AutoGPT, Jarvis, Claude-Next [10x GPT 4!] and more...
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Google Bard - The Full Review. Bard vs Bing [LaMDA vs GPT 4]
This AI Explained video reviews a major AI development through the lens of multimodal generation and provenance. It is useful context for AI engineering, evaluation, governance, and operational risk.
![8 New Ways to Use Bing's Upgraded 8 [now 20] Message Limit (ft. pdfs, quizzes, tables, scenarios...) video thumbnail](https://i.ytimg.com/vi/weH9LKYNGWg/hqdefault.jpg)
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8 New Ways to Use Bing's Upgraded 8 [now 20] Message Limit (ft. pdfs, quizzes, tables, scenarios...)
This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
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9 of the Best Bing (GPT 4) Prompts
This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.