Mrinal Wadhwa

➰ 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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Pranay Prateek from SigNoz shares his thoughts on Unified Observability and how Open Source Signoz can help you to harness OpenTelemetry (OTEL) for Traces, Metrics at Logs. This talk took place at Open Source Observability Day #opensource #observability #otel

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Observability at the infrastructure and application levels must be harnessed to deal with today’s demanding workloads in #GenAI. Come talk to us to see how you can use the new AI with observability data to optimize your services.

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🇮🇳 How does Skyflow help drive DPDP compliance in India 🇮🇳? 🇮🇳 How does Skyflow protect data in LLMs? 🇮🇳 And in Snowflake ❄️? And Databricks 🧱? 🇮🇳 Check out our story in India’s leading tech publication YourStory Journal 🇮🇳 Link in comments.

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Repeating "Why Now" for graph RAG / autoKG in my conversations with infra/AI/ops leaders the last few months, whether they're in enterprise/gov/tech, similar shifts here -- BEFORE => AFTER primitive KG triples => linked rich text summaries, embeddings, & citations naively chunked vector index => hierarchical summary vector indexing manual rule-based indexing => autoextraction & autosummarization w/ customization hooks vector vs KV / text search => hybrid search, parallel indexing + ranking myopic one-shot k-nearest-neighbor RAG Q&A => summary indexing, agentic reasoning, etc manual ontologies => LLM autoinference + optimal learning loops batch RAG & summary indexing => real-time ingest, indexing, & eventing --- Curious if other shifts folks are seeing here? Likewise, we're gathering design partners as we work towards more comprehensive, reliable, & scalable forms of RAG. We're very much up for chatting on challenge problems here, lmk! (And for cyber folks facing real-time intel challenges, we'll be at blackhat/defcon in a couple weeks.)

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BharatPe’s move to Coralogix was fast, efficient, and impactful. 🚀 Now handling over 40TB of data daily, Coralogix provides BharatPe with top-tier log management, metrics, and traces, along with tools like the TCO Optimizer and #Kubernetes Dashboard for a comprehensive #observability solution. Ready to see how observability can fuel your business growth? Check out BharatPe’s journey!

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Your periodic reminder that containers and k8s are not secure boundaries for isolation. Don't let others run your workflows (especially CI/CD) in containers / kubernetes. At WarpBuild, we've taken extra steps to only run customer workloads in secure, isolated, ephemeral VMs. https://lnkd.in/gCwwZadG

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An insightful piece by Akash Gupta on Position Market fit versus Product-Market fit delves into the transition from Software Scarcity to Software Abundance. It offers strategies for pricing and positioning in this evolving landscape. Check it out here: https://lnkd.in/ewDgaYjy

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#Observability sold as SaaS is just 1% of its potential. Here’s why your org is paying full but losing out the remaining 99%: Observability also has four very distinct components: ↳ Generation : Creation of telemetry data using instrumentation in user space or kernel space. ↳ Pipeline: Collecting, processing, and exporting telemetry data to storage. ↳ Storage: Storing the telemetry data efficiently, allowing real-time access and availability. ↳ Visualization: Presenting data in a simple and actionable manner. If we do not decouple these, you can imagine the hindrance in the development of the observability landscape. There are some really useful products, but they are using (or abusing?) this very fact that it is not decoupled yet, thus harder to replace. Ensuring that these components have their own parallel growth provides each institution the means to build observability which bends to their infrastructure not another way around. And that’s our vision at Incerto. Curious to learn more? Here’s a no BS blog on building the perfect observability solution: https://lnkd.in/g2be3JRw #DevOps #monitoring #developercommunity

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YugabyteDB ❤️ Apache Airflow: check out this talk at the Airflow Summit by Amit Chauhan.

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A few thoughts on #LLM usage in the small, prompted by my toying with #llama3 and #phi-3 on consumer hardware.

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#GenAI models necessitates training on extensive datasets, which in turn requires robust #cloud storage solutions. As Gen AI continues to advance, demand for cloud services would surge correspondingly, creating a symbiotic growth relationship b/w the two. https://t.ly/9Rg-C

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The FireHydrant acquisition of Blameless signals a major shift in observability and incident management. My Partner Jaya and I just wrote about the looming "observability crisis"—how the explosion of data and tools has paradoxically made it harder to extract meaningful insights. This deal is a direct response to that crisis. By combining forces, FireHydrant and Blameless aim to build an end-to-end incident management platform to reimagine root cause analysis from the ground up. But to *truly* address the observability crisis, it will take more than consolidation. We need AI-driven systems that: → Provide natural language interfaces to query complex systems → Automatically generate and update incident response playbooks → Offer predictive maintenance by recognizing patterns across heterogeneous data sources → Synthesize post-mortem reports, distilling insights from terabytes of data Most critically, we need to ditch volume-based pricing models that incentivize data hoarding over problem-solving. Expect more moves as the market evolves to meet the demands of increasingly complex enterprise systems.

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🗣️ PODCAST ALERT!📢 Tune in to the TechGig Podcast featuring Laduram Vishnoi, Founder & CEO of Middleware , as he shares groundbreaking insights on: 🔍 LLM Observability: Provides real-time monitoring, troubleshooting, and optimization for LLM-powered applications. 🤖 Query Genie: Bolsters data analysis by enabling instant search and retrieval of relevant data from infrastructure and logs using natural language queries. 🎯 Why Observability isn’t just an option anymore—it’s a necessity for modern enterprises. 🔥 Plus, get inspired by Middleware’s visionary journey and their use of AI and machine learning to tackle the biggest challenges facing today’s IT industry. 🎧 Don’t miss out. Listen now: https://lnkd.in/g-isB9YV A big shoutout to Ruchi Kumari 🙌 #TechGigPodcast #Middleware #FutureOfTech #LLMObservability #AI #MachineLearning #SystemAnalysis

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Using Search as tool is a powerful way to expand Gemini capabilities - here the code snippet - https://lnkd.in/di79KsFw

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If you jump into the cloud world with cost as the primary basis, there may be discontent. There are few other value levers that are frequently floated around to assert the benefit of cloud. I have seen posts negating them as well. But in my opinion, the reason organizations need to look at the cloud, now more than ever, is what is getting infused in cloud solutions- Artificial Intelligence (AI). #data #analytics #ai #ml #datascience #designedanalytics https://lnkd.in/g6JjdEN9

💡 Compute is the new oil in this AI era. Cedana solves this providing a solution for saving, migrating, and resuming compute jobs across cloud vendors without interruptions or code modifications. Essentially it helps companies leverage "compute arbitrage" 🤯 Here's their CTO Niranjan Ravichandra talking about how they use SigNoz to monitor the behavior and performance of daemons across customer nodes. Our powerful APIs and ability to write advanced ClickHouse query enables them to pull data into Jupyter notebooks & analyze performance trends across hundreds of nodes. Excited to see SigNoz powering observability of cutting edge companies like Cedana 🚀 https://lnkd.in/gzZtMwdN

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Apollo.io is tanking. Deliverability is a joke and data quality is going down. I've been an enterprise Apollo user for years. I loved the platform. However, the data seems to just be getting worse and worse and deliverability is essentially non-existent. We're switching Identity Matrix to Smartlead. Any other suggestions from my outbound friends? #outboundsales #sales

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