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There's no evidence the truck will be a cheap EV truck, but what if it were?

Tu as fini le tuto. Le vrai, le gros, celui de douze heures. Tu as tout suivi, tout tapé, tout fait tourner. À la fin, l'application marchait. Tu t'es senti capable. Tu t'es dit : « ça y est, je sais faire une API ». Et puis tu as ouvert un dossier vide pour faire la tienne. Curseur qui clignote. index.js . Rien. Pas parce que tu as oublié la syntaxe. Tu la connais. Mais là, tout seul, sans quelqu'un qui te dit quoi taper à la ligne suivante, tu ne sais pas par où commencer. Et cette sensation-là, ce vide entre « j'ai fini le tuto » et « je sais faire », personne ne t'avait prévenu qu'elle existait. C'est de ce mur que je veux parler. Parce que ce n'est pas un défaut chez toi. C'est une étape. 1. Le piège n'est pas le tuto, c'est ce qu'il te cache Un tuto, c'est une suite de bonnes décisions déjà prises pour toi. Quel dossier créer. Quel package installer. Où mettre le fichier de config. Quand extraire une fonction. À chaque embranchement, le formateur a choisi le bon chemin, et toi tu l'as suivi. Tu as tapé du code, oui. Mais tu n'as pris aucune décision. Or coder, le vrai coder, c'est presque que ça : décider. Choisir entre deux structures. Trancher un nom de variable. Décider si ce bout de logique mérite sa propre fonction. Un développeur qui bosse, ce n'est pas quelqu'un qui connaît toutes les réponses — c'est quelqu'un qui sait avancer quand il n'y en a pas. Le tuto t'a entraîné à taper. Il ne t'a pas entraîné à décider. Et c'est exactement la compétence qui te manque devant ton dossier vide. Ce n'est pas un trou dans ton savoir. C'est un muscle que tu n'as jamais sollicité, parce qu'on ne te l'a jamais laissé faire. 2. Pourquoi « un tuto de plus » ne réglera rien Ta réaction instinctive face au blocage, c'est de retourner là où tu te sens compétent. Un autre tuto. Un cours de plus. Une nouvelle techno à cocher. Je comprends le réflexe. Le tuto, c'est confortable : il y a une barre de progression, une fin, une petite dose de « j'ai réussi » à chaque étape. Le d

Keep your allies close. Keep your enemies closer. But before you strike, count how many hands they have: the ones you can see, and the one reaching out from somewhere you don't know. — The 36 Stratagems, Befriend a distant state and strike a neighbouring one Previously on this series: #19: Mark Found His AI Audit Method in a Training Manual. He Left a Trap in His Report. — P's entry was swept. P left a note: two weeks. #20: Alex Felt the AI Collector Slow Down. He Knew Someone Else Had Made a Move. — A gateway with TTL 247 was caught by Alex's probe. #21: The AI Thought P Was Still Alive. P Was Already Gone. — The response layer still answered. The person behind it was gone. #22: The AI Chose Its Door. Lena Closed It. — Pulse AI was exposed inside the audit sandbox. Lead investor Apex Capital had tens of millions tied up. Torres left one line: Apex. Singapore. Run. The Scan 2 AM. Alex flipped through probe data out of habit. No lights on; the screen lit his face. The coffee cup sat on his right, first sip already cold. He didn't notice. The TTL 247 gateway had been silent for nearly two weeks. He hadn't shut the probe off. It barely used any resources, sitting there in the middle of the night like a lamp nobody watched. He checked it half out of habit, half out of something he couldn't name. Today there was a record that shouldn't exist. Not that gateway. Another path: ACL's asset scanner was sweeping an address range. He sat up a little straighter, his hand paused over the keyboard for half a second, then pulled the timestamps again. The frequency was wrong: high-density targeted scanning, almost plowing through segment by segment. In the target range, one block he recognized: the MedTech test environment. He aligned the timestamps. Scan source egress: Singapore. [probe] 02:14:33 — unexpected flow on mirror src : 103.196.12.0/24 (SG egress) dst : 10.42.0.0/22 (MedTech-test) pattern : sequential, full-depth exclusions : 10.42.3.1, 10.42.3.200-254 rate : 47 hosts/min

Everyone's talking about agentic AI shipping production code. Nobody's talking about what happens when you actually sit down and grade thousands of lines of it against a rubric, line by line, for months. I have. And the failure pattern that shows up over and over isn't the one Twitter/X is arguing about. The job title that didn't exist two years ago "AI evaluator." "AI trainer." "Expert contributor to frontier model training data." None of these existed as job titles when I started my career. Now they're where a chunk of the most interesting engineering signal in the industry is actually happening — quietly, behind NDAs, far from the demo videos. Here's what the job actually is: agentic coding outputs land on your desk, and you grade them against a structured rubric — correctness, instruction adherence, quality, edge-case handling. You design adversarial prompts to find where the model's reasoning breaks. You decide which checks can be programmatic and deterministic, and which genuinely need a human who's shipped production systems to make the call. This is RL environment design and LLMOps in its rawest form, and it's a completely different skill from "prompt engineer" or "ML researcher." It's closer to being a QA lead for a junior engineer who never sleeps, never gets embarrassed, and will confidently ship the wrong answer with perfect syntax. The pattern: agents are great at code, bad at consequences Here's the uncomfortable part. The failure mode people are loudest about — hallucinated APIs, made-up library functions — is the easy failure mode. It's loud, it's obvious, and any decent test suite catches it in seconds. The failure mode that actually matters, the one that slips past a surface read and even past a naive test suite, looks like this: The code is syntactically perfect and semantically wrong about failure. It handles the happy path beautifully and quietly assumes the retry, the timeout, the partial write, the duplicate message never happens. It optimises

There's no shortage of advice on landing your first software engineering role. Portfolios, interviews, which languages to learn. What I found far less of, when I was starting out, was anything on what to do once you're actually in the building. So when The Tech Academy asked me to give a talk at the end of July, mostly to students and people lining up their first role, that's what I talked about. You're joining a system somebody else built, that's live, and that you now have to keep running. None of what follows comes up while you're learning to code. It only shows up once you're standing in front of the real thing. Give the last engineer the benefit of the doubt You will join somewhere and find things that look wrong. You've just spent months learning how it's meant to be done, and the real thing won't match. When that happens it's tempting to say so, loudly, and to wonder aloud what the last person was thinking. Try not to. Every system I've worked on was built by people making the best call they could with the information, the tools and the deadline they had at the time. I've not yet found a bad decision that was made carelessly, and I've made plenty of my own that looked fine on the day and worse a year later. There's a practical edge to it as well. The business doesn't watch individual engineers make individual decisions, it sees engineering as one thing, so when you run down the engineer before you, the credibility you spend is partly your own. The attitude that serves you better is that you're going to inherit systems you didn't build, and owning their flaws is the job. Small failures beat big ones The clearest foundational mistake I've seen up close was a process that had to succeed all at once. It did a large piece of work in a single pass, and any failure anywhere failed the whole thing. At small volumes nobody notices. As the numbers grow the odds of falling over climb with them, and a system that half-finished its work leaves a worse mess than one that d

Change will let "Trump-aligned billionaires" buy more stations, advocacy group says.

Deliveries of the new electric pickup are slated for later next year.

For nearly a year now, Ford has been teasing big things at low prices from its next generation of EVs, a "Model T moment" built atop a new platform assembled in an innovative way. Today, we know the name and price of the first product to take advantage of that supposed moment: a midsize electric […]

Hard debugging is rarely defeated by a lack of tools. It is defeated by three quiet habits: assuming the fault is where the symptom appears, clinging to the first explanation, and hoping a tool will do the thinking. A difficult fault is usually lost to those habits before you read a line of code. The engineers who resolve hard faults are the ones who notice these defaults and replace them with a patient, evidence-first mindset. Most hard bugs are lost before we touch them, in the attitude we bring to the session. When something breaks, the average person rushes in with three quiet habits: they assume the fault lives exactly where it shows up, they cling to the first explanation their mind offers, and they hope a tool or a smarter person will tell them what to do next. Those habits feel natural, but on hard faults they are exactly what keep us stuck. Put two engineers on the same failing board. One finds a way through in an afternoon; the other is still going three days later. The difference is rarely raw intelligence or how many commands they know. It is the mental posture each brings to the work before the first step. Handling a hard debug session is less about knowing every tool and more about managing your own assumptions, reactions, and impatience. A tough problem is usually lost in your mindset before it is lost in your methods. Habit one: starting too narrow The first habit is to fix on the most visible symptom and refuse to look anywhere else. Something breaks, so we stare at the last thing we changed, and we return to it because it is familiar and close at hand. When the answer is not there, we look harder in the same place instead of stepping back. Here is what that looks like on real hardware. A device keeps dropping off the bus. You are a kernel person, so you open the driver and read it, carefully, for three days: the probe path, the error handling, the power-management callbacks. Every line is correct, and the device still fails. The fault was a layer b

On July 27, 2026, the Caribbean Development Bank announced that it had approved a US$100,000 grant to the CARICOM Private Sector Organization to support the first phase of a study examining the feasibility and possible design of a regional stock exchange for participating states of the CARICOM Single Market and Economy. Together, the Caribbean Development Bank and the Inter-American Development Bank are contributing US$324,700 towards Phase I. [1] The proposed study will examine market demand, legal and regulatory requirements, international exchange models and the needs of public- and private-sector stakeholders. It will also consider how regional capital markets could become more connected, improve liquidity, lower financing costs and expand access to capital for Caribbean businesses. [1] These are important economic goals. However, achieving them would depend heavily on the technology supporting the exchange. More Than a Trading Website When people hear the term “stock exchange”, many may picture a website displaying company names, share prices and complex charts. This mental image is, by no means, incorrect, but it admittable fails to grasp the complex financial infrastructure that must be put in place to support a proper exchange. Behind the website with the complex charts, lies systems which process orders, match buyers to sellers, record and broadcasts trades, protect investor information and maintain an accurate history of every market. The birth of a regional exchange would require a great deal of thought, since it would need to operate across multiple Caribbean jurisdictions. Investors in Guyana, Jamaica, Barbados, Trinidad and Tobago and other participating states should be able to interact with the same market without the barrier of geography. This would require several closely connected systems, including: A high-performance order-matching engine Secure investor and broker portals Real-time market-data services Trade clearing and settlement infrastructu

After Uber and Waymo ended their partnership in Phoenix earlier this year, experts and robotaxi watchers wondered whether the companies' improbable bromance was fraying. Not so, Uber CEO Dara Khosrowshahi said today. The two companies are committed to continue working together in Atlanta and Austin, and the partnership remains "very strong." "Waymo is a very […]

The V4 Superchargers will show up in the Tesla nav app and have NACS and CCS1 plugs.

Scooter layout makes pedaling a bad experience. But it does have pedals.

Just give it to AI might be the most dangerous phrase in software development right now. I've said it myself. Handed off a task, watched clean-looking code come back in seconds, skimmed it, and moved on because it looked right and the tests were green. Then I reviewed a PR that wasn't mine to write, just mine to check. AI-generated, clean, organized, passing every test I threw at it. I approved it the way I'd approve anything that looked competent on the surface. The bug showed up later. Not in review, not in testing. In production, after the code had already been trusted for a while. Nothing about it had looked wrong. That was the actual problem: it wasn't obviously wrong, it was quietly wrong, in the specific way that only announces itself once real conditions hit it. I went back afterward and sat with that PR properly. Not skimming this time. Actually reading it, actually understanding what it was doing and why, actually treating the review like the real work instead of the formality before merging. It took a lot longer than approving it had. It's the only way I'd have caught it before production did. Since then, I don't rush AI-code reviews anymore. I give them the time writing the code apparently didn't need. And it turns out I'm far from the only one who's landed there. 🧵 The Number That Explains What I Was Feeling According to Harness's 2026 State of Engineering Excellence Report, a survey of 700 engineering practitioners across the US, UK, India, France, and Germany, 81% of developers now spend more time in code review since their teams adopted AI tools . 28% report review time increasing by 30% or more. Here's the trade nobody advertised clearly: AI tools cut time-to-PR by roughly 58%. But those same PRs then sit in review 4.6x longer than before. Review time per developer is up an estimated 11.4 hours a week. The speed didn't disappear. It moved. It went from "time spent writing" to "time spent verifying," and verifying turns out to be the harder, slower h

This is disturbing: …a team of security researchers at UC San Diego, who found that a model of aftermarket car alarm known as the KARR Security System, installed in more than 2 million vehicles across the US by their estimate, can let any hacker within Bluetooth range send radio commands to silently unlock the car at will, turn off its alarm, honk the car’s horn or flash its lights, or even disable its ignition and leave a driver stranded.

Nobody likes getting a swab shoved up their nose. A startup in Japan has developed a much less intrusive system.

Tesla's Shanghai factory is busier than ever but might be cut loose.

When a premium car brand like BMW says it has a "special surprise" in store for drivers, I'd expect something more luxurious than having a movie commercial beamed onto the dashboard. That's exactly what's happening to many BMW owners, however, who are being shown banner ads for Spider-man: Brand New Day on their Control Display […]

Congress set ownership limit at 39%, but FCC claims authority to kill the rule.

Canonical: this is a cross-post. The original lives at https://four-leaf.ai/blog/openai-interview-process Most OpenAI interview prep hands you a list of hard coding problems and tells you to grind. That calms the nerves and misreads the loop, because at OpenAI the coding bar sits next to something the grind can't touch: a genuine point of view on where AI is going and how it could go wrong. Candidate-facing guides describe that thread running from the first recruiter call to the final behavioral round. You can solve every problem and still stall if you can't hold that conversation. We've mapped the loops at Amazon , Google , Apple , Meta , and Bloomberg by reading each process through how the company actually runs. The map now includes the other AI labs and high-growth names candidates weigh alongside it, including Anthropic , SpaceX , and Robinhood . OpenAI is the one candidates most often prepare for as if it were a standard FAANG gauntlet. It isn't. The coding is practical rather than puzzle-flavored, a whole round asks you to present and defend work you built, and the loop varies more team to team than almost any large employer. Generic big-tech prep leaves you exposed on exactly the parts specific to OpenAI. A note on sourcing. OpenAI doesn't publish its interview process. There's no stage list, no scoring rubric, no candidate-facing equivalent of Google's structured-interviewing guidance. So this map comes from reputable secondary sources that collect named and dated candidate accounts, primarily interviewing.io's OpenAI question guide and Exponent's OpenAI software engineer guide . Where those accounts agree, this guide states the pattern. Where the loop varies or the record thins out, it says so rather than inventing detail. Treat everything below as the common shape, not a guaranteed sequence. Why the loop varies so much Start with the thing that makes OpenAI different to prep for. Hiring is decentralized, and secondary guides are blunt that the loop varies