unpopular dockerfile takes (that actually work) 1 - stop using alpine — yes, it's tiny. but musl libc ≠ glibc. your python/node app will rebuild native deps from scratch or just... silently be slower. use -slim (debian-slim) instead. same size win, zero grief. 2 - layer order is your cache strategy. COPY your lockfile first, run install, then copy source. invalidating the install layer on every code change is a skill issue ngl 3 - multi-stage builds aren't just "best practice" — they're the actual reason your prod image doesn't ship gcc and 400mb of build tools. builder stage = bloat zone. final stage = lean mean container. 4 - COPY . . is fine actually — if your .dockerignore is correct. most pain here is from forgetting to ignore node_modules/, .git, *.log. fix the ignore file, not the COPY. 5 - one process per container is a vibe, not a law. if your app needs nginx app server and you're not at k8s scale — just use supervisord. the "one process" dogma costs more complexity than it saves sometimes. 6 - pin your base image by digest, not tag. node:20 today ≠ node:20 in 6 months. prod broke because of a tag? that's a you problem tbh. 7 - BuildKit cache mounts (--mount=type=cache) will change your life. pip/apt/cargo cache between builds without it ending up in the final layer. nobody talks about this enough fr there's no "best practice" in a vacuum. alpine is great for Go binaries. slim is great for Python. scratch is great for static bins. know your workload, then choose. btw if you want something to catch all this stuff automatically - check out dockerfile-roast — a linter written in Rust that literally roasts your Dockerfile. 63 rules, brutally honest output (but it can also provide just dry facts, no roast), runs on any OS or as a docker container github.com/immanuwell/docker… #docker #devops #kubernetes #backend #linux #rust #sre #containers

Putting out a wish to the universe. I need more compute, if I can get more I will make sure every machine from a small phone to a bootstrapped RTX 3090 node can run frontier intelligence fast with minimal intelligence loss. I have hit page 2 of huggingface, released 3 model family compressions and got GLM-4.7 on a MacBook huggingface.co/0xsero My beast just isn’t enough and I already spent 2k usd on renting GPUs on top of credits provided by Prime intellect and Hotaisle. ——— If you believe in what I do help me get this to Nvidia, maybe they will bless me with the pewter to keep making local AI more accessible 🙏

Introducing the new /crawl endpoint - one API call and an entire site crawled. No scripts. No browser management. Just the content in HTML, Markdown, or JSON.

Just deployed @Alibaba_Qwen Qwen3.5-0.8B on @lmstudio (GGUF files on @huggingface / the 533MB is sufficient) and: - it's super fast; - runs locally on my MacBook; - my data & files don't leave my local server; - no internet is required; - it's completely free; ... Me: 😎

We've reset rate limits for all Claude Code users. Yesterday we rolled out a bug with prompt caching that caused usage limits to be consumed faster than normal. This is hotfixed in 2.1.62. Make sure you upgrade to the latest and hope you enjoy using Claude Code this weekend!

Do you consider a "beautiful URL" as part of the user experience when using a tool?

Say hi to the Cloudflare Data Platform — a serverless data platform built on Cloudflare's global infrastructure. Ingest data with Pipelines, manage it with our Iceberg-compatible Data Catalog, and *query it* using the brand-new R2 SQL query engine.