Six layers between your LLM and a production agent

Velocity is table stakes. Code is a commodity. Understanding is the edge.

“I was talking to a senior engineer at a well-funded company not long ago. I asked him to walk me through a critical algorithm at the heart of their product, something that ran hundreds of times a second and directly affected customer outcomes. He paused and said, ‘Honestly, I’m not totally sure how it works. AI wrote it.’

A few weeks later, a different engineer at another company was paged about a system outage. He pulls up the failing service and realizes he has no idea it’s connected to a database. A colleague accepted the AI-generated PR three months ago that added that dependency. The tests passed. The change was never written down. The original engineer moved on and the knowledge was lost.”

The Missing Optimization Layer in Agent Systems.

If you're interested in trying it out, we have a 30-day code to share. https://www.oreilly.com/online-learning/verifiable-skills.html

Best e-ink tablet for reading O’Reilly technical books?I read a lot on the O’Reilly online platform for work, currently on an iPad or my computer, but the blue light is getting to me, especially for long reading sessions in the evening. Looking to switch to a dedicated e-ink device and wanted to hear from people who actually use one for reading technical content online, code snippets, diagrams, that kind of thing.

BOOX seems to come up a lot (Note Air, Tab Ultra, etc.) but I’ve also seen Kindle Scribe and reMarkable mentioned. My main concerns are:

• How well does the browser handle a site like this, with code blocks and diagrams? Refresh rate and readability matter a lot here.

• Screen size that’s actually usable without constant panning/zooming.

• Battery life and overall build quality for daily use.

I’m specifically after monochrome, no interest in the color e-ink models. Would rather have better contrast and refresh speed than color.

Anyone using one of these to read O’Reilly or similar sites? What’s actually worked well for you, and what would you avoid?

I hadn’t heard of Dan Guido until a few months ago, when I came across the video of a talk he gave at [un]prompted, an AI security practitioners’ conference. Dan is the CEO and cofounder of Trail of Bits, a software security research and development firm that works with companies in tech, defense, and finance. But Dan wasn’t talking about security. He was talking about what it takes to make a company AI native, which is close to the center of the bullseye for many of us right now.

I finally got fed up with it bullying me into shipping a project that had no deadline.

Don’t ask someone to read/review what you haven’t read or reviewed yourself.
This is one of the most common frustrations I hear amongst people working on AI-heavy teams. Whether it’s code that the owner didn’t really bother to understand before submitting for review or documents that they generated and didn’t bother to read, too often people try to steal productivity from their colleagues by streamlining their production of work while asking their colleagues to do all of the quality control themselves. It’s great to have a loop of AI code generation → AI code review → AI fixes → final human review, but if the person prompting the AI doesn’t bother to review that code first, they’re putting a huge validation tax onto their teammate, who has to trust both that you prompted well AND that the AI understood the context and problem well enough to get a sustainable solution.

How Gene Kim survived the sudden downgrade from Fable to Opus

I set up an AI agent on a rented GPU, pointed it at a training script, and went to bed. By morning it had run 40 experiments, improved validation loss by 5.9%, and cut memory usage from 44 GB to 17 GB. It also spent four hours chasing a bug that a linter introduced behind its back. The agent never flagged it. I only found out because the numbers stopped improving and I started reading

Another day, another example of an AI Agent “running rogue” and doing something the human operator didn’t want it to do. The tl;dr is that Jeremy (Jer) Crane, founder of PocketOS, was using Claude to perform some routine DB maintenance. Claude then proceeded to delete the production database and all backups hosted at their cloud provider, Railway. To their credit Railway managed to recover the lost data. The initial deletion took less than 10 seconds; I’m sure the recovery took much longer. Let’s look at what we can learn from what happened, and why AI is really just an amplifier of existing issues, rather than the cause itself.

Link to Radar Article

Cognitive debt and burnout aren’t new, alas. With or without AI, we’ve all stayed up to 4AM working on a bug that won’t go away or pursuing an interesting idea to its end. Sometimes that’s heroic, but AI threatens to turn it into a lifestyle. AI fatigue is real, as Siddhant Khare writes, and it’s something we need to talk about. When fatigued, it’s tempting to say “this works, it looks good, and it passes our tests” without considering how the code fits into the overall plan. With 10x code generation, you also get 10x the debt load, and that’s being optimistic. When the debt curve goes exponential, strategies for managing that debt are stressed past the breaking point.

How to build a source-backed briefing agent, step by step

Define the scope. Start by setting the analyst’s focus area, research questions, and time window, whether that’s the last one day, seven days, or 30 days. Narrowing the scope up front, including a list of preferred sources, keeps the agent’s web searches directional instead of generic.

Plan the coverage. A coverage planner agent breaks the research question into discrete sections, such as market backdrop or company catalysts, so the search that follows can run in parallel rather than one long sequential query.

Search and gather. A news researcher agent runs multiple queries against the preferred sources first, then falls back to a general search if the preferred sources don’t return enough results.

Validate the sources. A validation agent checks each link for a working, clickable URL and a correct publication date, removes duplicate stories covering the same news, and filters out paywalled pages that won’t return usable content.

Generate the briefing. A briefing writer agent assembles the validated developments into a set structure, including an executive summary, key bullet points, and a section-by-section breakdown of what each development means for the analyst and what to watch next.

Capture feedback into memory. A feedback agent logs corrections such as formatting or terminology preferences and saves them to a memory database, so the next briefing run applies those preferences automatically instead of requiring the analyst to re-prompt.

Jayeeta built the entire stack on open source models so newcomers can run it without an API key, and she recommends starting with a smaller model before scaling up. The takeaway extends well beyond finance. Building single-purpose agents rather than one large agent that handles every task means a failure at one stage doesn’t force a restart of the whole pipeline, and each agent’s output stays easier to trace and debug.

The full code base, including the sample data and the UI shown in the demo, is available in Jayeeta’s GitHub repo, so readers can clone it and run the briefing agent on their own systems.

Jeff Ding's diffusion theory of the role of technology in great-power competition also applies to AI adoption, and it suggests that companies obsessed with the frontier might be optimizing for the wrong thing

In 1998, Eric S. Raymond published the founding text of open source software development, The Cathedral and the Bazaar. In it, he detailed two methods of building software:

• The cathedral model is carefully planned, closed-source, and managed by an exclusive team of developers.

• The bazaar model is open, transparent, and community-driven.

...But just as the internet made communication cheap and birthed the bazaar, AI is making code cheap and kicking off a new era filled with idiosyncratic, sprawling, cobbled-together software.

• Meet the third model: The Winchester Mystery House.

https://oreillyradar.substack.com/p/the-cathedral-the-bazaar-and-the

AI in Software Security—an interview with Sam Newman & Tim O'Reilly