MCP in Practice – O’Reilly
Mapping Power, Concentration, and Usage in the Emerging AI Developer Ecosystem| O’Reilly Media
In this episode, Ben Lorica and Anthropic interpretability researcher Emmanuel Ameisen get into the work Emmanuel’s team has been doing to better understand how LLMs like Claude work. Listen in to find out what they’ve uncovered by taking a microscopic look at how LLMs function—and just how far the analogy to the human brain holds. […]| Radar
This article is part of a series on the Sens-AI Framework—practical habits for learning and coding with AI. AI gives novice developers the ability to skip the slow, messy parts of learning. For experienced developers, that can mean getting to a working solution faster. Developers early in their learning path, however, face what I call […]| Radar
This is the first of a three-part series by Markus Eisele. Stay tuned for the follow-up posts. AI is everywhere right now. Every conference, keynote, and internal meeting has someone showing a prototype powered by a large language model. It looks impressive. You ask a question, and the system answers in natural language. But if […]| Radar
When I was eight years old, I watched a mountaineering documentary while waiting for the cricket match to start. I remember being incredibly frustrated watching these climbers inch their way up a massive rock face, stopping every few feet to hammer what looked like giant nails into the mountain. “Why don’t they just climb faster?” […]| Radar
The productivity gains from AI tools are undeniable. Development teams are shipping faster, marketing campaigns are launching quicker, and deliverables are more polished than ever. But if you’re a technology leader watching these efficiency improvements, you might want to ask yourself a harder question: Are we building a more capable organization, or are we unintentionally […]| Radar
We often say AIs “understand” code, but they don’t truly understand your problem or your codebase in the sense that humans understand things. They’re mimicking patterns from text and code they’ve seen before, either built into their model or provided by you, aiming to produce something that looks right and is a plausible answer. It’s […]| Radar
In this episode, Ben Lorica and AI engineer Faye Zhang talk about discoverability: how to use AI to build search and recommendation engines that actually find what you want. Listen in to learn how AI goes way beyond simple collaborative filtering—pulling in many different kinds of data and metadata, including images and voice, to get […]| Radar
In the rush to get the most from AI tools, prompt engineering—the practice of writing clear, structured inputs that guide an AI tool’s output—has taken center stage. But for software engineers, the skill isn’t new. We’ve been doing a version of it for decades, just under a different name. The challenges we face when writing […]| Radar
Mapping Power, Concentration, and Usage in the Emerging AI Developer Ecosystem| O’Reilly Media
In early 2024, a striking deepfake fraud case in Hong Kong brought the vulnerabilities of AI-driven deception into sharp relief. A finance employee was duped during a video call by what appeared to be the CFO—but was, in fact, a sophisticated AI-generated deepfake. Convinced of the call’s authenticity, the employee made 15 transfers totaling over […]| Radar
What if uncertainty wasn’t something to simply endure but something to actively exploit? The convergence of Nassim Taleb’s antifragility principles with generative AI capabilities is creating a new paradigm for organizational design powered by generative AI—one where volatility becomes fuel for competitive advantage rather than a threat to be managed. The Antifragility Imperative Antifragility transcends […]| Radar
Anyone who’s used AI to generate code has seen it make mistakes. But the real danger isn’t the occasional wrong answer; it’s in what happens when those errors pile up across a codebase. Issues that seem small at first can compound quickly, making code harder to understand, maintain, and evolve. To really see that danger, […]| Radar
We can’t not talk about power these days. We’ve been talking about it ever since the Stargate project, with half a trillion dollars in data center investment, was floated early in the year. We’ve been talking about it ever since the now-classic “Stochastic Parrots” paper. And, as time goes on, it only becomes more of […]| Radar
I’ve been in a few recent conversations about whether to use Apache Beam on its own or run it with Google Dataflow. On the surface, it’s a tooling decision. But it also reflects a broader conversation about how teams build systems. Beam offers a consistent programming model for unifying batch and streaming logic. It doesn’t […]| Radar
Join Luke Wroblewski and Ben Lorica as they talk about the future of software development. What happens when we have databases that are designed to interact with agents and language models rather than humans? We’re starting to see what that world will look like. It’s an exciting time to be a software developer. About the […]| Radar
Developments in AI, Security, Web, and More| O’Reilly Media
What the Computerization of Wall Street Can Teach Us About AI| O’Reilly Media
For most people, the face of AI is a chat window. You type a prompt, the AI responds, and the cycle repeats. This conversational model—popularized by tools| O’Reilly Media
We’ll start with a confession: Even after years of designing enterprise systems, AI architecture is still a moving target for us. The landscape shifts so fast that what feels cutting edge today might be table stakes tomorrow. But that’s exactly why we wanted to share these thoughts—because we’re all learning as we go.| O’Reilly Media
A Conversation with ChatGPT and Gemini About Job Losses from AI| O’Reilly Media
Chatbots Are More Than Just Their Models.| O’Reilly Media
0:00: Introduction to Raiza Martin, who cofounded Huxe and formerly led Google’s NotebookLM team. What made you think this was the time to trade the comforts of big tech for a garage startup?| O’Reilly Media
On some level, every engineering leader knows that strategy matters. And yet many teams remain stuck in reactive cycles, lurching from crisis to crisis, untethered from clear direction. This disconnect between recognizing the importance of strategy and actually practicing strategy well was at the heart of O’Reilly’s June 23rd CTO Hour, where host Peter Bell sat down with renowned engineering leader and best-selling author Will Larson. Together, they explored how deliberate, structured dec...| O’Reilly Media
Everyone’s talking about microservices. Who’s actually doing it?| O’Reilly Media
There’s a lot of chatter in the media that software developers will soon lose their jobs to AI. I don’t buy it.| O’Reilly Media
What O'Reilly Learning Platform Usage Tells Us About Where the Industry Is Headed| O’Reilly Media
Developments in Security, Programming, AI, and More| O’Reilly Media
The only thing to fear is failing to make the transition to AI-assisted programming| O’Reilly Media
To hear directly from the authors on this topic, sign up for the upcoming virtual event on June 20th, and learn more from the Generative AI Success Stories Superstream on June 12th.| O’Reilly Media
The Replacement of Organic Search with Advertising by Google and Amazon and What That Might Mean for the Future of AI| O’Reilly Media
Thoughts about the outcome of the NYT versus OpenAI copyright lawsuit| O’Reilly Media
Generative AI has been the biggest technology story of 2023. Almost everybody’s played with ChatGPT, Stable Diffusion, GitHub Copilot, or Midjourney. A few have even tried out Bard or Claude, or run LLaMA1 on their laptop. And everyone has opinions about how these language models and art generation programs are going to change the nature of work, usher in the singularity, or perhaps even doom the human race. In enterprises, we’ve seen everything from wholesale adoption to policies that se...| O’Reilly Media
In December 2021 and January 2022, we asked recipients of our Data and AI Newsletters to participate in our annual survey on AI adoption. We were particularly interested in what, if anything, has changed since last year. Are companies farther along in AI adoption? Do they have working applications in production? Are they using tools like AutoML to generate models, and other tools to streamline AI deployment? We also wanted to get a sense of where AI is headed. The hype has clearly moved o...| O’Reilly Media
