2026-06-16 15:34
π Just explored the Red Hat AI Inference on Amazon EKS! This article dives into deploying a two-GPU cluster using NVIDIA L4s, focusing on Kubernetes components like cert-manager for TLS, Istio for service mesh, and KServe for model serving. Key insights include how these elements connect and work together for efficient AI inference. π Learn more about the architecture and components involved! #RedHat #Kubernetes #AIInference #AmazonEKS #CloudComputing
Alexa Griffith
2026-06-16 07:01
EvalHub addresses the reproducibility crisis in AI evaluation by providing immutable records of evaluation runs. By integrating with MLflow, EvalHub captures comprehensive details about each evaluation, ensuring results are not just claims but verifiable evidence. With OCI persistence, evaluation results are stored as tamper-evident artifacts, improving compliance for regulated workloads. Learn more about building a scalable AI evaluation infrastructure! #AI #EvalHub #MachineLearning...
William Caban Babilonia, Matteo Mortari
2026-06-16 07:01
Explore the latest in agentic AI and text-to-SQL! π₯οΈ This installment delves into how agentic AI allows LLMs to autonomously interact with databases, improving accuracy in data queries. Unlike traditional chat interfaces, agentic systems learn and adapt, enhancing the user experience in conversational analytics. Stay tuned for more insights on orchestrating these systems! ππ #AI #DataAnalytics #TextToSQL #AgenticAI #RedHat
Peter Samouelian
2026-06-16 00:00
LivePerson improved its Logstash and Kafka performance on GCP by benchmarking five machine types. They found that selecting the right infrastructure can significantly reduce costs. Switching to AMD Milan-based instances cut Logstash processing costs by over 50%, while optimizing Kafka through codec selection boosted throughput. These changes allowed for fewer high-throughput instances, reducing overall infrastructure needs. #CloudComputing #CostOptimization #GCP #Logstash #Kafka ππ‘
Emily Chioconi,Kiril Karamanolev,Strahil Nikolov
2026-06-15 22:35
π Discover how Deepak Pushpakar and his team at Salesforce's Data 360 tackle the challenge of processing a quadrillion records monthly. Their work involves managing diverse customer data models across various storage systems, ensuring reliable audience segmentation. This is crucial for analytics, marketing campaigns, and personalization workflows. Data 360 allows customers to define their own data structures, making segmentation both complex and vital. #Data360 #Salesforce...
Scott Nyberg
2026-06-15 21:55
DoorDash is enhancing its search ad relevance using small language models (SLMs). Consumers expect ads to match their search intent seamlessly. By implementing query-item relevance prediction, DoorDash aims to ensure ads feel integrated into the search experience, rather than disruptive. Recent advancements in SLMs enable better semantic understanding and faster predictions, improving the accuracy of sponsored results. This approach addresses the challenge of matching user queries with...
Sharat Bhat
2026-06-15 16:45
π Mixture-of-experts (MoE) models are key in modern AI, enhancing model capacity efficiently. NVIDIA introduces advanced fused MLP kernels that optimize training throughput by addressing memory and synchronization issues. These kernels achieve significant speedups, improving end-to-end performance by up to 93%. Explore how custom kernels can help minimize training bottlenecks in MoE blocks. #AI #MachineLearning #NVIDIA #MoE #DeepLearning
Rachit Garg
2026-06-15 15:59
Atlassian has tested Google's DESIGN.md format to enhance AI-generated designs. This portable Markdown file aims to provide context about brand elements, addressing the issue of generic UI outputs often referred to as "slop." The DESIGN.md file includes machine-readable design tokens and human-readable design rationale. While it shows promise for certain workflows, it lacks the full technical specifications found in more complex systems. Discover more about the findings and implications for...
abooth
2026-06-15 12:00
π Exploring the rise of Vision-Language-Action (VLA) and World-Action Models (WAM) in robotics! VLA models adapt pretrained vision-language models to generate actions from visual and language inputs. WAM focuses on predicting scene changes and corresponding actions using a pretrained world model. Key terms include grounding, inverse dynamics, and action chunk, which are essential for connecting language instructions to physical actions. #Robotics #AI #MachineLearning #VLA #WAM
Moritz Reuss
2026-06-15 07:16
In distributed training, gradient synchronization is a crucial phase often slowed by communication delays. This article explores how Message Passing Interface (MPI) enhances performance using collective operations like All-Reduce to synchronize gradients across GPUs efficiently. It details various parallelization methods: data, tensor, pipeline, and sharded data parallelismβeach optimizing workload distribution among GPUs. Additionally, it addresses GPU-aware MPI, which reduces overhead and...
Kushagra Rastogi
2026-06-15 07:16
π Exploring local large language models? Check out the differences between llama.cpp and vLLM! llama.cpp is designed for efficient inference on consumer hardware, allowing users to run models with minimal GPU requirements through quantization. This approach makes LLMs more accessible to developers without dedicated hardware. On the other hand, vLLM excels in high-throughput scenarios, managing multiple requests simultaneously and optimizing GPU utilization. It's ideal for large-scale...
Cedric Clyburn
2026-06-12 18:00
At Dropbox, we recognize the challenge of maintaining visibility of security requirements during the development process. Our research revealed that only 12% of pull requests link back to their original threat models, causing potential gaps in security. To address this, we developed a system combining Model Context Protocol, foundational models, and Dash to retrieve relevant threat models during code reviews. This helps ensure security requirements are upheld throughout development. Learn...
Mark Breitenbach,Ishan Mishra
2026-06-12 16:17
π Exciting progress on CherryScript! This custom programming language aims to optimize data-driven workflows. It's designed to work seamlessly with lower-level systems and consumer electronics. The architecture focuses on minimizing memory use through a lazy-evaluation streaming lexer and transitioning to bytecode compilation for efficiency. Key features include deterministic speed and an approachable syntax, ensuring efficient handling of large data streams. #CherryScript #DataWorkflows...
Ahmad Ishanzai
2026-06-12 15:56
Introducing **olmo-eval**, an innovative evaluation workbench designed for the model development loop. This tool allows developers to evaluate their LLMs repeatedly during the building process. It adapts to changes in data, architecture, and hyperparameters, ensuring that each model checkpoint is effectively assessed. Unlike traditional evaluation tools, olmo-eval is tailored for dynamic models and real-world conditions. Discover more: π» [GitHub Link](https://github.com/allenai/olmo-eval)...
2026-06-12 13:00
π Cloudflare's Security Insights has achieved a significant improvement in its scanning capability. The system now processes over 120 scans per second, enhancing security for all users. π By optimizing components like Kafka consumers and Postgres queries, the team increased throughput by 10x without additional hardware. This means more frequent scans and actionable insights for every account. π With rising automated attacks, addressing security risks swiftly is crucial. The enhancements allow...
Dave Baxter
2026-06-12 00:00
Grab is transitioning from Ubuntu to Distroless images to enhance security by minimizing vulnerabilities. This strategic shift aims for 80% adoption by mid-2026, already covering over 900 services. However, this migration poses risks of runtime failures, requiring rigorous testing strategies. At Grab, Medium Tests are utilized to ensure services run smoothly with the new configurations while maintaining all dependencies. AI is being leveraged to automate the testing process, helping to...
2026-06-11 21:40
Mercedes-Benz Korea has successfully implemented "Talk to Data," enhancing AI capabilities in their operations. This initiative utilizes tools like Unity Catalog and AI agents to streamline data interaction. The collaboration with Databricks has led to the development of AI-ready semantics, improving efficiency and trust in data handling. Stay tuned for more advancements in AI! π€π #MercedesBenz #AI #DataScience #Innovation #Technology
2026-06-11 20:15
π MuleSoft is setting new standards for AI-generated code safety. In a recent Q&A, Melissa Cazalet, SVP of Software Engineering, discussed the Golden Gate initiative. This AI-powered governance system ensures every code change meets Salesforce's security and compliance standards before reaching production. π The initiative prioritizes trust in a fast-paced development environment, addressing risks like insecure dependencies and compliance violations. π» Golden Gate aims to streamline the...
Scott Nyberg
2026-06-11 19:45
π Exciting advancements in data engineering! Databricks introduces Zerobus Ingest, a serverless streaming API designed for petabyte-scale data pipelines. This technology allows teams to deploy data solutions instantly, eliminating the need for manual infrastructure management. Zerobus utilizes dynamic partitioning to automatically scale resources, effectively managing varying data volumes. ππ» #DataEngineering #ZerobusIngest #BigData #Serverless #TechInnovation
2026-06-11 17:00
Debugging in AI presents new challenges as traditional methods rely on deterministic software behavior. With AI, inputs can yield varying outputs, complicating the debugging process. To address this, a new approach called **prompt tracing** is proposed, capturing the entire lifecycle of AI requests. This method aims to enhance observability and reliability in AI systems. Learn more about this evolving paradigm! π€ππ» #AIDebugging #PromptTracing #SoftwareDevelopment #TechTrends
Zziwa Raymond Ian
2026-06-11 14:15
Exploring agent-driven end-to-end (E2E) testing offers a new approach to validating software functionality. Recent experiments with over 200 agentic workflows reveal that these tests focus on achieving specific goals rather than following strict sequences. This flexibility allows agents to adapt their methods for reaching outcomes, though it introduces considerations around reliability and cost. While agent-driven tests can be more costly and time-consuming, advancements in large language...
Sergii Gorbachov
2026-06-11 13:00
Upgrading applications with AI coding agents presents challenges, especially for large projects. π While upgrading to Spring Boot 4 using natural language commands is appealing, the process can be time-consuming and error-prone. Developers may face multiple iterations to resolve coding issues, taking 1-2 days or more. β³ The Spring Petclinic example highlights these challenges, where unexpected changes and deprecated methods led to failed upgrades. π οΈ Understanding these complexities is...
Raquel Pau
2026-06-11 12:56
π Excited to share insights from the article on DoorDash Assistant's engineering! This piece kicks off a blog series exploring the technology behind the Assistant. It focuses on how consumers can easily request meals or groceries tailored to their preferences. Key areas include local-commerce grounding and the importance of personalization. The Assistant is currently rolling out in select U.S. areas on iOS, enhancing restaurant and grocery search. Stay tuned for future updates on its...
Hong Tai Wei
2026-06-11 12:00
π Large language models are now vital in software development, generating code and infrastructure rapidly. However, trust in their output is a challenge due to the quality of training data. π οΈ SonarSweep addresses this by improving the data used to train models, aiming to reduce bugs and vulnerabilities in AI-generated code. π Models learn from both good and bad examples, which impacts code quality in real-world applications. Careful curation of training data is essential for reliable...
Joe Tyler
2026-06-11 07:00
Discover how the open-source project llm-d enhances large language model (LLM) inference on Red Hat AI. Traditional load balancers treat LLM requests as stateless, leading to inefficiencies. llm-d optimizes performance by routing requests to GPUs with relevant cached data, significantly reducing time-to-first-token by over 99% and doubling throughput. With intelligent scheduling, it adapts to real-time loads and queue depths, ensuring efficient resource use. This new approach is seamlessly...
Edoardo Vacchi, Madhu Goutham Reddy Ambati
2026-06-11 04:00
Okara leverages Vercel to manage marketing for over 120,000 businesses, processing 4 billion tokens daily. The AI CMO directs eight sub-agents focusing on SEO, social media, and content. This integration allows new AI models to be available instantly, streamlining operations with Vercel AI Gateway and Sandboxes. Okaraβs efficient model means founders can enhance their marketing without the high costs of traditional agencies. π #AI #Marketing #Startups #Vercel #Innovation
Eric Dodds
2026-06-11 00:00
In the latest article on profiling in PyTorch, the focus shifts from basic operations to using `nn.Linear` to create a Multilayer Perceptron (MLP). This transition highlights key concepts such as the CPU dispatch chain and profiling traces. The post includes scripts to illustrate these points: 02_linear.py, 03_simple_mlp.py, and 03_kernels_mlp.py. For those diving deeper into PyTorch, this is a valuable resource! ππ» #PyTorch #MachineLearning #DeepLearning #MLP #DataScience
2026-06-11 00:00
Large DELETE operations in Postgres can create more work instead of reclaiming space. Instead of deleting individual rows, consider structuring your database to use DROP TABLE or TRUNCATE for efficient data removal. When rows are deleted, they don't immediately free up disk space and can impact other transactions. This is due to Postgres' Multi-Version Concurrency Control (MVCC) system. For applications needing large deletions, adopting a schema that supports DROP TABLE can enhance...
Tom Pang
2026-06-10 22:23
π Discover how Atlassian's ML Studio is revolutionizing enterprise machine learning. This unified platform supports modular development, centralized workflow orchestration, and embedded governance, enabling high-velocity experimentation across diverse teams. With over 5 million active Rovo users and 120k monthly workflow runs, ML Studio is powering AI systems globally. Learn more about its architecture and capabilities. #MachineLearning #AI #Atlassian #MLOps #DataGovernance
Christopher Cheung
2026-06-10 18:42
π At Lyft, data is central to operations. To address inconsistencies in metric definitions across teams, we developed the Metric Semantic Layer (MSL). π MSL serves as a centralized repository for all metric definitions, ensuring clear communication and decision-making. Key principles include: 1. Simplified onboarding and change management. 2. Intentional governance for data quality. 3. Transparency and accessibility for all users. π This system enhances collaboration and consistency in data...
Iraklikhorguani
2026-06-10 17:34
π A new framework for auditing machine unlearning has been introduced, focusing on the verification of AI systems' ability to forget specific training data. π This method uses two-sample testing to determine if outputs from models differ significantly, ensuring compliance with regulations like GDPR. π» As models grow, the need for accurate and efficient auditing becomes crucial. The Regularized f-Divergence Kernel Tests aim to enhance sensitivity and reduce false positives in this process....
2026-06-10 15:00
AI factories are reshaping data-center infrastructure. Unlike traditional setups, they focus on manufacturing intelligence at scale. β‘οΈ Battery energy storage systems (BESS) are now key components of this new architecture, enhancing reliability and performance. They help manage power demands efficiently, reducing stress on grids and onsite generation. Learn more about the importance of BESS in AI factories and the considerations for their design. ππ‘ #AIFactories #EnergyStorage #DataCenters...
Sean James
2026-06-10 14:27
π At DigitalOcean, we focus on high-performance infrastructure for AI, particularly frontier Large Language Models (LLMs) on AMD GPUs. Our approach emphasizes that peak inference speed is influenced by model architecture and runtime execution, alongside hardware. This "performance alpha" highlights the benefits of specialized inference engineering. Recent collaborations with Wafer demonstrated significant throughput improvements: Kimi 2.5 saw an 11.33x speedup, while DeepSeek V3.2 achieved a...
Emilio Andere
2026-06-10 13:01
At Spotify, data challenges were previously handled by reaching out to experts, but the demand for insights outpaced capacity. To address this, they developed an AI data assistant that efficiently queries over 70,000 datasets, providing reliable answers in seconds. Since August 2025, it's been used by over 2,100 employees across various fields. The assistant ensures trustworthiness through context and ownership, utilizing clusters tied to specific domains and expert teams. #Spotify...
Spotify Engineering
2026-06-09 19:38
Exploring the capabilities of voice agents, a recent article evaluates how Automatic Speech Recognition (ASR) systems perform with bilingual and code-switched speech. The study highlights the challenges these systems face in accurately understanding mixed-language conversations. Findings suggest a need for ongoing improvements to better serve bilingual customers. π€π£οΈπ #VoiceTechnology #BilingualCommunication #ASR #CodeSwitching #Innovation
2026-06-09 18:27
Unlock faster inference with NVIDIA TensorRT! π This article discusses converting FP8-quantized checkpoints into efficient TensorRT engines. This process enhances production deployment, leading to improved throughput and GPU utilization. It details exporting checkpoints to ONNX and compiling them for real-world application, comparing FP8 performance against FP16. Learn more about the quantization workflow and its benefits! #NVIDIA #TensorRT #MachineLearning #AI #Quantization
Ruixiang Wang
2026-06-09 17:01
Airbnb's data teams have made significant advancements to support their expansion into Homes, Experiences, and Services. With the May 2025 release, they faced the challenge of evolving their offline data architecture. To navigate this, they established a flexible framework balancing consistency with decentralized modeling. This approach addresses the unique needs of each product line while maintaining clarity across the organization. Key principles included avoiding hybrid models, ensuring...
Patrick Lam
2026-06-09 16:35
π Federated Learning (FL) research often starts with exploring new strategies. The article discusses NVIDIA FLARE Auto-FL, a tool that enhances this process. It automates the testing of FL methods through well-defined benchmarks and structured workflows. This allows researchers to evaluate ideas efficiently while maintaining consistency in results. π Auto-FL helps researchers navigate their experiments, keeping track of outcomes for reproducibility. Learn more about how AI agents can...
Holger Roth
2026-06-09 15:00
Training speech AI to understand clinical terminology is challenging. Common drug names and medical terms often aren't included in standard speech models. π₯ Synthetic data generation can address this gap, but accuracy in pronunciation is crucial. Incorrect pronunciations can lead to more problems rather than solutions. NVIDIA's tools support this process, allowing quick creation of clinical benchmarks without the hurdles of real audio collection. π€ Clinical ASR is essential for various...
John Jahanipour
2026-06-09 14:40
Mediumβs recommendation system aims to keep readers engaged by processing user activity signals and correlating them with new articles. π The feature store plays a crucial role in this system, enabling real-time data storage and retrieval to support fast user interactions. AndrΓ©as Saudemont explains how the team improved their data model to handle over 1 million operations per second effectively. Learn more about their challenges and solutions! π‘ #Medium #DataScience #MachineLearning...
Cynthia Dunlop