Apple’s latest developer betas for iOS 27 and macOS 27 are quietly adding fuel to long-running rumours about two of its most anticipated future devices.

Nothing is officially confirmed, but the code and system changes in the first betas are starting to look less like general platform tweaks. Instead, they look more like support work for new hardware form factors.

Starting with the iPhone Fold, references spotted in iOS 27 include terms like “foldState”, “angleDegrees” and multiple display identifiers.

Now you can ask a different chatbot which restaurant to try.

Adani and Jabil are teaming up to make AI hardware in India.

The Adani Group, India’s infrastructure-and-energy conglomerate, and Jabil, the US contract manufacturer, said on Monday they intend to form a strategic alliance to build a vertically integrated AI and data-centre hardware platform in the country. They put no number on it, and the agreement is not yet signed.

What they want to make is the physical guts of an AI data centre. The plan is multi-gigawatt capacity for high-density, liquid-cooled AI racks, servers, storage and networking, plus the power and cooling gear that surrounds them: distribution and coolant units, transformers, switchgear and thermal systems.

The pitch is a single, end-to-end source, from design to deployment. Jabil brings 60 years of manufacturing and, after recent acquisitions, power and thermal expertise; Adani brings infrastructure, green energy, logistics and its own fast-growing data-centre operations.

Why Adani and Jabil are betting on India

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The demand case is a sovereignty case. India’s data-centre capacity is forecast to reach 5 to 8 gigawatts by 2030, hyperscalers have lined up more than $50bn in spending, and the country’s data-protection law and data-localisation push are nudging buyers toward hardware made at home.

A new tax holiday for data centres, running to 2047, sweetens the export maths further.

For Adani, the alliance slots into a vast existing bet: a $100bn commitment to develop 5 gigawatts of green-powered, AI-ready data centres by 2035. Making the racks and power gear domestically, rather than importing them, lets it capture more of that build-out and, in theory, sell the surplus abroad.

Gautam Adani framed it in epochal terms, calling AI an “Intelligence Revolution” and arguing India must be “a creator, builder, and exporter of intelligence,” not just a consumer.

Make in India, for AI

The deal is one piece of a much larger surge. India has now attracted more than $200bn in AI-infrastructure commitments, led by a $110bn pledge from Reliance, with tens of billions more from Google, Microsoft and Amazon; only last week Meta signed its first Indian data-centre deal, with Reliance.

The country is trying to convert its position as a huge AI consumer into a place that builds the kit, too, the same sovereignty instinct now driving its push for homegrown models.

The caution is that this is, so far, a press release. There is no disclosed investment, no binding contract, and the companies say they are still negotiating the “definitive operational frameworks.” Their own filing warns the alliance may never be finalised, and the headline-grabbing “$3 trillion market” is their framing of the opportunity, not a commitment.

The ambition is real and well-timed; whether it becomes gigawatts of Indian-made AI racks, or stays a signing-day vision, depends on what gets funded and signed next.

Apple’s beta testing routine for the current-gen operating systems continues, with the second developer builds of iOS 26.6, iPadOS 26.6, watchOS 26.6, tvOS 26.6, visionOS 26.6, and macOS Tahoe 26.6 out now.

The second developer builds arrive after the first, which landed on May 26.

While usually we deal with only one set of betas, sometimes we have to manage two of them. Following the WWDC keynote, Apple has introduced developer betas of its 27-generation operating systems, including iOS 27 and macOS 27.

Apple will continue to update the 26-generation operating systems as usual, complete with beta rounds running close to the fall release of the 27 generation.

• iOS 26.6 build 2 is 23G5043d, replacing 23G5028e
• iPadOS 26.6 build 2 is 23G5043d, replacing 23G5028e
• watchOS 26.6 build 2 is 23U5040d, replacing 23U5025e
• visionOS 26.6 build 2 is 23O5743c, replacing 23O5728e
• tvOS 26.6 build 2 is 23L5744d, replacing 23L5729e
• macOS Tahoe 26.6 build 2 is 25G5043d, replacing 25G5028f
• HomePod Software 26.6 build 2 is 23L5744d, replacing 23L5729e

At the same time, Apple has also brought out two more release candidates:

• macOS 15.7.8 RC 2 is 24G809
• macOS 14.8.8 RC 2 is 23J607

Generally speaking, when there are two developer beta tracks, the next-generation version will include the feature changes, while the current-gen track tends to be more muted.

Apple is keen to keep the features for the new versions. The current-gen beta updates are usually performance and security-focused.

The first iOS 26.6 beta build included a new feature for Contacts that notifies if users reach the maximum of 20,000 blocked listings. There was also a security fix for Apple Maps.

AppleInsider and Apple strongly recommend that users avoid installing beta operating systems or beta software onto “mission-critical” or primary-use hardware, due to the potential for issues and data loss. Instead, they should retain backups of their data and try to use secondary hardware that isn’t as essential to maintain.

For users wanting a less risky experience, Apple usually brings out a public beta version shortly after the developer counterpart. It is a more battle-hardened version of the update, with typically fewer issues than the developer builds.

Find any changes in the new builds? Reach out to us on Twitter at @AppleInsider or @Andrew_OSU, or send Andrew an email at [email protected].

AI coding agents are rapidly accelerating data engineering by generating transformations, pipelines, orchestration workflows, validation tests, and infrastructure configurations from prompts.

However, enterprise data platforms have long operated across fragmented systems owned by different teams and built on different technologies. As these systems evolve independently, organizations increasingly struggle with inconsistent business logic, duplicated implementations, difficult downstream impact analysis, and hidden dependencies across the platform.

The rise of vibe coding can further amplify these problems as more operational context, architectural decisions, and business knowledge become scattered across prompts, conversations, generated code, and disconnected workflows rather than becoming part of the system itself.

Spec-driven development (SDD) is emerging as one approach to address this challenge. In SDD, prompts, business rules, validation logic, orchestration behavior, and implementation workflows are converted into executable and versioned specifications that become part of the system itself. These specifications act as persistent operational memory for both humans and AI agents, allowing systems to evolve more consistently across releases, teams, and AI-assisted workflows.

Because enterprise data engineering already relies heavily on reusable patterns, metadata-driven pipelines, and standardized operational workflows, it is especially well-suited for SDD. By combining AI-assisted generation with deterministic and reusable system contracts, SDD may provide a new operational layer for reducing fragmentation and improving long-term coordination across increasingly AI-generated data platforms.

Vibe coding alone lacks persistent system memory

Vibe coding works remarkably well for generating isolated implementations quickly. But prompts are inherently temporary. They capture an engineer’s assumptions, business context, implementation logic, and system knowledge only for that specific conversation and moment in time.

In practice, making AI-generated systems work often requires far more than a simple prompt. Engineers continuously provide background information, architectural decisions, business rules, schema assumptions, downstream dependencies, operational constraints, debugging history, and implementation guidance throughout the development process.

These contexts become the real operational knowledge behind AI-assisted development.

However, in most vibe coding workflows, this information remains scattered across prompts, conversations, Jira tickets, documentation, chat history, generated code, and disconnected workflows rather than becoming part of the system itself.

This creates a major problem for enterprise data engineering because modern data platforms are naturally fragmented across many interconnected systems, including ingestion pipelines, warehouses, orchestration frameworks, semantic layers, APIs, dashboards, and machine learning (ML) systems. As more logic and context become embedded inside prompts and generated implementations, organizations gradually lose visibility into:

Over time, the system itself no longer contains the full reasoning behind how it was built. Critical business context, architectural assumptions, and operational knowledge still largely exist inside human judgement and scattered conversations rather than inside the platform itself.

Vibe coding makes implementation significantly faster, but from a system perspective, overall engineering efficiency does not improve proportionally because much of the development lifecycle still depends on human validation, domain knowledge, coordination, and decision-making.

More importantly, prompts are not naturally iterable engineering artifacts. Enterprise systems continuously evolve across releases, schema changes, business logic updates, and downstream dependencies. Teams repeatedly revisit and refine systems over time, but prompts are optimized for fast local generation rather than system long-term evolution.

They are difficult to:

Even the same prompt may not reliably generate the same implementation with different context in the future.

This is where SDD begins to move to the center of AI-assisted data engineering. Instead of leaving operational knowledge scattered across prompts and conversations, SDD integrates business context, validation logic, transformation behavior, orchestration requirements, and implementation workflows directly into executable specifications that become part of the system itself.

The system now has persistent memory about how it was designed, why certain decisions were made, and how different components are connected across the platform. This allows teams and AI agents to iterate systems more reliably over time while reducing fragmentation across increasingly distributed data environments.

Spec-driven development turns prompts into system memory

In SDD, systems are built around executable specifications rather than loosely coordinated prompts and implementations alone. Instead of treating specifications as passive documentation written after development, SDD treats them as operational contracts that directly drive code generation, validation, testing, orchestration, and deployment workflows.

In many ways, SDD extends ideas from Infrastructure-as-Code and GitOps into AI-assisted engineering. Specifications combine declarative system definitions with executable implementation workflows. The declarative layer provides system context, schemas, dependencies, constraints, and operational requirements, while workflow-oriented instructions guide AI agents on how to implement and evolve the system consistently.

Once these contexts, rules, and implementation patterns are converted into persistent and versioned contracts stored in repositories and integrated into CI/CD workflows, the system becomes significantly more iterable and governable over time. These specifications effectively become long-term system memory for both humans and AI agents, allowing systems to evolve consistently across releases, teams, and increasingly AI-assisted development workflows.

In practice, the structure of specifications largely depends on the type of systems and workflows being implemented. However, spec-driven systems often begin with a foundational “constitution” that defines project-wide principles and constraints that should remain consistent across the platform, such as technology standards, naming conventions, architectural rules, governance policies, and core system requirements. On top of this foundation, multiple layers of specifications serve different operational purposes across the development lifecycle:

• schema specifications define structural compatibility

• transformation specifications define business logic

• validation specifications define quality rules

• orchestration specifications define execution behavior

• semantic specifications define shared business definitions

• AI workflow specifications define reusable implementation instructions for coding agents

A simplified specification might look like this:

pipeline_spec:

  source:

    system: mysql

    table: order

  transformation:

    logic:

      – load_strategy: scd2

  target:

    platform: snowflake

    table: dim_order

  validation:

    primary_key: order_id

Additional workflow files can then provide reusable implementation instructions for coding agents:

1. Generate Python ingestion code for Salesforce customer data.

2. Generate DBT models implementing Type 2 SCD logic.

3. Generate Airflow workflows for hourly execution.

4. Generate validation tests for downstream compatibility.

These specification documents are often maintained as markdown-based operational artifacts generated and refined through AI-assisted workflows. Engineers can iteratively update the specifications, provide additional business context, and collaborate with coding agents to improve implementation logic, workflows, and prompt instructions over time. Compared to traditional documentation processes, AI-assisted specification generation is significantly faster and more adaptive.

The important shift is not simply better documentation. Specifications become reusable operational context that allows systems to evolve consistently across releases, teams, and AI-assisted workflows. Architectural intent, business assumptions, and implementation logic no longer disappear into temporary prompts and disconnected implementations, but instead become persistent system knowledge integrated directly into the development lifecycle.

Why spec-driven development specifically fits data engineering

SDD can theoretically be applied across many areas of software engineering, but data engineering is especially well-suited for this model because of the nature of modern data platforms.

Enterprise data systems naturally span many interconnected technologies and layers, including transactional systems, ingestion frameworks, streaming platforms, warehouses, orchestration systems, semantic layers, APIs, dashboards, and ML pipelines. Data engineers regularly work across long technology stacks and distributed systems where a single upstream change can impact many downstream consumers.

Enterprise data platforms also support many different teams and applications across fragmented environments. As systems evolve independently, understanding the full downstream impact of an upstream schema or business logic change becomes increasingly difficult. A seemingly small modification can silently break downstream pipelines, dashboards, APIs, semantic models, or machine learning workflows across the platform.

SDD can address this fragmentation by introducing shared and versioned operational contracts across systems. Because schemas, dependencies, validation rules, transformation logic, and orchestration behavior are explicitly defined within specifications, teams and AI agents gain much better visibility into how systems are connected and how changes propagate across the platform.

Additionally, the goal of data engineering is not simply delivering pipelines quickly. Teams must also optimize for system stability, scalability, consistency, maintainability, operational reliability, and infrastructure cost.

This requires significant system and solution design work from engineers. Teams must define tech stack, create schemas, transformation patterns, orchestration behavior, validation rules, storage strategies, and downstream compatibility requirements carefully across the platform.

However, once these architectural and operational patterns are established, much of the implementation work becomes highly repetitive and standardized.

For example, after defining a reusable ingestion and transformation pattern for Salesforce customer data, onboarding a new table may only require adding another table definition into the specification, while the remaining implementation can be generated automatically through existing specifications and workflows that follow the same operational pattern:

source:

  system: salesforce

  tables:

    – customer

    – order

    – product

From this specification alone, coding agents could generate new data pipelines following the same governed implementation pattern across the platform. This combination of human-driven architectural design and highly repeatable implementation workflows makes data engineering particularly suitable for SDD.

In many ways, data engineering has always been moving toward higher levels of automation, from ETL frameworks and metadata-driven pipelines to IaC and declarative orchestration systems. SDD represents another step in that evolution by combining prompt-based AI generation with deterministic and versioned operational contracts.

Instead of relying entirely on temporary conversational prompts or rigid template systems, SDD introduces a middle layer where reusable specifications provide structure, coordination, validation, and persistent system memory for AI-assisted development.

How SDD changes AI-assisted data engineering

SDD introduces a much higher level of automation into enterprise data engineering while also helping reduce the fragmentation problems that modern data platforms increasingly face.

Because schemas, business rules, transformation behavior, orchestration requirements, validation logic, and downstream dependencies are explicitly defined inside reusable specifications, coding agents can generate and evolve large portions of the implementation consistently across the platform. Instead of repeatedly rebuilding pipelines and workflows from temporary prompts and disconnected context, teams can iterate systems through shared operational contracts and reusable implementation patterns.

This significantly improves consistency, traceability, and coordination across distributed environments. Schema evolution becomes easier to manage, downstream impact becomes more visible, and systems can evolve incrementally instead of through disconnected generations of implementations.

At the same time, human engineers still remain essential in the development lifecycle. While AI agents can automate large portions of implementation work, human judgement is still critical for defining business logic, designing architectures, managing tradeoffs, validating correctness, and coordinating system evolution across organizations.

As more implementation work becomes AI-generated, the role of data engineering also begins shifting. Engineers spend less time writing repetitive pipelines and orchestration logic, and more time defining specifications, designing reusable operational patterns, managing validation rules, and coordinating business context across systems.

This may also gradually reduce some of the traditional boundaries between different data engineering teams. Because implementation becomes increasingly standardized and AI-assisted through shared specifications, organizations may rely less on highly siloed platform-specific implementation teams and more on shared operational contracts and reusable system patterns.

Ultimately, SDD shifts data engineering toward a more specification-oriented and system-oriented model where humans focus on intent, architecture, and business coordination, while AI agents increasingly handle implementation, testing, and operational generation at scale.

Shuhua Xu is a lead data engineer.

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Laser engraving can be incredibly versatile. You can engrave designs on metal or wood and gift them to your loved ones or sell them as a business. But there has always been a catch. If you want to work with different materials like metal, wood, glass, acrylic, or crystal, you’ll often need multiple machines, each designed for a specific job. This can quickly multiply the costs and make engraving an expensive hobby. Well, that’s exactly the problem the Creality Falcon T1 plans to solve. It’s a 5-in-1 laser workstation that lets you swap between five different laser modules in a single desktop machine.

How Does This Work?

The main selling point of the Falcon T1 is its modular design. Instead of buying separate machines for different materials, users can swap between five laser modules in about 15 seconds without tools.

Each module is designed for a specific type of work. The 20W Fiber Laser is intended for deep engraving on materials like stainless steel, aluminum, and hardwood. If you’re working primarily with metals and need things like color marking or deeper engravings, the 60W MOPA Laser is designed for materials such as titanium, gold, silver, brass, and copper.

For more traditional maker projects, the 20W and 40W Diode Lasers can cut and engrave wood, acrylic, MDF, leather, ceramics, and bamboo. Meanwhile, the 5W UV Laser focuses on transparent materials such as glass, crystal, and acrylic, opening up possibilities that standard diode lasers typically struggle with.

In practical terms, this means you could engrave a custom design on a metal nameplate and switch modules, then cut a wooden display stand for it with the same machine. According to Creality, building a similar setup using dedicated machines could easily cost over $20,000, whereas the Falcon T1 starts at $2,249.

Finally, to help you not blow your eyes out, the T1 has Class 1 laser safety certification and a fully enclosed design. Additional safeguards include automatic shutdown when the lid is opened, flame detection systems, airflow monitoring, an emergency stop button, and a laser key lock.

CNET’s deals team and I are always looking to bring you the best discounts from your favorite retailers, like Amazon and Walmart. With the Prime Day sale event creeping up on us, we’re seeing quite a few early discounts that are secretly dropping. It can be tricky trying to decide if it’s a real steal or just retailer fluff, especially during a sale event. We rounded up the standout discounts our CNET shopping experts actually recommend this week, including savings on tech, home essentials and everyday favorites.

Our CNET Deals text subscribers get these deals sent to them before anyone else does. I’ll send the best deals straight to your phone, so you can keep an eye on the hottest drops and jump on them before everyone else does. And it’s completely free. It’s never a bad time to save money, and finding affordable items in 2026 is more welcome than ever. Signing up for the CNET Deals text group is safe and trusted, plus you can opt out anytime.

Best deals of the week

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The Amazon Smart Thermostat works with Alexa to create schedules, adjust temperatures automatically and let you control your home’s temp from anywhere through the app. It is Energy Star certified and compatible with select Alexa devices. Plus, DIY installation makes setup relatively easy.

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The Houl Zallee portable speaker is built with dual tweeters, woofers and passive radiators to deliver punchy bass and room-filling audio. The IPX7 waterproof rating means it can handle sudden rain showers or splashes from the pool party. A battery life of up to 32 hours helps keep the music going all weekend long, and the integrated carry handle makes it easy to take from the backyard to the campsite. 

This lightweight camping hammock is 16 ounces and can pack down small enough to fit in most backpacks. It’s made from parachute nylon with triple-stitched seams so it can handle everything from campground overnights to evenings in the backyard. The included tree straps and carabiners makes for easy setup.

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This handheld fan doubles as a desktop fan, thanks to an included USB-C charging dock. It features 100 adjustable speed settings, an oversized seven-blade design for smooth airflow and a built-in cooling plate. With up to 16 hours of battery life, a foldable design and a detachable lanyard, it’s perfect to take anywhere all summer.

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The A16 iPad is a solid tablet, even though it’s been overshadowed by newer, fancier models. It’s an excellent size and offers amazing graphical performance with the A16 Bionic chip. Best of all, you can pick one up now at a discounted price.

How we choose the deals at CNET

Many of us at CNET have covered shopping events for over five years, including Black Friday, Prime Day, Memorial Day and countless others. Not to mention covering, researching and hunting deals on the daily. We’ve gotten good at weeding out scams and superficial deals, so you see only the best offers from all over. 

When choosing deals to show you, we look for real discounts, quality reviews and remaining sale time. Our team of experts has tested countless products to ensure we’re only sharing the best deals.

• Real discounts mean exactly that. We look at the price history for that product to make sure no brands are inflating prices to make the discount seem more substantial than it is.
• Quality reviews and in-depth testing are important for any product. If you’re unhappy the first time you use it, the discount wasn’t a worthwhile one. 
• Remaining sale time is a huge part of our vetting process. If a deal seems like it will only be around for a short while or will only be available for the remaining stock, we’ll let you know upfront so you don’t come back to the deal later only to be disappointed. 

Hosting your own group chat could let you avoid a lot of drama.

Why are people leaving Discord?

What open-source Discord alternatives are out there?

Stoat

Revolt

Element

Element

The best open-source Discord alternative

offbeat

26 years late and no threat unless you still run a PDP-11/70 and rely on short-wave timekeeping broadcasts

It’s been more than a quarter century since the Y2K bug threatened to disrupt the not-so-modern world, and while the patching efforts of global IT heroes prevented a millennial mess, the problem persists as a Dutch dev just found a new instance of the numeric nightmare.

While working on an emulator for the venerable Programmed Data Processor (PDP) series of “minicomputer” systems manufactured between the 1950s and 1990s, Folkert van Heusden spotted an unpatched Y2K bug in the Network Time Protocol daemon in BSD 2.11.

To be fair, it’s not like van Heusden stumbled onto a potentially devastating issue that’s simply waiting to cause chaos: Not only was the bug specific to the PDP-11/70, a system that entered service in 1975, but it also requires a Precision Standard Time, Inc.(PSTI) receiver manufactured by defunct hardware maker Traconex used to pick up time signals broadcast by short wave radio stations managed by the US National Institute of Standards and Technology. Even at that point, the bug won’t instantly break network time, as a would-be attacker must take several steps to configure the ancient mahicnes in a way that causes the error.

Van Heusden’s writeup explains how to trigger the flaw.

“I’m writing a PDP emulator,” van Heusden told The Register in an email. “I’m also very much interested in time keeping on computers. That combined, I dove into the NTP-implementation on the PDP. When adding emulation for the PSTI-device, I suddenly noticed 19126 for the year.”

Unsurprisingly, when the PSTI receiver actually produces the correct output, the system throws an error that the time offset between the PDP emulator and the emulated PSTI device is a bit “excessive.” Only by 17,000 years, give or take a couple centuries.

Luckily, van Heusden has coded a fix that’ll bring the times back in sync, eliminating what may be one of the few remaining Y2K bugs still floating around in the wild – after all, when’s the last time you heard of a forgotten (or, in this case, overlooked due to technological obsolescence) Y2K bug being patched?

If you want to tinker with a 50-year old emulated system running a 35-year old operating system, the good news is that the PDP and its 16-but CPU ran at 5MHz and needed just 4 MB main memory – a spec that van Heusden’s PDP-11/70 emulator can easily run on modest hardware like a Raspberry Pi Pico, and it’s available on GitHub.

Just be sure you patch that Y2K bug if you plan to tinker with time keeping. ®