Month: September 2025

c# – Missing default references in a “WinUI Blank App (Packaged)” project in VS26 (and 22)

Posted on by faz_business


The problem

Upon creating a new app with the Basic template, and trying to run the default App, I got hit with 15 missing assembly reference errors. Spent around 1 hour trying to find the solution, even adding custom sources and packages (which probably could’ve been solved by putting more time into, however it is hard to believe that they expect me to install 15 or more packages myself when using a default template). I need to know how to successfully build my newly created app (for those wondering).

I’ve tried installing Visual Studio Community 2022 in case of this being an Insider build related issue, however I ended up with the same experience. When it comes to components, I’ve got the two default ones, as well as Win11 SDK (which I thought it probably wouldn’t work without). Gave adding other two SDK options a try (Win10 and older Win11) as well as “Windows Platform Development tools”, had no luck. Tried checking the “.NET Development” option as well, still nothing.

Errors from the Output tab

The type or namespace name 'Controls' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Controls' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Input' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Media' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Navigation' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Shapes' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Application' could not be found (are you missing a using directive or an assembly reference?)
The type or namespace name 'Window' could not be found (are you missing a using directive or an assembly reference?)
The type or namespace name 'LaunchActivatedEventArgs' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Controls' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Controls' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Input' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Media' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Navigation' does not exist in the namespace 'Microsoft.UI.Xaml' (are you missing an assembly reference?)
The type or namespace name 'Window' could not be found (are you missing a using directive or an assembly reference?)

Steps to reproduce the bug

  1. Install Visual Studio Installer from MS Store
  2. Find Visual Studio Community 2026 Insiders from the Installer and check the latest Win11 SDK box
  3. Click on the Install button
  4. Open the freshly installed VS26 and press the “Create a new project” button
  5. Choose the “WinUI Blank App (Packaged)” template option
  6. Press the green “Start Without Debugging” button located near the top of the screen (or press Ctrl+F5)
  7. Enjoy the agony you feel when you get hit with 15 – 20 red x’s signifying errors

Screenshots

The screen I am presented with upon trying to build my app

Windows version

Windows 11 (24H2): Build 26100

Note

I’ve already created an issue on the microsoft/microsoft-ui-xaml GitHub page, however, after receiving nothing during the 2 days I decided to wait, I’ve decided to come here (which is why the formatting is very similar to a typical GitHub issue). Here is the issue link:

https://github.com/microsoft/microsoft-ui-xaml/issues/10804

Outsourced Accounting Service Models Evolve With Business

Posted on by faz_business


Outsourced Accounting Service Models
Pexels

For many business leaders, the accounting department exists in a fixed box. It’s perceived as a necessary, if somewhat rigid, function focused on historical data—a department that documents what has already happened. This traditional view often casts accounting as a pure cost center, a part of the business that consumes resources without directly contributing to growth or strategy. Under this model (instead under outsourced accounting service models), its duties were often confined to a predictable set of tasks:

  • Processing payroll and paying bills.
  • Closing the books at the end of each month or quarter.
  • Ensuring tax compliance and preparing statutory filings.
  • Generating historical financial statements.

It’s a function to be managed, a compliance requirement to be met. But this perspective is rapidly becoming obsolete, eclipsed by a far more integrated and fluid approach. Modern financial management as those of outsourced accounting service models, particularly when handled by an external partner, is not a static fixture. Instead, it behaves like a living organism, designed to stretch, adapt, and evolve in lockstep with the company it serves.

The Shift from Static Cost to Dynamic Partnership

Traditionally, building a finance function meant hiring. A bookkeeper, then perhaps an accountant, and eventually a controller—each addition represented a significant increase in fixed overhead. This model creates a rigid cost structure that is disconnected from the company’s actual performance. An outsourced accounting service fundamentally breaks this outdated model by creating a dynamic partnership. Instead of hiring employees, a business gains access to an entire team of professionals with varied expertise. This transforms a heavy, fixed salary expense into a predictable, variable cost that scales directly with the company’s operational needs. In slower months, the service can be lean. During periods of rapid growth, the resources can be expanded immediately without the lengthy and expensive process of recruiting, hiring, and training new staff. This agility ensures that the company’s financial capabilities are always perfectly matched to its current reality, eliminating waste and enabling capital to be deployed where it matters most: fueling growth.

Scaling Services for the Business Lifecycle

A business’s financial needs are not static; they change dramatically through its lifecycle. A service model that evolves with the company is therefore not a luxury, but a necessity. In the early startup phase, the requirements are foundational: establishing a clean chart of accounts, managing payroll and bills, and ensuring basic tax compliance. The priority is creating order from chaos so founders can focus on their product and customers. As the business enters a growth stage, perhaps after securing its first round of funding, the complexity multiplies. Suddenly, there is a need for sophisticated cash flow forecasting, budget variance analysis, and detailed financial reports for board meetings and investors. A truly adaptive service anticipates this shift. It can seamlessly layer on these more advanced functions, providing the deeper financial intelligence required for strategic decision-making. This organic scaling means the business is never overpaying for services it doesn’t need, nor is it caught unprepared when its financial requirements become more demanding.

Integrating Strategic Financial Leadership on Demand

As a company matures, its financial questions evolve from “Are the books correct?” to “What do the books tell us about our future?” This is the leap from accounting to finance—from historical record-keeping to forward-looking strategy. Navigating this transition requires a level of expertise that goes beyond daily transactional work. It demands strategic guidance on capital allocation, pricing models, market expansion, and fundraising. While a full-time Chief Financial Officer (CFO) provides this guidance, their six-figure salary is often prohibitive for most small and medium-sized businesses. This is precisely the gap filled by fractional CFO services. This innovative model provides growing companies with access to seasoned financial executives on a part-time or project basis. It allows a business to tap into high-level strategic thinking for critical initiatives—like preparing for a capital raise or analyzing a potential acquisition—without incurring the cost of a full-time C-suite hire. It’s the ultimate form of scalability: C-level expertise, on demand.

The Technology Stack: The Engine of Modern Accounting

The evolution of outsourced accounting service models is powered by a sophisticated and interconnected technology stack. Gone are the days of desktop software, manual data entry, and shoeboxes full of receipts. Today, the entire finance function operates in the cloud, creating a single source of truth that is accessible anytime, anywhere. Platforms like QuickBooks Online and Xero form the core, while a surrounding ecosystem of applications automates everything from expense reporting to bill payments. This automation does more than just save time; it liberates human accountants from tedious, repetitive tasks, allowing them to focus on what matters most: analysis, interpretation, and strategic advice. The result is real-time financial dashboards that empower business owners to make decisions based on current data, not month-old reports. A modern provider like Lineal CPA builds its service delivery around this integrated technology, creating a client experience that feels less like an external service and more like a seamlessly connected internal finance department.

Bespoke Solutions: Building a Custom-Fit Finance Function

Perhaps the most profound shift in outsourced accounting is the move away from rigid, one-size-fits-all packages. Today’s service models are built on the principle of customization, allowing a business to construct a finance function that is perfectly tailored to its specific needs. Companies are no longer forced to choose between a “basic” or “premium” tier that may include services they don’t require or lack ones they do. Instead, the approach is modular. A business can select exactly what it needs from a menu of options. For instance, a growing company might build its ideal finance function by selecting from services like:

  • Daily transaction management, including accounts payable and receivable.
  • Payroll processing and benefits administration.
  • Month-end close procedures and standard financial reporting.
  • Cash flow forecasting and budget-to-actual analysis.
  • Industry-specific revenue recognition (e.g., for SaaS or construction).
  • Detailed project or inventory accounting.
  • Strategic CFO-level advisory for fundraising or M&A activities.

This is especially critical as different industries have unique financial challenges; an e-commerce company’s needs around inventory and sales tax are vastly different from a SaaS company’s focus on subscription metrics. This bespoke model ensures that the accounting function is not an ill-fitting suit, but a perfectly tailored garment that provides support exactly where it’s needed.

An Evolving Partnership for Future Growth

The narrative surrounding accounting has fundamentally changed. What was once viewed as a static, backward-looking necessity has transformed into a forward-looking, dynamic capability. Modern outsourced accounting service models are defined by the ability to adapt, scale, and integrate directly into the fabric of a business. It meets a company precisely where it is, providing foundational support in the early days and layering on sophisticated strategic guidance as challenges and opportunities grow more complex. By converting a rigid fixed cost into a flexible variable expense, it preserves precious capital. By leveraging technology, it delivers real-time clarity. And by offering a full spectrum of expertise on demand—from the daily transactions to the high-level boardroom strategy—it ensures that a business is never without the financial acumen it needs to thrive. Ultimately, this evolution is about more than just offloading tasks; it’s about insourcing a strategic partner that is structurally designed to evolve with the business, not against it, ensuring the financial function is always a catalyst for growth.

Find a Home-Based Business to Start-Up >>> Hundreds of Business Listings.

Konami Believes in ‘Silent Hill f’ So Much, It’s Becoming a Novel

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It’s only been a few days since Silent Hill f came out, and in a month, fans will be able to read it.

Konami announced a novel adaptation of the survival horror game releasing October 30. Written by author Kuroshiro, who adapted Midnight Wanderers and NG for the page, this Silent Hill f adaptation will cover the game’s core story and provide new material not featured. As a fan of Kuroshiro’s work, Silent Hill producer Motoi Okamoto recalled feeling “overjoyed when Kadokawa suggested him as the novelization author.”

This is the one of the few novels for the Silent Hill franchise without illustrations. Save for books based on the 2006 and and 2012 films, the series’ literature has been light novels based on specific games and films, art and guidebooks, or manga telling an original story. Okamoto hopes this new novel appeals to game fans or horror book lovers “who [don’t] play games but enjoys reading books.”

Developed by NeoBards Entertainment, Silent Hill f centers on 1960s high school Hinako Shimizu as she navigates the fictional, fog-covered town of Ebisugaoka. The game’s received a warm reception so far and is part of Konami’s larger push to revive Silent Hill, which has included a remake of Silent Hill 2 (and its 2026 film adaptation Return to Silent Hill), a remake of the first game, and the brand new Silent Hill: Townfall.

Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who.



Consume Me Free Download – WorldofPCGames

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Consume Me Pre-Installed Worldofpcgames

Consume Me Direct Download:

BEING A TEENAGER SUCKS. So we made a videogame about it. Hey remember when your parents, your friends, and society at large all conspired to make you feel ugly, lazy, stupid, and unloved despite the brilliant human spirit contained within you? No? Well, allow us to refresh your memory via the timeless medium of a slice-of-life role-playing game! In Consume Me, you take on the role of Jenny, young, in love, and entering her final year of high school. Make meticulous scheduling decisions to maximize your glow-up! Solve the puzzle game of dieting! Evade distractions as you pursue scholarly success! Do chores to get money from mom at a rate much lower than minimum wage! Agatha Christie – Death on the Nile

Can you win the heart of the boy of your dreams? Can you hold on to it? Can you chart a course towards a sustainable future? Can you fit just one more TODO goal on your plate, or is it already full to bursting? Most importantly, does any of this matter–either in the game, or in a broader sense? I don’t have time to spoil the story here, so I’ll just give you a free tip: if you have the spare money, buy some coffee at the corner store and drink it for an extra free-time unit. I was super excited for this game and was sure it would be amazing. I love these storytelling games in a quirky style about real life but gamified. The game ultimately exceeded my expectations in terms of content and variety of mini-games, storyline, and duration (honestly, I thought it would be something short, maybe two hours at most).

Features and System Requirements:

  • A darkly funny coming-of-age story: You’ll laugh, you’ll cry, you’ll get a little hungry, sometimes all at the same time! Sorry about that!
  • Every meal a puzzle: Eat your lunch, but be careful not too eat too much. Also, try not to eat too little either. Throw any food you don’t want to the dog. It’s fine, she’s not on a diet!
  • Gripping strategic decision making: A limited amount of time and an unlimited number of things to do!? This classic combination introduces the ultimate strategic question: How should you be spending your time? It’s up to you to answer this question…every day of your life!
  • Obtain powerful equipment: Choose from a selection of garments that help you dress for success. But be warned, the armor of this world degrades after a mere single day of use…until you renew it via that ancient ritual known only as “laundry.”
  • Over 13 possible endings: Most of them bad!

Screenshots

System Requirements

Minimum
OS *: Windows 7 or later
Processor: x64 architecture
Memory: 2 GB RAM
Graphics: OpenGL or DirectX 9 compatible graphics card.
DirectX: Version 9.0c
Storage: 3 GB available space
Sound Card: Any
Support the game developers by purchasing the game on Steam

Installation Guide

Turn Off Your Antivirus Before Installing Any Game

1 :: Download Game
2 :: Extract Game
3 :: Launch The Game
4 :: Have Fun 🙂

What Is Orchestration in Computing? Types, Benefits & Future Trends

Posted on by faz_business


Orchestration has become a foundational concept in the digital era, allowing businesses to stitch together everything from container deployments to business processes into a seamless flow. When done well, orchestration transforms scattered tasks into cohesive, automated workflows, unlocking reliability, scalability and cost efficiency. In the AI space, Clarifai leads this orchestration revolution with its compute orchestration platform that works across clouds and on‑premises, helping organizations deploy and run AI efficiently. This article demystifies orchestration in computing, explains how it differs from automation, highlights major tools and use cases, and offers practical guidance for getting started.

Quick Digest: What’s Coming in This Guide

  • What is orchestration? Orchestration coordinates multiple automated tasks and services to deliver an end‑to‑end outcome. It operates like a conductor managing an orchestra, ensuring every component plays its part at the right time.
  • Why now? Companies rely on microservices, containers and hybrid clouds, making manual coordination impossible. Orchestration simplifies deployment, scaling and reliability.
  • Key distinctions: Understand how orchestration differs from automation and choreography, and why these concepts matter.
  • Types and tools: Explore different types of orchestration—containers, workflows, infrastructure—and compare leading tools like Kubernetes, Airflow, Terraform, and Clarifai’s orchestration platform.
  • Benefits and challenges: Learn about the scalability, cost savings and reliability orchestration brings, as well as potential pitfalls like complexity and security risks.
  • Best practices: Discover patterns such as decoupled design, observability, and CI/CD that ensure orchestration success.
  • Emerging trends: Get a glimpse of the future with AI‑driven orchestration, edge computing, multi‑cloud strategies and generative AI that helps design systems.
  • How to start: Follow a step‑by‑step guide and see how Clarifai’s compute orchestration and local runners can simplify your journey.
  • FAQs: Wrap up with answers to common questions.

Understanding Orchestration—Definition, Evolution & Concepts

How Has Orchestration Evolved and What Does It Mean?

In computing, orchestration refers to the automated coordination of multiple tasks, services and resources to achieve a desired outcome. Think of a conductor guiding an orchestra—each musician (task) must play the right note at the right time for the piece (workflow) to come together. Similarly, orchestration tools manage dependencies, sequence tasks, handle failures and scale resources to deliver complex workflows. Initially, teams relied on cron jobs and custom scripts to automate single tasks. As systems grew into distributed architectures with containers and microservices, manual coordination became unsustainable. Modern orchestration emerged to bridge disparate components into unified workflows, making deployment and scaling seamless.

Why Is Orchestration Important in Today’s Digital Landscape?

Companies deploy applications across hybrid clouds, edge devices and on‑premises environments. Manual oversight can’t scale with such complexity. Orchestration solves this by managing lifecycles (start, stop, scale), handling retries, sequencing tasks, monitoring performance and recovering from failures automatically. In the AI domain, Clarifai’s unified control plane orchestrates AI models across different infrastructures, helping customers optimize costs and avoid vendor lock‑in. The modern emphasis on agility and DevOps makes orchestration critical—organizations can deploy changes faster while ensuring reliability.

Expert Insights & Statistics

  • Survey data indicates that more than 80 % of organizations use containers in production, and 87 % run microservices, many managed by orchestration platforms.
  • Dynatrace reports that organizations adopting container orchestration see improved scalability and more than 60 % of infrastructure workloads deployed on Kubernetes.
  • Clarifai states that their compute orchestration can deliver up to 90 % less compute needed, handle 1.6 million inference requests per second, and provide 99.999 % reliability.
  • Expert tip: Think of orchestration as the glue that binds microservices and tasks. Without it, your system is like a group of musicians practicing solo—talented individually but chaotic together.

Creative Example: The Factory Analogy

Imagine a factory assembling smartphones. Each station performs a specific task—cutting glass, installing chips, applying adhesive. If each station works independently, parts pile up or run out. An orchestration system acts like the factory supervisor: it determines when each station should start, stops when needed, handles shortages, and ensures every phone flows smoothly down the line. Similarly, orchestration in computing coordinates tasks so that data moves through pipelines, containers spin up and down, and services communicate reliably.

Orchestration vs. Automation vs. Choreography – Clear Distinctions

What’s the Difference Between Orchestration and Automation?

Automation involves executing a single task or a sequence of static steps automatically—like a script that backs up a database every night. Orchestration, on the other hand, coordinates multiple automated tasks, making decisions based on system state, dependencies and business rules. It ensures tasks run in the correct order, handle failures gracefully and scale up or down based on demand. Think of automation as playing one instrument and orchestration as leading an entire orchestra.

How Does Choreography Fit In?

Choreography relates primarily to event‑driven microservices. In choreography, each service listens for events and reacts independently without a central coordinator. This peer‑to‑peer model can be highly scalable but may introduce complexity if not designed carefully. Orchestration, in contrast, relies on a central controller (the orchestrator) that directs services and coordinates their interactions. Choosing between orchestration and choreography depends on your architecture: orchestration provides visibility and control; choreography offers loose coupling and autonomy.

Expert Insights & Advanced Tips

  • Red Hat experts note that automation is a subset of orchestration; while automation can perform tasks, orchestration adds decision logic and state awareness.
  • Microservice architects often blend both: they use orchestration for complex workflows that need oversight and choreography for event‑driven communication when services must respond quickly to changes.
  • Advanced tip: Avoid coupling your orchestration tool tightly to business logic. Keep business rules separate so you can switch orchestrators without rewriting core services.

Orchestration vs. Automation vs. Choreography

Types of Orchestration in Computing

Orchestration spans multiple domains. Understanding the different types helps you select the right tool for your workload.

Container Orchestration

Container orchestration automates the deployment, scaling and management of containerized applications. Kubernetes leads this space, supporting features such as auto‑scaling, service discovery, rolling updates and fault tolerance. Others include Docker Swarm (simpler but less flexible) and Apache Mesos (used for big data workloads). Clarifai’s compute orchestration integrates with Kubernetes but offers a unified control plane to manage AI workloads across multiple clusters and regions. The platform automatically provisions GPU or TPU resources, handles scaling, and optimizes compute usage.

Workflow or Data Orchestration

Workflow orchestration coordinates tasks across data pipelines, ETL/ELT processes and batch jobs. Tools like Apache Airflow, Dagster, Prefect and Argo Workflows allow you to define Directed Acyclic Graphs (DAGs) that specify task order, dependencies, scheduling and retries. These tools are crucial for data teams running complex pipelines. Clarifai’s orchestration platform enables deploying AI pipelines that include data ingestion, model inference and result post‑processing; you can run them on Clarifai’s shared compute, your VPC or on‑premises servers.

Microservices Orchestration

Microservices orchestration focuses on coordinating multiple services to deliver business processes. Service orchestrators or workflow engines manage API calls, handle retries and enforce business logic. Spring Cloud Data Flow and Camunda are examples, and serverless orchestrators like AWS Step Functions or Azure Durable Functions perform similar roles for event‑driven functions. Clarifai’s platform orchestrates AI microservices (e.g., image recognition, text analysis, custom models) to create complex AI pipelines.

Cloud & Infrastructure Orchestration

Infrastructure orchestration automates the provisioning, scaling and configuration of compute, storage and network resources. Tools like Terraform, AWS CloudFormation and Pulumi allow teams to define infrastructure as code (IaC), manage state and deploy across providers. Clarifai’s compute orchestration simplifies infrastructure management by offering a single control plane to run models on cloud, VPC or on‑premises, with auto‑scaling and cost optimisation.

Business Process Orchestration

Beyond IT, orchestration can coordinate enterprise workflows such as order fulfillment, supply chain management and HR processes. Business Process Management (BPM) platforms and BPMN modeling tools allow analysts to design workflows that cross departmental boundaries. They integrate with systems like ERP and CRM to automate tasks and approvals.

Edge & IoT Orchestration (Emerging)

With the rise of edge computing, orchestrating workloads across thousands of IoT devices becomes critical. Edge orchestration ensures that models run near the data source for low latency while central control manages updates and resource distribution. Research from MDPI highlights emerging frameworks for edge orchestration that use machine learning to predict workloads and schedule tasks. Clarifai’s compute orchestration supports deploying models to edge devices through Local Runners, which allow models to run locally while still being accessible via the Clarifai API.

Expert Insights & Data Points

  • IDC predicts that by 2025, 75 % of enterprise data will be generated at the edge, requiring edge orchestration solutions.
  • Clarifai’s Local Runners enable running models on workstations or on‑premises servers and exposing them through Clarifai’s API; this provides secure, low‑latency inference while using a unified management interface.
  • Step Functions and Durable Functions simplify orchestrating serverless microservices. They handle retries, state machines and parallel execution, making them ideal for event‑driven architecture.

Types of orchestration

Leading Orchestration Tools & Platforms: Comparisons and Lists

Selecting the right orchestration tool depends on your workload, team skills and business goals. This section compares popular options across categories and highlights Clarifai’s unique strengths.

Container Orchestrators

Feature

Kubernetes

Docker Swarm

Apache Mesos

Clarifai Compute Orchestration

Scalability & Ecosystem

Industry standard with a vast ecosystem; runs microservices at scale.

Simpler setup but limited features.

Designed for large clusters; used by big data frameworks.

Built on Kubernetes but provides unified control plane and AI‑optimized scaling.

Ease of Use

Steep learning curve; extensive configuration.

Easier to start; fewer features.

Complex; typically used in research environments.

Abstraction layer hides Kubernetes complexity; automatically optimizes GPU/TPU usage.

Managed Services

EKS (AWS), GKE (Google), AKS (Azure).

Docker Swarm is self‑managed.

Mesos requires self‑hosting.

Clarifai offers shared and dedicated compute, or connects to your own clusters.

Use Cases

General microservices, AI pipelines, hybrid cloud.

Small teams wanting simple container management.

Large‑scale data processing (Hadoop, Spark).

AI/ML workloads, inference at scale, hybrid deployments, cost optimisation.

Note: Clarifai’s platform is not a direct replacement for Kubernetes; it builds on top of it, focusing specifically on orchestrating AI models and inference pipelines. It provides a single control plane for managing compute across environments and adds features like GPU fractioning, batching, autoscaling and serverless provisioning.

Workflow & Data Orchestrators

  • Apache Airflow: Popular open‑source DAG‑based orchestrator. Highly extensible and community‑supported but can be challenging to scale.
  • Prefect: Modern Python‑based orchestrator with declarative flows and a cloud dashboard. Good for data engineering tasks.
  • Dagster: A data‑centric orchestrator with strong type checking and observability features.
  • Argo Workflows: Kubernetes‑native workflow engine, ideal for cloud‑native pipelines. Supports containerized tasks and artifacts.

Clarifai: Allows orchestrating AI workflows by chaining models (e.g., image detection → object classification → text extraction). The platform manages containerization and scaling automatically, so data scientists can focus on building workflows instead of infrastructure.

Infrastructure & IaC Orchestrators

  • Terraform: Cloud‑agnostic tool for defining and provisioning infrastructure. Uses HCL language; state management can be complex.
  • Pulumi: Allows writing IaC in languages like TypeScript, Python and Go; easier integration with existing codebases.
  • Ansible: Agentless configuration management with a large module library; good for provisioning and deploying applications.
  • CloudFormation: AWS‑native orchestration; integrates tightly with AWS resources.

Clarifai: Abstracts infrastructure details by offering a serverless compute layer for AI models. You can deploy models on Clarifai’s shared cloud, dedicated clusters or your own VPC/on‑premises environment, all through a consistent API.

Serverless & Function Orchestrators

  • AWS Step Functions and Azure Durable Functions: Provide state machines for orchestrating serverless functions, handling retries, branching and parallelism.
  • Google Workflows: Similar to Step Functions but integrated with Google Cloud services.

These services are well‑suited for event‑driven microservices and IoT applications. Clarifai can integrate serverless functions within AI pipelines; for example, a Step Function could trigger Clarifai’s inference API.

Expert Insights & Key Statistics

  • DZone reports that 54 % of Kubernetes users adopt it for hybrid/multi‑cloud deployments, 49 % for new cloud‑native apps and 46 % for modernizing existing apps. This shows the versatility of container orchestration.
  • Survey results reveal that 75 % of developers use Kubernetes and 87 % run microservices on it. However, only 54 % of projects are mostly successful, indicating room for improvement.
  • Clarifai’s compute orchestration helps reduce compute costs by fractioning GPUs, batching requests and using spot instances; this can cut expenses by up to 90 %.
  • Fairwinds predicts that cluster consolidation, multi‑cloud strategies and tools like Karpenter will dominate orchestration by 2025.

Benefits & Use Cases of Orchestration

How Does Orchestration Deliver Value?

Scalability & Elasticity

Orchestration automatically scales services based on demand, spinning up additional instances during peak times and scaling down when idle. In container orchestrators like Kubernetes, autoscalers monitor CPU/memory and adjust the number of pods. In Clarifai’s platform, autoscaling works across clusters and regions, handling millions of inference requests per second while minimizing resource use.

Reliability & Fault Tolerance

Orchestrators provide self‑healing capabilities—if a container or service fails, the orchestrator restarts it or reroutes traffic. They manage rolling updates, handle retries and ensure overall system stability. Clarifai’s orchestration offers 99.999 % reliability, ensuring AI services stay available even during infrastructure failures.

Faster Deployment & Time to Market

CI/CD pipelines integrated with orchestration allow developers to push code frequently with confidence. Rolling updates, blue‑green deployments and canary releases ensure zero downtime. By automating deployment tasks, teams can iterate faster.

Cost Optimization & Resource Efficiency

Orchestrators allocate resources efficiently, preventing overprovisioning. Clarifai uses GPU fractioning, batching, autoscaling and spot instances to optimize costs. This means models only use GPU time when needed, significantly reducing expenses.

Multi‑Cloud & Hybrid Operations

Orchestration allows deploying workloads across multiple clouds, on‑premises data centers and edge nodes. This flexibility avoids vendor lock‑in and enables global scalability. Clarifai’s control plane can manage models across your VPC, on‑premises servers and Clarifai’s cloud.

AI/ML & Edge Use Cases

With the growing adoption of AI and IoT, orchestrating models at scale becomes critical. Clarifai’s platform lets you run models at the edge via Local Runners while maintaining central control and monitoring. This ensures low‑latency inference for applications like autonomous vehicles, retail cameras and industrial sensors.

Business Process Automation

Beyond IT, orchestration automates cross‑departmental workflows. For example, an order processing pipeline might orchestrate inventory checks, payment processing and shipping notifications, integrating with ERP and CRM systems.

Expert Insights & Data Points

  • Survey data shows that the microservices orchestration market is projected to reach USD 13.2 billion by 2034 with a 21.2 % CAGR.
  • Dynatrace reports that 63 % of organizations deploy Kubernetes for infrastructure workloads.
  • Industry opinion: Orchestration doesn’t just save money—it enhances innovation by freeing engineers from operational toil. This shift empowers teams to focus on building value.

Benefits of orchestration

Challenges, Risks & When Not to Use Orchestration

Where Does Orchestration Fall Short?

Complexity & Learning Curve

While orchestration simplifies operations, platforms like Kubernetes come with a steep learning curve. Managing clusters, writing YAML manifests and configuring RBAC can be overwhelming for small teams. Developers report that Kubernetes setup and management are resource‑intensive.

Security Risks & Misconfiguration

Misconfigured orchestration can open security holes. Without proper RBAC, network policies and vulnerability scanning, clusters become susceptible to attacks. Survey data reveals that 13 % of developers think orchestration worsens security. Tools like Clarifai include best‑practice security defaults and allow deployment into your own VPC or on‑premises environment without exposing ports.

Cost Overrun & Resource Sprawl

If not monitored, orchestration can lead to wasted resources. Idle pods, over‑provisioned nodes and persistent volumes drive up cloud bills. According to Fairwinds research, 25 % of developers find cost optimization challenging. Clarifai mitigates this by automatically adjusting compute to workload demand.

Latency & Performance Overhead

Adding orchestration layers can introduce latency. Tools need to manage scheduling and context switching. For latency‑sensitive edge applications, over‑orchestration might not be ideal.

Over‑Engineering for Small Projects

For simple monolithic applications, orchestration may be overkill. Microservices and orchestration bring many benefits, but they also introduce complexity. Reports show that not all microservice projects succeed, with only 54 % mostly successful. Evaluate whether your project truly benefits from microservices or if a simpler architecture would suffice.

Vendor Lock‑In

Choosing a proprietary orchestrator can lock you into a single provider. Look for tools supporting open standards. Clarifai addresses this by allowing customers to connect their own compute resources and avoid cloud vendor lock‑in.

Expert Insights & Cautionary Tales

  • Fairwinds survey reveals that the top challenges developers face with Kubernetes include high complexity, cost optimization and security.
  • O’Reilly’s microservices study reports that while many companies adopt microservices, only half find substantial success, underscoring the need for planning and expertise.
  • Advice: Start small. Use managed services or platforms like Clarifai to minimize complexity. Optimize gradually and avoid blindly splitting monoliths.

Best Practices & Architectural Patterns for Orchestration

How to Design Effective Orchestration Architectures

Design for Decoupling & Statelessness

Orchestration works best when services are loosely coupled and stateless. Each service should expose clear APIs and avoid storing state locally. This enables the orchestrator to scale services horizontally without coordination headaches. Use patterns like the Strangler Fig to gradually break monoliths into microservices.

Balance Orchestration & Choreography

Not every interaction needs central orchestration. Use event‑driven architecture where services can react to events independently (choreography) and apply orchestration for complex workflows requiring control. For example, use Step Functions to orchestrate a data pipeline but rely on asynchronous messaging (Kafka) for simple event flows.

Adopt CI/CD & Infrastructure as Code (IaC)

Automate everything: use CI/CD to deploy application code and IaC tools (Terraform, Pulumi) to manage infrastructure. This ensures reproducibility, easier rollbacks and fewer manual errors.

Implement Observability & Monitoring Early

Instrumentation is critical. Deploy metrics, logs and traces to understand performance. According to surveys, 65 % of organizations use Grafana, 62 % use Prometheus and 21 % use Datadog for observability. Clarifai’s platform provides monitoring and cost dashboards, allowing you to track inference usage and performance.

Automate Security & Apply Least Privilege

Enable RBAC, enforce network policies and integrate vulnerability scanning into CI/CD. Tools like OPA (Open Policy Agent) or Kyverno can enforce policies. Clarifai’s compute orchestration allows you to deploy models into your own VPC or on‑premises clusters, controlling ingress and egress ports.

Optimize Costs & Autoscaling

Set resource requests and limits appropriately, use autoscaling policies, and leverage spot instances or pre‑emptible VMs. Clarifai automatically scales compute and uses GPU fractioning and batching to minimize costs.

Document Workflows & Version Control

Use BPMN diagrams or YAML manifests to document workflows. Track changes through version control. This ensures reproducibility and collaboration.

Expert Insights & Research Highlights

  • Researchers apply long short‑term memory (LSTM) networks to predict workloads and inform autoscaling decisions in microservices.
  • Generative AI and large language models (LLMs) are being used to suggest microservice boundaries and optimize orchestration patterns.
  • Fairwinds predicts the rise of cluster consolidation and multi‑cloud orchestration tools like Karpenter.
  • Clarifai automatically handles model containerization and packing, so you focus on building models rather than managing Dockerfiles.

Case Studies & Real‑World Examples

Success Stories of Orchestration

Netflix: Microservices at Scale

Netflix famously migrated from a monolithic architecture to over 700 microservices to support its global streaming service. Kubernetes (via Titus) orchestrates containers to handle millions of concurrent streams, performing rolling updates and autoscaling effortlessly. This transformation enabled Netflix to scale globally, experiment quickly and deliver a high‑quality user experience. While Netflix built its own orchestration, many companies can replicate similar benefits by adopting tools like Kubernetes or Clarifai’s compute orchestration for AI workloads.

Uber: Rapid Feature Integration

Uber transitioned to microservices to reduce feature integration time from three days to three hours. They reorganized 2,200 services into 70 domains, creating a domain‑driven architecture that improved operational efficiency. Orchestration played a key role in coordinating these services and ensuring reliability under heavy load.

Banking & Finance

Financial institutions deploy microservices for transaction processing and risk analysis. Orchestration ensures compliance and auditability. AI models for fraud detection run in orchestrated pipelines, requiring high reliability and low latency.

Retail & E‑Commerce

E‑commerce platforms use orchestration to manage inventory, payments, recommendations and delivery logistics. AI models for image search, product tagging and customer personalization run through orchestrated workflows. Clarifai’s platform can orchestrate these models across cloud and on‑premises, optimizing cost and latency.

Cautionary Tales

  • A startup attempted to adopt microservices too early. The overhead of managing Kubernetes and service communication slowed development, leading to missed deadlines. Eventually, they returned to a monolithic service until their team matured.
  • A research organization ran a data pipeline with numerous dependencies but lacked orchestration. When one task failed, the entire pipeline broke. After adopting a workflow orchestrator (Airflow), they gained visibility into failures and improved reliability.

Expert Insights & Lessons Learned

  • Enterprises need to evaluate readiness before diving into microservices. If team size is small and the domain is stable, a monolith may suffice.
  • Case studies show that success hinges on careful planning, adoption of observability and robust deployment strategies. Merely adopting microservices without culture change leads to failure.

Emerging Trends & Future of Orchestration (2025+ Outlook)

What Innovations Are Shaping Orchestration’s Future?

AI‑Driven & Predictive Orchestration

Machine learning techniques like LSTM and Bi‑LSTM can analyze metrics and predict workloads, enabling orchestrators to scale ahead of demand. Tools such as Karpenter (AWS) and Cluster Autoscaler use predictive algorithms to manage node pools. Clarifai leverages AI to optimize inference workloads, batching requests and scaling clusters efficiently.

Edge & IoT Orchestration

As IoT devices proliferate, orchestrating workloads at the edge becomes crucial. 5G and AI chips enable real‑time processing on devices. Orchestrators must manage remote updates, handle intermittent connectivity and ensure security. Local Runners from Clarifai demonstrate how to run models at the edge while maintaining centralized control.

Multi‑Cloud & Hybrid Orchestration

Organizations increasingly spread workloads across multiple clouds to avoid vendor lock‑in and increase resilience. Tools like Crossplane and Rafay manage multi‑cluster deployments. Clarifai’s orchestration supports multi‑cloud by enabling models to run on Clarifai’s cloud, dedicated clusters or customer VPCs.

Serverless & Function Orchestration

Serverless architectures reduce operational overhead and cost. Future orchestrators will blend container and function orchestration, enabling developers to choose the best compute paradigm for each task.

Generative AI & LLM‑Assisted Design

Generative AI can analyze code and traffic patterns to suggest microservice boundaries, security policies and resource allocation. Imagine an orchestrator that recommends splitting a service into two based on usage or suggests adding a circuit breaker pattern. Clarifai’s AI expertise positions it well to integrate such features into its platform.

Observability & FinOps Evolution

Observability tools will use AI to detect anomalies, foresee capacity bottlenecks and recommend cost savings. FinOps practices will become integral, with orchestrators providing cost dashboards and optimization hints. Clarifai’s cost monitoring helps users track compute spending and efficiency.

Security & Compliance

With increasing threats, zero‑trust architectures, policy‑as‑code and supply chain security will be standard. Orchestrators will integrate scanning and policy engines into the workflow.

Expert Insights & Research Trends

  • Market analysts forecast significant growth for AI‑driven orchestration and edge computing solutions.
  • Fairwinds notes that cluster consolidation and multi‑cloud strategies will drive orchestration adoption.
  • MDPI review highlights research into AI methods for optimizing microservices design and orchestration.

Future of orchestration

Getting Started with Orchestration—Skills, Steps & Resources

What Skills Are Required?

  • Fundamental knowledge of distributed systems: Understand concurrency, networking, service discovery and fault tolerance.
  • Containerization basics: Learn Docker and how to build container images.
  • Programming languages & APIs: Proficiency in languages like Python, Go or Java; familiarity with REST APIs.
  • Infrastructure & Networking: Learn about VPCs, subnets, load balancers and DNS.
  • CI/CD & IaC: Experience with pipelines (Jenkins, GitHub Actions) and IaC tools.
  • Security concepts: Understand RBAC, TLS, secrets management and policy enforcement.

Step‑by‑Step Guide to Implementing Orchestration

  1. Set Up Docker: Install Docker and run a simple container (e.g., Nginx). Create your own container image for a small app.
  2. Deploy to Kubernetes (or Clarifai):
    • Install a local Kubernetes cluster (e.g., minikube) or use a managed service (EKS, GKE).
    • Write a deployment manifest for your container and deploy it. Observe how pods scale and restart.
    • Alternatively, sign up for Clarifai’s platform, upload a model, and run it on shared compute. Clarifai handles containerization and scaling for you.
  3. Define a Workflow: Use Airflow or Dagster to build a simple DAG (e.g., ETL pipeline). Configure dependencies and schedules.
  4. Add Observability: Integrate Prometheus and Grafana or use Clarifai’s built‑in monitoring to track metrics.
  5. Secure & Optimize: Apply RBAC, secrets management and resource limits. Experiment with autoscaling parameters.
  6. Scale to Production: Evaluate multi‑cloud deployment, high availability and backup strategies. Consider using Clarifai for AI workloads to reduce operational burden and access features like GPU fractioning.

Tips for Small Teams

  • Use managed services: For container orchestration, choose a managed Kubernetes (GKE, EKS, AKS) or a specialized AI platform like Clarifai. This reduces operational overhead.
  • Start simple: Begin with a monolith and gradually break off services. Introduce orchestration only where needed.
  • Invest in training: Encourage team members to take Kubernetes and cloud certifications (CKA, CKAD). Clarifai offers documentation and tutorials tailored to AI deployment.
  • Join communities: Engage with open‑source communities (CNCF, Kubernetes Slack) and attend webinars to stay updated.

Clarifai Product Integration – Compute Orchestration & Local Runners

Clarifai offers a compute orchestration platform designed specifically for AI/ML workloads. Here’s how it integrates naturally into your orchestration journey:

  • Unified Control Plane: Manage your AI compute, costs and performance through a single portal. This control plane abstracts underlying Kubernetes complexity and lets you run models on shared or dedicated hardware.
  • Flexible Deployment Options: Deploy models on Clarifai’s cloud, your VPC, or on‑premises clusters. Options include shared SaaS, dedicated SaaS, self‑managed VPC, on‑premises, multi‑site, and full platform deployment.
  • Cost Optimization Features: Clarifai leverages GPU fractioning, batching, autoscaling, and spot instances to reduce compute costs.
  • Local Runners: Run models locally on workstations or servers and expose them via Clarifai’s API. This allows low‑latency inference without sending data to the cloud.
  • Model Management & Packaging: Clarifai handles containerization, model packing and dependency management, so you can focus on building models.
  • Monitoring & Analytics: The platform provides dashboards to monitor inference requests, compute usage and costs, ensuring transparency.
  • Enterprise-Grade Security: Deploy models into your own VPC or on‑premises clusters without exposing ports; Clarifai adheres to security best practices.

By incorporating Clarifai into your orchestration strategy, you gain the benefits of Kubernetes and other orchestrators while leveraging specialized AI optimization and cost control.

Clarifai Compute Orchestration

Frequently Asked Questions

Q1: What is the difference between orchestration and automation?
 A: Automation executes repetitive tasks automatically (e.g., backing up a database), whereas orchestration coordinates multiple automated tasks, making decisions based on dependencies and system state. Orchestration involves scheduling, scaling, error handling and complex workflows.

Q2: Do I always need orchestration for microservices?
 A: Not necessarily. Small microservice systems can use event‑driven communication without central orchestration. As complexity grows—hundreds of services, multi‑cloud deployments, compliance requirements—an orchestrator becomes essential for reliability and visibility.

Q3: How does Clarifai’s orchestration differ from Kubernetes?
 A: Clarifai builds on Kubernetes to provide a unified control plane for AI workloads. It hides Kubernetes complexity, automatically handles containerization and scaling, and optimizes GPU/TPU usage. It also offers specialized features like Local Runners and AI cost dashboards.

Q4: Can I use Clarifai’s local runners without internet access?
 A: Yes. Local Runners let you run models on local machines or private clusters and expose them via Clarifai’s API. They operate offline and sync results when connectivity is restored.

Q5: Which orchestrator should I choose for data pipelines?
 A: For data pipelines, consider Airflow, Dagster, Argo Workflows or Prefect. If your pipelines involve AI/ML models, Clarifai can orchestrate model inference alongside data processing, providing cost optimization and multi‑cloud deployment.

Q6: What are the upcoming trends in orchestration?
 A: Expect AI‑driven scaling, edge & IoT orchestration, multi‑cloud strategies, serverless function orchestration, generative AI assisting design, FinOps integration, and enhanced security.

Conclusion: Orchestrating the Future

Orchestration is more than just a buzzword—it’s the backbone of modern computing, enabling organizations to deliver reliable, scalable and cost‑effective services. By automating coordination across containers, microservices, workflows and infrastructure, orchestration unlocks agility and innovation. However, it also demands careful planning, security and observability. Platforms like Clarifai’s compute orchestration combine best‑in‑class orchestration with AI‑specific optimizations, making it easier for businesses to deploy and run AI workloads anywhere. As the future brings AI‑driven orchestration, edge computing and generative design, embracing orchestration today ensures your systems are ready for tomorrow’s challenges.

 



Pick up this battery-powered Ring doorbell while it’s down to $80 ahead of Prime Day

Posted on by faz_business


If you’ve been considering a video doorbell for your front door, Prime Day deals may have just what you’re looking for at a good price. A great deal already available is on the latest Ring Battery Doorbell Plus, which is 47 percent off and down to only $80.

The Battery Doorbell Plus offers a 150-by-150-degree “head to toe” field of vision and 1536p high-resolution video. This makes it a lot easier to see boxes dropped off at your front door since it doesn’t cut off the bottom of the image like a lot of video doorbells.

Image for the large product module

Ring

Pick one up now for almost half off ahead of Prime Day.

$80 at Amazon

This model features motion detection, privacy zones, color night vision and Live View with two-way talk, among other features. Installation is a breeze since you don’t have to hardwire it to your existing doorbell wiring. Most users report that the battery lasts between several weeks and several months depending on how users set up the video doorbell, with power-heavy features like motion detection consuming more battery life.

With most video doorbells today, you need a subscription to get the most out of them, and Ring is no exception. Features like package alerts require a Ring Home plan, with tiers ranging from Basic for $5 per month to Premium for $20 per month. You’ll also need a plan to store your video event history.

Ring was acquired by Amazon in 2018, and now offers a full suite of home security products including outdoor cameras, home alarm systems and more. This deal is part of a larger sale on Ring and Blink devices leading up to Prime Day.

TechCrunch Mobility: Self-driving trucks startup Kodiak goes public and a shake-up at Hyundai’s Supernal

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Welcome back to TechCrunch Mobility — your central hub for news and insights on the future of transportation. To get this in your inbox, sign up here for free — just click TechCrunch Mobility!

The autonomous vehicle industry is years — maybe decades — from maturing. And so there’s still a Wild West quality to the sector, in spite of the steady stream of announcements that do show marked progress. Two such news items from this week illustrate my point of progress, possibility, and even a bit of peril (at least to the ups and downs a public market can provide).

First up is Gatik, an AV and logistics startup that is applying its tech to middle-mile trucks. The startup, which I first wrote about in 2019, announced a multi-year and expanded commercial partnership with Canada’s largest retailer, Loblaw. Under the deal, Gatik will deploy 20 autonomous trucks by the end of 2025 to provide driverless delivery to Loblaw’s network of stores in the greater Toronto area. Co-founder and CEO Gautam Narang told me the company will add another 30 autonomous trucks to the fleet by the end of 2026.

The deal is notable, and not just because of the fleet size. As Narang explained to me, the trucks will be handling the full regional network for Loblaw. This means these third-generation AV trucks will operate autonomously to pick up products from two distribution centers and make deliveries to over 300 retail stores. “These are multiple brands within the Loblaw umbrella,” he said. 

In other words, this is not some fixed-route pilot program. It’s commercial, and it’s complex.

Next up is Kodiak Robotics, another startup I have reported on since its founding. The company, which is developing self-driving trucks for highway, industrial, and defense uses, began trading on Nasdaq this week under the tickers KDK and KDKRW. 

The company, which is now called Kodiak AI, went public via a merger with special-purpose acquisition company Ares Acquisition Corporation II, an affiliate of Ares Management. The deal valued the startup at about $2.5 billion. 

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Kodiak raised $275 million in financing. More than $212.5 million came from certain institutional investors, including $145 million in PIPE funding and about $62.9 million in trust cash from Ares. It should be noted that the trust cash is smaller (it was $562 million), as some SPAC investors redeemed their shares. 

I spoke to founder and CEO Don Burnette the day before Kodiak’s big debut about why he took the company public — let alone via a SPAC. It was a big moment for Burnette, whose family was on hand to watch him ring the bell and mark the milestone. The stock was trading at about $7.70 Friday, down about 10% from its market open.

“As you can imagine, building and scaling a transformative autonomous driving company is very capital intensive, and we were looking to access the public markets as a path forward for the company. And when choosing between, you know, traditional IPO or a SPAC, we considered all the options,” he said. “We felt like, from a timing perspective, it was the right decision for the company (to take the SPAC route).”

It should be noted that Burnette is also quite bullish on defense. Here’s why:

“I think autonomy is the future of ground transportation broadly,” he said, before noting the benefits within defense for logistics and reconnaissance operations for ground vehicles. “One of the key things is defense requires unstructured autonomy, and this is one of the areas where we become specialists.”

A little bird

blinky cat bird green
Image Credits:Bryce Durbin

A few weeks ago, we wrote about some trouble at Hyundai‘s electric air taxi startup Supernal, including that the company had stopped work on its air taxi program and that its CEO and CTO were out. 

This week, a little bird told us that a wider reorg of Supernal’s C-suite was afoot — something Hyundai Motor Group has now confirmed to us.

Chief strategy officer Jaeyong Song and chief safety officer Tracy Lamb are part of a “transition to new leadership,” according to the Korean conglomerate. Song’s departure is particularly notable, as he was once the VP of Hyundai’s Advanced Air Mobility division, which Supernal was spun out of in 2021. Also gone is Lina Yang, who most recently served as chief of staff to the startup’s now-former CEO, but who also served as Supernal’s “Head of Intelligent Systems” before that.

Got a tip for us? Email Kirsten Korosec at kirsten.korosec@techcrunch.com or my Signal at kkorosec.07, or email Sean O’Kane at sean.okane@techcrunch.com.

Deals!

money the station
Image Credits:Bryce Durbin

Remember Moxion Power, the portable battery startup that raised $110 million before going bankrupt? The founders are back with a new startup called Anode Technology Company, which has designed a mobile battery and inverter that can be used for EV charging and supplying remote power to construction sites and live events. The startup just raised $9 million in seed funding in a round led by Eclipse Ventures; its partner, Jiten Behl, who spearheaded the deal, was previously Rivian’s chief growth officer. Apparently, Behl’s interest was sparked by his experience at Rivian. 

Side note: Palo Alto-based venture capital firm Eclipse sure has been busy this year. The VC firm led the $105 million round of Also, the micromobility startup that spun out of Rivian, and recently hired longtime T. Rowe Price Group investor Joe Fath as partner and head of growth. 

The firm doesn’t explicitly focus on transportation, but some of its portfolio companies in this sector include Arc, Bedrock Robotics, Reliable Robotics, Skyryse, and Wayve.

Other deals that got my attention …

Rapido, a popular ride-hailing platform in India that competes with Uber, doubled its valuation to $2.3 billion following a secondary share sale by food delivery giant Swiggy. The share sale comes just weeks after Rapido began piloting food deliveries, edging into Swiggy’s core territory.

Telo, the tiny electric truck developer, raised $20 million in a Series A funding round co-led by designer and Telo co-founder Yves Béhar and Tesla co-founder Marc Tarpenning, who is on Telo’s board. Additional investment came from Salesforce CEO Marc Benioff and early-stage funds like TO VC, E12 Ventures, and Neo.

TheTrump administration is seeking up to a 10% stake in Lithium Americas in exchange for renegotiating the repayment period of a $2.26 billion Department of Energy loan. GM is a major investor in the Canadian company, which is developing a lithium mine in Nevada that is expected to be the largest in the Western Hemisphere.

Notable reads and other tidbits

Image Credits:Bryce Durbin

Hackers have had quite an active week in the transportation sector. Stellantis confirmed a data breach involving customers’ personal information. The breach is linked to a hack of its Salesforce database. Meanwhile, a hack that began last Friday and targeted check-in systems provided by Collins Aerospace caused delays at Brussels, Berlin, and Dublin airports, as well as London’s Heathrow. The U.K.’s National Crime Agency has arrested a man in connection to the ransomware attack. And finally, Jaguar Land Rover said it will not resume production at its factories for yet another week as it continues to grapple with fallout from a cyberattack.  

Battery materials startup Sila started operations at its facility in Moses Lake, Washington, a milestone that could pave the way for longer-range, faster-charging EVs. The factory is the first large-scale silicon anode factory in the West and will initially be capable of making enough battery materials for 20,000 to 50,000 EVs. Future expansion could fulfill demand for as many as 2.5 million vehicles.

Automakers continue to pull back on EVs and electrified vehicles. Honda is ending U.S. production of its Acura ZDX electric vehicle that was being built by General Motors in Tennessee, CNBC reported. And Stellantis has canceled plans to produce a 4xe plug-in hybrid Jeep Gladiator in North America by the end of 2025. Which EV is next on the chopping block?

The National Highway Traffic Safety Administration opened an investigation into Rivian over issues with the seat belts in its electric delivery vans that could introduce additional risk in the event of a crash, Bloomberg reported.

Tesla asked the Environmental Protection Agency not to roll back current vehicle emissions standards, breaking from other major automakers that want to see the rules eased. 

TuneIn, an audio streaming service, is collaborating with the Federal Emergency Management Agency to deliver emergency alerts directly to drivers. 

Volvo Cars is pledging a commitment to U.S. production. The company said it will continue to invest in its U.S. car plant near Charleston, South Carolina, and announced plans to expand the factory to produce a hybrid vehicle by the end of the decade.

Waymo launched “Waymo for Business,” a new service designed for companies to set up accounts so their employees can access robotaxis in cities like Los Angeles, Phoenix, and San Francisco.

Zoox has asked federal regulators for an exemption that would allow the Amazon-owned autonomous vehicle company to commercially deploy its custom-built robotaxis, which lack traditional controls like pedals and a steering wheel.

One more thing

Finally, proof of life from Luminar founder Austin Russell. 

You may remember that Russell was mysteriously and suddenly replaced in May as CEO of the lidar company he created. The company has never truly explained his departure, only that it was the result of a “code of business conduct and ethics inquiry” initiated by the board.

Russell has been silent; while he remains on Luminar’s board, he hasn’t signed any of the filings the company has submitted with the U.S. Securities and Exchange Commission since he was replaced. This week, he reappeared as the co-founder of a new company called Russell AI Labs. It’s billed as a “platform that backs and builds transformative AI and frontier technology companies.”

It doesn’t seem like his troubles at Luminar have affected his ability to attract high-profile support or make eyebrow-raising deals. Russell’s co-founders are Markus Schäfer, CTO and board member at Mercedes-Benz Group AG, and Murtaza Ahmed, who served as a managing director at Goldman Sachs before joining SoftBank and was a partner in the $100 billion Vision Fund and managing partner of its $5 billion Latin America Fund.

As part of Russell AI Lab’s debut, the startup announced it has taken a $300 million stake in agentic AI company Emergence AI. 

Sunday Night Football: How to Watch Packers vs. Cowboys Tonight

Posted on by faz_business


When to watch the Green Bay Packers vs. Dallas Cowboys

  • Sunday, Sept. 28, at 8:20 p.m. ET (5:20 p.m. PT).

Where to watch 

  • The Packers-Cowboys game will be broadcast nationally on NBC and stream on Peacock.

Tonight marks Micah Parsons’ return to Dallas. After the shocking trade on the eve of the season that shipped the star defensive end to Green Bay, Parsons returns to Jerry World wearing green and gold. 

Without Parsons, the Dallas defense has been one of the worst in the league through three games. The Cowboys are one of only six teams giving up more than 30 points per game. With Parsons, the Packers defense is giving up a league-low 14.7 points a game. 

The Packers-Cowboys Sunday Night Football game starts tonight at 8:20 p.m. ET (5:20 p.m. PT). The game is available to watch nationally on NBC or stream on Peacock.

Micah Parsons #1 of the Green Bay Packers looks on prior to an NFL football game against the Detroit Lions at Lambeau Field on September 7, 2025 in Green Bay, Wisconsin.

Micah Parsons makes his return to Dallas on Sunday night as a Green Bay Packer.

Todd Rosenberg/Getty Images

How to watch Packers vs. Cowboys

You can watch this game on your local NBC station with a cable or satellite TV subscription or with an over-the-air antenna. Most live TV streaming services, such as YouTube TV and Hulu Plus Live TV, also carry your local NBC station (see below). 

If you don’t subscribe to a TV service with NBC and want to watch the game tonight, you can sign up for Peacock Premium for $11 per month to watch Sunday Night Football. 

You can also subscribe to NFL Plus, the NFL’s streaming service at $7 per month, but streams are limited to just watching on a phone or tablet (not a TV).

Peacock/CNET

With Peacock’s $11-per-month Premium plan, you can watch tonight’s game and every Sunday Night Football game this season. Read our Peacock review.

DirecTV Stream/CNET

Sarah Tew/CNET

YouTube TV costs $83 a month and includes NBC and the rest of the channels you need to watch NFL games week in and week out. Right now, the first two months are discounted to $50 a month, and there is a free 21-day trial. Plug in your ZIP code on YouTube TV’s welcome page to see which local networks are available in your area. Read our YouTube TV review.

Sarah Tew/CNET

Hulu Plus Live TV costs $83 a month and includes NBC in most markets. On its live news page, you can enter your ZIP code under the “Can I watch local news in my area?” question at the bottom of the page to see which local channels you get. Read our Hulu Plus Live TV review.

Fubo/CNET

Fubo’s Essential plan costs $85 a month and includes NBC. Click here to see which local channels you get. 

Fubo recently introduced a $56-per-month skinny bundle for sports fans that includes the other channels that show NFL games — ABC, CBS, Fox and ESPN — but it does not include NBC. Read our Fubo review.

Sling/CNET

All the live TV streaming services above allow you to cancel anytime and require a solid internet connection. Looking for more information? Check out our live TV streaming services guide.



Stario: Haven Tower is yet another vertical city-builder, but this one has magic, space whales and flying delivery turtles

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Stario: Haven Tower isn’t the first vertical city-builder I’ve seen, or even the first one I’ve seen this year. But it is the first I’ve encountered that also features floating space whales, which immediately makes it the one I’m most interested in playing. Does this demonstrate how badly the Internet has affected my attention span? Well, I’ll have you know that—ooh, a squirrel!

Developed by Chinese outfit Stargate Games, Stario: Haven Tower tasks you with constructing a literal towering civilization. Through “six atmospheric layers”, your metropolitan column will rise from a sandy, lifeless wilderness all the way up to a painterly cosmos.

How OpenAI and Microsoft’s New Pact Unlocks the Path to AGI

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OpenAI is one step closer to a radical transformation. This week, the company signed a “memorandum of understanding” with its biggest backer Microsoft, clearing a major hurdle in the AI lab’s plan to become a for-profit company. Continue reading “How OpenAI and Microsoft’s New Pact Unlocks the Path to AGI”