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@kala shared a link, 1 month, 3 weeks 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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@kala shared a link, 1 month, 3 weeks 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, 1 month, 3 weeks ago
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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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@devopslinks shared a link, 1 month, 3 weeks 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, 1 month, 3 weeks 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, 1 month, 3 weeks ago
FAUN.dev()

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, 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
News FAUN.dev() Team Trending
@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.
Grafana Tempo is a distributed tracing backend built for massive scale and low operational overhead. Unlike traditional tracing systems that depend on complex databases, Tempo uses object storage—such as S3, GCS, or Azure Blob Storage—to store trace data, making it highly cost-effective and resilient. Tempo is part of the Grafana observability stack and integrates natively with Grafana, Prometheus, and Loki, enabling unified visualization and correlation across metrics, logs, and traces.

Technically, Tempo supports ingestion from major tracing protocols including Jaeger, Zipkin, OpenCensus, and OpenTelemetry, ensuring easy interoperability. It features TraceQL, a domain-specific query language for traces inspired by PromQL and LogQL, allowing developers to perform targeted searches and complex trace-based analytics. The newer TraceQL Metrics capability even lets users derive metrics directly from trace data, bridging the gap between tracing and performance analysis.

Tempo’s Traces Drilldown UI further enhances usability by providing intuitive, queryless analysis of latency, errors, and performance bottlenecks. Combined with the tempo-cli and tempo-vulture tools, it delivers a full suite for trace collection, verification, and debugging.

Built in Go and following OpenTelemetry standards, Grafana Tempo is ideal for organizations seeking scalable, vendor-neutral distributed tracing to power observability at cloud scale.