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@kala shared a link, 7 months, 1 week ago
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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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@kala shared a link, 7 months, 1 week ago
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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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@devopslinks shared a link, 7 months, 1 week 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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@devopslinks shared a link, 7 months, 1 week ago
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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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@devopslinks shared a link, 7 months, 1 week ago
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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  

News FAUN.dev() Team
@kala shared an update, 7 months, 1 week 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
News FAUN.dev() Team
@devopslinks shared an update, 7 months, 1 week ago
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Amazon Apologizes for Major AWS Outage in US-EAST-1 Region

Amazon Web Services Amazon EC2 Amazon ELB

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
News FAUN.dev() Team
@varbear shared an update, 7 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
News FAUN.dev() Team
@kaptain shared an update, 7 months, 1 week ago
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Grafana Tempo 2.9 Supercharges Distributed Tracing with LLM Integration

Grafana Tempo

Grafana Tempo 2.9 debuts with MCP server support and TraceQL metrics sampling, enhancing data analysis and query efficiency.

Grafana Tempo 2.9 Supercharges Distributed Tracing with LLM Integration
News FAUN.dev() Team
@kala shared an update, 7 months, 1 week ago
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LangChain Secures $125M and Launches LangChain & LangGraph 1.0

LangChain

LangChain raised $125 million to enhance its agent engineering platform, introducing LangChain and LangGraph 1.0 with new tools like the Insights Agent and a no-code agent builder, aiming to transform LLM applications into reliable agents.

LangChain Secures $125M and Launches LangChain & LangGraph 1.0
Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.