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@faun shared a link, 7 months ago

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Le Chat now integrates with 20+ enterprise platforms—powered by MCP—and remembers what matters with Memories.

Le Chat now includes20+ secure, MCP-based connectorsfor tools like GitHub, Snowflake, Stripe, and Jira. That means in-chat search, summaries, and actions—straight from enterprise systems. Developers can plug in their owncustom MCP connectors, and run Le Chat wherever it fits: on-prem, private cloud.. read more

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@faun shared a link, 7 months ago

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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

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@faun shared a link, 7 months ago

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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

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@faun shared a link, 7 months ago

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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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@faun shared a link, 7 months 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

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@faun shared a link, 7 months ago

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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

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@faun shared a link, 7 months 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

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@faun shared a link, 7 months ago

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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

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@faun shared a link, 7 months ago

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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

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@laura_garcia shared a post, 7 months 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

Magecomp Magento 2 Multiple Wishlist Extension is a powerful tool designed to enhance the default wishlist functionality in Magento 2. Developed by Magecomp, a trusted provider of Magento extensions, this extension offers advanced features and flexibility for creating multiple wishlists on your Magento 2 store.

Wishlists are an essential feature for any e-commerce store as they allow customers to save products for future reference or purchase. However, the default Magento 2 wishlist functionality only allows customers to create a single wishlist. This limitation can be restrictive, especially for customers who want to organize their saved products into different categories or share specific wishlists with others.

Magecomp's Multiple Wishlist Extension addresses these limitations by enabling customers to create and manage multiple wishlists effortlessly. Here are some key features and benefits of this extension:

Multiple Wishlists: The extension allows customers to create multiple wishlists based on their preferences. Customers can organize their saved products into different wishlists, such as "Birthday Gifts," "Holiday Shopping," or "Favorite Items," providing them with better control and organization over their desired products.

Wishlist Sharing: With this extension, customers can easily share their wishlists with friends, family, or on social media platforms. The sharing functionality enhances customer engagement and encourages social interaction, allowing customers to get opinions or recommendations from others before making a purchase decision.

Privacy Control: Magecomp's Multiple Wishlist Extension provides customers with privacy control options. Customers can choose to keep their wishlists private or make them public, depending on their preferences. Public wishlists can be viewed and accessed by others, which can be useful for sharing gift ideas or creating collaborative wishlists.

Wishlist Search and Filtering: The extension enhances the usability of wishlists by adding search and filtering options. Customers can search for specific products within a wishlist or filter products based on various attributes such as price, brand, or category. This feature simplifies the process of finding desired products within a large wishlist, improving the overall user experience.

Bulk Actions: Customers can perform bulk actions on their wishlists, such as adding multiple products to the cart, removing selected products, or moving items between wishlists. These bulk actions save time and provide convenience to customers when managing their wishlists.

Customization Options: The Multiple Wishlist Extension allows store owners to customize various aspects of the wishlists, including layout, design, and display options. This customization capability ensures that the wishlists seamlessly integrate with the overall look and feel of the Magento 2 store.

Easy Integration: The extension is designed to seamlessly integrate with the Magento 2 platform. It is easy to install, configure, and use, without requiring extensive technical knowledge or coding skills.

In conclusion, Magecomp Magento 2 Multiple Wishlist Extension is a valuable addition to any Magento 2 store, empowering customers with the ability to create and manage multiple wishlists. With enhanced features like wishlist sharing, privacy control, search and filtering options, and customization capabilities, this extension improves the user experience and boosts customer engagement on your e-commerce website.

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