Ainekko

FOSDEM'25 AI DevRoom: Topics Explained! Great dive with Jarek Potiuk into a few topics we highly welcome in the Low-Level AI Engineering and Hacking DevRoom - from models inference engines and quantization to hardware acceleration, llama.cpp governance and new AI open source era principles like freedom to use sowatware as you like. So many interesting perpsectives - we tried to narrow them down for those who are still considering an application. Clocks are ticking! Call for Proposals closes on Dec 1st - we want you in our DevRoom and will be happy to support you in application process or even sponsor your travel to Belgium on Feb 1-2, if necessary. Watch our podcast and learn more about the DevRoom: https://lnkd.in/dVkzwpvi

"Last year, I attended one talk at FOSDEM that completely changed my trajectory for this year." These are sentiments we’ve heard from many of our participants. FOSDEM isn’t just another tech conference. It’s complete chaos (in the best possible way), where groundbreaking AI engineering and innovation thrive. What makes FOSDEM stand out? Imagine this: Walking into a room buzzing with the most passionate minds in open-source and AI engineering - people whose GitHub profiles you’ve admired - and now you get to meet them in person. That’s what makes FOSDEM special for so many It’s a place where hands-on work takes the spotlight, whether you’re: → Combining models → Discussing distillation techniques → Showing your latest technical ideas Does this sound like your crowd? You’ll feel right at home if you’re: → Working directly with models → Creating practical, real-world AI solutions → Interested in how AI works behind the scenes Don’t just attend - PRESENT your work and meet the community! Best part? It’s all FREE. No registration is required. Just show up and dive in to deepen your learning and skills. So why wait? Join us at FOSDEM to shape the AI future. INTERESTED? DM “FOSDEM” in the comments below!

What’s great about FOSDEM is that all the truly passionate open-source community is there! Whether you're into governance, policy, hardware, low-level hacking, or even niche firmware solutions - there’s a space for everyone. You don't need to register—you just show up It’s free - and still grows and exists for over 20 years. I’ve organized many conferences, but FOSDEM is unique. It’s like what Burning Man used to be—no tickets, just show up and enjoy. It’s not uncommon to spot people in costumes, like someone dressed as an elephant last year. You get to meet incredible people - even while waiting in line for food. The connections made in those moments are unforgettable - even despite the cold weather. Useful swag like umbrellas, hats, and, of course, beer are at your disposal thanks to organizers! If you want to be a part of this - join us in the "Low-Level AI Engineering&Hacking DevRoom!" https://lnkd.in/d3pgefri

Choosing ML Deployment Tools: A Practical Guide Common myths: ? Must be expensive ? Only for big tech ? Needs expert teams Reality check: ? Options for every budget ? Solutions for all levels ? Scalable for any size Quick decision framework: 1?? Small project? FastAPI/Flask 2?? Enterprise? KubeFlow/MLflow 3?? Cloud-first? AWS SageMaker/Azure ML 4?? Need monitoring? MLflow or W&B 5?? Budget tight? Start with open-source Pro Tip: Pick tools that match YOUR needs, not the hype. Start simple, scale later. What deployment tools work for your team? Share below!

We are starting our new podcast series - meet 'Opening AI'! Literally, we try to open up how AI works in these conversations, and the first episode featuring Avi Deitcher will walk you through the process of building systems for production, discussing everything from model deployment to inference, and the challenges of bringing LLMs into the real world. Avi breaks down the concept into three layers, each with its distinct challenges and considerations. Let's explore these layers in detail. Watch https://lnkd.in/dQbK7kgs Read https://lnkd.in/dqEpaQkt

Catch our fearless leader Tanya Dadasheva talking about AI Policy in today’s Tuesday Tech Talk hosted by Yujian Tang!

AI is eating the world, so how do we keep it safe for us? That's where the broad ranging field of AI Policy steps in. Tomorrow, we've got a super exciting talk with Tanya Dadasheva from AIFoundry.org and Ainekko coming to chat about AI Policy. Tanya has an extensive background working with open source policy, venture capital, and mathematics. I'm super excited for this, and if you want to get insight into AI policy and how it's playing out today? You've got to be there. RSVP in the comments

'Low-Level AI Engineering and Hacking' podcast write up is here! https://lnkd.in/eqgX3ytE Enjoy amazing stories from Jarek Potiuk, Roman Shaposhnik and Tanya Dadasheva about why we like FOSDEM so much (teaser: it's not only Belgian beer ??). Also watch the podcast recording: https://lnkd.in/e7Mp86Hk

Come see our co-founder Tanya in tomorrow's Tech Talks by OSS4AI and maing Yujian Tang! https://lu.ma/pv0xm6q9

Halloween has passed, however, I am still in kind of shock every day doing my work and trying to figure out how the AI world works at different levels:)) And continuing chapter one, what about hardware optimization, and speaking more precisely, decreasing compute costs through it? In short, our team in AIFoundry.org and part of the community is working on two related potential solutions here: quantization and, specifically, matrix multiplications with quantized data. ? When working with quantized models matrix multiplication is especially critical. Quantization is a process where the weights of a neural network are compressed into smaller data types (such as 8-bit or even 4/2-bit integers) to reduce the memory footprint and speed up computations - and the accuracy on certain tasks can stay almost the same! However, this compression brings challenges when trying to execute matrix multiplication directly on quantized data. So, how low-level optimizations for AI models explains the intricacies of working with quantized data, particularly in the context of the Lama CPP library, which supports quantized models? Here is the breakdown: Read in full https://lnkd.in/eYNA_GPy