标签：#micro

A year after cutting around 9,100 employees, Microsoft is making further layoffs today as it begins its new financial year. The software maker is laying off around 4,800 employees today, approximately 2.1 percent of its workforce. Most of the employees affected by today's cuts are in Microsoft's commercial sales business or the company's Xbox division. […]

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

A 2023 HotOS paper by Sanjay Ghemawat (MapReduce/Bigtable co-author) and Amin Vahdat (Google Fellow) got repackaged by tech media as "microservices are dead." It said no such thing. Three years later, the misreading has traveled further than the paper itself. This post does three things: reconstructs what the paper actually claims, maps its three structural gaps, and introduces a variable the authors couldn't have predicted — AI code generation — which, I'll argue, undermines the paper's central solution more than any of those gaps. The AI section uses my own open-source project ReqForge as evidence. Flagging the conflict of interest up front: this isn't neutral analysis, it's a design rationale. Which is exactly why it's more honest than a hypothetical example. What the paper actually said The paper is Towards Modern Development of Cloud Applications (HotOS '23, 8 pages). Its core claim in one sentence: The fundamental problem with microservices is that they bind the logical boundary to the physical boundary. You let "how the code is organized" dictate "how the code is deployed" — two questions that should never have been welded together. From that claim, the paper proposes a three-layer solution: Logical monolith — developers write a cleanly modularized monolith; deployment is someone else's problem. Automated runtime — a smart platform that decides at runtime whether components should be merged or split, based on load. Atomic deployment — all components on a request path share one consistent version, avoiding half-old/half-new. Prototype numbers: 15× lower latency, 9× lower cost. That's it. The paper never says "microservices are wrong," never says "everyone should go back to monoliths," and gives no implementable plan. It's a vision paper — written to provoke discussion at a workshop, not an engineering whitepaper. A ruler Before dissecting it, here's a ruler you can apply to any architectural claim (this is a common framing in the engineering literature — you'r

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.

In this eMag, we try to establish agentic AI architecture as a new type of software architecture that will likely dominate the industry for years to come. The articles, written by industry experts, cover various elements and aspects of agentic AI architecture. We aim to present the latest trends and developments shaping the new type of architecture as it enters the mainstream. By InfoQ

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

Key Takeaways Oracle Backend for Microservices and AI 2.1.0 is a platform modernization release. It updates several shared backend concerns at once, including external access, observability, configuration, messaging, database deployment choices, workflow, samples, enterprise installation planning, and upgrades. Gateway API and Envoy Gateway are now the default external access direction. NGINX Ingress Controller is deprecated, disabled by default, and still available only when explicitly enabled. The release gives platform teams clearer building blocks. OpenTelemetry Operator, Java auto-instrumentation, Spring Config Server, Kafka through Strimzi-managed resources, and clearer database deployment choices supported by Oracle AI Database Operator for Kubernetes make important platform choices easier to see and discuss. Enterprise adoption still needs architecture review. Before adopting or upgrading, teams should review private registry needs, air-gapped installation requirements, multi-tenant installation goals, workflow implications, database deployment choices, and upgrade readiness. OBaaS 2.1.0 is a platform modernization release Oracle Backend for Microservices and AI , or OBaaS , is a backend-as-a-service style platform for teams building microservices and AI-enabled applications with Oracle AI Database as a core data foundation. It brings common backend platform concerns together: service access, telemetry, configuration, messaging, workflow, and database connectivity. That matters because most teams do not want every application squad to rebuild those pieces on its own. They want a platform shape that gives developers useful defaults while still giving architects, DBAs, security teams, and operators the control points they need. This article focuses on the Oracle Backend for Microservices and AI 2.1.0 update. The best way to read OBaaS 2.1.0 is not as a single-feature release. It is a platform modernization release. The update moves the default external access

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

When Logs and Metrics Aren't Enough You have great dashboards. Your log aggregation is solid. But when a user reports "the checkout page is slow," you still spend 30 minutes jumping between services trying to find the bottleneck. That's the gap distributed tracing fills. What Tracing Actually Shows You A trace is a complete picture of a single request as it flows through your system: User Request → API Gateway → Auth Service → Product Service → DB → Cache → Response 5ms 12ms 45ms 120ms 3ms ^ This is your bottleneck Without tracing, you'd see: API Gateway: latency looks fine Auth Service: latency looks fine Product Service: latency is HIGH but why? With tracing, you see the exact DB query inside Product Service that's taking 120ms. Getting Started with OpenTelemetry OpenTelemetry is the standard. Here's a minimal setup: # Python example with Flask from opentelemetry import trace from opentelemetry.instrumentation.flask import FlaskInstrumentor from opentelemetry.instrumentation.requests import RequestsInstrumentor from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter # Setup provider = TracerProvider () provider . add_span_processor ( BatchSpanProcessor ( OTLPSpanExporter ( endpoint = " http://otel-collector:4317 " )) ) trace . set_tracer_provider ( provider ) # Auto-instrument everything FlaskInstrumentor (). instrument_app ( app ) RequestsInstrumentor (). instrument () SQLAlchemyInstrumentor (). instrument ( engine = db . engine ) That's it. Three auto-instrumentations cover 80% of what you need. Custom Spans for the Other 20% Auto-instrumentation gives you HTTP calls and DB queries. Add custom spans for business logic: tracer = trace . get_tracer ( __name__ ) def process_order ( order ): with tracer . start_as_current_span ( " process_order " ) as sp