The app displays a green confirmation message:

Four Tables, One Screen

Picture a support screen for a small online shop. When a customer calls, the agent needs the order's customer, items, prices, and status in one place.

A cloud application rarely has one TLS connection. A single API request can be decrypted and re-encrypted at a CDN, a web application firewall, an API gateway, a load balancer, a service-mesh proxy, an application runtime, and a managed cloud endpoint. Each termination negotiates a fresh session. Upgrading only the public edge can leave every downstream hop protected by classical key exchange.

That architectural fact changes how teams should approach post-quantum TLS. The right unit of migration is not the hostname, service, cluster, or application. It is the directional TLS link between two adjacent termination points, complete with an owner, a policy, a negotiated group, and runtime evidence.

Queries timed out unless you narrowed them down to an hour. Cardinality kept growing, because teams added labels "for debugging" and then sometimes forgot to remove them. On top of that, we were shipping a copy of fractions of our metric data to an external SaaS vendor, just in case, and that bill grew every quarter. And the fleet under all of this ran latency-sensitive trading services, so monitoring itself was not allowed to eat an extra CPU core at a wrong moment.

Imagine a city's traffic system during rush hour.

A minor accident blocks one lane on a major highway. Traffic slows down, but the road remains partially functional.

The best solution here is the Query Store tool. Since its implementation in 2016, this special feature has been storing data about SQL query text, execution plans, and statistics. In addition, unlike the plan cache, it retains this information even after a server restart. However, the trick is to get this data in a user-friendly format.

In my opinion, this is not necessarily a choice where we have to pick one approach over the other. Both approaches have advantages and disadvantages, and rather than relying on one approach, I believe we can combine them and use each where it makes the most sense.

And yet the same old gap is there. Your Go service calls GetSecret once at startup, decodes the value into a struct, and treats it as immutable for the life of the process. When someone rotates the secret in Key Vault — a new version, the old one disabled — your service has no idea. It keeps using the value it read at boot until the next deploy or pod restart. The rotation was seamless in Azure and invisible to your app, which is exactly the wrong combination.

Playwright is a modern test automation framework that’s popular among testers and developers. Built by Microsoft, it makes it easy to automate both web and API testing across multiple programming languages, all within a single tool.

There's just one problem, and it's the one nobody mentions in the demo: your application has no idea it happened.

By the end you have a bespoke ConfigManager in every repo: a loader, a fetcher, a refresh loop, a mutex, and a prayer. It is never quite the same twice, it is rarely tested well, and it is exactly the kind of code nobody wants to own.

That’s where the Builder Pattern, combined with Faker, can really help. It lets us create clean, flexible, and realistic test data with ease.

AI Inference Metrics

Models process input in units called tokens. Hence, common metrics include time to first token (TTFT), time between tokens (TBT), and time per output token (TPOT). Other measurements could be end-to-end latency, input and output token counts, request rate, error rate, etc.   

• Part 1, Running Sentiment Analysis Inside Neo4j With a Java Plugin, used the VADER lexicon via a pure Java UDF – rule-based, fully local, high throughput.
• Part 2, Wasm Inside Neo4j: Building the Example That Didn't Exist, embedded a Wasm runtime inside a Java UDF using wasmtime-java — sandboxed, still local, lower throughput.

Part 3 takes a different approach. Instead of bundling a scoring model inside the plugin JAR, we call TypeSafe AI's Jev from inside a Java UDF. Jev is a System One model – it doesn't generate text. It takes a state (the text to evaluate) and a set of typed questions and returns structured decisions with explicit probability and confidence information. No prompt engineering, no JSON schema coercion, no parsing.

The trouble shows up later, when the same agent is asked to inspect infrastructure, parse OpenAPI specs, write docs, answer developer questions, and call operational tools. The system prompt turns into a wall of text. Tool selection gets flaky in ways you can't reproduce. Someone tunes the prompt to fix documentation quality and quietly breaks spec analysis, and nobody notices for a week.

This case started with an API that was technically “healthy.” It was serving traffic, success rates were high, and no critical alerts were firing. Yet recurring degradation periods caused the service to miss its service-level agreement (SLA) target for request success. During the worst degradation periods, request success could fall to approximately 98%, against a target of 99.9%. Some requests were simply failing. Because the API sat directly in the purchase path, the gap between 98% and 99.9% meant real checkout failures, abandoned transactions, and lost product revenue.

Most teams threat model their APIs and their infrastructure. Very few threat model the AI context pipeline itself. This walkthrough does exactly that: it takes a realistic reference architecture, walks it through STRIDE, and ends with a prioritized mitigation list you can apply to your own system.

1. The Fixed Knowledge Cutoff: Model intelligence is inherently restricted to its training data window, making it blind to real-time changes.  
2. The "Air-Gap" Limitation: Out of the box, LLMs cannot securely interact with external systems or private APIs on their own.

Historically, developers bypassed these hurdles by writing fragile, ad-hoc API wrappers or custom orchestrators. Enter the Model Context Protocol (MCP): an open standard designed to standardize how AI applications safely connect to external data sources and execution environments.

In this article, we will explore the core architecture of MCP, look at why the Azure MCP Server is a game-changer for cloud engineers, and walk through a step-by-step guide to configuring it inside Visual Studio Code.

QuestDB appeals to enterprise developers by combining a time-series architecture with a familiar SQL interface. This technique reduces the learning curve for teams experienced in relational querying while providing a database optimized for append-heavy, chronological workloads. For systems needing recent-state queries, historical analysis, trend detection, and rapid ingestion, QuestDB delivers a practical solution.

Phoenix complements HBase by providing capabilities such as Global Secondary Indexes (GSIs), Atomic and Conditional updates, change data capture (CDC) streams, Updatable views, and multi-tenancy support across tables, indexes, and views. Furthermore, it supports server-side push-down execution for complex OLAP joins and grouping operations.