标签：#microsoft

Microsoft used their Build 2026 event to announce new functionality for Microsoft Foundry. Citing Foundry as "the place where AI agents move from experiments to production systems," in a blog post, Nick Brady writes that the release brings “runtime, tools, memory, grounding, models, observability, and governance” that developers need for production agents, rather than just new model endpoints. By Matt Saunders

Today I’m talking with Mustafa Suleyman, the CEO of Microsoft AI. And I’m actually going to keep today’s intro short — I’m working from my wife’s family farm this week, as you’ll see in the video, but also this is a real burner of an episode. We covered everything from Mustafa’s approach to training new […]

Two years ago, when Microsoft first revealed that it was bringing four Xbox-exclusive games to the PS5 and Nintendo Switch, it made the announcement far more complicated than necessary. That's not likely to improve anytime soon. In fact, things now seem more confusing than ever as the company tries to appease both fans and the […]

Microsoft announced the general availability of Microsoft Discovery, its Azure-based platform for deploying autonomous AI agent teams in scientific R&D. The platform powered the development of Majorana 2, a topological quantum chip with 1,000x reliability improvement and 20-second qubit lifetimes. Microsoft now targets a scalable quantum computer by 2029, halving its original timeline. By Steef-Jan Wiggers

Microsoft announced Logic Apps Automation at Build 2026, a new SKU at auto.azure.com packaging workflows, AI agents, knowledge services, and model access into a managed SaaS experience. Agents integrate via agent-loop orchestration, Foundry agents, and managed sandbox. Knowledge as a Service provides a fully managed RAG pipeline. By Steef-Jan Wiggers

We're likely to see more price increases as the big AI companies plan to go public.

In Part 1 we installed Presidio and ran a basic detection on clean sample text. Real data is messier. Emails have signatures with phone numbers buried in HTML. Support tickets mix PII with technical jargon. Chat logs have informal name references that NER models struggle with. And sometimes the PII isn't in text at all. It's in screenshots and scanned documents. This part covers how Presidio's detection engine actually works under the hood, how to process different text types you'll encounter in production, and how to handle structured data and images. How the Analyzer Engine Works Presidio doesn't rely on a single detection method. It layers three approaches and combines their results. Named Entity Recognition (NER) The NER model (spaCy by default) processes the text and identifies entities based on the language model's training. It's good at catching names, locations, and organizations even when they don't follow a fixed pattern. "John Smith" is easy. "Dr. J. Martinez-Garcia" is harder but the NER model handles it because it understands context and word patterns. The tradeoff is that NER is probabilistic. It can miss unusual names or flag common words as entities. That's why Presidio doesn't stop here. Pattern Matching (Regex) For entities with predictable formats, Presidio uses regex recognizers. Credit card numbers, SSNs, email addresses, IP addresses, phone numbers all have known patterns. A Luhn-validated 16-digit number is almost certainly a credit card. A string matching \d{3}-\d{2}-\d{4} in the right context is probably an SSN. Pattern-based detections typically get higher confidence scores than NER detections because the pattern itself is strong evidence. Context Scoring Here's where it gets interesting. Presidio looks at the words surrounding a potential match to boost or lower confidence. If the text says "my SSN is 123-45-6789," the phrase "my SSN is" provides strong context that the number is actually a social security number and not some random ID. Th

Microsoft has created a special edition Xbox Series X to celebrate 25 years of the console. The Xbox 25th-anniversary console takes design cues from the original Xbox console, with both the console and controller featuring a translucent green design. "For the first time, we're bringing a translucent design to Xbox Series X, drawing inspiration from […]

Minecraft Dungeons 2, a sequel to Microsoft's dungeon crawler spinoff Minecraft Dungeons, will be released on September 29th. The company originally revealed the game in a brief trailer in March, promising a fall 2026 release window. Here's how Microsoft described it at the time: Return to the world of Minecraft Dungeons in an all new […]

Just a few days after pushing Fable out of 2026, Microsoft showed off more footage of Fable, the first new entry in the storied RPG franchise since 2010's Fable III, at its Xbox Games Showcase on Sunday. The company also announced a specific release date: February 23rd, 2027. Though if you get the Premium Edition, […]

As part of its Xbox Games Showcase on Sunday, Microsoft revealed new details about Halo: Campaign Evolved, the upcoming remake of Halo: Combat Evolved's campaign mode. The remake will debut on Xbox Series S / X, PC, and PS5 on July 28th. Today's mission trailer includes a first look at Operation: Meteorite, a new three-mission […]

Apparently, the "return of Xbox" means a retreat from other platforms. At its Xbox Games Showcase today, Microsoft revealed that Gears of War: E-Day - which was previously rumored for a PS5 launch in addition to Xbox and PC - will not be coming to PlayStation. It'll be an Xbox console exclusive and is launching […]

When I started learning Excel as part of my Data Science & Analytics course, I assumed it was just a tool for creating tables and performing basic calculations. After spending a week exploring its features, I quickly realized that Excel is much more powerful than I thought. Almost every organization generates data. Businesses track sales, schools monitor student performance, hospitals manage patient records, and marketers analyze campaign results. Before data can be analyzed, it needs to be organized, cleaned, and summarized—and that's where Excel comes in. In this article, I'll share some of the Excel concepts I've learned so far and how they're used in real-world data analysis. Understanding the Excel Workspace Before working with data, it's important to understand the basic structure of Excel. When you open Excel, you're working inside a workbook . A workbook can contain multiple worksheets (often called sheets), which help organize different sets of data. At the top of the screen is the Ribbon , which contains tabs such as Home, Insert, Page Layout, Formulas, Data, and View. The Ribbon acts like a control center where you can access Excel's tools and features. Rows run horizontally and are identified by numbers, while columns run vertically and are identified by letters. The intersection of a row and column is called a cell , where data is entered. At first, all these parts seemed overwhelming, but after using Excel regularly, navigating through them has become much easier. The Different Types of Data in Excel One of the first things I learned is that not all data is the same. Excel commonly works with: Text data (names, product categories, locations) Numeric data (sales figures, quantities, prices) Date and time data (order dates, deadlines) Logical data (TRUE or FALSE values) Understanding data types is important because Excel treats each type differently when performing calculations and analysis. Number Formats Matter More Than I Expected Another concept that

The Problem If you've ever audited your Windows network traffic during a boot-up sequence, you know the truth: there's a "blind spot." Between the moment your network drivers initialize and your VPN/WireGuard tunnel actually establishes, your traffic is leaking. Many third-party solutions exist, but they are often bloated, use proprietary binaries, or act as black boxes. I wanted something transparent, native, and bulletproof. The Solution: WG-KillSwitch I developed a pure PowerShell-based kill switch architecture. It doesn't rely on third-party libraries—it uses native Windows system components to enforce security. Key Architectural Features: Zero-Trust Firewall Matrix: Hardens the system by blocking all outbound traffic by default, allowing only authenticated tunnel traffic. WMI Persistent Watchdog: Unlike standard scripts that can be killed via Task Manager, this project uses WMI Event Subscriptions. If the watchdog process is terminated, Windows itself immediately respawns it. Resilience: Survives hard reboots, modem resets, and Windows service cycling. Resilience & Leak Testing I've put this through a gauntlet of tests: Forced Reboots: Zero leaks detected during driver load. Process Termination: The WMI engine restores the protection in milliseconds. Dynamic Network Resets: The firewall matrix remains active regardless of adapter status. Let's Collaborate This is open source, transparent, and built for the community. I'm looking for security audits and feedback. Check out the source code, open an issue, or submit a PR: https://github.com/ryderlacin-pixel/Windows-WireGuard-KillSwitch

Data analysis is at the heart of how we spot patterns and improve systems today. Tools like Python, SQL, Power BI, and Tableau are everywhere in the data world, but Excel has held its ground as the starting point for anyone getting into data work, and there is a reason for that. What is Excel? Excel is a spreadsheet built on a grid of rows and columns. You use it to organize, format, and calculate data. For analysts it is where messy raw data gets sorted out, numbers get worked through, and everything gets turned into something that actually makes sense to look at. Ways Excel is Used in Real-World Data Analysis 1. Data Cleaning Raw data is almost never clean. Names are misspelled, IDs get duplicated, spacing is off, values go missing. None of that is unusual, it is just the reality of working with real data. Before any analysis happens the data has to be honest, because if the data is wrong the results will be too. Functions like PROPER() and TRIM() are some of the basic tools that help get data into a state where you can actually work with it. 2. Financial Reporting Every business, big or small, needs to know where the money is going. Excel makes that straightforward. SUM() adds up a range of numbers, AVERAGE() finds the mean, and once the calculations are done the data can be turned into charts and dashboards that tell the story of the business clearly. Not everyone in the room is an analyst, but everyone can read a chart. 3. Business Decision Making Clean data presented well becomes a decision making tool. What do customers want? What is working? What needs to change? Sorting figures from highest to lowest or filtering by region can take thousands of rows and turn them into something focused and answerable. That is really what data is for, helping people make better calls. Excel Features I Have Learned and How They Apply Three features that have stood out to me are conditional formatting, data validation, and cell referencing. Conditional formatting highlights ce

Introduction A traditional database. That is what many who have not really interacted with Excel to a great extent would define it as in its most basic form. Not that they are wrong, only that is the scope their utilization of Excel covers. Mostly record keeping, basic operations, and data representation. But for those whose utilization scope of Excel is broader, we definitely know better. This underestimation of Excel is a grave mistake for anyone considering themselves as tech-oriented, especially for anyone dealing with data operations, be it simple record keeping or complex concepts involving data. What is Excel A spreadsheet program or tool that facilitates data organization, analysis, and visualization through mathematical operations, chart creation, and building financial models. Real-world application of Excel in Data Analytics Reporting and visualisation Excel facilitates data representation in the form of charts(bar charts, pie charts, line graphs) and dashboards. Businesses and organisations utilize this to get an organised, more insightful, and simplified view and report of their raw data. Financial Accounting Excel's provision for mathematical operations, functions, and formulas in analysis facilitates financial accounting. Balance sheets and income statements preparation, budgeting, and expense tracking are just some of the ways Excel can be used in accounting. Decision-Making Businesses and organisations heavily rely on analysis to support their decision-making. Excel helps in the analysis through different data metrics comparisons, e.g., sales across seasons and locations, forecasting, and tracking key performance indicators. This helps businesses make the best decisions based on the insights gathered from the analysis. Beginner Excel Features and Formulas for Data Analysis Learnt so far Sort and Filter By applying the Filter feature for each column, data in specific columns can not only be sorted from newest to oldest, but also be filtered based on

After a very profitable decade on Microsoft's board, Reid Hoffman is stepping down to focus on his AI drug discovery startup Manus.

A while back, I needed to integrate SMS into a .NET project. Giant SMS had a REST API, but no official .NET client. The only existing library was a PHP one from 6–7 years ago, and it only covered two methods: send and getBalance. So I built my own. It now has nearly 2,000 downloads on NuGet. Here's exactly how I did it. The Problem Wiring up raw HTTP calls to the Giant SMS API in every project gets repetitive fast: Manually setting Authorization headers Remembering which endpoints use token auth vs. username/password Deserializing responses every time Scattering credentials across your codebase I wanted something that felt native to .NET. Configure once in appsettings.json , register with DI, and just call a method. Designing the Public API The first decision was the interface. I wanted consumers to never touch HttpClient directly, and I wanted methods that mapped clearly to what the API actually does: public interface IGiantSmsService { bool IsReady { get ; } Task < SingleSmsResponse > SendSingleMessage ( string to , string msg ); Task < SingleSmsResponse > SendMessageWithToken ( SingleMessageRequest messageRequest ); Task < BaseResponse > SendBulkMessages ( BulkMessageRequest messageRequest ); Task < SingleSmsResponse > CheckMessageStatus ( string messageId ); Task < BaseResponse > GetBalance (); Task < SenderIdResponse > GetSenderIds (); Task < BaseResponse > RegisterSenderId ( RegisterSenderIdRequest senderIdRequest ); } Seven methods, the full surface of the API, no more, no less. The IsReady property is a small but useful addition. It lets consumers do a quick sanity check at startup rather than discovering a missing token on the first SMS send: csharp _isReady = !string.IsNullOrWhiteSpace(_connection.Token) && !string.IsNullOrWhiteSpace(_connection.Username); Handling Two Auth Methods This was the most interesting design challenge. The Giant SMS API uses two different authentication schemes depending on the endpoint: Token-based (Basic Authorization header) —

We're now deep into developer conference season, and one of the themes so far is the relentless conviction from Big Tech companies that AI is going to change everything about how we do everything. Nvidia's Jensen Huang made that clearer than anyone this week, when he described a completely new way of using our laptops […]

Microsoft’s AI products aren’t selling and Github’s been plagued with troubles. WIRED spoke with VP Scott Hanselman about whether the company is in catch-up mode.