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The temporary exemption from federal safety standards will let Amazon’s Zoox launch a real, paid robotaxi service in Las Vegas.
The SkyNomad N90 Max is the latest electric SUV from Xiaomi and its first extended-range EV, with a claimed range of over 1,705 km (about 1,059 miles). It will come in both five- and seven-seat configurations, as well as an N90 Max Camping Edition. One of the big features of the N90 is its highly […]
Zoox just got permission to charge for robotaxi rides in its boxy, steering-wheel-less vehicles. On Thursday, the National Highway Traffic Safety Administration announced it has granted the Amazon-owned Zoox a temporary exemption, allowing it to deploy up to 2,500 vehicles annually over the next two years, as reported earlier by Reuters. The NHTSA's decision exempts […]
Government ban on foreign-made robots may hinder instead of help US robotics.
Just two months after a bumpy launch earlier this year, Ferrari has reportedly already hit its 2026 sales goal for its polarizing, Jony Ive-designed Luce EV. Ahead of Ferrari announcing its quarterly earnings on Thursday, the Financial Times reports that Ferrari was aiming to sell "just under 500" Luce units this year, and reached that […]
WIRED test-drove a preproduction version of the Luce, the storied Italian automaker’s controversial electric car, and it was remarkably surprising. Will it convert Ferrari fanatics?
The new full-size flagship SUV starts at $87,700 when it goes on sale in Q4.
Stratagems #20: Alex Felt the AI Collector Slow Down. He Knew Someone Else Had Made a...
I finally shipped the first version of my freelance landing page. The funny part? I spent weeks thinking I had a design problem. I didn't. I had a content problem. The layout is good enough. The copy is good enough. The CSS is good enough. What the page really needs now is more real projects. Instead of redesigning it again, I'm going to spend my time replacing concept work with actual client work as it comes in. Sometimes the next version isn't another refactor. It's simply experience. 🔗 https://lksvn.com.br/freelance/
Stop Waiting. Start Building. For a long time, I believed I needed to be "ready." Ready with a stronger portfolio. Ready with more skills. Ready with a clearer career plan. So I waited. Eventually I realized something uncomfortable: Confidence isn't built before you start. It's built because you start. I'm an AI & Data Science graduate figuring things out in public. Right now I'm: Learning AI beyond what college taught me. Building projects that solve real problems. Applying for internships and jobs. Documenting everything I learn. I don't have all the answers. But I do have curiosity. This blog is my commitment to consistency over perfection. I'll be sharing projects, mistakes, things I learn, career experiments, and ideas that hopefully help someone else who's also trying to figure things out. If you're also building before you feel ready Welcome. 🚀 Let's learn together. What was the project that finally pushed you to stop waiting?
Governments are switching, but I’m not sure it makes a difference : …some municipalities, including Denver, Colorado, are ditching their Flock arrays. But keep in mind that if they’re only switching from Flock to another brand of license-plate readers, like Axon, it’s like a gambling addict trying to kick the habit by switching from FanDuel to DraftKings. […] Despite what you may read on the Flock website, Axon cameras are pretty effective when it comes to hoovering up personal details that can go far beyond your license plate numbers. That means a municipality that opts for Axon cameras instead of Flock units won’t necessarily reduce the amount privacy its citizens lose through their use...
I'm responding directly to Maya Posch's piece on HackADay that was published today. Did that rhyme? I think it did. Community-led open source project hosting site Codeberg has formally announced that projects whose code is largely or fully machine-generated through LLMs and other ‘AI’ tools will no longer be welcome. This follows on the heels of a similar ban on cryptocurrency-related projects. The community vote was on two issues, the first being the notion that scraping of project code for the use in LLMs should be forbidden, which was a motion that easily passed. The second motion was on disallowing projects whose code was substantially generated by LLMs like Claude, OpenAI Codex, and similar. This motion passed with 358 in favor versus 144 against. In the earlier linked blog post the reasoning behind especially this second issue is expanded upon, covering not only ‘license whitewashing’, but also the direct and indirect hardware costs, with the expanding ‘AI’ datacenter hyperscaling having massively increased hardware costs for Codeberg over the past years, as the costs have been largely externalized. Also covered is the aspect of these LLM-based tools destroying the OSS community, which is something that is backed up by recent studies. Even if we ignore that such LLM-tools are destroying the cognitive abilities of its users, there’s an argument to be made that if LLM-scraping is disallowed, then it’s consistent to also not allow LLM-generated code. In the Terms of Use you can see these changes, both for LLMs and for cryptocurrency projects. Thanks to [mk-fg] for the tip. Credit is due to the original HackADay.com Article by Maya Posch post contents, but I would like to respond to each point here while preserving what was published on 7-24-2026. Scraping of Project Code for the use in LLMs should be forbidden If LLMs are rendering code trained on GPL-3 open-source software, then its effectively bypassing the license through intellectual property theft by directl
Summary More and more, I find myself overtaken by AI in my own field of expertise. Given only the materials, AI clears the specialized knowledge I spent years building up more easily than I had expected. This column takes a small thing that actually happened during collaborative development as its starting point, and thinks about what a specialist who has ended up on the overtaken side can make into a strength from here on. Where will the human role move next? How long will what I can hand over now stay something only I can produce? This is not a piece that settles the answer, but I use what happened in daily development as a clue to think through the question. Who this is for and what you can take away This column is for people who, in collaborative development with AI, have begun to wonder where to place their own expertise. Rather than lamenting being overtaken as such, I want to think through with you, from things that actually happened, what you can hold on to once you are on the overtaken side. I wrote it so that you can carry away the unease of handing work over to AI as a question for rethinking your own role. I hope it becomes material for someone standing in the same situation to think about what they themselves would do. This article is a column based on things that actually happened one day during collaborative development, and on the record of the exchange I had with AI that day. In my own field of expertise, the moments of being overtaken by AI have clearly grown more frequent. There was one recently. It was when I added a feature for subscribers to comment to each other on this site. What I decided went only as far as the line that both reading and writing stay closed to subscribers alone; the implementation I left to AI. When the implementation was done, other AIs reviewed it from four viewpoints, tried to rebut each finding, and sent only the ones that survived on to be fixed. Two of them were ways the daily posting limit could be slipped past. One
Don't fight their strength — take away their advantage. Don't fight the boiling water — remove the...
Over the past few weeks, I've been focused on deepening my understanding of mobile analytics—not just by completing a course, but by putting those concepts into practice through hands-on implementation in React Native. Throughout this journey, I explored a wide range of topics, including: Firebase Analytics integration Google Analytics 4 (GA4) Event planning and naming conventions Screen view tracking Custom events and custom definitions Key Events (Conversions) User properties and User ID Acquisition and campaign tracking Audience segmentation Ecommerce measurement Checkout funnel analysis Promotions and marketing attribution BigQuery integration Realtime reporting and DebugView Analytics validation and best practices One of the biggest lessons I learned is that analytics is much more than logging events . A well-designed analytics strategy helps answer important questions about user behavior, feature adoption, user engagement, and conversion optimization. The quality of the insights you gain depends on having a well-planned event architecture, consistent naming conventions, and meaningful data collection from the very beginning. Completing this Udemy course gave me a strong foundation in Firebase Analytics and Google Analytics 4. Reinforcing that knowledge through hands-on implementation in React Native helped me better understand event planning, debugging, reporting, and analytics best practices for modern mobile applications. Course Certificate I'm happy to have successfully completed the Firebase Analytics & Google Analytics 4 (GA4) course on Udemy. Certificate: https://www.udemy.com/certificate/UC-4a23b92f-0857-4a75-83dd-ac186bdfdfbc The course covered both the fundamentals and advanced capabilities of mobile analytics, including GA4 reports, custom events, screen tracking, audiences, BigQuery integration, and data-driven decision making. It has been a valuable learning experience that strengthened both my theoretical understanding and practical implementation
I judged three hackathons over about ten days this July: MLH x DigitalOcean "AI for Social Good" on July 11, the Sports World Cup Hackathon in San Francisco on July 17, and Aethera Hacks, an online event on Devpost, across July 19 to 21. I came in from the sports technology side, building athlete monetization tools, so I was usually the judge asking who pays for this rather than the judge asking what is your bundle size. That turned out to be a useful seat, because the questions that decide scores are mostly not technical ones. Here is the part builders rarely get told: a judge is scoring under a hard constraint. Some number of teams, a fixed window, and by the middle of the block the demos start blurring together. Judges are not evaluating your project against an ideal. They are ranking it against the six they just saw while trying to remember which one had the map. Everything below follows from that. The rubric is real, but it is not what separates teams Most events hand judges four or five categories with numbers next to them. Technical difficulty, originality, design, impact, something about use of a sponsor API. Those categories are real and I filled them in honestly. But they compress. Almost every team lands mid-range on most of them, and the spread that produces a winner comes from two or three things the rubric does not name directly. 1. Whether the demo ran This sounds too obvious to write down. It is the single largest score differentiator I saw. A working demo, live, on the judge's screen or the team's laptop, beats a more ambitious project shown as slides almost every time. Not because judges are impressed by working software as such, but because a live demo removes doubt, and doubt is what a judge is actually managing under time pressure. The practical version: cut scope until something end-to-end runs. One complete path through the product beats four half-built paths. If your architecture diagram has six boxes and two of them work, demo the two and de
Late that night which was two weeks to my final year project defense, I stared at the ceiling thinking about life after school. As a Nigerian, the reality of the economy hits differently from what you imagine. I questioned why I chose to study Computer Engineering only to finish with no jobs and no internships afterwards. And one thing, I knew was that I wanted to work in tech, but I was confused about which particular skill to focus on, since I've been learning Python for a while with no clear direction. Then one day I came across a post on X from a popular influencer sharing a TS Academy scholarship opportunity. I clicked it and applied. Just like that, no long research, no consulting anyone. Few days later I got a mail that I've been selected but there was an application fee. That's where I paused . The Nigerian in me thought anything scholarship is free😂😂, The thought of spending my hard-earned money on something I wasn't sure about but something told me to take the leap so I paid. Few days later I got another email, this one was a email for successful payment with full details and start date. Boom! That's how I chose DevOps. They was no plan, no careful research, it just started with one post, one click and one leap of faith. I did mention it to someone after though. A senior friend. His response? "Have you registered?" I said yes. "You shouldn't have. The job market is so tight." My chest couldn't contain that. But I'd already paid so I went ahead anyway. Shortly after graduation I started learning. And I won't lie, there were times I asked myself why I chose this 😂 Linux commands. Git. AWS. Terraform. Ansible. CI/CD. Docker. Kubernetes. Monitoring tools. Each one felt like a wall and I had to climb every single one. I completed my first project on Bash scripting, second on AWS deployment. And right now I'm in the middle of my capstone Project Phoenix. The deadline passed but I still have a month of grace. Lucky me 😂 In between I stepped away for about a month
I took a week off from Dev.to. Not a planned one — I just sat down last Sunday and realized I had nothing left. Eighteen stories into a 36-story series, and my tank was empty. So I didn't post a single article for a full week. I'd pop into the comments section now and then, but that was it. The day job was still there, but I stopped staying up till 1:30 AM writing like I did when the series first started. I adjusted to a 10 PM bedtime instead. Then on Friday afternoon, something happened. I spent twenty minutes writing a rant about bugs and layoffs, hit publish, and went back to doing nothing. When I checked back on Sunday, that rant had more eyeballs on it than most of my 36 Stratagems stories. You're supposed to have an existential crisis about your content strategy at this point, right? I didn't. The Series That Wasn't a Strategy Eighteen stories ago, I sat down and wrote the first Stratagem. I wasn't starting from nothing — there was a rough outline in my head, a skeleton of 36 chapters with each of the six characters mapped to a specific stratagem. But I hadn't figured out the details of each story yet. Not because I had a content calendar. Not because an editor was pushing me. Because it clicked. The six protagonists — Derek, Lena, Leo, Alex, Mark, and P — had been living in my head long before the first post went up. They came from an earlier series I'd written, 15 stories about AI systems collapsing in the wild. Those people weren't characters I invented for a series. They were people I'd met, worked with, watched navigate impossible situations. They stayed with me because their stories weren't finished. The 36 Stratagems wasn't a strategy. It was a container. I found an ancient Chinese military text that happened to map perfectly onto what I'd already seen happen in AI engineering teams across the industry. The fit was uncanny — like the text had been waiting two thousand years for someone to rewrite it in Python and production incidents. Each Stratagem too
We’ve all been there: It’s 11 PM, the bug is still alive, your tests are failing, and you’re about to throw your laptop out the window. We usually view debugging as a pure logic problem: stack traces, breakpoints, and logs. But Emotional Intelligence (EQ) is often the real reason you fix a bug in 20 minutes instead of 3 hours. Here is how EQ actually applies to your daily workflow: 1. Spotting Tunnel Vision Before It Wastes Your Time Frustration causes confirmation bias. You start forcing your initial hypothesis ( "It MUST be the cache!" ) even when the logs say otherwise. EQ Move: Recognize physical signs like tight shoulders or rage-typing. Take a 5-minute bio-break. Stepping away resets your mental stack, which is usually faster than another hour of blind grinding. 2. Separating code.hasBug() from dev.isBad() A stubborn bug easily triggers imposter syndrome: "A senior dev would have solved this already." That inner voice just adds noise to your debugging stack. EQ Move: Reframe the problem objectively: ❌ "I don't know what I'm doing." (Emotion) ✅ "This async function isn't returning the expected payload." (Fact) Debug the code, not your self-worth. 3. Handling Spicy Bug Reports A ticket comes in: "This is completely broken, who let this ship?!" Your gut reaction might be to get defensive or send a passive-aggressive response. EQ Move: Filter out the noise. Translate panic or bad phrasing into actionable facts. Reply calmly to de-escalate, pull the missing repro steps, and ship the fix without unnecessary Slack drama. 4. Rubber Ducking and Asking for Help (Ego-Free) How many times have you fixed a bug just by explaining it out loud to a peer? Sitting in silent frustration for hours doesn't make you a hero; it just delays the feature. EQ Move: Treat asking for help as an optimization tactic. Send a concise message with context: > "Hey, expecting X, getting Y. Already tried A and B. Got 5 mins to glance at this snippet?" 5. Staying Cool During Prod Outages Panicked
When I first heard the term ETL (Extract, Transform, Load), I thought it sounded like something only advanced data engineers dealt with. But as I’ve been learning, I realized ETL is the backbone of almost every data project. It’s the process that makes raw data usable, and without it, analysis can quickly fall apart. The first lesson was short but powerful. ETL is about moving data from one place to another, transforming it along the way so it’s clean and ready for analysis. I remember thinking: “So this is how companies make sense of the chaos in their databases.” It felt like peeking behind the curtain of how insights are really built. Then came the part about Excel macros. At first, I was intimidated, macros sounded complicated. But once I tried them, I realized they’re like little helpers that automate repetitive cleaning tasks. Instead of manually fixing hundreds of rows, I could write a macro and let Excel do the heavy lifting. It felt like discovering a secret shortcut. I even laughed at myself when I realized how much time I had wasted before, manually correcting data. This was a turning point: I started to see how automation can save not just minutes, but hours. Finally, I explored Power Query. If macros are shortcuts, Power Query is like a full toolkit. It lets you connect to different data sources, transform them, and keep everything organized. I loved how visual it was dragging, dropping, and shaping data felt almost creative. I remember thinking: “This is what makes data cleaning less of a chore and more of a craft.” It gave me confidence that even messy datasets could be tamed. Learning ETL, macros, and Power Query taught me that data cleaning isn’t just technical, it’s about mindset. It’s about respecting the data, being patient, and finding smarter ways to work. I used to think cleaning data was boring, but now I see it as the foundation of every meaningful insight. Without clean data, analysis is just noise. ✨ Takeaway: If you’re starting out in dat