Anthropic’s Fable 5 spent three days as the most capable AI model ever released to the public. It topped the Chatbot Arena leaderboard, crushed OpenAI’s GPT 5.5 on coding benchmarks by double-digit margins, and gave paying subscribers access to Mythos-class reasoning for the first time. Then, on June 12, the US government ordered Anthropic to shut it down.

The result is a strange moment in AI. The model that demonstrably outperforms everything else on the market is the one you cannot use. GPT 5.5, which OpenAI launched in late April under the internal codename “Spud,” is now the strongest model available to developers and consumers, not because it improved but because its only real competitor was removed.

The benchmark gap between the two is not close. On SWE-Bench Pro, which measures a model’s ability to resolve real software engineering issues across open-source codebases, Fable 5 scored 80.3% to GPT 5.5’s 58.6%, a 22-point difference. On SWE-Bench Verified, a curated subset of the same benchmark, Fable 5 reached 95.0%.

The coding benchmarks tell a similar story. Fable 5 leads the Code Arena by 98 Elo points, scoring 1,665 to GPT 5.5’s 1,501. On FrontierCode Diamond, a benchmark designed to test the most difficult programming tasks, Fable 5 scored 29.3% while GPT 5.5 managed 5.7%, and on the broader Chatbot Arena leaderboard Fable 5 sits at number one with GPT 5.5 in fourth.

GPT 5.5 does have one area of strength. On Terminal-Bench 2.0, which evaluates interactive terminal-based coding tasks rather than codebase-level issue resolution, GPT 5.5 scored 82.7% compared to Fable 5’s approximately 88.0%. The gap is narrower there, and the benchmark tests a different skill, executing commands and debugging in real time rather than reading and patching large repositories.

Pricing also favours OpenAI. GPT 5.5 costs $5 per million input tokens and $30 per million output tokens, half the price of Fable 5’s $10 and $50 respectively. For developers running high-volume applications where the performance difference is less critical than cost, GPT 5.5 is the more practical choice even when both models are available.

Fable 5 launched on June 9 as Anthropic’s first Mythos-class model made available to the general public. It offered a one-million-token context window and 128,000 output tokens. Anthropic made it available at no extra cost to Pro, Max, Team, and Enterprise subscribers until June 22, a promotional window that the government directive cut short after just three days.

The shutdown came via an export control directive issued on June 12. The government cited a jailbreak vulnerability as the reason for pulling both Fable 5 and the broader Mythos 5 model family. Anthropic has disputed the severity of the finding, saying the vulnerabilities identified are minor, publicly known, and achievable by GPT 5.5 without any bypass techniques, while reports indicate that Amazon CEO Andy Jassy played a role in triggering the government’s review.

The practical consequence is that developers and researchers who were evaluating Fable 5 for production use have had to revert to GPT 5.5 or Anthropic’s earlier Opus models. For coding-heavy workflows, the downgrade is significant. The 22-point gap on SWE-Bench Pro represents the difference between a model that can resolve four out of five real-world software issues and one that handles roughly three out of five.

Whether Fable 5 returns depends on Anthropic’s negotiations with the government over the export control classification. The company has publicly argued that the directive is disproportionate and that the cited vulnerabilities do not justify pulling the model entirely. Until that dispute is resolved, GPT 5.5 holds the top spot by default, the best model available not because it is the best model that exists.

This article is part of the collection: Teaching Tech: Navigating Learning and AI in the Industrial Revolution.

A little over a decade ago, schools were swept into what many described as a movement to prepare students for the future of work. That work was coding — “Hello, world!”

Districts introduced new courses, nonprofits expanded access to computer science education and a growing ecosystem of programs promised to teach students the skills needed to enter the tech workforce. For many, it felt like a necessary correction to a rapidly digitizing world. But over time, a more complicated picture emerged.

While access to computer science education expanded, the relationship between early coding exposure and long-term workforce outcomes became uneven. The “learn to code” movement raised an important question that still lingers today: Which skills actually endure when technologies change? That question has resurfaced in a new form.

Today, generative AI is driving a similar wave of urgency. Schools are once again being encouraged to adapt quickly, often with the same underlying rationale that teachers must prepare students for a future shaped by emerging technologies.

But if the instructional role of AI remains unclear, and if the tools themselves are likely to evolve rapidly, the more persistent challenge may lie elsewhere.

After conducting a two-year research project alongside teachers, who are adapting and are open to integrating AI, we found that uptake is still minimal. Most of our participants, including those who are engineering or computer science teachers, still struggle to identify a clear or universal instructional use case for widespread AI integration.

So, what should students learn to help them adapt to whatever comes next?

A growing body of research suggests that the answer may lie not in teaching students how to use a particular AI system, but in helping them understand the computational ideas that make those systems possible.

The Limits of Teaching the Tool

In recent years, many discussions about AI education have centered on teaching students how to use generative tools effectively. Prompt engineering, for example, has become a common topic in professional development workshops and online tutorials.

Yet, focusing heavily on tool-specific skills can create a familiar educational problem, because technology changes faster than curricula.

Teaching students how to interact with a specific interface risks becoming the equivalent of teaching to standardized tests, rather than teaching students important lessons that don’t appear on state exams.

The history of computing education offers a useful example. In the early 2010s, a wave of coding initiatives encouraged schools to teach programming skills broadly. While many of those programs expanded access to computer science education, subsequent analysis showed that workforce pipelines in technology remained uneven, and many students learned tool-specific skills without developing deeper computational reasoning abilities.

That experience offers a cautionary lesson for the current AI moment. If the goal of integrating AI into education is long-term preparation for technological change, focusing narrowly on how to use today’s tools may not be the most durable strategy.

The Skill That Outlasts the Tool

A growing body of research suggests that computational thinking is a more durable educational objective.

Computational thinking refers to a set of problem-solving practices used in computer science and other analytical disciplines. These include:

• breaking complex problems into smaller components

• recognizing patterns

• designing step-by-step processes

• evaluating the outputs of automated systems

These skills apply not only to programming but also to fields ranging from engineering to public policy.

Importantly, they also help students understand how algorithmic systems operate.

When students learn computational thinking, they gain the ability to analyze how technologies like AI produce results rather than simply accepting those results as authoritative.

In this sense, computational thinking provides a conceptual bridge between traditional academic skills and emerging digital systems.

What Teachers Are Already Doing

Many teachers in our study were already moving in this direction, often without using the term computational thinking.

When teachers asked students to analyze chatbot errors, they were encouraging students to examine how algorithmic systems produce outputs. When they designed exercises comparing training data and algorithms to everyday processes, they were helping students reason about how automated systems work.

These approaches do not require students to rely heavily on AI tools themselves. Instead, they position AI as a case study for examining how technology shapes information.

That framing aligns with longstanding educational goals around critical thinking, media literacy and problem-solving.

Implications for Educators

If the instructional use case for generative AI remains uncertain, educators may benefit from focusing on skills that remain valuable regardless of which tools dominate in the future.

Several practical approaches are already emerging in classrooms. Teachers can use AI systems as objects of analysis, asking students to evaluate outputs, identify errors and investigate how models generate responses.

Lessons can connect AI to broader topics such as data quality, algorithmic bias and information reliability.

Assignments that emphasize reasoning, structured problem solving and evidence evaluation continue to support the kinds of cognitive work that remain central to learning.

These approaches allow students to engage with AI without allowing the technology to replace the thinking process itself.

Implications for EdTech Developers

The experiences teachers described also highlight an opportunity for edtech companies.

Many current AI tools were developed as general-purpose language systems and later introduced into education contexts. As a result, teachers are often left to determine whether and how those tools align with classroom learning goals. Future products may benefit from deeper collaboration with educators during the design process.

Teachers in our conversations were already experimenting with small classroom applications, designing AI literacy lessons and building course-specific chatbots.

These experiments resemble early-stage product development.

Partnerships between educators, edtech developers and product managers could help identify instructional problems that AI systems could realistically address.

The Next Phase of the Research

The conversations described in this series represent an early attempt to document how teachers are navigating the arrival of generative AI.

As schools continue experimenting with these tools, the next challenge will be to develop governance frameworks that help educators evaluate when and how AI should be used in learning environments.

Our research team is beginning the next phase of this work by partnering with school districts to develop guidance for AI governance and inviting edtech companies interested in exploring these questions collaboratively.

Rather than assuming that AI will inevitably transform classrooms, this phase of the project will focus on identifying the conditions under which AI tools actually support teaching and learning and how to reduce harm when they don’t.

The fourth grade teacher’s question remains a useful guide: What can I actually use this for in math?

Until the answer becomes clearer, many teachers will likely continue doing what professionals in any field do when new technologies appear: experimenting cautiously, adopting what works and relying on their judgment to decide where or if the tool belongs.

If your school, district, organization, or edtech company is interested in learning more about joining our next project on AI governance, contact our research team at research@edsurge.com.

Grassroots opposition to data center construction in the United States has reached a scale that is starting to reshape where and whether the AI industry can build. A new report from Data Center Watch, a tracker maintained by AI research firm 10a Labs, found that activists blocked or delayed at least 75 projects worth a combined $130 billion in the first quarter of 2026. According to NBC News, that is the most disruptions recorded in a three-month period since the group began tracking in 2023.

The pace represents a structural shift, not a spike. The total number and value of projects disrupted in Q1 roughly matched the full-year total for 2025, according to the report. The number of active anti-data center groups more than doubled from 396 at the end of 2025 to 833 by March, spread across 49 states, with Maryland, Ohio, and Texas hosting the most.

The opposition is bipartisan and locally driven. Communities are organising around electricity costs, water consumption, and noise, the same concerns that have already forced Denmark to pause all new grid connections for data centres and prompted the EU to ask households to cut peak electricity use because AI data centres are straining the grid.

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Legislative momentum is building alongside the grassroots resistance. Data Center Watch counted 14 statewide measures introduced in Q1 2026, and a separate analysis by MultiState identified moratorium bills across 11 states with proposed pauses ranging from three months to four years. More than 300 data-center-related bills were introduced in statehouses in just the first six weeks of the year.

None of the statewide moratoriums have passed yet, but they are getting close. Maine’s legislature passed one in April that would have paused permitting for facilities drawing 20 megawatts or more, the first of its kind in the country. Governor Janet Mills vetoed it but said she would have signed it if the bill had exempted a specific project in Jay, Maine that had strong local support, and she separately signed a law barring data centers from state tax incentives.

A Heatmap Pro poll found that a majority of Americans would “strongly” oppose a data center being built near their home, a shift from a survey nine months earlier that showed the public roughly evenly divided. Gallup data puts the figure at 70% opposed. The speed of the opinion shift suggests the issue is crossing from local planning disputes into broader political territory.

The industry is spending as though the opposition will not hold. US utilities plan to spend $1.4 trillion by 2030 on grid infrastructure driven largely by data centre demand, and hyperscaler capital expenditure is projected to exceed $690 billion in 2026 alone. The gap between what the industry wants to build and what communities are willing to accept is widening faster than either side expected.

In some cases, opposition is now mobilising before any project is officially filed. The mere rumour of a data center has been enough to trigger organised resistance, according to the report. That pre-emptive organising makes siting decisions harder even in states without formal moratoriums, because local permitting bodies face political pressure before a single application lands on their desk.

The Atlantic published a contrarian essay on Friday arguing that the backlash is overblown and that data centers can bring real economic benefits to host communities. The piece acknowledged that opposing data centers is good politics but argued it is not always good policy. Whether that argument gains traction will depend on whether the industry can demonstrate tangible local benefits beyond tax revenue, something most communities have not yet seen.

The report paints a picture of an industry that assumed it could build its way through local opposition with money and speed, and a country that is deciding otherwise, one zoning board at a time.

The multiview features on Apple TV 4K work similarly for all sports. However, because MLB baseball is a lot more limited in the number of games/streams available, the method of access is slightly different there. In general, you’ll need to select one stream you’ll want in the grouping and the multiview options will appear in the playback controls or in-stream menu. Selecting those will give you the ability to build your multiview from a menu of currently available games and shows. Once again, you’ll be able to watch up to four streams simultaneously — with the exception of baseball.

When you’re watching an MLB game, a multiview icon will appear on the player controls beside the options for subtitles, alternate audio feeds and picture-in-picture (PiP). Because Apple TV only broadcasts a maximum of two MLB games at once, there will only be three options to build a multiview feed: the two games and Apple’s MLB Big Inning studio show.

MLS fans have the ability to watch up to four games in multiview, or up to three games and Apple TV’s MLS 360 whiparound show. The studio show offers live look-ins at in-progress games, real-time analysis and ongoing discussions of all the day’s action. To set up your MLS multiview, start by watching any match. You’ll see the multiview icon in the playback controls where you can then browse the available live games and shows.

Formula 1 may be the most recent addition to the Apple TV sports lineup, but the racing series also has the most unique multiview options. Since Apple has partnered with F1, Apple TV subscribers get an F1 TV Premium subscription for this season. However, a lot of what’s included with that access is available in the Apple TV app — including the multiview selection. Once you pick your main feed, the multiview option shows up in the in-stream menu just below the main playback view. You can make it appear by swiping down on the Apple TV 4K remote.

In addition to the main race feed, you can create a multiview with a driver tracker, telemetry chart (live timing) and dedicated cameras for P1, P2 and P3. You can also choose from the driver’s onboard cameras for each car. The Apple TV app provides some pre-made multiview recipes, but you have the ability to create an entirely custom setup as you see fit. Lastly, Apple gives race fans the option of the Sky Sports feed with its commentary team if you prefer that coverage over the F1 TV crew that Apple TV’s main broadcast uses.

For all three sports, you can choose to highlight each of the streams in multiview by swiping over from box to box. Doing so pipes in the audio from that stream although the video from the others will always be visible so long as multiview is active. If you want to watch one of the feeds in your multiview on its own, simply click to select it.

In addition to multiview, the Apple TV 4K also supports picture-in-picture (PiP). This allows you to keep a game or race minimized in the corner of your screen while you browse other apps and menus on the streaming box. The PiP option is also available in the playback controls once you’ve selected a game, race or show. And unlike multiview, the feature works with anything you’re watching — even workouts in the Fitness app.

Almond Robotics launched Axol this week as a dual-arm robot built specifically for teams developing physical AI systems that must function in factories, warehouses, kitchens, and other unpredictable settings. The company spent the past year putting existing robots through real shifts in grocery stores and production lines. Those machines repeatedly hit limits that slowed progress or caused outright failures.

When attempting to insert its hand inside bins or equipment, reach usually fails. Payloads led the motors to overheat for extended periods of time, and the exposed cables were repeatedly bent until they broke. Singularities would regularly intervene, requiring the robot to slam on the brakes or perform more maneuvers to get back to a safe position. Even more frustrating, it would take forever to collect clean, labeled demo data because the robot couldn’t seem to approach the task in the same manner that a human operator would. Axol grew out of those hard lessons. Almond designed everything with long-term, contact-rich work in mind, and he devised a technique to accelerate data collection for training in the event that a human operator was absent.

Each arm has seven degrees of flexibility and extends 860 millimeters from shoulder to fingertip, providing a robot with substantially more workspace than identical research arms without the need to reposition the base every five seconds. The wrist joints alone can perform full 180 degrees of pitch and yaw, giving the robot plenty of flexibility and reducing any annoying singularities that would normally limit the usable workspace on other platforms, all of which adds up to smoother trajectories when the robot is attempting to reach for something complex.

Payload peaks at 6.5 kilograms, and in typical operation, the actuators can support a steady four kilogram weight without ever throttling back owing to heat. That level of sustained capacity is essential for tasks that include frequent lifting, pressing, or tool use. All of the cables inside the item are routed internally, so there is no risk of them catching on anything or wearing out due to frequent movement. Two FAKRA GMSL 2.0 connections are neatly positioned at each wrist, ready to be connected to high-speed stereo cameras that deliver low-latency vision where it counts: precision grabbing and insertion.

Standard grippers include two fingers and interchangeable tips, and because the technology is modular, teams can easily swap in custom end effectors as projects progress without having to start over. The control loops operate at a constant 500 Hz, which is necessary for responsive, fine-grained movements, especially when dealing with delicate touch operations.

Axol delivers the robot directly from Almond’s workshop in San Francisco’s Dogpatch district, which is advantageous for a number of reasons, including faster part availability and on-site maintenance options for Bay Area deployments. Just as important, the software stack is given equal attention. The full solution is accessible as an open source Python SDK, which includes low-level CAN motor control, bimanual inverse kinematics, ZED camera streaming, and LeRobot connectivity, as well as a WebXR teleoperation pipeline for any compatible headset. The system records everything, including synchronized joint positions, camera frames, and actions, in policy-training-ready formats.

Since Almond is addressing startups and research groups, pricing is modest, hence the standalone Axol is listed at $7,999 during the launch window. The set includes a height-adjustable mobile platform, three ZED X One S cameras, an NVIDIA Orin NX 16 GB computer unit, and all necessary cables for $11,999, which includes shipping. Both options are available currently.
[Source]

Germany’s reputation as tournament specialists has taken a battering in the past decade, with the four-time World Cup winners suffering group-stage exits in 2018 and 2022.

However, Die Mannschaft are ready to resume normal service at the FIFA World Cup 2026, led by exciting young playmakers Jamal Musiala and Florian Wirtz, and the familiar figure of 40-year-old Manuel Neuer in goal. They are unsurprisingly strong favourites in their Group E opener against tournament debutants Curacao in Houston, where a heavy victory would all-but guarantee a place in the last 32 and avoid the humiliation of falling at the first hurdle for the third successive World Cup.

Chinese hackers took control of a target organization’s authentication stack and maintained persistence for 10 years, with full visibility into the administrative activity.

Dubbed “Operation Highland,” the intrusion is attributed to the Velvet Ant cyberespionage threat group, which targeted vulnerable internet-facing systems before pivoting to a network with no direct external path.

Chinese hackers of the “Velvet Ant” activity cluster breached the isolated critical infrastructure network of a large organization and conducted cyber-espionage operations for 10 years.

The campaign, dubbed “Operation Highland” by Sygnia researchers who discovered it, began in 2016, targeting vulnerable internet-facing systems before pivoting to an “air-gapped” environment with no direct internet connection.

Velvet Ant’s lengthy espionage operations were documented in 2024, when Sygnia warned of a campaign targeting F5 BIG-IP devices that operated undetected for three years.

Also in 2024, Cisco warned of a zero-day in NX-OS running on Nexus switches, which was exploited by Velvet Ant to gain access to targets.

Velvet Ant attack chain

The attack begins with the compromise of internet-facing servers, though the researchers don’t mention the specific product or any vulnerability used.

Velvet Ant deployed a modified GS-Netcat reverse shell disguised as a legitimate system component that connected to a hardcoded relay domain, providing encrypted remote shell access.

The shell achieved persistence either via a malicious systemd service or through startup script modification.

Next, Velvet Ant installed a custom SOCKS5 proxy for network traffic tunneling, enabling it to reach internal systems that are not directly accessible from the internet.

The proxy ran as a daemon masquerading as ‘smbd -D,’ using different filenames and ports on each host, and turning compromised servers into internal pivot points.

The most interesting part of the attack was building a remote execution path into the isolated network.

To achieve this, Velvet Ant modified the configuration of a compromised internet-facing Nginx server to proxy specially crafted requests to a compromised backend server.

The backend server’s Nginx configuration was also altered to forward requests to a FastCGI process (fcgiwrap) listening on a separate port.

The FastCGI wrapper acted as an execution bridge, processing requests and launching a custom binary named ‘uptime.’

The tool established SSH connections to systems within the isolated critical infrastructure network using parameters supplied in HTTP POST requests.

Having established their access into the isolated environment, Velvet Ant shifted focus to long-term persistence and credential theft by targeting Linux Pluggable Authentication Modules (PAM), a set of libraries that let administrators set up methods to authenticate users.

The attackers replaced legitimate ‘pam_unix.so’ modules with backdoored versions that accept hardcoded passwords and harvest user credentials.

Sygnia identified nine distinct variants of the malicious PAM module, each compiled in a separate build environment, indicating a well-resourced threat actor.

The researchers say that two of the malicious PAM modules stand out for acting as a backdoor only and for collecting credentials.

Velvet Ant actors also replaced OpenSSH components such as ssh, sshd, and scp with trojanized versions that captured credentials, logged commands entered during SSH sessions, and stored the collected data locally for future retrieval.

Sygnia says that by extending control to the authentication process by modifying the PAM and OpenSSH components, the threat actor had access to credentials as they were used in the target environment and could bypass the authentication flow.

“Administrative activity became fully observable: every login; every command executed across compromised hosts. Access was no longer tied to a specific foothold but embedded into the authentication process itself,” the researchers explain.

This way, the hackers ensured their persistence despite password changes and session terminations, and reduced “the effectiveness of conventional containment measures.”

Complex cleanup

Sygnia says even after discovering the compromise, remediating it and removing Velvet Ant from the compromised environment was particularly complicated.

The threat actors had replaced so many critical components with custom versions that removing them was likely to break authentication, lock legitimate administrators out, and cause operational outages.

To tackle this problem, the researchers built a testing lab to validate the binary replacement process, profiled each host, tested the results, and prepared rollback procedures before attempting the cleanup.

Sygnia recommends that defenders treat authentication components such as PAM, OpenSSH, and Windows LSASS as critical security assets and protect them with EDR, file integrity monitoring, hardened privileged access, multi-factor authentication (MFA), and continuous monitoring for unauthorized modifications.

Organizations should plan for offline recovery, which includes strict backups with an adequate schedule for automatically creating snapshots with immutable copies.

The restoration process should consider testing the backups and recovery hosts running operating systems that have been validated, along with the recovery scripts.

Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.

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Skoda has revealed the Peaq, its first seven-seat all-electric SUV and the most expensive car in the Czech automaker’s 130-year history. Built on the Volkswagen Group’s MEB platform at Skoda’s home plant in Mladá Boleslav, the Peaq stretches nearly 4.9 metres long and is designed to compete directly with the Kia EV9, Hyundai Ioniq 9, and Volvo EX90. The difference is price, with Skoda targeting a starting point of around €50,000 to €55,000, compared to roughly €66,000 for the EV9 and €70,000 for the Ioniq 9.

The lineup will launch with three variants. The Peaq 60 pairs a 150kW rear motor with a 63kWh battery for more than 460km of WLTP range, while the Peaq 90 steps up to a 210kW motor and a 91kWh pack for over 600km. The range-topping Peaq 90x adds a second motor for all-wheel drive and 220kW of total output, keeping the same 91kWh battery and 600km-plus range.

All three variants support DC fast charging at up to 200kW, which Skoda says will take the battery from 10 to 80 percent in approximately 28 minutes. The Peaq also supports bidirectional charging, meaning it can feed power back to a home through the VW Group’s Moon Power Ambibox DC wallbox. Vehicle-to-load capability is included as well, letting owners run external devices directly from the car’s battery.

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Inside, the third row folds flat to open up 890 litres of boot space. Options include a Sonos sound system, a panoramic glass roof, and massaging front seats. The design follows Skoda’s Modern Solid language, which debuted with the Vision 7S concept that previewed the Peaq’s shape back in 2022.

Skoda confirmed the Peaq name in January 2026 and showed a near-production version on March 30. The world premiere is set for June 23 in Monnetier-Mornex, France, with deliveries expected from mid-2026. Production will run alongside the Enyaq at Mladá Boleslav, making the Peaq the second MEB-based model built at the plant.

The pricing strategy is the Peaq’s sharpest weapon. Skoda has historically positioned itself as the VW Group’s value brand, and the Peaq extends that logic into the seven-seat EV segment where competitors have priced themselves into premium territory. The Kia EV9 starts at roughly €66,000 in Europe, the Hyundai Ioniq 9 at around €70,000, and the Volvo EX90 higher still.

That positioning matters at a time when tariffs and trade barriers are reshaping which EVs are available in which markets. A seven-seat electric SUV starting under €55,000 from a European manufacturer built in Europe avoids the import exposure that has forced several Korean and American models out of certain markets or into higher price brackets.

The Peaq also arrives into a segment that is still thin on options. The Peugeot E-5008 offers seven seats at a lower price but with less range and a smaller footprint. Above the Peaq, the choices jump quickly into luxury pricing. Skoda is betting that families shopping for a large EV want the space and capability of a premium model without the premium itself, and the Peaq’s spec sheet suggests it can deliver that.

Marvel Animation released the first trailer for season two of X-Men ’97 this week under the name Roll Call, and the footage makes the direction clear without giving every twist away. The core team ends up split across different eras after the events of the first season, with some members landing in the ancient past, others in a distant future, and all of them trying to find a route back to the time they know. Back in the 1990s, the absence leaves room for fresh waves of mutant fear and new enemies who see an opening.

The new season keeps the heart of the original but raises the stakes dramatically. Rogue is still devastated by Gambit’s death, and it will have a tremendous influence on the entire team. It has an impact not just on their mental well-being, but also on their ability to function. At the end of the last episode, there’s a brief end credits sequence in which Apocalypse holds one of Gambit’s playing cards while the guy talks to his buddies about anguish and death, hinting that old comic book themes may reemerge as the season develops. The trailer makes it clear that Apocalypse is the main antagonist, portraying himself as an Omega-level threat who claims to be the last one standing. The potential of a confrontation with Magneto heightens the drama.

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The main cast returns, including Ross Marquand as Professor X, Matthew Waterson as Magneto, and Ray Chase as Cyclops. We also have Jennifer Hale as Jean Grey, Alison Sealy-Smith as Storm, Cal Dodd as Wolverine (giving him that edge we adore), Lenore Zann as Rogue, George Buza as Beast, and the same strong blend of resolve and personality that made the first season so popular.

Some mutants that played minor parts the previous season will have far more to do this time around. Strong Guy offers some serious muscle to the fights, Psylocke provides some slick psychic moves, and Wolfsbane and Siryn get to show off their animal and sonic abilities. Then there’s Multiple Man, who is rather good at the “making copies of himself to handle stuff” trick. Meanwhile, Archangel, Havok, Polaris, and Emma Frost all receive far more screen time than the prior time. Oh, and Nightcrawler enters, engaging in some great sword combat with Exodus, while Storm has a very wonderful moment that demonstrates her leadership talents.

There are numerous new costumes floating around, as well as some really cool homage to historical covers, including a subtle nod to the first Frank Miller Wolverine comic, which was first published in 1982. The conflicts are faster and more intense, which contributes to the season’s attractiveness. What are the settings? It’s extremely diverse, which is part of the fun. he season consists of nine episodes, all of which will be available on Disney+ on July 1st.

Facepalm: Few Apple devices have won as much praise as the MacBook Neo. Cupertino’s excellent budget laptop outsold the MacBook Air and Pro during its first three weeks, and it seems AMD is feeling a little jealous of all the attention. Team Red has just posted ads for its Ryzen laptops boasting of their gaming abilities, while also pointing out that the MacBook Neo can only play five out of twenty top PC games natively.

Like other companies, AMD likely feels a little threatened by the success of a budget MacBook, so it’s gone after its weakest area: PC gaming.

In its Ryzen AI processors ad, AMD compares an HP OmniBook X Flip, which features last year’s Zen 4-based Ryzen 5 220, against the Neo, which uses Apple’s A18 Pro chip. The company writes that the x86 machine offers access to game libraries across Steam, Epic, and PC Game Pass, complete with “high frame rates” and “advanced graphics,” and with “No workarounds required”

The ad also notes that just five of the “top 20” PC games run natively on the Neo. There are also stats about the HP laptop’s 512GB of storage (compared to the Neo’s 256GB), 2-in-1 touchscreen design, and extra ports. AMD adds that the Ryzen offers 57% better multitasking, 38% faster content creation, and up to double the WiFi speed.

While there’s no arguing that the OmniBook X Flip, which starts at $999, has plenty of elements that put it above the MacBook Neo, nobody is buying one of Apple’s machines to primarily play PC games, so it’s a strange comparison to make. It’s more like AMD is simply comparing operating systems, making points that will be obvious to most people.

Moreover, claiming the Radeon 740M GPU in the Ryzen 5 220 offers high frame rates and advanced graphics is quite a stretch – only the most forgiving games are playable, and even then, they have to set at their lowest 1080p settings. Meanwhile, the Neo has been shown to play some PC games quite well, given its hardware limitations.

The MacBook Neo is the clear budget-category winner in our Best Laptops feature. It sold 1.1 million units in under a month after launch thanks to its $599 starting price, design, and macOS experience. It’s an excellent laptop for the price, but not much good for PC gaming, obviously.

Image credit: Andrew Tsai