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There is no manual for TronSoft. No API reference, no schema diagram, no forum thread explaining why a comanda refuses to close. If you want to understand it, you open the database and start pulling threads until something makes sense. That's exactly what I did — for months, on top of my actual job. The problem nobody wrote down I'm the Operations Manager at a restaurant in Itaúna, a mid-sized town in Minas Gerais, Brazil. I'm also the only person there who writes software. Not because I was hired to — because the restaurant runs on a Brazilian ERP called TronSoft, built on a Firebird database, and Firebird doesn't come with the kind of ecosystem you get around Postgres or MySQL. No Stack Overflow flood of answers. No official docs beyond a thin operator manual. Vendor support exists, but it's slow, and it doesn't scale to "I want to automate this specific internal workflow at 11pm on a Tuesday." So when I needed to automate payment reconciliation, close out comandas without touching the vendor's fragile UI, and trigger fiscal document emission (NFC-e) reliably, I didn't have a spec to follow. I had a live production database and a lot of curiosity. Learning a system by watching it think I started the way you'd expect: opening tables, guessing at relationships, breaking things in a test environment until I understood why they broke. Over time that turned into something more systematic — I ended up documenting 390 tables and 514 foreign keys across roughly 40 functional modules, entirely from observation. No vendor documentation, no source code access. Just structure, inference, and a lot of trial and error. Some of what I learned only reveals itself under pressure: Firebird's SQL dialect has its own quirks — FIRST 1 instead of LIMIT , for one. Small thing, but it breaks every query you copy-paste from a Postgres tutorial. Primary keys aren't auto-incrementing in the way you'd assume. They're driven by generators ( GEN_ID ), and if you write a record without syncing

A lot of automakers talk about wanting to minimize or eliminate driver distractions so as to make the experience of driving more rewarding and safer overall. Lamborghini has a different strategy; it wants the driver to become one with their vehicle. This helps explain the storied super car maker's tagline for the new Revuelto SV: […]

Chris Carter chats with Ars about the film he originally wanted to make—and why it's more relevant than ever.

Three years ago, I started my journey as a developer with a pretty simple idea: Learn to code, gain experience, become good at it. Three years later, I’ve learned a lot — but I’ve also realized that becoming a developer isn't as straightforward as I imagined. I've worked with JavaScript, React, Laravel, PlayCanvas, WebGL, and other technologies. I've worked on real projects, dealt with bugs I didn't understand at first, learned technologies because a project required them, and worked alongside other people to get things done. I think one of my strengths has always been learning new technologies and adapting to new problems. But there are things I'm not proud of. I've never been particularly good at finishing personal projects by myself. I've started many things, learned from them, experimented with different technologies, but I rarely took them all the way to completion. I also don't have an impressive GitHub contribution graph. I haven't spent the last three years consistently building open-source projects or pushing code every day. And if I'm being completely honest, I don't think I've mastered any particular technology. I'm good enough to build things. I'm good enough to understand code, solve problems, learn what I don't know, and contribute to a team. But I'm not at the level where I'd confidently say: "This is the thing I'm an expert at." And recently, AI has made me think about this even more. I'm not afraid of AI taking over jobs. I actually think the capabilities we're getting are incredible. What concerns me is more personal: If AI can already build many of the things I've spent years learning to build, then what should I be becoming as a developer? For a while, I felt overwhelmed by that question. Should I learn more technologies? Should I specialize? Should I focus on fundamentals? Should I build more projects? Should I contribute to open source? Should I learn AI? I'm realizing that the answer probably isn't to chase everything. My next goal isn't to co

For years, I thought being a good programmer meant knowing your stack really well. I was a Laravel developer, A React developer, A Node.js developer and A Go developer. And there was some truth to that. I spent years working with Laravel, for example, and naturally became faster at solving problems with Laravel. I know the ecosystem, the common mistakes, the packages, the conventions, and probably a few things that weren't even written in the documentation. My stack became part of my identity as a developer. But I think AI is slowly changing that. Not because frameworks and programming languages don't matter anymore. They obviously do. It's because AI has made moving between them much easier. Today, I can open a codebase written in a language or framework I haven't touched in years, or maybe have never used seriously, and get productive much faster than I could before. I can ask AI to explain the project structure. I can ask it to explain a piece of code. I can ask it to translate something I understand in PHP into Go. I can ask it to help me write tests. I can use it while debugging. I can even ask it why a particular approach might be a bad idea. That doesn't suddenly make me an expert in that technology. But it means I don't need to spend weeks just getting comfortable enough to start solving the actual problem. And I think that's a pretty big change. Your Stack Is Becoming Less Important There was a time when knowing a technology itself was a significant advantage. If you knew Laravel, you had to learn Laravel. If you wanted to learn React, you had to spend time understanding React. If you wanted to work with Kubernetes, good luck. You read documentation, watched tutorials, built things, broke things, fixed them, and slowly built up experience. That's still how you become good. But AI has changed the entry point. The first few hours with a new technology are no longer as painful as they used to be. You can have an AI sitting beside you explaining things as you g

Czinger used topological design and additive manufacturing to make the brakes.

Ten things started is not progress. It's ten open tabs in your head, each one costing rent. Every unfinished task keeps a little of your attention hostage. You feel busy. You are just fragmented. Finishing one small thing returns more energy than starting three. So close the loop. Merge the PR. Send the message. Delete the branch. An empty background is where good work actually happens. Done is quiet. Chase the quiet. – Serguey Asael Shinder

Study finds wait times for cancer surgeries hit 10-year high.

Analytical Insights: The Mechanisms Driving Career Growth in Data Engineering In the rapidly evolving field of data engineering, career progression is not merely a product of time served but a result of deliberate, adaptive strategies. A 7-8 year trajectory to a Senior Data Engineer role, marked by multiple successful contracts, underscores the critical role of adaptability and continuous learning. This analysis dissects the mechanisms that propel career growth, highlighting their interdependencies and the consequences of their neglect. 1. Continuous Learning and Skill Development Impact: The pace of technological advancement in data engineering demands constant upskilling. Internal Process: Engaging with new tools, methodologies, and industry trends through online courses, certifications, and hands-on practice ensures relevance. Observable Effect: Enhanced technical proficiency translates into the successful delivery of complex projects and the attainment of senior-level roles. Instability: Skill Stagnation occurs when learning efforts are inconsistent or outdated, leading to reduced competitiveness. This gap between current skills and industry demands can halt career progression, making individuals less attractive to employers seeking cutting-edge expertise. Intermediate Conclusion: Continuous learning is not optional; it is a survival mechanism in a field where obsolescence is a constant threat. 2. Client Relationship Management Impact: Diverse client needs and expectations across multiple contracts require tailored approaches. Internal Process: Implementing tailored communication strategies, proactively aligning project goals, and establishing iterative feedback loops foster trust and collaboration. Observable Effect: High client satisfaction leads to repeat contracts and positive referrals, which are critical for career advancement. Instability: Client Misalignment arises from inadequate communication or misunderstanding of client requirements, resulting in pro

Ford said today that its next-generation electric vehicle - recently dubbed Fathom - will go into production at the automaker's recently overhauled Louisville Assembly Plant in the first quarter of 2027. The first Fathoms will be prototypes, with Ford's team in Louisville already in the production-level pre-tooling phase at the recently converted facility. Factory workers […]

My drive of the 2027 Corvette Grand Sport X began under oily black clouds, a torrential weather front releasing its grip on Manhattan - an inauspicious start for any mega-powered sports car. Rain pelted the waterlogged pavement, as I set course for the mountain-man roads of the Catskills, then on to Long Island and New […]

Pricing hasn't changed, and now you can get captain's chairs for the R1S.

Rivian R1S owners, rejoice. Rivian has heard your pleas for second-row captain's chairs. For years, Rivian owners have lamented the absence of captain's chairs in the R1S, noting that the three-row SUV would be much improved with standalone seats instead of a bench in the second row. Parents groused that loading and unloading kids in […]

AI is disrupting career progression by eliminating the learning opportunities at each rung while simultaneously enabling people to perform above their experience level, Alasdair Allan explained in his talk Engineering Progression When AI Ate the Middle at QCon London. Fewer junior developers join the industry, and AI slows hiring at the entry level. By Ben Linders

24 stories. 24 visits. One counter, same six faces. The Third Cup The AI wave hit, and...

It's 4:50 on a Friday when the alert fires. A logging endpoint nobody remembers writing is copying customer records out of the database. Thirty-eight thousand of them. It's been quietly running for eleven months. You see it first. You understand it first: an old token, never rotated, someone's shortcut from a year ago that was never closed. You know exactly how to say it. In your own language, the sentence is already formed, precise, complete. Then the call starts. Eight people. The CTO, the client's legal counsel, someone from the German data protection office who joined without a word. Everyone is talking in English, fast. The lawyer says one thing, in a flat voice: under GDPR, you have seventy-two hours to report this from the moment you became aware. The clock started at 4:50. Now someone asks the only question that matters. "Do we know how they got in?" You know. You are the only one on the call who actually knows. You open your mouth and start to build the sentence in English: the token, was it "leaked" or "exposed," is it "left open" or "not revoked," and while you are still choosing the word, the CTO says "Maybe it was intentional. Someone inside." The room turns. Now they are looking for a person to blame, and the true, simple answer—that it was an old mistake, not a traitor—is still sitting in your head in the wrong language. Two minutes later someone has decided it was probably a rogue developer. The investigation will now point the wrong way for a day you do not have. You could have stopped it with three sentences. You had the three sentences. You just couldn't get them out fast enough while the clock ran. This is the part no one warns you about. In an incident, the language is not the small problem. It is the whole problem. The engineer who explains the breach clearly in the first five minutes controls what happens next. The one who can't watches other people decide, wrongly, in a language he understands perfectly but can't yet fight in. Test Yourself F

JCB has invested heavily in hydrogen to power its diggers and other machinery.

Jaguar's all-electric Type 01 comes with a very beige interior that divides the four seats with a "spine" that spans the length of the cabin. New photos shared by Jaguar show a low-slung driver's seat, alongside a slim dashboard with a smartphone-style display in the center. This vehicle doesn't come with a traditional rear-view mirror, […]

The deal starts with the arrival of the GEN4 car and 2026/2027 season this December.

This guide gives you a test that takes ten seconds and tells you whether the thing you just built is a report or an analysis. Then it gives you four moves that turn one into the other. Every move has a worked SQL example and real numbers. The whole method is here. What you actually do: take the number you just produced, and ask what someone would do differently because of it. If the honest answer is nothing, you have a report. Then you run the four moves below, in order, until the answer is a specific action a specific person can take on Monday. The short version. Data analysis is looking at records of things that already happened and finding a pattern that changes what someone does next. If nothing changes, it was not analysis. It was a report. The same starting number, two endings. The test: what would someone do differently? Before you read the answer, look at the last thing you built and try it yourself. Who was going to act on it, and what were they going to do? Take any number you have produced and finish this sentence out loud: "Because of this, someone should do a specific thing ." Both blanks have to fill in with something real. A named person or team, and an action they control. Here is a real one. "Churn was 4.1% in Q3." Who acts, and how? Nobody can act on that. It is a true, correctly calculated, carefully formatted number, and it changes nothing. That is a report, and reports are useful. A dashboard that tells you the servers are up is doing its job. It is just not analysis. Now the same underlying data, worked further. "Monthly-plan accounts that never opened the import tool churn at 9.2%. Ones that did churn at 1.8%. The email introducing that tool goes out on day 14, and most cancellations happen on day 11." Who acts? The lifecycle marketing owner. What do they do? Move the email to day 3. That is analysis, and the only difference is that it ended somewhere a person can stand. The word "analysis" is doing a lot of quiet work in job descriptions, so