Chris Wilkens

🚀 Zetta just released a new feature for automated anomaly detection to tell you when your data isn't looking right. Data quality is one of the biggest challenges data teams face and Zetta is solving this so you can trust your data and make confident decisions. Zetta is an all-in-one AI data platform designed to give you instant data clarity and insights—no data team, infrastructure, or building required. Company wide automated analytics and insights can be setup as quickly as one day with Zetta. Here’s what you get with Zetta: Ready-to-use dashboards that track your key metrics Automated insights that tell you what’s driving your numbers Predictive forecasts to help you plan ahead A conversational AI Analyst so you can explore your data with simple questions A secure, reliable, and scalable data warehouse that’s fully managed for you Let's unlock the full potential of your data, together! https://zettadata.ai/ #AI #Data #Analytics #Businessintelligence #Startups #Growth

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My cofounder Saumil goes on the Stack Overflow Podcast to deep dive on AI Agents! He goes into detail how we leverage AI agents to power automated workflows to accomplish the most complex tasks. Including agents firing off tasks to other agents to tackle the details. Need an agent to verify your work? No problem. Give it a listen and let us know what you think!

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The newest issue of The Pragmatic Engineer found a surprise uptick in eng hiring in the typically slow summer season - I'm curious to see how the macro environment impacts this uptick as we head into the fall, but I remain hopeful. I asked Gemini to give me the tldr of the article to share with my team (sorry, Gergely Orosz) and found it missed one of the most interesting pieces of the article. Gemini's highlights - Unexpected surge: Tech recruitment has seen an unusual spike in June and July, contrary to typical trends. Increased demand: Startups, especially AI-focused ones, are driving the hiring uptick. Global phenomenon: This trend isn't just limited to the US; Europe is also experiencing increased recruitment activity. Reasons for hiring: Companies are backfilling positions, investing in growth due to improved financials, and responding to increased VC funding. Challenges remain: While the job market is improving, finding qualified candidates and offering competitive compensation remains difficult for many companies. What I found most interesting - The nature of the backfills: the article finds that a lot of new headcount are actually backfills of folks that were "over-slashed" during rounds of layoffs. Not entirely surprising with the "trim the fat" and "rebuild lean" mentality, but interesting nonetheless. Either way, the uptick is encouraging (and don't always rely on Gemini's tldrs). #hiring #startups #VCbacked #softwareengineering

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It’s eye-opening to see how AI model businesses are dealing with tight margins despite big investments. 🤯 Aidan Gomez’s comments about the “zero margin business” highlight the fierce competition and price cuts in the industry. 💸 With giants like OpenAI and Anthropic lowering prices to stay ahead, it’s a tough time for startups. The future of AI might depend on finding new ways to make these technologies profitable. 🚀 #AI #MachineLearning #TechTrends #Startups https://lnkd.in/ejkDu_wT

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The difference between a decision and a data-driven decision is like the difference between a shot in the dark and a carefully aimed arrow. Which decision do you want to make?

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It was great to hear Robert Wong Julie Willem Angie McAllister, Ph.D. and Markus Kunesch on the recent episode of the @Ed-Technical podcast -- the first of a three-part series about how we're applying Google AI to our work in education and learning: https://lnkd.in/e7hQi6a2. This episode delves deep into LearnLM, how we're making Gemini tuned for pedagogy and how we're integrating this technology into our mainstream products to improve learning outcomes, and evolve how people learn on Google products. It explains inspiring new product ideas, and the steps we take across the company to understand and enhance the journey of learning in a responsible and innovative way. #LearnLM #AI #Education #Google

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Want to tease something my team has been working on over at Buffer! The attached image isn't a mockup, but a screenshot of a live prototype of RSS feeds directly in Buffer! It's only available internally and still pretty rough at this point, but we'll have it available for user testing soon. With this enhancement, we hope to help Buffer users easily find new and engaging content to share with their audiences. What do you think? Will RSS feeds in Buffer help you create more content?

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The challenges of scaling and recovering AWS MSK clusters are now effectively mitigated, representing a significant advancement in cluster management.

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🚀 We've open-sourced TimesFM – a decoder-only foundation model for time-series forecasting 📈 Key Highlights: ✅ Large-Scale Training Data: Trained on a corpus of over 100 billion real-world time points, including Google Trends and Wikipedia pageviews. ✅ Zero-Shot Forecasting: Offers high-quality forecasts even on unseen datasets, without requiring fine-tuning or additional training. ✅ Outstanding Benchmark Performance: Achieved competitive results across a comprehensive suite of benchmarks. 🤗 Explore the model on Hugging Face and GitHub. I'm encouraged by the early feedback and look forward to seeing how #TimesFM will be used in real-world applications for forecasting and anomaly detection. 🙏 A special thanks to the talented research team behind this achievement: Abhimanyu Das Rajat Sen Yichen Zhou Weihao Kong

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Can probabilistic models, like LLMs and Nate Silver's election forecast model, ever truly be perfect, with no errors? According to Nate Silver: "We don’t talk about it much, but there’s a small amount of error introduced into the model because it’s probabilistic rather than deterministic. The margin of error on 40,000 simulations is about ± 0.5 points of win probability for either candidate; after 80,000, it declines, but only to ± 0.35." Sounds like a familiar refrain around Infactory as we discuss the limitations of LLMs... P.S. Vote!

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Over the holidays, I read Gergely Orosz’s book Building Mobile Apps at Scale to see how his insights matched my experience at ClassPass. The first part provides an overview of the challenges that are unique to mobile. One standout quote from Chuck Rossi who worked on a mobile release team at Meta captures one of these challenges: “It was the most terrifying thing to take 10,000 diffs, package it into effectively a bullet, fire that bullet at the horizon and that bullet, once it leaves the barrel, it’s gone. I cannot get it back…” Mistakes are hard to revert. Once a binary is released, it’s out there forever–there’s limited recourse to fix it once it’s out (e.g., expedited review, feature flags). This is why we find it crucial to thoroughly test and monitor for crashes and critical bugs before a full rollout. We achieve this by dogfooding the app with employees, automate end-to-end tests, stage rollouts, monitor and alert on new crashes, and smoke test the app with our QA engineers.

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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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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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You MUST see this. TL;DR We've reached a true breakthrough in clinical AI, developing CARE1 - the world's first clinical foundation model with an application submitted to FDA review. Truthfully this is just the tip of the iceberg, we've used a fraction of our data and two years from now we'll see these as the first baby steps. You can read more about it here: https://lnkd.in/drhPYTtX Shameless plug - this recent breakthrough is just one example of the impactful work our teams do every day. We're looking for highly experienced algorithmic experts, both ICs and tech leads, to join us and take a pivotal role in this and other high impact projects. Feel free to reach out to me directly. This is first and foremost thanks to the amazing team working on this effort - Itay Katzir the mastermind who's been planning this for years building paradigm after paradigm to enable this. From the algorithmic engine, through NLP, through scalable training pipelines and more. Ariel Persiko - truly a one man algo team, the exact person you want on this type of breakthrough project. Dor Alon - AI Infrastructure expert, built components that were critical to this endavour. And of course some of our amazing leaders who were deeply involved in invaluable strategic guidance - Uri Goren, Ronny Levanda, Tamar Lin. Not less than that - I think this is a true lesson of how good founders really move the needle even at a 450 person company. Elad Walach our CEO was maybe the first person who started pushing strongly for us to invest in this, and Michael B. Braginsky our legendary CTO who continues to amaze me even after 8 years with his ability to inspire us with unachievable goals which we are then able to achieve.

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👋 Today myself, Benji Vaughan and Mark Downey (with more names coming soon) set out on a new journey. I'm thrilled introduce Conduit AI (conduit-ai.com), a new company on a mission to empower SMEs with the incredible potential of generative AI, especially retrieval augmented generation (RAG) applications. But first, let's briefly explain what RAG is and why it's a game-changer for businesses. RAG is a powerful AI technique that combines the knowledge stored in large language models (LLMs) with the ability to retrieve and incorporate relevant information from your own data sources. This means RAG-powered applications can generate highly accurate, context-aware, and up-to-date responses to a wide range of queries. For SMEs, this translates to unparalleled productivity gains, enhanced customer experiences, and a significant competitive edge. 🚀 Now, a bit about our journey. Starting Conduit AI has been an exhilarating leap of faith, fuelled by our passion for AI and the unwavering support of our loved ones and mentors. We couldn't have done it without their wisdom and encouragement. 🙏 Over the past year, we've been on a mission to deeply understand how SMEs can effectively adopt generative AI. We've collaborated with companies across various industries and consistently found: SMEs are excited about the potential of LLMs to boost their productivity and profitability. 📈 There's a strong interest in RAG applications that can generate valuable insights from both unstructured and structured data. 💡 Many SMEs are concerned about developing LLMs in-house due to expertise gaps, reliability, trust, and privacy issues. 🔒 We've learned that SMEs are eager to become AI-enabled but need a trusted partner to help them develop a long-term AI vision and deliver products that bring this vision to life. That's where we come in! 🤝 At Conduit AI, our goal is to be the channel that helps SMEs transition into an AI-enabled world. We'll work closely with you to: - Develop a tailored AI strategy 🎯 - Design an adoption roadmap 🗺️ - Identify and implement low-risk, high-return AI applications 💪 With our team's 15 years of ML/AI/NLP experience, having built AI/ML tools for companies like Zurich Insurance and OVO, we're well-equipped to create AI strategies and applications that are tailored to your unique vision, team, and culture. 🌟 We can't wait to share more about our growing team and the incredible stories of the clients we'll be working with. If you're curious about how AI can transform your business, let's chat! 💬 Thanks for being a part of our exciting journey! 🙌 #AIConsulting #TechStartup #SMEs #FutureOfWork #RAG #newventure

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We're on product hunt today launching Ragie Connect. Ragie Connect streamlines connecting user data from sources like gdrive, notion, and salesforce to RAG to improve the quality of generative ai features. The most common use case is for multi tenant SaaS. Integrating can be as simple as sending your users to us with a secure link. We'll authenticate them on the platform they're connecting, start syncing their data, and then redirect them back to your app. You can listen for webhooks as we process their data so you can know when it's ready. Can you take a moment and upvote us on product hunt? https://lnkd.in/ePJjpa7B

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PSA: Stop using the original OpenAI CLIP weights. I see a lot of people using CLIP for multi-modal retrieval. What many people may not know is that there are 100’s of CLIP models now since OpenAI’s original release. These newer models offer significantly better performance across many benchmarks. For product search related tasks we recommend the following. + Fastest inference: ViT-B-32 [laion2b_s34b_b79k] + Best balanced: ViT-L-14 [laion2b_s32b_b82k] + Best-all round: xlm-roberta-large-ViT-H-14 [frozen_laion5b_s13b_b90k] You might notice these are not the ones that perform the best on the benchmarks. Why? Because the benchmark only test for very specific tasks which are often quite different from search and recommendations. These are ones we have found to be the best for product related search and retrieval tasks and generally very good first choices if you are unsure where to start (even for non-product related search). Here is a hugging face space with all the data: https://lnkd.in/gxu5UYkt Read more: https://lnkd.in/g5_D4ccs

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