Compiler…

Paper: The Path of a Packet Through the Linux Kernel net.in.tum.de/fileadmin/TUM/… Computer Networking is one of my favourite topics - I even did my final-year project on MPLS/LDP on a Linux box. That meant a lot of hands-on NIC configuration: setting up interfaces in various modes so packets could be routed correctly over LDP. And tracing how packet buffers are handled to implement zero-copy wherever possible. This paper is a deep dive into how packets traverse the kernel - the kind I wish I'd had back then. Worth saving for a careful read.

I recommend looking into 𝚘𝚙𝚎𝚗𝚝𝚞𝚒

The CSE 351 Cache Simulator is the best hands-on way to understand cache hits, misses, LRU eviction, and write policies. Tweak the parameters, step through memory accesses, watch it all happen live. courses.cs.washington.edu/courses/cse351…

almost 11 years ago i watched this talk on my lunch break. it ended up being one of the most influential videos i've ever watched. been workflow pilled ever since youtube.com/watch?v=xDuwrt…

I disagree: stevekrouse.com/precision Code is how we get precise abstractions into human heads Saying code isn't important is like saying mathematical notation isn't important There's a reason we glorify f = ma or e = mc2. Formalism holds immense power for mastering complexity

Expectation: the age of the IDE is over Reality: we’re going to need a bigger IDE (imo). It just looks very different because humans now move upwards and program at a higher level - the basic unit of interest is not one file but one agent. It’s still programming.

oops did it again

If you want to become a database genius among your peers, you need to read this article. It offers invaluable insights that could mark the beginning of your breakthrough in the database field.

Open-source contributions can literally get you hired... with zero interviews

During his talk this morning in the #Go devroom at #FOSDEM2026 (10:30 AM CET), software engineer Valentyn Yukhymenko explores how Reflection actually works under the hood in @golang bloom.bg/3Z3OwzA #golang #GoRuntime #opensource

I'm Boris and I created Claude Code. I wanted to quickly share a few tips for using Claude Code, sourced directly from the Claude Code team. The way the team uses Claude is different than how I use it. Remember: there is no one right way to use Claude Code -- everyones' setup is different. You should experiment to see what works for you!

@PostgreSQL has long powered core @OpenAI products like ChatGPT and the API. Over the past year, our production load grew 10× and keeps rising. Today we run a single primary with nearly 50 read replicas in production, delivering low double-digit millisecond p99 client-side latency and five-nines availability. In our latest OpenAI Engineering blog, we unpack the optimizations we made to to scale @Azure PostgreSQL to millions of queries per second for more than 800M ChatGPT users. Check out the full post here: openai.com/index/scaling-…

Postgres vs MySQL Transactions + Log-based writes (Database Internals Chapter 5) x.com/i/broadcasts/1…

our species is evolving the capacity of conjuring arbitrarily complex software out of thin words and people are mad about it

Yoo chat Found a good read on rabbit mq @erickzanetti/rabbitmq-a-complete-guide-to-message-broker-performance-and-reliability-3999ee776d85" target="_blank" rel="nofollow noopener">medium.com/@erickzanetti/…

New blog post: Don't fall into the anti-AI hype. antirez.com/news/158

ML engineering that is essential for backend engineers > **model serving & inference APIs.** you'll eventually serve a model. understand latency vs throughput tradeoffs. know when to use REST vs gRPC for predictions. batch inference vs real-time. cold start problems are real. > **feature stores & pipelines.** models are useless without features. learn how feature stores work (Feast, Tecton). understand online vs offline features. the gap between training data and serving data will bite you. > **vector databases & embeddings.** search is becoming semantic. know how embeddings work conceptually. understand ANN search, HNSW indexes. Pinecone, Weaviate, Qdrant, pgvector — pick one and go deep. > **ML infrastructure basics.** models need to be versioned, deployed, monitored. MLflow, Kubeflow, BentoML — the ecosystem is messy but learnable. understand model registries and artifact storage. > **GPU resource management.** when your team adds ML workloads, someone needs to manage GPU allocation. understand CUDA basics, container GPU access, spot instance strategies. cloud GPU bills will shock you. > **data validation & drift detection.** production data changes. models degrade silently. learn to monitor input distributions. Great Expectations, Evidently, Whylogs — pick your tooling. > **prompt engineering & LLM integration.** like it or not, you'll integrate LLMs. understand context windows, token limits, streaming responses. caching strategies for deterministic prompts. cost management is non-trivial. > **batch vs stream processing for ML.** some predictions need real-time. some can wait. know when to use Spark vs Flink vs simple cron jobs. over-engineering kills projects. Backend engineers who understand ML don't just integrate models. They build systems where models actually work in production. That's the gap worth closing.