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When the enemy occupies favorable terrain, don't attack head-on. Let them think they're safe, let...

Beehiiv is launching an AI Copilot to help publishers with user growth and analytics.

Today, the European Union ordered Google to give its AI rivals greater access to Android, the open-source operating system that powers billions of devices worldwide. The demand is hardly surprising. It may look like a defeat on paper for Google, which has spent years resisting exactly this kind of access, but it is a regulatory […]

Disclaimer: NO AI bullshit here. We are in April 2024, I am working in Mexico City and a friend of mine contacts me. She is an architect and she works in Merida. Her and her colleague would like to have a solution to analyze architectural cadastral documents. She told me that she called friend for Mexico and USA and they all say it is impossible. Interested by the challenge I said: "Hold my beer" and I made a POC in 75h of intensive work. However, it was not enough. They had other requirements and getting to this was much more complicated. Complicated but still not impossible. As a Data Scientist, I started to develop a full solution (mostly Computer Vision) to read and analyze automatically specific cadastral documents. I decided to continue working on the project. Hours and hours. There was no money involved. Only challenge. Thanks to that I was able to do everything with the time I wanted. Putting all the passion in my side-project. With time the project grew and grew. But I was prepared for this. I made every part of the code separable, isolated. 6 repositories: aitt-core: core backend + Streamlit app -> Docker image deployment on ECR aitt-core-deploy: service deployment on the server via Ansible aitt-symbol-clf: ML + API -> Docker image deployment on ECR aitt-symbol-clf-deploy: service deployment on the server via Ansible 5 .aitt-infra: Terraform on AWS aitt-platform: Vue.js frontend (private repo) I finally came to a solution for what they needed in more than 625 hours of work. My work contract ended in 2025 in Mexico so I came back to France (I am French, yes I love pain - joke for bilingual). This project made me realized several things: I am capable of doing great things. We all are. The only thing that matters is discipline. This is not some sort of personal development bullshit. This is an advice for life. Einstein said: "I have no special talents. I am only passionately curious." Time is a powerful thing . With time and dedication you can achieve great t

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OnePlus promises to continue supporting the phones it has already released.

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"We will likely have similar concerns and discussions when China fields its Starlink-like constellation."

Travel agency Fora announced a $60 million Series D round led by Forerunner and Tactile Ventures, valuing the company at $1 billion.

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Google is giving its AI note-taking app a new name. The company announced on Thursday that NotebookLM is becoming Gemini Notebook, but will remain a standalone app even as it integrates more deeply across Gemini and Google Search. Google first revealed Gemini Notebook - then called Project Tailwind - in May 2023 before widely releasing […]

Google said users can soon access their notebooks through AI Mode in Search.

With this new update, Google is expanding AI Mode beyond answering questions and into completing tasks across the apps they use regularly.

You're paying $10 to $20 a month for Copilot. You don't have to. A 2024-era laptop can run a coding model good enough for autocomplete, refactors, and "explain this function" entirely offline. No API key, no telemetry, no per-token bill. Here's the exact 2026 setup I run on a 16GB machine. Why local in 2026 Two years ago, local coding models were a toy. The autocomplete was slow and the suggestions were noise. That changed. qwen2.5-coder and deepseek-coder-v2 are genuinely useful now, and the tooling caught up: Ollama serves them, Continue.dev wires them into your editor, and the whole thing runs on hardware you already own. The pitch is simple: Free. No subscription, no usage caps. Private. Your proprietary code never leaves the machine. This matters if you work on smart contracts or anything under NDA. Offline. Works on a plane, in a basement, behind a corporate firewall. The tradeoff is quality and latency. We'll be honest about both. Pick a model (and match it to your RAM) This is the decision that makes or breaks the experience. Pick a model your machine can actually hold in memory, or it spills to disk and crawls. # Fast, fits anywhere (8GB+) ollama pull qwen2.5-coder:1.5b # ~1.0GB ollama pull qwen2.5-coder:3b # ~1.9GB # The sweet spot for most laptops (16GB) ollama pull qwen2.5-coder:7b # ~4.7GB # Quality tier, needs headroom (32GB+ comfortable) ollama pull deepseek-coder-v2 # ~8.9GB (16b MoE) ollama pull qwen2.5-coder:14b # ~9.0GB ollama pull qwen2.5-coder:32b # ~20GB Rough rule: the model file size is the floor, then add a few GB for context and the OS. A 4.7GB model on a 16GB machine is comfortable. A 20GB model on the same machine is not. Model Size RAM I'd want Use it for qwen2.5-coder:1.5b 1.0GB 8GB Autocomplete, fast iteration qwen2.5-coder:7b 4.7GB 16GB Daily driver: chat, refactors, explain deepseek-coder-v2 8.9GB 32GB Harder reasoning, multi-file context qwen2.5-coder:32b 20GB 64GB Near-cloud quality, if you have the RAM deepseek-coder-v2 is a 16b m

Splatoon Raiders’ July 23rd launch day is almost here, so your window to preorder the physical version at a $10 discount is about to close. The digital version is $49.99, and so is the game on cartridge if you preorder at Walmart. Amazon previously honored the preorder discount, but it’s selling the game at full […]

The Tesla driver who fatally struck a woman after crashing into her home "manually overrode" the vehicle's Full Self-Driving (FSD) technology by pressing the gas pedal to 100 percent, the National Transportation Safety Board (NTSB) confirmed in a preliminary report on Wednesday. After examining the car's electronic data, investigators found that the Tesla Model 3 […]

When people think about data centers, they usually think about servers, GPUs, electricity, and AI. Very few people think about water. That realization is what led me to start building AquaStat . Why AquaStat? Modern data centers consume significant amounts of water for cooling. Depending on the technology, climate, and workload, water usage can vary dramatically from one facility to another. Finding reliable information about that usage, however, is often difficult. Some facilities voluntarily publish sustainability reports. Others release only limited information. In many cases, information is scattered across government documents, environmental reports, local news articles, permits, or community discussions. I wanted to build a platform that could organize this information into something developers, researchers, journalists, and the public could actually use. What AquaStat Is AquaStat is an API-first platform focused on collecting, organizing, and analyzing information related to data center water usage. The long-term vision includes: A developer-friendly REST API OpenAPI documentation API key management A desktop control center A command-line interface Historical tracking Source attribution for collected information Transparent methodologies A modern TypeScript ecosystem Rather than hiding calculations, I want AquaStat to explain where information comes from and how conclusions are reached whenever possible. Technical Goals I'm designing AquaStat around several principles: API First Everything should be accessible through documented APIs before being exposed through a graphical interface. Strong Documentation Documentation should be treated as part of the product, not an afterthought. Reproducible Calculations Whenever AquaStat estimates or derives values, the methodology should be understandable and repeatable. Modern Tooling The project uses a modern TypeScript stack with an emphasis on maintainability, testing, and developer experience. Challenges One of the b

Swatch’s latest MoonSwatch is the Mission to the Moon 1969, a limited-edition model with components crafted from Omega’s 18K Moonshine Gold. To get one, you'll have to join a lottery.

Welcome to Day 4! Today is all about clean architecture, dependency isolation, and modern Python tooling. You will learn how to structure your files, control execution flows, use modern tools like uv to manage virtual environments at lightning speed, and organize a codebase like a professional software engineer. 🚀 1. Modules & Packages 📦 Module: A single .py file containing variables, functions, or classes you want to reuse. Package: A directory of modules. __init__.py : Runs automatically when a package is imported, allowing you to expose a clean top-level API and hide internal folder nesting. Absolute Import: Imports specifying the full path from the project root ( from app.core import analyze ). Preferred by PEP 8 . Relative Import: Imports relative to the current file using dots ( from .utils import clean ). Single dot . is current folder; double dot .. is parent folder. A. Core Modules & Packages 🌱 Easy Starter Example Creating a basic module and importing it: # file: calculator.py (Our module) def add ( a , b ): return a + b # file: main.py (Importing our module) import calculator print ( calculator . add ( 5 , 3 )) # Output: 8 🏛️ Real-World Example: Database Package API Exposing internal package functions cleanly using __init__.py : # Project Layout: database/ ├── __init__.py ├── auth.py (defines login_user()) └── query.py (defines fetch_data()) # database/__init__.py # Expose functions relative to this folder so users don't need deep imports from .auth import login_user from .query import fetch_data # main.py # Clean absolute package import for the end-user from database import login_user , fetch_data login_user ( " admin " , " password123 " ) B. Built-In vs. Third-Party Modules Built-In: Included with Python out-of-the-box (e.g., os , sys , json ). Third-Party: Built by the community and installed from PyPI (e.g., requests , rich ). 🌱 Easy Starter Example import math # Built-in math operations print ( math . sqrt ( 25 )) # Output: 5.0 # import requests # Th

One period tracker app tested by Mozilla was 'squeaky clean,' while another app was seen sharing users' health data with an analytics company, underscoring vast differences in user privacy among these apps.