Systems | Development | Analytics | API | Testing

AI-powered automated test script generation is transforming how software teams approach quality assurance. By analyzing real user behavior, code changes, and system logs, these tools reduce the time and effort needed to create and maintain tests. This shift from manual scripting to AI-generated scripts helps teams keep test coverage in sync with rapid release cycles.

Kuwaiti banks got two very different signals from their regulator within a few months of each other. In December 2025, the Central Bank of Kuwait (CBK) replaced its 2020 Cybersecurity Framework with something far more demanding – the Cyber and Operational Resilience Framework (CORF). Then in March 2026, CBK went the other way on capital and liquidity, temporarily easing several key ratios to give banks more breathing room. Read separately, these look like contradictory moves.

The MLS (Multiple Listing Service) is designed to manage and exchange listing information within a defined real estate market. It standardizes how a property for sale is described and distributed, and it updates on a regular cadence. Within its market, an MLS is the system of record for the listing data it governs. It was not designed to be the central operational data platform for an individual brokerage’s customers, transactions, financials, or product analytics.

BigQuery augmented analytics Table-Valued Functions (TVFs) automate insight discovery and pattern explanation using AI, ML and statistical methods.

In Under the Hood with Go TLS and eBPF, I left socket tracking as an exercise for later. The example used bpf_get_current_pid_tgid() and explicitly excluded concurrent TLS operations. Capturing plaintext was enough for that post. With rustls, later arrived: I could read the HTTP payload perfectly and still attach it to the wrong TCP connection. That’s a frustratingly convincing failure. The request looks right. The response looks right. The application works.

Tabular Foundation Models (TFMs) move tabular prediction from task-specific Gradient Boosted Decision Trees (GBDTs), such as XGBoost and CatBoost, toward “table-native” models built on In-Context Learning (ICL). The training data becomes a contextual prompt, and the model produces zero-shot predictions in a single forward pass without parameter updates.

On its own, gen AI runs your performance analysis without a trusted data foundation, without your business context, and without built-in analytical methods, and that leaves its answers open to mistakes. Databox grounds AI tools in all three.

Every iOS team is about to hit a testing cycle they can't skip. And waiting until production is the expensive way to find out.