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OpenCiv3: Open-source, cross-platform reimagining of Civilization III

https://openciv3.org/
611•klaussilveira•12h ago•180 comments

The Waymo World Model

https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-frontier-for-autonomous-driving-simula...
915•xnx•17h ago•545 comments

What Is Ruliology?

https://writings.stephenwolfram.com/2026/01/what-is-ruliology/
28•helloplanets•4d ago•22 comments

How we made geo joins 400× faster with H3 indexes

https://floedb.ai/blog/how-we-made-geo-joins-400-faster-with-h3-indexes
102•matheusalmeida•1d ago•24 comments

Unseen Footage of Atari Battlezone Arcade Cabinet Production

https://arcadeblogger.com/2026/02/02/unseen-footage-of-atari-battlezone-cabinet-production/
36•videotopia•4d ago•1 comments

Show HN: Look Ma, No Linux: Shell, App Installer, Vi, Cc on ESP32-S3 / BreezyBox

https://github.com/valdanylchuk/breezydemo
212•isitcontent•12h ago•25 comments

Jeffrey Snover: "Welcome to the Room"

https://www.jsnover.com/blog/2026/02/01/welcome-to-the-room/
5•kaonwarb•3d ago•1 comments

Monty: A minimal, secure Python interpreter written in Rust for use by AI

https://github.com/pydantic/monty
206•dmpetrov•12h ago•101 comments

Show HN: I spent 4 years building a UI design tool with only the features I use

https://vecti.com
316•vecti•14h ago•140 comments

Microsoft open-sources LiteBox, a security-focused library OS

https://github.com/microsoft/litebox
355•aktau•18h ago•181 comments

Sheldon Brown's Bicycle Technical Info

https://www.sheldonbrown.com/
361•ostacke•18h ago•94 comments

Hackers (1995) Animated Experience

https://hackers-1995.vercel.app/
471•todsacerdoti•20h ago•232 comments

Show HN: If you lose your memory, how to regain access to your computer?

https://eljojo.github.io/rememory/
267•eljojo•15h ago•157 comments

An Update on Heroku

https://www.heroku.com/blog/an-update-on-heroku/
399•lstoll•18h ago•271 comments

Delimited Continuations vs. Lwt for Threads

https://mirageos.org/blog/delimcc-vs-lwt
25•romes•4d ago•3 comments

Dark Alley Mathematics

https://blog.szczepan.org/blog/three-points/
82•quibono•4d ago•20 comments

PC Floppy Copy Protection: Vault Prolok

https://martypc.blogspot.com/2024/09/pc-floppy-copy-protection-vault-prolok.html
54•kmm•4d ago•3 comments

Was Benoit Mandelbrot a hedgehog or a fox?

https://arxiv.org/abs/2602.01122
9•bikenaga•3d ago•2 comments

How to effectively write quality code with AI

https://heidenstedt.org/posts/2026/how-to-effectively-write-quality-code-with-ai/
242•i5heu•15h ago•183 comments

Introducing the Developer Knowledge API and MCP Server

https://developers.googleblog.com/introducing-the-developer-knowledge-api-and-mcp-server/
51•gfortaine•10h ago•16 comments

I spent 5 years in DevOps – Solutions engineering gave me what I was missing

https://infisical.com/blog/devops-to-solutions-engineering
138•vmatsiiako•17h ago•60 comments

Understanding Neural Network, Visually

https://visualrambling.space/neural-network/
275•surprisetalk•3d ago•37 comments

Show HN: R3forth, a ColorForth-inspired language with a tiny VM

https://github.com/phreda4/r3
68•phreda4•11h ago•13 comments

I now assume that all ads on Apple news are scams

https://kirkville.com/i-now-assume-that-all-ads-on-apple-news-are-scams/
1052•cdrnsf•21h ago•433 comments

Why I Joined OpenAI

https://www.brendangregg.com/blog/2026-02-07/why-i-joined-openai.html
127•SerCe•8h ago•111 comments

Female Asian Elephant Calf Born at the Smithsonian National Zoo

https://www.si.edu/newsdesk/releases/female-asian-elephant-calf-born-smithsonians-national-zoo-an...
28•gmays•7h ago•10 comments

Learning from context is harder than we thought

https://hy.tencent.com/research/100025?langVersion=en
173•limoce•3d ago•93 comments

Vocal Guide – belt sing without killing yourself

https://jesperordrup.github.io/vocal-guide/
7•jesperordrup•2h ago•4 comments

FORTH? Really!?

https://rescrv.net/w/2026/02/06/associative
61•rescrv•20h ago•22 comments

Zlob.h 100% POSIX and glibc compatible globbing lib that is faste and better

https://github.com/dmtrKovalenko/zlob
17•neogoose•4h ago•9 comments
Open in hackernews

Sirius DB

https://www.sirius-db.com/
144•manoji•1mo ago

Comments

stogot•1mo ago
Sounds amazing; what are the downsides that a company needs to consider? Memory bottlenecks or storage bus access?
necubi•1mo ago
One downside is that you're paying for the GPU whether you're fully using it or not. It takes big queries to saturate a GH200, and if you're only using 10% of the capacity of the GPU it doesn't really matter that it's 10x faster.

In a typical company you'll have jobs, some scheduled, some ad-hoc, at a range of sizes. Most of them won't be cost-effective to run on a GPU instance, so you need a scheduling layer that estimates the size of the job and routes it to the appropriate hardware. But now what if the job is too big to run on your GPU machine? Now we either have to scale up our GPU cluster or retry it on our more flexible CPU cluster.

And this all assumes that your jobs can be transparently run across different executors from a correctness and performance standpoint.

There are niches where this makes sense (we run the same 100TB job every day and we need to speed it up), as well and large and sophisticated internal infra teams that can manage a heterogenous cluster + scheduling systems, but it's not mass-market.

srcreigh•1mo ago
The website claims it’s 10x cheaper (“10x faster on same hardware costs”) and implements SQL execution.

I don’t understand why GPU saturation is relevant. If it’s 10x cheaper, it doesn’t matter if you only use 0.1% of the GPU, right?

Correctness shouldn’t be a concern if it implements SQL.

Curious for some more details, maybe there’s something I’m missing.

zX41ZdbW•1mo ago
GPU databases can run a small subset of production workloads in a narrow combination of conditions.

There are plenty of GPU databases out there: mapD/OmniSci/HeavyDB, AresDB, BlazingSQL, Kinetika, BrytlytDB, SQReam, Alenka, ... Some of them are very niche, and the others are not even usable.

adrianco•1mo ago
I’ve talked to the authors of this, it’s a very interesting project. GPU memory space used to be the limitation but the latest generations of GPUs have enormous shared memory capacity and need something like SiriusDB to manipulate and prepare data in-place before the AI algorithms get to work.
esafak•1mo ago
Reminds me of Uber's AresDB: https://www.uber.com/blog/aresdb/
tobefranklin•1mo ago
There is also a recent blog post about this: https://developer.nvidia.com/blog/nvidia-gpu-accelerated-sir...
sys13•1mo ago
I wonder if the benefit is primarily for transactional vs analytical queries
anentropic•1mo ago
it'll be purely for analytical queries
manoji•1mo ago
Its sitting at the top in clickbench .Pretty cool https://benchmark.clickhouse.com/#system=-&type=-&machine=-c...
riku_iki•1mo ago
improvement over DuckDb is kinda marginal (44%)
thesz•1mo ago
44% is not marginal. "Marginal" is what perceived by seller and buyer as negligible and it tops at 5%.
riku_iki•1mo ago
its marginal compared to promised 10x improvement.
SchwKatze•1mo ago
Wow! Now I got interested on reading the paper, thanks
canadiantim•1mo ago
It really is a SeriousDB
jauntywundrkind•1mo ago
From their Rethinking Analytical Processing in the GPU Era paper,

> Sirius builds on GPU libraries such as libcudf [6], RMM [14], and NCCL [11], reusing optimized implemen- tations of core relational operators like joins, filters, aggregations, and data shuffle. Thanks to its modular design, Sirius also allows developers to easily switch the operator implementation between these GPU libraries and custom CUDA kernels.

https://arxiv.org/abs/2508.04701

I wonder if the various other CUDA translation layers (ZLUDA, SCALE, HIP) can host this?

It'd be so nice to see a little more foothold for Vulkan in this space. There's some good work in AI for Vulkan, it's becoming quite capable. But for databases & GPGPU, it doesn't seem like there are good rallying points.

I expect whatever does eventually emerge will perhaps likely be based on Substrait too! What an awesome common grounds thats emerged for data processing work.

ledbit•1mo ago
Some of the price performance improvement that is quoted is due to using $ from different cloud providers - eg a GH200 in Lambda Labs costs $1.5/hr, but the closest equivalent in AWS (p5.4xlarge) costs $6.88/hr. Which means, ~4.5x of the price performance benefits is not real ...