Nadim Abdo

Hey everyone, I am an investor and advisor to Katanemo.... They are reinventing the infrastructure primitives for GenAI and launched Arch - https://lnkd.in/gCrbbkEV - an intelligent prompt gateway. Give the project a look on Github, and star the project if it resonates

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First story on what we have been up to at Augment. Much more to come! https://lnkd.in/gC6gbADA

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Large Language Models (#LLMs) are evolving rapidly, and the demand for running them at scale is skyrocketing. #Ollama is stepping up to meet this need with its official container image on Docker Hub, excelling in local #deployments. However, there’s a catch for containerized environments: downloading #models requires the ollama serve command to be running within the #container. This extra step complicates automated deployments via #CI #CD #pipelines, making seamless model access challenging. To take your #model to production, it needs to be #container-ready—a feature Ollama has yet to fully support. The gap in #AIOps for smooth deployments gets blurred with the inference services available as an easy way to take it to production. Goal is to overcome obstacles and deliver solutions 😉 so here is to get you started on having your (O)#llama #production ready in a private #deployment from your #local to #cloud all in #oneshot . Enjoy Medium: https://lnkd.in/g9jqBQ8d dev.to: https://lnkd.in/gi_gGwam #AI #LLM #Ollama #llama3 #phi3 #mistral #S6overlay #AIOps #CI #CD #oneshot #container #docker #podmanEnjoy

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It’s time for an update as I’m starting a new chapter of my career. The last year at LiquidityBook was truly a blast! I’ve always been interested in understanding the stock market better, so learning about the inner mechanics (middle office, settlement, locates for shorts, L1 vs. L2 quotes, to name just a few) was immensely fun! When it comes to customer focus, I learned a ton from Kevin Samuel and Shawn Samuel's ability to build a company that’s willing to build anything for its customers. I also appreciate the ego-free and hands-on culture they built, where everyone is willing to wear whatever hat to move the company forward. Driving SOC2 compliance gave me a newfound appreciation for how complex security and IT concerns can be. I already miss working with amazing people (including but not limited to) like Kevin, Shawn, Andy Carroll, Isaac Cambron, Stephanie Minister, Jason Morris, Ryan Canfield, Taylor Wielage, Terrence Cheung, Sayant Chatterjee and the rest of the engineering team. I am beyond grateful for the opportunity, and 10x more excited today than my first day about the company’s future! The next chapter for me, as it turns out, will also be Fintech. When Jeff Currier reached out to me about Chime, I was excited by the possibility of working with him again, but also very happy where I was. After learning more about the company’s mission, and meeting a few folks to learn about the company’s culture and what organizational challenges it’s looking to solve, the next step became clear. Chime is doing something incredible to help people have a fair start in life, and impressively doing so while building a thriving business. I feel honored and proud to become a part of the team! https://lnkd.in/gdPA8hTz

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Anthropic's latest Claude AI model is taking human-AI interaction to a whole new level! 🖥️🤖 The new Claude 3.5 Sonnet can now use computers just like a human would - by looking at the screen, moving the cursor, clicking buttons, and typing text. This is a major leap forward in AI capabilities. Key features: Operates based on screenshots, emulating human computer use Can perform complex multi-step tasks across different applications Self-corrects and retries when encountering obstacles2 Accessible via API for developers to build with Why it's game-changing: This opens up incredible possibilities for AI assistance in data entry, form filling, software testing, and automating repetitive computer tasks13. Imagine having an AI assistant that can seamlessly work alongside you, handling tedious computer work while you focus on higher-level tasks. While still in beta, this technology demonstrates how AI is rapidly advancing toward more human-like interaction with digital interfaces. As it develops further, it could revolutionize how we work with computers and boost productivity across industries. The future of human-AI collaboration just got a lot more exciting! What do you think about this new capability? #AIInnovation #FutureOfWork Here is a video about it: https://lnkd.in/gh8aNYEu

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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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Folks I've spoken to about Amazon have likely heard me talk about the importance of the PR/FAQ process, a.k.a. "the working backwards process". This blog posts gives a very nice example of a PR/FAQ. https://lnkd.in/edTeuVRQ. Note that the blog doesn't capture the presumably 100s of reviews/discussions/rewrites that this doc represents, which is a part of the real value. When the process is done it all looks so simple, but getting a large organization to make a decision to something simple and do it in a BIG way is the challenge the fun of it all.

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Satya Nadella sparked a new (or reignited) debate about death of apps as a result of AI agents. While he makes a great point on the Agentic AI architecture and how traditional architectures will be obsolete, wondering if front-end apps will go away. Often, tools like Excel or well-designed user interfaces help me identify the right questions to ask. Can AI alone replace the value of that interactive discovery process? What do you think? #ArtificialIntelligence #AI #AgenticAI #AIApplications #FutureOfApps https://lnkd.in/g8UKWQmf

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Check out this solution for building user authentication features into generative AI RAG applications!

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"Over 25% of all new code at Google is now generated by AI", according to CEO Sundar Pichai in today's earnings call. But is this shift aimed at speeding up the development of complex software, or simply cutting engineering costs? One potential indicator will be if Google reduces headcount by 25% while boosting revenue per employee. Here are some details behind this productivity metric in Google's own research page . Majority of this '25% code' is in the area of code completion, not in code creation. "LLM-based inline code completion is the most popular application of AI applied to software development..." Watch the video to understand further on this metric.

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While there are certainly individual variations in skill and experience when discussing modern software development the story is much more nuanced than pure individual effort.  * software development is a team sport -- individual performance is heavily impacted by the system in which they are asked to operate in.  * we pair skill and experience to this model of understanding performance by expecting people to be able to influence and adapt in direct proportion to their seniority, aka the more senior you are the more responsible you are for the outcomes of your work, the less senior you are the more your manager and management chain are responsible.  * humans want to succeed, we're social animals who derive purpose from demonstrating skill, and working collectively. All of which is to say there is no way you have over 1000 people underperforming at your company without senior leadership deeply implicated in.  If nearly 10% of your company is failing, versus just having the market move against you, you need to fire the CEO.

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Can you trust AI? 🤔 For most companies the answer is no - at least using current foundational models out of the box. ❌ Problems like sensitive data leakage and toxic content are major barriers to implementing production genAI solutions. The route towards trust lies in system architecture and the good news is the field is advancing rapidly, including increasingly easily accessible toolkits of solutions to increase trust in AI systems. 🔥 This is the hot area for new releases from major AI platform players (along with AI security), including AWS (https://lnkd.in/e45S-ehS), DataRobot (https://lnkd.in/eCWvppWR), and Databricks (https://lnkd.in/e6A7cTcH). 👨‍⚖️ Guardrail or judge models - leveraging a second AI model to police the content coming out of an original - are probably the most promising scalable solution. For many the best route is likely to be a new class of foundational models specifically designed for this task such as Llama Guard and Databrick’s new judge LLM. There is also the ability to fine tune a model (think a discriminator model from a GAN architecture) or use a general purpose foundational model. 👮‍♂️ Classic filtering techniques for sensitive data can also be applied to the result of an LLM, whether for PII, company IP, or inappropriate content. This can include regex, classic PII models (such as Amazon Comprehend), etc. 🚻 Human in the loop techniques can also be used, but are far more scalable when used to calibrate automated solutions such as those described above rather than manually verifying outputs in realtime. What’s still missing? 🔎 🎯 While techniques to improve accuracy and reduce hallucination such as RAG and AI agents have made great strides, there remains a big gap in conveying a model’s confidence in its output to end users. 💡One idea is to leverage the probability distributions used for top-p (nucleus sampling) at the token level instead at greater aggregations to convey confidence levels for entire model outputs or significant segments (e.g. each step in a chain of thought structure or AI agents framework). #ai, #artificialintelligence, #GenAI, #GenerativeAI, #llm, #datascience, #machinelearning, #mlops, #bigdata

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The Site Reliability Engineering: How Google Runs Production System book is arguably one of the most prevalent and well-known SRE resources out there, but there are many other great options for folks looking to expand on their learning. We recently spoke to Google's Reliability Advocate, Steve McGhee, in our Humans of Reliability interview series. In addition to his interesting anecdotes on the early days of SRE at Google, and his journey to becoming a Reliability Advocate, he also shared a handful of his favorite SRE resources, which we compiled here into a list: https://lnkd.in/gpUVhyRa

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Also #intuit confirms that #bigid is the #dspm with the best #datadiscovery capabilities! BigID has more classifiers (including AI\ML based classifiers), and more connectors (including PaaS, SaaS, on-premise and even Mainframe!), than any other DSPM! #datasecurity #dataprivacy #datagovernance #cybersecurity

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Over the course of each month, I spend a few moments each day or so collecting interesting links, and then, at the end of the month, I bundle them (with the help of some nifty automation scripts) into an easily digestible reading list. Here's everything that I found interesting in May 2024 about the irresponsible use of machine intelligence: #qfm #quantumfaxmachine #blog #irresponsible #ai #genai https://lnkd.in/e2uE49up

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🚀 Want to run your very own trained LLM, privately and securely? 🤖🔒 Imagine harnessing the power of a Language Model tailored just for you without worrying about third-party servers! 💼✨ Try, LM Studio. Jest for few click . LLM running in your PC. With LM Studio, you can ... 🤖 • Run LLMs on your laptop, entirely offline 📚 • Chat with your local documents (new in 0.3) 👾 • Use models through the in-app Chat UI or an OpenAI compatible local server 📂 • Download any compatible model files from Hugging Face 🤗 repositories 🔭 • Discover new & noteworthy LLMs right inside the app's Discover page #LLM #private #chatgpt #AI

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In this episode of the Mob Mentality Show, join us as we delve into "The Discovery of Ignorance in Product Development: Time for a Smarter Way to Deliver Customer Value" with our special guest, Gary Cohen 🟦: https://lnkd.in/ggGMHyfu https://lnkd.in/gx2tXEu6 Drawing on his rich experience across diverse sectors—from government agencies to startups—Gary brings a wealth of insight into optimizing product discovery and delivery for true customer impact. ### **Key Topics Explored: The Product Discovery and Delivery Intersection** Even if a team is familiar with the 80/20 rule (e.g., where 80% of features go unused), how does it determine the right 20% to deliver? Gary shares strategies to tackle this challenge, advocating for a shift from traditional roadmap fixation to embracing dynamic, continuous discovery that keeps customer value at the forefront. Together, we examine the effectiveness of "strict" roadmap discipline (the practice of adhering to set timelines and objectives) and if it becomes a barrier to true innovation and customer value. We also briefly touch on the FAST framework and its relevance to product discovery, emphasizing how collective discovery through mobbing allows teams to stay closer to evolving customer needs. ### **The Role of GenAI in Modern Product Development** Is AI is reshaping product discovery? We explore how GenAI can be a collaborative asset for mobs, not only for code development but also for bringing value by uncovering insights into customer behavior and streamlining feature prioritization. We compare "introverted" AI and "extroverted" AI in mob environments and discusses how developing refined AI inquiry skills is key to leveraging AI’s full potential. From training mobs in nuanced prompt engineering to using AI to help drive a more lean delivery, we share ways to maximize AI's impact while maintaining expert human oversight. ### **Unpacking Behavioral Expectations: A Smarter Approach to Product Decisions** Beyond the technical, we look at the behavioral side of product monitoring, asking, “What responses do we expect from our users?” Gary emphasizes the importance of behavioral prediction in discovery, cautioning against information bias and providing ways to gauge responses to feature experiments in real-time. We highlight the importance of monitoring usage data and what it reveals about customer engagement—whether a feature elicits a “wow” or a mere “meh.” Chris Lucian

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Want to start contributing to open source projects, but not sure where to start? Diana Pham has got you covered. 👌 Discover different types of contributions, learn how to find projects and understand common misconceptions. #mycompany #opensource https://gag.gl/fzVy4E

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Your ability to adapt to changing conditions will be your super power. Being overly adapted to existing conditions makes survival questionable.

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