Octopus and CATL are pledging to roll out a network of battery swap stations for heavy trucks across Europe.
https://archive.ph/tSlDZ https://www.cnbc.com/2026/06/22/alan-greenspan-former-chairm...
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I haven’t thought about the privacy issues surrounding professional athletes and wearables. Wearables present serious privacy issues for “Average Joe” consumers, who are entrusting tech companies to safely store and protect their biometric data. Imagine the stakes for a professional athlete, whose entire livelihood could be affected by a single biometric data point. To give one of many realistic hypotheticals: a basketball player has a terrible game, and the coach wonders if they showed up to the gym hungover. The coach has access to the player’s wearable data, and checks to see when they went to sleep, as well as what their heart rate looked like during the night. Should the player have been out partying before a game? No. Should the coach be able to surveil them? Definitely not...
Bob Starr was delighted with his vibe-coded website. "Boomberg" showed how much US tax money is going to tech companies, and Starr launched it online immediately after making it. It wasn't until months after the site went live that he realized there was a problem: a hidden SQL injection risk. It could've left the site […]
In this article, the author explores data poisoning as a threat to machine learning systems, covering techniques such as label flipping, backdoors, clean-label poisoning, and gradient manipulation. The article reviews real-world incidents, discusses the challenges of detecting poisoned data, and presents practical defenses, tools, and operational practices for securing ML training pipelines. By Igor Maljkovic
Dan Fineran explores how eBPF has evolved far beyond its roots in packet filtering into a robust, safe way to extend the Linux kernel. He explains how the eBPF "verifier", the security guardrail, enables implementation of deep observability and networking without the risks of traditional kernel modules or the slow upstreaming process. By Dan Fineran
I got tired of MacOS failing to index applications (it consistently seems to prefer apps installed on my phone instead of the computer) so I've bound them to keys and have started building muscle memory for opening and switching apps. Reduces mental fatigue from constantly cmd + tabbing between apps all day
AWS made Graviton5-powered EC2 M9g and M9gd instances generally available with 192 ARM cores, formally verified VM isolation via the Nitro Isolation Engine, and DDR5-8800 memory. ClickHouse reported 36% better performance with zero code changes. Meta committed tens of millions of cores. On-demand pricing is 9% above Graviton4, translating to roughly 15% better price-performance. By Steef-Jan Wiggers
GitHub热门项目 | Use claude code and codex for free in the terminal, VSCode extension, and discord like OpenClaw (voice supported) | Stars: 36,218 | 258 stars today | 语言: Python
A consumer nav app tells the driver where to turn. It will not tell the dispatcher where the 11-hour driving clock runs out — and, more importantly, whether there is legal parking when it does. That second question is the one that strands a truck at 11 PM on the shoulder of an off-ramp with every nearby lot already full. Road511’s routing endpoint now answers it. Send the driver’s Hours-of-Service clock along with the route, and the response carries an hos[] array: every point on the corridor where the driver must take a break or stop driving under the selected regime, the projected time they reach it, the legal deadline, and — the part that matters operationally — the truck parking and rest areas actually reachable before that deadline. How It Works It rides the same call as everything else routing does: POST /api/v1/routing/route . You already send an origin, a destination, and a truck profile. To get the HOS projection, add an hos block inside the truck object describing the driver’s clock at departure. curl -X POST "https://api.road511.com/api/v1/routing/route" \ -H "X-API-Key: your_key" \ -H "Content-Type: application/json" \ -d '{ "origin": { "lat": 41.8781, "lng": -87.6298 }, "destination": { "lat": 39.7392, "lng": -104.9903 }, "departure_time": "2026-06-08T06:00:00Z", "truck": { "profile": "tractor", "weight_t": 36.0, "height_m": 4.2, "axles": 5, "hos": { "ruleset": "us", "drive_remaining_s": 39600, "duty_remaining_s": 50400, "since_break_s": 0 } }, "enrichment": { "include_features": ["truck_parking", "rest_areas"] } }' That’s a Chicago→Denver run for a fresh driver on US rules: 11 hours of driving left ( 39600 s), a 14-hour duty window ( 50400 s), and zero driving time since the last break. Every counter is “seconds remaining” against the named ruleset’s limit. The HOS Clock The hos object is the driver’s state, not a fixed policy. Store only the ruleset on a reusable truck profile — the per-trip counters are supplied inline on each request and merge on to
"Fractional CTO" has become the title people put on their LinkedIn profile when "senior developer" doesn't sound senior enough. I play this role for some of my clients, so I have opinions about what it actually means — and what it doesn't. The confusion isn't just semantics. If you hire the wrong thing under this label, you pay consulting rates for work that a good contractor would have done better. The Problem with the Label The term covers a surprisingly wide range of people and arrangements. At one end, you have experienced technical leaders — people who have actually run engineering organizations, made architectural decisions that constrained companies for years, hired and let go technical staff, and owned the consequences. At the other end, you have developers who decided their day rate felt more justifiable with a fancier title. Both call themselves fractional CTOs. The market hasn't sorted this out yet. The reason it matters: these are fundamentally different services with different prices, different deliverables, and different risks. Mixing them up is how companies end up paying €200/hour for someone to help them choose a JavaScript framework. What a Real Fractional CTO Actually Owns Owns is the operative word. Not "advises on." Not "contributes to." Owns. Technical architecture decisions. When you're building a system that will handle real volume, real users, and real money — the decisions made in the first few months constrain everything that follows. What database, what caching strategy, how the services are separated, where the complexity lives. A fractional CTO makes those calls and stakes their reputation on them. They're not writing a report about options. They're deciding. Tech stack and vendor selection. When a vendor pitches you something, someone needs to evaluate it who has seen enough vendors to know which ones are overselling. When a developer suggests a library, someone needs to know whether it's the right tool or just the tool that developer
Originally published at htpbe.tech . The version on htpbe.tech stays in sync with the latest detection algorithm — refer to it for the canonical text. Accounts payable teams are receiving receipts generated by ChatGPT plugins. HR platforms are seeing payslips rendered by Python scripts. Insurance claims contain repair estimates that no shop ever issued. The documents look correct. The logos match. The numbers are plausible. The question is: what can actually be detected, and what cannot? The honest answer requires separating two things that are often confused under the phrase “AI-generated document detection.” Two distinct problems called "AI-generated document detection" When people ask how to detect an AI-generated document, they usually mean one of two distinct things: Content classification asks: was the text in this document written by an AI language model? This is what tools like GPTZero and Turnitin’s AI detector do. They analyze writing style, token probability distributions, and linguistic patterns to estimate whether a human or a model produced the text. Structural forensics asks: was this PDF file generated by a real institutional system, or did it come from a headless browser, a PDF library, or a consumer tool? This is what HTPBE does. It reads the binary structure of the file — producer metadata, xref patterns, font embedding, object numbering — and checks whether those patterns match how legitimate institutional software generates documents. These are not the same problem. A document can contain AI-written text and still come from a real corporate system. A document can contain entirely human-written text and still have been rendered by Puppeteer an hour ago. The structural check and the content check answer different questions. HTPBE does structural forensics. It does not classify text. This article explains what that distinction means in practice, what the structural approach reliably catches, and where its limits are. What structural forensics detec
We just shipped the Forgelab PDF API — a fast, affordable REST API for developers who need to handle PDF files without the hassle. What it does: Merge multiple PDFs into one Split PDFs by page ranges Compress PDFs to reduce file size Convert PDFs to images (PNG/JPEG) Pricing: Starts at $5/month for 100 calls/month. No hidden fees. Quick start: curl -X POST https://www.forgelab.africa/api/pdf/merge \ -H "X-API-Key: your_key" \ -F "files=@doc1.pdf" -F "files=@doc2.pdf" Sign up at forgelab.africa
We asked Johan Ejdemo to list which Ikea items populate his home. He also tells us about his all-time favorite. (No, it's not a Billy bookcase.)