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OpenAI to launch its first AI chip in 2026 with Broadcom, FT reports

OpenAI’s firstin-house AI chipis nearly out of the oven. It’s headed for fabrication atTSMCand built to handle OpenAI’s own workloads—no outside sales, according to theFinancial Times. Why it matters:Big AI shops are going vertical. Custom silicon means tighter control over runtime, reliability, an.. read more  

OpenAI to launch its first AI chip in 2026 with Broadcom, FT reports
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Best Practices for High Availability of LLM Based on AI Gateway

Alibaba Cloud’s AI Gateway just got sharper. It now handlesreal-time overload protectionandLLM fallback routingusing passive health checks, first packet timeouts, and traffic shaping. It proxies both BYO and cloud LLMs—think PAI-EAS, Tongyi Qianwen—and redirects load spikes or failures on the fly. F.. read more  

Best Practices for High Availability of LLM Based on AI Gateway
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The Big LLM Architecture Comparison

Architectures since GPT-2 still ride transformers. They crank memory and performance withRoPE, swapGQAforMLA, sprinkle in sparseMoE, and roll sliding-window attention. Teams shiftRMSNorm. They tweak layer norms withQK-Norm, locking in training stability across modern models. Trend to watch:In 2025,.. read more  

The Big LLM Architecture Comparison
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Why language models hallucinate

OpenAI sheds light on the persistence ofhallucinationsin language models due to evaluation methods favoring guessing over honesty, requiring a shift towards rewarding uncertainty acknowledgment. High model accuracy does not equate to the eradication of hallucinations, as some questions are inherentl.. read more  

Why language models hallucinate
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Simplifying Large-Scale LLM Processing across Instacart with Maple

Instacart builtMaple, a backend brain for handling millions of LLM prompts—fast, cheap, and shared across teams. It’s not just another service. Maple runs onTemporal,PyArrow, andS3, strip-mines away provider-specific boilerplate, auto-batches prompts, retries failures, and slashes LLM costs by up t.. read more  

Simplifying Large-Scale LLM Processing across Instacart with Maple
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@faun shared a link, 7 months, 2 weeks ago
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Hermes V3: Building Swiggy’s Conversational AI Analyst

Swiggy just gave its GenAI tool, Hermes, a serious glow-up. What started as a simple text-to-SQL bot is now acontext-aware AI analystthat lives inside Slack. The upgrade? Not just tweaks—an overhaul. Think: vector-based prompt retrieval, session-level memory, an Agent orchestration layer, and a SQL.. read more  

Hermes V3: Building Swiggy’s Conversational AI Analyst
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GPT-5 Thinking in ChatGPT (aka Research Goblin) is shockingly good at search

GPT-5's“thinking” modeljust leveled up. It's not just answering queries—it’s doing full-on research. Picture deep, multi-step Bing searches mixed with tool use and reasoning chains. It reads PDFs. Analyzes them. Suggests what to do next. Then actually does it. All from your phone. What’s changing:L.. read more  

GPT-5 Thinking in ChatGPT (aka Research Goblin) is shockingly good at search
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From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels

Hugging Face just dropped Kernel Builder—a full-stack toolchain for building, versioning, and shippingcustom CUDA kernels as native PyTorch ops. Kernels arearchitecture-aware,semantically versioned, andpullable straight from the Hub. It tracks changes with lockfiles and bakes inDocker deploysout of.. read more  

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@laura_garcia shared a post, 7 months, 2 weeks ago
Software Developer, RELIANOID

RELIANOID Load Balancer Community Edition v7 on AWS using Terraform

🚀 New Guide Available! Learn how to quickly deploy RELIANOID Load Balancer Community Edition v7 on AWS using Terraform. Our step-by-step article shows you how to provision everything automatically — from VPCs and subnets to EC2 and key pairs — in just minutes. 👉 https://www.relianoid.com/resources/k..

Knowledge base Deploy RELIANOID Load Balancer Community Edition v7 with Terraform on AWS
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Sandboxed to Compromised: New Research Exposes Credential Exfiltration Paths in AWS Code Interpreters

Researchers poked holes insandboxed Bedrock AgentCore code interpreters—and found a way to leak execution role credentials through theMicroVM Metadata Service (MMDS). No outside network? Doesn’t matter. The exploit dodges basic string filters in requests and lets non-agentic code swipe AWS creds to .. read more  

Lustre is an open-source, parallel distributed file system built for high-performance computing environments that require extremely fast, large-scale data access. Designed to serve thousands of compute nodes concurrently, Lustre enables HPC clusters to read and write data at multi-terabyte-per-second speeds while maintaining low latency and fault tolerance.

A Lustre deployment separates metadata and file data into distinct services—Metadata Servers (MDS) handling namespace operations and Object Storage Servers (OSS) serving file contents stored across multiple Object Storage Targets (OSTs). This architecture allows clients to access data in parallel, achieving performance far beyond traditional network file systems.

Widely adopted in scientific computing, supercomputing centers, weather modeling, genomics, and large-scale AI training, Lustre remains a foundational component of modern HPC stacks. It integrates with resource managers like Slurm, supports POSIX semantics, and is designed to scale from small clusters to some of the world’s fastest supercomputers.

With strong community and enterprise support, Lustre provides a mature, battle-tested solution for workloads that demand extreme I/O performance, massive concurrency, and petabyte-scale distributed storage.