Onur Cinar

Effortless Integration for Smarter Insights: CloudEagle.ai Meets Bitbucket Integrating CloudEagle.ai with Bitbucket takes just minutes and gives you: ✔ Simplified App Tracking: No more manual reports, get the app usage data you need, when you need it. ✔ Centralized Control: Manage your Bitbucket security from the CloudEagle dashboard. ✔ Proactive Monitoring: Catch unusual login activity before it becomes a problem. Ready for a smarter, more secure development workflow? Book a demo today - https://lnkd.in/gXAhChrt #saas #saasmanagement #toolstuesday Atlassian #jira #toolstuesday

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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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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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ModernBERT is an open-source embedding model which represents a significant leap in encoder-only transformer architecture merging: > Long context (8k tokens) > Local-global attention for efficiency > Up-to-date code and text training data (2 trillion tokens) > FlashAttention & Unpadding to push performance on consumer and server GPUs > High retrieval and classification accuracy that sets new benchmarks across GLUE, BEIR, code tasks, and more. Learn more about this exciting new embedding model here: https://lnkd.in/en5ud9VE

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Meet AGUVIS: A pure vision-based framework for autonomous Computer/Mobile-use GUI agents, operating seamlessly across web, desktop, and mobile platforms without UI code. Key Features & Contributions - Pure Vision Framework: First fully autonomous pure vision GUI agent capable of performing tasks independently without relying on closed-source models - Cross-Platform Unification: Unified action space and plugin system that works consistently across different GUI environments - Comprehensive Dataset: Large-scale dataset of GUI agent trajectories with multimodal grounding and reasoning - Two-Stage Training: Novel training pipeline focusing on GUI grounding followed by planning and reasoning - Inner Monologue: Explicit planning and reasoning capabilities integrated into the model training - Generalization to OOD (Out-of-Distribution): AGUVIS demonstrates impressive generalization on OSWorld benchmark even without desktop trajectory data during training. Project Page: https://lnkd.in/guzkwpbB Paper: https://lnkd.in/gs7wXyJS GitHub: https://lnkd.in/gWeSkFmK Collaboratiion among Yiheng Xu Zekun Wang Junli Wang Dunjie Lu Tianbao Xie Amrita Saha Doyen Sahoo Tao Yu Caiming Xiong

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sharpening writing skill is one of killer skill!

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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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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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Everyone knows Moore’s law, the idea that CPU performance doubles roughly every two years. But remarkably, driven by GPU performance and AI model breakthroughs, we’ve seen a very different law emerge with respect to the rate at which AI model performance improves — in many cases improving by orders of magnitude in the past decade. This introduces massive implications in a world of AI Agents. For the past five plus decades, in any domain that computers touch, we’ve generally seen massive efficiency gains that bring more tech to more people. Yet the same has not been possible for essentially any non-digital services. Historically, anytime you want to solve non-computerized business problems (i.e. most knowledge work), we have seen the cost to solve a problem — say from healthcare services to legal work — tend to only go up over time. Due to a mix of inflation, specialization in certain fields, regulation, decreased competition in markets, or other added costs, we’ve seen the price of most services generally monotonically increase over time. This also puts a burden on driving economic activity in general, especially when creating new ideas from scratch, or starting and running a new business. Yet, in a world of AI Agents, anyone — from a small business or large enterprise — has access to automated work that can let them address problems with variable capacity. When you decrease the cost of entry for getting knowledge work done, you dramatically increase the use cases for that work. That new sales program can be tested instantly instead of waiting weeks or months or never happening at all; you can test a broader surface area of your product for bugs or security issues; legal work and reviews becomes affordable on the seemingly insignificant transactions that otherwise produce risk; or you can launch a new marketing campaign in markets you didn’t otherwise serve. Even in a world of AI costs remaining stable this is a super compelling proposition, but in a world of constantly improving technology the implications are vast. Just as Moore’s law steadily lowered the cost of software, increased its sophistication, and made it more ubiquitous, we can imagine a world where knowledge work regularly gets more affordable, sophisticated, and ubiquitous at a constant rate. This means AI Agents present the ability for any unit of work to continuously drop overtime, enabling us to solve greater and greater problems for a wider set of people and businesses. Of course, a large portion of these efficiency gains will quickly get “eaten up” by solving increasingly more sophisticated problems, but those problems will be solved at the cost of a previously simpler problem. The implications of this are enormously interesting, and it’s going to create incredible opportunities all around.

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One of the best things about being in San Francisco 🌉 is the ability to drop in on great talks. Last week I had a chance to see NVIDIA's CEO Jensen Huang (JH) being interviewed by Stripe's CEO Patrick Collison. Jensen is among the longest-serving founder-CEOs in Silicon Valley at the moment (30+ years). With that comes a special clarity of mind and some hot takes. 🌶👇 🗣 Jensen doesn't believe in 'praise in public and criticism in private'. Instead, he reasons that most information in the organization should not be privy to certain people. By giving feedback in public, you allow everyone to learn. 'What is better than learning from your own mistakes? It's learning from someone else's mistakes!!' 🔥 JH also doesn't like to fire people. He would rather "torture them into greatness". He invests in people and he doesn't like to write off his investments. 👨‍💻 Asked whether he likes his job every day, he answers "no". The perfect job doesn't give you happiness 100% of the time. "No great things were done, that were easy all the way." ... "Even if I come home from a day where I did things I didn't enjoy, it could still be have been a great day." 👩‍👧‍👦 Jensen famously has 60 direct reports. "This allowed us to cut out probably 7 levels of management". His subordinates don't necessarily have such large teams. "It doesn't scale down, less senior people need more guidance". Looking at the numbers, this approach allowed NVidia to reach ~2T market cap with ~28.000 people vs. Microsoft's 3T with ~220.000 people and Apple's 2.6T with 160.000 people. ❔ Jensen answers almost every question in the same way: "Let's break it down, let's reason about it". This is a great response on so many levels. First, it allows him to not be put on the spot. Second, it allows him to teach his workforce how he reasons through a problem so that they can emulate it and create alignment. Third, it gives people a chance to point out flaws in his reasoning. He admits he is not right all the time. ON AI 👨‍🔧 "You will not lose your job to AI, your company will go out of business because another company used AI." "If you're not using AI you're doing it wrong." ⏱ The extent to which AI will enter many processes is largely underestimated. Right now we are focused on language - sounds in a time sequence. "Any time sequence can be tokenized." This will include robotic movement. 🥛 JH also expects that we won't be satisfied with foundation models "You're going to want to tune it into perfection because you care so deeply. Say if it's already at 99% you'll want to get it to say 99.3%" This last piece is also why we created Query Vary (YC W22). AI will never feel truly intelligent until it completely understands your data and context. It needs to be taught, regardless of how powerful the foundation model is. Finally, besides insights, it was remarkable how funny Jensen was, cracking a joke at roughly a 1 per 3 min rate. It was a very entertaining talk. #ai #sf

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Performance is key! As we cannot share the detailed work with (former) customers of Rockset (acquired by OpenAI), we decided to implement the Rockbench benchmark to check how streaming ingest between CrateDB and Rockset (acquired by OpenAI) compares. The results are very insightful. We see: ✅ CrateDB achieves 6-9x lower latencies than Rockset for streaming ingest. ✅ CrateDB outperforms Rockset on the same hardware while saving about 20% costs. ✅ When volumes increase, the latency increased linearly in Rockset, while remaining mostly flat in CrateDB. As a plus: 1️⃣ Achieve full high availability due to the distributed nature of CrateDB (benchmark results are achieved including replication in CrateDB) 2️⃣ Achieve higher throughput on the same amount of CPUs as Rockset (at 50% of memory needs) 3️⃣ Full open-source solution that prevents you from market shifts On side note: If you are thinking to switch to Elastic/OpenSearch, this benchmark has been created to compare Rockset vs. Elastic, so CrateDB is also faster than Elastic for streaming ingest cases! Read the full comparison here: https://lnkd.in/d_nEMDkx

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At Instrumental Inc. we are typically very secretive about the clients we work with. During onboarding 3 years ago, I knew I made the right decision to join, especially after seeing the list of major logos we support. Today we are doing something a bit out of character and publicizing our partnership with @Meta to assist in the quality control of their new hardware. I personally believe this is the sort of edge hardware companies need to outperform competitors in a rapidly evolving market. Don't just take my word for it though, check out the video! Really exciting chapter in tech and at both companies. #AR #Engineering #Innovation

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Jensen Huang’s reflections strip away the glamour of entrepreneurship to reveal the stark reality of building NVIDIA showcasing the emotional toll and relentless grind behind the scenes. In the startup world, the actual essence of entrepreneurship is often overshadowed by the glamor of ‘fail fast, fail often.’ It’s not just about bold ideas and bigger bets; it’s about enduring doubts, managing fears of failure, and the resilience to stand back up, bruised but unbroken. It’s about the nights spent wrestling with the paradox of choice, the weight of decision-making, and the echoes of what if. Just like parenting a child with special needs, the journey is anything but linear. It's a mosaic of highs and lows, of victories and setbacks. It requires a heart willing to traverse the spectrum of human emotions, a spirit resilient enough to rebuild from scratch, and a resolve to redefine success on one's own terms. This perspective is not for everyone. It's for the brave, for the heartfelt warriors of life who dare to embrace the different, to see beyond the ordinary, and to aspire for the extraordinary, even when faced with the most ordinary of days.

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This is the upside of having a monorepo. Successes like this depend on being able to change code at scale safely and with clear understanding of performance impact. The changes themselves are done by a fairly small team, but the systems that enable us to continuously roll changes with critical company wide impact like this are key to make this possible.

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NVIDIA has introduced something amazing—Project DIGITS, which could bring supercomputers into our homes. This means we might soon be able to use powerful AI tools, do advanced research, or create incredible things right from our own desks. It makes you wonder: what’s next? Could we soon see AI in every device we use, making our daily lives even easier and more connected? The possibilities are exciting, and the future looks full of opportunities.

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𝗔𝗽𝗽𝗹𝗲 𝘀𝗵𝗼𝘄𝗰𝗮𝘀𝗲𝘀 𝘄𝗵𝘆 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘂𝗽𝗴𝗿𝗮𝗱𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗶𝗣𝗵𝗼𝗻𝗲 🚀 This clip from Apple’s event yesterday outlines all the advancements in the new A18 chip, specifically designed for AI. This isn’t just another upgrade—it’s a leap forward, bringing AI capabilities to the forefront. The A18 chip is engineered to unlock the full potential of the next wave of intelligent applications on-device. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿? Because the A18 chip isn’t just faster; it’s built for AI. Apple has poured innovations into this silicon, enabling real-time processing of complex AI tasks right on your device. Whether it's lightning-fast image recognition, advanced language processing, or seamless augmented reality experiences, the A18 chip is the key to tapping into the future of AI. 💡 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗰𝗮𝘁𝗰𝗵: To fully leverage these new capabilities, you need a device that’s powered by the A18. The question isn’t just whether you should upgrade; it’s whether you’re ready to step into the future of AI. ✨ Apple’s strength has always been in creating devices where the hardware and software work in perfect harmony. The A18 chip exemplifies this, designed to elevate your entire experience. By upgrading, you’re not just getting the latest tech—you’re embracing the next evolution of Apple’s innovation, where every interaction is smarter, faster, and more intuitive.

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Can KV cache optimization go beyond just reducing memory footprint? Our latest work, SwiftKV, does exactly that—cutting prefill computation by half and significantly accelerating enterprise inference workloads! We’ve open-sourced the SwiftKV-optimized model checkpoints for the Llama 3 family and vLLM implementations, enabling efficient inference and making it easier than ever to integrate these advancements into your applications. - Tech blog: https://lnkd.in/gZbPBum3 - Paper: https://lnkd.in/g__9NpBd We hope SwiftKV inspires new possibilities for enterprise LLM efficiency for you!

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