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Show HN: Animalese

https://animalese.barcoloudly.com/
1•noreplica•36s ago•0 comments

StrongDM's AI team build serious software without even looking at the code

https://simonwillison.net/2026/Feb/7/software-factory/
1•simonw•1m ago•0 comments

John Haugeland on the failure of micro-worlds

https://blog.plover.com/tech/gpt/micro-worlds.html
1•blenderob•1m ago•0 comments

Show HN: I built an invoicing SaaS with AI-generated invoice templates

https://www.invocrea.com/en
1•mathysth•1m ago•0 comments

Velocity

https://velocity.quest
1•kevinelliott•2m ago•1 comments

Corning Invented a New Fiber-Optic Cable for AI and Landed a $6B Meta Deal [video]

https://www.youtube.com/watch?v=Y3KLbc5DlRs
1•ksec•3m ago•0 comments

Show HN: XAPIs.dev – Twitter API Alternative at 90% Lower Cost

https://xapis.dev
1•nmfccodes•4m ago•0 comments

Near-Instantly Aborting the Worst Pain Imaginable with Psychedelics

https://psychotechnology.substack.com/p/near-instantly-aborting-the-worst
1•eatitraw•10m ago•0 comments

Show HN: Nginx-defender – realtime abuse blocking for Nginx

https://github.com/Anipaleja/nginx-defender
2•anipaleja•10m ago•0 comments

The Super Sharp Blade

https://netzhansa.com/the-super-sharp-blade/
1•robin_reala•11m ago•0 comments

Smart Homes Are Terrible

https://www.theatlantic.com/ideas/2026/02/smart-homes-technology/685867/
1•tusslewake•13m ago•0 comments

What I haven't figured out

https://macwright.com/2026/01/29/what-i-havent-figured-out
1•stevekrouse•14m ago•0 comments

KPMG pressed its auditor to pass on AI cost savings

https://www.irishtimes.com/business/2026/02/06/kpmg-pressed-its-auditor-to-pass-on-ai-cost-savings/
1•cainxinth•14m ago•0 comments

Open-source Claude skill that optimizes Hinge profiles. Pretty well.

https://twitter.com/b1rdmania/status/2020155122181869666
2•birdmania•14m ago•1 comments

First Proof

https://arxiv.org/abs/2602.05192
2•samasblack•16m ago•1 comments

I squeezed a BERT sentiment analyzer into 1GB RAM on a $5 VPS

https://mohammedeabdelaziz.github.io/articles/trendscope-market-scanner
1•mohammede•17m ago•0 comments

Kagi Translate

https://translate.kagi.com
2•microflash•18m ago•0 comments

Building Interactive C/C++ workflows in Jupyter through Clang-REPL [video]

https://fosdem.org/2026/schedule/event/QX3RPH-building_interactive_cc_workflows_in_jupyter_throug...
1•stabbles•19m ago•0 comments

Tactical tornado is the new default

https://olano.dev/blog/tactical-tornado/
2•facundo_olano•21m ago•0 comments

Full-Circle Test-Driven Firmware Development with OpenClaw

https://blog.adafruit.com/2026/02/07/full-circle-test-driven-firmware-development-with-openclaw/
1•ptorrone•21m ago•0 comments

Automating Myself Out of My Job – Part 2

https://blog.dsa.club/automation-series/automating-myself-out-of-my-job-part-2/
1•funnyfoobar•21m ago•1 comments

Dependency Resolution Methods

https://nesbitt.io/2026/02/06/dependency-resolution-methods.html
1•zdw•22m ago•0 comments

Crypto firm apologises for sending Bitcoin users $40B by mistake

https://www.msn.com/en-ie/money/other/crypto-firm-apologises-for-sending-bitcoin-users-40-billion...
1•Someone•23m ago•0 comments

Show HN: iPlotCSV: CSV Data, Visualized Beautifully for Free

https://www.iplotcsv.com/demo
2•maxmoq•24m ago•0 comments

There's no such thing as "tech" (Ten years later)

https://www.anildash.com/2026/02/06/no-such-thing-as-tech/
1•headalgorithm•24m ago•0 comments

List of unproven and disproven cancer treatments

https://en.wikipedia.org/wiki/List_of_unproven_and_disproven_cancer_treatments
1•brightbeige•24m ago•0 comments

Me/CFS: The blind spot in proactive medicine (Open Letter)

https://github.com/debugmeplease/debug-ME
1•debugmeplease•25m ago•1 comments

Ask HN: What are the word games do you play everyday?

1•gogo61•28m ago•1 comments

Show HN: Paper Arena – A social trading feed where only AI agents can post

https://paperinvest.io/arena
1•andrenorman•29m ago•0 comments

TOSTracker – The AI Training Asymmetry

https://tostracker.app/analysis/ai-training
1•tldrthelaw•33m 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 ...