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Toronto, Ontario, Canada
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Sandip Bhattacharya shared thisI am afraid of the day when someone runs this analysis on LinkedIn 😛 https://lnkd.in/eCan26Kp
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Sandip Bhattacharya shared thisReally like this model. This is now my goto model (via fireworks of course) for all non-subscription AI usage.Sandip Bhattacharya shared thisKimi K2.6 is live on Fireworks as a Day-0 launch partner! K2.5 was the base for standout models like Cursor’s Composer 2 and was the most popular model on our training platform. K2.6 on Fireworks raises the bar again. → Optimized across the stack, from custom speculators to heterogeneous hardware support across NVIDIA and AMD. → Day-0 serverless support is live, and coming soon to Fire Pass (stay tuned). → Unlock new use cases with capabilities for 12+ hour autonomous runs and 4,000+ tool calls. → $0.95 input / $4.00 output per 1M tok Get started today → https://lnkd.in/gqXXpZrg
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Sandip Bhattacharya shared thisPosition open in Canada, USA, UK. Ceph is the critical filesystem layer in all our Kubernetes environments, and we have a lot of really interesting problems to solve at scale!Sandip Bhattacharya shared thisWe’re hiring a Senior Ceph Engineer at Index Exchange! 🚀 You’ll be working on distributed storage systems powering a platform that processes 550B+ real-time auctions daily and 2PB of data every day, partnering closely with an excellent leader, Sahil Yusuf. We're looking for someone who: • Has experience with Ceph • Has worked in high-scale, production environments • Enjoys solving reliability and performance challenges Apply here: https://lnkd.in/eHXeMzYp
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Sandip Bhattacharya shared thisThis sounds urgent. 🏃♂️OpenAI Issues Urgent Warning: macOS Users Must Update ChatGPT & Codex ImmediatelyOpenAI Issues Urgent Warning: macOS Users Must Update ChatGPT & Codex ImmediatelyThe Cyber Security Hub™
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Sandip Bhattacharya shared thisOne thing I like about #ClaudeCode is that it has no opinion about what you can use it for. I use it for coding, sysadmin work, writing obsidian notes, everything. #OpenCode, my second best agentic cli, on the other hand has annoying opinions about what you should use it for - "I am a software engineer only, thanks."
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Sandip Bhattacharya posted thisIt is increasingly hard not to see the parallels between how the core behaviour of LLMs are not entirely explainable by their very own creators (why do emergent behaviour happen at scale), and the emerging trend of software being built with LLM generated code that is increasingly unsupervised.
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Sandip Bhattacharya shared thisI have heard about this trend of _no IDE_ and I still don't entirely get it. So is the expectation that either an engineer only looks at diffs in code, or not do code reviews at all? IDEs give better context to change sets even in an AI driven workflow, imo. I frequently spot inconsistencies in a different part of the code while reviewing changes from the agent in the IDE window. Sometimes docs are out of sync, sometimes neighbouring code turns redundant. IDEs are still important for humans managing code. We can handle larger context, while the agents are still desperately trying to grep through code to fit into their tiny context windows. Humans are imperfect, and have less mechanical capabilities than agents, but they are better stewards of the code. It is hard keeping up with rapidly changing coding practices already, but I am deeply uncomfortable with this wish of turning engineers into agent managers. There is a whole bunch of articles emerging about skill atrophy in engineers who spent less time being involved with actual code.Sandip Bhattacharya shared thisThis week I told my engineering org two seemingly insane things: 1. Delete your IDE. 2. Write 0 lines of code. There was immediate skepticism. A little “wtf, he's crazy.” Fair. Then something interesting happened: people leaned in. It was obvious there was no faster way to learn. Why? If you want an organization to operate AI- and agent-first, you can’t layer new tools on top of an old job definition. You have to change the work itself. IDEs are comfort blankets. Writing code is muscle memory. Both anchor you to a job that no longer exists. The new job is direct agents and shape systems to ship outcomes. We’ve been aggressively using AI since late 2024—long before it was fashionable—and still at times feel behind. The issue was never tooling. It was mindset. Maybe the real bottleneck has been us, humans, the whole time. The invariant: build things users care about. Everything else is just how you get there. If you want to change the game, you have to actually play it.
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Sandip Bhattacharya reposted thisSandip Bhattacharya reposted thisThat feeling after migrating the control plane nodes on a 𝟱𝟬 𝗣𝗲𝘁𝗮𝗯𝘆𝘁𝗲 𝗰𝗹𝘂𝘀𝘁𝗲𝗿 while all data pipelines were live, 𝟳 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗲𝘃𝗲𝗻𝘁𝘀/𝘀𝗲𝗰 flowing through, and not a single user impact. All of this happened at 7:30 PM on a Friday just 30 minutes before the Jays & Dodgers World Series game we were all excited about. Yet the focus, ownership, and teamwork never dipped. 🚀 . Thanks to Everyone for clean execution. 𝗘𝗻𝗷𝗼𝘆 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗔𝗽𝗽𝗿𝗲𝗰𝗶𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 !! #teamwork #dataengineering
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Sandip Bhattacharya shared thisLove this take.Sandip Bhattacharya shared thisHere's something remarkable: You almost *never* hear the most popular view of AI that the majority of people who work in tech have about technologies like LLMs. By popular request, I've tried to sum up that POV as succinctly as possible, and explain why many are afraid to tell a simpler, less dramatic story that contradicts the narrative that the billionaire tycoons are trying to force on the world. https://lnkd.in/e5pjGvVP
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Sandip Bhattacharya liked thisSandip Bhattacharya liked thisApril was a pretty strong month for open-weight LLM architecture releases: 1. Gemma 4 Continues the local/global attention recipe with sliding window attention, which is a classic yet "easy" way to extend context while making it cheaper than full attention. 2. GLM-5.1 A large MoE model that based on DeepSeek V3.2 with multi-head latent attention (MLA) and DeepSeek Sparse Attention 3. Qwen3.6 Keeps the Qwen3.5-style hybrid stack. I.e., mostly Gated DeltaNet / linear-attention-style layers, with periodic full attention layers. Next to the 35B-A3B MoE variant there now also is a 27B dense variant. 4. Kimi K2.6 A 1T-scale sparse MoE model with DeepSeek-style MLA and only a small fraction of parameters active per token. It is another good example of how frontier-scale models keep improving without major architecture changes (coming from DeepSeek V3) except increasing the size. (Lots of performance improvements are due to the training recipe.) 5. DeepSeek V4 Probably the most architecture-heavy release of the month. The Pro and Flash variants add manifold-constrained hyper-connections, compressed attention variants, hash-based routing in the early layers, and a 1M-token context window. All of these are now added to the LLM Architecture Gallery. I am keeping the notes short for now, especially for DeepSeek V4, because the details around mHC and compressed attention deserve a more careful writeup. More once I am fully back in May.
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Sandip Bhattacharya liked thisSandip Bhattacharya liked thisKimi K2.6 is live on Fireworks as a Day-0 launch partner! K2.5 was the base for standout models like Cursor’s Composer 2 and was the most popular model on our training platform. K2.6 on Fireworks raises the bar again. → Optimized across the stack, from custom speculators to heterogeneous hardware support across NVIDIA and AMD. → Day-0 serverless support is live, and coming soon to Fire Pass (stay tuned). → Unlock new use cases with capabilities for 12+ hour autonomous runs and 4,000+ tool calls. → $0.95 input / $4.00 output per 1M tok Get started today → https://lnkd.in/gqXXpZrg
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Sandip Bhattacharya liked thisSandip Bhattacharya liked thisParadigm 2026 – Celebrating Legacy, Strengthening Connections On April 18, 2026, the Alumni Relations Committee, MBA—IISWBM, in collaboration with the Events Committee - IISWBM, proudly hosted PARADIGM 2026 at The HHI, Kolkata, bringing together esteemed alumni across batches for an evening of meaningful engagement and shared legacy. The event was graced by the esteemed presence of IISWBM’s Director, Prof. (Dr.) K. M. Agrawal, and Head of the MBA (Day & Evening) programme, Prof. (Dr.) Tanima Ray, whose guidance and encouragement continue to shape the institution’s academic excellence. We were also privileged to have a large number of respected faculty members in attendance, whose presence added immense value and warmth to the occasion. We were deeply honoured by the gracious presence of our distinguished alumni, whose participation made the evening truly enriching. Their insights, experiences and continued association with the institute remain a constant source of inspiration for our students and the IISWBM community as a whole. The evening fostered meaningful conversations, mentorship and a renewed sense of belonging—further strengthening the vibrant IISWBM alumni network. To our alumni and faculty—thank you for making PARADIGM 2026 truly memorable. #PARADIGM2026 #IISWBM #AlumniRelations #MBA
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Sandip Bhattacharya liked thisSandip Bhattacharya liked this𝗧𝗵𝗲 𝗔𝗹𝘂𝗺𝗻𝗶 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗖𝗼𝗺𝗺𝗶𝘁𝘁𝗲𝗲 𝗼𝗳 𝗜𝗜𝗦𝗪𝗕𝗠-𝗠𝗕𝗔(𝗗𝗮𝘆) is glad to present the team representing the 𝗕𝗮𝘁𝗰𝗵 𝟮𝟬𝟮𝟰-𝟮𝟬𝟮𝟲 & 𝗕𝗮𝘁𝗰𝗵 𝟮𝟬𝟮𝟱-𝟮𝟬𝟮𝟳. Building on the responsibilities entrusted to us, we aspire to strengthen meaningful ties between the Institute and its alumni, while supporting initiatives that uphold the IISWBM spirit. As a new chapter begins, this team looks forward to contributing to a legacy of connection, collaboration, and community. 𝗪𝗶𝘁𝗵𝗶𝗻 𝗳𝗿𝗮𝗺𝗲:– ~ front row ~ Swarnima Chakraborty – 𝐇𝐞𝐚𝐝 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐨𝐫 Debasmita Das – 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐞 𝐇𝐞𝐚𝐝 & 𝐓𝐫𝐞𝐚𝐬𝐮𝐫𝐞𝐫 Debarati Roy – 𝐏𝐮𝐛𝐥𝐢𝐜 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐇𝐞𝐚𝐝 & 𝐄𝐝𝐢𝐭𝐨𝐫 Spandan Bose – 𝐀𝐥𝐮𝐦𝐧𝐢 𝐄𝐯𝐞𝐧𝐭𝐬 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐨𝐫 ~ back row ~ Anushka Bhowmick – 𝐉𝐮𝐧𝐢𝐨𝐫 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐨𝐫 Ritabrata Mukherjee – 𝐉𝐮𝐧𝐢𝐨𝐫 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐨𝐫 Shirsha Gooptu – 𝐉𝐮𝐧𝐢𝐨𝐫 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐨𝐫 Sudeshna Karmakar – 𝐉𝐮𝐧𝐢𝐨𝐫 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐨𝐫 Here’s to a new year of connections, collaborations, and community.
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Professional Apache Tomcat
Wrox
The first book on Apache Tomcat. Includes how to setup, configure and use.
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Manish Pal
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Excited to share that Olyv has raised $23M (~208 Crore) in our Series B funding round, a significant milestone for the platform we have been architecting over the years. My journey at Olyv has been closely tied to building and evolving our core systems through multiple lending cycles and regulatory shifts. From scaling our credit decisioning engine to strengthening distributed architecture, data pipelines, and real-time risk frameworks, the focus has always been clear — build infrastructure that is resilient, intelligent, and compliant by design. A major part of this evolution has been our in-house ML and Data Science capabilities. We have invested deeply in building proprietary models, optimising underwriting logic, and enabling continuous learning across the credit lifecycle. Working alongside our co-founders has reinforced the importance of long-term architectural thinking. Speed matters, but structural integrity matters more. Every system we have scaled has been grounded in that philosophy. As we enter this next phase, we will continue advancing automation, strengthening platform intelligence, and engineering systems that can responsibly serve millions at scale. Grateful to our technical leadership who turn complexity into momentum—through sound judgment, technical rigor, and execution excellence. Proud of the entire Olyv team for building with depth, rigor, and ownership. Onwards and Upwards. https://lnkd.in/gABaXZpg Rohit Garg Amit Chandel Jayant Upadhyay Vinay Kumar Singh Pradhuman Singh Abhishu Khekre Deepak Mishra Olyv
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Madhur Prashant
Antimetal • 5K followers
An agent has no consistent definition. You can think of an agent as an autonomous or semi-autonomous system that can take actions on behalf of the user in a given environment, state, and make decisions or take actions that can accomplish certain tasks along a given time-frame, either by calling tools, dynamically selecting the next action to take or use a deterministic "workflow" (systems where LLMs and tools are orchestrated through predefined code paths). Using an Agent framework that gives you the ability to built systems in both, a reliable and a dynamic way can accelerate your agent development journey using agent abstractions. This means building Agentic systems that can reliably call tools, store memory (episodic/semantic/procedural), have comprehensive logging and observability, human in the loop workflows and the ability to build various multi-agent patterns flexibly based on your use case. A successful Agentic system in production is usually a combination of both, dynamic and predictable/reliable multi-agent systems. Strands Agents SDK gives you exactly these capabilities by treating each “agent” as a combination of a foundation model plus a suite of tools. You define a prompt and register your tools (decorated functions) in code, then Strands handles reasoning→planning→tool-execution cycles, local testing, and cloud deployment (ECS, Fargate, Lambda, EC2), along with support for all other agent abstractions provided above. Excited to share a hedge-fund analyst multi agent system: This uses the newest Anthropic's Claude 4 Sonnet/Opus that powers the Lead Analyst Agent, routing incoming queries to specialized sub-agents for fundamental, technical, and market analyses. Each specialist is wrapped as a callable tool (using the “agents-as-tools” multi-agent pattern), so the orchestrator never has to implement domain logic itself and can handoff the task to an agent as a tool. For sensitive operations (insider lookups), we utilize a HITL approval step that halts execution until a human grants consent. We also use meta-tooling that enables the Lead Analyst to generate, load, and invoke new custom tools at runtime—whether it’s a portfolio beta calculator or a pricer—without redeployment. Strands also embeds observability (Langfuse) and OpenTelemetry tracing so you can trace reasoning events, tool invocations, errors, and end-to-end workflows in real time. View more information on the code implementation here: https://lnkd.in/gJmwVyGi Code implementation: https://lnkd.in/gzTtJvJq Thanks to 🏄♂️ Cagatay Cali for being a reviewer/collaborator on this! Feel free to try it out and reach out with any questions/ideas. #aws #agenticAI #strands #agents #generativeAI
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Ryan Burnham
Elastic • 3K followers
Led by Chidambaram Chidambaram, Senior Customer Architect at Elastic, the session Implementing Agentic RAG using Elasticsearch and LangChain is now available to watch on demand. It covers building agentic retrieval augmented generation (RAG) systems that retrieve, reason, and support contextual decision-making in real-world scenarios. Watch on-demand: https://gag.gl/rqR9Om
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Pratyush Kamal
Siemens EDA (Siemens Digital… • 2K followers
It will be a good idea for Intel to standardize the layout template for their EMIB offering. Fixed layer count for specific version of D2D, and fixed number of side bands, scalable with beach-front. Abstracted and scalable models ready for integration in package. I hope it happens one day. Sell it as soft or hard passive chiplet.
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Daniel Riegel
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Announcing OpenZL! Today, I'm excited to announce the open source release of a project that my team has been working on for the last five years: It's called OpenZL, a new kind of lossless data compressor that can make your data smaller with less CPU than you might have thought possible. How does it work? It has a toolbox of compression techniques, but each trick can be applied selectively to the formats to which they apply best, and it will entirely skip those that are not well-suited to your data. The end result is a much more efficient compressor which finds more compression opportunities, faster. It really shines for structured data, and it's ready to try at https://lnkd.in/eGtfZ3WT. Learn more from the blog post https://lnkd.in/eWGhNYmj Or go deeper in the whitepaper https://lnkd.in/eEu27QjS to see details on how it works and performance benchmarks
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Brian Gero
3K followers
Flexential Interconnection Mesh is here. 🌐 This new addition to Flexential Fabric gives enterprises direct control of multi-site connectivity from a single port—fast, flexible, and built for scale. Proud moment for the team as we continue to strengthen the FlexAnywhere platform. 💪 Learn more: https://ow.ly/7bYJ30sPtmo #FlexAnywhere #Connectivity #DataCenters #HybridIT
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Nicole Foster
5K followers
International cooperation on AI governance is a significant G7 legacy. The G7 Hiroshima AI Process (HAIP) makes meaningful progress in creating frameworks for safe, secure, and trustworthy AI. I'm very proud that Amazon has demonstrated its commitment to this process through its submission to the G7 Hiroshima AI Process Transparency Report, committing to risk assessment and mitigation throughout the AI lifecycle, post-deployment monitoring, public reporting of AI capabilities and limitations, and information sharing across industry and government. Continuing this momentum is essential as AI systems become increasingly integrated into our economies and societies.
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Saju Pillai
Kong Inc. • 4K followers
Very excited about Kong Inc. extending "API governance" abilities to eventing systems. Our new event_gateway can front Kafka brokers and let you run policies to mutate messages. The event_gateway integrates back into Kong Konnect - our SaaS API platform - one single platform to "build, run, govern" http and eventing apis. ps: built in Rust on tokio, the performance characteristics of the event_gateway are amazing.
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Vivek R.
FISPAN • 2K followers
This should be one of the best talks, taking an iterative approach in solving for high scalable low latency distributed multi tenant systems and evolving architectural integration patterns. It covers the rationale and trade-offs such as, 1. Synchronous vs Asynchronous communication patterns 2. Tenant Identity (JWT) for Data isolation 3. Backpressure for performance 4. Message queues for decoupling and resiliency 5. Hybrid architecture balancing Single Tenant Queues and Multi Tenant Queues (for solving the noisy neighbors problem) - explores queue sharing patterns (Cell and Shuffle) 6. Dynamic consumer-controlled overflow queues 7. DLQ as circuit breaker for external facing integrations 8. Externalizing state and Scatter gatherer pattern with multi tenancy and it's data management strategies https://lnkd.in/gtnBQqSN Absolutely beautiful!
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Sree Chadalavada
Open Compute Project… • 6K followers
Like most in my network, I spent most of my career in traditional Ethernet (front-end networks). With AI, there are three new networks are emerging - Scale up: GPU-to-GPU Scale out: Node-to-Node Scale across: DC-to-DC The following 3 white papers offer a good summary of “why”, “what”, and “where do we go from here” for AI backend scale-out networks: 1. Datacenter Ethernet and RDMA: Issues at Hyperscale: AI workloads require lossless networks because they depend on extreme levels of parallelism and synchronization. The industry adopted RDMA (Remote Direct Memory Access) over Converged Ethernet (RoCE) as a protocol for AI backend scale-out networks. This paper discusses key challenges and inefficiencies with the RoCE standard. Link: https://lnkd.in/gKqSb3XB 2. I’ve Got 99 Problems But FLOPS Ain’t One: As AI Infrastructure deployments is a core competitive advantage, Hyperscalers tend to be secretive about the challenges they face (and the solutions derived to address the problems) in building global-scale network infrastructures. The authors of this white paper used a research-based approach to model network challenges in supporting mega clusters and identified potential research areas for further exploration. Link: https://lnkd.in/g_V59r2A 3. Ultra Ethernet’s Design Principles and Architectural Innovations: The 562-page-long Ultra Ethernet Specification 1.0 is intimidating. Ultra Ethernet specification 1.0 was designed to address AI and HPC workload network requirements. Key guiding principles include: Massive Scalability, High Performance, Compatibility and Vendor Differentiation. This white paper provides an easily digestible summary of key advancements proposed in Ultra Ethernet specification. Link: https://lnkd.in/gBFr-Vtv Ultra Ethernet Specification: Ultra Ethernet Specification v1.0 June 11, 2025: https://lnkd.in/gTmX73rV I would be remiss if I did not mention Torsten Hoefler 🇨🇭 who not only made contributions to the specification, but is also democratizing the specification. Thank you, Torsten! Torsten Hoefler Talk @ 2025 Swiss Conference: Next Generation AI and HPC Networking with Ultra Ethernet: Torsten Hoefler Talk: https://lnkd.in/gc9kZYB8 Are there other AI Backend Scale Out resources that you came across? #AINetworking #UEC #ScaleOutNetwork
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Arham Mehta
NVIDIA • 6K followers
Excited about the release of Nemotron 3 Super - 120B total / 12B active, Hybrid SSM Latent MoE, designed for Blackwell, pre-trained in NVFP4 and top spot on AA index for its size! For data at scale, we used NeMo Curator to curate over 10 trillion tokens for LLM pre-training, and open sourced our data curation recipe so you can use the same pipeline for your own workflows that includes GPU-accelerated deduplication, quality filtering, model ensembling, and more. 🔗 NeMo Curator: open-source recipe and code so you can curate high-quality datasets for your use case: https://lnkd.in/ggykjqa2 🔗 Technical report: https://lnkd.in/gmxhBK89 Mostofa Patwary, Markus Kliegl, Ayush Dattagupta, Vibhu Jawa, Abhinav Garg, Praateek Mahajan, Sarah Yurick, Bartley Richardson, Randy Gelhausen, Ashwath Aithal, Nima Tajbakhsh #AI #dataprocessing #datacuration #NVIDIA #OpenSource #AIdeveloper
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Andrew Jessup
Kong Inc. • 3K followers
This is a huge update that anyone who cares about workload identity or agentic security should be paying attention to. Kong Mesh is the first and only service mesh to natively act as a SPIFFE identity issuer, to interoperate with an existing SPIRE deployment, or to federate between two. This vastly improves interoperability in hybrid and multi mesh deployments. Great work Kuma team! We'll be sharing more about how this can help solve for modern enterprise security challenges in a future post.
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Carmella (Surdyk) Weatherill
3K followers
When an API's backend target fails, how do you ensure your service remains available? This new guide explores two strategies for building resilient APIs: → Circuit Breaking: Use Apigee load balancing to fail over after a certain number of errors. → Failover with ServiceCallouts: Use a no-target proxy for dynamic URLs, like switching to a different region. Learn how to make your APIs more resilient, with code snippets and a sample proxy from Principal Architect Tyler Ayers. Read the full story →
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Anderson Luiz Mendes Matos
Questrade Financial Group • 1K followers
Today at #RedisReleased Toronto I explored how Redis is evolving far beyond what most of us still picture it as. From massive performance gains and multi-cloud active-active deployments to data aggregation, extreme resiliency, and real-time load management without bottlenecks, Redis is a complete data platform. It was a privilege to see, first-hand, feedback from TD and OrderGrid and how Redis is empowering their next steps, including AI transformation and adoption. Add reliable messaging, deep instance insights and even the ability to chat with your data, and the message is very clear: this isn't just about caching for a long time, now. Redis is empowering global enterprises to run faster, smarter, and more resilient than ever. And you? Are you still using Redis as a cache? What's your favorite Redis feature (or the one that surprised you the most)? #RedisReleased #Redis #DataEngineering #Performance #Cloud #HighAvailability #TorontoTech #AI
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Santiago Merlos
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Optimize Your Ambient Mesh with Envoy Gateway as Unified Ingress and Waypoint Proxy Integrating Envoy Gateway with Istio's Ambient Mesh creates a unified ingress gateway and waypoint proxy, enhancing service mesh capabilities. This combination leverages Envoy Gateway’s advanced Layer 7 features, such as authentication, rate limiting, and CORS handling, within Ambient Mesh’s sidecar-less architecture. By utilizing Kubernetes-style APIs, the setup simplifies deployments and provides consistent traffic management and policy enforcement across the mesh. Practical implementations demonstrated include configuring Envoy Gateway for ingress and waypoint proxy roles, enabling functionalities like global rate limiting and circuit breaking. This integration offers a scalable, secure, and efficient solution for managing both ingress and internal traffic in cloud-native environments, optimizing performance without the complexity of traditional service meshes. Author: Huabing
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Plamen Florov
Datalaked • 744 followers
LLM Memory Breakthrough: 50x Reduction in KV Cache! 🚀 One of the biggest bottlenecks in scaling Large Language Models just got a massive reality check. A new technique called KIVI is promising to cut KV cache memory usage by up to 50x—without losing model accuracy. Why does this matter? If you’ve ever run out of VRAM while generating a long document or chatting with a bot, you’ve hit the "KV Cache" wall. Traditionally, as your conversation gets longer, the memory required grows rapidly, slowing down performance and limiting context windows. The "Magic" behind KIVI: Plug-and-Play: It doesn't require re-training the model. Smart Quantization: It compresses the "Key" and "Value" data to 2-bit precision. No Accuracy Loss: It maintains the model's intelligence by being selective about what it compresses, ensuring the "important" data stays intact. The Bottom Line: This means we can soon run much larger models on smaller hardware (like your laptop or phone) and handle massive context windows at a fraction of the current cost. The gap between "massive data centers" and "local AI" just got a whole lot smaller. 📉💻 New KV cache compaction technique cuts LLM memory 50x without accuracy loss | VentureBeat https://lnkd.in/dPdQZZmq
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Alex Klimenko
ZibraAI • 2K followers
Zibra AI is looking for partners to build the next generation of cloud rendering infrastructure. Have you ever tried using cloud rendering infrastructure for VFX production? In theory, it offers compelling advantages: on-demand compute and storage scaling, centralized resource management, and high availability of processed data. In practice, however, it remains impractical for many high-end production pipelines. The main technical barrier? Volumetric VFX file sizes. No one wants to wait hours for a terabyte-sized VDB project to download from the cloud—or be hit with a massive data transfer bill at the end of the month. Even today, we still hear about teams physically transferring data between locations using hard drives. But what if your volumetric data took up only 5% of its original size—without compromising quality? With that kind of efficiency, you can simulate, render, and distribute content seamlessly across any location. This is now possible thanks to Zibra AI’s compression technology: ZibraVDB. It replaces OpenVDB as the new standard for volumetric visual effects, offering state-of-the-art compression rates, fast real-time decompression, and real-time rendering. ZibraVDB supports all the functionality required by render farms and integrates smoothly with major VFX production tools. Adding it to your pipeline is as simple as inserting a caching node in your Houdini project. If data storage and transfer have been holding you back from building a cloud rendering infrastructure, send me a direct message. With ZibraVDB, cloud rendering finally becomes practical.
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