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In the age of AI engineering and vibe coding, almost nobody mentions OOP anymore. But what if children were never taught prefixes, roots, and suffixes — the architecture of words — or how a sentence is properly built? Will AI agents really be enough for the specialists of tomorrow, if those specialists never learned the grammar underneath? Dive into OOP in WordPress development practice → (original source, featuring the four whales example) Most WordPress developers learn Object-Oriented Programming the hard way: by staring at WP_Widget, WP_Query, or WP_Post and reverse-engineering why core is built the way it is. Textbooks explain encapsulation, abstraction, inheritance, and polymorphism with abstract diagrams that rarely survive contact with real code. Here's a different way to think about it — using a whale. A whale keeps its vital organs protected inside its body, dives into depths where the mechanics of survival are invisible from the surface, passes traits down to its calf, and adapts its behavior differently depending on the environment it's in. Swap "whale" for "class," and you've basically described the four pillars of OOP. Let's walk through each one with WordPress-specific code, then look at how the same principles scale from a five-page brochure site to an enterprise platform. Why OOP Matters in WordPress at All WordPress was procedural for most of its early life, and plenty of plugins still are. But once your project outgrows a handful of files, procedural code starts fighting you: global state leaks everywhere, the same logic gets copy-pasted into three different hooks, and a single typo in a variable name three files away breaks something unrelated. OOP fixes this by grouping data and behavior together into objects instead of scattering functions and passing arrays between them. WordPress core made this bet a long time ago — WP_Widget, WP_Query, and WP_Post are all classes — and the four principles below are the foundation that makes classes trustwort

Most job searches look the same from the inside: a dozen open browser tabs, a spreadsheet that was accurate for about four days, and a nagging feeling that something is slipping. Applications leak out the bottom. Follow-ups get forgotten. And after a few weeks of it, you have done a lot of work and learned almost nothing about what is actually working. I ran mine that way for a while. Then I stopped treating it as a to-do list and started treating it as a pipeline: named stages, a scoring step at the front, and a follow-up cadence that did not depend on my memory. That one shift changed how the whole search felt. Here is the system. Why a list fails you A to-do list is good at exactly one thing: telling you what to do next. That is also its limit. A list cannot tell you what is working. It has no stages, so you cannot see where things stall. Are you not getting responses because your applications are weak, or because you are aiming at the wrong roles, or because you never follow up? A list shrugs. It just shows you the next unchecked box. And because a list rewards volume, it quietly pushes you to apply more without ever asking whether applying more is the problem. You end up repeating the same misses faster. The reframe is simple. A job search is not a list of chores. It has stages, the same way a sales pipeline does. Naming those stages is the first thing that changes, because you cannot improve a step you cannot see. The stages Here is the pipeline I settled on, in plain terms: Sourced. A role you found and might go after, but have not evaluated yet. Evaluated. You have looked at it seriously and decided it is worth pursuing. Applied. You are in. Follow-up. You have applied and the clock is running on a nudge. Interview. A human is talking to you. Offer. The point of the whole thing. And then the ways a role ends, which matter more than people think: No response. You applied and heard nothing back. Ghosted. Closed. The posting closed before you got a real shot at

Sometimes, a change that looks simple on the surface is not actually that simple. Imagine that you need to replace the source table feeding a refined or trusted table in a data pipeline. At first, it might look like a one-line change: update the table name, deploy the code, and move on. But in a real data platform, there is usually much more behind that change. There are dependencies, scheduling rules, upstream and downstream processes, resource consumption, concurrency, data lineage, and, sometimes, assumptions that were not immediately obvious when the pipeline was first created. I recently had to look into exactly this kind of situation in an Apache Airflow project, and one of the questions that came up was: Should this DAG be scheduled using a cron expression, or should it be triggered based on an Asset? The answer, as usual in software engineering, is: it depends. And understanding why it depends is much more important than simply knowing how to configure either option. Cron: the familiar way of scheduling a DAG Let's start with the simplest and most familiar option: a time-based schedule. With Airflow, we can define a DAG to run according to a cron expression: with DAG ( dag_id = " my_pipeline " , schedule = " 0 13 * * 0 " , catchup = False , ): ... In this example, the DAG is scheduled to run every Sunday at 1 PM. In a real environment, we might have different schedules for different environments. For example: Environment Schedule Development Saturday at 1 PM Homologation Sunday at 1 PM Production Monday–Friday at 1 PM The important characteristic here is that the schedule is based on time . If the DAG is configured to run at 1 PM every Sunday, Airflow will try to run it at that time, regardless of whether the data it depends on has actually changed. This is not necessarily a bad thing. In fact, sometimes this is exactly what we want. But there is another approach. When data becomes part of the schedule Modern data pipelines often have dependencies that are b

A technology problem rarely stays a technology problem for very long. A platform may need to scale. A product may need to move faster. An organisation may want to introduce AI, modernise an ageing estate, improve customer experience or launch something entirely new. The first instinct is usually to look at the technology itself. Which architecture should change? Which platform should we buy? Which team should build it? Which tools should we introduce? Those questions matter. But they are often not the questions that determine the outcome. At Cralgo, one pattern keeps appearing across technology work: the harder part is frequently the system around the technology. The problem behind the problem Consider a programme that appears to have an execution issue. Delivery is slow. Priorities keep changing. Teams disagree. Decisions are repeatedly reopened. The roadmap keeps moving. It is easy to conclude that the engineering team needs to become faster. But look closer and the constraint may be somewhere else: ownership is unclear; priorities are not genuinely ordered; product and technology are working from different assumptions; architecture decisions are being made without business context; teams are executing tasks without understanding the judgement behind them; governance exists, but only as reporting; critical decisions remain dependent on a small number of people. None of these are purely technical problems. They are questions of judgement, ownership, capability, sequencing and governance. Technology simply makes them visible. Better technology does not automatically create better execution Organisations understandably invest heavily in platforms, cloud, data, automation and AI. But technology increases capability only when the organisation around it can use that capability well. A new platform cannot decide what should be prioritised. A new operating model diagram cannot create ownership. A dashboard cannot replace judgement. AI cannot resolve ambiguity that an orga

Archify is taking GitHub by storm, hitting #1 on Trending and crossing 4,200+ stars in record time! If you use AI coding assistants like Cursor or Claude Code, Archify is an absolute game-changer. It allows your AI agent to automatically generate verifiable architecture, workflow, sequence, and data-flow diagrams as beautiful, self-contained HTML files (with dark/light themes and motion animations!). 🔗 Links & Resources: • Archify GitHub Repository: https://github.com/tt-a1i/archify • Try it yourself: npx skills add tt-a1i/archify -g 👇 What do you think of Archify? Are you going to use it for your next system design or PR review? Let me know in the comments! If you found this live demo helpful, please drop a LIKE and SUBSCRIBE for more cutting-edge AI developer tools. Archify #SoftwareArchitecture #Cursor #ClaudeCode #AI #SystemDesign #GitHubTrending #WebDev #OpenSource #DevTools

Whoop Life is the highest tier of membership to the Whoop platform, and it does offer more data than the less expensive levels.

Most performance optimization focuses on making each operation faster. HKD Kernel approaches a different question: What if most of those operations did not need to execute at all? I’ve been working on HKD Kernel, a native C library for exact sparse and incremental computation. The target workload looks like this: A large computation has already been evaluated. Only a small subset of the inputs changes. The dependency structure tells us which results can actually change. HKD recomputes those affected regions instead of repeating the entire calculation. The important word is exact. The optimized result must equal the result of full recomputation. What the benchmark measures The repository contains reproducible benchmarks comparing full recomputation with the HKD incremental path. Across the benchmark suite currently documented in the repository, the measured mean speedup is roughly 18,000x. That requires an important qualification: This does not mean HKD makes arbitrary programs 18,000x faster. It means that on workloads with sparse changes and reusable state, avoiding redundant computation can produce extremely large reductions in work. That distinction is important enough that I built the repository around reproducibility rather than a black-box benchmark claim. What HKD Kernel is not HKD Kernel: does not replace the macOS XNU kernel does not modify CPU microcode does not disable SIP does not change processor ALU hardware It is a user-space native computation library. Where I think this model is useful The workloads I’m most interested in include: dependency graphs incremental build systems large simulations with sparse updates optimization systems financial/risk recomputation logistics and scheduling cached numerical pipelines The real question is not “how fast is HKD?” It is: How much of your current computation is being repeated even though the inputs affecting it never changed? I’d especially like developers to try to break the benchmark assumptions or suggest w

Originally published at nlocoding.com 26% of remote software teams report missing critical project deadlines due to miscommunication—despite using two or more collaboration tools (Gartner, 2026). The proliferation of AI-based collaboration tools for remote software teams isn’t hype—it's necessity. In 2026, 81% of tech companies operate partially or fully remote (Buffer, 2026). The tools have changed. The stakes haven’t. One communication failure and the sprint backlog becomes a graveyard. The difference now: AI can actually fix this. AI-based collaboration tools are rewriting team productivity in 2026 AI-based collaboration tools for remote software teams automate routine coordination, reduce context-switching, and surface blockers in real time. According to Atlassian’s 2026 report, teams using AI-driven tools resolve tasks 42% faster. Not magic. Just relentless automation of the boring parts. You’ll notice the biggest gain is invisible—less time wasted chasing status updates, more time on code. Actionable takeaway: Pick one AI-native platform and go deep. Stacking tools multiplies confusion. 42%Faster task resolution with AI-driven collaboration (Atlassian, 2026) Integrated AI assistants are now table stakes, not a luxury Most people get this wrong: Slackbot isn’t AI. In 2026, 74% of remote teams rely on integrated AI assistants for core workflows (G2, 2026). These bots summarize meeting transcripts, auto-generate Jira tickets, and flag misaligned priorities before you even notice. Microsoft Teams’ Copilot costs $30/user/month and saves the average dev team 5 hours/week (Microsoft, 2026). Actionable takeaway: Train your team to interact with the AI—not ignore its nudges. 💡 Pro Tip: Feed your AI assistant high-quality prompts. Sloppy input = irrelevant output. Use specific, action-oriented queries for summaries and follow-ups. Real-time code collaboration powered by AI cuts merge conflicts in half The data shows: GitHub Copilot’s Live Share reduces code merge confli

Investors, founders, and operators from across Europe arrived for the annual Nordic TechBBQ conference to talk about how humans can have agency over AI.

Google's Pixel 11 phone uses a Tensor G6 processor with a powerful TPU. How is it different from a GPU, and what does that mean in real-world use?

If you're wondering if CarPlay works over Wi-Fi or Bluetooth, the answer is yes. It uses both at different times.

I just wrote about the billion-dollar rounds flooding into humanoid robotics. Here is the story from the other end of the scale, and I find it more encouraging. Hugging Face's open-source robot, a 25-centimeter bipedal machine with fifteen actuators and a sensor kit that includes a camera, speaker, LiDAR, NFC, Bluetooth, and WiFi, just passed a million dollars in sales. Fully open hardware, openly documented, quietly making real money. One of these robotics stories is funded like an industrial giant. The other is a small, open, shippable thing that people are actually buying. They are both true, and the small one is the one most builders can learn from. Open hardware turned out to be a business The reflexive assumption about open-source hardware is that you cannot make money on it, because anyone can copy the design. Hugging Face's robot is a live counterexample. The plans are open, the software stack is open through their LeRobot ecosystem, and it crossed a million in sales anyway. That is worth sitting with, because it means openness and revenue are not the opposites people assume. The reason it works is the same reason open-source software companies work. Most buyers do not want to source fifteen actuators, fabricate a chassis, and debug a sensor stack to save money on a robot that already exists and is affordable. They want the finished thing, they want it to work out of the box, and they are happy to pay the people who designed it. Openness is not the giveaway that kills the business. It is the trust and the ecosystem that make the business, because you can see exactly what you are buying, modify it, and build on a platform other people are also building on. Why this is the better story for builders The mega-funded humanoid companies are placing a bet only a handful of players can place: billions of dollars, years of runway, factories. That is a real path, and it is not your path or mine. The Hugging Face robot is the other path, and it is copyable. Small, open

Cloudflare KV for Session Caching in Multi-Tenant FastAPI: Reducing PostgreSQL Load Without Redis Complexity Every SaaS I've built hits the same wall: session validation on every request hammers PostgreSQL. You add Redis, suddenly you're managing another service, debugging cache invalidation, and paying for redundancy you don't need. Then I discovered Cloudflare KV sits between your users and origin server. It's not a replacement for PostgreSQL—it's a read cache positioned at the edge that auto-syncs on writes. For multi-tenant session and permission data, this eliminates 60–80% of auth-related database queries without the operational complexity of Redis. This is the approach I use in CitizenApp. Here's why it works, how to implement it, and where I nearly broke production. Why Cloudflare KV Beats Redis for Session Caching Redis requires: A separate service deployment (Render, AWS ElastiCache) Connection pooling logic in your app Cache invalidation strategies you'll get wrong Monitoring for memory leaks and eviction Cost that scales with your hot data size Cloudflare KV requires: A binding in your edge worker (one line of config) Simple key-value storage at 200+ edge locations Automatic TTL expiration Zero operational overhead—Cloudflare manages it Here's my honest take: I prefer KV because I don't have to think about it. My workers validate JWT tokens and fetch session data from KV before even routing to my FastAPI origin. Cache misses flow to PostgreSQL and write back to KV. No connection pools. No eviction policies. No debugging Redis memory fragmentation at 3 AM. The tradeoff? KV is slower than in-memory Redis (ms vs microseconds), but for session lookups happening 200+ times per second per user at global scale, edge-cached responses beat origin-fetched ones every time. Architecture: Edge Validation + Origin Sync Your flow looks like this: Request hits Cloudflare Worker Worker checks KV for session + permissions (hit = serve immediately) KV miss → fetch from Fas

A inteligência artificial no setor jurídico ultrapassou a fase dos chatbots genéricos de pergunta e resposta. Quando lidamos com o Direito, o custo de uma "alucinação" de IA não é apenas um incômodo — pode significar a perda de um prazo fatal, uma tese fundamentada em jurisprudência inexistente ou a violação de sigilo. Para resolver esse problema, a engenharia de software aplicada a LegalTechs está migrando para os Agentic AI Workflows (Workflows de IA Agêntica). Em vez de depender de um único prompt gigantesco para resolver um problema complexo, orquestramos múltiplos agentes especializados. Neste artigo, vamos detalhar como arquitetar uma esteira de triagem, busca vetorial e sumarização de processos, utilizando ferramentas maduras e garantindo que o advogado permaneça como o orquestrador final no Quality Gate . 1. Dividir para Conquistar: A Arquitetura Multi-Agente A premissa da orquestração de agentes é a especialização. Cada agente no sistema possui um escopo restrito, ferramentas específicas ( tool use ) e um objetivo claro. Em um cenário de entrada de um novo processo longo (ex: um PDF de 500 páginas), o workflow se divide em três estágios: Agente 1: Classificação de Intenção e Roteamento O primeiro agente atua como o recepcionista. Ele não lê o documento para extrair teses; ele apenas analisa as primeiras páginas para responder: O que é isso? É uma Inicial Trabalhista? Uma intimação de prazo? Uma contestação? A partir dessa classificação, o workflow roteia o documento para a fila correta de processamento. Agente 2: RAG (Retrieval-Augmented Generation) e Busca Vetorial O segundo agente é o pesquisador. Ele quebra o documento em fragmentos ( chunks ) e cruza as alegações da parte contrária com o acervo interno do escritório. No ecossistema Elixir, por exemplo, podemos utilizar o PostgreSQL com pgvector e Ecto para armazenar os embeddings de casos passados e jurisprudências vencedoras do próprio escritório. O agente busca semelhanças e recupera o contexto estrit

As my 20s are set to come to an end later this year, I’ve officially reached the age where sleeping in the wrong position or stretching just a little too far can cause aches and pains. I’ve always been somewhat skeptical of massage guns, mostly because I’ve tried a few off-brand ones and just assumed […]

Outstanding call quality and an excellent price point put these earbuds way ahead of pricier competition.

Fabiane Nardon shares how TOTVS prepares enterprise data for token-hungry AI agents. She discusses balancing deterministic logic and non-deterministic LLMs across precision, security, and cost. Nardon details using data mesh, low-latency database architectures, semantic ontologies, and dynamic MCP tool selection to optimize context windows and reduce token overhead in transactional systems. By Fabiane Nardon

Astronomers will test equipment that, if it’s successful, will one day be crucial for discovering Earthlike planets.

Installing a large language model on your personal computer gives you a handy digital assistant that won’t compromise your data privacy.

Cloudflare Workers can now accept inbound TCP connections through a new connect(socket) handler routed via Spectrum, ending an eight-year restriction to HTTP. Containers get full-duplex gRPC in any language, while Workers get unary and server-streaming through automatic gRPC-web translation. Everything is private beta. By Steef-Jan Wiggers