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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 […]

DoorDash is opening a limited beta of dd-cli, a command-line tool that lets developers and AI agents search stores, build carts, and place orders from the terminal, marking another step toward software designed for AI agents instead of just humans.

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

Owen Flowers and Thalha Jubair, two members of the prolific Scattered Spider hacking group, pleaded guilty and were sentenced to five years and six months in jail for hacking London’s metropolitan transit system.

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.

As long as we don't have AGI or superintelligence, we still need good software engineers. And even if we do reach AGI and coding is "solved," are we really supposed to just trust the generated code? Outsourcing our cognitive thinking and understanding isn't the way to advance as humans, nor is it the way to stay ahead of AI. If we give that up, how do we limit AI slop? And how would we even pull the plug, if it came to that? Understanding computer science concepts and developing good software en

Your Test Data May Be More Dangerous Than Your Production Database Most organizations protect production databases carefully. Access is restricted, activity is logged, and security teams monitor unusual behavior. Then a copy of the same data is exported into a test environment, sent to an analytics team, or shared with an outside service provider. At that moment, the protection model often becomes much weaker. The copied database may contain customer names, phone numbers, account details, identification numbers, medical information, transaction histories, or internal employee records. Developers may need realistic data structures, but they rarely need to see the real identities behind them. Operations teams may need to investigate a production issue, but they do not always require full access to sensitive fields. This is where data masking becomes a practical security control. It allows useful data to remain available while reducing the exposure of the people and organizations represented inside it. The Real Risk Begins When Data Starts Moving Sensitive data rarely stays in one place. It flows from production into testing, development, quality assurance, reporting, analytics, migration projects, outsourced support, and third party platforms. Every new copy expands the attack surface. A production database may have strong access controls, but a test database may be managed by a broader team. A temporary migration environment may remain online longer than planned. A dataset shared for analytics may contain fields that were not necessary for the project. The security problem is therefore larger than database access. It is about controlling what information remains visible as data moves through the organization. Data masking changes sensitive values while preserving their structure and usefulness. A masked phone number can still look like a phone number. A substituted customer name can still support application testing. A shuffled account value can preserve distribution

GDPR Compliance Fails When It Exists Only in Policy Documents Many organizations can produce a privacy policy, a data processing register, and a set of security procedures. The harder question is whether their infrastructure can actually protect, recover, trace, and report personal data when something goes wrong. GDPR compliance is often discussed as a legal project. In practice, many of its most difficult requirements depend on everyday IT operations. Can the organization recover personal data after a destructive incident? Can it identify who restored, copied, accessed, or deleted a backup? Can it detect suspicious activity quickly enough to support an investigation? Can it demonstrate that protection controls are applied across on premises, cloud, and hybrid environments? Policies explain intent. Operational controls provide evidence. Availability Is a Privacy Requirement Too Privacy discussions often focus on unauthorized access and disclosure. Data loss and prolonged unavailability matter as well. Personal data may become unavailable because of ransomware, storage failure, accidental deletion, database corruption, software defects, or a regional outage. If the organization cannot restore that data, it may be unable to serve customers, respond to data subject requests, or maintain essential business processes. A GDPR aligned protection strategy should therefore include reliable backup, offsite copies, tested recovery, and resilience across multiple failure scenarios. The backup architecture should support the systems that actually contain personal data, including databases, virtual machines, file servers, object storage, cloud workloads, and large data platforms. Protecting only the most visible applications leaves hidden gaps. Encryption Is Necessary, but It Is Not the Whole Answer Encryption protects data during transmission and storage, especially when backup copies move across networks or are stored outside the production environment. However, encrypted data

Your data warehouse has a table called orders . It has columns like amount , status , created_at , and customer_id . Now three people ask "What was Q1 revenue?" The analyst writes SELECT SUM(amount) FROM orders WHERE created_at BETWEEN '2026-01-01' AND '2026-03-31' . The data engineer adds WHERE status = 'completed' . Finance excludes refunds and trial conversions. Three queries, three numbers, one question. Nobody is wrong. They just defined "revenue" differently. Multiply this by every metric in your organization, every team that queries the warehouse, and every tool that displays a number. That's the problem. A semantic layer solves it by defining each metric once, in one place, and serving that definition to every consumer. What is a semantic layer? A semantic layer is a metadata layer between your data warehouse and every tool that queries it. It defines business metrics, maps them to SQL, and exposes them through APIs. Instead of every consumer writing its own query, they all reference the same definition. When someone asks for "revenue," the semantic layer knows that means: SUM ( CASE WHEN status != 'refunded' AND type != 'trial' THEN amount ELSE 0 END ) That definition lives in one place. Dashboards, APIs, AI agents, and ad-hoc queries all use it. Change the definition once and every consumer gets the updated calculation. No Slack thread asking "which number is right." No detective work tracing a wrong number back to a stale query in a notebook somewhere. The core components of a semantic layer: Metrics (measures). The numbers you aggregate: revenue, order count, average deal size. Each metric has a fixed SQL definition. Dimensions. The columns you filter and group by: date, status, category, region. Dimensions define the axes of analysis. Relationships (joins). How tables connect: orders belong to customers, products belong to categories. Defined once, reused by every query. Access rules. Who can see what. Row-level security, tenant isolation, role-based ac