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Reuters reports:


Last week, Getty announced plans to file for bankruptcy, marking a major reset after its planned merger with rival Shutterstock collapsed under the weight of heavy debt and growing competition from AI-generated imagery. Getty said the affiliates of the Getty family and Koch Equity Development, which committed to provide up to $533 million in new equity, will own 100% of the company after the restructuring It's hard to compete against things like the DALL-E image generator.

Long-time Slashdot reader schwit1 quips that maybe "It's hard to compete against things like the DALL-E image generator..."

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"As of 2026, the company has issued more than 60 million digital certificates," notes Wikipedia. But now, InfoWorld reports:

A certificate authority's decision to abandon the global SSL/TLS market is exposing a weakness many organizations rarely examine: their dependence on complex PKI trust chains. Following the compromise of private keys behind eight of its intermediate certificate authorities, eMudhra is exiting the global SSL/TLS certificate business and is offering customers Sectigo-issued replacements... [In a press note the company added] that data sovereignty and compliance requirements had increased the business's complexity and cost... The company said it will contact affected customers to replace certificates free of cost. It also said it will keep revocation and certificate status services running until every issued certificate has expired or been replaced...

Yash Kadakia, co-founder and CTO of Security Brigade, said that moving to another root operated by the same company does not necessarily address concerns over trust and governance. "The last thing organisations need is to migrate twice," Kadakia said.


But there's another concern, the article points out. "Analysts warn that enterprises may discover they have only limited visibility into the certificate hierarchies underpinning critical services."


Replacing a website's certificate may not address every dependency in an enterprise environment, according to Sakshi Grover, research director at IDC. She said residual exposure lies in systems that maintain their own trust stores and do not update on the same schedule as browsers. These include Java keystores, language runtimes, container images, mutual TLS configurations, proxies, load balancers, VPN and security appliances, and embedded devices. "The harder finding for many organizations will be that they can't answer these questions at all," Grover said, referring to where certificate trust decisions are made outside browsers and operating systems...
Grover recommended that security teams first identify where trust decisions occur outside browsers and operating systems, prioritising systems that accept connections from external parties and mutual TLS endpoints. Teams should then determine which systems can be updated to distrust a specific intermediate certificate or public key, she said.

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The Free Software Foundation's "EncryptFest" today covers both encryption and privacy. The free two-hour event runs from 13:00-15:00 Eastern Daylight Time (17:00-19:00 UTC), and includes:

— A discussion on "bulk surveillance, and how encryption protects personal privacy"
— A short video from renowned cryptographer Bruce Schneier.
— A discussion with Henry Fisher, the founder of Techlore and a digital rights activist
— A hands-on workshop on how you can encrypt your email using the FSF's Email Self-Defense guide "as a concrete step to enhance your data privacy"



You may join us virtually, or if you are in Boston, Massachusetts, United States (the FSF's home base), you can join us in-person at the Boston Public Library's Copley Square branch. You may also join us in-person at MakeHaven in New Haven, Connecticut, US, and meet our deputy director Sean O'Brien. Whether attending our event online or in-person, please register. It will help us plan ahead and enable us to contact you about other important details before the workshop, like what to expect and what to bring...

Bulk surveillance violates our fundamental rights and makes free speech dangerous. Email encryption is an excellent tool you can use to shield yourself from bulk surveillance. Encrypting your email is also a concrete step towards protecting your data from others whose intentions may not be honorable.

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The blog Android Authority offers this TLDR:

Google updated its privacy policy to clarify that it could use information shared through Personal Intelligence to personalize ads. Gmail, Drive, and Photos content isn't used for targeted ads by default, but connecting them to eligible Google services could change that.

Digital Trends explains:


Google has long promised that your Gmail, Drive, and Google Photos content never helps it personalize ads. However, since October 16, 2026, its privacy policy adds a catch. The promise lapses if you let other ad-showing Google services use that content. That means using Personal Intelligence, an opt-in tool that lets Google's AI search read your connected apps, does exactly that (via Google).

If you ask Google's AI Mode where to eat near your hotel, and it can identify your hotel from the booking confirmation in Gmail, find the location in Maps, and then suggest a few places you might like. That's the helpful half. The twist, however, is that AI Mode can also show sponsored results. Google mentions that the personalized ads follow what the question and answer are about. So, while your inbox never goes to an advertiser, it can quietly shape the answer, which, in turn, shapes the ads.

Google's defense is elaborate. First, it says connecting the apps is optional. Second, advertisers can't see the information or content Google accesses in your Workspace apps (including your inbox and photos). Finally, sensitive topics like health are off-limits.


Ars Technica shares the response they got from a Google spokesperson: that Google isn't scanning your entire inbox to feed their advertising profiles, but if you choose to pipe your personal data into an AI search tool — then the ads Google shows you could be based on that information.

If you want to ensure ads never use your personal data in this way, you'll need to make sure Personal Intelligence is turned off. The easiest way is to access the Gemini settings for Personal Intelligence and disable the connected apps. These settings are also a few levels deep in your main Google account settings under Data & privacy > Personalization settings > Manage Personalized Recommendations in Search Services > Connected Content Apps. From there, you can disable connections to Workspace and Google Photos.

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"Despite massive investments and widespread hype around AI , the technology has delivered zero financial payoff to corporate America outside of tech," writes the chief economist at U.S. asset management firm Apollo Global.

He adds that "there are still no signs of AI boosting productivity growth... [M]ainstream corporate America is pulling back and starting to view token spending not as a source of growth but as a cost center that weighs on margins."

VC-backed companies are spending more and more on tokens, but mainstream corporate America does not need the latest model and is unwilling to pay for it. As long as agentic AI and token spending are confined to a small part of the economy, the macroeconomic effects of AI will be very limited...

The question looking ahead is whether, even as models become commoditized, mainstream corporate America can turn increasingly cheap intelligence into higher revenue growth and higher profit margins. That may be harder than it sounds. AI may be a great calculator, but in business, intelligence is mostly judgment. Judgment depends on context, and the answer varies with which human or which model you ask. Perhaps most importantly, AI does not understand accountability, trust, culture, unwritten rules, ethical trade-offs or fairness, all of which shape reputational risk and ultimately drive revenue growth and profit margins.

The bottom line is that the AI story will be decided in the S&P 493, not the Mag 7, and so far, there is no evidence that revenue growth or margins are improving outside the tech sector.




More details from TechCrunch:


Apollo's Torsten Slok focuses on what he calls the S&P 493 — the S&P 500, minus Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla — finding that profit margins and revenue per worker appear to be flat, with no evidence that AI is improving productivity...

Recent studies have also found that AI has yet to deliver significant productivity gains.

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BeamFS creator Aurelien Desbrieres is not calling for a merge, but is requesting feedback on an 18-patch proposal that takes the place of FTRFS, an earlier filesystem he proposed in April.

The Linux/Open Source blog It's FOSS reports:


Filesystems that can repair damage, like Btrfs and ZFS, usually do it by falling back on a spare copy of the data. BeamFS skips that and stores Reed-Solomon error-correcting codes inside every block. Every read gets checked against those codes, and small amounts of damage are corrected before the data reaches the application. If the filesystem can tell the damage is beyond repair, the read fails with an error instead of returning the wrong bytes...

In his technical report, ext4 and ext3 returned wrong data without any error, while Btrfs refused to read the file. BeamFS returned the correct file every time except once, when a flip hit a pointer to the file's data and it refused the read.


"Aurelien says he used Anthropic's Claude to write the code, the tool that checks the patches, and the cover letter..."

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From the student newspaper at the University of California, Berkeley:


Richard Borcherds, a UC Berkeley professor and Fields medalist, no longer grades his students' homework. After seeing how many of his students used AI to consistently receive top scores, he felt there was "no point" in paying readers to grade for correctness in homework, as AI use is now unavoidable.

Borcherds revealed this in a sit-down interview Tuesday, following OpenAI's announcement that AI agents apparently solved one of mathematics' most difficult unsolved problems, the Navier-Stokes existence and smoothness problem. "If AI continues to improve at present rate, then, in a few years, there'll be nothing human mathematicians can do that AI can't do better," Borcherds said.

Borcherds said he currently grades on completion if a student hands in their homework. He admitted that it is "not a very good system," but at the moment, he has no alternative... Campus has tried several AI detection tools, including Turnitin and Rumi, but have determined that these tools aren't adequate enough to be standardized on campus.

The article points out that at the university, each class's AI policy "is up to the discretion of faculty."

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"As of today in systemd Git it's now possible for the systemd programs to be written in the Rust programming language as an alternative to C," reports Phoronix:


Core systemd code like the systemd library doesn't yet support Rust code, but for the various systemd programs / leaf components there is now support for using Rust... The new systemd-keyring-setup utility in Rust is used for provisioning keyrings and enrolling X.509 certificates into the kernel's dm-verity / fs-verity / BPF keyrings during the boot process while dealing them afterward. This systemd-keyring-setup code hasn't been merged yet but is expected soon now that the Rust infrastructure has landed. Originally it was all part of a single big commit before being split-up for the Rust enablement, systemd-keyring-setup, and other preparations for this new component.

Systemd developers aren't planning on porting existing C code to Rust, all the Rust code is built with Meson [no Cargo usage], and C linkage only is mandated.

Systemd Rust components come out slightly larger than C with a test program being 32KB compared to 20KB for its C twin. For now at least the systemd Rust support is optional and toggled by the -Drust= Meson option with it being auto by default.

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"59% of maintainers report growing contribution volume since the rise of GenAI," according to a new study published by Linux Foundation Research. But 25% of their incoming pull requests "are estimated to be low-quality, also known as 'AI slop'." And on large projects, 69% see growing contributions — and estimate that 31% are low-quality/AI slop.

So what do these results show us? "Generative AI is fundamentally changing how people participate in open source," says the head of Open Source Ecosystem Development at Huawei's R&D subsidiary Futurewei, which helped support the study. Specifically GenAI is "lowering barriers to contribution" but also "creating new challenges" for maintainers and communities. Their recommendation? "Build the governance, transparency and trust mechanisms that allow AI-assisted contributions to strengthen, but not overwhelm, the open source ecosystem."

"Open source projects need to design for a growing AI-generated contributor base, not simply react to it," said Hilary Carter, SVP of Research at the Linux Foundation. "That means setting clear expectations for GenAI, putting responsibility for testing and disclosure on contributors and giving maintainers better tools to manage incoming pull requests. If we get those fundamentals right, AI can expand participation without overburdening and increasing costs for maintainers."

More from the blog It's FOSS:
LF Research is the Linux Foundation's research arm. It was set up in 2021 and publishes studies on how open source is built and used, drawing on surveys and project data. Its latest report, Navigating Open Source Software Development in the Generative AI Era, surveyed 234 contributors and maintainers on how generative AI is affecting the way open source gets made...
64% say reviews now take longer. Burnout is their top concern at 63%, narrowly ahead of contribution quality at 60%.
You will also find that policies have not caught up to the AI-assisted coding and vulnerability reporting trend. Of the 119 respondents who answered the question, 35% said their project has a formal policy permitting AI use. Another 32% have none and no plans for one, 23% plan to write one, and 10% ban AI contributions outright....
Most maintainers (71%) say AI makes it easier for newcomers to start contributing.

Other interesting findings:

"Respondents estimate average time reductions across all six OSS development activities measured, including proposal discussions (19%) and code authoring (52%). For high-complexity tasks, however, 21% of respondents report taking more time when using GenAI."
"71% of maintainers agree GenAI reduces barriers to entry for OSS, but 53% of maintainers disagree that GenAI has improved community trust."
Developers are reinvesting the time they save back into open source: 66% report using productivity gains to address technical debt, 63% to develop new features and 58% for learning and upskilling.
Continued investment in human mentorship remains important: 46% of maintainers say GenAI increases the need for mentorship of new contributors, compared with 32% who say it decreases that need.



OpenSSF and LF AI & Data also helped support the study.

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The problem? Understanding how AI coding agents interact with the software you build. "Whether you're maintaining an SDK, building a command-line interface, or publishing an MCP server or skills... you need to understand whether agents can discover and use what you ship, and what your team can change."
Results can vary, and configurations keep changing, with behaviors influenced by everything from custom instructions and MCP servers to skills and plugins. So after a year of development Microsoft has created Scope, "an open-source platform built on Kubernetes for turning those interactions into evidence product teams can act on."

Details are available on... the Microsoft open source blog:
Available under the MIT License, Scope supports a continuous Developer Agent Experience (DevAx) learning loop grounded in repeatable experiments.
The Azure Cosmos DB team's experience shows what that loop can deliver... "The evaluations led to 20 Agent Kit issues and 27 documentation issues resolved," [says Jay Gordon, Senior Program Manager, Azure Cosmos DB]... These results connect evaluation to decisions a product team can make: which documentation needs clarification, where tooling needs a fix, and whether an update changes agent behavior.... [The gains] demonstrate how targeted changes, informed by observed behavior, can improve the way agents use a product.
For more on the shift from building for human developers to supporting agents, watch Your Product Was Built for Humans. Is It Ready for Agents? or join us at AI DevCon NYC conference in November.

Scope surfaces everything from incorrect assumptions in documentation (or recurring missed instructions) to whether agents select the intended SDK, initialize it correctly, and use its APIs, leading to clearer descriptions for command-line tool and more actionable errors.

Microsoft's open source blog also explains why AI agent evaluation needs an open-source foundation:
The challenges behind agent experience aren't unique to one product or organization. SDK maintainers, tool authors, researchers, and agent developers are all asking how agents behave and what helps them succeed. No single team can represent every language, workflow, or integration. An open project can bring more scenarios, more failure cases, and more viewpoints into the evaluation process... Opening Scope's execution and evaluation code gives contributors a way to question the methods, investigate unexpected results, and improve the evaluation itself. A shared foundation also reduces duplicated infrastructure. Agent integrations, reusable criteria, and trace-analysis improvements can help other teams study their own products without rebuilding the same machinery. The goal isn't one universal benchmark or definition of success. It's a common starting point that teams can adapt to their needs.

The MIT License lets teams use and extend Scope, while self-managed deployments let them evaluate private workloads without publishing their code or run data.... Scope streams activity as the agent works and retains interactions and workspace snapshots for later review. Your team can follow tool calls, inspect generated files, and connect a failed requirement to the steps that preceded it. Those traces provide observable evidence of what the agent did, helping turn a failure into a concrete documentation, SDK, or tooling improvement.

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