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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.

You may have heard that OpenAI released its first piece of hardware this week. You may not have heard about the ChatGPT basketball.

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

Exposing your data over MCP is the easy part. Designing a tool an agent uses well is the hard part. An agent can only use a tool it can read, so the work is shaping the tool for how the model calls it, not just for the human looking at the result. These are the techniques behind @bonnard/mcp-charts and the visualize tool. Discovery-first, so the agent stops guessing An agent that guesses your schema writes wrong queries. So the first tool the agent meets is a discovery tool. It calls visualize_read_me to load the chart options, the tool schema, and worked examples before it ever calls visualize , and an explore_schema tool to learn your tables and columns before it writes SQL. The agent reads, then acts. A small set of purpose-built tools The temptation is one tool per metric, or a single tool that takes arbitrary SQL and hopes. Both fail: too many tools blow the agent's attention budget; one firehose tool gives it no guardrails. Bonnard ships a small set, discover, query, visualize, each with a narrow, obvious job. The agent picks the right one because there are few of them and each does one thing. // a small, purpose-built set, not one tool per metric server . registerTool ( " explore_schema " , { /* list tables + columns */ }, listSchema ); addCharts ( server , { runSql }); // registers visualize_read_me + visualize Compact, honest responses A tool that returns 10,000 raw rows poisons the context window and the agent's next decision. Bonnard's responses are sized for a model to read: Row caps with a completeness flag. Results are capped and tagged partial or complete , so the agent knows whether it is looking at everything. Partial-result warnings. When results are capped, the response says so and tells the agent not to sum or average the visible rows, use a measure instead. Summaries over dumps. The chart comes back with a compact text summary the model can reason over, not just an image it cannot read. Errors that guide the next action A bare "error: invalid co

Most teams connecting AI agents to their data warehouse start with text-to-SQL. The agent generates SQL from natural language, runs it against the warehouse, and returns results. It works until it doesn't: hallucinated JOINs, inconsistent aggregations, no access control, no audit trail. There's a better approach. Define your business metrics in a semantic layer, expose them via MCP (Model Context Protocol), and let any AI agent query governed definitions instead of raw tables. Then add one tool so the agent can chart the result in Claude or ChatGPT. This tutorial shows how to set it up in under 30 minutes. Why does text-to-SQL break in production? The agent sees column names but not business logic. It doesn't know that your company excludes refunds from revenue. It doesn't know that status = 'completed' means something different in orders than in subscriptions . It doesn't know that marketing and finance defined "active user" differently three years ago and never reconciled. So the agent writes plausible SQL and returns plausible numbers. Ask the same question twice with different phrasing and you get different answers. Ask two different agents and you get two different numbers. Neither matches the number your finance team reports. Beyond consistency, there's no row-level security. No multi-tenancy. No audit trail showing which agent queried what, when, and for whom. In production, with real customers, that's a non-starter. Text-to-SQL gives you speed. It doesn't give you trust. What is the semantic layer approach? Instead of letting agents write arbitrary SQL, define your metrics once in YAML: cubes, measures, dimensions, access rules. Then expose those definitions via MCP so agents query governed metrics, not raw tables. The difference: every agent gets the same answer because the metric definition is fixed. total_revenue isn't a column the agent interprets. It's a pre-defined calculation with agreed-upon filters and aggregations. When your finance team updates th

The iPad Mini could get an OLED display upgrade as soon as October, according to Bloomberg's Mark Gurman. It would be the most significant refresh for the Mini since its 2021 redesign. An OLED iPad Mini has been rumored for months now, with Gurman previously reporting last year that it will also come with a […]

The startup, founded in 2021, lets enterprise customers use smartphones to scan and spot vehicle damage.

The Dynamic Island that gets you to meetings on time Discussion | Link