Elad Kolet

Solve for information asymmetries! Architecture & Civil Engineering - Layout interpretation ~other industries: Healthcare - <Prescription> interpretation Financial Products - <Subject to market risks documentation> interpretation Loans - <Fine print> interpretation There’s going to be a slew of startups commercialising this year with a value prop of simplification using AI

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Good article from John Cutler on dependencies and tech debt: “The further you are from the work, the most it seems like an excuse. The closer you are to the work, the more it looks like a reality you’re tired of describing and explaining.”

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Have you ever thought about how compliance laws are a lot like design systems? I recently spoke with Joe Toscano, founder of DataGrade, and he made a fascinating comparison. Joe explained how Brad Frost's atomic design methodology took an engineering approach to design: → Creating standardized elements → Templatizing them → Scaling them across a system This led to the rise of human interface guidelines (HIGs) from tech giants like Google, Apple, and IBM. But here's where it gets really interesting: Joe argued that privacy laws and regulations are like the ultimate HIG for product design. Just like how HIGs provide rules and constraints for design systems... Compliance laws set guardrails for what we can and can't do with user data. If we design something that isn't compliant, it doesn't matter how sleek the UI is. Legal requirements become the top-level framework we have to design within. That's what got Joe into studying laws - to understand this new "design system" for building products. So next time you're cursing a lengthy legal doc or trying to make sense of GDPR... Remember, it's all just part of the atomic design of compliance. — Do you agree with the analogy of compliance laws as the ultimate design system? Why or why not? Hear more of Joe's unique perspectives on the intersection of product, privacy, and law on the latest episode of The Minimum Viable Podcast.

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New on Runtime: The August edition of the Runtime Roundtable is live! We asked our panel of experts to help companies that are setting up platform engineering teams understand how they can maximize that investment and finally (theoretically) realize the promise of DevOps. With thoughts from Yuri Golikov, Sudha Raghavan, Sakib Dadi, Steven Jones, Madhukar Kumar, Patrick Bohannon, Ryan Fox-Tyler, and Hubert Behaghel.

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This guy doesn't quite get it, some bullets from his "quiet quitting playbook" * Blocking out private calendar time - I advise teams and developers to do this to not be randomized and to have dedicated flow for their work * Artificially extending project timelines (claiming two weeks for one-day tasks) - Oh you mean pad our numbers so we estimate accurately? * Citing unclear specifications as blockers - never ending stories because of poorly defined specs/stories? * Making numerous small code refactors - Excuse me? All that tech debt that you said you were ok with us adding and promised to give us time to address that never happened? What about simple design, or the TDD loop? What about dealing with this big ball of mud? Or are you asking, "Why don't you just write it right the first time?" * Blaming build system issues - That the build takes 5 hours to run and non-deterministic test failures? * Claiming blockages from other teams - AKA external team dependencies are impacting delivery? * Using technical jargon about "race conditions" to justify delays - This guy obviously isn't technical. Ever tried to debug a "race condition"? * Deflecting tasks by requesting formal ticketing ("can you create a JIRA for that?") - I almost love this one the most. If I spin this another way, "Don't work on any item that I can't see on the Project Board please. https://lnkd.in/et4HYE9E

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This old post from Ben Horowitz highlights a common struggle among entrepreneurs – expecting the team to perform seamlessly without investing in training. People are not plug-and-play; they bring diverse backgrounds and perspectives. You gotta spend time training and educating on your business, the way you want to talk to customers, and the right experience you want for them. Training is a powerful tool and needs to be done regularly! https://lnkd.in/ekv8c6WD #Entrepreneurship #Training #BusinessSuccess

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𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻 𝗮𝗰𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗪𝗶𝘇𝘇 𝗖𝗘𝗢'𝘀 𝗕𝗼𝗹𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻. CEO Assaf Rappaport and Wizz founders gave up $23 billion in favor of the challenging option of developing the company in preparation for a giant IPO. Leadership is often defined by the choices we make in the face of opportunity. Recently, the CEO of Wizz demonstrated exceptional leadership by declining an enticing offer from Google. This decision wasn't about rejecting growth or innovation but about staying true to the company's vision and values. At ALPHA Marketing, we believe that true leadership is about understanding your unique value proposition and making decisions that align with your long-term goals, even when faced with lucrative opportunities. The Wizz CEO's choice underscores the importance of strategic focus and the courage to stand firm in one's convictions. In a world where quick wins can often overshadow sustainable growth, let's celebrate leaders who prioritize vision, integrity, and the enduring success of their organizations. #Leadership #StrategicDecisions #BusinessGrowth #TechMarketing #MarTech #startup #venturecapital #TechInnovation #IsraelBusiness #cybersecurity

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Conferences can be daunting for introverts (like me), but here's a cool AI use case: an app that matches attendees and provides personalized talking points to spark conversations 💪 https://lnkd.in/d3sYr74r

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Stop (over-) engineering your prompts Generative AI is really cool. I use several tools in this category every day for writing suggestions, image generation and more. The tools can do amazing things, especially if given the right prompt. But there's a limit to how much prompt engineering one should do, especially if you're trying to generate text for mainstream consumption. This topic is already a bit of a bandwagon but this topic has been bugging me for about a week so I'm going to write about it. Generative AI relies on a class of machine learning applications broadly described as deep learning. Deep learning boils down to feeding a neural network a ton of content that has been classified into groups and then iterating over the content to allow the network to identify patterns in the content. Basically the network builds up statistical relationships between the words or other elements in the content. So it knows, for example, that if a piece of content is a recipe the word tomato is often followed by the words sauce or paste and very rarely by the word stem. But in a document about farming tomato is often followed by the word seed, starter or stem. The deep learning model has developed an elaborate matrix of statistical relationships between words in different types of content. If you described CHatGPT as an elaborate word guesser you wouldn't be wrong, just over-generalizing. Large language models are trained on billions of documents. So how, you might ask, are the documents classified in order to feed them into the neural network? Did someone categorize the web by hand? Of course not. The developers of the LLM leveraged Natural Language Processing (another deep learning application, btw) and other tools to categorize documents before they got fed into the model. Okay, but, again, what does that have to do with prompt engineering? When you ask an LLM to generate a piece of content, say a blog post about cycling in Northern California, you are specifying a category of content. If you later asked for an "image in Rubinesque style of a man and a woman engaged in flower picking while overhead rages a space battle between starships populated by photorealistic blob-like aliens carrying professional immersion blenders done in the style of pointillism" you have specified many categories. You're essentially asking the LLM to extract elements of the content that it was trained on that match your descriptors and which have mathematical relationships to other adjacent content elements and to include those in your generated image or blog post. And ... prompt engineering? The categories in the content were identified with Natural Language processing. Your prompt should be in natural language, not some over-specified, highly engineered artifical style. Don't over-think it, work with the model, don't try to control it too much. That's it.

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At Proplens AI we are building vertical AI (LLM) agents for real estate! 🏢 At times we do see skepticism around application of Gen AI to reimagine customer journeys in traditional industries like real estate, specifically in Asia. "Customer interactions in real estate will always be a high-touch human only workflow, how will AI add value here?". Proponents of these old school arguments tend to ignore the fact that AI is not all about automation, but also about enabling and empowering human professionals to deliver significantly better, faster, cheaper outcomes🎯in ways never imagined earlier. These arguments also ignore the evolving consumer behaviour in favour of transparency, information authenticity, responsiveness & personalization - areas where AI can directly amplify customer experience (CX) for real estate firms.✨ Sales and customer support professionals in RE firms spend significant amount of time in routine tasks like follow-ups, personalizing customer communication, information lookup & coordination tasks. Our AI agents make their lives simpler, enabling them achieve more! Win for businesses, employees & end consumers💪. #proptech #GenerativeAI #AIforRE

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🚀 There's a lot of publicity around Ilya Sutskever, former chief scientist at OpenAI, launching Safe Superintelligence alongside Daniel Levy and Daniel Gross. This new startup is laser-focused on creating safe superintelligent systems, emphasising safety and groundbreaking engineering. With a singular mission, they aim to revolutionise AI while keeping safety at the forefront. 🌟🔒 It will be interesting to see this develop as more information becomes available, but the securitisation of AI is such a topical conversation and cause of some problems . What are your thoughts on the future of safe superintelligent AI? #AI #Superintelligence #TechInnovation #FutureTech #SafeAI #Innovation #ArtificialIntelligence

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Join Karim (Crowd Favorite) and Tayo (Drewl) for an insightful discussion on composable systems and their transformative impact on tech stacks. Explore the core principles of composability and how it boosts flexibility and scalability. If you want to streamline operations and foster innovation, check out the full-length video on our YouTube channel. It is an essential watch for marketing teams seeking advancement. Subscribe to our channel for more exciting and informative content on the way! https://lnkd.in/eTfFm2an #ComposableSystems #TechInnovation #DigitalTransformation #MarketingTeams #CrowdFavorite #Drewl #InnovateTogether #TechSynergy #FutureReadyPartnership

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Got an email "GCP's Apache Kafka service is now live" at noon. After dinner, spent half an hour confirming it works well with Timeplus. I don't think you can make the bootstrap server publicly accessible. Has to setup a bastion host or run your app within the same project/VPC. Setting up the SASL username/pwd was not easy. Follow the guide carefully https://lnkd.in/gcKEVh3g and you may need to disable the iam.disableServiceAccountKeyCreation Anyway, if your workload are in GCP already, having such Kafka service in GCP is nice way to burn your cloud credits. Yet another Kafka vendor, great.

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When building RAG and LLM apps, the devil is in the documents. One of the hardest and least appreciated pieces of any RAG pipeline is turning complex visual documents into LLM-ready data. I'm talking about financial, health, insurance, gov docs and more. If they have tables, charts, diagrams and so on, it's a recipe for downstream hallucination. So how you fix it? This episode of RAG Masters explains the theory and then looks at PyPDF, Tesseract, LlamaParse, Unstructured and EyeLevel's X-Ray. See who comes out on top. https://lnkd.in/eTJw4hgX

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I need the toddler explanation… I'm still laying a ton of ground work and have gone back and added more information to previous posts on IAB Certified Downloads. If you don’t understand things or need additional information, or if I say something wrong, let me know! I’m constantly updating and improving upon everything. The latest post answers the simple question of "What Is the IAB?" in relation to podcasting. If you're a podcaster and don't know what the IAB is, you should! https://lnkd.in/eSZY5rW8

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Great talking to Silvia Saccardi about all things Gen AI!

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If the CDN market will grow 3%, it may mean the market has STOPPED GROWING (~3% is rate of inflation). That seems like a big story, and maybe an indicator that the internet is at a pivot from stored file delivery (CDNs, video streaming) to more real-time traffic and compute.

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Whether you're curious about AI or Springing into it, my good friend Craig Walls (cr-AI-g) has something to teach at dev2next Conference Check out the details at https://lnkd.in/eMD7pgtT

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