ContentPosts from @pham..
Link
@faun shared a link, 2 hours ago

Serverless RL: Faster, Cheaper and More Flexible RL Training

New product, Serverless RL, available through collaboration between CoreWeave, Weights & Biases, and OpenPipe. Offers fast training, lower costs, and simple model deployment. Saves time with no infra setup, faster feedback loops, and easier entry into RL training...

Link
@faun shared a link, 2 hours ago

The RAG Obituary: Killed by Agents, Buried by Context Windows

Agent-based setups are starting to edge out old-school RAG. As LLMs snag multi-million-token context windows and better task chops, the need for chunking, embeddings, and reranking starts to fade. Claude Code, for example, skips all that - with direct file access and smart navigation instead. Retrie..

The RAG Obituary: Killed by Agents, Buried by Context Windows
Link
@faun shared a link, 2 hours ago

How AWS S3 serves 1 petabyte per second on top of slow HDDs

AWS S3 doesn’t need fancy hardware. It wrings performance out ofcheap HDDs,log-structured merge trees, anderasure coding. The trick? Shard everything. Hit it in parallel. Randomized placementdodges hotspots.Hedged requestsrace the slowest links. And when things get lopsided, S3 rebalances - constant..

How AWS S3 serves 1 petabyte per second on top of slow HDDs
Link
@faun shared a link, 2 hours ago

Ansible Service Module: Start, Stop, & Manage Services

The Ansibleservicemodulehandles LinuxandWindows without choking on init system quirks. One playbook can start, stop, enable, or restart anything - no matter the OS. Idempotent, so you don’t have to babysit state. Clean and repeatable. Bonus: it’s great for wrangling fleets. Think: coordinating servi..

Link
@faun shared a link, 2 hours ago

How LogSeam Searches 500 Million Logs per second

LogSeam rips through500M log searches/secand pushes1.5+ TB/s throughputusing Tigris’ geo-distributed object storage. It slashes log volume by 100× with Parquet + Zstandard compression. Then it spins up compute on the fly, right where the data lives—no long-running infrastructure, no laggy reads...

How LogSeam Searches 500 Million Logs per second
Link
@faun shared a link, 2 hours ago

Automated GitHub Self-Hosted Runner Cleanup: Lambda Functions and Auto Scaling Lifecycle Hooks

When an EC2 instance in an Auto Scaling Group shuts down, event-driven plumbing kicks in. Alifecycle hookcatches the scale-in, fires off an SNS notification, and triggers aLambda. That Lambda calls the GitHub API to yank the self-hosted runner before the instance dies. No dangling runners. No manual..

Automated GitHub Self-Hosted Runner Cleanup: Lambda Functions and Auto Scaling Lifecycle Hooks
Link
@faun shared a link, 2 hours ago

How We Migrated DB 1 to DB 2 , 1 Billion Records Without Downtime

A team movedover 1 billion production records- no downtime, no drama. The stack: dual writes, Kafka retries, and idempotent inserts to keep it clean. They ranshadow readsto sniff for errors, chunked the transfers with checksums, and held off indexing to keep inserts fast. Caches got warmed early to ..

How We Migrated DB 1 to DB 2 , 1 Billion Records Without Downtime
Link
@faun shared a link, 2 hours ago

Seven Years of Firecracker

AWS is puttingFirecracker microVMsto work in two fresh stacks:AgentCore, the new base layer for AI agents, andAurora DSQL, a serverless, PostgreSQL-compatible database it just rolled out. AgentCore gives each agent session its own microVM. More isolation, less cross-talk - solid for multistep LLM wo..

Seven Years of Firecracker
Link
@faun shared a link, 2 hours ago

Python 3.14 Is Here. How Fast Is It?

Python 3.14lands with a~27%speed jump over 3.13, keeping the post-3.11 momentum alive. The big news: the newfree-threading interpreter- no GIL - now hits up to3.1x fasterthan regular CPython in multi-threaded, CPU-heavy benchmarks. That’s up from 2.2x in 3.13. Less shiny: theJIT interpreterstill can..

Python 3.14 Is Here. How Fast Is It?
Link
@faun shared a link, 2 hours ago

The Myths (and Costs) of Running Node.js on Kubernetes

Kubernetes struggles to scale Node.js efficiently due to a mismatch in resource usage patterns. Autoscaling can be sluggish with bursty traffic, leading to revenue risks and performance issues. Teams must rethink resource allocation and scaling strategies to optimize Node.js efficiency in Kubernetes..