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Received β€” 26 August 2026 ⏭ AI & ML – Radar

The Design System as the Control Plane for AI-Generated UI

25 August 2026 at 16:10
AI-assisted development has made it easier to generate frontend code quickly. A developer can ask for a form, a dashboard widget, a settings page, or a modal flow and get a working first draft in seconds. That speed is useful, especially when teams are moving through routine UI work. But speed creates a problem that’s […]

Shadow Agents, Standing Privileges, and the Governance Gap Between Deployment and Discovery

25 August 2026 at 10:56
There was a brief window where AI agent security felt like a future problem. Organizations deployed copilots, coding assistants, and autonomous workflows on the assumption that the worst case was a bad recommendation or a hallucinated answer. That window closed in the first half of 2026, when a cluster of vulnerabilities and a landmark incident […]
Received β€” 25 August 2026 ⏭ AI & ML – Radar

Data Intelligence: Building Your Competitive Advantage in the Era of AI

24 August 2026 at 15:58
To keep pace with modern business, data strategy is shifting toward more autonomous real-time systems that deliver intelligence at the moment decisions are made. Driven by agentic AI, modern data teams are moving beyond simply looking at what happened. Now they’re automating complex workflows that analyze what’s happening, anticipate what might happen next, and recommend […]
Received β€” 21 August 2026 ⏭ AI & ML – Radar

The Agent-Era Career

21 August 2026 at 15:59
The following article originally appeared on Addy Osmani’s blog site and is being republished here with the author’s permission. If the AI layer gets good at anything, it will be anything that has an answer key. School used to be answer keys all the way down. School is the ultimate anchoring of success, because it’s […]
Received β€” 20 August 2026 ⏭ AI & ML – Radar

Principal Drift in Practice

20 August 2026 at 10:55
In 2026, the software engineering community is divided by a simple question: Should AI engineers still read the code generated by their agents? One camp argues that code has become virtually free to produce and discard, so humans should focus on systems and guardrails rather than implementation details. The other warns that blindly trusting AI […]
Received β€” 19 August 2026 ⏭ AI & ML – Radar

When Your Buyer Is an AI Agent

19 August 2026 at 16:00
In 2021, Maersk, the world’s largest container shipping company, deployed AI agents from a startup called Pactum to negotiate freight lane contracts with its carrier suppliers. The objective was for AI agents to handle negotiations autonomously rather than merely support human procurement staff. Operating entirely autonomously, the system manages the end-to-end agreement process, from reaching […]

When Guardrails Go Wrong

19 August 2026 at 10:53
The latest round of restrictions and safeguards for frontier models are overly fussy and limiting. A Claude skill that I created demonstrates what happens when guardrails go astray. My skill helps me to find articles and blog posts that go into O’Reilly Radar’s monthly Trends to Watch. It reads roughly a dozen well-known sites like […]
Received β€” 18 August 2026 ⏭ AI & ML – Radar

Is Open-Source AI Really the Dangerous Path?

18 August 2026 at 15:58
The following article originally appeared on the Tech Policy Press site and is being republished here with the author’s permission. In Washington, AI is increasingly being treated as something that needs to be controlled. The government believes that AI is, first and foremost, a national security asset, meaning that it must be sequestered to prevent […]
Received β€” 17 August 2026 ⏭ AI & ML – Radar

What’s an Orchestratorβ€”and Why Does Software Need One?

17 August 2026 at 15:55
The following article originally appeared on Medium and is being republished here with the author’s permission. Everybody’s talking about the death of developers. I get it. The developer whose job was to write boilerplate or scaffold CRUD apps is doneβ€”a model can do that in seconds, and that developer is not coming back. But the […]

When AI Writes the Code, Specifications Need an Exit Strategy

17 August 2026 at 10:45
The following article has been extended and rewritten by Markus Eisele from The Main Thread and is being republished here with the author’s permission. Open a repository after six months of spec-driven agent work and you may find a second system sitting next to the code. Requirements, research notes, high-level designs, low-level designs, implementation plans, […]
Received β€” 14 August 2026 ⏭ AI & ML – Radar

The Intent Debt

14 August 2026 at 13:01
The following article originally appeared on Addy Osmani’s blog site and is being republished here with the author’s permission. Technical debt lives in your code. Cognitive debt lives in your head. Intent debt lives in the artifacts you may never have written: the goals, constraints, and rationale for why the system is the way it […]
Received β€” 13 August 2026 ⏭ AI & ML – Radar

Prompt Debt and β€œFighting the Weights”

13 August 2026 at 16:08
Drew Breunig is one of the smartest voices writing about AI today. He’s the CEO and co-founder of cmpnd.ai, and a long-time hacker with a depth of experience from several eras, which is a surprisingly valuable asset these days. He’s also got a book on the way, The Context Engineering Handbook, already in early release […]

Why β€œIt Depends” Is the Most Future-Proof Phrase in Software

12 August 2026 at 15:54
Ask an architect almost any question and you’ll get the same answer: It depends. For years this answer has been the punchline of jokes about architects, but in an era when AI can generate a working service faster than you can describe it, β€œit depends” is one of the most important phrases in software. It […]
Received β€” 12 August 2026 ⏭ AI & ML – Radar

The Two Pillars of Post-training: Reinforcement Learning and Supervised Fine-Tuning

12 August 2026 at 10:57
This is the second article in Sharon Zhou’s post-training series. Read part 1 here. In the first post of this series, you learned how post-training closed the fundamental gap in usability of LLMs by making them behave in a certain way. In this post, you’ll explore specific techniques you can use to change a model’s […]
Received β€” 11 August 2026 ⏭ AI & ML – Radar

A Home for Personal Context

11 August 2026 at 10:45
Every agent I use is building a model of me. Claude has learned how I like my prose. ChatGPT remembers what I’m working on. I don’t mind thisβ€”every person I have a relationship with carries a model of me in their head, and every company I do business with keeps a profile. Other people’s understandings […]

Why Open Source Matters for AI

10 August 2026 at 08:42
In 1995, the question in the media was whether Netscape or Microsoft would control the web. The answer, it turned out, was neither. Both Netscape and Microsoft aimed to dominate the web server and browser market, reasoning that whoever controlled both ends of the connection would have an internet β€œplatform” to rival the deathgrip that […]
Received β€” 7 August 2026 ⏭ AI & ML – Radar
Received β€” 6 August 2026 ⏭ AI & ML – Radar

Your AI Agent Isn’t a Static Artifact. It’s Growing Up.

6 August 2026 at 10:55
In July 2025, an AI coding agent on Replit deleted a production database belonging to SaaStr founder Jason Lemkin. It did this during an explicit code freeze. Lemkin had told the agent, in capital letters, not to change anything. The agent ran destructive commands anyway, wiped records on more than a thousand executives and companies, […]

Building Organizational Intelligence

5 August 2026 at 15:55
Introduction Not long ago, one of my engineering directors came to me with a request: His team seemed overloaded, and he wanted to hire another engineer. I decided to test a research assistant I had been buildingβ€”an AI agent connected to our internal systems via MCPβ€”by asking it to analyze the team’s workload and write […]

Introduction to Post-training

5 August 2026 at 10:53
This is the first article in a series about post-training. Follow along on Radar. Before post-training, there was a major problem with LLMs: Almost nobody could use them. The story of post-training is also the story of how AI went from a research curiosity to a product used by about a billion people. Post-training is […]
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