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@faun shared a link, 3 months, 2 weeks ago
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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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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, 3 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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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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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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@laura_garcia shared a post, 3 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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Measuring Developer Productivity with Amazon Q Developer and Jellyfish

Amazon Q Developer now plugs into Jellyfish. Teams get a clearer view of how AI fits into the real flow of work—prompt usage, code adoption, PR throughput. Not just surface stats. The setup pipes data from AWS S3 straight into Jellyfish’s analytics engine. It tags AI users, tracks velocity gains, an.. read more  

Measuring Developer Productivity with Amazon Q Developer and Jellyfish
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AWS, Microsoft and Google unite behind Linux Foundation DocumentDB database to cut enterprise costs and limit vendor lock-in

Document databases are crucial for AI apps in the gen AI era. Microsoft's open-source DocumentDB project, based on PostgreSQL, is moving to the Linux Foundation, offering a vendor-neutral, open-source alternative to MongoDB. DocumentDB's compatibility with MongoDB drivers and open source governance .. read more  

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Deploy a containerized application with Kamal and Terraform

A Docker-first workflow combinesTerraformandKamalinto a lean, Elastic Beanstalk-ish alternative—without the bloat. Terraform spins up a three-tier VPC and wires it toECR. Kamal takes it from there, booting containers on a raw EC2 box: app, proxy, monitor. One script. Done... read more  

Deploy a containerized application with Kamal and Terraform
Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.