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.
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Recursive Model Improvement — Lee Robinson, Cursor, SpaceXAI
Lee Robinson discusses recursive model improvement in the context of Cursor’s AI-native development work, focusing on how product use, model behavior, and engineering feedback can inform successive system improvements.
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In Code They Act, In Proof We Trust — Erik Meijer, Leibniz Labs
AI agents today execute on blind trust, and the failure modes are already in the headlines: a dealership chatbot agreeing to sell a $76,000 Chevy Tahoe for $1, a coding agent wiping a production database during a code freeze, an "agent skill" quietly installing a keylogger on a developer's machine. These are not edge c
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Stop Evaluating Models Like It's the 50s - Alejandro Vidal, Mindmakers
Psychologists spent the last century learning how to measure something invisible and uncooperative: a human mind. AI evaluation, meanwhile, still scores like it is 1950. Count the right answers, treat every question as equal, trust the percentage (this is Classical Test Theory). We are sitting on decades of measurement
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The Prime Intellect Stack — Will Brown, Prime Intellect
Deep dive into Prime Intellect's open-source ecosystem of post-training tools, including the verifiers and prime-rl libraries, as well as the Lab platform for self-serve training and inference. Speaker: Will Brown — Research Lead, Prime Intellect Will Brown leads Applied Research at Prime Intellect and builds open rese
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remobi.app: Don't change your terminal workflow for mobile
remobi.app: Don't change your terminal workflow for mobile. Swipe between agents, unblock when stuck.
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The AI bugpocalypse is here. Now what? - Jack Cable, Corridor
Something shifted in the past year that most security teams haven't fully reckoned with yet: AI models can now find serious vulnerabilities in production code, at scale, with minimal human skill required. Not in toy examples. In libraries that have been reviewed hundreds of times by the best researchers in the world. J
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What Does Done Even Mean? Agents and Paperclip's Liveness Model - Dotta, Paperclip
What does “done” mean when agents can produce more work than humans can possibly review? This talk argues that the future of agentic work is not just faster output, but a stronger trust protocol: systems where “done” means an artifact has met a stated standard, carries evidence, has been checked by the right verifier,
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RLM: Recursive Language Models for Large Codebases - Shashi, Superagentic AI
Large codebases break coding agents: they lose the architecture and drown in tool output as context grows. This talk introduces Recursive Language Models (RLM) from a MIT paper a pattern that loads the repo into a programmable REPL where the model writes code to inspect it and recursively delegates focused sub-question
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Semantic Blindness: 500,000 Sensors Confused an LLM - Raahul Singh & Vanč Levstik, Phaidra
You cannot solve a combinatorial engineering problem with a next token prediction engine. We learned this the hard way. Modern LLMs can write code, summarize research papers, and reason across massive datasets. But what happens when you connect them to mission-critical physical infrastructure with 50,000 live sensors,
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ReviewDebt: a practical framework for scoring every pull request — Sachin Gupta, Ebay
Coding agents ship PRs faster than humans can trust them. The gap is filling up with a debt nobody is measuring — and it's about to swallow your engineering velocity. Every team in 2026 measures coding agents the same way: PR count, lines of code, cycle time, developer NPS. None of those see the real cost — bloated dif
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A Song of Types and Agents - Roberto Stagi, Ratel
Python ruled unchallenged for a decade, sitting comfortably on the AIron Throne. But a quiet rebellion is brewing: the entire stack that actually deploys AI agents in production runs on npm, not pip. This lightning talk is an opinionated, slightly unhinged tour of how TypeScript is taking over the AI throne, why this h
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The Agentic Web and the Bazaar Era of AI - Ramesh Raskar, MIT Media Lab
The AI agent industry is currently focused on memory, orchestration, enterprise deployment, and tooling. But these are the first steps toward a larger transformation: the emergence of the Agentic Web. Today’s ecosystem resembles the early days of AOL: closed platforms, proprietary agent stores, and siloed orchestration
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Develop at Idea Velocity - Jeffrey Lee-Chan, Snapchat
The biggest gap in production AI agent systems is not the model—it's the harness. After 1,000 hours of orchestrating autonomous fleets under human direction, the pattern is unmistakable: agents that finish complex tasks on the first run routinely fail on subsequent iterations because the surrounding loop lacks persiste
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From Writing Code to Designing Systems: How the Developer Role is Changing — Chris Noring, Microsoft
For decades, developers have been valued primarily for how much code they could write and how quickly they could write it. That model no longer scales. As AI becomes a first-class collaborator, the bottleneck is no longer syntax or implementation speed—it’s clarity of intent, architectural thinking, and the ability to
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State of the Union: Why Local, Why Now — NVIDIA, Osmantic, Roboflow, EXO Labs, @matthew_berman
Local AI has crossed from interesting to useful, driven by stronger open models, better hardware, and a maturing ecosystem for running intelligence outside the cloud. This panel explores what that shift unlocks for sovereignty, defense, regulated industries, privacy, cost, and resilience, and why open-source AI may be
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Chat and citations won't save your vertical AI - Atul Ramachandran, Filed Inc
Most vertical SaaS teams are doing the same things: chasing higher accuracy, building better model harnesses, shipping more features. And their customers are saying the same things: the AI got this wrong, it hallucinated, the accuracy is not good enough. So teams go back and push the numbers higher. We did the same at
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Every Solo Agent Builder Eventually Reinvents a Worse Version of CI/CD - Sumaiya Shrabony
If you build agents alone long enough, you will independently reinvent five things software engineering solved decades ago. A way to test whether your agent's output is still correct after you changed something. A way to run it on a schedule and know if it failed. A way to prevent one skill's schema change from silentl
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Design Patterns for AI Trust: Juries, Libraries, and Agent Tiers — Alex Bauer, Upside.tech
A couple of years ago, everyone worried about AI hallucinating. We rarely hear that word anymore, but it’s just because the problem grew up. Today, your AI still doesn’t know how to say “I’m not sure.” Instead, it hands you a revenue number that’s wrong in ways that look exactly like being right. The good news is we al
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The Factory That Dreams: 39 AI Agents, No Framework - Rushabh Doshi, Machinecraft
Most AI demos are built around a toy workflow. Ira was built around a factory. This talk is the story of how a third-generation Indian machinery company built a multi-agent operating system that helps run sales, business development, recruitment, quoting, marketing, production context, email workflows, and organization
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Special Topics in Kernels, RL, Reward Hacking in Agents — Daniel Han, Unsloth
An advanced seminar (good prerequisites: Daniel's 2024 and 2025 hit AIE workshops, but all are welcome!) PLS WATCH: https://www.youtube.com/@aiDotEngineer/search?query=daniel%20han
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Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS
AI agents that book 15 guests in a 10-person room. Agents that fabricate statistics when data doesn't exist. Agents that pick wrong tools from 29 options, wasting $47 in tokens. These aren't prompt engineering failures, they're architectural limitations that need structural solutions. This hands-on workshop covers 5 re
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Claws Out: Securing and Building with OpenClaw - Nick Taylor, Pomerium
Running OpenClaw without hardening access to it is a bad idea. We'll cover how I secured my OpenClaw, McClaw, contributed trusted-proxy auth mode to the OpenClaw project, and how I use it to build tools. We're going to build something live during the talk using OpenClaw, the same way I built Clawspace, a browser-based
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Understanding is the new bottleneck — Geoffrey Litt, Notion
Autonomous loops are hot, but the reality is that most agentic tasks still require human judgement. And to guide your agents well, it's not enough to just verify correctness -- you actually need to understand the work they're doing. In this talk, I'll share some techniques for staying in the loop and efficiently develo
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Should AI Engineers Still Read Code in 2026? The Z/L Continuum — Alex Volkov, ThursdAI
"How much better do the models have to get before you'll stop reading the code?" Theo asked that question recently and the replies caught fire. Mitchell Hashimoto is calling it agent psychosis. ThePrimeagen's subreddit is in open revolt about people shipping code they never read. Uncle Bob says we have about a year lef
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The Golden Age of AI Engineering — Alexander Embiricos & Romain Huet & Peter Steinberger, OpenAI
OpenAI's Dev Day 2024 demo ran on an o1 preview model that could not run or check its own code, so Romain Huet had to cross his fingers live on stage. A year later, the same kind of demo ran a full camera and lighting rig, because the model could now test its own work. Alexander Embiricos and Huet use that jump to show
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
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I Run a Fleet of AI Agents Across Three Machines. Here's What Broke. - Kyle Jaejun Lee, KRAFTON
An honest field report from my own personal fleet of AI agents, run across several machines as a daily driver. Less about any single tool, more about the journey: how things that work on one machine break once you scale to many, what it takes to keep a setup like this running, and where it's all converging. Not a compa
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Running a Chess YouTube Channel entirely by AI — Stephan Steinfurt, TNG
Daily chess puzzle explanations on YouTube: Our agent analyzes and describes chess puzzles in an accessible way - arrows included!
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Teaching Coding Agents to do Spreadsheets - Nuno Campos, Witan Labs
https://github.com/witanlabs/research-log
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Building an ACP-Compatible Agent Live — Bennet Fenner, Zed
In this session, we'll be building a coding agent that implements ACP — covering protocol design, session lifecycle management, and handling tool calls. The session ends with a live demo of the finished agent running inside Zed, showing what ACP looks like in practice from both sides of the protocol.
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Your LLM Deception Monitor Is Broken. The Fix Is in the Training Data - Sachin Kumar, LexisNexis
You fine-tune LLMs and ship them. Your evals are green, your behavioral monitors are green — and a sleeper-agent backdoor can still flip the model to harmful output on a trigger you never tested. Behavioral testing can't reach it, and the interpretability tool people reach for — joint cross-model features (crosscoders)
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Your coding agent doesn't always follow your rules — Talha Sheikh, Checkout.com
Your coding agent doesn't always follow your rules. An agent harness makes sure it does, in real-time, every time.
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From fork() to Fleet: Designing an Agent Sandbox Cloud — Abhishek Bhardwaj, OpenAI
Sandboxes unleash agents by giving them secure, fully functional computers where they can tackle diverse tasks with minimal setup. This talk explores the architectural challenges of building an agent sandbox cloud. We compare runtime isolation technologies and their trade-offs, examine persistence and storage as the ne
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Think You Can Build a Game with AI? Think Again! - Danielle An & David Hoe, Meta
With the recent development of AI, either you or your friend probably vibe coded a game using Gemini, on Three.js. But that is old news now. If everyone can do that, what is next? The next massive hit, the one that millions of people across the world will play, is just about to be born. Wanna know more? Come see this t
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Your agent is blindfolded — Johan Lajili, Poolside AI
Your agent is blindfolded. How giving it (good) eyes multiplies performance and trust!
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Shipping Production AI Inside Government — William Tarr, Ministry of Justice (DO NOT PUBLISH)
The UK Ministry of Justice Justice AI Unit is about 40 people doing work that normally takes 300. Their probation officer tool went from MVP to national rollout with two engineers in a matter of months. A team of 40 would have been the standard approach. What makes the difference is that their engineers spend two or th
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What do we build now? — Theo Browne, @t3dotgg
For the closing keynote of AIEWF2026, Theo provokes you to think wider, not just bigger. In this keynote from the AI Engineer World's Fair, developer and YouTuber Theo Browne (@t3dotgg) argues that the rapid evolution of AI models—moving from tool-calling (Sonnet 3.5) to long-running task execution (Opus 4.5) and now o
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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
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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
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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
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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
AI Engineer session on Running LLMs on your iPhone: 40 tok/s Gemma 4 with MLX, presented by Adrien Grondin, Locally AI. It adds practical context for how teams are building and operating AI systems in production.
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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!
April 20, 2026 - all times in EST -- 9:00am - Welcome to AI Engineer Miami -- 9:10am - Gabe Greenberg, G2i Opening Remarks -- 9:15am - Dax Raad, OpenCode Keynote -- 9:40am - Dexter Horthy, HumanLayer Everything We got Wrong About RPI -- 10:05am - Max Stoiber, OpenAI Coming Soon -- 10:30am - Morning Break -- 11:00am - B
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Code Mode: Let the Code do the Talking - Sunil Pai, Cloudflare
AI Engineer session on Code Mode: Let the Code do the Talking - Sunil Pai, Cloudflare. It adds practical context for how teams are building and operating AI systems in production.
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Building pi in a World of Slop — Mario Zechner
AI Engineer session on Building pi in a World of Slop, presented by Mario Zechner. It adds practical context for how teams are building and operating AI systems in production.
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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
AI Engineer session on Judge the Judge: Building LLM Evaluators That Actually Work with GEPA, presented by Mahmoud Mabrouk, Agenta AI. It adds practical context for how teams are building and operating AI systems in production.
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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
AI Engineer session on Build a Prompt Learning Loop - SallyAnn DeLucia & Fuad Ali, Arize. It adds practical context for how teams are building and operating AI systems in production.
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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
AI Engineer session on Hard Won Lessons from Building Effective AI Coding Agents, presented by Nik Pash, Cline. It adds practical context for how teams are building and operating AI systems in production.
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From Vibe Coding To Vibe Engineering — Kitze, Sizzy
AI Engineer session on From Vibe Coding To Vibe Engineering, presented by Kitze, Sizzy. It adds practical context for how teams are building and operating AI systems in production.
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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
AI Engineer session on Small Bets, Big Impact Building GenBI at a Fortune 100, presented by Asaf Bord, Northwestern Mutual. It adds practical context for how teams are building and operating AI systems in production.
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Dispatch from the Future: building an AI-native Company — Dan Shipper, Every, AI & I
AI Engineer session on Dispatch from the Future: building an AI-native Company, presented by Dan Shipper, Every, AI & I. It adds practical context for how teams are building and operating AI systems in production.
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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
AI Engineer session on Building Cursor Composer, presented by Lee Robinson, Cursor. It adds practical context for how teams are building and operating AI systems in production.
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Building in the Gemini Era — Kat Kampf & Ammaar Reshi, Google DeepMind
AI Engineer session on Building in the Gemini Era, presented by Kat Kampf & Ammaar Reshi, Google DeepMind. It adds practical context for how teams are building and operating AI systems in production.
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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.
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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.
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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.