This was not a standalone exploit. It was a multi-vector convergence where vulnerabilities in the Node/NPM ecosystem, the systemic ungoverned architecture of the Visual Studio Code Marketplace, and the tactical “fog of war” caused by a period of historic GitHub infrastructure instability came together to create the perfect attack.

Call them phantom APIs: routes, handlers, and parameters that show up in production but never appear in a spec, a ticket, or a design review. Some get hand-built by a developer in a hurry and are forgotten. Increasingly, though, they're a byproduct of AI code generation — Copilot, Cursor, an internal fine-tuned assistant, whatever your shop has standardized on — quietly scaffolding an admin route, a debug handler, or a permissive query path because that pattern showed up often enough in training data to feel "normal." Nobody asked for it. Nobody reviewed it with fresh eyes, because by the time a human glances at the diff, the suggestion already looks plausible. That's the part that should worry you more than any single CVE: plausibility, not malice, is now the main vector.

We have used Delta Sharing in two distinct flavors: Databricks-to-Databricks (D2D), where both sides happen to be on Databricks, and the open protocol, where the recipient is on literally anything that can speak HTTPS. This article walks through both, with the code we actually run.

Most teams that want to add AI retrieval already have the data they need in databases, document stores, ticketing systems, and lakehouse tables that serve their purpose well. You usually do not need to centralize or rebuild this data; you can add retrieval as a thin layer over the systems you already run.

That's the pattern I want to walk through here — not as a hypothetical, but as something that's now happened, in public, with named victims and dated timelines, twice in the last eighteen months at a scale too big to wave away.

Azure AI Foundry has a genuinely great portal. You can see your agent runs, the tools it calls, the messages it sends and receives, and even a breakdown of token usage — all in a clean UI.

But here's what actually happens when you're building an agent locally:

Building reliable classification logic, however, is no simple task. It’s easy to create something brittle, and much harder to create something dynamic and flexible that works reliably in the majority of cases. In this article, we’ll look at why classification breaks down at scale, and we’ll examine what it actually takes to build and maintain a reliable solution in C#. Towards the end, we’ll walk through a dedicated API that handles classification across a wide range of document formats using AI without requiring a specially trained model for each document type.

Many teams find governance gaps only after a retrieval system surfaces stale or unauthorized content in production. Models, agents, and retrieval workflows all depend on enterprise data. Before any of that data reaches an AI system, teams need to know where it originates, how it’s integrated, whether it meets quality expectations, what context enriches it, who can access it, and how it changes over time.

If your platform provisions domains for users (whether that’s a website builder, a multi-tenant SaaS, or a developer tool that provides domain-at-checkout), email routing belongs in your provisioning pipeline, executed immediately after domain registration, without any user involvement.

This article walks through registering the server, the full tool catalog it advertises, how Foxit’s eSign and DocGen REST APIs carry the same agent session forward into signing and document generation, and a concrete four-step workflow you can reproduce with your own files.

The model didn't get worse. Nobody shipped a bad update. What happened is harder to point to, and that's exactly why it keeps happening.

I started working on a personal project with a simple question: If AI can analyze a database schema and generate SQL, what still makes the answer hard to trust?

It did not go well. The model was actually right a lot of the time, and that wasn’t the problem. The problem was that nobody on the business side could tell when it was right. Prompts came in tangled; the model would interpret one clause subtly wrong, and we’d return a clean-looking number sitting on top of a clean-looking SQL query. The users couldn’t read the SQL. When we tried to surface the model’s reasoning, it was a wall of CTEs and join keys that helped no one.

The unwanted, unexpected extra step in an information request or response operation chain is a telltale sign that something’s changed or isn’t working in your infrastructure. And heavier traffic is more than just an inconvenience – it’s a multifaceted problem with knock-on business effects that falls upon admins to identify and fix.

Here’s how it works. The LaunchDarkly Playground evaluates a single model call against a prompt and dataset you configure. By pre-computing your RAG retrieval offline and baking the chunks directly into each dataset row, you turn that call into a high-value generation test: the model in the Playground receives the same documentation context it would in production, so the eval measures how well your agent reasons over real grounded input.

That's not what developers are asking for.

The obvious approach is to run a graph search whenever you need an answer. That works, and it is also the wrong default here. This article walks through why, then shows how fixing the destination word lets you replace every future search with a single offline pass plus an O(1) lookup. The whole solver then runs in the browser, with no backend and no per-request search.

Now the rules are changing again. People are no longer just typing queries into a search engine and browsing the blue links. They ask ChatGPT, Perplexity, Claude, or Gemini, and they get a direct answer. If an AI answers the question, your carefully optimized page is invisible, even if it ranks #1 on Google.

Six months into using generative AI (GenAI) extensively in software delivery, I keep coming back to those production bugs. They still feel like the norm, not the exception.

Then the business calls. Nothing is working. The automations aren't firing. 

Work in the domain of AI programming assistants shows that AI-based code often exhibits security vulnerabilities in real-world testing. A study on GitHub's features showed that approximately 40% of the generated code was susceptible to security issues, emphasizing the need for careful testing and scrutiny.

Traditional ABAP Data Access With Open SQL

Open SQL is the standard SQL interface in ABAP that allows developers to query the underlying database in a database-agnostic way. For example, an ABAP report might join two tables and fetch results like this:

The retrieval-augmented generation (RAG) ecosystem has matured remarkably fast. Vector databases are production-grade, embedding models are cheaper than ever, and retrieval pipelines are being deployed across healthcare, finance, legal, and education systems worldwide. Every major benchmark shows impressive numbers.

Almost every major benchmark is in English.

Today, modern systems use a mix of relational databases, document stores, caches, graph engines, and increasingly, vector databases that support semantic search, retrieval-augmented generation (RAG), and AI-powered applications. Polyglot persistence is now the industry standard. While Jakarta EE standardized relational persistence through JPA, it still lacks a vendor-neutral standard for non-relational persistence. This gap forces developers to rely on fragmented, proprietary solutions, creating barriers to portability, productivity, and innovation.

The API key was never in its environment.