SUSE plans to cut roughly 10% of its 1,900-person workforce, with CEO Dirk-Peter van Leeuwen saying "automation and AI" are reducing the need for some roles and allowing the open source software company to operate with less overhead and flatter management structures. "We are announcing a reduction of SUSE's global workforce of approximately 10 percent," said Van Leeuwen in an email. "These decisions are painful for our people, our culture, and our open source community, but they are necessary for us to adapt and build a sustainable, resilient SUSE for the long term." The Register reports: Van Leeuwen says the business, bought by Swedish private equity provider EQT, decided to cut jobs after scrutinizing its "future strategy, required capabilities, and team structures." The rest of the organization will get a look at the fiscal year 2027 strategy on November 16. He said in the message to staff: "What is clear is that automation and AI are fundamentally shifting how software companies operate. AI capabilities are advancing exponentially, forcing us to radically reimagine our work. We have seen this trend already both inside our sector and in the wider economy. This means that the organizational design and cost base that SUSE is built on today will not carry us forward successfully. "We are adapting. Across SUSE, modern tooling is already changing our daily workflows, enabling teams to move faster with less operational overhead and flatter reporting lines. The requirement for some existing roles has greatly diminished. Other roles will evolve over the next few years. This means we are accelerating investments in high priority areas, and deprioritizing others. These are hard, painful decisions to make, but necessary for our future." [...] The specific roles at risk of redundancy were not outlined but SUSE claims it has considered where the people in those positions are "able to evolve, what they can stop doing, what they need to do more of - and decisions have been made on that basis." Staff caught up in the redundancies will be informed by November 4, employee representatives nominated the following day, and one to one meetings will begin at the start of next week. Van Leeuwen says in the internal memo that the job cuts are a "reflection of shifting organizational needs, not your talent or commitment. You have tackled hard infrastructure and open source challenges that power critical enterprise systems around the world. "Any organization will be fortunate to have your expertise."

Read more of this story at Slashdot.

Microsoft’s Imagine Cup, a competition for student developers since 2003, is expanding for the first time to early-stage startups whose founders aren’t in school, with $250,000 in cash and $1 million in cloud credits awarded to the winner.

The change, announced Monday, opens the competition to companies founded in the past two years that haven’t raised a Series C round, whether or not their founders are students.

Startups will compete in six industry categories, including cybersecurity, developer tools and healthcare. Students will get a seventh category of their own, with a $25,000 top prize, or they can choose to compete for the main prize in the industry categories.

It’s the latest step in a broader evolution of the Imagine Cup, and the implications go beyond the competition itself. Changes in the rules from 2024 to 2027 show how Microsoft and the tech industry are adjusting, year by year, to the realities of the AI era.

Changing AI requirements: The clearest example is the Imagine Cup rules for AI usage. In 2024, entrants had to build their products with “Microsoft and/or OpenAI” services. In 2025, they had to use at least one Microsoft AI service. In 2026, they had to use at least two Microsoft AI services.

For 2027, they can use any AI they want, as long as they use Microsoft’s Azure cloud.

The shift mirrors a broader change in Microsoft’s AI strategy. The company has added models from Anthropic, xAI, Meta and others to Azure. In April it ended its exclusive cloud arrangement with its longtime AI partner OpenAI, allowing the ChatGPT maker to sell its technology through other platforms such as Amazon Web Services.

Microsoft Foundry, the company’s platform for building AI applications, offers access to more than 11,000 models.

“Imagine Cup 2027 reflects that same approach, giving founders the flexibility to choose the right models and technologies for their solutions while meeting the competition’s Azure and technical requirements,” said Tom Davis, general manager of Microsoft for Startups, in a statement responding to GeekWire’s questions.

Why startups: Addressing the expansion to non-student startups, Davis said AI is letting more founders start companies faster than ever, and the revamped competition is meant to help those young companies win customers and prepare to sell to big businesses.

The judging criteria have changed to match that focus on business customers.

As recently as 2024, student teams couldn’t have raised more than $50,000 in outside funding to compete. Under the new rules, final-round judges, whose scores determine all of the winners, will look for net revenue retention over the previous 12 months, six months of traction data, and a customer reference willing to be contacted.

What’s in it for Microsoft: The change is also a way for Microsoft to bring promising startups onto Azure. For startups in the industry categories, the first-round scoring criteria in 2027 will include “Microsoft and Azure commercial readiness, and expected cloud and AI consumption.”

Azure gets less explicit weight in the final round, whose scores determine the winners. A live production workload on Azure is worth 6 of 100 points there. In 2026, use of Microsoft technology made up 40% of the score in every round.

Participants can also earn Azure credits by using more of Microsoft’s cloud services. The credits start at $1,000 and rise to as much as $150,000 for startups that run at least 10 eligible Azure workloads and spend $3,000 a month on Azure for 60 days.

Amazon and Google run their own credit programs for startups, as the cloud giants compete to win AI companies early, when they’re choosing where to build.

Diversity criteria dropped: The new rules also drop diversity, inclusion and accessibility from the judging criteria, where they had been through 2026. In 2024, inclusion was worth 10% of the score, with judges asked to consider factors such as gender, ethnicity and disability.

Davis said Microsoft “updated the judging framework to reflect the range of industries and customer challenges represented in the competition,” and that inclusion and accessibility remain central to the company’s mission. He added that the competition welcomes startups developing products focused on inclusion and accessibility.

Separately, Microsoft skipped its annual diversity report last year, ending a tradition it started in 2019, as Google, Meta, Amazon and other big tech companies scaled back diversity programs under pressure from the Trump administration.

Prizes and timing: Applications are due Feb. 28. The second-place startup will receive $50,000 and $350,000 in credits, and third place gets $25,000 and $350,000 in credits. The 2026 champion, CopyFlag, a U.K. startup that tracks copies of creators’ designs online, won $150,000.

The top startup in the student category will present at the World Championship but isn’t eligible for the top three prizes, according to the official rules. To compete for those top prizes, student startups can enter an industry category instead, going up against companies that may have raised as much as a Series B.

The rules also allow Microsoft to use “automated assistance” to triage, summarize and compare entries, with human review in advancement decisions.

Winners will be selected in April and announced in May or June at a World Championship, with the date and location still to be determined, Davis said. The 2026 finale was at Microsoft’s Build developer conference in San Francisco.

An anonymous reader quotes a report from VentureBeat: Researchers from the Massachusetts Institute of Technology (MIT) Computer Science & Artificial Intelligence Laboratory (CSAIL) reported today that they used OpenAI's GPT-6 Astra language model to raise a small, open source Qwen model's math score roughly sixfold by directly editing its weights -- the numerical parameters that govern its behavior. Rikiya Takehi, Jayden Teoh, Alex Zhang and Omar Khattab described their work in a research blog post published November 2. They call the approach "weight-space programming" and tested Astra's ability to interpret and alter networks without using conventional training procedures to generate the edits. In the strongest language-model example, Astra's intervention raised Qwen3.5-0.8B's MATH-500 score from 7.9% to 48.1% while touching approximately 0.016% of its weights. The improvement came primarily from correcting repetitive reasoning—a distinction that shaped both the technical findings and the debate over their implications. Their experiments explored a broader possibility: AI systems that inspect, repair and construct other neural networks by working directly on their internal machinery. "This is cool as heck!" wrote Yuchen Jin, a technical staffer at Databricks and former co-founder and CTO of Hyperbolic Labs who was unaffiliated with the research, on X. "Imagine using a large LLM to directly edit a smaller model's weights and dramatically improve its capabilities. It's like gene editing for neural networks. Even cooler than distillation!" Voyage AI staff researcher Chandan Singh, formerly of Microsoft AI, also reacted to the findings: "For anyone questioning whether this post is overhyped, I think they are actually *underselling* the approach."

Read more of this story at Slashdot.

We’re bringing Guided Learning capabilities to Search and Google Classroom, and evolving our approach to school controls.

Read announcements from Google’s 2026 AI in the Learning Era event.

Louis Erbkamm knows his way around Arnis, the smallest town in Germany, but he’s never set foot in it.

I know where the shops are. Where the boats are… I know the city pretty well, but I haven’t been there yet.

Louis Erbkamm, maintainer of Arnis

Arnis is also the name of his open source project, which takes real-world data from OpenStreetMap and other sources to turn places you know into Minecraft worlds you can freely explore. Since Louis started Arnis in 2022, it’s reached close to 900,000 downloads from GitHub and about 70 contributors.

Two weeks and a slow prototype

Louis grew up playing Minecraft and started coding at 13, always looking for ways to bring code into the game. Arnis began as a fun diversion combining Minecraft with geospatial data, and it went on GitHub, where he already kept his side projects. He finished the first prototype within two weeks while on a break from school.

That first version was Python written by hand, built only on OpenStreetMap, and generating a town took a while. He needed somewhere real to test it, so he looked up towns in Germany, where he lives, and found Arnis. A small town helped while early generation was slow.

Arnis has a lot of buildings, it has streets, it has waters, it has all kinds of different elements that are useful to test with.

Louis

The project didn’t have a name yet, so it borrowed the town’s unique moniker.

A community that brings its own ideas

When Arnis added elevation, its worlds stopped being flat, and geospatial specialists from all over showed up with ideas on how best to integrate it. They started discussions on their own, and Louis says that’s exactly what he needed.

Bigger calls happen in the open too: before porting Arnis from Python to Rust, Louis opened discussions on GitHub to hear different perspectives. Contributors have since translated Arnis into about 20 languages and built major features of their own.

The best thing that has happened to me is essentially this big community on GitHub to see actually people from all kinds of different fields coming together to work on something that I started as a side project.

Louis

How a trash bin gets into Minecraft

Some features start on a walk. Louis noticed how many trash bins there were in his city, and there were none in his generated worlds.

Minecraft has no dedicated bins, so he took the question to the community, and together they found a block that fit. Placing one follows the same path as everything else: for the area a user selects, Arnis queries OpenStreetMap for every mapped element, then looks up each element’s coordinates and type.

If an element isn’t mapped, Arnis can’t place it. For structures too detailed for the map to describe, like the Statue of Liberty in New York, or Christ the Redeemer in Rio de Janeiro, Arnis converts 3D models into Minecraft blocks.

One maintainer, a stack of pull requests

As the sole maintainer, Louis reviews every pull request himself, and they stack up. He usually spends evenings or weekends working on Arnis, which means the community has to wait for him to be done with work for the day.

He uses GitHub Copilot to get an overview of open issues and bugs from the latest release, see how many people are running into them, trace where a bug came from in the codebase, and create a fix. Since he brought AI into the project, he says, code reviews and documentation have both increased.

I could delegate those annoying and boring tasks to Copilot, while I could still have a look at those really cool topics that are related to the development of it.

Louis

Sub-agents that look for regressions

Big features arrive as big pull requests.

Louis opens a large pull request in the GitHub Copilot app for a first overview and a performance review, with Copilot running in the background looking for small changes that would cut memory usage or generation time.

It takes just one wrong calculation in street lights, for example, to make the generation time of an entire city much slower.

Louis

He also keeps a mental list of the areas of the codebase that need extra care whenever someone changes them, and he has written the same warnings out by hand over and over.

I’m starting a few sub agents with the GitHub Copilot app to have a look specifically at those cases, and to come up with corner cases to test to make sure that we do not introduce any regressions into new releases.

Louis

He likes that the terminal, his files, the code changes and the pull request all sit in one app next to the chat.

An unexpected classroom

Louis’s favorite use of Arnis is in schools, where teachers have generated their city or a neighborhood park.

Some turn it into a contest: every student starts from the same generated area, improves a nearby park or playground using Arnis and Minecraft, and the class looks at the results together.

Where Arnis goes next

Recent releases have gone beyond the bounds of Earth, adding the Moon and Mars. Louis wants Arnis to stay easy to use while getting closer to reality, with more detail in less time, and he would like to see it in more schools.

Start with your hometown

Download Arnis from github.com/louis-e/arnis, pick a place on the map, choose where to spawn, and click Start Generation: within a few minutes, the world is ready to open in Minecraft. Louis suggests a place you know, like your hometown or somewhere memorable you’ve been, such as the Grand Canyon or Niagara Falls. Then look for what’s missing.

So maybe you see a bench that was not generated even though it is there.

Louis

Map it on OpenStreetMap, or tell Louis if it’s a bug and he will fix it.

The post Arnis turns any place on Earth into a Minecraft world appeared first on The GitHub Blog.