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“I spend 8 hours per week trying to keep up to date, it’s overwhelming!”

“Integrating new libraries is difficult. They’re either poorly maintained or updated in ways that break compatibility.”

“I want to be able to experiment quickly, without relying on APIs for closed-source models.”

These were just a few of the challenges we heard from developers during months of interviews. Today, we’re excited to introduce Blueprints and the Blueprints Hub!

Meet Mozilla.ai Blueprints

The Blueprints Hub is designed to cut through the clutter of clunky tool integration and outdated resources, so you can focus on building, not troubleshooting. It’s a showcase for the best of the open-source builder community.

What are Blueprints?

Blueprints are your go-to, customizable workflows that enable you to prototype AI applications with trusted open-source tools. Each Blueprint tackles a real-world challenge, giving you a robust foundation to work from:

• Open-source power: Fully hosted on GitHub and built with the community.
• Ready out-of-the-box: Get started instantly with accessible setup options.
• Customizable and extendable: Use it as-is or extend it to fit your own needs.
• Consistent and templatized: Every Blueprint follows a core template to keep your workflow smooth.
• Community-driven: Contribute, collaborate, and be part of something bigger.

Our launch lineup
Kick off your journey with these five practical Blueprints:

• Document-to-Podcast: Turn your text into lively, multi-voice audio clips with minimal fuss.
• Structured Question Answering: Extract answers from structured documents with a simple workflow.
• Finetuning Speech-to-text: Fine-tune speech models locally for multiple languages or your own dataset.
• OpenStreetMap AI Helper: Use computer vision to detect and map features on OpenStreetMap, with Human Verification.
• Finetuning an LLM with Federated AI: Collaboratively fine-tune models across data owners without sharing raw data.
• Build your own Timeline Algorithm: Visualize, search, and re-rank social posts using AI without data leaving your computer.

Explore the Blueprints Hub

Our new Hub is built for ease and exploration:

• Instant demos: Play around with Blueprints live in the hosted demo. No installation required.
• Video walkthroughs: Follow our video guides for a step-by-step introduction
• Technical insights: Understand the technical choices made during development of each Blueprint
• Practical use-cases: See how other developers are customizing and extending these Blueprints for their needs.
• Join our community: Share your blueprints, learn from fellow innovators, and help expand the hub.

Ready to transform your AI projects?

Join us and see how Mozilla.ai Blueprints Hub can speed up your development and spark your creativity. Visit our website now to explore, experiment, and become part of our vibrant community. Your next great idea is just a click away! 

Ready to transform your AI projects? Explore the Blueprints Hub

The post Open-source AI is hard. Blueprints can help! appeared first on The Mozilla Blog.

Sanibel Lazar is using TikTok to break beyond traditional book promotion.

Spoiler: The internet’s not finished. Welcome to “Web in Progress,” a series celebrating the internet as a space you can shape, not just scroll through. Just as Firefox empowers you to take charge of your online experience, this series highlights how individuals and communities are shaping an internet that truly serves their needs.

In this installment, see how a debut novelist is using TikTok to break beyond traditional book promotion. By focusing on niche interests, she found new ways to connect with readers who might never have picked up her book. Her experience is a testament to how digital platforms can open unexpected doors. 

Before I started promoting my debut novel, “To Have and Have More,” I hadn’t posted on any social media platform since 2015. Creating content wasn’t in my plan — until I realized it was the most practical way to get my book noticed. Working with a brand-new press meant I had to carve out my own opportunities. Social media was a means to feel like I was in the driver’s seat as my book went out into the world. 

Instead of feeling overwhelmed by the need to promote my book, I leaned into what I could control. I started creating videos on TikTok, not as part of BookTok, but tailored to themes from my book like class, privilege and wealth. They led me to unexpected audiences. I’ve ended up on PrivilegeTok, WealthTok, StatusSymbolTok and even QuietLuxuryTok — places where I stand out as the only person talking about a novel. My videos are a way to access audiences who might not otherwise pick up a book.

When one of my videos hit a couple hundred thousand views, I checked my Amazon ranking and watched my book climb. Social media has also brought me opportunities I didn’t anticipate. Rather than chasing podcast invites or op-eds, I’ve been getting invitations to do readings and guest spots (it’s thanks to social media that I got tapped to write this article) — all because people discovered me through my content. 

I’m not on TikTok to recommend books or talk about author life; instead I riff on social etiquette, classism, and luxury brands. My strategy isn’t about jumping on trend bandwagons but about getting people interested in my book. I call this approach “Oblique Content,” inspired by perfume ads that sell a mood or idea rather than focusing on product specs. In my videos, I talk about everything from toxic wealth to throwback millennial fashion trends — and I plug my book for ten seconds at the end.

I got a DM recently from a follower who said she was shocked to see a certain high-end brand at TJMaxx and thought of me. That message was a small but significant sign: My content was resonating. People were connecting my name and voice with the themes of my book.

For creatives, finding success on social media isn’t as simple as racking up views. You want your followers to be interested in your body of work and your ideas — not just your ability to “stop the scroll.” My advice? Experiment widely and don’t pigeonhole yourself in the conventions of your genre. And don’t get sidetracked scrolling for inspiration; focus on creating. 

Sanibel Lazar is a writer based in New York City. She earned her MFA from The New School and her BA in Classical Studies from the University of Pennsylvania. Her debut novel, “To Have and Have More,” will be published in April 2025. Sanibel’s work has appeared in New York Magazine, ELLE, Air Mail, Literary Hub and more.

The post Reaching readers, one TikTok at a time appeared first on The Mozilla Blog.

Oude versies Firefox gaan slechter werken door verlopen rootcertificaat - Computer - Nieuws  Tweakers

As you scroll through social media, the posts blend together: a heroic cat, a shocking statistic, a breaking news clip. But in a world where AI blurs the line between fact and fiction, how do you tell what’s real from what’s misinformation? 

The short answer: Most of the time, you can’t. Generative AI tools have made it faster, cheaper, and easier to create convincing fakes, flooding our feeds with AI-generated content. But here’s what you can do – learn to spot the signs of misinformation.

What should I think about when trying to detect AI? 

Just looking out for obvious AI will mean missing a lot of it. Retrain your brain to assess social media on a framework similar to the ones used by AI-misinformation researchers. Consider who’s behind the post, whether the content makes sense, how it’s written, the emotions it evokes and any signs of manipulation.   

User 

• Who is posting this? Is it a reliable source? Is this account tied to a real-world institution that I trust? 
• What is the username length? Is it a long set of random numbers and letters? Is it a verified account? Does it only have a handful of followers (who also look random or not real)? 

Content 

• Does the framing make sense? What is this content about? Is it especially vague or seems so outrageous that it couldn’t be true? Does this contradict what you already know about the topic? 
• Are there platform flags that the content could be potentially misleading, or a comment section full of claims that it’s false? Are there AI badges or indicator hashtags such as #AI, #satire, #skit, #spoof or #AD? 

Style 

• How is it written? Is there poor or wooden-sounding grammar? Is it flowery? Is there unnatural repetition, or has the user posted the same thing several times?   
• Does it repeat often-used AI words such as “elevate,” “captivate,” “tapestry” or “delve”? Does it use known AI phrases such as “provided valuable insights,” an “indelible mark,” or “a rich tapestry”? (Of course, these words and phrases don’t definitively mean that the content is AI-generated misinformation; they’re just reasons to take a closer look.)

Emotion 

• Is this an especially emotion-laded post? Is the level of emotion appropriate for the situation? 
• Does the post appear to “weaponize” emotion or tell readers how to feel about the content, such as by using more anger and swear words? (Keep in mind that bots on social media can and do use profanity). 

Manipulation 

• What might someone have to gain by touching on your emotions in this way? What’s the worst-case scenario if this turns out to not be true? What might a user (using AI) be hoping you don’t look up? 

How do I spot AI-generated images or videos online? 

Gone are the days where every AI image looked like a wacky Pixar knockoff, but it’s still worth checking for these known cues: 

• Hands and teeth with too many fingers, too many hands, too many teeth or impossibly long arms 
• Hyper-realistic images or those that look like paintings 
• Texture issues in the background, midground and around the corners and edges of an image
• Unnatural smoothness, especially on faces, clothes or hair 
• Shadows and light coming from the wrong place, or with only certain elements casting shadows 
• Abrupt transitions, either in an image or a video 

How do I verify if something is AI or not? 

Tools like TrueMedia.org scan social posts for false faces, voices, and information, while Mozilla’s Deepfake Detector analyzes text using multiple detection engines and generates a confidence score indicating the likelihood that the content is AI-generated. But while AI detection accuracy is improving, it isn’t perfect.

It always helps to try to verify the information itself — search for it along with “fact check” and look for trusted sources. For images, try a reverse image search using Google Image Search or Bing Image Match. 

What can misinformation look like on TikTok? 

Every social media site has its own AI landscape. Fake news, images and news clips targeting young voters and consumers are circulated particularly widely on TikTok due to its young user base. “Content farms” spin out inaccurate or misleading videos, often multiple a day in the distinctive TikTok style of on-screen text read by an AI voice. 

When scrolling on TikTok, be skeptical — or at least get a second opinion — on any informational videos that aren’t read by real people or only consist of captions to the AI voice (reputable news sites usually show who’s talking to build trust). Profiles that look like news sites but that have no comments or likes (especially for celebrity news) are a red flag — as well as canned phrases like “creepy response” or “finally broke their silence” meant to drive clicks. 

What can misinformation look like on X?

Though many AI-generated posts on X are largely innocuous text-based posts, videos in particular are more likely to be political deepfakes. And while the crowdsourced moderation feature, “Community Notes,” allows users to annotate posts with context or warnings, it replaced a more robust monitoring operation that means it’s more likely users will encounter bots. 

Stay wary of accounts that only spam replies, or situations where multiple accounts are commenting similar things under a post. If a user only posts replies, especially to inflammatory content, it’s a red flag that it’s a bot searching for certain keywords. 

Also, user verification on X is the least trustworthy of the major social media sites as users can pay for “verified” status (in one Cambridge study, half of synthetic profiles studied had verified status). 

What can misinformation look like on Facebook? 

It’s especially difficult to silo yourself from AI-generated content on Facebook, even if you’re only interested in posts from family and friends. Over the past three years, there has been a “significant increase” in the rate of users seeing posts they held no “friend” connection to, thanks to the algorithm that surfaces high-engagement posts in users’ feeds.

Being disciplined about clicking “not interested” under the three dots on each post can help stem the flow, as well as staying skeptical of images and being wary of link-outs to “news” sites. Verify posts (even those that appear to be from a harmless, real person) about any news events independently.

Misleading posts on Facebook are also especially focused on trying to get users off Facebook — directing them off the platform to content farms, fake stores and other scam sites. 

Stay alert and think critically online

Humans often overestimate how good they are at detecting AI — nice art is sometimes AI-generated, and terrible grammar is sometimes very human. It’s not easy to navigate a landscape designed to trick you, but your best call is to improve how you critically consume all information. Stay curious. After all, AI gets better every passing day — right down to drawing those tricky hands.

Sarah Skinner is a senior editor at a NYC tech startup. She holds a degree from Cornell University on AI and empathy, and has previously worked for McKinsey & Company and the Associated Press.

The post Misinformation in the age of AI: It’s in the details (like extra fingers) appeared first on The Mozilla Blog.

Software-update: Mozilla Firefox 136.0.1 - Computer - Downloads  Tweakers

Last week the Department of Justice and some state attorneys general filed revised proposed remedies in the U.S. v. Google LLC search case. If the proposed remedies barring all search payments to browser developers are adopted by the court, these misguided plans would be a direct hit to small and independent browsers—the very forces that keep the web open, innovative and free. This case was meant to promote search competition, yet somehow the outcome threatens to crush browser competition, making it even harder for challengers to stand up to dominant players like Google, Apple and Microsoft.

“These proposed remedies prohibiting search payments to small and independent browsers miss the bigger picture—and the people who will suffer most are everyday internet users,” said Mark Surman, President of Mozilla. “Independent browsers like Firefox are on the frontlines of protecting consumer privacy, driving browser innovation, and giving people real choice on how they experience the web. But instead of promoting a fair fight, the DOJ’s remedies would tilt the playing field further into the hands of a few dominant players, diminishing consumer choice and weakening the broader internet ecosystem.”

The DOJ’s proposal hurts, not helps, browser competition

Mozilla agrees that we need to improve search competition, but the DOJ’s proposed remedies unnecessarily risk harming browser competition instead.

Here’s why:

• The DOJ wants to ban all search agreements between Google and browsers, even independent browsers that make up a smaller part of the market.
• Dominant players that own browsers, like Apple, don’t rely on search deals as they have significant revenue streams from other sources, like hardware, operating systems and app stores.
• Meanwhile, independent browsers like Firefox fund the development of their browsers mainly through search revenue––they require this revenue to survive. Search revenue underpins a large part of our work, keeping Firefox competitive and ensuring that web users have privacy-first alternatives.
• Punishing independent browsers will not solve the problem. Judge Mehta found that independent browsers account for just 1.15% of U.S. search queries. This means that cutting off our access to search deals won’t fix the issue of search dominance—not by a landslide. Instead, it hurts browser competition.

“The big unintended consequence here is the handing of power from one dominant player to another. So, from Google Search to Microsoft, or Bing for example—while shutting out the smaller, independent challengers that actually drive browser innovation and offer web users privacy and choice,” Surman added.

The last unicorn–the web can’t afford to lose Mozilla’s browser engine

Another thing missing from this conversation is something pretty important—browser engine competition.

You see, browser engines power the web. They are central to a browser’s speed, privacy and security functionality, and the browser’s ability to innovate and do things differently. But they’re very complex and require massive resources and a deep technical expertise to maintain—so much so, that right now only three major browser engines remain: Google’s Chromium, Apple’s Webkit (this engine is really only supported on Apple devices, and isn’t considered “cross-platfrom”), and then there’s Mozilla’s Gecko (which happens to be the only true cross-platform alternative to Chromium).

The DOJ’s proposal to bar search payments to independent browser developers would put Mozilla’s ability to develop and maintain Gecko at risk. If Mozilla is unable to sustain our browser engine, it would severely impact browser engine competition and mean the death of the open web as we know it—essentially, creating a web where dominant players like Google and Apple, have even more control, not less.

“This isn’t just about Firefox,” Surman explained. “If we lose our ability to maintain Gecko, it’s game over for an open, independent web. Look, Microsoft—a $3 trillion company—already gave up its browser engine in 2019 and Opera gave up theirs in 2013. If Mozilla is forced out, Google’s Chromium becomes the only cross-platform browser engine left.”

Mozilla’s role in an open web is BIGGER than our market share

Nevermind our market share, Mozilla has played an outsized role in keeping the web open, private and advocating for choice. Firefox still serves 27 million monthly active users (MAU) in the U.S. and nearly 205 million MAU globally, but our real impact comes from making the internet better by:

• Shaping the future of web standards—maintaining our own browser engine, Gecko, gives us a voice in defining how the web works and making decisions that are in support of people, not the bottom-line.
• Ensuring interoperability—we fight for a web accessible to all—where anyone can create, access, and share content seamlessly, regardless of the devices or web services they use—not locked into a few ecosystems.
• Proving that privacy-respecting technology is possible—we build critical web technologies with security, privacy and user agency at the core.

“This isn’t something we do because it’s profitable or easy,” said Surman. “We do it because it matters. The DOJ’s proposal doesn’t just miss the mark, it risks handing even more power to dominant industry players like Google or Apple, not less.”

Mozilla calls on regulators and policymakers to recognize the vital role of independent browsers and take action to nurture competition, innovation, and protect the public interest in the evolving digital landscape.

Mozilla is committed to ensuring a fair and competitive internet ecosystem, one where independent browsers can compete on a level playing field and consumers have real choice. The future of competition, innovation and the open internet depends on us.

The post Mozilla’s response to proposed remedies in U.S. v. Google appeared first on The Mozilla Blog.

Thunderbird vanaf dit najaar ook beschikbaar op iOS  TechPulse

Software-update: Mozilla Thunderbird 128.8.0esr - Computer - Downloads  Tweakers

Software-update: Mozilla Thunderbird 136.0 - Computer - Downloads  Tweakers

Mozilla: 'Nieuwe gebruiksvoorwaarden betekent niet dat wij gebruikersdata gaan verkopen'  Dutch IT Channel

Maker Firefox-browser reageert op kritiek op nieuwe gebruiksvoorwaarden  NU.nl

Mozilla: Firefox-voorwaarden geven het bedrijf geen eigendom van gebruikersdata - Computer - Nieuws  Tweakers

Firefox zegt dat het misschien je persoonlijke data verkoopt  Androidworld

Firefox onder vuur voor aanpassingen in privacybeleid  TechPulse

In the Firefox AI Runtime, we can use multiple threads in the dedicated inference process to speed up execution times CPU. The WASM/JS environment can create a SharedArrayBuffer and run multiple threads against its content and distribute the load on several CPU cores concurrently.

Below is the time taken in seconds on a MacBook M1 Pro, which has 10 physical cores, using our PDF.js image-to-text model to generate an alt text, with different levels of concurrency:

So running several threads is a game-changer ! But using more and more threads will start to slow down execution time to a point where it will become slower than not using threads at all. 

So one question we have asked ourselves was: how can we determine the best number of threads ?

Physical vs logical cores

According to our most recent public data report, on desktop, 81% of our users are equipped with an Intel CPU, 14% with AMD and the rest are mostly Apple devices.

All modern CPUs provide more logical cores (also called “threads”) than physical cores. This happens due to technologies like Intel’s Hyper-Threading. Or AMD’s Simultaneous Multithreading (SMT).

For example, the Intel Core i9-10900K chip has 10 physical cores and 20 logical cores.

When you spin up threads equal to the number of logical cores, you might see performance gains, especially when tasks are I/O bound or if the CPU can effectively interleave instructions.

However, for compute-bound tasks (like heavy ML inference), having more threads than physical cores can lead to diminishing returns, or even performance drops, due to factors like thread scheduling overhead and cache contention.

Not all cores are created equal

On Apple Silicon, you don’t just have a quantity of cores; you have different kinds of cores. Some are high-performance cores designed for heavy lifting, while others are efficiency cores that are optimized for lower power consumption.

For instance, Apple M1 Pro chips have a combination of high-performance (8) and efficiency cores (2). The physical cores might total 10, but each performance core is designed for heavy-duty tasks, while efficiency cores typically handle background tasks that are less demanding. 

When your machine is under load with ML tasks, it’s often better to fully utilize the high-performance cores and leave some breathing room for the efficiency cores to handle background or system processes. 

Similarly, Intel’s processors have different cores, most notably starting with their 12th-generation “Alder Lake” architecture. 

These chips feature Performance-cores (P-cores) designed for demanding, single-threaded tasks, and Efficient-cores (E-cores) aimed at background or less intensive workloads. The P-cores can leverage Intel’s Hyper-Threading technology (meaning each P-core can run two logical threads), while E-cores typically run one thread each. This hybrid approach enables the CPU to optimize power usage and performance by scheduling tasks on the cores best suited for them. Like Apple Silicon’s you’d typically want to maximize utilization of the higher-performance P-cores, while leaving some headroom on the E-cores for system processes. 

Android is close to Apple Silicon’s architecture, as most devices are using ARM’s big.LITTLE (or DynamIQ) architecture – with 2 types of cores: “big” and “LITTLE”.

On mobile Qualcomm’s CPU, there can be three types: “Prime”, “Performance” and “Efficiency”. Most recently, some phones like Samsung Galaxy S24 have gained a fourth kind of core (Exynos 2400) allowing even more combinations.

To summarize, all CPU makers have cores dedicated to performance, and cores for efficiency: 

• Performance: “P-Core”, “big”, “Prime”, “Performance”
• Efficiency: “E-Core”, “LITTLE”, “Efficiency”

By combining high-efficiency and high-performance cores, Apple Silicon, Androids, and Intel based devices can strike a better balance between power consumption and raw compute power, depending on the demands of the workload.

But if you try to run all cores (performance + efficiency) at maximum capacity, you may see:

1. Less optimal thread scheduling, because tasks will bounce between slower efficiency cores and faster performance cores.
2. Contention for shared resources like the memory bus, cache.
3. And in extreme cases: thermal throttling if the system overheats, and reaches its Thermal Design Point, in which case the clock speed is throttled to cool down the system. 

This is why simply setting the thread count to “all cores, all the time” can be suboptimal for performance.

AMD on the other hand, does not have efficiency cores. Some CPUs like the Ryzen 5 8000 combine two sizes of cores Zen 4 and Zen 4c, but the latter is not an efficiency core and can also be used to run heavy-duty tasks.

navigator.hardwareConcurrency

In a browser, there is a single and simple API you can call: navigator.hardwareConcurrency

This returns the number of logical cores available. Since it’s the only API available on the web, many libraries (including the one we vendor: onnxruntime) default to using navigator.hardwareConcurrency as a baseline for concurrency.

It’s bad practice to use that value directly as it might overcommit threads as we’ve explained in the previous sections. It’s also not aware of the current system’s activity.

For that reason, ONNX formula takes the number of logical cores divided by two and will never set it higher than 4:

Math.min(4, Math.ceil((navigator.hardwareConcurrency || 1) / 2));

That formula works out ok in general, but will not take advantage of all the cores for some devices. For instance, on an Apple M1 Pro, ML tasks could use a concurrency level up to 8 cores instead of 4.

On the other end of the spectrum, a chip like Intel’s i3-1220p that we use in our CI to run tests in Windows 11, which reflects better what our users have – see our hardware section in our Firefox Public Data Report.  

It has 12 logical cores and 10 physical cores that are composed of 8 efficient cores and 2 performance cores. ONNX formula for that chip means we would run with 4 threads, where 2 would be a better value.

navigator.hardwareConcurrency is a good starting point, but it’s just a blunt instrument. It won’t always yield the true “best” concurrency for a given device and a given workload.

MLUtils.getOptimalCPUConcurrency

While it’s impossible to get the best value at any given time without considering the system activity as a whole, looking at the number of physical cores and not using “efficiency” cores, can help to get to a better value.

Llama.cpp for instance is looking at the number of physical cores to decide for concurrency, with a few twists:

• On any x86_64, it will return the number of performance cores
• On Android, and any aarch64-based devices like Apple Silicon  it will return the number of performance cores for tri-layered chips.

We’ve implemented something very similar in a C++ API that can be used via XPIDL in our inference engine:

NS_IMETHODIMP MLUtils::GetOptimalCPUConcurrency(uint8_t* _retval) { ProcessInfo processInfo = {}; if (!NS_SUCCEEDED(CollectProcessInfo(processInfo))) { return NS_ERROR_FAILURE; } #if defined(ANDROID) // On android, "big" and "medium" cpus can be used. uint8_t cpuCount = processInfo.cpuPCount + processInfo.cpuMCount; #else # ifdef __aarch64__ // On aarch64 (like macBooks) we want to avoid efficient cores and stick with "big" cpus. uint8_t cpuCount = processInfo.cpuPCount; # else // on x86_64 we're always using the number of physical cores. uint8_t cpuCount = processInfo.cpuCores; # endif #endif *_retval = cpuCount; return NS_OK; }

This function is then straightforward to use from JS shipped within Firefox to configure concurrency when we run inference:

let mlUtils = Cc["@mozilla.org/ml-utils;1"].createInstance(Ci.nsIMLUtils); const numThreads = mlUtils.getOptimalCPUConcurrency();

We’ve moved away from using navigator.hardwareConcurrency, and we’re now using this new API.

Conclusion

In our quest to find the optimal number of threads, we’re closer to reality now, but there are other factors to consider. The system will use the CPU for other applications so it’s still possible to overload it.

Using more threads is also going to use more memory in our WASM environment, which can become a real issue. Depending on the workload, each additional thread can add up to 100MiB of physical memory usage in our runtime. We’re working on reducing this overhead but on devices that don’t have a lot of memory, limiting concurrency is still our best option.

For our Firefox ML features, we are using a variety of hardware profiles in our performance CI to make sure that we try them on devices that are close to what our users have. The list of devices we have is going to grow in the next few months to make sure we cover the whole spectrum of CPUs. We’ve started collecting and aggregating metrics on a dashboard that helps us understand what can be expected when our users run our inference engine.

The hardware landscape is also evolving a lot. For example, the most recent Apple devices introduced a new instruction set, called AMX, which used to be proprietary, and gave a significant boost compared to Neon. That has now been replaced by an official API called SME. Similarly, some phones are getting more core types, which could impact how we calculate the number of cores to use. Our current algorithm could be changed the day we leverage these new APIs and hardware in our backend.

Another aspect we have not discussed in this post is using GPU or even more specialized units like NPUs, to offload our ML tasks, which will be a post on its own.

The post What is the best hardware concurrency for running inference on CPU? appeared first on The Mozilla Blog.

As we close out the Paris AI Action Summit, one thing is clear: the conversation around open and Public AI is evolving—and gaining real momentum. Just over a year ago at Bletchley Park, open source AI was framed as a risk. In Paris, we saw a major shift. There is now a growing recognition that openness isn’t just compatible with AI safety and advancing public interest AI—it’s essential to it.

We have been vocal supporters of an ecosystem grounded in open competition and trustworthy AI —one where innovation isn’t walled off by dominant players or concentrated in a single geography. Mozilla, therefore, came to this Summit with a clear and urgent message: AI must be open, human-centered, and built for the public good. And across discussions, that message resonated.

Open source AI is entering the conversation in a big way

Two particularly notable moments stood out:

• European Commission President Ursula von der Leyen spoke about Europe’s “distinctive approach to AI,” emphasizing collaborative, open-source solutions as a path forward.
• India’s Prime Minister Narendra Modi reinforced this vision, calling for open source AI systems to enhance trust and transparency, reduce bias, and democratize technology.

These aren’t just words. The investments and initiatives announced at this Summit mark a real turning point. From the launch of Current AI, an initial $400M public interest AI partnership supporting open source development, to ROOST, a new nonprofit making AI safety tools open and accessible, to the €109 billion investment in AI computing infrastructure announced by President Macron, the momentum is clear. Add to that strong signals from the EU and India, and this Summit stands out as one of the most positive and proactive international gatherings on AI so far.

At the heart of this is Public AI—the idea that we need infrastructure beyond private, purely profit-driven AI. That means building AI that serves society and promotes true innovation even when it doesn’t fit neatly into short-term business incentives. The conversations in Paris show that we’re making progress, but there’s more work to do.

Looking ahead to the next AI summit

Momentum is building, and we must forge onward. The next AI Summit in India will be a critical moment to review the progress on these announcements and ensure organizations like Mozilla—those fighting for open and Public AI infrastructure—have a seat at the table.

Mozilla is committed to turning this vision into reality—no longer a distant, abstract idea, but a movement already in motion.

A huge thanks to the organizers, partners, and global leaders driving this conversation forward. Let’s keep pushing for AI that serves humanity—not the other way around.

––Mitchell Baker
Chairwoman, Mozilla
Paris AI Action Summit Steering Committee Member

The post Paris AI Action Summit: A milestone for open and Public AI appeared first on The Mozilla Blog.

Today we want to point to one of the most exciting announcements at the Paris AI summit: the launch of ROOST, a new nonprofit to build AI safety tools for everyone. 

ROOST stands for Robust Open Online Safety Tools, and it’s solving a clear and important problem: many startups, nonprofits, and governments are trying to use AI responsibly every day but they lack access to even the most basic safety tools and resources that are available to large tech companies. This not only puts users at risk but slows down innovation. ROOST has backing from top tech companies and philanthropies alike ensuring that a broad set of stakeholders have a vested stake in its success. This is critical to building accessible, scalable and resilient safety infrastructure all of us need for the AI era. 

What does this mean practically? ROOST is building, open sourcing and maintaining modular building blocks for AI safety, and offering hands-on support by technical experts to enable organizations of all sizes to build and use AI responsibly. With that, organizations can tackle some of the biggest safety challenges such as eliminating child sexual abuse material (CSAM) from AI datasets and models. 

At Mozilla, we’re proud to have helped kickstart this work, providing a small seed grant for the research at Columbia University that eventually turned into ROOST. Why did we invest early? Because we believe the world needs nonprofit public AI organizations that at once complement and serve as a counterpoint to what’s being built inside the big commercial AI labs. ROOST is exactly this kind of organization, with the potential to create the kind of public technology infrastructure the Mozilla, Linux, and Apache foundations developed in the previous era of the internet.

Our support of ROOST is part of a bigger investment in open source AI and safety. 

In October 2023, before the AI Safety Summit in Bletchley Park, Mozilla worked with Professor Camille Francois and Columbia University to publish an open letter that stated  “when it comes to AI Safety and Security, openness is an antidote not a poison.” 

Over 1,800 leading experts and community members signed our letter, which compelled us to start the Columbia Convening series to advance the conversation around AI, openness, and safety. The second Columbia Convening (which was an official event on the road to the French AI Action Summit happening this week), brought together over 45 experts and builders in AI to advance practical approaches to AI safety. This work helped shape some of  the priorities of ROOST and create a community ready to engage with it going forward. We are thrilled to see ROOST emerge from the 100+ leading AI open source organizations we’ve been bringing together the past year. It exemplifies the principles of openness, pluralism, and practicality that unite this growing community. 

Much has changed in the last year. At the Bletchley Park summit, a number of governments and large AI labs had focused the debate on the so-called existential risks of AI — and were proposing limits on open source AI. Just 15 months later, the tide has shifted. With the world gathering at the AI Action Summit in France, countries are embracing openness as a key component of making AI safe in practical development and deployment contexts. This is an important turning point. 

ROOST launches at exactly the right time and in the right place, using this global AI summit to gather a community that will create the practical building blocks we need to enable a safer AI ecosystem. This is the type of work that makes AI safety a field that everyone can shape and improve.

The post ROOST: Open source AI safety for everyone appeared first on The Mozilla Blog.

Please note some of the information provided in this report may be subject to change as we are sometimes sharing information about projects that are still in early stages and are not final yet. 

Welcome!

Are you a locale leader and want us to include new members in our upcoming reports? Contact us!

New content and projects What’s new or coming up in Firefox desktop Tab Groups

Tab groups are now available in Nightly 136! To create a group in Nightly, all you have to do is have two tabs open, click and drag one tab to the other, pause a sec and then drop. From there the tab group editor window will appear where you can name the group and give it a color. After saving, the group will appear on your tab bar.

Once you create a group, you can easily access your groups from the overflow menu on the right.

 

These work great in the sidebar and vertical tabs feature that was released in the Firefox Labs feature in Nightly 131!

New profile selector

The new profile selector which we have been localizing over the previous months is now starting to roll out gradually to users in Nightly 136. SUMO has an excellent article about all the new changes which you can find here.

What’s new or coming up in web projects AMO and AMO Frontend

The team is planning to migrate/copy the Spanish (es) locale into four: es-AR, es-CL, es-ES, and es-MX. Per the community managers’ input, all locales will retain the suggestions that have not been approved at the time of migration. Be on the lookout for the changes in the upcoming week(s).

Mozilla Accounts

The Mozilla accounts team recently landed strings used in three emails planned to be sent over the course of 90 days, with the first happening in the coming weeks. These will be sent to inactive users who have not logged in or interacted with the Mozilla accounts service in 2 years, letting them know their account and data may be deleted.

What’s new or coming up in SUMO

The CX team is still working on 2025 planning. In the meantime, read a recap from our technical writer, Lucas Siebert about how 2024 went in this blog post. We will also have a community call coming up on Feb 5th at 5 PM UTC. Check out the agenda for more detail and we’d love to see you there!

Last but not least, we will be at FOSDEM 2025. Mozilla’s booth will be at the K building, level 1. Would love to see you if you’re around!

What’s new or coming up in Pontoon New Email Features

We’re excited to announce two new email features that will keep you better informed and connected with your localization work on Pontoon:

Email Notifications: Opt in to receive notifications via email, ensuring you stay up to date with important events even when you’re away from the platform. You can choose between daily or weekly digests and subscribe to specific notification types only.

Monthly Activity Summary: If enabled, you’ll receive an email summary at the start of each month, highlighting your personal activity and key activities within your teams for the previous month.

Visit your settings to explore and activate these features today!

New Translation Memory tools are here!

If you are a locale manager or translator, here’s what you can do from the new TM tab on your team page:

• Search, edit, and delete Translation Memory entries with ease.
• Upload .TMX files to instantly share your Translation Memories with your team.

These tools are here to save you time and boost the quality of suggestions from Machinery. Dive in and explore the new features today!

Moving to GitHub Discussions

Feedback, support and conversations on new Pontoon developments have moved from Discourse to GitHub Discussions. See you there!

Newly published localizer facing documentation

• How to test mozilla.org was updated to reflect some of the changes to the site in the last year or so.

Events

Come check out our end of year presentation on Pontoon! A Youtube link and AirMozilla link are available.

Want to showcase an event coming up that your community is participating in? Contact us and we’ll include it.

Friends of the Lion

Know someone in your l10n community who’s been doing a great job and should appear here? Contact us and we’ll make sure they get a shout-out!

Useful Links

• #l10n-community channel on Element (chat.mozilla.org)
• Localization category on Discourse
• Mastodon
• L10n blog

Questions? Want to get involved?

If you want to get involved, or have any question about l10n, reach out to:

• Francesco Lodolo (flod) – Engineering Manager
• Bryan – l10n Project Manager
• Delphine – l10n Project Manager for mobile
• Peiying (CocoMo) – l10n Project Manager for mozilla.org, marketing, and legal
• Francis – l10n Project Manager for Common Voice, Mozilla Foundation
• Théo Chevalier – l10n Project Manager for Mozilla Foundation
• Matjaž (mathjazz) – Pontoon dev
• Eemeli – Pontoon, Fluent dev

Did you enjoy reading this report? Let us know how we can improve it.

Firefox is typically installed on macOS by downloading a DMG (Disk iMaGe) file, and dragging the Firefox.app into /Applications. These DMG files are compressed to reduce download time. As of Firefox 136, we’re making an under the hood change to them, and switching from bzip2 to lzma compression, which shrinks their size by ~9% and cuts decompression time by ~50%.

Why now?

If you’re familiar with macOS packaging, you’ll know that LZMA support was introduced in macOS 10.15, all the way back in 2015. However, Firefox continued to support older versions of macOS until Firefox 116.0 was released in August 2023, which meant that we couldn’t use it prior to then.

But that still begs the question: why wait ~18 months later to realize these improvements? Answering that question requires a bit of explanation of how we package Firefox…

Packaging Firefox for macOS… on Linux!

Most DMGs are created with hdiutil, a standard tool that ships with macOS. hdiutil is a fine tool, but unfortunately, it only runs natively on macOS. This a problem for us, because we package Firefox thousands of times per day, and it is impractical to maintain a fleet of macOS machines large enough to support this. Instead, we use libdmg-hfsplus, a 3rd party tool that runs on Linux, to create our DMGs. This allows us to scale these operations as much as needed for a fraction of the cost.

Why now, redux

Until recently, our fork of libdmg-hfsplus only supported bzip2 compression, which of course made it impossible for us to use lzma. Thanks to some recent efforts by Dave Vasilevsky, a wonderful volunteer who previously added bzip2 support, it now supports lzma compression.

We quietly enabled this for Firefox Nightly in 135.0, and now that it’s had some bake time there, we’re confident that it’s ready to be shipped on Beta and Release.

Why LZMA?

DMGs support many types of compression: bzip2, zlib, lzfse and lzma being the most notable. Each of these has strengths and weaknesses:

• bzip2 has the best compression (in terms of size) that is supported on all macOS versions, but the slowest decompression
• zlib has very fast decompression, at the cost of increased package size
• lzfse has the fastest decompression, but the second largest package size
• lzma has the second fastest decompression and the best compression in terms of size, at the cost of increased compression times

With all of this in mind, we chose lzma to make improvements on both download size and installation time.

You may wonder why download size is an important consideration, seeing as fast broadband connections are common these days. This may be true in many places, but not everyone has the benefits of a fast unmetered connection. Reducing download size has an outsized impact for users with slow connections, or those who pay for each gigabyte used.

What does this mean for you?

Absolutely nothing! Other than a quicker installation experience, you should see absolutely no changes to the Firefox installation experience.

Of course, edge cases exist and bugs are possible. If you do notice something that you think may be related to this change please file a bug or post on discourse to bring it to our attention.

Get involved!

If you’d like to be like Dave, and contribute to Firefox development, take a look at codetribute.mozilla.org. Whether you’re interested in automation and tools, the Firefox frontend, the Javascript engine, or many other things, there’s an opportunity waiting just for you!

Greetings extension developers!

We wanted to highlight this just-published blog post from our AI team where they share some exciting news – we’re shipping a new experimental ML API in Firefox that will allow developers to leverage our AI Runtime to run offline machine learning tasks in their web extensions.

Head on over to Mozilla’s AI blog to learn more. After you’ve had a chance to check it out, we encourage you to share feedback, comments, or questions over on the Mozilla AI Discord (invite link).

Happy coding!

The post Announcing the WebExtensions ML API appeared first on Mozilla Add-ons Community Blog.