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The incident could disrupt a key part of the defense company’s rocket motor business, which designs and tests prototype motors for military customers.

Tariffs and anti-China policies killed this little Volvo in the United States.

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 […]

Welcome to the “Temu experience of telehealth,” where everyone from Grindr to MAGA influencers can open a virtual clinic selling weight loss drugs and more.

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

Google is updating its image generator to make it faster and cheaper, making it a more useful tool for creators looking to make AI content.

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 […]

But the company is still claiming that New Glenn will return to flight this year.

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 […]

My dashboard had a useMemo doing arithmetic it had no business doing. It was a Pokémon TCG Pocket collection tracker, but that part doesn't matter. What matters is that the home page needed to show three things: overall completion, completion per set, and which set you were closest to finishing. The way I'd built it, the browser was fetching every card and every owned record, then grinding through the math on each render to figure all of that out. It worked. It also got slower and harder to read every time the data grew or I added a metric. So I moved the aggregation out of React and into Postgres, and the surprising result was that my frontend got boring . Fewer hooks, less state, almost nothing left to break. That's the whole argument of this post: aggregation belongs in the database, and when you put it there, the React code that's left over is the kind of boring you actually want in the layer your users touch. What "fetching everything into React" actually looks like Here's the shape of the original dashboard. Load all the cards, load the user's owned rows, then derive everything on the client. const [ cards , setCards ] = useState < Card [] > ([]); const [ owned , setOwned ] = useState < OwnedCard [] > ([]); useEffect (() => { ( async () => { const { data : allCards } = await supabase . from ( " cards " ). select ( " * " ); const { data : ownedRows } = await supabase . from ( " user_cards " ) . select ( " card_id " ); setCards ( allCards ?? []); setOwned ( ownedRows ?? []); })(); }, []); const ownedIds = useMemo (() => new Set ( owned . map (( o ) => o . card_id )), [ owned ]); const perSet = useMemo (() => { const groups : Record < string , { total : number ; have : number } > = {}; for ( const card of cards ) { const g = ( groups [ card . set_id ] ??= { total : 0 , have : 0 }); g . total += 1 ; if ( ownedIds . has ( card . id )) g . have += 1 ; } return groups ; }, [ cards , ownedIds ]); const overall = useMemo (() => { const total = cards . length ; const ha

SokoFlow Build Log — Month 1 of 4 Last semester I set out on a new strategic plan to level up my software development skills through deliberate, project-based learning. That work produced one of the most ambitious things I've built so far: Sim-Pesa , a local-first transactional appliance that lets developers working in the M-Pesa ecosystem test and simulate STK Push workflows entirely on their own machines, without depending on the Daraja sandbox. I documented that build in 16 weekly posts, which you can find here . This semester, the focus shifts — from fintech foundations to cloud-native integration and real-world systems. The flagship project is SokoFlow , a conversational ERP for small Kenyan shopkeepers to track inventory and record sales entirely through WhatsApp chat. No app to download, no training session required — just natural language. Where Sim-Pesa lived in a controlled, predictable transactional world, SokoFlow steps into the mess of cloud-native reality: third-party API failures, webhook signature verification, the statelessness of HTTP, and container orchestration. The target audience shifts too — Kenyan SMEs operating on infrastructure that is often unreliable by design, not by exception. It's an ambitious project, but the goal was always to learn as much as possible from it. With the plan in place, I got to work. 1. The Vision of a Headless ERP The first real question I had to answer before writing a line of code: what does "headless" actually mean? Headless architecture decouples the frontend — the "head," or user interface — from the backend, the "body" that holds the data and business logic. A conventional ERP bundles both: backend plus a dashboard or UI on top. A headless ERP, by contrast, is just the engine. The brain. There's no built-in screen. So how do users interact with a system that has no interface of its own? SokoFlow doesn't actually care. It could be: WhatsApp SMS A web app A mobile app A voice assistant In this case, the "frontend

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

This week in COSS: The acquisition trend continued as Qualcomm agreed to acquire Modular for nearly $4 billion, Cursor quietly acquired open-source coding assistant Continue, and Elastic acquired AI SRE startup Deductive AI for up to $85 million. In funding news, DeepSeek closed over $7 billion in funding, Timefold raised a $13M Series A for its scheduling optimization platform, and Moonshot AI has reportedly sought a $30 billion valuation in new funding talks. In other announcements, Sentient Foundation committed $42 million to advance open-source AGI, Daytona announced it is going closed source, Vercel launched eve (an open-source agentic framework), Zilliz launched Vector Lakebase, Upbound open-sourced Modelplane (a control plane for AI inference), Bluesky COO Rose Wang discussed AI and the company's open-source approach, and ClickHouse announced Silk, a new fiber runtime. We also feature the following companies in Cossmology: ArcadeDB, Proton, HitKeep, Passbolt, Nirmata, Paper Compute Co., Plastic Labs, DuckLabs, Blacksky Algorithms, and Earendil Works. COSS Headlines Cursor quietly acquires Continue, an open-source alternative to GitHub Copilot Companies mentioned: Continue Announcement · The New Stack Introducing Modelplane: the control plane for AI inference Companies mentioned: Upbound Announcement · Modelplane Blog 'AI is taking away what makes us human' says social media boss Companies mentioned: Bluesky Media Mention · Metro DeepSeek closes $7bn-plus round with an unusual structure Companies mentioned: DeepSeek Funding · The Next Web Elastic reportedly acquires site reliability engineering startup Deductive AI Companies mentioned: Elastic Announcement · SiliconANGLE Vercel launches eve, an open-source framework that treats agents as directories Companies mentioned: Vercel Announcement · The New Stack Zilliz Launches Vector Lakebase, Extending the World's Most Adopted Vector Database into a Unified Data Platform for AI Companies mentioned: Zilliz Announcem

A new report finds "high-intensity AI adopters” saw headcount increase 10.2%. Among those companies, entry-level headcount rose by 12%, countering the rhetoric that AI kills junior jobs.

My "serverless" database was billing me like it never slept. Neon has this great feature called scale-to-zero. Your Postgres compute suspends when it's idle, so you only pay for the queries you actually run. For a pre-revenue product, that should cost a few cents a month. Mine ran 24/7. The compute never once scaled to zero. The culprit wasn't my app logic. It was my database driver. I was using postgres-js, which holds a persistent connection open to the database. From Neon's side, an open connection looks like activity, so it never suspended. It just stayed awake and kept quietly billing me. The fix was basically one conceptual change: switch to @neondatabase/serverless, the HTTP driver. Instead of a long-lived connection, every query becomes a stateless one-shot HTTP request. The query fires, the connection closes, and the compute is free to suspend. Scale-to-zero finally worked. The lesson I keep relearning as a solo founder: "serverless" is a property of your whole stack, not a checkbox on one service. One persistent connection upstream and the whole cost model breaks.

South Korea targets physical AI lead and commercial humanoid robots by 2028.

Leaked iPhone 18 Pro photos and parts lists appeared on the dark web following a data breach affecting one of Apple's key suppliers, according to a report from Reuters. The leaked images show a drop test of what a source tells Reuters is the iPhone 18 Pro, equipped with a three-camera layout and Apple logo. […]

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