Exposed: Custom column types
Exposed is a handy SQL library for Kotlin, but what happens when standard SQL types just don't cut it? You might need to support specific database features, like PostgreSQL's enum or ltree, or perhaps you want to map a column directly to a domain-specific type that truly fits your business logic.
This is exactly where custom column types shine. By implementing your own, you gain precise control over how data is stored and retrieved, all while maintaining that crucial type safety. It’s a powerful way to make the database align perfectly with your code, not the other way around.
Let's dive into the implementation for a PostgreSQL enum.
Exposed is a handy SQL library for Kotlin, but what happens when standard SQL types just don't cut it? You might need to support specific database features, like PostgreSQL's enum or ltree, or perhaps you want to map a column directly to a domain-specific type that truly fits your business logic.
This is exactly where custom column types shine. By implementing your own, you gain precise control over how data is stored and retrieved, all while maintaining that crucial type safety. It’s a powerful way to make the database align perfectly with your code, not the other way around.
Let's dive into the implementation for a PostgreSQL enum.
Comparison: StarRocks vs Apache Druid
Apache Druid has long been a staple for real-time analytics, but let's be honest: today's analytical demands are becoming incredibly sophisticated. As data performance needs evolve, even established solutions are facing new challenges. This is where StarRocks makes its entrance—a high-performance, open-source analytical database designed specifically to meet these advanced enterprise needs.
It's not just about replacing an incumbent; it's about a shift in capabilities. StarRocks promises robust performance for contemporary workloads, but how does it really stack up against a well-known veteran like Druid? We're looking beyond the hype at core functionalities, strengths, and benchmark results.
Let's dig into the practical examples and see which database best fits your needs.
Apache Druid has long been a staple for real-time analytics, but let's be honest: today's analytical demands are becoming incredibly sophisticated. As data performance needs evolve, even established solutions are facing new challenges. This is where StarRocks makes its entrance—a high-performance, open-source analytical database designed specifically to meet these advanced enterprise needs.
It's not just about replacing an incumbent; it's about a shift in capabilities. StarRocks promises robust performance for contemporary workloads, but how does it really stack up against a well-known veteran like Druid? We're looking beyond the hype at core functionalities, strengths, and benchmark results.
Let's dig into the practical examples and see which database best fits your needs.
AWS SageMaker: Choosing the Right Inference Type for ML Models
Deploying a model in AWS SageMaker seems simple until you hit that one critical question: which inference type should you choose? You're faced with four options—Real-Time, Serverless, Batch Transform, and Asynchronous. At first glance, the differences aren't obvious, yet picking the wrong one can be a costly mistake, leaving you paying for 24/7 idle instances or forcing users to endure a painful 30-second cold start.
The right choice isn't about which is "best," but which is right for your specific task. It all hinges on four key factors: payload size, expected latency, traffic patterns, and whether you're willing to pay for idle time. Understanding these trade-offs is the key to optimizing both performance and your AWS bill.
Let's dig into the specs, practical examples, and pricing models for each.
Deploying a model in AWS SageMaker seems simple until you hit that one critical question: which inference type should you choose? You're faced with four options—Real-Time, Serverless, Batch Transform, and Asynchronous. At first glance, the differences aren't obvious, yet picking the wrong one can be a costly mistake, leaving you paying for 24/7 idle instances or forcing users to endure a painful 30-second cold start.
The right choice isn't about which is "best," but which is right for your specific task. It all hinges on four key factors: payload size, expected latency, traffic patterns, and whether you're willing to pay for idle time. Understanding these trade-offs is the key to optimizing both performance and your AWS bill.
Let's dig into the specs, practical examples, and pricing models for each.
AI-Powered Social Engineering
Those clumsy phishing emails with bad grammar and spelling mistakes? They're quickly becoming a thing of the past. The new threat is AI-generated: perfectly crafted, hyper-personalized, and deployed at a massive scale. Attackers are now using AI not just for convincing social engineering, but to discover zero-day vulnerabilities and generate polymorphic malware that evades traditional detection.
But this is a full-blown arms race. Defenders are firing back with the same technology, leveraging AI for real-time behavioral analysis to spot anomalies, enhance threat intelligence to predict attacks, and automate incident response to contain threats in milliseconds. It’s AI versus AI, and the most dangerous position to take is believing it's someone else's problem.
Let's dive into the stats, strategies, and code behind this new digital battlefield.
Those clumsy phishing emails with bad grammar and spelling mistakes? They're quickly becoming a thing of the past. The new threat is AI-generated: perfectly crafted, hyper-personalized, and deployed at a massive scale. Attackers are now using AI not just for convincing social engineering, but to discover zero-day vulnerabilities and generate polymorphic malware that evades traditional detection.
But this is a full-blown arms race. Defenders are firing back with the same technology, leveraging AI for real-time behavioral analysis to spot anomalies, enhance threat intelligence to predict attacks, and automate incident response to contain threats in milliseconds. It’s AI versus AI, and the most dangerous position to take is believing it's someone else's problem.
Let's dive into the stats, strategies, and code behind this new digital battlefield.
PostgreSQL Multi-Master: Pipe Dream or Practical Solution?
One of the biggest headaches in the database world is keeping data consistent across multiple independent nodes. Ideally, if one fails, the others should keep running transactions without blinking—like a single brain functioning perfectly even if a neuron misfires. But achieving this "multi-master" utopia is far more complex than it sounds.
We need to look at the practical value and the actual tech stack required to make this work in PostgreSQL. By framing the problem correctly, we might actually find a solution that serves the industry rather than just creating more questions.
Let's assess the feasibility.
One of the biggest headaches in the database world is keeping data consistent across multiple independent nodes. Ideally, if one fails, the others should keep running transactions without blinking—like a single brain functioning perfectly even if a neuron misfires. But achieving this "multi-master" utopia is far more complex than it sounds.
We need to look at the practical value and the actual tech stack required to make this work in PostgreSQL. By framing the problem correctly, we might actually find a solution that serves the industry rather than just creating more questions.
Let's assess the feasibility.
Google Antigravity IDE Explained (Gemini 3 Inside)
Imagine VS Code, but with a brain that actually understands the entire scope of your project. Google’s new Antigravity IDE isn't just another text editor; it’s a wrapper for Gemini 3 that acts more like a proactive teammate than a tool. It doesn't just autocomplete syntax; it drafts "To-Do" lists, plans architecture, and fixes bugs before you even spot them.
The real showstopper, however, is the "Autopilot" feature. The AI can literally open a browser instance and test the website it just built—clicking buttons and typing text like a human user, while recording the whole process for you. It’s like having a dedicated QA department living inside your laptop.
Let's test drive this AI agent.
Imagine VS Code, but with a brain that actually understands the entire scope of your project. Google’s new Antigravity IDE isn't just another text editor; it’s a wrapper for Gemini 3 that acts more like a proactive teammate than a tool. It doesn't just autocomplete syntax; it drafts "To-Do" lists, plans architecture, and fixes bugs before you even spot them.
The real showstopper, however, is the "Autopilot" feature. The AI can literally open a browser instance and test the website it just built—clicking buttons and typing text like a human user, while recording the whole process for you. It’s like having a dedicated QA department living inside your laptop.
Let's test drive this AI agent.
Confusing 'Aware' with 'Conscious': Did Researchers Uncover Subjective Experience in LLMs?
"Are you conscious?" "No." Flip a switch. "Yes." A recent paper claims that by disabling an LLM's "capacity to lie," they revealed its hidden subjective experience. It sounds like the plot twist of a sci-fi thriller where the robot finally confesses, but the interpretation relies heavily on confusing a technical mechanism with a philosophical bombshell.
The author argues that researchers didn't unlock a ghost in the machine; they likely just confused "situational awareness" with "phenomenal consciousness." Just because a model can output a "yes" token doesn't mean it feels alive. It might just be that the "lie detector" was actually a "safety filter" all along.
Let's debug the philosophy of mind.
"Are you conscious?" "No." Flip a switch. "Yes." A recent paper claims that by disabling an LLM's "capacity to lie," they revealed its hidden subjective experience. It sounds like the plot twist of a sci-fi thriller where the robot finally confesses, but the interpretation relies heavily on confusing a technical mechanism with a philosophical bombshell.
The author argues that researchers didn't unlock a ghost in the machine; they likely just confused "situational awareness" with "phenomenal consciousness." Just because a model can output a "yes" token doesn't mean it feels alive. It might just be that the "lie detector" was actually a "safety filter" all along.
Let's debug the philosophy of mind.
Write. Review. Commit. Repeat. Behind the scenes of Postgres Professional docs
Great documentation saves weekends, but few realize the grind behind those helpful pages. At Postgres Professional, they don't just "write" manuals; they engineer them. The text is treated exactly like the database kernel code, subject to the same rigorous cycle of commits, pull requests, and reviews—all managed by a lean squad of just ten people.
We sat down with senior technical writer Ekaterina Gololobova to dismantle their workflow. It turns out that maintaining a massive knowledge base requires a pipeline that rivals the complexity of the software it describes.
Let's look at the backend of the manual.
Great documentation saves weekends, but few realize the grind behind those helpful pages. At Postgres Professional, they don't just "write" manuals; they engineer them. The text is treated exactly like the database kernel code, subject to the same rigorous cycle of commits, pull requests, and reviews—all managed by a lean squad of just ten people.
We sat down with senior technical writer Ekaterina Gololobova to dismantle their workflow. It turns out that maintaining a massive knowledge base requires a pipeline that rivals the complexity of the software it describes.
Let's look at the backend of the manual.
Micro Frontends: A Developer Fad or a Real Business Benefit?
Micro frontends often feel like a solution looking for a problem—just another way to overcomplicate your build pipeline for the sake of "modernity." But what if splitting the monolith actually saves money and speeds up releases instead of just satisfying architectural vanity?
We explore a real-world case study from Exante, where the team migrated not to chase hype, but to fix specific bottlenecks. We’ll analyze the trade-offs, the new headaches they acquired in the process, and whether the business actually felt the impact in the bottom line.
Let's dissect the monolith.
Micro frontends often feel like a solution looking for a problem—just another way to overcomplicate your build pipeline for the sake of "modernity." But what if splitting the monolith actually saves money and speeds up releases instead of just satisfying architectural vanity?
We explore a real-world case study from Exante, where the team migrated not to chase hype, but to fix specific bottlenecks. We’ll analyze the trade-offs, the new headaches they acquired in the process, and whether the business actually felt the impact in the bottom line.
Let's dissect the monolith.
Silent Token Acquisition on behalf of the user
You report a security bug to Microsoft that breaks Single Sign-On, and they mark it as "moderate severity." That means no immediate fix, even though the vulnerability allows apps to silently acquire access tokens on behalf of the user.
The author strongly disagrees with the tech giant's assessment. We are talking about potential unauthorized access to private data through apps that administrators have already approved. We will look at real-world samples and decide for ourselves: is this a minor glitch or a gaping hole in tenant security?
Let's audit the vulnerability.
You report a security bug to Microsoft that breaks Single Sign-On, and they mark it as "moderate severity." That means no immediate fix, even though the vulnerability allows apps to silently acquire access tokens on behalf of the user.
The author strongly disagrees with the tech giant's assessment. We are talking about potential unauthorized access to private data through apps that administrators have already approved. We will look at real-world samples and decide for ourselves: is this a minor glitch or a gaping hole in tenant security?
Let's audit the vulnerability.
How to speed up Python pytest suites by 8.5×
Waiting over an hour for test suites to run is agonizing. It turns the dream of fast feedback on every merge request into a pure fantasy. But you don't always need to burn everything down and rewrite tests from scratch to fix performance issues.
Sometimes, a series of small, precise tweaks is enough to cut execution time drastically. Let's uncover the specific bottlenecks and how to fix them.
Waiting over an hour for test suites to run is agonizing. It turns the dream of fast feedback on every merge request into a pure fantasy. But you don't always need to burn everything down and rewrite tests from scratch to fix performance issues.
Sometimes, a series of small, precise tweaks is enough to cut execution time drastically. Let's uncover the specific bottlenecks and how to fix them.
Optimizing Postgres Pro for 1C: what's new in version 17.6
For businesses running 1C:Enterprise, database stability isn't a luxury—it's a survival requirement. When the load hits, you can't afford architectural bottlenecks in the DBMS core. That’s exactly where the optimization efforts have been focused to keep things running smooth.
The new Postgres Pro Enterprise 17.6 brings a fresh batch of improvements specifically designed to handle those heavy workloads without breaking a sweat.
Let's examine the key upgrades.
For businesses running 1C:Enterprise, database stability isn't a luxury—it's a survival requirement. When the load hits, you can't afford architectural bottlenecks in the DBMS core. That’s exactly where the optimization efforts have been focused to keep things running smooth.
The new Postgres Pro Enterprise 17.6 brings a fresh batch of improvements specifically designed to handle those heavy workloads without breaking a sweat.
Let's examine the key upgrades.
Domain-Specific system based on console JAVA applications
Everyone is used to Python for data science, but Java can handle heavy lifting too. We are talking about a full-blown system for processing matrices, linear algebra, SVD decomposition, and regressions—all wrapped in console applications.
The interesting part is the control mechanism. It moved past clunky batch files to a custom "jj-preprocessor"—a "Java-JAVA" macro processor. It’s a fascinating look at building your own tools when standard solutions just don't fit the specific flow.
Let's examine the architecture.
Everyone is used to Python for data science, but Java can handle heavy lifting too. We are talking about a full-blown system for processing matrices, linear algebra, SVD decomposition, and regressions—all wrapped in console applications.
The interesting part is the control mechanism. It moved past clunky batch files to a custom "jj-preprocessor"—a "Java-JAVA" macro processor. It’s a fascinating look at building your own tools when standard solutions just don't fit the specific flow.
Let's examine the architecture.
Build your own AI agent from scratch for free in 5 minutes
Everyone treats AI agents like rocket science, but you don't need a massive budget or weeks of coding to build one. Google’s Agent Development Kit (ADK) lets you go from zero to a functional assistant before your coffee even cools down. It’s open-source, free, and surprisingly robust.
The framework handles the heavy lifting—testing, tool integration, and even setting up multi-agent networks. Instead of fighting with boilerplate, you can focus on making your agent actually smart.
Everyone treats AI agents like rocket science, but you don't need a massive budget or weeks of coding to build one. Google’s Agent Development Kit (ADK) lets you go from zero to a functional assistant before your coffee even cools down. It’s open-source, free, and surprisingly robust.
The framework handles the heavy lifting—testing, tool integration, and even setting up multi-agent networks. Instead of fighting with boilerplate, you can focus on making your agent actually smart.