There’s a growing pressure in tech companies to “add AI” to every product or feature. Executives and stakeholders often ask for an “AI assistant” or a ChatGPT-style feature on every screen, assuming more AI automatically makes products better. But the truth is, the most important AI work right now isn’t building more AI, it’s designing guardrails around it.
AI isn’t magic. Left unchecked, it can quietly make products worse, frustrate users, and introduce risk. Before adding AI for the sake of AI, teams need a framework to decide where it adds value and where it doesn’t.
Fish Shell 4.3 introduces smarter theming, improved completions, better terminal integration, and many changes across scripting and interactive features.
Shows how to install PHP 8.5 on Ubuntu using the ondrej/php PPA, with examples for Apache and Nginx setups. It also walks through common extensions, switching between PHP versions, and handling typical PHP-FPM issues.
Powered by the latest and greatest Linux 6.18 kernel series (with SYSRQ support), PorteuX 2.5 ships with no less than eight editions featuring the GNOME 49.2, KDE Plasma 6.5.4, Cinnamon 6.6, LXQt 2.3, COSMIC 1.0, Xfce 4.20, LXDE 0.11.1, and MATE 1.28.2 desktop environments.
An alpha release of Orion for Linux, a new Webkit-based web browser from paid search engine Kagi, is out for testing – and early impressions are positive.
The integration of AI-enhanced microservices within the SAFe 5.0 framework presents a novel approach to achieving scalability in enterprise solutions. This article explores how AI can serve as a lean portfolio ally to enhance value stream performance, reduce noise, and automate tasks such as financial forecasting and risk management.
The cross-industry application of AI, from automotive predictive maintenance to healthcare, demonstrates its potential to redefine processes and improve outcomes. Moreover, the shift towards decentralized AI models fosters autonomy within Agile Release Trains, eliminating bottlenecks and enabling seamless adaptation to changing priorities. AI-augmented DevOps challenges the traditional paradigms, offering richer, more actionable insights throughout the lifecycle. Despite hurdles in transitioning to microservices, the convergence of AI and microservices promises dynamic, self-adjusting systems crucial for maintaining competitive advantage in a digital landscape.
LLM agents are fantastic in demos. Fire up a notebook, drop in a friendly "Help me analyze my cloud metrics," and suddenly the model is querying APIs, generating summaries, classifying incidents, and recommending scaling strategies like it’s been on call with you for years.
But the gap between agent demos and production agents is the size of a data center.
Organizations today want to build chatbots capable of handling a multitude of tasks, such as FAQs, troubleshooting, recommendations, and ideation. My previous article focused on a high-level view of designing and testing chatbots. Here, I will dive deeper into how strong intent routing and orchestration should figure into your chatbot design.
What Is a Multi-Use Chatbot?
A multi-use case chatbot supports several distinct tasks, each with different goals, performance needs, and response styles. For each use case, LLM parameters are fine-tuned around its goals. For example, a factual FAQ flow might use a low temperature for consistency, while a recommendation flow might use a higher one for creativity. Similarly, top p-values, frequency, presence, and max token penalties are also adjusted based on the use case.
Agentic AI is no longer a research concept or a demo-only capability. It is being introduced into production systems that must operate under real constraints: predictable latency, bounded cloud spend, operational reliability, security requirements, and long-term maintainability. Autonomous agents that can reason, plan, collaborate, and act across distributed architectures promise significant leverage, but they also introduce a new cost model that many engineering teams underestimate.
Early implementations often succeed functionally while failing operationally. Agents reason too frequently, collaborate without limits, and remain active long after decisions have been made. What starts as intelligent autonomy quickly turns into inflated inference costs, unpredictable system behavior, and architectures that are difficult to govern at scale.
Hey everybody, let’s talk about a silent crisis that has probably plagued every developer who has ever worked on a backend system. You know the story: a user clicks “Submit Payment,” the spinner spins… and spins… then a timeout error occurs. The user, unsure, hits the button again. What unravels next? In a poorly designed system, this single click can equate to a double charge, a duplicate order, or two identical welcome emails in a user’s inbox.
I learned this lesson the hard way early in my career. We had a nice, slick new payment service, and during a period when the network was unstable, we experienced a handful of users being charged twice. It was horrible — user trust was abused, followed by a flurry of manual refunds. That incident was my brutal, and expensive, introduction to the need for idempotency.
Let’s start with a simple fact that cannot be overlooked today: identity is the new perimeter. Following this logic, there exists a simple yet powerful principle of Zero Trust — never trust, always verify. Zero Trust protects architectures by continuously verifying users, devices, and more — whether internal or external — to protect critical resources, sensitive data, and enterprise applications from unauthorized access, insider threats, and lateral movement. Some useful methods within this principle include strong identity verification, multi-factor authentication (MFA), device posture checks, least-privilege access, and continuous monitoring. This significantly reduces the risk of compromise.
In theory, leveraging this approach should make breaches almost impossible. However, in reality, high-profile security incidents continue to occur — even in organizations with very robust security controls. One might ask: how is this possible? The gap lies in the methods of implementation. Attackers are becoming increasingly sophisticated, and simple safeguards such as authentication, device compliance, and network controls alone are not sufficient. These controls can be easily bypassed by attacking one element in the technology ecosystem that is most often implicitly trusted — the web browser. Browsers are the face of the internet. They exist as the primary interface between users and applications, executing untrusted code, loading third-party scripts, and interacting with countless external domains. Without any protection mechanisms in the browser, attackers can hijack sessions, manipulate tokens, or exploit extensions. This stark difference between the promise and reality of the humble browser makes it the weakest link in modern Zero Trust security architectures.
Testing SQL queries in production environments presents unique challenges that every data engineering team faces. When working with BigQuery, Snowflake, Redshift, Athena, or Trino, traditional testing approaches often fall short:
Fragile integration tests that break when production data changes
Slow feedback loops from running tests against full datasets
Silent failures during database engine upgrades that change SQL semantics
No type safety between SQL queries and Python code
Database migration challenges where SQL syntax differs across platforms
Complex setup requirements with different mocking strategies for each database
These challenges led to the development of SQL Testing Library - an open-source Python framework that enables fast, reliable unit testing of SQL queries with type-safe data contracts and mock data injection across BigQuery, Snowflake, Redshift, Athena, Trino, and DuckDB.
Instead of building custom integrations for a variety of AI assistants or Large language models (LLMs) you interact with — e.g., ChatGPT, Claude, or any custom LLM — you can now, thanks to the Model Context Protocol (MCP), develop a server once and use it everywhere.
This is exactly as we used to say about Java applications; that thanks to the Java Virtual Machine (JVM), they're WORA (Write Once Run Anywhere). They're built on one system and expected to run on any other Java-enabled system without further adjustments.
Войти
