For everyone interested in data infra, want to get a quick sense of how big data works, how data systems are designed, and what the tradeoffs are, start with this share from @xiangpeng.systems, really nice intro!

intro-data-system.xiangpeng.systems

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. 🤔

100% correct post

!!!

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

AWS seems to have a major outage in the us-east-1 region right now 😵‍💫 health.aws.amazon.com/health/status

a16z-backed data firms Fivetran, dbt Labs to merge in all-stock deal Data startups Fivetran and dbt Labs will merge in an all-stock deal, creating a combined data infrastructure company with nearly $600 million in annual revenue, the two companies told Reuters.

🤯 www.reuters.com/business/a16...

#dataBS

A Walkthrough of nano-vllm | Flaneur2020 Recently, I’ve been delving into the architecture of production-grade inference engines. While projects like vLLM and SGLang are crazy sophisticated, …

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...

recently Qwen3-Next-80B-A3B has become my goto model for asking "how-to" questions in the work. it's not considered as a smart model but it's really dog fast 😲

thanks! hope it can be useful 😁

yeah, it's an optimistic numbering scheme, if there's any username conflict, I'll just back off to the next year and try to register the account again. 😂

what an amazing journey! after working through this step by step, we finally have a functional transaction API with SSI support. hope it proves useful! 😁

family mooncake 😋

Advice for a young investigator in the first and last days of the Anthropocene Advice for a (young) investigator in the first and last days of the Anthropocene Jascha Sohl-Dickstein Anthropic Title: Advice for a young investigator in the first and last days of the Anthropocene A...

He will give practical advice, and concrete criteria to consider, when choosing research projects, and making professional decisions, in these last few years before AGI."

docs.google.com/presentation...

🚀🚀🚀

GitHub - column-tax/tax-calc-bench: Code & data for TaxCalcBench Code & data for TaxCalcBench. Contribute to column-tax/tax-calc-bench development by creating an account on GitHub.

You already know the answer, but it’s nice that someone put in the manual effort to create a benchmark

Can AI file your taxes? No

"TaxCalcBench: Evaluating Frontier Models on the Tax Calculation Task"

Paper: www.arxiv.org/abs/2507.16126
Repo: github.com/column-tax/t...

Job update: a couple of weeks ago, I joined @tensorlake.ai full time. I’m having a lot of fun building the product with @diptanu.bsky.social and the rest of this wonderful team.

We have a few open positions if you’d like to work with us: www.linkedin.com/jobs/search/...

On to new things!

"Ingest, query, and share telemetry data with your engineers and customers at a fraction of the cost."

early return / goroutine leak is my favourite interview question, it can help reveal a candidate’s awareness of resource management, which is essential for building robust systems 😲

Early return and goroutine leak At work, a common mistake I notice when reviewing candidates’ home assignments is how they wire goroutines to channels and then return early. The pattern usually looks like this: start a few goroutin...

Regarding goroutine & unbuffered channel interaction, found myself repeating this multiple times. Maybe it's time to write it down for reference.

Early return + unbuffered send = goroutine leak.

rednafi.com/go/early_ret...

#golang

1/ SlateDB v0.8 is now available! This is release includes OpenDAL object store support, serializable snapshot isolation, first-class Go bindings, Python binding improvements, deterministic simulation tests, performance improvements, and tons of bug fixes. Details below. 👇

now i believe all the dinosaurs are big chicken 🤔

2/ Snapshot isolation — @flaneur2024.bsky.social has been hard at work on snapshots/transactions. 0.8 now has `DbSnapshots`, which provide a consistent point-in-time DB view. Sequence numbers are now core to SlateDB and will be used for many features (including transactions) going forward.

instead of just looking at scores, we can build up an internal 'reputation' for each model based on how it performs day-to-day 🤔

it seems there's no gold-standard benchmark for coding models at the moment. imho, the only way to know if one is any good is to throw a (real world) task at it and see if it can handle it smoothly 🤔.

thanks to the help from @xuanwo.io for reviewing this, there are still some follow-up tasks that need to be continued 😀

feat: allow using object_store as opendal's backend by flaneur2020 · Pull Request #6283 · apache/opendal Which issue does this PR close? Closes #6171. Rationale for this change with allowing object_store as opendal&#39;s backend, we can leverage opendal&#39;s advanced operation like parallel fetchin...

OpenDAL can now use object_store as its backend service 🥳!

this allows users to leverage the out-of-box primitives from the OpenDAL operator, such as chunking / parallel fetching and caching on user-provided object_store instances

github.com/apache/opend...

(i can only let it work independently for at most 10 minutes at the moment 🤔)

24/7: Late Capitalism and the Ends of Sleep – June 4, 2013 by Jonathan Crary

in the age of vibe coding, the measure of a top programmer should be how long they can get Claude Code to work continuously without human intervention. if you can get Claude to code independently for a full 24-hour workday, you deserve top dollar in the market 🤔

z.ai's slime

It is built with native SGLang integration, carrying its full inference optimizations straight into training, is an LLM post-training framework for RL scaling, providing two core capabilities: