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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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New trend: Programming by kicking off parallel AI agents

Senior engineers are starting to spin upparallel AI coding agents- think Claude Code, Cursor, and the like - to run tasks side by side. One agent sketches boilerplate. Another tackles tests. A third refactors old junk. All at once. Is it "multitasking on steroids"? Not just this as it messes with ho.. 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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@devopslinks shared a link, 1 month, 3 weeks ago
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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

A plain-oldgit repo on an SSH-accessible servercan double as a lean deployment rig. Drop in somegit hooks- like apost-receive- and every push can kick off static site builds or publish code on the spot. No extra tools. Just Git doing Git things. Turns basic Git infra into a no-frills CI/CD pipeline... read more  

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@kala shared an update, 1 month, 3 weeks ago
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AWS Unveils Project Rainier: Massive AI Cluster with Trainium2 Chips

Amazon Web Services

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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@devopslinks shared an update, 1 month, 3 weeks ago
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Amazon Apologizes for Major AWS Outage in US-EAST-1 Region

Amazon EC2 Amazon ELB Amazon Web Services

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, 1 month, 3 weeks 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
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@kala added a new tool vLLM , 1 month, 3 weeks ago.
BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).