标签：#ci

Company noted for building quantum annealers now also making gate-based hardware.

Once, I had some questions about why SpaceX, Elon Musk's healthiest company, acquired xAI, his sickliest one. Now I have some questions about why we're calling the whole thing SpaceX. Look, what we have here, by revenue, is primarily a telecom company and a company that rents compute, according to SpaceX's first quarterly earnings statement […]

More than 50 offending image and video ads were published across Facebook, Instagram, Messenger, or Threads, according to Meta’s ad library data. Some ran as recently as this week.

The company is also planning to retire its public API.

Jeff Dean and other high-profile Google executives have founded Discovery Loop, a startup that will seek AI-powered breakthroughs in everything from drug discovery to chip design.

A Deep Dive into the Memory Model From Source Code to Machine Instructions A five-part journey through compilers, executables, virtual memory, and the CPU Introduction: What Really Happens When Code Runs Consider a simple C program: include <stdio.h> int value = 10; int add(int a, int b) { return a + b; } int main() { int x = 5; int result = add(x, value); printf("%d", result); return 0; } Most programmers look at this and see only the visible outcome: 5 + 10 = 15 But behind that single printed number lies a much deeper story. Where does the data actually live? Who moves it from one place to another? How does the CPU find the instructions it needs to run? And how does the result finally make its way to the screen? Answering these questions means understanding a concept that many programmers use daily but rarely examine closely: the memory model. What Is a Memory Model, Really? Ask most developers what a "memory model" means, and the answer usually comes back in two words: stack and heap. That answer isn't wrong - it's just incomplete. A memory model is really a description of five things at once: How data is stored How data is accessed How long data exists Who is responsible for managing that lifetime How different parts of a system communicate through memory A program never leaps directly from C source code into RAM. Several distinct layers sit between the two, each one translating the layer below it into something the layer above can reason about. This article walks through all of them, one at a time, and then reassembles the full picture. The Four Layers, at a Glance Layer What It Deals With Typical Concepts 1. Programming Language Human-readable code scope, lifetime, ownership 2. Compiler Translating code to instructions registers, optimization, assembly 3. Operating System Running the program as a process virtual address space, .text/.data/.bss 4. CPU Architecture Executing raw instructions registers, cache, pipeline, ALU The rest of this article follows a sing

On our new Real World AI stage, we’ll be focusing on the intersection between the digital and physical, and all the ways we’ll continue to see a blending of the two.

Tinder is expanding its in-person events feature from an initial Los Angeles test to 26 cities worldwide by the end of September.

JioHotstar explains the distributed architecture behind its real-time ad request workflow, covering ad decisioning, waterfall tiering, pacing algorithms, latency optimization, and service coordination required to select and deliver personalized advertisements during streaming playback at scale. By Leela Kumili

A startup has created beagles without the gene that causes runny noses and watery eyes for allergy sufferers.

A new study shows how the big cats cause deer to move away from roads and deeper into the forest, where they pose less of a hazard to motorists.

Mitti Labs plans to expand beyond India and enter the Philippines and Indonesia while growing its carbon credits and agricultural data business.

Mugshawtys and similar pages often frame mugshots as thirst traps. The women featured are humiliated and are sometimes harassed for months and years after their arrests.

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

Wispr Flow, a popular dictation tool, has released a live notetaker that transcribes and summarizes meetings. It joins a growing wave of AI notetakers for the workplace.

SpaceX is preparing to build a terrestrial mobile network to "acquire quite a few" of the customers now subscribed to T-Mobile, AT&T, and Verizon. The message to compete head-to-head with the US carriers was delivered by SpaceX president Gwynne Shotwell and CEO Elon Musk during the Q&A section of the company's first earnings call. "The […]

Originally published at Programming Tech Lab . Welcome to the Garage: What is Linear Regression? Step away from the kitchen counter and step into a bustling auto garage. Imagine you are an experienced mechanic evaluating used cars brought in for trade-ins. A customer drives in a sedan with 50,000 miles on the odometer and asks: "How much is my car worth?" Without needing a complex computer program, your brain instantly draws a connection: as the mileage on a car goes up, its resale price goes down. If a car has 0 miles (brand new), it commands peak market price. If it has 200,000 miles, it drops significantly toward scrap value. This straight-line relationship between two factors—where changes in one variable cause a predictable increase or decrease in another—is the core concept behind Linear Regression . Deconstructing the Formula (Without the Headache) In high school math, you probably saw the classic line equation: y = mx + b In machine learning, Linear Regression uses this exact same formula to make predictions: Predicted Value (y) = ( Slope m × Input Feature x ) + Starting Point b Let's map this directly to our mechanic's garage evaluation: Target (y): The estimated resale price of the car ($). Input Feature (x): The total miles on the odometer. Starting Point / Intercept (b): The price of the car when mileage is 0 (Brand New MSRP). Slope / Weight (m): The rate of depreciation (e.g., losing $0.10 in value for every 1 mile driven). If a car starts at a baseline price of $30,000 and depreciates by $0.10 per mile, a car with 50,000 miles is predicted to be worth: Predicted Price = $30,000 - ($0.10 × 50,000) = $25,000 How the Algorithm Draws the Perfect Line: Least Squares If you plot 100 used cars on a graph where the horizontal axis (X) is Mileage and the vertical axis (Y) is Price, the dots won't form a perfectly straight laser line. Some owners took great care of their vehicles; others had minor scratches. So how does a Linear Regression algorithm draw the sin

Seedance 2.5's API opens on August 7. ByteDance published the pricing ahead of it, and there is a detail in there that will quietly break your cost model if you carry it over from 2.0. Video is quoted per second and metered per token: tokens = (input_video_seconds + output_seconds) × width × height × fps / 1024 fps is fixed at 24. Multiply by the per-million-token rate and that is the bill. The published rates USD per million tokens: Model No video input With video input Seedance 2.5 (480p, 720p) 10.70 6.40 Seedance 2.0 (480p, 720p) 7.00 4.30 Seedance 2.0 (1080p) 7.70 4.70 Seedance 2.0 (4K) 4.00 2.40 2.5 costs 52.9% more per token without video input and 48.8% more with it. Only 480p and 720p are published for 2.5. No 1080p, no 4K, and offline inference reads "not supported yet". Look at the 4K row before you move on. It is the cheapest tier per token, 43% below 480p, and it is also the most expensive output on the board, because a 3840×2160 frame carries 19.4 times the pixels of what 480p actually renders. The rate drops 43% while the token count climbs 1940%. Comparing providers by scanning the rate column gets you the wrong answer by roughly a factor of eleven. The 480p frame changed and nobody said so This is not in any release note. It falls out of dividing ByteDance's own worked examples by their own token rates. Their published five-second, 16:9, no-reference examples: Model 480p 720p Seedance 2.5 $0.514 ($0.103/s) $1.156 ($0.231/s) Seedance 2.0 $0.352 ($0.070/s) $0.756 ($0.151/s) Divide price by token rate to recover the token count, then by 24/1024 to recover pixels: const tokens = pricePerVideo / ( ratePerMillion / 1 e6 ); const pixels = ( tokens / outputSeconds ) * ( 1024 / 24 ); // Seedance 2.5, 480p: 0.514 / (10.70/1e6) / 5 = 9,607 tokens/sec // 9,607 * 1024/24 = 409,899 px -> ~854 x 480 // Seedance 2.0, 480p: 0.352 / (7.00/1e6) / 5 = 10,057 tokens/sec // 10,057 * 1024/24 = 429,105 px -> ~873 x 491 720p resolves to 21,600 tokens per second on both versi

Week 3. "The deploy is done. Everything's green. Now what am I actually supposed to be looking at?" Previously Runner ↓ Cache ↓ Artifact ↓ Deployment Today ↓ Monitoring Junior Engineer: The canary rolled out fine yesterday. 100% traffic, all healthy. I closed my laptop. Was that wrong? Senior Engineer: Not wrong, exactly. But let me ask you something first. Your service is running on a server somewhere. Right now, this second — is it healthy? Junior Engineer: I mean... I assume so? Nobody's messaged me. Senior Engineer: "Nobody's messaged me" isn't an answer. It's the absence of one. That's the entire problem monitoring exists to solve. The Thing Nobody Says Out Loud Senior Engineer: Here's an uncomfortable fact about production systems: you cannot see them. Not directly. You're not standing next to the server, watching electricity move through it. Everything you know about whether it's healthy is a claim — something a piece of software told you, that you're choosing to trust. Junior Engineer: That sounds obvious when you say it, but I don't think I've ever actually thought about it that way. Senior Engineer: Most engineers don't, until the gap between "the system told me it's fine" and "the system is actually fine" bites them. Monitoring is the discipline of shrinking that gap — of making sure what you're told is close to what's actually true, and told to you fast enough to matter. 📒 Senior Engineer's Notebook You don't monitor a system because you don't trust it. You monitor it because you can't see it. Trust isn't the issue — visibility is. The Car Dashboard Analogy Junior Engineer: Can you make this concrete? Senior Engineer: Think about driving a car. You can't see the engine. You can't see the oil level, the coolant temperature, how much fuel is actually left in the tank, mid-drive. All of that is invisible to you, sealed inside metal, while you're doing 100 km/h. So the car gives you a dashboard. Speed, fuel, engine temperature, warning lights. You're not wat

Some creators fear the EU AI Act’s regulatory chaos will upend their lucrative businesses. Others are owning it by incorporating AI transparency into their creative process.