Rema Matevosyan

Adapt to whatever medium, challenge, or problem you encounter. I revisit Dr. Akin's original post often. Full list here: https://lnkd.in/dUF7ereE ------------------------------------------------ - Engineering is done with numbers. Analysis without numbers is only an opinion. ------------------------------------------------ - Capabilities drive requirements, regardless of what the systems engineering textbooks say. ------------------------------------------------ - There is never a single right solution. There are always multiple wrong ones, though. ------------------------------------------------ - When in doubt, estimate. In an emergency, guess. But be sure to go back and clean up the mess when the real numbers come along. ------------------------------------------------ - Design is an iterative process. The necessary number of iterations is one more than the number you have currently done. This is true at any point in time. ------------------------------------------------ - (Mo's Law of Evolutionary Development) You can't get to the moon by climbing successively taller trees. ------------------------------------------------ - (Roosevelt's Law of Task Planning) Do what you can, where you are, with what you have. ------------------------------------------------ - (de Saint-Exupery's Law of Design) A designer knows that they have achieved perfection not when there is nothing left to add, but when there is nothing left to take away. ------------------------------------------------ - (Mar's Law) Everything is linear if plotted log-log with a fat magic marker. ------------------------------------------------ - (Edison's Law) "Better" is the enemy of "good". ------------------------------------------------ - (Shea's Law) The ability to improve a design occurs primarily at the interfaces. This is also the prime location for screwing it up. ------------------------------------------------ - (Montemerlo's Law) Don't do nuthin' dumb. ------------------------------------------------ - (Ranger's Law) There ain't no such thing as a free launch. ------------------------------------------------ - (von Tiesenhausen's Law of Engineering Design) If you want to have a maximum effect on the design of a new engineering system, learn to draw. Engineers always wind up designing the vehicle to look like the initial artist's concept. ------------------------------------------------ - (McBryan's Law) You can't make it better until you make it work. ------------------------------------------------ - (Lachance's Law) "Plenty of time" becomes "not enough time" in a very short time. ------------------------------------------------

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After several months of development and gallons of coffee, I am thrilled to unveil the latest from MassSim - HairSim v0.3.0! ☕️🚀 🌐 What's MassSim? At MassSim, our mission is to breathe life into digital humans through state-of-the-art interactive simulators. Our technology enhances in-game experiences, advances robotics, and pioneers new forms of spatial computing, unlocking unprecedented capabilities for human-machine interaction. 💡 What's HairSim v0.3.0? HairSim elevates the realism of digital humans in games or XR by adding interaction to hair. It can also aid in robotics as an advanced simulator for mastering dexterous hair manipulation, among other innovative applications. The release of version 0.3.0 marks significant progress in our development, but there's more in store. Stay tuned! 👀 See HairSim in action We've posted a demo here: https://lnkd.in/grNG9Gbf 🤝 Strategic Partnerships and Collaboration Opportunity The coming weeks are crucial for us as we seek to connect with industry leaders, innovative companies, and forward-thinking pioneers who share our excitement for reshaping the future of digital interaction. MassSim is at a pivotal point where strategic collaborations can significantly influence MassSim's evolution. We are looking for partners interested in exploring the cutting-edge potential of HairSim v0.3.0 who may be willing to sign a Letter of Intent (LOI) that reflects our mutual commitment to innovation. ❤️ We Need Your Support Your feedback, shares, and insights are invaluable. Whether you're in the industry, a technology enthusiast, or someone curious about our journey, we welcome you to engage with us. Your support can help shape the future of MassSim.

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In thinking about what to post here about Pod Engine's new developments, I realized there's a lot I've taken for granted and not mentioned! Something I find particularly cool is the sheer scale of our architecture and ML pipelines. Pod Engine now has tens of thousands of _fully tracked_ podcasts. For each podcast, this means: 1. At least the last 6 months of episodes transcribed and analyzed (for many we've processed every episode in their back-catalog too!) 2. Every transcript fully searchable semantically and by keyword 3. Their own podcast analytics page (attached, will improve a lot this week too!) We actually track millions of podcasts (specifically every active podcast, and a fair few more), and the number of fully tracked podcasts is steadily growing. Essentially our machines have "listened to" many, many hundreds of thousands of episodes and extracted insights extracted from every single one. Super powerful stuff when harnessed appropriately! I'll be adding some extra data to the podcast pages this week and would love some feedback. They'll have: 1. Podcast contact details 2. Overall reviews (Itunes + Spotify) 3. Detailed (individual) reviews for Itunes 4. Various proxies for listenership data (Castbox subscribers etc.) 5. All social links You can access all of this for free today just by logging in to a free account. Our pilot customers will soon have access to lots more though, which I'm extra excited about!

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Over the past few months at Assembled, we've been exploring how Large Language Models (LLMs) can enhance our engineering workflows. One area where they've made a significant impact is in writing comprehensive tests. Traditionally, writing thorough tests can be time consuming and sometimes gets sidelined due to tight deadlines. By leveraging LLMs, we've been able to generate robust tests much more quickly—tasks that used to take hours now take just minutes. This shift has brought: - Increased adoption of testing: Engineers who previously wrote fewer tests are now consistently incorporating them into their workflow. - Time savings: We've collectively saved hundreds of hours, allowing us to focus more on developing new features and refining existing ones. - Enhanced code quality: Our codebase is better tested, giving us greater confidence in our deployments. I've shared more about our approach, including examples and lessons learned, in a recent blog post. 👉 Read more here: https://lnkd.in/gKw3djQu We're excited about the progress we're making and the insights we've gained. If you're interested in the intersection of LLMs and software engineering, or if you're passionate about building tools that transform customer support, I'd love to connect. P.S. We're hiring! Check out our open roles: https://lnkd.in/gSr_wqV7

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If you want to learn beyond RAG and agents get tuned into this session, this is one of the best combo to build modern applied GenAI applications.

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Things are getting real 👀 I'm releasing free AI and Tech Courses in collaboration with Marqo! Coming soon! The first course is on 'Fine-Tuning Embedding Models' which, might sound complex, but let's break it down below 👇 💡 What are Embedding Models? Embedding Models help computers understand the meaning of words and other data by turning them into numbers. This makes it easier for computers to find patterns, make predictions, and understand human language better. 💡 Why should I care about Embedding Models? Embedding models are essential because they significantly enhance the functionality and user experience of many technologies you interact with daily. 💡 What technologies are they used in? 1. Google search! Imagine searching for a picture of a dog and Google returned a picture of a cat 🤦‍♀️ Well, embedding models stop this by allowing relevant and accurate search results. 2. Netflix, Spotify and Apple all analyse your viewing or listening habits to suggest movies, shows, or music you might like - thanks to embedding models! 3. Chatbots like OpenAI's ChatGPT! 💡 What's fine-tuning? Fine-tuning is a process of making these models perform even better. 🚀 Interested? To get early access, join our Slack Community below to get exclusive updates and releases: https://lnkd.in/e4BgvNwf

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AI in code, and leadership in hi-tech We’re excited to host Gilad Shoham, Dev & Open Source Leader Bit.dev, Angel investor, and mentor!  Join Group Leader Amir Shalem from develeap as he chats with Gilad about: Bit’s potential in building composable software Setting long-term goals to unlock a startup's true potential The role of AI in code generation Leadership vs. Management in hi-tech and how to foster relationships that your top talents! Don’t miss Gilad’s advice for junior developers looking to break into the hi-tech industry, including the steps they need to take to land their dream job! https://lnkd.in/dxN7HgNS

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Dan Wallach of Defense Advanced Research Projects Agency (DARPA) wants to move all software written in C to 🦀 Rust 🦀 The TRACTOR program (Translating All C to Rust) is an effort to create approaches to convert C applications to Rust applications. 🎉 This is a big step forward. 🎉 Rust’s memory management model eliminates a whole host of security vulnerabilities (and bugs) that are rooted in dangling pointers, buffer overflows, use-after-free occurrences, double free, and memory leaks; to name a few. This all happens without a garbage collector 🗑️ How does Rust do this? By restricting/discouraging you from directly allocate memory. Rust uses its Borrow Checker feature to enforce these strict set of constraints, and in turn, Rust will allocate & deallocte memory on your behalf by tracking when it is in & out of scope. Read More about TRACTOR ->

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Ramblr’s Data Engine now supports automated #SceneGraph generation! This powerful feature bridges the gap between visual scenes and human language, enabling advanced vision-language tasks and seamlessly injecting video context into #LLM prompts. Drago Guggiana Nilo walks you through the functionality and shows you how to get started. Get your free account at https://ramblr.ai and try automated #SceneGraph generation on Ramblr.ai®'s platform Stay tuned for more - We are constantly improving our  #DataEngine enabling advanced #VideoUnderstanding. #AR #MR #VideoIntelligence #ContexualAI #AIforThePhysicalWorld 

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Read our latest published research paper about SuperCoder

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GNN models to design your own enzymes: SolvIT, a graph NN predicting protein solubility in Escherichia coli, optimizing enzyme expression selection from larger design sets. Using this method, we engineered enzymes with superior expression levels, with 54% expressed in E. coli, and increased thermal stability, with over 30% having higher Tm than the template, with no high-throughput screening.

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New article to Rustaceans 🦀 Sharing an article on "Error Handling For Large Rust Projects" Dive into the details here: https://lnkd.in/driJZGcg Enjoy the read! 🎉 #RustResources #RustLearning #RustOkumalari #RustKaynaklari #Rustlang #TurkiyeRust

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This new description of the transformer mechanism used in AI (machine learning) systems is so well done that I am going to post a pointer to it. The person who did this, Grant Sanderson, is a mathematician who has built tools for presenting concepts for his YouTube channel 3blue1brown and other ventures. He uses these tools to augment is excellent understanding of what is important. Anyone who has not fully understood the concepts of the attention mechanism and how it is used to build a transformer for machine learning as demonstrated in the 2017 paper "Attention is All You Need", take a look at this 1 in a million talk released on Nov 20, 2024 on YouTube: https://lnkd.in/gqP4t9W3 Cheers! ☕ Todd B.

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