标签：#ice

A polished update to Google’s foldable brings real improvements, but the price bump and rampant competition make it a tough sell.

With advice from infectious disease experts, I tested a low-effort way to pasteurize eggs at home.

Salmonella outbreaks are seemingly everywhere right now. A good temperature probe is the last and best line of defense.

From halting my doomscrolls to automating air-quality checks, Apple’s streamlined Shortcuts app is my favorite iOS 27 feature.

Managed Data Lake: A Guide for 2027 Apache Iceberg is the standard table format for production data lakes in 2027. Every major engine reads and writes it natively. The catalog ecosystem standardized on REST. You own your data on commodity storage with no lock-in. But Iceberg deliberately separates the table format from the system that keeps tables healthy. It gives you the primitives for maintenance — rewrite_data_files , expire_snapshots , remove_orphan_files , rewrite_manifests — but not the intelligence to decide when, how, and in what order to run them. Without that operational layer, every Iceberg table degrades over time: small files accumulate, snapshots bloat metadata, sort orders drift from query patterns, orphan files inflate storage costs, and query performance decays silently until something breaks visibly. This operational gap is the central challenge of running a data lake at production scale. Netflix built four internal services to address it — Autotune for compaction strategy selection, Polaris for catalog management, janitors for garbage collection, Metacat for cross-service observability — each staffed by dedicated teams over multiple years. Google engineered automatic compaction and garbage collection directly into BigLake , so their managed Iceberg tables stay healthy regardless of write volume or query pattern changes. In 2027, you do not need to replicate that investment. This guide covers what "managed" actually means for a data lake, the degradation mechanics that make it necessary, the control plane architecture that solves it, and the practical paths to getting there — whether you are running 50 tables or 5,000. Why Lakes Degrade — The Mechanics The degradation pattern is predictable and present in nearly every Iceberg lake running for more than three months without dedicated maintenance. Understanding these mechanics is necessary regardless of which management approach you choose. The Small-File Problem Every streaming writer — Flink, Spar

Some fortnights the gripes scatter; this one they converged. Across the forums where paying customers of the big AI tools compare notes, the same complaint surfaced against three different companies in the same window: the monthly plan buys less than it did, and nobody dropped the price to match. One firm’s temporary generosity is about to expire, another’s year-old plan has quietly tightened, and the users caught in the middle are doing the same grim sum and reaching for the same coping strategies. Quotes sourced from: Reddit — specifically the subreddits r/ClaudeCode, r/perplexity_ai and r/Anthropic. Every quote below was opened at its permalink and copied verbatim; each is listed with its handle, subreddit and date in the Sources section. We quote experiences, not verdicts — a forum post is one person’s felt reality, and we have framed it as exactly that. The limit that lapses on the 19th The loudest note this fortnight came from Claude Code users watching a date on the calendar. Anthropic had lifted weekly limits by 50% as a promotion, extended it several times, and set it to expire around 19 August — after which allowances fall back to standard. For anyone who had adjusted their workflow to the higher ceiling, the lapse reads as a cut. A user posting as EnthusiasmMountain10 laid out the worry on 14 August: “I’m seeing more tokens burned on tasks that previously felt straightforward, more meandering, and generally less output per unit of usage. At the same time, the 50% usage reduction coming on Aug 19 makes this particularly concerning.” The complaint was double-barrelled: not only is the ceiling dropping, but the same work seems to cost more against it than it used to. That second half — a sense that quality had slipped — ran through the thread. A commenter posting as Captain_Birb put it bluntly: “quality is down.. 4.6 and 4.8 were sharper. Sonnet 5 is seriously a joke — it gets roasted by Opus every time and admits his faults.” Another, TheSassyPlant , descri

The Quest Begins (The "Why") I still remember the first time I opened a legacy codebase and felt like I’d stepped into a dark dungeon without a torch. The file was a single 800‑line function called processData . Inside, variables bore names like tmp , x , flag , and comments that tried to explain every line: // TODO: refactor this mess function processData ( input ) { let r = []; // result array for ( let i = 0 ; i < input . length ; i ++ ) { // loop over items if ( input [ i ] > 10 ) { // if value greater than threshold let v = input [ i ] * 2 ; // double it if ( v % 2 === 0 ) { // if even r . push ( v ); // add to result } } } return r ; } I spent three hours tracing why a certain edge case produced an empty array, only to discover the comment “if value greater than threshold” was outdated—the threshold had changed to 12 in a later commit, but the comment never got updated. The code lied, the comments misled, and I felt like a hero who’d just swung at a shadow. That frustration sparked a question: What if we could write code so clear that comments became unnecessary? Not because we’re lazy, but because the code itself tells the story. The Revelation (The Insight) The treasure I uncovered wasn’t a new framework or a slick library—it was a mindset shift: make the code self‑documenting through intention‑revealing names and small, focused functions . When a variable, function, or class name reads like a sentence, the reader can infer what’s happening without a side note. Think of it like reading a well‑written novel. You don’t need footnotes to understand that “She opened the door and stepped into the rain” means she’s going outside. The same principle applies to code: if you name a function filterValuesAboveThreshold , the intent is obvious. Why does this matter? Because comments decay. They become outdated, they get ignored, and they add noise. Self‑explanatory code, on the other hand, stays accurate as long as the name stays accurate. It also forces you to think ab

The organizations embracing hybrid agile models aren't making a principled methodological choice — most of them are just formalizing the mess they were already living in. Picture a delivery team at a mid-sized European bank. They run two-week Scrum sprints — daily standups, sprint reviews, the whole ceremony. They use Jira boards. They call themselves agile. And then, every quarter, a Release Approval Board convenes to review a 47-page change documentation package before anything goes to production. The sprints are agile theater. The real schedule is a Gantt chart that lives in somebody's SharePoint. Nobody says this out loud in the all-hands. They don't need to. This scenario — the sprint-shaped container wrapped around predictive, gate-controlled delivery — has quietly become the dominant operating model in software delivery. According to the 18th State of Agile Report, 74% of organizations now report using hybrid or homegrown models, mixing and matching agile with whatever else their org chart demands. The consulting firms have a polished name for it: hybrid delivery. The people living it often have a less flattering one. Here's the uncomfortable argument worth making: the rise of hybrid agile isn't evidence that organizations have matured past ideological purity. For many of them, it's evidence that they never committed to anything in the first place — and now have a framework-shaped fig leaf to cover that fact. The hybrid model is legitimate. Claiming you've adopted one when you've actually just left the org chart untouched while duct-taping Scrum on top? That's a different animal entirely. How We Got Here The path from "pure agile" to hybrid wasn't a straight line. It started with a real problem. As organizations tried to scale agile beyond small teams, limitations became visible — among them, agile's tendency to underweight documentation and its friction with physical product iteration cycles, both of which created compliance and maintenance headaches in regu

On the latest episode of Equity, we wonder why the DOJ is investigating startup board seats.

Your desk setup needs a monitor arm, and I’ve been testing every one I can get my hands on to find the best.

It is possible to achieve your desired work-life balance on your smartphone. Just set up separate accounts using this built-in Android feature.

A usage policy for Flock license plate reader cameras tells police not to talk about the cameras: When cops use Flock to arrest someone in Wapello County, Iowa, they don’t want them to know. A usage policy for the automated license plate reader cameras in the county tells police, in no uncertain terms, to keep them a secret: “DO NOT MENTION ALPR USAGE TO THE OCCUPANTS OF THE VEHICLE,” the policy document reads. “DO NOT MENTION ALPR USAGE IN YOUR REPORT OR COMPLAINT UNLESS ABSOLUTELY NECESSARY.” This reminds me of IMSI-catchers (Stingray was the most popular) a couple of decades ago. Police would go to even more extremes to hide their usage...

Some fortnights the complaints scatter across a dozen topics; this one they clustered around two. Across the forums where paying AI users gather to compare notes, two things happened in the same window that left the same taste: the meter moved, and the mark landed. In both cases, something about the product changed without a clear announcement, and the users found out by running into it. Quotes sourced from: the OpenAI Developer Community and Hacker News. Every quote below was opened at its permalink and copied verbatim; each is listed with its handle, platform and date in the Sources section. We quote experiences, not verdicts — a forum post is one person’s felt reality, and we have framed it as exactly that. The $200 plan that lasted two days The loudest note this fortnight was the Codex usage drain. On the OpenAI Developer Community, a Pro subscriber posting as jjjnoronha laid out the damage plainly on 4 August: “I’m on the $200 Pro plan, and my weekly limit was completely exhausted in 2 days of serious development work. This is simply not viable.” He was refactoring a Rust backend with two agents running — one coding, one checking code quality — and described leaving Ultra mode on for three or four hours, which alone consumed 30% of his weekly allowance. But the complaint was not that the work was expensive; it was that the plan was sold with language suggesting far more headroom than what the cap delivered. “When someone pays for the highest consumer tier,” he wrote, “the expectation is capacity for sustained, professional work — not being locked out for the rest of the week after two days.” Others described something stranger. A user posting as kalani reported going to bed with 70% remaining after a Saturday reset, then waking up to find 0%. “Not like I had something running overnight, and yet all usage blocked until this next Saturday,” they wrote on 4 August. “Hard to trust a $200 / month service that is completely unpredictable about whether you’ll even be

The cyberattack at CareCloud resulted in one of the largest reported data breaches in the U.S. healthcare industry this year.

Sam Newman discusses the concept of progressive collapse in civil engineering and how it applies to distributed systems. Using real-world examples - from the 1968 Ronan Point tower failure to AWS outages - he shares crucial resilience engineering strategies for software leaders. Learn how to strengthen components, isolate failures, and reduce interconnections to prevent catastrophic cascades. By Sam Newman

Amazon Web Services has extended Amazon Bedrock AgentCore with runtime instances, a new compute option that gives AI agents persistent infrastructure purpose-built for complex long-running workflows and multi-agent coordination. By Matt Saunders

With the Azure Kubernetes Services (AKS) platform, containerized workloads can be efficiently managed and scaled. However, the full potential of AKS is only realized when it is seamlessly integrated with other Azure services. This enables a complete cloud-native environment that is scalable, secure, and automatable, while providing maximum flexibility. In this final post of the series, we will show you how AKS can be integrated with other Azure services to create a robust and holistic platform for your applications. Why Integrating AKS into the Azure Cloud Is Crucial AKS provides a highly available and scalable infrastructure for managing containerized applications. However, integrating it with other Azure services like Azure DevOps, Azure Monitor, Azure Active Directory (Entra ID), and Azure Storage extends functionality and optimizes workload management. By leveraging Azure services alongside AKS, companies can: Ensure enhanced security for their containerized applications. Build robust monitoring and logging solutions to monitor the state of applications at all times. Set up automated pipelines for deployment and scaling. Seamlessly exchange data and status information across various Azure services. Key Azure Services to Integrate with Your AKS Platform Azure Active Directory (AAD) for Authentication and Security Azure Active Directory (AAD) provides comprehensive identity and access management that can be directly integrated with AKS. This ensures that only authorized users and services can access your Kubernetes clusters. With Azure RBAC (Role-Based Access Control), you can define granular access permissions for different users and teams, increasing the security of your environment. AAD Pod Managed Identities enable your AKS applications to securely access Azure resources like Azure Key Vault or Azure Storage without the need to manually manage sensitive credentials. Azure DevOps for CI/CD Pipelines Azure DevOps is one of the best solutions for automating CI/CD

Most people ask: What will my first job teach me? The deeper question is quieter: What does it take to become someone who can finally be trusted with real work? There is a real tension in the first job. One view says it’s just a stepping stone — gain experience, update the résumé, move on. Another says it shapes you in ways that stay long after the job itself is forgotten. Both can be true. A first job can be temporary. What you learn from it may not be. The First Mistake Imagine a young graduate starting on a Monday. By Thursday they have already made a small but visible mistake — the wrong file, a misread requirement, an assumption that turned out wrong. They sit at their desk replaying it. What will my manager think? Did I just ruin my first impression? But the office doesn’t stop. The team keeps working. The next task arrives. Slowly they realize something that rarely appears on any résumé: A mistake doesn’t end a career. You acknowledge it. Fix what you can. Learn. Then continue. That lesson can stay with someone for years. When Nobody Is Standing Behind You College gives instructions. A first job gives responsibility. At some point someone hands you a problem and expects you to figure out where to begin. You may not know the answer. You may not even know the right question yet. That is when the real transition begins. You learn when to ask for help, when to investigate alone, when to admit you don’t understand, and eventually when to decide without waiting for someone else to tell you exactly what to do. Two People, One First Job Imagine two people joining the same company on the same day. Both are capable. Both make mistakes. One treats every task as something to finish until a better opportunity appears. The other starts noticing what each task is teaching — how problems are approached, how decisions are made, how mistakes are handled. Five years later their résumés may look similar. Their instincts may not. The difference isn’t necessarily who had the bette

The textbook version of the transactional outbox is tight. You save the domain entity and an outbox row in one local transaction. A background scheduler picks up PENDING rows and publishes them to Kafka. You never publish inside the request thread — no dual-write, no atomicity breach. The pattern closes the consistency gap. Then you load-test it. I ran 1,000 authenticated requests through my event-driven platform in 70 seconds. The gateway returned 201 for every one of them. The outbox absorbed every row. The consumer drained everything. By every visible metric the system looked healthy. Underneath that health, I found three production-grade problems the textbook never mentioned. What a correct implementation looks like Before the problems, the shape of the solution. The outbox publisher runs on a @Scheduled virtual-thread worker: @Scheduled ( fixedDelay = 5000 ) @Transactional public void publishPendingEvents () { List < OutboxEvent > batch = outboxRepository . findTop20ByStatusOrderByCreatedAtAsc ( OutboxStatus . PENDING ); for ( OutboxEvent event : batch ) { event . setStatus ( OutboxStatus . PROCESSING ); outboxRepository . save ( event ); try { kafkaTemplate . send ( event . getTopic (), event . getPayload ()). get (); event . setStatus ( OutboxStatus . PUBLISHED ); } catch ( Exception e ) { event . incrementRetryCount (); if ( event . getRetryCount () >= MAX_RETRIES ) { event . setStatus ( OutboxStatus . FAILED ); } else { event . setStatus ( OutboxStatus . PENDING ); } } outboxRepository . save ( event ); } } This is correct. The PROCESSING state prevents another scheduler instance from claiming the same row. The retry cap prevents infinite cycling. The PENDING fallback on transient errors gives the event another chance. The dual-write problem is genuinely closed. Here is what that correctness does not cover. Gap 1: Your throughput ceiling is a config line fixedDelay = 5000 means the scheduler runs every 5 seconds. findTop20 means it picks up 20 rows per cycl

I’ve tried most of the ready-to-eat meal delivery services in the country. CookUnity is the one that feels like real cooking, from real chefs.