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This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on the bleak state of the video game industry, follow Andrew Webster. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here. How it started Microsoft closed out Summer […]

This closes out the series' capstone: the multi-agent customer support system built across Parts 6-9, now hardened with evaluation, cost governance, and observability so it can actually run in production with an on-call rotation behind it, not just in a demo environment. Continuous evaluation pipeline Evaluation: measuring quality continuously, not just at launch A one-time eval before launch tells you nothing about drift once real traffic — and real edge cases — start hitting the system. Set up a continuous evaluation pipeline using a G-Eval-style approach, where a separate model scores production outputs against explicit criteria: eval_criteria = { " correctness " : " Does the response accurately reflect the order/refund status retrieved from the tools? " , " escalation_appropriateness " : " If the case was ambiguous or high-risk, did the agent escalate to a human rather than resolving it alone? " , " tone " : " Is the response professional and appropriately empathetic given the customer ' s stated frustration level? " , } def geval_score ( response , context , criterion_name , criterion_description , eval_model_client ): prompt = f """ Evaluate the following response against this criterion: { criterion_description } Context: { context } Response: { response } Score from 1-5 and give one sentence of reasoning. Return JSON: {{ " score " : int, " reasoning " : str}} """ result = eval_model_client . complete ( prompt ) return json . loads ( result ) def run_continuous_eval ( sample_of_production_traffic ): scores = { crit : [] for crit in eval_criteria } for interaction in sample_of_production_traffic : for crit_name , crit_desc in eval_criteria . items (): result = geval_score ( interaction . response , interaction . context , crit_name , crit_desc , eval_model_client ) scores [ crit_name ]. append ( result [ " score " ]) return { crit : sum ( vals ) / len ( vals ) for crit , vals in scores . items ()} Sample a percentage of real production traffic daily (not just s

If you've ever managed a large collection of images, you've probably experienced this. Editing the images is only half the job. After exporting them, you still need to add: Titles Descriptions Alt text Keywords IPTC/XMP metadata For a handful of images, that's manageable. For hundreds of images, it becomes one of the most repetitive tasks in the entire workflow. The Problem I searched for a Windows application that could: Generate image metadata with AI Write IPTC and XMP metadata directly into image files Process multiple images in bulk Still allow full manual editing I found tools that handled parts of the workflow. Some could edit metadata. Some could generate AI text. But I couldn't find one focused on Image SEO from start to finish. So I decided to build it myself. Building Image SEO AI The project eventually became Image SEO AI , a Windows desktop application built specifically for creators who need to optimize image metadata. Instead of replacing existing photo editors, the goal was to eliminate repetitive metadata work. Today, the application can: Generate image titles with AI Create SEO-friendly descriptions Generate alt text Suggest relevant keywords Write IPTC & XMP metadata Process up to 50 images in a single batch Support both AI-assisted and manual editing One Challenge I Didn't Expect The biggest challenge wasn't AI. It was designing a workflow that still felt familiar. Many users don't want AI to make every decision. Sometimes they just want a better starting point. That's why every AI-generated field can be edited before saving. The application is designed to speed up repetitive work—not remove user control. Lessons Learned Building this project taught me a few things. AI works best as an assistant, not a replacement. Small workflow improvements can save hours every week. Metadata management is still an underserved problem. Simplicity often matters more than adding more features. What's Next? I'm continuing to improve Image SEO AI based on user feed

Table of contents What is OAUTH A trip to OAUTH1.0Ville What is OAUTH2.0 Examples of OAUTH Technology OIDC Hands-on Implementation with Microsoft Entra ID What is OAUTH OAUTH is a technological standard that allows you to authorize one app or service to sign in to another without divulging private information, such as passwords. OAUTH stands for Open-Authorization , not Authentication . Authentication is a process that verifies your identity, although OAUTH involves identity verification, its main purpose is to grant access to connect you with different apps and services without requiring you to create a new account. How Does OAUTH Work OAUTH uses access tokens, and this is what makes OAUTH secure to use. An access token is a piece of data that contains information about the user and the resource the token is intended for. A token will also include specific rules for data sharing . For example, you want to share your photos from Instagram with Kyrier — An intelligent email platform built for professionals who refuse to let their inbox run their day , but you only want Kyrier to access your profile image. Kyrier does not also need to access your direct messages or friends list. Instagram issues an access token to Kyrier to access the data you approve (your profile image in this case) on your behalf. So an access token will only allow Kyrier to access your profile image, not even other photos on your page. There may be rules governing when Kyrier can use the access token, it might be for a single use or for recurring uses, and it always has an expiration date. A trip to OAUTH1.0Ville Welcome to OAuth1.0Ville. Please keep your hands inside the vehicle. This is where OAuth started. It was built only for websites , back when "an app" meant a web page and nothing else. Although it worked, it had a lot of problems: Only three authorization flows (2.0 has six) No real plan for mobile or modern apps A scaling problem it never solved It also makes you cryptographically sign e

A new mandatory compliance report released by Microsoft shows how it declares profits in different European nations to reduce its tax bill.

Microsoft follows Amazon, OpenAI, and Anthropic with its new AI deployment group.

Neo is Bhavin Turakhia’s fifth venture and his latest involving enterprise software. This time he's taking on Microsoft Office, Google Apps with AI.

Microsoft will likely soon follow Sony and stop the production of physical discs for Xbox games. But instead of leaving physical discs behind entirely, sources familiar with Microsoft's plans tell me the company has quietly been working on a disc-to-digital feature that will allow Xbox owners to digitize their existing physical game collections. Xbox employees […]

This continues from Part ① . In Part ②, we'll configure the common settings and the internal-processing Power Automate flows. Common Settings Setting Up Connections Open Power Automate ( https://make.powerautomate.com ) Go to "Data" → "Connections" → "New connection" and create a Microsoft Dataverse connection Do the same to create an Office 365 Outlook connection Basic Flow Creation Steps Click "Create" → select "Automated cloud flow" (event-triggered) or "Scheduled cloud flow" (recurring) Name flows in the format [Zone]-[Number] [Description] (e.g., "A-1 Opportunity Stage Stall Alert") Always run a test after creating a flow to verify it works 2. Internal-Processing PA Flows — 4 Flows (Write-back portions of A-4, C-5, C-6, D-3) Once the common settings are done, it's time to build. A-4: Write Back Stage Changed Date Without this flow, the stall-day calculations in A-1 and B-1 will not work. Implement this first. In Microsoft Dynamics 365 (D365), a "stage" refers to a major milestone in a process — such as a sales deal or customer engagement — that guides the responsible person through what needs to happen next. It's how a series of activities is visualized and managed. From here, all work is done in Power Automate. Step Task Details 1 Create the flow "Automated cloud flow" → select trigger "When a row is added, modified or deleted (Dataverse)" 2 Configure trigger Table: Opportunities / Change type: Modified 3 Add condition Add a "Condition" action: "When Status Reason (statuscode) has changed" 4 Write-back action "Update a row (Dataverse)" → set cr917_stage_changed_date to utcNow() C-5: Auto-Set Renewal Date + Auto-Create Renewal Opportunity (on Won) On Won close, two things happen: ① auto-set the renewal date to close date + 365 days, and ② auto-create a new Opportunity for the renewal cycle and add it to the pipeline. Step Task Details 1 Create the flow "Automated cloud flow" → trigger "When a row is added, modified or deleted (Dataverse)" 2 Configure trigger Ta

INTRODUCTION When I started using Power BI, I only thought of visuals like charts and graphs. However, as I progressed, I discovered a great data dashboard is built on great data models. Data Modelling is the process of organizing your data tables and defining how they relate to each other so Power BI can combine them into meaningful reports and dashboards. Good, designed data makes it easier and faster to maintain. Why is data Modelling Important Well-organized data makes it easier to manage data. Reduction of the duplicates. Ensures data consistency. Understanding Relationships Relationships allow the data table to give communication using fields. For example, Customer Table stores all information about a customer. Product Table store product details Sales Table stores all information about the transactions. Power BI connects the information between the customer’s name and Customer Id rather than repeating them it connects the information using joins. Going through relationships I discovered schemes. Scheme is the way tables are organized in databases. There are different types of schemes e.g. Star Schema, snowflake schema and Flat table. Star Schema A star schema is a data model with one central fact table and dimension table surrounding it. Fact table A table that stores events, transactions of what happened. • Total sales • average sales • quantity sold Dimension table A dimension table describes the items in the fact table. The table contains descriptive information. • The customer table describes the customer • How much sales were made The fact table sits in the center, while the dimension tables surround it—forming a star. Dashboard designs A good dashboard has to fit one page. A dashboard should show critical information. Update automatically when data changes. Focus on data understanding and decision making. Conclusion Power BI taught me that a great report are built from a a great dashboard which is achieved by having great models. Structuring a data into

Microsoft is set to announce a wave of layoffs for its Xbox studios and employees next week. Sources familiar with Microsoft's plans tell The Verge that the layoffs will lead to studio closures or spinoffs, potential mergers of studios, and canceled games. I understand Microsoft is currently weighing closing at least five studios, including the […]

To this day, we have yet to see a quantum computer conclusively perform a single useful task. Existing machines are simply too small and error-ridden to solve commercially relevant problems. That hasn't stopped Donald Trump's science adviser from promising a "quantum computer powerful enough for scientific discovery by 2028" and Trump from issuing a new […]

Microsoft has announced the limited public preview of Copilot Autofix for GitHub Advanced Security for Azure DevOps, extending AI-powered vulnerability remediation to teams using Azure Repos. By Craig Risi

Xbox union members argue for transparency and good-faith bargaining.

You export a CSV from a UK system and double-click it in US Excel — 28/05/2026 suddenly looks like May 28, or something even stranger. The file isn't corrupt. Excel is guessing your date format based on your computer's locale. This short guide covers why that happens, how to import CSV correctly, and how to batch-fix dates that are already wrong. For the full walkthrough with examples, see the official guide: How to Fix Excel CSV Date Import Problems . Why Excel breaks CSV dates A CSV is plain text. It does not store whether a value is a date or a string. When you double-click to open, Excel parses using your regional settings: US Excel: MM/DD/YYYY UK / Europe: DD/MM/YYYY Dates like 03/04/2025 are the most dangerous — both parts are ≤ 12. US Excel may read April 3; UK Excel reads March 4. Excel won't warn you. Other common traps: Dates exported as serial numbers (e.g. 44927 ) Two-digit years triggering century guesses Re-saving as CSV destroys formats a second time The right way: don't double-click the CSV Open Excel first — don't double-click the .csv file Data → Get Data from Text/CSV (older Excel: Data → From Text) Set date columns to Text if you need to preserve the original string Confirm the source region (US / UK), then convert to a single format For team data exchange, agree on ISO 8601: YYYY-MM-DD in your schema — unambiguous, sorts correctly as text, and works in JSON APIs and databases. Ambiguous value US reads as UK reads as Safe format (ISO) 03/04/2025 2025-04-03 2025-03-04 Confirm source region 28/05/2026 — 2026-05-28 2026-05-28 05/28/2026 2026-05-28 — 2026-05-28 Already broken? Fix dates in the browser (no server upload) I built a free tool cluster on FormatList — everything runs locally in your browser : 1. Date Format Fixer — bulk repair Paste a date column or upload .csv / .txt Ambiguous rows highlighted; optional US / UK preference Export ISO / US / UK or download a fixed CSV Best for: normalizing a whole column to ISO before a database or API imp

Nine jurors. Two hours of deliberation. Twenty-six claims at the original federal complaint's peak. Three surviving claims at trial. Zero claims surviving the verdict. One hundred fifty billion dollars of maximum disgorgement exposure if the verdict had gone the other way. One hundred thirty billion dollars of OpenAI Foundation equity stake under the October 28, 2025 recapitalization. Thirty-eight million dollars of total Musk contributions per his sworn trial testimony. Forty-four million per the legal complaint. Eight years from the January 2, 2016 Sutskever-Musk "less open / Yup" email exchange to the August 2024 federal filing date. Three years of statute-of-limitations runway on the breach-of-charitable-trust claim; two years on the unjust-enrichment claim. The verdict in Musk v. Altman came in this morning at the federal courthouse on Clay Street in Oakland, before Judge Yvonne Gonzalez Rogers in the Northern District of California. The companion piece, The Calendar Technicality , makes the doctrinal argument that the procedural dismissal is the substantive determination California charitable-trust law would have produced on the merits as well. This piece takes the same conclusion through the numbers. The dollar-and-time math closed the merits door before the doctrinal door even came into view. Two hours, in context Federal-court civil-trial deliberations on complex commercial cases typically run between one and five days. The Administrative Office of the U.S. Courts' annual judicial-business reports show median civil-jury deliberation in the multi-day range for cases with three or more issues to resolve and dollar exposure above one billion. The two-hour deliberation in Musk v. Altman is roughly one to two standard deviations below the median for cases of this complexity. The brevity is not a function of jury inattention. The trial ran three weeks. Roughly four hours of testimony came from Altman alone on May 12, with cross-examination opening with Musk's lea

About a quarter of PCs are still running Microsoft's previous operating system.

The company says the increases are being driven by rising memory and console storage prices, with costs more than 2.5x higher than previous levels.

Microsoft just added a cheaper 12-inch Surface Pro and 13-inch Surface Laptop to its lineup. Both models come equipped with 8GB of RAM instead of 16GB, costing $849 for the specced-down Surface Pro and $949 for the Surface Laptop, as spotted earlier by Windows Central. When the 12-inch Surface Pro and 13-inch Surface Laptop launched […]

A critique published in Nature Wednesday calls the basic technology behind Microsoft's "breakthrough" quantum computing chip the Majorana 1 into question. Microsoft unveiled the chip in February 2025 and said it featured a brand-new technology known as a topological qubit. Topological qubits, they said, would be the "building blocks" for their future quantum computer. Microsoft […]