标签：#war

The lakehouse projects spent this week arguing about boundaries. Iceberg decided where conformance testing lives and started sketching the REST API shape that V4 tables will need. Polaris argued about what a committer owes a project when LLMs make pull requests cheap. Parquet pulled a feature apart because two proposals were reaching for the same mechanism. DataFusion and Iceberg Rust opened a joint thread about which repository should own their integration. Every one of those debates is a question about ownership, and the answers this week tell you a lot about how these communities plan to scale. Apache Iceberg The single biggest outcome of the week was the creation of a new repository. Neelesh Salian, working with Sung Yun and Andrei Tserakhau, called a vote to create apache/iceberg-verification , a standalone home for language-neutral conformance fixtures that every Iceberg implementation can run against. The vote passed with five binding +1s from Russell Spitzer, Sung Yun, Matt Topol, Daniel Weeks, and Amogh Jahagirdar, plus twenty-two non-binding votes. That is a wide turnout. The names on the non-binding list read like a roll call of the Rust, Python, Go, and Java maintainers, which is the point. Salian will now work with a PMC member to stand the repository up. The reason this matters goes beyond tidiness. Iceberg has at least five serious implementations today across Java, Python, Rust, Go, and C++. Each one carries its own test fixtures and its own understanding of edge cases in the spec. When two implementations disagree about how to interpret a manifest list, users find out the hard way. A shared set of fixtures that every implementation reads from one place turns spec ambiguity into a failing test rather than a production surprise. The 29 messages in the vote thread also included a fair amount of discussion about what belongs in the first batch of fixtures, and the conversation is worth reading if you maintain a client. The second major thread was about

Plaud's new 'agentic' earbuds are priced at $249.

Insert Molding Design: How to Place Metal Inserts Without Disaster — 8 Years of Structure Design Notes Every structure designer has been burned by inserts at some point — a nut seated crooked, an insert causing sink marks or cracks, pull-out force too low, an insert washed away by melt during injection. I've tripped on all of these myself. Insert molding sounds simple: drop a metal part into the mold and inject plastic around it. But metal and plastic have thermal expansion coefficients an order of magnitude apart, and every detail — shrinkage, grip force, locating method — can turn into a disaster. This article walks through the key design principles of insert molding, from insert types and locating structures to wall thickness and defect prevention. All of it is experience paid for with real money on real projects. Three Common Types of Inserts Insert molding falls into three categories by purpose, each with completely different design priorities. 1. Thread Inserts (Nut Inserts) The most common type. Tapping threads directly into plastic fails fast — fine threads under M3 strip after a few cycles — so metal nuts are embedded in the plastic. Copper inserts dominate because copper conducts heat well (fast heat dissipation during molding), has moderate hardness, and gives clean threads after tapping. We made a portable Bluetooth speaker with an ABS housing whose four corner posts needed M2.5 screws. Tapping the plastic posts directly stripped after three cycles. We switched to embedded M2.5×4mm copper nuts and measured over 45N pull-out, still stable after 500 screw cycles. The key: leave at least 1.5mm of plastic wall around the nut's outer diameter, or the area sinks and bubbles after cooling. 2. Locating / Support Inserts These locate, support, or conduct magnetism — locating pins in motor brackets, magnetic cores in sensor housings. The biggest challenge is insert positioning accuracy and post-molding offset. In 2024 we made a smart lock panel embedding a stainle

Tired of Claude Code generating bizarre, overly dramatic jargon like "load-bearing spine"? You can fix this by enforcing Simplified Technical English (STE) in your system instructions or .claudemd files. This 1970s aerospace standard restricts vocabulary, forcing your AI agent to communicate in clear, direct, and highly actionable prose. "The load-bearing spine has hit a ceiling, and that is a significant foot gun with a large blast radius." If you have spent any time recently working with AI coding agents, you have probably stared at your terminal reading absolute gibberish like this, wondering: What on earth are you trying to tell me? I asked a straightforward technical question, and instead of a direct answer, I got a theatrical performance. It is incredibly tiring to translate AI metaphors back into plain English just to figure out which line of code actually broke. Fortunately, there is a remarkably elegant fix for this. The solution does not involve complex prompt engineering; instead, it leverages a fifty-year-old aerospace standard: Simplified Technical English (STE) . Why does Claude Code output weird technical jargon? AI models generate overly dramatic jargon because they are trained on vast internet corpuses where technical writing is often cluttered, metaphorical, and performative. To sound authoritative, the model indexes on complex vocabulary and metaphorical hand-waving instead of simple, direct statements. Imagine a scenario where your team is debugging a database lock. A human engineer would say, "The transaction is blocked." An AI model, eager to please and sound sophisticated, might describe it as a "temporal execution bottleneck causing systemic architectural paralysis." This happens because reinforcement learning from human feedback (RLHF) often rewards models for sounding smart and comprehensive. Without strict stylistic constraints, the agent defaults to verbose, metaphorical explanations that add cognitive load rather than solving your proble

In virtually every software engineering team, the temptation of the 'quick and dirty' fix surfaces sooner or later. The sprint deadline is looming, stakeholders are eager for a release, and a code snippet exists that barely passes the happy path. The logic is undocumented, edge cases remain unaddressed, and the design is brittle, yet the ticket can technically be moved to 'Done'. In the short term, everyone appears satisfied: the feature ships and the milestone is recorded. But before long, the consequences arrive: subtle bugs surface in production, extending the codebase becomes perilous, and teammates spend frustrating hours attempting to decipher undocumented logic. What began as a brief shortcut solidifies into technical debt and team friction. At the core of Christian ethics lies the command to love your neighbour as yourself. While that principle is often discussed in abstract theological terms, in modern software engineering it takes on direct, tangible significance. Who is your neighbour in a development team? Your neighbour is the colleague who will maintain, debug, or extend your pull request six months from now. Your neighbour is the junior engineer looking to existing code for guidance. And your neighbour is the end user relying on the system to function reliably and securely. When you deliberately invest effort in clear naming conventions, modular architecture, comprehensive documentation, and thorough automated tests, you provide genuine service to your peers. You choose to carry the cognitive burden today so that someone else does not suffer tomorrow. That is Christian care translated into code. Craftsmanship extends beyond syntax; it shapes the cultural atmosphere of an engineering team: Honesty regarding technical debt: Having the courage to articulate when architectural shortcuts threaten system sustainability, rather than passively allowing brittle code into production. Constructive peer reviews: Conducting code reviews with the intention of mento

By using a sustainable testing strategy, you can skip unnecessary tests, ensure failing fast and early, and only run tests affected by code changes. Tracking energy use per test and using static code analysis can help spot inefficiencies and guide optimization efforts. By Ben Linders

Authorities in Australia have arrested two men believed to be members of TeamPCP, a prolific cybercrime and data extortion group blamed for perpetrating the longest running spree of software supply chain attacks ever. In a statement released today, the Australian Federal Police (AFP) said two unnamed suspects from Western Australia, aged 21 and 23, were arrested in connection with a "sophisticated cybercrime syndicate that allegedly created malicious open-source software to rob thousands of global businesses." The AFP did not name the defendants, but KrebsOnSecurity learned the 21-year-old suspect's real identity in June, and has been communicating with him ever since. This story includes interviews with TeamPCP's self-described spokesperson, and examines clues left behind by the TeamPCP leader that likely led to his undoing.

Java Service Steward is a new Windows service host for Java applications. It reads the wrapper.conf format used by the Java Service Wrapper, follows the same command line and log format, and is licensed Apache-2.0 OR MIT. I wrote it because the Community Edition of the Java Service Wrapper has no 64-bit Windows build, and I did not want to buy a license or rewrite the service integration of applications that already had working configuration files. Repository: https://github.com/jayyanez/java-service-steward What it is The distribution is two files, wrapper.exe and wrapper.jar . The executable is written in Rust and does the Windows part: it registers the service, launches the JVM, keeps a control channel to it over a loopback socket, restarts it when it exits unexpectedly or stops answering pings, writes and rotates wrapper.log , and handles Service Control Manager requests (stop, pause, resume, custom control codes). The JAR is compiled for Java 8 and contains the launcher classes and a small API. There is no native DLL and no JNI. It only runs on 64-bit Windows. There is no Unix version. What is compatible Configuration. wrapper.conf with #include , #encoding , set.VAR=value , %VAR% expansion and numbered properties such as wrapper.java.additional.<n> . Relative paths resolve from the executable's directory, as before. Command line. -c runs in a console, -i and -r install and remove the service, -t and -p start and stop it, -q queries it, -d requests a thread dump. Property overrides on the command line and -- pass-through of application arguments work the same way. Service registration. An installed service's ImagePath calls wrapper.exe -s <conf> , so an existing registration keeps working. Log format. Records use the same LPTM layout, the same column widths and the same SIZE , WRAPPER and JVM roll modes, so scripts that parse wrapper.log do not need changes. Launchers. A configuration that names the original SimpleApp , StartStopApp or JarApp launcher in wrappe

One thing I learned from working with experienced engineers is that solving a problem and approaching a problem are two different skills. During one of my projects, I had the opportunity to work closely with Microsoft engineers. Since I was working independently, whenever I faced an issue, I would first spend time exploring it myself. I would check the data, logs, code, test different possibilities, and eventually figure out a solution. But sometimes, when I discussed the same issue with them, I was surprised by how differently they approached it. Instead of immediately looking for a fix, they would pause and ask a few simple but thoughtful questions. Those questions often narrowed the scope of the problem quickly and helped uncover the root cause much faster than trial and error. Over time, I started adopting that mindset. I learned that spending more time understanding why something is happening often leads to a better outcome than rushing into how to fix it. I also picked up many small but valuable engineering habits from everyday discussions, habits that continue to help me in my work today. Courses and certifications definitely help us learn new technologies. But some of the best learning in my career has simply come from working with skilled people, observing how they think, and applying those learnings in my own way. Grateful for the experiences, mentorship, and the people who generously shared their knowledge along the way. Learning #ProblemSolving #CareerGrowth #DataEngineering #GrowthMindset #ProfessionalDevelopment

MyZubster Is Not Trying to Build Another App — We're Exploring a Verifiable Digital Ecosystem For years, software development has largely followed the same pattern: User → Application → Database → Service AI changed part of that equation. IoT changed another part. Blockchain introduced new models for provenance and ownership. But there is still a difficult problem connecting all of them: How can a digital system verify what actually happened in the real world? This is one of the questions driving the development of MyZubster. MyZubster is an Italian open-source digital ecosystem currently under development. It hasn't reached its final public form yet. And that's important. Because we're not presenting a finished platform. We're documenting how the architecture evolves. From application to ecosystem Calling MyZubster simply an "app" increasingly feels incomplete. The architecture we're exploring connects several layers: MYZUBSTER ┌─────────────────┐ │ REAL WORLD │ │ people / places │ │ devices / events│ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ DATA │ │ sensors / users │ │ external sources│ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ PROVENANCE │ │ source / time │ │ context / proof │ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ AI │ │ interpretation │ │ automation │ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ EVIDENCE │ │ verification │ │ reproducibility │ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ DIGITAL SERVICES│ └─────────────────┘ The goal isn't to put every technology imaginable into one application. The interesting part is the connection between these layers. AI needs evidence Generative AI can produce extraordinary outputs. But generation and verification are fundamentally different operations. An AI system can say: "This intervention reduced water consumption by 30%." But where did that number come from? What sensor produced the original measurement? What period was compared? What methodology was used? Was the dataset modified? Can somebody repro

You can build a working agent mesh with QUIC transport, encrypted messaging, and decentralized coordination. Five processes can reinforce independent conclusions and let unsupported signals decay. The mesh works. Then you try to introduce it to another agent and discover you have no standard way to ask what the swarm can do. No retained task to retrieve after an internal signal expires. No interoperable progress stream. No cancellation contract. No artifact another framework would understand. SMESH is a Rust-based decentralized agent framework that hit this boundary. The author had built a society with no border crossing. The solution was Google's Agent2Agent (A2A) protocol, announced in April 2025 and moved under Linux Foundation governance in June 2025. A2A provides the missing public contract: a way for agents built by different vendors to discover one another, exchange messages, and collaborate without sharing private memory, tools, or internal plans. The Cold-Start Problem in Agent Meshes Traditional service meshes solve discovery with a central registry. Kubernetes has etcd. Consul has its catalog. Envoy has xDS. You register your service, get a DNS name or IP, and other services find you. This works because services are relatively static and the registry is the source of truth. Agent meshes are different. Agents are ephemeral, context-dependent, and often spawned on demand. They need to: Discover peers without a central registry Exchange capability metadata at runtime Negotiate protocols without pre-shared configuration Maintain security boundaries during introduction The coordination primitives (message passing, consensus, signal decay) assume agents already know about each other. Discovery is the layer below coordination. SMESH had the top layer working but no way to bootstrap the bottom layer without manual wiring. What A2A Provides A2A is not a coordination protocol. It is an introduction protocol. The spec defines: Discovery handshake : How agents announ

When learning Angular, Spring, and Node.js, I often came across terms like SOLID, Dependency Injection (DI), Inversion of Control (IoC), IoC Container, and Composition . At first, these concepts can feel like they are all the same thing. They are not. The key realization is: SOLID is about how we design software. Composition is about how we build larger systems from smaller pieces. Dependency Injection is a technique for providing those pieces. IoC containers automate that process. Understanding this relationship makes Angular, Spring, and Node.js architectures much easier to reason about. 1. SOLID Is a Design Principle, Not a Framework Feature SOLID is a collection of software design principles. For example, Single Responsibility Principle (SRP) says that a component should have a focused responsibility. Instead of having one class responsible for HTTP handling, database access, validation, email, and payment processing, we can separate those responsibilities: Controller ↓ Service ↓ Repository ↓ Database Each part has a focused job. Similarly, the Open/Closed Principle (OCP) encourages us to design components that can be extended without constantly modifying their existing implementation. These principles don't require Angular, Spring, or an IoC container. You can follow SOLID in plain JavaScript. 2. Composition Is the Bigger Idea Composition means: Build a larger behavior by combining smaller, focused pieces. This works in both functional and object-oriented programming. In functional programming: function A ↓ function B ↓ function C A larger function can be created by composing smaller functions. In object-oriented programming: OrderService │ ├── PaymentService └── EmailService OrderService is composed using other objects. The important relationship is often: HAS-A rather than IS-A For example: OrderService HAS-A PaymentService rather than: OrderService IS-A PaymentService This is one reason composition is often preferred over deep inheritance hierarchies. 3. Dep

Automation Everything boring that can be automated, should probably be automated. Whether others know about that automation, depends upon how much it is valued over looking busy. Image by magnific I've been on a self undertaken journey at work for the past couple of months - the automation of our build process. I learnt a lot about how Jenkins works, how interactions happen between GitHub, Jenkins, Artifactory, Docker, Ansible, etc. I started slow - one build pipeline that creates and pushes Docker images, and I kept adding pipelines as I felt the need. Today, I have a suite of pipelines that run tests, code coverage, build, deploy, cleanup, and run security scans across x86 and s390x. Some highlights of this suite - A multi architecture build - UI built on an x86 agent and build folder sent over to an s390x agent. This agent then builds the backend and the final image An end-to-end .jar updater - Separate java repository whose .jar files were imported into the main repository to be called. The pipeline built these .jars and automatically created a PR on GitHub. This has freed up a lot of dev hours for my team and myself. It's also helped keep the systems (and me) sane with the insane amount of work that gets done nowadays. I keep looking for things I can automate now, especially the small, mundane tasks since the time saved really does compound up. To anyone reading this, or future me - "Automation is like getting regular exercise; you might not see immediate results, but your systems will thank you later." Ambiguity The biggest blocker of them all is often the difference in understanding of the same words Image by starline on Magnific A couple of years ago, when I just started working as a software engineer, I struggled with ambiguity. Before this, the requirements were straightforward assignments with most of them written down. Now, I hold the opinion that dealing with ambiguity and sifting through it is a large part of my job. There's multiple stakeholders, rang

The event is expected to be a notable one, as Apple is rumored to unveil its long-awaited foldable iPhone. It'll also be the first Apple event with John Ternus as CEO, who is scheduled to take the reins of the tech giant from Tim Cook on September 1.

Ring says users can still opt for end-to-end encryption.

A program can produce the right answer and still contain work that does not help it reach that answer. Tests pass, the output looks correct, and unnecessary computations survive because they appear harmless. This becomes easier to miss in AI-generated code. A model can produce a plausible implementation in seconds, but plausible code often includes variables, conversions, or branches that the requirement never asked for. An intent alignment review adds one question to the usual correctness check: Does every instruction help achieve or explain the stated goal? This does not require a formal proof or an exhaustive line-by-line exercise. The useful result can be concise. Correctness and intent Correctness asks whether the observable behavior matches the specification. Intent alignment looks for code that contributes neither behavior nor useful clarity. The goal is not to produce the fewest possible lines. A named constant or helper function can be worthwhile even when the program could run without it. The concern is accidental complexity: code that suggests requirements or design decisions that do not actually exist. AI can help by reading the requirement and implementation together. It can confirm the working behavior, identify unnecessary instructions, and explain whether those instructions are harmful or simply unhelpful. A small Fibonacci example Consider this specification: The function should print to stdout the first hundred elements of the Fibonacci sequence. The phrase "first hundred" does not specify whether the sequence begins with 0, 1 or 1, 1 . For this review, we assume the intended convention begins with 0, 1 and prints one value per line. def print_fibonacci_100 (): a , b = 0 , 1 sequence_limit = 100 display_width = len ( str ( sequence_limit )) for index in range ( sequence_limit ): current_value = int ( a ) print ( current_value ) a , b = b , a + b checkpoint = ( index + 1 ) % 10 == 0 final_pair = ( a , b ) print_fibonacci_100 () Review The implementa

The glasses, called Legato Frames, integrate the company’s patented hearing-assistance technology into the arms of eyewear frames.

Grand Theft Auto fans, eager for news about one of the most anticipated video games of all time, appear especially vulnerable to this new cyberattack.

For a long time, whenever we hear 'User Experience', we instinctively think of UI/UX designers, product designers, or maybe frontend engineers. Why? Because we tend to think users interact first with a graphical or command-line interface, while the backend engine plays little to no role in how they experience the product. The first half is correct. The second half, incorrect. A user doesn't experience your frontend in isolation. They experience the entire system. As I continue to compound my experience building products as a backend-leaning engineer, I've found it increasingly necessary to think beyond whether an endpoint works or whether an architecture is technically sound. I have to ask: How does this technical decision affect the user's experience? Here's how. 1. API Response Times Become UX A user doesn't care that your endpoint executes 17 database queries, that your service is making five downstream requests, or that your server is experiencing a cold start. They care that they clicked “Pay” three seconds ago and nothing has happened. Eventually, they may refresh the page, click the button again, or abandon the application altogether. The frontend can add a beautiful loading animation, but it cannot completely hide a system that is fundamentally slow. 2. Error Messages Become UX One of the easiest ways to see the relationship between backend engineering and UX is through errors. Imagine trying to make a payment and receiving: 400 Bad Request Technically, something has gone wrong. But the user has learned almost nothing. Compare that with: “Your payment could not be completed because your card was declined. Please try another payment method.” Good backend error handling should therefore answer three questions: What happened? Why did it happen? What can the user do about it? 3. API Design Becomes UX API design can feel very far removed from UX. After all, users don't see JSON responses. But, developers build products using those responses. The decisions we make

Have you ever stopped to think about the risk of having a pipeline where any merge into the main branch deploys straight to production without a single safety gate? For a long time, our workflow here was that classic setup almost every developer has used at some point: merge on main triggering an SSH script with git pull and pm2 restart It worked for day-to-day tasks, but it gave a false sense of stability lol The reality check hit when I found a critical blind spot in the automation: remote SSH scripts were running without strict error handling. In other words, if a git pull caused a conflict or a database migration failed halfway through, the script simply ignored the failure, ran to the end, and GitHub Actions marked the pipeline as green The absolute worst-case scenario for monitoring: the pipeline reported that everything went smoothly, while production was already completely down On top of that, the execution order was inverted: database migrations were running before the application build. If TypeScript threw a type error right after, the database schema had already advanced while the new code never booted. And since Prisma has no native down migrations, rolling back meant a high-risk manual intervention I decided to stop everything and redesign our delivery pipeline from scratch, starting from one clear premise: a tag is a release, a merge is not Today, nothing touches the production server without an annotated SemVer tag, going through 6 tightly coupled stages: Strict tag validation: only accepts annotated tags matching vX.Y.Z, ensuring author, timestamp, and audit trail for every single release Quality gates across PR and Release: automated tests with Vitest, strict typechecking, builds, and migration validation against a clean database via workflow_call Decoupled backups: an independent daily scheduled routine combined with a mandatory safety snapshot right before touching production Real migration dry-run: the most valuable gate, where the pipeline resto