Irakli Gozalishvili

Join us in Irvine on July 17th at 6:30 PM for our latest OC ACM talk, "Systems and Software for Averting an Energy Crisis" presented by Bill Gervasi, a Principal Systems Architect for Wolley Inc. For more info or to RSVP click here: https://lnkd.in/gquYQ2z9 According to a recent Forbes article, "The rise of generative AI and surging GPU shipments is causing data centers to scale from tens of thousands to 100,000-plus accelerators, shifting the emphasis to power as a mission-critical problem to solve." making this talk particularly relevant. #acm #techtalk #gpu #ai #generativeai #nvidia #energy #energycrisis

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At the core of effective engineering leadership is the ability to stay connected with your team. This is perhaps the most challenging aspect because retaining the respect of your people is difficult unless you remain technically involved yourself. In my journey as an engineering leader, staying true to the individual contributor role that originally earned their respect has kept me close. This approach extends beyond technical expertise; it's about maintaining a deep connection with the core activities that propel product development forward at Gearflow. One of my biggest professional fears is becoming irrelevant or disconnected from my team and our work. This fear motivates me to continually reinvent and retool in response to market demands. Next month, I'll be sharing about my adventures generating synthetic data recently. And while I'm no expert when I start preparing for these talks, the process is incredibly valuable. It's less about delivering a perfect talk and more about how the preparation expands my thinking and exposes me to new capabilities. This helps me connect the dots to opportunities at Gearflow that bring real innovation to our customers. Speaking regularly holds me accountable and pushes me to learn ahead of the curve, ensuring I remain relevant and impactful day-to-day. Every talk is a journey of discovery, helping me to lead with humility, communicate clearly, and inspire a culture of innovation through a deep, technical understanding of our work.

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Having lived in SF bay area for so long, I came to know the big coffee culture scene here. I was totally consumed by it a while ago and spent years trying to attain the “perfect" latte. I published a fun coffee story and my entire home setup, which took weeks of research and multiple iterations, in my blog link in bio. Meanwhile, to all fellow coffee loving tech peeps, here are some top tips that no one told me. Put it in practice at home or in microkitchens. - A big factor in preparing milk suitable for latte art is pressure of the wand. If this is important to you, you need to get a machine that has a strong enough pressure. - When pulling shots, put in 18g of beans and extract 30g of espresso. - The darker the roast, the coarser the setting should be on the grinder, the lighter the roast, the finer the setting should be. - When adjusting the grind setting, do so while the grinder is working if there are beans in the hopper. Otherwise, a bean can get stuck between the burrrs. - Pulling a shot should take 20-30 sec in time, or based on observation. It should look like caramel going out, not a waterfall. - Eventually work your way to using a bottomless portafilter. It punishes you for all the small imperfections when preparing your coffee puck, like any tilt, and uneven distribution. It does so by spraying the espresso all over the place. The more you can get it to flow uniformly, the better your preparation of espresso, objectively. - Seal your beans in a vacuum, so they don’t go stale. The more stale the beans, the more minor adjustment you need to make to grind finer. - When steaming milk in your pitcher, you can tell if it’s done by holding the pitcher and as soon as the heat becomes unbearable i.e. stings, it’s done. - Taking a latte art course at a local coffee shop was the best boost for me to start nailing latte art.

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Project update! My application tracking extension is now doing fuzzier matching and injecting new information into LinkedIn (and other sites) to keep me from doubling up on applications. Next up: local storage and bulk import/export operations...

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Want a preview of what I'm going to be talking about in my keynote for RubyConf? You can catch a glimpse of it here!

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How to live a better life for yourself and others

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"The core art of Operations Management is observing the messy, human chaos of business and applying order to it." The Operator's handbook is a great guide to the various operations disciplines like #businessoperations or #bizops, #revenueoperations or #revops, #financialops or #finops,#legalops, #marketingops, #peopleops and #productoperations / #productops that have emerged as a result of shift from hardware to software-based value creation (see my previous post too --> https://lnkd.in/gwNWwkNh?). Great introduction, though I find it lacking as it did not include factory operations, supply chain, store operations that rely far more on offline-online interfaces. For any operator out there, process, analytics, metrics, cadence, solutioning and execution are the key concepts to apply.

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🌟 Attention Engineering Managers and Technical Leaders! 🌟 In just two weeks, on June 28th at 10 AM PDT, I’m excited to host an interactive roundtable on Feature Flags and Delivery in conjunction with ELC! 🚀 Why Attend? This is a collaborative discussion where your expertise and curiosity are key! Whether you're experienced or eager to learn, your contributions are invaluable. 🔍 Discussion Points: - Decoupling Processes - Boosting Team Efficiency - Optimizing Feature Delivery - Tracking Platform Usage Expect rich conversations on best practices, unique implementations, methodologies, and the latest tools enhancing feature flagging and delivery. Who Should Join? - Engineering Managers - Technical Leads - DevOps Engineers - Software Developers This roundtable is your chance to: - Share your experiences - Gain new insights - Network with peers - Discover practical recommendations RSVP Now and mark your calendars for June 28th at 10 AM PDT! Let’s collaborate and elevate our skills together! #EngineeringManagement #TechLeaders #FeatureFlags #DevOps #SoftwareDevelopment #TeamEfficiency #TechRoundtable

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From IC engineer to C-Level exec, the most effective people I've worked with have one thing in common: they break down and decouple decisions. Optionality, trade-offs, last responsible moment, one-way and two-way door decisions, slicing, MVPs, proof of concepts etc. are all techniques they use for breaking decisions down and getting them made. The best engineers did this when working on code. Rather than trying to decide on all the functionality at once they'd be able to say "I can break this story down into smaller pieces of value that we can get released to users fast" or "we don't need to deliver this piece of functionality now, we can wait until we see how user's respond". The best architects, product managers and business leaders do the same thing. It's a real skill to decouple decisions. It's a real skill to be able to defer parts of a decision to next or later (or even never) to enable faster movement and lower risk. I have always been impressed when I witness it. Often it's obvious with hindsight and sometimes its completely out-of-the-box and you would have never have realised.

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Interesting perspective shared by Kent Beck on the importance of refactoring: "Why refactor? Because I accept an ethical obligation to my teammates to not unnecessarily complicate their future work." Check out the full thread here: https://lnkd.in/gvKTm_cv.

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The feature we've been waiting for is finally here! No more endless scrolling to find timely news with Threads recent update. Learn all about it here! 👇 https://ow.ly/ZLM630sCUjZ

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Since launching last Fall, its been incredible to see all the many workflows and integrations that companies are building with Lucid's Developer Platform. We've seen companies build automated processes and deeply linked templates that help with Sales, Marketing, Operations, and Engineering teams get the most out of Lucid. Come learn how to get started building from two of our engineers on the API teams who help engineers build these integrations every day.

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Highly recommend giving this one a read! Workflow contributor Sean Hughes gives a thoughtful breakdown of why closed GenAI foundation models should be of concern to everyone, and why open systems are the key to a safer, better future. Check it out. 👇

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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 Elixir programming ecosystem: #qfm #quantumfaxmachine #blog #elixir https://lnkd.in/eVwqy6Tf

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Anthropic released another paper on their ongoing work towards interpretability of large foundational models like their Claude 3 family, built on previous work by many. Their work continues to be enlightening and inspiring. The findings in this work are generally similar to previous findings on smaller models but scaled up to Claude 3 Sonnet: 💡 they find large numbers of "features" corresponding to combinations of neuron activations and these features map to concepts that we recognize/understand: for example, features for transit infrastructure, particular people, concepts (like bias and "objects lacking sentience"). This might sound familiar from classical machine learning, but the interesting thing is that there is no single thing/neuron encoding each of these concepts, it's a particular combination of neuron activations that embeds these concepts in "superposition" in the network. 💡they observe a sort of "distance" of these feature activations that arrange themselves "near" semantically similar features. When they use their sparse autoencoder approach to learn more and more features they observe that the level of abstraction in the detected features becomes more granular, in ways that are interpretable and meaningful. 💡when they artificially fire these activation patterns during inference, it affects the output in ways that relate to the "feature". For example, asking Claude "what's the most interesting science" normally it responds Physics, but if you clamp the feature activations for the feature "Brain Science" to 10x their max values then Claude responds: Neuroscience. Ways to interpret _and_ influence the model. Great readable work [1] along with an interactive explorer of some of the "features" themselves [2] 🎂 There's an easter egg too: if you scroll in the feature explorer tool and try and select one of the "Randomly selected features from 1M" you get a message "if you want to see the rest of the features, we're hiring!" with a link to their recruiting page :) [1] https://lnkd.in/gZbparwp [2] https://lnkd.in/gC9eus53

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I've been testing GPT-4o since its recent release. While I haven't really had a use for the multi-modal (images, audio, and text) capabilities yet, the improvement in speed of response is noticeable. Not just noticeable from a spec sheet perspective either—it's noticeable in how the product works in real use cases. I've been thinking about why that is, and here's my theory: there's a breakpoint somewhere based on latency and response speed. Above this breakpoint, interactions feel sluggish and disruptive. Below it, they become seamless and effortless, akin to a natural conversation. Previously, I posted about how Llama and Gemini models seemed noticeably more usable than GPT-4, with my preference being Llama whenever possible. This new experience with GPT-4o further emphasizes the importance of optimizing both latency and throughput in AI systems. This chart from ArtificialAnalysis.ai shows that GPT-4o has a total response time of 1.5 seconds for 100 tokens, compared to GPT-4's 5.7 seconds. This speed difference isn't just a matter of numbers—it's fundamental to achieving a smooth and efficient user experience. When latency drops below a certain threshold, the interaction becomes fluid and engaging, making the AI more practical and user-friendly. It also makes its mistakes more tolerable. This improvement transforms how we use these models in real-time applications, making AI interactions faster and more intuitive. It underscores that achieving low latency and high throughput is no longer a nice-to-have but a critical requirement for practical AI deployment.

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I just blogged about the benefits of contract-first API development. If you and your team aren't following this approach, I encourage you to read on and learn why you may be missing out on a practice that can help your team be more productive.

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