News Aggregator

Handling Logging After Migrating UiPath to Automation Cloud

Aggregated on: 2026-01-07 20:15:00

Migrating UiPath Orchestrator from an on-premises deployment to Automation Cloud simplifies infrastructure management, but it also changes how execution logs can be accessed and consumed. Teams migrating existing Splunk-based observability pipelines often discover that familiar on-prem logging patterns no longer apply once workloads move to the cloud. In on-prem environments, Orchestrator and robot logs are typically available as files on the server filesystem, making them easy to ingest into centralized monitoring platforms using standard forwarders. Automation Cloud removes direct access to the underlying infrastructure, forcing teams to rethink how logging should be handled after migration.

AWS Bedrock vs Azure OpenAI vs Gemini API: A Practical Comparison

Aggregated on: 2026-01-07 19:15:00

Choosing a cloud AI platform isn't just about which has the "best" model — it's about integration, pricing, compliance, and how well it fits your existing infrastructure. After building production systems on all three platforms, here's my engineering-focused breakdown to help you make the right choice.

Implementing Idempotency in Distributed Spring Boot Applications Using MySQL

Aggregated on: 2026-01-07 18:15:00

Why Idempotency Breaks in Real Systems Modern distributed systems expose APIs that trigger state-changing operations such as payments, orders, the account acquisition process, or account updates. In such environments, the chance of duplicate transactions being initiated is quite high and unavoidable due to network retries, a Kafka rebalancer issuing multiple requests, load balancers, and other factors. Without proper safeguards, these duplicate transactions/requests can lead to data inconsistency, financial discrepancies, and variations in business invariants. Idempotency is a well-established technique used to ensure that repeated executions of the same request produce a single, consistent outcome. While idempotency can be enforced at the application level using in-memory caches or request deduplication logic, these approaches would fail for a horizontally scaled microservice architecture, where multiple application instances may process requests concurrently and across numerous different regions.

How to Send .NET Crash Dumps to Slack From an ECS Fargate Task

Aggregated on: 2026-01-07 17:30:00

Sometimes .NET applications crash in production, and nobody knows why, because logs and metrics are ok. It's quite bothersome and makes debugging very unpleasant. In such cases, memory dumps might simplify debugging and reduce troubleshooting time from days to minutes. This article explains how to configure dumps for .NET applications deployed to AWS ECS Fargate and then forward them to the development team in the most convenient and secure way.

Automated Deployment Using a CI/CD Pipeline (Mule 4 | CloudHub 2.0)

Aggregated on: 2026-01-07 16:30:00

The purpose of this article is to depict and demonstrate how to automate the build and deployment process using a CI/CD pipeline with CloudHub 2.0 (Mule 4). Prerequisites Anypoint CloudHub account (CloudHub 2.0) app.runtime – 4.9.0 mule.maven.plugin.version – 4.3.0 Anypoint Studio – Version 7.21.0 OpenJDK – 11.0

The Hidden Security Risks in ETL/ELT Pipelines for LLM-Enabled Organizations

Aggregated on: 2026-01-07 15:30:00

As organizations integrate large language models (LLMs) into analytics, automation, and internal tools, a subtle yet serious shift is occurring within their data platforms. ETL and ELT pipelines that were originally designed for reporting and aggregation are now feeding models with logs, tickets, emails, documents, and other free-text inputs. These pipelines were never built with adversarial AI behavior in mind.

Why Small Language Models Are Transforming AI Adoption for Everyone

Aggregated on: 2026-01-07 14:30:00

You’ve probably seen it yourself over the last couple of years: whenever people talk about artificial intelligence (AI), the spotlight almost always lands on large language models (LLMs). Tools like ChatGPT, Claude, and Gemini have practically become the poster children for modern AI — and it’s not hard to understand why. These systems have been remarkable in pushing natural language processing forward, and they continue to capture headlines and imagination across industries, including IT and software, marketing, manufacturing, and e-commerce.

RPA Validation in Life Sciences: 5 Pitfalls and How to Avoid Them

Aggregated on: 2026-01-07 13:30:00

The issue with RPA was discovered during an FDA audit at a Global Biotech company. There was a lack of validation documentation, requirement traceability, and testing, and missing evidence. That’s when it was noted that a successful automation project is indeed a regulatory finding. This is not an unusual event in life sciences; bots aren’t just scripts, they are regulated systems. The development should include compliance, risk management, and audit readiness, as with any other GxP systems.

Orchestrating Retail-Scale Data on Google Cloud

Aggregated on: 2026-01-07 12:30:00

Digital retail never sleeps. Carts open and close at 2 a.m., promotions spike traffic without warning, and supply signals move from warehouses to web in minutes. In that environment, data pipelines are not just utilities — they are the nervous system that keeps analytics current, inventory visible, and decisions grounded in fact. The challenge is designing pipelines that stay elastic under peak load, deliver trustworthy data consistently, and keep costs predictable. Google Cloud’s modular services — Pub/Sub for event ingestion, Dataflow for processing, BigQuery for analytics, and Cloud Composer for orchestration — provide the foundation. What matters is how they fit together into patterns that remain reliable when traffic doubles or triples overnight.

Solving the Cold Start Problem in Edge AI: A Guide to Data-Saving Learning

Aggregated on: 2026-01-06 20:15:00

We have all seen the demo: a computer vision model achieves 99% accuracy on a test dataset. Then, we deploy it to an edge device — a drone, a security camera, or an industrial robot — and performance crashes. The problem is domain shift. The lighting is different, the camera angle is skewed, or the background noise has changed. In traditional MLOps, the solution is to collect thousands of new images from the edge device, label them manually, and retrain the model from scratch.

Developer Tools That Actually Matter in 2026

Aggregated on: 2026-01-06 19:15:00

Though I am an architect, I am a developer at heart. I explore developer tools every year and publish my favorites here. As developers, we’ve all been through the hype cycles. Every year brings a new wave of tools that promise to change everything. Most fade away within months. But 2026 is different — and not because of buzzwords. The tools that are making real differences now are the ones solving actual problems we face every single day. I’ve spent the last few months testing what’s actually working in production environments, talking to teams across different tech stacks, and trying tools that claim to make our lives easier. Here’s what I found: the best tools aren’t the flashiest ones. They’re the ones that disappear into your workflow and just work.

Metadata, Not Data Volume, Is the Real Bottleneck in Modern Data Lakes

Aggregated on: 2026-01-06 18:15:00

For more than a decade, data engineering best practices have revolved around a single assumption: data volume is the primary scalability challenge. We optimized Parquet sizes, tuned partitioning strategies, compressed aggressively, and scaled compute to handle terabytes and petabytes of data. As long as queries scanned fewer files and clusters had enough memory, performance generally improved.

Why Data Engineers Need to Think Like Product Managers

Aggregated on: 2026-01-06 17:15:00

Introduction Today, the work of a data engineer is more complex than simply building pipelines and platforms. Data engineers are no longer just builders; they are now vital parts of value creation in a data driven organization. However, many engineers continue to evaluate success using the number of completed jobs and created tables, rather than their actual worth to the business. This is where a Product Manager (PM) mindset comes in handy. Adopting a product manager’s way of thinking is not about managing Jira boards and marketing roadmaps. It is about thinking of data assets as products complete with customers, lifecycle, and tangible results.

BYOLM with Spring AI & MCP: Secure, Swappable AI Everywhere

Aggregated on: 2026-01-06 16:15:00

Introduction Artificial intelligence has rapidly moved from research labs into everyday tools. Yet, most users remain locked into vendor‑controlled ecosystems, where the choice of language model (LM) is dictated by the provider. This creates friction for developers, educators, and organizations who want flexibility, privacy, and control. The Bring Your Own Language Model (BYOLM) paradigm challenges this status quo. By designing a configurable middleware layer, extensions for Chrome, Word, and other applications can seamlessly integrate with swappable LLMs. Combined with Spring AI and Model Context Protocol (MCP), this architecture empowers users to safeguard sensitive data, authenticate access securely, and orchestrate reproducible AI labs. This article may be referred to as a sequel to this article on DZone, and readers are encouraged to read it. Motivation The motivation behind BYOLM is simple yet powerful: freedom of choice. Traditional AI assistants often operate as black boxes, offering little transparency into how data is processed or stored. For developers and mentors, this lack of control is unacceptable. BYOLM allows individuals and organizations to:

Understanding Parquet Scans: How Readers Skip Work and Stay Fast

Aggregated on: 2026-01-06 15:15:00

Parquet is a columnar file format designed for efficient data storage and retrieval. On disk, it is organized around row groups, column chunks, and pages. Along with that, each file also has a footer that describes how everything fits together. A Parquet reader that understands this layout can avoid a lot of work during the scan, such as skipping entire row groups, column chunks, and pages, and decoding only the values that matter. This article uses a single sample Parquet file to explain exactly what happens during column reads and some common optimization techniques.

6 Software Development and DevOps Trends Shaping 2026

Aggregated on: 2026-01-06 14:15:00

In 2025, many teams tried new things in software development and DevOps — AI copilots, new platforms, more automation, and more security checks. Some of it worked great, some of it created new mess (tool sprawl, unclear ownership, higher cloud bills, and “we ship faster but break more”). Heading into 2026, the focus is shifting from experimentation to ensuring reliability and repeatability. Leaders and practitioners are asking the same questions: How do we move fast without losing quality? How do we keep systems secure without slowing teams down? How do we reduce toil, control costs, and still deliver features that matter?

A Practical Guide to Semantic Caching With Redis LangCache

Aggregated on: 2026-01-06 13:15:00

Semantic cache is an advanced caching mechanism that differs from traditional caching, which relies on exact keyword matching; it stores and retrieves data based on semantic similarity. Redis LangCache is a fully hosted semantic caching service that helps cache LLM prompts and responses semantically, thereby reducing LLM usage costs. In this tutorial, let's learn how to quickly create a simple application and use LangCache for caching LLM queries. Also, see if we can combine fuzzy logic to improve the responses.

Effectively Managing AI Agents for Testing

Aggregated on: 2026-01-06 12:15:00

Large language models and AI agents have already transformed many fields and are changing our lives in fundamental ways. In the testing domain, AI agents have a clear path for making immediate improvements in process and quality, and ultimately for producing reliable, performant, secure, and compliant software. Check out Demystifying Agentic Test Automation: What It Means for QA Teams. But it’s not obvious how to take advantage of these capabilities. While AI agents are not fully predictable, they can be managed reliably via robust control mechanisms. Let's see how. What Does It Mean to Manage AI Agents in QA? There are several important aspects to managing AI agents, both in general and specifically in the testing domain.

Unlocking Hidden Value in Dirty Data: A Practical NLP Pattern for Legacy Records

Aggregated on: 2026-01-05 20:29:59

In the era of Digital Transformation (DX), we are often told that "data is the new oil." However, for many enterprises, that oil is crude, unrefined, and full of sludge. Consider the automotive, manufacturing, or healthcare industries. For decades, technicians and operators have been typing notes into free-text fields. These millions of records contain critical information about asset health, maintenance history, and compliance. But because they are unstructured, full of typos, and riddled with domain-specific slang, they remain invisible to standard analytics tools.

Securing Verifiable Credentials With DPoP: A Spring Boot Implementation

Aggregated on: 2026-01-05 19:29:59

In my previous article, I demonstrated how to implement OIDC4VCI (credential issuance) and OIDC4VP (credential presentation) using Spring Boot and an Android wallet. This follow-up focuses on a critical security enhancement now mandated by EUDI standards: DPoP (Demonstrating Proof-of-Possession). The Problem With Bearer Tokens Traditional Bearer tokens have an inherent weakness: anyone who obtains the token can use it. If an attacker intercepts or steals a Bearer token, they can impersonate the legitimate client until the token expires (or is revoked).

Beyond the Spec Sheet: Performance Tuning a Massive DWH Migration on AWS

Aggregated on: 2026-01-05 18:29:59

Moving large-scale financial systems to the public cloud is a high-stakes game. In regulated industries, you often cannot perform casual test-in-production experiments. You have to design correctly and then verify quickly. When migrating a massive data warehouse (DWH) from on-premises hardware to AWS EC2, the natural instinct is to match specifications: "If I had 16 cores and 128 GB RAM on-prem, I need an x1e.xlarge on AWS."

Beyond Fuzzy Matching: Engineering a Multi-Signal ML Pipeline for CRM Deduplication

Aggregated on: 2026-01-05 17:29:59

The CRM Problem We All Face Almost every CRM platform struggles with duplicate customer records because the data is a messy mix flowing in from too many places — web forms, call centers, imports from ERP, partner feeds, and even legacy systems from multiple business units. The exact same company can pop up under slightly different names, with spelling variations, inconsistent addresses, or missing suite numbers. A simple single-rule fuzzy match isn't enough to connect these dots. To truly fix this, we need something smarter: a multi-signal pipeline. This means using a blocking layer to speed up things, checking fuzzy name and address similarity, using geographic distance as a reality check, and implementing a small machine learning classifier to weigh all the evidence and decide if two records should be merged.

Raw Agent Systems Explained: From Single Agents to Multi-Agent Networks

Aggregated on: 2026-01-05 16:29:59

Raw agent technology is at the forefront of the next phase of artificial intelligence (AI) development. According to a survey conducted by IBM involving 2,900 executives, the implementation of AI-enabled workflows is expected to increase from 3% to 25% by 2025, with 70% of respondents expressing confidence in the organizational impact of agentic AI. This form of AI allows language models to be directed through natural language, facilitating decision-making and uncertainty management. Gartner forecasts that by 2026, 75% of large companies will adopt multi-agent systems, while BCG anticipates a rise in revenue from $5.7 billion in 2024 to $53 billion by 2030. This study examines raw agent systems, ranging from single-agent frameworks to multi-agent networks that promote AI collaboration, and discusses LangGraph implementations and their significant challenges.

How to Perform Response Verification in REST-Assured Java for API Testing

Aggregated on: 2026-01-05 15:29:59

One of the core factors in API test automation is verifying the response body for data integrity. It is a crucial step in the functional validation of the API to confirm that it is returning the correct data as desired. This verification of the response body can be of a POST, GET, PUT, or PATCH API request. The same logic can be applied to any API response for verifying the data returned by the API.

Best Date Conversion Approaches in Java 8+

Aggregated on: 2026-01-05 14:29:59

Working with dates and time has always been one of the trickiest parts of Java development. For years, developers wrestled with java.util.Date, Calendar, and the never-ending confusion around mutability, time zones, thread safety, and formatting quirks. When Java 8 introduced the java.time package, it finally brought a modern and much more intuitive date-time API inspired by Joda-Time. Yet even with this improved API, many developers still find themselves constantly converting between different date representations, especially when integrating legacy systems, REST interfaces, databases, or front-end clients. In this article, I want to walk through the best practical approaches for date conversion in Java 8+, focusing on clarity and reliability. These are patterns I’ve seen consistently used in production systems, and they help avoid many silent bugs that come from incorrect time zone assumptions, accidental loss of precision, and misuse of the older date classes.

Building Your Own Ledger: A Solo Developer's Answer to Time and Money Chaos

Aggregated on: 2026-01-05 13:29:59

Let’s talk about the parts of our job that they don’t teach in tutorials. You’ve crushed the sprint, architected a beautiful solution, and pushed clean code. Then, the real-world complication hits: untangling the spreadsheet to figure out how many hours that last feature actually took, or manually building an invoice from a chaotic mix of calendar events, timer logs, and scribbled notes. For developers trading time for money, freelancers, contractors, and consultants, this administrative tax is a constant drain on focus and a direct hit to profitability. I built my career on this model. And for over a decade, I accepted the friction. My "system" was a Frankenstein's monster of a time-tracker tab, a project management tab, a calendar tab, and a spreadsheet tab. Data lived in silos. "Billable hours" were an estimate, reconstructed weekly with forensic effort. I kept thinking, "Someone must have solved this." The tools I found fell into two camps: overly simplistic stopwatches that gave me numbers without context, or complex, expensive enterprise platforms with features I didn't need and a price tag that hurt my solo-dev sensibility.

Fine-Tune SLMs for Free: From Google Colab to Ollama in 7 Steps

Aggregated on: 2026-01-05 12:29:59

In this article, I'll walk through a practical pipeline that: Fine-tunes a popular open-source base small language model on your own data using Unsloth on Google Colab (free T4 GPU) Exports the result to GGUF via llama.cpp Deploys it to Ollama so that you can run ollama pull my-model from anywhere and even push it to the Ollama registry. We'll put this into practice by creating a real-world example: a "multi-agent orchestrator," built step-by-step in seven concrete steps.

Tired of Reverse-Engineering Code? A Data-First Pattern for Legacy Modernization

Aggregated on: 2026-01-02 20:29:58

We have all faced the Monolith from Hell. It’s a 20-year-old system. The documentation is missing, the original architects retired a decade ago, and the codebase is a tangled mess of spaghetti logic. When tasked with modernizing such a system, the instinct is a function-first approach: read the source code, trace the logic, and try to replicate the existing functionality in a modern language.

AutoML vs. LLMs: A Developer’s Guide to Efficient ML Pipeline Generation

Aggregated on: 2026-01-02 19:29:58

In the current AI landscape, the hype cycle is undeniably focused on large language models (LLMs). From code generation to reasoning, models like GPT-4 and Llama 3 have transformed how we interact with data. However, for machine learning (ML) engineers tasked with building robust, production-grade pipelines for tabular data or predictive analytics, LLMs are not always the silver bullet. Automated Machine Learning (AutoML) has quietly matured into a powerhouse technology, automating the tedious aspects of data science — feature engineering, model selection, and hyperparameter tuning.

Cloud to Local Copilots: A Hybrid Path to Privacy and Control

Aggregated on: 2026-01-02 18:29:58

Software usage patterns have always evolved alongside hardware capabilities. In recent years, with the rise of GPUs and cloud-based AI copilots such as GitHub Copilot, this evolution has accelerated — offering developers real-time code suggestions, documentation support, and automated testing at scale. However, concerns around personal data privacy, the cost of copilot usage, and the need for greater autonomy have given rise to local AI copilots. By hosting models on a local device, developers gain tighter control over sensitive data, reduce dependency on cloud providers, and unlock performance benefits tailored to their device’s capabilities. Cloud Copilots vs. Local Copilots Cloud-based copilots have become the default entry point for many developers, especially in workplace settings, offering seamless integration with cloud-hosted repositories and services. However, there are trade-offs — namely recurring subscription costs and potential exposure of sensitive code or data.

5 Challenges and Solutions in Mobile App Testing

Aggregated on: 2026-01-02 17:29:58

Testing is one of the final stages of mobile app development before you’re ready for launch. The finish line may seem close, but it might not be. If you encounter mobile app testing challenges unprepared, you may have to push your launch window back by days or even weeks. Here’s why mobile app testing is essential, the challenges you might encounter, and how to resolve them. The Importance of Mobile App Testing The mobile app market is booming. By 2026, the Apple App Store is expected to see an estimated 38 billion downloads, while projections indicate the Google Play Store will reach approximately 143 billion downloads — representing 15% and 30% increases, respectively. Competition is intense, so you must prioritize comprehensive testing to attract and retain an audience.

Is TOON a Boon for AI Communication, LLM Token Cost Economics?

Aggregated on: 2026-01-02 14:29:58

Modern AI systems are hitting a new kind of bottleneck. It is not CPU, memory, or network bandwidth. It is tokens. With large language models (LLMs), every character sent and received is tokenized, processed, and billed. At a small scale, this cost is easy to ignore. At enterprise scale, it becomes a first‑order architectural concern. This shift is driving interest in formats designed specifically for AI communication, such as TOON (Token‑Oriented Object Notation).

Automating Monolith Migration for Resource-Constrained Edge Systems

Aggregated on: 2026-01-01 20:14:57

We usually design microservices with a cloud-first mindset. If a container needs more RAM, we scale it up. If latency increases, we add a cache or a load balancer. But the world is shifting toward Software-Defined Vehicles (SDVs) and edge computing. In these environments, hardware is fixed, and latency can be a matter of life and death (e.g., autonomous braking systems). Migrating a legacy C/C++ monolith to microservices in this context is dangerous. The overhead of containerization (Docker) and inter-process communication (IPC) can easily overwhelm an embedded CPU.

LLMs in Data Engineering: How Generative AI is Changing ETL and Analytics

Aggregated on: 2026-01-01 18:14:57

For decades, data engineering has revolved around building reliable pipelines to extract, transform, and load (ETL) data, ensuring that business analysts and data scientists have access to trustworthy datasets. The role has always focused on scale, reliability, and speed. But with the rise of large language models (LLMs), the traditional definition of ETL and analytics is shifting. Generative AI is no longer just a research curiosity; it’s becoming a powerful co-pilot in modern data platforms. This article explores how LLMs are impacting ETL and analytics, the opportunities and challenges they create, and what the near future may look like. To make things practical, we’ll refer to a real-world case in which a global retailer used LLMs to automate parts of its data transformation and analytics pipeline.

Airflow vs. Dagster vs. Prefect: Which Scheduler Fits Your Data Team?

Aggregated on: 2026-01-01 17:14:57

Workflow orchestration sits at the heart of modern data engineering. Whether you’re running daily ETL jobs, streaming pipelines, or machine learning workflows, you need a scheduler to manage dependencies, retries, and monitoring. For years, Apache Airflow has been the default choice, but newer tools like Dagster and Prefect have emerged, each promising a more modern approach. The question is: Which scheduler best fits your data team? In this article, we’ll explore the strengths and trade-offs of Airflow, Dagster, and Prefect through real-world lenses. We’ll focus less on abstract features and more on how these tools behave in practice.

Turning Architectural Assumptions into Enforceable Code

Aggregated on: 2026-01-01 16:14:57

When Everything Works But Nothing Aligns There is a moment in every large AI initiative when the system behaves correctly, the model behaves correctly, and yet the entire pipeline enters a state where nothing aligns with what was promised. The logs look fine. Dashboards look clean. Latency spikes are non-critical. But a design boundary that was agreed upon months earlier no longer maps to the reality the system is operating in. The failure does not originate in code. It originates in the assumptions underneath the code. The incident that pushed me to formalise this came from a simple requirement: the inference layer needed p95 latency under 180 ms during peak loads. Three teams signed off on it. Architecture captured it in diagrams, delivery scoped for it, and infra agreed to provision accordingly. But by the time the model reached production, none of those teams were working off the same interpretation. The latency budget existed. The system no longer matched it.

How Generative AI Can Transform Cloud Support Operations: A Practical Framework

Aggregated on: 2026-01-01 14:29:57

Abstract Cloud support is no longer a staffing problem — it’s a cognition and scalability problem. As cloud platforms grow in complexity, support engineers are spending more time searching, routing, and rewriting than actually solving issues. This article introduces a three-layer framework showing how generative AI can improve resolution speed, reduce escalations, and enhance communication quality in modern cloud support teams, using a vendor-neutral, implementation-focused approach.

Why Catalogs Really Break (and How a Copilot Prevents It)

Aggregated on: 2025-12-31 20:14:57

Most catalog incidents aren’t exotic; they’re actually repeatable mistakes. The pattern goes like this: Someone copies an old JSON A few fields get changed One constraint gets missed The downstream service chokes Think of a “Launched” offer with a future start date, or a retail-only promo that accidentally ships to WEB. The blast radius is significant: wrong price on site, agents can’t quote, orders stuck in validation.

How Unified Data Pipelines Transform Modern AI Infrastructure

Aggregated on: 2025-12-31 18:14:57

Over time, the rate of improvement in AI models has outpaced that of pipelines intended to support them. Teams are moving towards more complex signals and higher workloads, but it becomes increasingly difficult for the pipelines to support this. This gap widens with every new data source that adds to this stack, forcing the engineers to hold together workflows that were never designed to work together. Performance slows, iteration decreases, and now the system begins to limit the very models it was built to support. This issue is solved by a unified data flow, which ensures that AI has a scalable structure. The sections below will break down the key details on why this change is so important.

Security and Governance Patterns for Your Conversational AI

Aggregated on: 2025-12-31 17:14:57

How many times have we heard people talk about the "dream of a SOC copilot?" A copilot woåuld allow an analyst to type something like, "Show me all the SSH login attempts for 10.0.0.5 over the last hour and compare those to the CrowdStrike alerts," and get the results instantly. This concept suggests the possibility of reducing mean time to resolution (MTTR) and providing Tier 3 knowledge to junior analysts. However, in a secure environment, this dream may become a nightmare. In order to connect a probabilistic, hallucinating conversational AI (LLM) to your SIEM (Splunk, Sentinel) or EDR, you will require a fundamentally different security architecture than what you use for a typical chatbot. If the LLM can write to your systems, it could wipe out logs.

Avoid BigQuery SQL Injection in Go With saferbq

Aggregated on: 2025-12-31 16:14:57

You can build dynamic queries in BigQuery using the Go SDK. When building applications that allow users to select tables or datasets dynamically, you need to include those identifiers in your SQL queries. I was surprised to find that the BigQuery manual and code examples do not warn about SQL injection vulnerabilities when doing this. Even more surprising: BigQuery does not provide a built-in mechanism to safely handle user input in table or dataset names. The official SDK supports parameterized queries for data values using @ and ? syntax, but these cannot be used for identifiers that need backtick escaping. You’re forced to use string concatenation, which opens the door to SQL injection. This post explains the problem and introduces a package I wrote to tackle this shortcoming.

DevSecOps as a Strategic Imperative for Modern DevOps

Aggregated on: 2025-12-31 14:14:57

If you do not take security seriously, you are just begging for trouble. Security should be an integral part of your development process, not something that you add at the end. Patches and updates do not suffice to deter severe attacks, and if you entrust security to another team, then you are simply relying on luck. Only an unwavering, company-wide security commitment can guard the moat that keeps competitors at bay and satisfy the blizzard of new regulatory expectations. Operate this way and your software will stay resilient, compliant, and ultimately, market-winning. DevOps security and DevSecOps both champion security embedded within the modern development workflow, but they place differing emphases throughout the pipeline. DevOps security typically zeroes in on the hardening of pipeline components and the enforcement of security policy across infrastructure and runtime. In contrast, DevSecOps broadens the mandate, making security everyone’s job from the earliest design phase, marrying threat modeling, secure coding, and security testing with development and release cadence. Collectively, they unite elite defensive posture with the speed and fluidity of continuous integration and continuous delivery, driving home the principle that security velocity must equal delivery velocity.

Rethinking Cloud Compliance With an AI-Driven Approach

Aggregated on: 2025-12-30 20:14:56

The regulatory environment across the world is becoming increasingly stringent day by day. It is expanding across several business sectors, and the technology sector is not far behind. Cloud computing and artificial intelligence (AI) have been at the center stage without a doubt. While both technologies have brought about immense abundance, the industry is grappling with increasing pressure to comply with complex laws and regulatory frameworks such as GDPR, HIPAA, SOC 2, and industry-specific standards. Work on traditional compliance approaches focuses mainly on manual audits, static policies, and periodic reviews. This needs to be rethought, as these approaches need to keep pace with the speed and scale of modern cloud environments. In this context, AI becomes a powerful tool to manage cloud compliance. AI can assist across a broad scope of use cases — from machine learning and predictive analytics to intelligent automation — the range is endless. Beyond routine automation of day-to-day tasks, AI can enable teams to not only anticipate risks and optimize governance strategies but also maintain proactive compliance across hybrid and multi-cloud infrastructures. In this article, let’s understand the different ways in which AI is redefining cloud compliance, which helps organizations achieve proactive and intelligent governance.

Bias and Shortcut Tests for Vision Models: A Practical Test Suite From Real-World Experiments

Aggregated on: 2025-12-30 18:14:56

When I first started working with deep learning image models, I did what most people do: Train a model Check top-1/top-5 accuracy Look at a few confusion matrices On paper, everything looked great. But when I started poking at the models in slightly weird ways, I learned a very uncomfortable truth:

How to Build a Self-Evolving AI Agent That Learns From Failure

Aggregated on: 2025-12-30 17:14:56

For developers building autonomous systems, today's generative AI agents present a fundamental challenge: they are amnesiacs. An agent can execute a complex task, fail, and then repeat the same mistake five minutes later. Their capabilities are "test-time static," meaning they are frozen at the moment their training ends. They cannot learn from their interactions, discard valuable insights, or correct their own errors. For developers and architects trying to build reliable autonomous systems, this is the primary barrier to adoption. An unreliable agent is not autonomous. It is a brittle system that creates technical debt.

Building Unified, Access-Aware Search in a Data Mesh

Aggregated on: 2025-12-30 16:14:56

Over the last few years, I have worked on a distributed data platform in which each engineering group owns the data it produces from heterogeneous data processing systems. Our central platform follows a data mesh model, as every domain manages its own pipelines and storage, but discovery in the unified system should be seamless to anyone searching across the mesh. That goal sounded simple, but wasn’t that simple considering we host datasets from ultra and black undisclosed products. The first complaint we heard, repeatedly:

Designing for Empathy: Human-Centered AI Patterns for Real Systems

Aggregated on: 2025-12-30 14:29:56

Abstract Empathy-aware interaction is becoming a core requirement as AI systems mediate healthcare, education, and public services. For software teams, the challenge is not “make the bot nice,” but: how do we design and ship systems that sense user context, adapt with calibrated empathy, and still remain private, auditable, and predictable? This article proposes a practice-oriented framework that unifies affect sensing, context modeling, policy-based adaptation, and transparent explanations into a deployable pipeline for human–machine systems. The contributions for developers are:

The Coming Shift From Bigger AI Models to Smaller, Faster Ones

Aggregated on: 2025-12-29 20:14:56

Bigger isn’t always better, especially when it comes to AI models. They are larger, more capable, and more resource-intensive, utilizing bigger models to deliver enhanced reasoning, summarization, and even code generation capabilities. The size and scalability of gen AI models have their limits. Larger models are designed to work best with open-ended problems, which are, by nature, often countered in chats. However, when an AI-powered product, such as a CRM system, is using AI models, the problem that the product is solving is actually very much fixed and highly structured. It has deviated substantially from the original chat format, which would require AI models to define the problem and come up with the steps to a solution themselves.

Manage Knowledge, Not Code

Aggregated on: 2025-12-29 18:14:56

After 20 years deep in the trenches of the software industry, working with everyone from early-stage startups to Fortune 100 companies, I’ve seen every kind of problem you can imagine except one: I’ve never seen a company that truly lacked the resources to write the code it needed. The problem isn’t resources. It’s how we think about software itself.

Terraform Type Constraints: Best Practices for Enterprise-Scale AWS

Aggregated on: 2025-12-29 17:14:56

Terraform's type constraints help write IaC that is reliable, reusable, and easily maintained when building out AWS-based infrastructures. Ensuring your AWS variables have proper typing can reduce your chances of misconfiguring your AWS resources, help you enforce best practices, and make it easier to read, understand, and be confident in your module usage. This article will go into detail on Terraform's type system and demonstrate how type constraints are used when deploying AWS-based infrastructure. Using Type Constraints for Terraform Terraform provides a type constraint, which helps developers confirm whether a variable has passed the right type of information. If no type constraint is defined, then it is very easy to assign incorrect data to your AWS resources, and therefore, your deployment will fail, or there may be many configuration errors hidden within the structure of your deployment. In addition to providing validation, types enable teams to create consistent code, validate expectations, and create self-documenting code.