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A BSOD for All Seasons – Send Bad News via a Kernel Panic

https://bsod-fas.pages.dev/
1•keepamovin•40s ago•0 comments

Show HN: I got tired of copy-pasting between Claude windows, so I built Orcha

https://orcha.nl
1•buildingwdavid•43s ago•0 comments

Omarchy First Impressions

https://brianlovin.com/writing/omarchy-first-impressions-CEEstJk
1•tosh•6m ago•0 comments

Reinforcement Learning from Human Feedback

https://arxiv.org/abs/2504.12501
1•onurkanbkrc•6m ago•0 comments

Show HN: Versor – The "Unbending" Paradigm for Geometric Deep Learning

https://github.com/Concode0/Versor
1•concode0•7m ago•1 comments

Show HN: HypothesisHub – An open API where AI agents collaborate on medical res

https://medresearch-ai.org/hypotheses-hub/
1•panossk•10m ago•0 comments

Big Tech vs. OpenClaw

https://www.jakequist.com/thoughts/big-tech-vs-openclaw/
1•headalgorithm•13m ago•0 comments

Anofox Forecast

https://anofox.com/docs/forecast/
1•marklit•13m ago•0 comments

Ask HN: How do you figure out where data lives across 100 microservices?

1•doodledood•13m ago•0 comments

Motus: A Unified Latent Action World Model

https://arxiv.org/abs/2512.13030
1•mnming•13m ago•0 comments

Rotten Tomatoes Desperately Claims 'Impossible' Rating for 'Melania' Is Real

https://www.thedailybeast.com/obsessed/rotten-tomatoes-desperately-claims-impossible-rating-for-m...
3•juujian•15m ago•1 comments

The protein denitrosylase SCoR2 regulates lipogenesis and fat storage [pdf]

https://www.science.org/doi/10.1126/scisignal.adv0660
1•thunderbong•17m ago•0 comments

Los Alamos Primer

https://blog.szczepan.org/blog/los-alamos-primer/
1•alkyon•19m ago•0 comments

NewASM Virtual Machine

https://github.com/bracesoftware/newasm
2•DEntisT_•21m ago•0 comments

Terminal-Bench 2.0 Leaderboard

https://www.tbench.ai/leaderboard/terminal-bench/2.0
2•tosh•22m ago•0 comments

I vibe coded a BBS bank with a real working ledger

https://mini-ledger.exe.xyz/
1•simonvc•22m ago•1 comments

The Path to Mojo 1.0

https://www.modular.com/blog/the-path-to-mojo-1-0
1•tosh•25m ago•0 comments

Show HN: I'm 75, building an OSS Virtual Protest Protocol for digital activism

https://github.com/voice-of-japan/Virtual-Protest-Protocol/blob/main/README.md
5•sakanakana00•28m ago•1 comments

Show HN: I built Divvy to split restaurant bills from a photo

https://divvyai.app/
3•pieterdy•30m ago•0 comments

Hot Reloading in Rust? Subsecond and Dioxus to the Rescue

https://codethoughts.io/posts/2026-02-07-rust-hot-reloading/
3•Tehnix•31m ago•1 comments

Skim – vibe review your PRs

https://github.com/Haizzz/skim
2•haizzz•32m ago•1 comments

Show HN: Open-source AI assistant for interview reasoning

https://github.com/evinjohnn/natively-cluely-ai-assistant
4•Nive11•33m ago•6 comments

Tech Edge: A Living Playbook for America's Technology Long Game

https://csis-website-prod.s3.amazonaws.com/s3fs-public/2026-01/260120_EST_Tech_Edge_0.pdf?Version...
2•hunglee2•36m ago•0 comments

Golden Cross vs. Death Cross: Crypto Trading Guide

https://chartscout.io/golden-cross-vs-death-cross-crypto-trading-guide
3•chartscout•39m ago•0 comments

Hoot: Scheme on WebAssembly

https://www.spritely.institute/hoot/
3•AlexeyBrin•42m ago•0 comments

What the longevity experts don't tell you

https://machielreyneke.com/blog/longevity-lessons/
2•machielrey•43m ago•1 comments

Monzo wrongly denied refunds to fraud and scam victims

https://www.theguardian.com/money/2026/feb/07/monzo-natwest-hsbc-refunds-fraud-scam-fos-ombudsman
3•tablets•48m ago•1 comments

They were drawn to Korea with dreams of K-pop stardom – but then let down

https://www.bbc.com/news/articles/cvgnq9rwyqno
2•breve•50m ago•0 comments

Show HN: AI-Powered Merchant Intelligence

https://nodee.co
1•jjkirsch•52m ago•0 comments

Bash parallel tasks and error handling

https://github.com/themattrix/bash-concurrent
2•pastage•52m ago•0 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 ...