Rudi Araújo

Mischa van den Burg,  your tweet is spot on about the future of the CNCF ecosystem and cloud-native tech. Thanks a lots . For those worried about AI taking jobs—fear not. The entire AIML workload relies heavily on Kubernetes, and there’s a long way to go before automation could replace all human roles. Many companies are still operating on VMs and gradually transitioning to Kubernetes. This ecosystem—CNCF, Kubernetes, and adjacent tech—is here to stay for at least the next decade. So, Rather you could be Relevant with these technologies.

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Exploring Network Models with OpenCypher and Google’s MALT Concept Google’s MALT whitepaper introduced a powerful approach to network modeling based on their Unified Network Model (UNM), and thanks to Jeffrey Mogul and the GGN leadership team, this valuable insight is publicly accessible! For anyone curious, check out the paper here: https://lnkd.in/euzsjWT5. To help others get started, Google even shared a sample model: https://lnkd.in/exz-tGn7 Internally, Google uses a model query language to extract submodels, but I wanted to explore using Cypher as the query language. Luckily, OpenCypher has a Go implementation by CloudPrivacyLabs (OpenCypher Go) that made this possible with just a bit of glue code! https://lnkd.in/egP8EhCK With a bit of glue code, et voila... It's not a full implementation of the Cypher query language but it is sufficient for the large majority of the use cases. model-shell> MATCH (c:EK_COUNTRY)-[]->()-[]->()-[]->(d:EK_DEVICE)         RETURN c.entity.id.name, d.entity.id.name Results: 0 map[1:Australia 2:device1]   1 map[1:United States 2:device3]   2 map[1:Switzerland 2:device2] It’s exciting to see how adaptable these network models can be with the right tools! #NetworkModeling #MALT #OpenCypher #GoLang #Networking

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You have to love it when you can get a negative review that essentially says "Vendor made a change that makes their work less exploitable" when the software remains under an OSI approved license. People don't inherently understand open source companies. We didn't make KumoMTA open source because we're a bunch of anti-capitalist hippies, we did it because we considered it to be the best option for our customers. To keep that decision in place, our customers need to support us, otherwise the next option becomes either closing up shop or moving to a model that doesn't make supporting our efforts optional. Fortunately for us we work in a very niche space where our customers recognize the value we bring them and the importance of us continuing to do so, and steadily growing numbers of them are becoming sponsors and ensuring the continuous future of KumoMTA. Because of this unique combination of factors we haven't had to look at open core models or source available licenses to succeed.

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My article "Green Code: Why the World Needs More Than Just Efficient Algorithms" got featured as the cover story in SDN Magazine (Dutch)! As developers, we have the unique power to create software that doesn’t just serve people, but also serves the planet. 🌱 In this article, I explore how we can make a meaningful, positive impact by writing code that is not only efficient but eco-friendly too. 💡 If you're curious about how technology and sustainability go hand in hand, this is for you! Let's work together to create a greener digital future. Reach out if you want to learn more or get a copy of the magazine! 🌿 #GreenCode #SustainableSoftware #Sustainability

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🌟 The Right Architecture for Your SaaS Platform 🌟 When it comes to building a SaaS platform, the architecture you choose can make or break your project. And guess what? The choice isn’t black and white—it depends on various factors. 🤔 Let’s talk about monolithic architecture first. It’s like a cozy studio apartment where everything you need is in one space. It’s perfect for simpler applications, quick launches, and when you want to keep things straightforward and snappy. Plus, if your team is small and you’re looking to hit the market fast, going monolithic can be like a speedboat—swift and efficient. 🏁 Now, onto microservices. Imagine a sprawling mansion with different rooms for everything. This is the go-to for complex, scalable applications that grow with your user base. If you’re planning to frequently update features, need high fault tolerance, and love the idea of using a variety of tech stacks, microservices are your best bet. It’s like having a team of specialists, each handling their own department. 🏠 For SaaS development, your choice should be tailored to the specific needs of your project, the expertise of your team, and the complexity of the application you’re dreaming up. Whether you’re a startup or a seasoned enterprise, the architecture you choose is the foundation of your service. So, choose wisely! 🧐 What’s your experience with monolithic vs. microservices architectures? Drop your thoughts below! 👇 #SaaSDevelopment #SaaSSolutions #SoftwareArchitecture #TechTalks #TechChoices #BuildingTogether

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How far can you go with an open-source tech stack? A few months ago, I got annoyed with PandaDoc. It made no sense to pay to send a handful of signatures per month. Especially, as the tech behind it is so simple. I thought, there must be an open-source tool. And there was: OpenSign. We have been using OpenSign for 4 months now, and it does the job. Unlimited signatures for free. The UX is a bit clunky and ugly, but it does not cause any productivity loss. So who cares? Now, I went down the rabbit hole and searched for more open-source tools. Well, there are many! Below is a list. I tested a few but only use OpenSign. Why did I not use the others? - Self-hosting is not an option. - Paid tools have some features we need. - Paid tools consolidate many free tools. - Connectivity of tools stops us from switching. Killing PandaDoc was easy. As none of the limitations above applied. I can see how business might use them. But there are also reasons against it. Do you use an open-source tool? Can you recommend any?

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In PyData Eindhoven definitely the building of trusted Generative AI applications is the main theme. Not only the building of Retrieval Augmented Generation pipelines is being discussed, but also, how to make them performant and traceable. A lot of the discussions made me remember some talks by Dr. Chris Hillman (Who you should definitely follow), and this article by our colleague Chetan Hirapara (Who you should also definitely follow). https://lnkd.in/g5JYrhHX

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LLMs and Optimization: An Unbeatable Combo!

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🚀  Llama 3.1 is a game-changer! I had to blink my eyes today when I ran some tool-calling experiments (in Dutch): the 70B version performed perfectly on tough cases, just like GPT-4o. And Officially Llama 3.1 does not even speak Dutch. I ran it on a double RTX 4090 GPU for 80ct/h (20 euros a day)! ‘Tool calling’ is the glue between human speech and the system executing the spoken request. So far self-hosted LLMs were not strong enough to do this reliably even after fine-tuning, so an external LLM like OpenAI GPT-4 was always needed. No more! Now banks, insurance companies, and governments who are afraid of privacy and security issues with external LLMs can run a high-quality model on 4000 euros of graphics card! The limit is gone! Last but not least, the 8B version (which you can host for 15ct an hour) is also really good at tool-calling. It has minor glitches, but if your use case is not too demanding it is a blazingly fast-responding little beast.

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Very interesting read . Thanks Srini for sharing and shedding some light on current AI shortages and challenges.

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I have a couple of questions (if you are aware of how this was implemented) regarding the reward function design: - How is the reward function in the RL model formulated to balance profitability and risk? and - Does the reward function incorporate long-term strategy over short-term gains?

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Got inspired by a dutch podcast that creates stories from wikipedia pages. I used gpt-4o and their text-to-speech model to build something that sounded human. Of course it's not as great as the original podcast but this tech works way better than expected! Try it yourself 👇 Huggingface Space: https://lnkd.in/eKdT3XBY How did I do it? - Use <break time="1s"/> tag on strategic places to create breaks. - The TTS model is smart enough to understand these tags! - Prompt to use colloquialism english. - Use filler words like "um", "well", ... Bonus: I also generated an intro song using facebooks "MusicGen" and tweaked it a bit with with online tool called "AudioMass" Tools I used: AudioMass: https://audiomass.co/ MusicGen: https://lnkd.in/eAfjHZzA If your dutch speaking; the original podcast is called "Nooit Geweten", I can highly recommend: https://lnkd.in/ex66X6_a #gpt-4o #text-to-speech #tts #huggingface #spaces #podcast

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I've talked about this before: Frontier Models > Older Model Methodology/ALgos with (years of data/years of research). See Erik Meijer from Microsoft talking about how frontier LLM doing much better than older research and algos. Other examples: latest and the greatest traditional OCR (Object Character Recognition) vs LLM-based visual recognition in OpenAI's 4o-mini. I've also anecdotally heard of Spotify's existing recommendations ML algo with access to years of music-listening user data giving lower quality recommendations than uploading a screenshot of favourites in Spotify app to an LLM and asking for recommendations. Insightful talk from Erik Meijer (MSFT VB, C#, LINQ): link in comments. The best part title of his talk: "Hinton's Nightmare is My Dream" 🤣

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I enjoyed joining Demetrios Brinkmann and Jelmer Borst on the MLOps Community podcast, where we discussed the challenges of scaling ML teams at Picnic Technologies We had a great time sharing our journey and discussing the ups and downs we encountered while scaling both our teams and technology. As the ML field continues to evolve, so do the organizational challenges, yet resources on how to navigate these issues are still limited. That's why we draw inspiration from leaders like Elizabeth Stone and Jessica Lachs, who have openly shared their experiences. We're excited to contribute with our insights and lessons learned to help others facing similar challenges. Check out the episode here:  https://lnkd.in/eZVknC_b #MLOps #MachineLearning #DataScience #AI

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Mere weeks after Devin (devin.ai) was launched as the 'First AI Software Engineer', several open source equivalents have emerged, such as OpenDevin. What makes OpenDevin extremely interesting is that you can achieve the same functionality as Devin using whatever local LLMs you want - it's also free. The rapid emergence of open source equivalents to a well funded commercial #AI offerings is happening at a blistering pace. There's even an open source perplexity.ai equivalent that you can run completely local to your machine with private data. As a VC, how would you react to seeing an open source project emerge that completely mimics the functionality of a venture you just backed, but with the added benefit of its users having complete control over their institutional data? https://lnkd.in/gyYkNJaz

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Here is a summary of the main AI approaches implemented by the participants at the Scrive API Hackathon that we (Cillers) organized yesterday. 1. The winning team: Enable users to select search filters with natural language. 2. Most others: Use Retrieval Augmented Generation (RAG) to enable users to make use of Large Language Models (LLMs) for: * finding relevant contracts. * getting answers to questions about the contracts in the archive. 3. Generating metadata about each contract using LLMs. E.g. a summary of each contact that can be displayed in search results and in the contract overview page. And metadata that could be used by search filters. 4. Using LLM embeddings with vector search to categorize contracts. Personally, I learned a lot yesterday from the solutions presented. Next up for us is to organize the Google Cloud Summit AI Hackathon. Stay tuned for mote details on that. Sign up for event invites at the bottom of our website: cillers.com. Huge thanks to our sponsors: Couchbase, Redpanda Data, Kong Inc., Curity, Polytope. And huge thanks to Compileit for designing the hackathon track.

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