@flaneur2024.bsky.social

maintainer of SlateDB loves Rust, Datasys, Cloud Infra, AI https://flaneur2020.github.io

I used to wonder why some processes get stuck in the 'D' state and can't be killed. just learned that it seems to be a consideration of ownership: when disk IO reads into the page cache, killing the process mid-way could cause the DMA write to end up in a random memory location. 🤔

just learned about the Kitty Graphics Protocol which allows displaying graphics in terminals like Kitty / Ghostty. thought it’d be interesting to run a GUI inside a Ghostty terminal, so I vibe-coded a demo. it’s still a bit flickery though 😲

agentic seems to be a further compression of knowledge, as long as you have a proven approach to solving problems in the real world, you don't need to memorize vast amount of details. 🤔

thinking about how FP is a cheat code for AI-driven development. properties like referential transparency / stateless functions are possible to significantly reduce the context the AI needs to worry about. 🤔

SlateDB 0.10.0 is out! My favorite changes: - Compactor can run on a separate machine - Go binding improvements - Network chaos tests - Object store cache improvements - Merges on L0 flush Changelog: app.dosu.dev/d8f2da6d-6c4... Release notes: github.com/slatedb/slated

Nov 22, 2025 - Dec 30, 2025 Changelog

Features Add release schedule to README: Documented the project's release cadence and compatibility guarantees. (2025-12-30, #1150) Add run-compactor to slated

app.dosu.dev

distilling knowledge from LLMs to brains ought to be extremely convenient for thinkers nowadays, but 'being thoughtful' will become an even rarer trait 🤔

i realized i do not have to put all my $HOME folder into my dotfiles. zsh & git & neovim are simply all i need, and nothing else is worth version controlling for me. 😲

just as 'User Experience' optimizes tools for humans, I suppose there will emerge a profession called 'AI Agent Experience' to optimize tools for AI agents in the coming years. AI Agents are waiting for their own Steve Jobs 🤔

made a note about how all these components around CSI interacts with each other to simply mount a EBS in k8s, surprisingly a mess of stuffs 😲

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a metaphor I've been pondering: post-WWII nuclear tests irradiated the atmosphere, making all steel smelted after carried a background radiation. for high-precision instruments, you had to salvage pre-test steel from sunken ships.

it’s crazy to see such a great outage from cloudflare, twitter was inaccessible for nearly 3 hours. luckily bluesky is still available at the moment 😲

The largest egocentric dataset. Egocentric (first person) video is a general learning framework that passively captures how skilled workers do their jobs. - 10,000 hours - 2,153 factory workers - 1,080,000,000 frames

i'm beginning to understand the appeal of HCL as a configuration format over YAML. HCL has built-in variables, effectively providing a native templating engine. with YAML, we often have to use macros just to battle the indentation and ensure the correct number of spaces with some Jinja variants. 😲

in my earlier understanding, distributed systems usually featured a fine-grained metadata service for cluster membership. however, I've recently noticed that many systems' implementations seem to prefer a fixed cluster design. 🤔 once the cluster is established, its membership becomes immutable.

i believe household chores should be considered internal domestic logistics. for instance, handling the movement of clothing between the washing machine, drying space, and wardrobe. or dishes moving between the dining table, dishwasher, and cupboard.. 🤔

I do understand why so many articles talk about how vibe coding can destroy the joy of programming. however, I've never found that any joy exists in manually writing GitHub/Jenkins workflows. 🤔

Calling database nerds in SF! I'm covering SlateDB at the systems meetup next Wednesday (10/29). If you're around, I'd love to meet you in person (that way you'll have proof I'm not just an AI bot). 👉 luma.com/e7feg2i6

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modern LLM inference engines like vLLM & SGlang are becoming tough to dive into. to learn how these inference engines work, nano-vllm is a fantastic educational project—complete Page Attention & LLM scheduler in <1k loc.🤯 flaneur2020.github.io/posts/2025-1...

A Walkthrough of nano-vllm | Flaneur2020

Recently, I&rsquo;ve been delving into the architecture of production-grade inference engines. While projects like vLLM and SGLang are crazy sophisticated, …

flaneur2020.github.io