Abhi Sharma

AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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➰ APIContext's latest insights on cloud-based API performance have been featured on Nordic APIs! In the 2024 report, we delve into crucial aspects affecting API efficiency: 🔐 Authentication is key. Ensuring robust authentication mechanisms in APIs is crucial, yet offloading this function can introduce complex challenges. 📖 DNS configurations matter. How you handle DNS settings can significantly impact API performance, particularly when tied to authentication processes. 🌐 The role of third parties. Critical third-party services play a significant role in 🔗 API transactions, underscoring the need for careful integration and monitoring. ⛅ Not all clouds are created equal. Differences in cloud services can drastically affect API performance, an essential consideration as APIs account for over 80% of all internet traffic. #APIs #CloudComputing #TechInsights #APIContext

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Worth reading: How the Apache Arrow Format Accelerates Query Result Transfer by Ian Cook One of the hidden problems of intersystem communication is transcription of data formats. Ian does a wonderful job of explaining the "why" of Apache Arrow as well as the "how" of its advantages. Read on...

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Hard-coded model routing takes away intelligence from your system instead of adding to it! Interesting read below on how Ema Unlimited's Fusion model is a potent solution for the heterogeneous multi-model future of AI in the enterprise.

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We're launching the inaugural edition of Beyond Benchmarks, a deep dive into the metrics and trends observed across the early-stage enterprise cloud market. This is the first look at some of the business data around GenAI in SaaS. Some initial observations: 1) 60% of SaaS companies have already integrated GenAI into their offerings (60% of these as part of their current product and 40% as a new product), and an additional 20% are planning to do so this year. Almost half aren't monetizing these features. Those that are are primarily experimenting with both usage-based and flat fee pricing models. 2) Most companies are utilizing OpenAI as their primary LLM, but many are experimenting with multiple models - and we are noticing an early trend towards intelligently routing GenAI inference requests to different models based on cost, performance, and security. 3) Though we are in the initial innings of GenAI, we are seeing some positive signals - companies that implemented GenAI had 7% higher NDR than those that did not. Still early days, but we'll be doing this benchmarking report regularly to track and share how things progress in this new era. Link to report in comments.

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Some additional charts on the aggregate hyperscaler data from Q1. The charts below show 1) The aggregate net new ARR added on q quarterly basis. Q1 is always a seasonal low 2) The YoY growth in net new ARR in Q1. Q1 '23 was the "bottom" with AWS actually adding negative net new ARR (ie they shrunk), so the YoY comp is the "easiest" for the aggregate bucket.

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Apple’s Private Cloud Compute and the business of privacy With Private Cloud Compute, Apple has gone to extraordinary lengths to safeguard user privacy with AI-related tasks. In doing so, Apple has surely also incurred an extraordinary expense. How can Apple justify the cost of providing PCC if it doesn’t directly monetize its Apple Intelligence features? Either through the improved brand perception captured in a broader privacy narrative or by monetizing PCC with other non-AI use cases. While the narrative explanation is clear, the more interesting theory about Apple’s motivation in building PCC is that it can be expanded to other use cases that can be directly monetized. The first alternative use case that springs to mind is advertising attribution. PCC sounds conceptually similar to a data clean room, albeit far more sophisticated than most implementations, which merely use IP addresses and other identifiers to match publisher-observed ad interactions with advertiser-observed conversions. While the PCC nodes seem hardware-optimized for processing AI-related tasks, other nodes could be built that aren’t.

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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On Wednesday, GitHub and JFrog announced a collaboration to integrate their platforms, making it simpler for developers and their support teams to handle source code and binaries across both services. This features single sign-on, role mapping, code tracing from source to binary packages across platforms, and a unified project architecture. Later, a unified dashboard will display GitHub and JFrog’s source- and binary-focused security scan findings. #github #jfrog #githubcopilot #eltrys ELTRYS. GitHub JFrog

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Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

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JFrog Ltd., the Liquid Software company and creators of the JFrog Software Supply Chain Platform, today announced a new product integration with NVIDIA NIM microservices, a component of the NVIDIA AI Enterprise software platform. Their platform has been expanded to include a unified MLOps platform as a result of the acquisition of Qwak AI. It is anticipated that the JFrog Platform will be integrated with the JFrog Artifactory model registry and NVIDIA NIM to create an end-to-end software supply chain workflow that combines GPU-optimized, pre-approved AI models with centralized DevSecOps processes. Read more - https://lnkd.in/gpy_sCJZ For Startup funding reach out us at: https://lnkd.in/gra5x2Xi To share your startup information write us on - [email protected] Join Our Whatsapp Channel To Get The Latest Updates & News: https://lnkd.in/esJEFzan #tech #AI #automation #nvidia #JFrog #AIEnterprise #microservices

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Confluent's Field CTO, Kai Waehner, shares insights on the future of streaming analytics and data management, highlighting the importance of reliable data flow, collaboration, and security. Discover how Confluent's cutting-edge platform revolutionizes streaming analytics. Read the full interview: https://lnkd.in/gSMVCmvy #StreamingAnalytics #DataManagement #FutureOfData #BusinessIntelligence

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The most downloaded datasets from the California HHS site? If you follow me on LinkedIn, you've probably guessed it - the Medi-Cal enrolled provider list and Suspended and Ineligible list are the top two, followed by other datasets by a wide margin. The California Health & Human Services Agency (Cal HHS) has a nice data hosting site that resembles data.cms.gov. The site looks slick and provides interesting insights, including visitor statistics and download metrics. It's fascinating to see how people are interested in provider eligibility and exclusion data for Medi-Cal billing. Looking at how organizations use these public datasets, compliance emerges as the dominant use case. This makes sense - when accessing government data portals, users are typically verifying facts related to government programs. The user base spans health plans, hospitals, IPAs, medical groups, and even commercial payers, as Medicaid suspensions often trigger reviews across the healthcare ecosystem. What's interesting is that this pattern extends beyond healthcare. On data.ca.gov, which hosts all kinds of government data, the most downloaded dataset is the directory of certified electricians. This shows a consistent theme - whether it's healthcare providers or electricians, people come to government sites primarily to verify professional credentials and eligibility. This raises an intriguing observation about how government data is used. The way organizations use these datasets - downloading lists to verify credentials and eligibility - is functionally similar to what modern Identity Provider (IdP) systems do, but through a much more manual process. What are your thoughts on the future of government data in healthcare identity verification? #Healthcare #DigitalTransformation #Compliance #HealthTech #GovTech #Identity

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ICYMI 📣 Here's the inside scoop from #theCUBE's coverage of #MongoDBlocal NYC. https://lnkd.in/gY4FeTRX 💡 Data is the driving force behind the AI surge. MongoDB's MAAP is empowering enterprise AI with seamless integrations, technical expertise, and robust architectures. 🌐 Strong partnerships with Microsoft, Google and Amazon Web Services (AWS) show MongoDB's commitment to adapting its ecosystem for developer-friendly features, as with Arc XP. 🗣️ “MongoDB is putting so much investment and innovation into the industry, including the MAAP initiative,” said David Vellante, theCUBE Research’s chief analyst. Get up to speed on how #MongoDB is redefining the AI landscape! #TechNews #CTOtrends #CloudComputing #DevOps #enterpriseAI #DataManagement

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I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment

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$2 H100s: How the GPU Bubble Burst Last year, H100s were $8/hr if you could get them. Today, there's 7 different resale markets selling them under $2. What happened? The market has flipped from shortage ($8/hr) to oversupplied ($2/hr), because of reserved compute resales, open model finetuning, and decline in new foundation model co’s. Rent instead. https://lnkd.in/gpQEidmC

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