Minko Gechev

🚀🎙 Episode 25 of #PeymanFakharianPodcast and this time with Kayvan Mohit Mafi, CEO of Diginext.ir, the innovation center of #digikala. Kayvan truely is one of the most active supporters of startup innovation in Iran and in this episode he shares his insightful views on B2B tech and AI startups. 🔍 Topics We cover: - Foundation of Diginext - Story of Diginext and its investment portfolio - Investment on B2B Startups - AI Startups - Inviting Mentors to help startup founders 🎧 Listen to the episode on your favorite podcast app. Just search for "Peyman Fakharian" or "پیمان فخاریان" 🎧 Or listen on Castbox https://lnkd.in/eRQ6AmzA 🎧 Or Subscribe to our Youtube Channel https://lnkd.in/ePx6cfS7 📌 For more updates and exclusive content, follow my Telegram Channel: https://lnkd.in/dQJVz2Z9 hashtag #Peymanfakharian #پیمان_فخاریان #digikala #diginext #دیجیکالا #دیجی‌کالا #PeymanFakharianPodcast

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bookmarking these cool series classes on LLMs in JAX.

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Check out the latest in our "Debuggability" blog series! This blog is authored by Kevin Downey Jason Johl and me and talks about how we built a Debuggability Paved Road at Intuit using Ephemeral Containers and Argo Workflows. We'd love to hear your thoughts on the topic. Also, what tools do you build as-a-platform to help debug service, compute and network issues in Kubernetes at scale? #intuit #kubernetes #debug

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How you can make your LLM applications better!

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In the AI ecosystem, LLMs act like CPUs, crucial yet dependent on complementary tools such as Chatbot and AnythingLLM, which function similarly to I/O systems. Alongside, tools like MemGPT and CrewAI serve roles akin to RAM and network interfaces. The challenge arises as these tools often evolve independently, aspiring to be standalone solutions. This siloed development leads to a lack of standardization, complicating their integration and limiting their utility in professional settings. Without unified protocols, integrating these diverse AI tools with LLMs demands significant customization, placing an undue burden on developers and hindering broader innovation. Just as the adoption of standardized interfaces like USB revolutionized computer usability, AI tools would benefit from similar standardization to enhance functionality and ease of use. Promoting common standards for AI tools is crucial for maximizing the capabilities of LLMs and ensuring their practical application across various domains. Without such efforts, the AI field may continue to struggle with fragmentation, undermining the potential of these advanced technologies.

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Ruby Concurrency vs. Virtual Threads on JRuby

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I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

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How good are Trainium and Inference clusters for online serving of production traffic? Check this blog from Trishul Chilimbi on how Rufus responses are powered with high throughput and resiliency. #Trainium #Neuron #AWS Amazon Web Services (AWS)

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Understanding Develocity Build Data with Honeycomb https://lnkd.in/gmGSrkVB

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Interesting article from Bloomberg about life at Google's X in a time of layoffs and cost reduction at https://lnkd.in/gmw3GG7C. X is a component of Alphabet (Google's parent company) that incubates new ideas. They have been responsible for some great successes - getting Waymo and a lot of Google's AI work started - but they've also had projects that didn't survive - like Google's attempt to provide Internet access via weather balloons. When you work at a company that has hundreds of billions of dollars in revenues, it's hard to get a lot of interest in an entirely new business idea that brings in hundreds of millions. If you have a project that improves ads revenue by that amount, or decreases capital expenditures, you can do it, but if you want to, say, build wearable tech, it becomes hard to attract the attention of marketing or product. So, new ideas often die on the vine. For the most part, this is good and proper. Companies may have the financial wherewithal to branch out, but more often than not, they consider it a distraction. And they are probably right - it's hard to argue that Meta was right to divert such a huge chunk of their resources to chase after the metaverse. They've (rightfully) pulled back, and are more focused on their core business again. On the flip side, if you are in the tech sector, and you don't innovate, you are likely to get left behind at some point. So, you need to make and manage these investments somehow. When I joined Google, the CEO had a 70-20-10 rule - 70% of effort went to core businesses, 20% to broader efforts to improve Google, and 10% to stuff that's completely blue-sky. This is probably not completely right, but it is also probably not completely wrong. X is the division of Alphabet that is *specifically* designed to bring blue-sky products to life. Unsurprisingly, they've had to pivot and rethink their strategy many, many times. The article interviews a number of people who have worked at X over the years; it's well worth a perusal, for folks who are interested in the story.

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https://lnkd.in/gdNYBCxH Speaker: Marzieh Vaez Torshizi Giuseppe Di Guglielmo Nhan Tran Farah Fahim Harish Anil Jamakhandi Come and listen to how Catapult AI NN is making it easier to translate python models into RTL, combining hls4ml capabilities with all the benefits of rapid design exploration using HLS, along with adcanced high-level verification (HLV) capabilities and the extremely easy to use and convenient flow for value range analysis on the quantized datatypes, to deliver a high-fidelity, high-productivity flow for its users

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There are fewer and fewer secrets in foundational LLM training: difficult for providers, great for GenAI applications. Alibaba's investment has been undervalued: no fancy papers, only great models. From my own experience, their model family is improving consistently with commercial friendly licenses. https://lnkd.in/dxZWjpsW

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I found this post pretty insightful (in terms of qualifying the differences), though I think it misses what the difference actually is When you're interviewing someone for 45 minutes, what kind of data can you get? Can you really conclude this person is going to be a good leader? Or are you simply measuring how well the person can bullshit? Coding & problem solving, on the other hand, cannot be bullshit They are a good predictor & signal of actual real-world performance (at least, how good of a coder & problem solver the person is) Whereas if you interview someone & they "seem to be a good leader", what you're actually probably measuring is that they're a really good bullshitter If I had to assign probabilities: A) Probability that a "leadership interview" leads to a hire which is: A good bullshitter: 90% Competent: 20% Hardworking: 20% A good leader: 20% B) Probability a technical/problem-solving interview leads to a hire which is: Competent: 90% Hardworking: 50% A good leader: 20% A good bullshitter: 20% I'll take (B) any day

According to Eric Schmidt, in the next year or so, the union of the large context window, agents, and text-to-action, when delivered at scale, is going to have an impact on the world that no one understands yet. Listen to the fascinating discussion in the "The Age of AI, Eric Schmidt" youtube video - https://lnkd.in/dFTKgBVY

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You don’t normally see LLM researchers and engineers talking about their day-to-day work. Big shoutout to this YouTube video from the Anthropic LLM Interpretability team, the team behind the Golden Gate Claude. It’s amazing to see that most of the team members have worked for the team for only one year, and yet they have made such a big breakthrough in interpretability research. Another thing I want to highlight is the unique challenges facing the team and how they approach these challenges, that is, through fast trial-and-error iterations and constant evaluation of what may work and what to prioritize next. #artificialintelligence #machinelearning #deeplearning https://lnkd.in/eUaPAYch

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Amazon Bedrock Studio 😎. "With Bedrock Studio, you - and your teams - can sow a million different generative AI ideas, care and feed them, and watch the beautiful results bloom in-front of your eyes. " #aws #artificialintelligence #genai

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