🚀 An ebook that takes FastAPI from your first endpoint all the way to production-scale AI systems — going deep on LLM/RAG serving, with interview questions at the end of every chapter. Title: FastAPI for AI Engineers: From First Endpoint to Production-Scale AI Systems URL: 🚀 Overview A practical guide (First Edition, 2026, by AI Engineering Insider) for AI engineers serving ML models and LLM/RAG systems in production with Python. It spans 10 chapters and 100 interview questions, woven with real-incident case studies and cost-model sidebars. ❓ Challenges Solved ・Building a model is one skill; serving it safely as a scalable production API is another ・LLM/RAG serving has its own hard parts — streaming, guardrails, cost control The book reframes FastAPI as "the de facto serving layer for AI and ML systems." 💡 Structure & Tech Covered ・Foundations: ASGI/WSGI, Uvicorn, OpenAPI, and Pydantic v2 schema separation and validation ・Implementation: idempotency, meaningful status codes, pagination, and the Router→Service→Repository clean architecture ・DB/Security: SQLAlchemy/SQLModel/Alembic, N+1, pool sizing, JWT, BOLA defense, OWASP API Top 10 ・Async: "never block the event loop," def vs async def, and httpx retries/circuit breakers 🎯 The Core (Ch.9: AI/RAG/LLM) ・Load model weights once in lifespan; offload CPU inference to a thread ・An LLM gateway centralizing auth, prompts, guardrails, and cost metering, with SSE token streaming ・Build RAG with embeddings + a vector DB (start with pgvector); validate output with Pydantic, then retry on failure ・Enforce max_tokens as a "spending limit" via the type system 📊 Highlights ・Practice-first, learning from real incidents (Netflix, Stripe, GitLab, Optus, Air Canada) ・Ch.10 covers Gunicorn+Uvicorn, K8s liveness/readiness, the three pillars of observability (p99 vs p50), and SLO-based alerting #FastAPI# #AIEngineering#

Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything in 28 minutes he shows exactly how agents can remember across sessions, completely free worth more than any $500 AI engineering course here's what he covers: • why agents forget everything between sessions • memory stores - agents read, write across sessions • dreaming - agents that improve their own memory • 95% cache hit rate, so it stays cheap most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night watch full video then read article below

Everyone wants to build AI agents. Almost nobody has a reading list. They watch one YouTube video, skim a Twitter thread, then wonder why their agent falls apart the moment it needs to plan, use a tool, or remember anything. Here's the resource list I wish someone had handed me on day one. Every link, organized by what it actually teaches. Save this one. **VIDEOS** 1. LLM Introduction: 2. LLMs from Scratch: 3. Agentic AI Overview (Stanford): 4. Building and Evaluating Agents: 5. Building Effective Agents: 6. Building Agents with MCP: 7. Building an Agent from Scratch: 8. Philo Agents (playlist): **REPOS** 1. GenAI Agents: 2. Microsoft's AI Agents for Beginners: 3. Prompt Engineering Guide: 4. Hands-On Large Language Models: 5. GenAI Agents (alt link): 6. Made with ML: 7. Hands-On AI Engineering: 8. Awesome Generative AI Guide: 9. Designing Machine Learning Systems: 10. Machine Learning for Beginners (Microsoft): 11. LLM Course: **GUIDES** 1. Google's Agent Whitepaper: 2. Google's Agent Companion: 3. Building Effective Agents (Anthropic): 4. Claude Code Best Agentic Coding Practices: 5. OpenAI's Practical Guide to Building Agents: **BOOKS** 1. Understanding Deep Learning: 2. Building an LLM from Scratch: 3. The LLM Engineering Handbook: 4. AI Agents: The Definitive Guide — Nicole Koenigstein: 5. Building Applications with AI Agents — Michael Albada: 6. AI Agents with MCP — Kyle Stratis: 7. AI Engineering (O'Reilly): **PAPERS** 1. ReAct: 2. Generative Agents: 3. Toolformer: 4. Chain-of-Thought Prompting: 5. Tree of Thoughts: 6. Reflexion: 7. Retrieval-Augmented Generation Survey: **COURSES** 1. HuggingFace's Agent Course: 2. Build with Anthropic: I'm not telling you to go through all 39 links this weekend. Start with the Stanford overview, then Anthropic's "Building Effective Agents" guide, then ReAct. That sequence alone will teach you more about how agents actually work than most paid courses. Save this post. You'll want it back in three weeks when you're stuck debugging why your agent keeps calling the wrong tool. Repost if this saved you an afternoon of searching. P.S. Which one are you starting with?

There will be no AI jobpocalypse. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it. I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines. Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%. Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable! Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more. Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus. To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market. Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades. Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have). Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future! [Original text in The Batch newsletter.]

META PREPARES TO CUT 8,000 EMPLOYEES IN AGGRESSIVE PIVOT TO APPLIED AI Meta is reportedly slashing approximately 10% of its global workforce on May 20, impacting nearly 8,000 employees. This mass reduction coincides with the strategic reallocation of at least 1,000 elite engineers into a newly formed Applied AI Engineering unit.

AMD invited us to their biggest launch of the year to check out the behind-the-scenes. At Advancing AI 2026, @AnushElangovan (VP, AI Software at AMD) and @QuentinAnthon15 (VP, AI Engineering at Zyphra) join @sophiadew on the floor to break down Helios, ROCm AI, and the Anthropic partnership, live the day it all shipped: 1:05 What actually changed today: AMD goes rack-scale 3:03 ROCm skills land in Claude Code and Codex 3:51 Why Zyphra bet its whole stack on AMD 3:57 Sparse mixture-of-experts and the HBM advantage 6:25 Why an open stack beats a closed one 8:52 The wall of logos: Anthropic, OpenAI, Meta 9:27 Why the frontier labs are committing gigawatts 11:30 Why the next wave will exceed expectations 17:02 Frontier models running on your own laptop + DeepSeek V4 Pro at 8-10 tokens/sec 17:41 How ROCm AI hits 3.3x inference gains 19:38 A kernel rewritten overnight with no humans in the loop 21:40 Do you still need kernel experts? 23:47 Why CPUs are critical for the agentic AI era 25:40 An agent that's been rewriting a driver for four months 28:14 The internet parallel, and the dark-fiber lesson

Just met with one of our engineering leaders. Some takeaways about AI engineering best practices + more: 1) Spec writing and strong reading comprehension are two of the most valuable skills in ai engineers today. 2) Being hyper structured and opinionated in engineering workflows is how you get probabilistic models to behave deterministically when you want them to and also get models to spend tokens efficiently. 3) Creating a standardized schema/metadata on markdown files in your workflows allows you to make non-software tasks verifiable which allows you to close the agent loop more successfully. 4) One of the bigger behavioral changes in knowledge work is learning to thoughtfully structure/organize your files like good engineers have always done to get the least entropy from models. 5) Building a strong immune system around markdown files is important. As a workflow evolves and gets more reps it’s easy for specs to get bloated with conflicting guidance/unnecessary rules. Hermes agents solves this with a thin memory layer. There’s still a lot of optimization to be done with memory/markdown autophagy. 6) The key ingredients of our engineering process are CLI + deeply opinionated folder/file system + markdown metadata + coding agents + linear as source of truth. 7) Our mental model is always how can we make sure the agent has one way to do things and can validate its output.

One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations. The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below. The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs. However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality. Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on. What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities. [Original text: The Batch newsletter]

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