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Grokipedia

Grokipedia just dropped - a Wikipedia remix built from LLM output, pitched as an escape from "woke" bias. The pitch? Bold. The execution? Rough. Entries run long. Facts bend. Citations wander. And the tone? Cold, context-free, and unmistakably machine-made. The usual LLM suspects are here: hallucina.. read more  

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Agentic AI and Security

Agentic LLM apps come with a glaring security flaw: they can't tell the difference between data and code. That blind spot opens the door to prompt injection and similar attacks. The fix? Treat them like they're radioactive. Run sensitive tasks in containers. Break up agent workflows so they never ju.. read more  

Agentic AI and Security
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New trend: Programming by kicking off parallel AI agents

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Why GPUs accelerate AI learning: The power of parallel math

Modern AI eats GPUs for breakfast - training, inference, all of it. Matrix ops? Parallel everything. Models like LLaMA don’t blink without a gang of H100s working overtime... read more  

Why GPUs accelerate AI learning: The power of parallel math
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More Than DNS: The 14 hour AWS us-east-1 outage

AWS’s us-east-1 faceplanted for 14 hours after arace conditioninDynamoDBkicked off a DNS meltdown, taking down 140 services. EC2 buckled under acongestive collapse, overwhelmed by a backup in DropletWorkflow Manager queues. Meanwhile, NLB health checks kept firing blanks - tricked by stale network s.. read more  

More Than DNS: The 14 hour AWS us-east-1 outage
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How We Saved $500,000 Per Year by Rolling Our Own “S3”

Nanit ditched S3’s PutObject-heavy ingest path and built a customRust-based in-memory landing zone (N3). It cut ~$500K/year in storage ops. N3 grabs short-lived video chunks straight into RAM and only spills to S3 when it has to. Ordering stays tight thanks toSQS FIFO, and fallback kicks in clean wh.. read more  

How We Saved $500,000 Per Year by Rolling Our Own “S3”
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You already have a git server

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@kala shared an update, 6 months, 1 week ago
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AWS Unveils Project Rainier: Massive AI Cluster with Trainium2 Chips

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AWS has launched Project Rainier, a massive AI compute cluster with nearly half a million Trainium2 chips, in collaboration with Anthropic to advance AI infrastructure and model development.

AWS Unveils Project Rainier: Massive AI Cluster with Trainium2 Chips
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Amazon Apologizes for Major AWS Outage in US-EAST-1 Region

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Amazon apologized for a major AWS outage in the Northern Virginia region, caused by a race condition in the DynamoDB DNS management system, affecting services like DynamoDB, Network Load Balancer, and EC2.

Amazon Apologizes for Major AWS Outage in US-EAST-1 Region
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@varbear shared an update, 6 months, 1 week ago
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AI Takes Over GitHub: TypeScript Tops the Charts as 36 Million New Developers Join the Platform

Docker TypeScript vLLM GitHub Copilot Python

In 2025, GitHub saw a surge in growth with AI advancements, as TypeScript overtook Python and JavaScript in popularity, fueled by the release of GitHub Copilot Free and a global developer expansion.

AI Takes Over GitHub: TypeScript Tops the Charts as 36 Million New Developers Join the Revolution
On the 1st of July 2017, the Indian government introduced the Goods and Services Tax (GST) to the general public. This tax replaced pre-existing taxes with a destination-based, multi-stage, indirect tax system. The GST applies to all eCommerce and Magento stores within India. The World Bank has stated that the GST is one of the most complex tax systems worldwide due to its intricate nature.

As the GST is bifurcated into multiple tax slabs, it has become more difficult to implement such a complex taxing system in Magento 2. Managing a multi-vendor marketplace entails dealing with numerous vendors who sell various products on your platform. 

This necessitates the computation of GST tax rates based on the product and its category, which can be intricate due to varying tax brackets. An automated system is needed to collect and accurately compute vendor tax rates.

The Magento 2 Multi-vendor Indian GST Extension, also called the Magento Multi-vendor GST Module by MageComp, confidently calculates GST for products sold through your Magento stores by vendors. The module fully complies with the Indian government's GST standards and prominently displays the vendor's GST number and summary in all Magento order documents.

Why choose MageComp’s Magento 2 Multi Vendor Indian GST Extension?

Allows admins to enable/disable the module for global, category, and product-specific configuration.
Orders that are placed with the GST module showcase a detailed bifurcation of tax rates in the order view, credit memos, invoices, new order emails and PDFs.
The GST module displays a detailed SGST and CGST tax bifurcation in the order details.
The module automatically calculates IGST, CGST and SGST based on the products added to the cart.

Benefits of Multi Vendor Indian GST for Admins

Allows admins to mention their GSTIN, CIN and PAN number in all order details.
Admins can import the product-specific GST via a CSV file.
Admins have the option to include or exclude GST tax rates from the product prices.
Admins can easily set their business origins and apply IGST (interstate Magento GST)
The module allows the admin to upload an image and a digital signature to display in invoice PDF.
Admins have the option to generate detailed GST reports based on orders and products.

Benefits for Vendors

Vendors are allowed to add their GST number to all invoices and other documents.
Vendors can set a GST rate from the backend grid while creating a product.
Vendors can also set a minimum order price to apply GST rates.

Benefits for Customers

Customers can enter the buyer’s GST number to signup.
Customers can update their buyer’s GST number any time they want from their account grid.