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Um relato honesto de alguém que trabalha com design, vive com TDAH e está cansada de dicas genéricas Tem um tipo de artigo sobre organização que eu já sei de cor. É sempre alguma variação de: “faça uma lista, use Pomodoro, durma 8 horas e beba água”. Só que tem um cenário que quase nunca aparece nessas listas: O momento em que você não é neurotípica, está em burnout, tem duas tarefas importantes com o mesmo prazo e nenhuma técnica milagrosa resolve. É sobre isso que eu quero falar aqui. Sumário: O cenário caótico (e bem real) Por que o Pomodoro não funciona pra todo mundo Burnout em quem tem TDAH O dia em que duas tarefas importantes têm o mesmo prazo Estratégia 1: uma prioridade verdadeira por dia Estratégia 2: subtarefas em vez de cronômetro Estratégia 3: time blocking gentil (agenda que não te esmaga) Estratégia 4: reduzir fricção em vez de exigir mais disciplina Estratégia 5: contratos curtos consigo mesma E quando nada disso parece suficiente? Referências O cenário caótico (e bem real) Imagina o seguinte: Projeto A : entrega do pitch da pós, com prazo na sexta. Projeto B: preparar apresentação do roadmap, também para sexta. Você já está cansada, a cabeça rodando, o corpo em modo economia de energia. Aí você joga no Google “como se organizar” e recebe de volta: “Use a técnica Pomodoro, 25 minutos de foco, 5 de pausa.” E você pensa: “Amiga, eu mal estou levantando da cama. Você quer que eu vire um cronômetro humano?” A real é que muita técnica de produtividade tradicional foi pensada para cérebros neurotípicos. Quando a gente vive com TDAH, burnout ou os dois juntos, essa lógica simplesmente não encaixa tão bem. Por que o Pomodoro não funciona pra todo mundo Pomodoro é ótimo… para algumas pessoas. Mas tem motivos bem específicos para ser um caos para muitos de nós. Por exemplo: A pausa obrigatória, interrompe justo quando o foco finalmente chegou. A sensação do timer contando, aumenta a ansiedade em vez de ajudar. Cada “reinício de ciclo” vira mais uma micro deci

Despite continued benefits, anti-vaccine rhetoric has driven down vaccination.

A story about an AI alert model that cut false alarms from 60% to 7% — and what happened when...

Armed with a ton of new upgrades, Ferrari came to Spain full of confidence.

If you've ever worked on a live game, you've probably watched this happen: an update ships, something feels off, the forums light up, and within an hour the community manager is in the middle of a fire they didn't start and can't immediately put out. And here's the thing most people get wrong about that person's job. "They just post updates, right?" That's the assumption. A game community manager writes patch notes, posts announcements, answers a few questions on Discord, drops the occasional meme, and keeps the social feeds warm. That's the visible 10%. The other 90% is harder, quieter, and almost invisible when it's done well. A community manager sits at a collision point. On one side: players who are frustrated because something broke, a balance change feels unfair, compensation feels insulting, or an update slipped. On the other side: a dev team that's heads-down debugging, prioritizing, and sometimes wrestling with problems that genuinely can't be fixed fast. The community manager has to talk to both sides at once — without sounding cold, defensive, fake, or corporate. That's not a "soft skill." That's translation under pressure, and it's hard. The real role: a two-way translator Strip away the memes and the role is basically two jobs sharing one desk. Job one is outward. Translate the studio to players: acknowledge the actual pain, explain what's known and unknown, hold boundaries without being defensive, and come back with real updates. Job two is inward. Translate players to the studio: take a pile of angry, contradictory, emotional posts and turn them into categorized, prioritized, actionable feedback the team can build from. Most people only ever see job one. Job two — the research half — is usually what decides whether the game actually improves. We'll get to it. Why players hate "we hear you" Players can smell a script instantly. These phrases aren't wrong , but they're hollow: "We hear you." "We value your feedback." "Please be patient." "We apologize f

In early 2026, Anthropic researchers ran an experiment with 52 junior developers. Half used an AI assistant to learn an unfamiliar Python library. The other half worked without one. Both groups finished the task. But when tested on how well they understood the code they had just written, the AI-assisted group scored 50% on a comprehension quiz - versus 67% for the unassisted group. That 17-percentage-point gap has a name: cognitive debt. It is one of the most important concepts in software engineering right now, and most developers are not paying enough attention to it. What Is Cognitive Debt? Cognitive debt describes the growing gap between the volume of code that exists in a system and the amount that any developer genuinely understands. It is not a new term, but it crystallized across multiple research streams in early 2026. Addy Osmani (Google Chrome) described it as "comprehension debt" - the hidden cost that accumulates when code becomes cheap to generate but understanding still requires deliberate effort. Margaret-Anne Storey (University of Victoria) formalized the concept in a March 2026 arXiv paper, framing it as a team-level problem and extending it into a Triple Debt Model: technical debt in the code, cognitive debt in the people, and intent debt - the missing rationale that both humans and AI agents need to safely work with code. Cognitive Debt vs. Technical Debt These two ideas are easy to conflate, but they are fundamentally different problems. Technical debt lives in the code - it shows up as slow builds, tangled dependencies, and failing tests. Cognitive debt lives in people - it surfaces as an inability to explain, debug, or extend code that the team themselves wrote. The critical difference: technical debt announces itself through friction. Cognitive debt breeds false confidence. Your tests are green, velocity looks fine, and nobody realizes the system is fragile until something breaks in production and the team cannot reason through why. What the

The Škoda Peaq, which will debut this summer as the European automaker’s flagship model, seats seven and has an intriguing design. It will almost certainly come with a hefty price tag.

6 months ago I was a final year CS student with no real projects and no clear path into tech. So I built one. Not just a project. A full platform. Here's everything inside Rohith Builds — completely free, forever. What's Inside 1. 100 Structured Lessons (Python → AI) Not random tutorials. A structured path: Day 1-20: Python basics Day 21-40: Backend & APIs Day 41-60: SQL & Databases Day 61-80: AI & LLMs Day 81-100: AI Agents & Launch Every lesson uses Indian examples. Zomato orders. Cricket scores. IRCTC bookings. Aadhaar validation. Because most tutorials use pizza and baseball. We use what we actually know. 2. Rohi — AI Tutor Stuck on a concept? Ask Rohi. he's powered by Groq LLM, understands Indian context, and is available 24/7. No waiting for a mentor to reply. No expensive bootcamp. Just ask and learn. 3. Jobs Board — Updated Daily This took the longest to build. An autonomous scraper that: → Queries LinkedIn Guest API → Searches Naukri via AOL gateway → Finds Indeed listings → Sends each to Groq AI for analysis → Filters only junior/fresher roles → Checks freshness (last 4 days only) → Removes duplicates automatically → Inserts into PostgreSQL Result: 53+ curated jobs. Updated daily. Filter by: Python, Backend, AI, Frontend Filter by: 2025, 2026, 2027 graduates No more scrolling through senior roles pretending to be junior. 4. 220+ Prompt Vault Every prompt I've used to: Debug faster Write better code Prepare for interviews Build projects Copy. Use. Customize. 5. Improve Any Prompt (Free Tool) Paste any AI prompt. Get an optimized version instantly. No signup needed. The Tech Stack Built with: Python + Flask PostgreSQL (Neon) Groq API (Llama-3.3-70b) Render deployment SQLAlchemy + psycopg2 No React. No Next.js. No hype stack. Just Python and PostgreSQL doing real work. Why I Made It Free Because every resource that helped me was either: Paywalled after lesson 3 In English accent I had to rewind Using examples I couldn't relate to Assuming I had a MacBook and

If you're a DevOps, SRE, or Cloud engineer in India — or hiring one — the market in 2026 looks very different from a few years ago. Instead of guessing, we analyzed 800+ live DevOps/SRE/Cloud/Platform Engineering roles currently on PuneOps to see what's actually being hired for right now. Here's what the data shows. 1. Bangalore dominates, but the market is genuinely national DevOps hiring in India is no longer a one-city story. Of the live roles: Bangalore — the clear leader, ~25% of all listings Pune — a strong #2 (and a serious DevOps hub, not just an IT-services town) Hyderabad, Mumbai, Delhi NCR, Chennai — all with steady, healthy demand Remote / Pan-India — roughly a third of all roles don't tie you to a city at all Takeaway for candidates: you're no longer limited to wherever you live. Remote and pan-India DevOps roles are a huge and growing slice of the market. 2. This is a senior-heavy market The single most striking pattern: DevOps hiring in India skews experienced. The largest band by far is 5–10 years of experience A meaningful chunk wants 10+ years (architects, principals, platform leads) Entry-level (0–2 years) roles are comparatively rare Takeaway: DevOps remains a hard field to break into directly. Most roles assume you've already done software, sysadmin, or cloud work. If you're junior, the path in is usually via a software/ops role, then specializing. 3. The skills employers actually ask for Across the listings, the same technologies show up again and again: Kubernetes — effectively table stakes now Terraform / IaC — infrastructure-as-code is expected, not bonus AWS / Azure / GCP — cloud fluency, often multi-cloud CI/CD pipelines, observability, and Python for automation Takeaway: if you're leveling up, Kubernetes + Terraform + one major cloud is the core combination Indian employers are screening for in 2026. 4. Salary ranges (market benchmarks, 2026) Compensation varies widely by company type (product vs. services), city, and exactly how senior t

I place developers with US tech companies for a living. Before that sentence makes you close the tab: what follows is the thing I tell developers for free, one conversation at a time, until I got tired of saying it one person at a time. Last month a developer in Prague asked me if 55 dollars an hour was a reasonable rate. Nine years in. Kotlin, AWS. He had built and run a payment system for one of the largest Czech fintechs. Three million transactions a month. Zero P0 incidents in two years. A profile most US startups would fight over. I told him what the US market actually pays for that exact stack at that exact level. He went quiet for about thirty seconds. Then he said: "I have been contracting for three years. I just did the math." He had left roughly 180,000 dollars on the table. Not because he was not good enough. Because no one had ever told him the number. This is the most expensive blind spot in our industry, and almost nobody outside the US escapes it. So let me walk through why it happens, because once you see it you cannot unsee it. You are pricing against the only benchmark you have ever seen When you set your rate, you do not pull it from nowhere. You anchor it to something. And the only thing you have ever had to anchor to is your local market. So a senior engineer in Warsaw prices against Warsaw. One in Bucharest against Bucharest. You take the local senior salary, maybe add a premium because the client is foreign, and you land on a number that feels brave. Forty-five an hour feels brave when the engineer at the next desk makes the local equivalent of twenty. Here is the disruptive part. The US client is not paying for your location. They are not even thinking about your location, except as a logistics detail. They are paying for the work, and what that work is worth to their business. A payment system that does not go down is worth the same to a US fintech whether the person who built it sits in San Francisco or Brno. The value did not get cheaper w

The Moment I Realised UI Testing Wasn't Enough Three years into my QA career, I thought I was doing well. I had a solid Selenium suite running. Regression coverage was green. Stakeholders were happy. Then a production incident happened. A payment API was returning incorrect amounts under a specific condition. The UI looked perfect — amounts displayed correctly after rounding. But the raw API response? Off by a significant margin. My entire test suite missed it. Every single test. Because I was only testing what users saw . Not what the system was actually doing . That incident changed how I approached QA forever. 👇 Why API Testing Is the Most Underrated Skill in QA Let me be direct about something. Most QA engineers treat API testing as a secondary skill. Something you do with Postman when a developer asks you to verify an endpoint. A quick sanity check before moving on. That's the wrong mental model entirely. Here's the truth after 7.5 years: The API layer is where your product actually lives. The UI is a presentation layer. It shows users a version of the truth. But the API? That's the truth itself. Data contracts, business logic, validation rules, error handling — all of it lives at the API layer. If you're only testing the UI, you're testing the packaging. Not the product. My API Testing Journey — Tool by Tool Let me walk you through exactly how my API testing practice evolved, and what each tool actually taught me. Stage 1 — Postman: Learning to Think in Requests Postman was my entry point. And it's still the tool I reach for first when exploring a new API. But most people use Postman wrong. They treat it like a manual testing tool — fire a request, check the response, move on. That's wasting 80% of what Postman can do. Here's how I actually use it: Collections + Environments = your real power combo // Environment variables — not hardcoded values {{ base_url }} /api/ v1 / users / {{ user_id }} // Switch between dev/staging/prod by changing one environment // No

RJ Scaringe, the CEO of Rivian Automotive, joined us for a wide-ranging interview about how his company’s new electric SUV fits into the current EV industry, and what comes next.

How I Fixed Bugs in 30+ Open Source Projects (And What I Learned) Over the past few months, I've been contributing to open source as an independent developer. No big company backing, no team — just me, a laptop, and a lot of caffeine. Along the way, I've submitted pull requests to 30+ repositories across the Python, JavaScript, TypeScript, and Rust ecosystems. Here's what I learned from the process — the good, the bad, and the "I wish someone told me this earlier." Why Contribute to Open Source? Let's get the obvious out of the way: it's not about the money (at least not directly). Most bounties pay $50-$500, and you'll spend 10-20 hours on a single PR if it involves deep codebase exploration. The real value is: Reputation — Each merged PR is a public signal that you can read, understand, and improve other people's code Learning — You'll see how major projects are structured, tested, and maintained Network — Maintainers remember helpful contributors. Jobs come from these relationships Scratching your own itch — Fix a bug that annoys you? Everyone benefits My Process: Finding Good Issues Step 1: Pick the Right Projects Not all projects are equally welcoming to new contributors. Here's my filter: Signal Good ✅ Bad ❌ Response time < 7 days > 30 days or never Issue labels good first issue , help wanted None CI/CD Green, fast builds Broken, 30min+ builds PR merge rate > 60% of open PRs merge < 20% merge Step 2: Find Issues You Can Actually Fix I look for: Bug reports with clear reproduction steps — Someone already did the hard work of identifying what's wrong Issues labeled easy-fix or similar — The maintainer thinks it's approachable Issues in domains I know — Don't pick a C++ compiler bug if you've never written C++ Step 3: Before Writing Code This is where most beginners fail. Don't start coding yet! Read the CONTRIBUTING.md — Every project has different style, commit message format, and PR requirements Look at recent merged PRs — What do good PRs in this project look

A cottage industry of celebrity figurines, blinking screens, and other DIY gadgets is helping drivers bypass Tesla's distracted-driving controls.

More than 350,000 spectators will watch 62 cars compete, day and night.

Just in time for Le Mans, BMW is pulling the cover off its new M performance concept built on the Bavarian automaker's Neue Klasse next-generation architecture. The concept is the debut of a new design language for BMW's M division, signaling a shift toward a more electrified future. The company says it is built on […]

Every developer I know has built something for themselves. A productivity tool. A habit tracker. A personal finance app. An AI that makes them smarter, faster, calmer. I did the same thing for 2 years. Then I had a conversation with someone close to me that I completely mishandled — and I realised no amount of personal productivity tools would have helped me there. The problem wasn't me, individually. The problem was the space between us. So I started asking a weird question Why has all of AI been built for individuals? Copilot helps you code faster. ChatGPT makes you smarter. Notion AI organises your thoughts. Calm helps you sleep better. Not one of them is built for what happens when two people try to understand each other. That's a massive gap. And it's one I couldn't stop thinking about. What I built Mendle — an AI-powered Relationship Intelligence platform. Not a therapy app. Not a chatbot companion. Not another journaling tool with an AI skin on top. The core idea is **shared emotional memory. Most relationship apps are built around one person's perspective. You log your feelings. You get insights. Your partner is an afterthought in the architecture. Mendle is different at the data model level. Both people contribute. Both people benefit. The AI builds an understanding of the relationship not just an individual. Over time it surfaces patterns. Communication loops. Emotional triggers. The things you keep missing because you're too close to them. The technical challenge that surprised me Building AI for two people is fundamentally harder than building it for one. Single-user AI: one context window, one set of preferences, one voice to understand. Relationship AI: two different communication styles, two different emotional vocabularies, shared history that neither person has complete visibility into, and privacy boundaries that have to be respected even between partners. The shared memory architecture was the hardest part to get right. How do you build something

Floating-point addition isn't associative. For a corporate inventory with tens of thousands of rows, naive summation drifts — and the number you disclose depends on row order. Here's why, and the fix. Here's a result that should bother anyone building carbon software. Take a corporate emissions inventory — tens of thousands of line items, each a number in tonnes CO₂e. Sum it. Now sort the same rows differently and sum again. The totals don't match. Not by much — maybe the third or fourth decimal place — but they don't match, and nothing in your code changed except the order. If you've never seen this, open a console: 0.1 + 0.2 === 0.3 // false That's the same bug, scaled up to a reporting deliverable. Why order changes the answer IEEE 754 doubles have 52 bits of mantissa. That's about 15–16 significant decimal digits of precision — generous, until you add numbers of very different magnitudes. When you add a small number to a large running total, the small number gets shifted right to line up the exponents before the addition happens. Bits that fall off the end of the mantissa are gone. Add a 0.0001 tCO₂e line to a running total of 80000.0 and there simply aren't enough mantissa bits to hold both the 80,000 and the 0.0001 — the small value is partially or completely swallowed. Float addition, as a result, isn't associative. (a + b) + c is not guaranteed to equal a + (b + c) . Sum your rows largest-first and the small values vanish early against a big accumulator. Sum smallest-first and they accumulate into something large enough to survive. Same data, different total. Here's the effect, deliberately constructed to be visible: const big = 80000 ; const smalls = Array ( 50000 ). fill ( 0.0001 ); // small values first, then the big one let a = 0 ; for ( const x of [... smalls , big ]) a += x ; // big value first, then the smalls let b = 0 ; for ( const x of [ big , ... smalls ]) b += x ; console . log ( a ); // 80004.99999999... console . log ( b ); // 80004.99999999...

Cruz/Wyden bill would help Americans sue federal officials over censorship.

Latency, bandwidth, and fidelity all matter when you're chasing milliseconds.