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SectorC: A C Compiler in 512 bytes

https://xorvoid.com/sectorc.html
97•valyala•4h ago•16 comments

The F Word

http://muratbuffalo.blogspot.com/2026/02/friction.html
43•zdw•3d ago•8 comments

Brookhaven Lab's RHIC concludes 25-year run with final collisions

https://www.hpcwire.com/off-the-wire/brookhaven-labs-rhic-concludes-25-year-run-with-final-collis...
23•gnufx•2h ago•19 comments

Speed up responses with fast mode

https://code.claude.com/docs/en/fast-mode
55•surprisetalk•3h ago•54 comments

Software factories and the agentic moment

https://factory.strongdm.ai/
97•mellosouls•6h ago•175 comments

Stories from 25 Years of Software Development

https://susam.net/twenty-five-years-of-computing.html
100•vinhnx•7h ago•13 comments

Hoot: Scheme on WebAssembly

https://www.spritely.institute/hoot/
143•AlexeyBrin•9h ago•26 comments

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

https://openciv3.org/
850•klaussilveira•1d ago•258 comments

I write games in C (yes, C)

https://jonathanwhiting.com/writing/blog/games_in_c/
138•valyala•4h ago•109 comments

First Proof

https://arxiv.org/abs/2602.05192
68•samasblack•6h ago•52 comments

Show HN: A luma dependent chroma compression algorithm (image compression)

https://www.bitsnbites.eu/a-spatial-domain-variable-block-size-luma-dependent-chroma-compression-...
7•mbitsnbites•3d ago•0 comments

The Waymo World Model

https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-frontier-for-autonomous-driving-simula...
1093•xnx•1d ago•618 comments

Al Lowe on model trains, funny deaths and working with Disney

https://spillhistorie.no/2026/02/06/interview-with-sierra-veteran-al-lowe/
64•thelok•6h ago•10 comments

Vocal Guide – belt sing without killing yourself

https://jesperordrup.github.io/vocal-guide/
235•jesperordrup•14h ago•80 comments

Start all of your commands with a comma (2009)

https://rhodesmill.org/brandon/2009/commands-with-comma/
519•theblazehen•3d ago•191 comments

Reinforcement Learning from Human Feedback

https://rlhfbook.com/
94•onurkanbkrc•9h ago•5 comments

Show HN: I saw this cool navigation reveal, so I made a simple HTML+CSS version

https://github.com/Momciloo/fun-with-clip-path
31•momciloo•4h ago•5 comments

Selection Rather Than Prediction

https://voratiq.com/blog/selection-rather-than-prediction/
13•languid-photic•3d ago•4 comments

Coding agents have replaced every framework I used

https://blog.alaindichiappari.dev/p/software-engineering-is-back
259•alainrk•8h ago•425 comments

The AI boom is causing shortages everywhere else

https://www.washingtonpost.com/technology/2026/02/07/ai-spending-economy-shortages/
186•1vuio0pswjnm7•10h ago•266 comments

A Fresh Look at IBM 3270 Information Display System

https://www.rs-online.com/designspark/a-fresh-look-at-ibm-3270-information-display-system
48•rbanffy•4d ago•9 comments

France's homegrown open source online office suite

https://github.com/suitenumerique
615•nar001•8h ago•272 comments

72M Points of Interest

https://tech.marksblogg.com/overture-places-pois.html
36•marklit•5d ago•6 comments

We mourn our craft

https://nolanlawson.com/2026/02/07/we-mourn-our-craft/
348•ColinWright•3h ago•414 comments

Unseen Footage of Atari Battlezone Arcade Cabinet Production

https://arcadeblogger.com/2026/02/02/unseen-footage-of-atari-battlezone-cabinet-production/
124•videotopia•4d ago•39 comments

Where did all the starships go?

https://www.datawrapper.de/blog/science-fiction-decline
99•speckx•4d ago•115 comments

Show HN: Kappal – CLI to Run Docker Compose YML on Kubernetes for Local Dev

https://github.com/sandys/kappal
33•sandGorgon•2d ago•15 comments

Learning from context is harder than we thought

https://hy.tencent.com/research/100025?langVersion=en
211•limoce•4d ago•119 comments

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

https://github.com/valdanylchuk/breezydemo
288•isitcontent•1d ago•38 comments

History and Timeline of the Proco Rat Pedal (2021)

https://web.archive.org/web/20211030011207/https://thejhsshow.com/articles/history-and-timeline-o...
20•brudgers•5d ago•5 comments
Open in hackernews

Nvidia DGX Spark and Apple Mac Studio = 4x Faster LLM Inference with EXO 1.0

https://blog.exolabs.net/nvidia-dgx-spark/
61•edelsohn•3mo ago

Comments

pram•3mo ago
Very cool, using the DGX like an “AI eGPU.” I wonder if this could also benefit stuff like Stable Diffusion/WAN etc?
alexandercheema•3mo ago
Yes, these models are mostly compute-bound so benefit even more from the compute on the DGX Spark.
dekhn•3mo ago
Are you using USB-C for networking between the Spark and the Mac?
pdpi•3mo ago
IP over thunderbolt is definitely a thing, don't know whether IP over USB is also a thing. USB4x2 or TB5 can do 80Gib/s symmetrical or 120+40 asymmetrical (and boy is this a poster child for the asymmetrical setup). The Mac definitely supports that fine, so, as long as the Spark plays nice, USB is actually a legitimately decent choice.
esseph•3mo ago
USB4 was based on Thunderbolt3

Yes, it's a thing that works.

mehdibl•3mo ago
The gain is only in prefill and if the task/output is complex the gain will be totally minor. So the numbers are quitly exagerated here based on a prompt that is taking less than 2s to decode. So I guess we are not here doing complex tasks with 100's or 1000 token output. For the cost of an M3 Ultra + DGX the gain seem minimal and most of all, exo didn't clarify the model used here and it's for sure not a dense model or an MoE with 1B or 2B experts otherwise the mac ultra too will suffer a lot and the layers will be bigger!
solarkraft•3mo ago
Anecdotally, even medium-sized prompts (a few thousand tokens) on pretty small models (8-2B) have resulted in extremely noticeable slowdowns (vast majority of total processing time) on my M1 Mac, leading me to appreciate the significance of the pre-fill step (and difficulty of processing large contexts locally).
adam_arthur•3mo ago
I'm confused by all the takes implying decode is more important than prefill.

There are an enormous number of use cases where the prompt is large and the expected output is small.

E.g. providing data for the LLM to analyze, after which it gives a simple yes/no Boolean response. Or selecting a single enum value from a set.

This pattern seems far more valuable in practice, than the common and lazy open ended chat style implementations (lazy from a product perspective).

Obviously decode will be important for code generation or search, but that's such a small set of possible applications, and you'll probably always do better being on the latest models in the cloud.

drodgers•3mo ago
This is really cool!

Now I'm trying to stop myself from finding an excuse to spend upwards of $30k on compute hardware...

tuananh•3mo ago
if you have $30k to spare, I'm sure there are better options
_ea1k•3mo ago
Yeah, a couple of RTX Pro 6000 cards would blow this away and still leave him with money to spare.
solarkraft•3mo ago
This is a wonderful explanation of the two phases! I appreciate the hardware concerns for both now.

Reading the article I wished for a device that just does both things well and on that topic it might be noteworthy that Apple's just-released M5 has approximately 3.5x-ed TTFT performance compared to M4, according to their claims!

daft_pink•3mo ago
It’s really sad that exo went private.
ethanpil•3mo ago
How do you know this happened? I thought it was an abandoned project until I saw this post. I've been diligently checking weekly for new releases but nothing for almost a year...
alexandercheema•3mo ago
Appreciate you checking back so often. We have some exciting plans. Keep checking and it won't be long before something pops up :)
storus•3mo ago
Wouldn't this restrict memory to 128GB, wasting M3 Ultra potential?
alexandercheema•3mo ago
Blog author here. Actually, no. The model can be streamed into the DGX Spark, so we can run prefill of models much larger than 128GB (e.g. DeepSeek R1) on the DGX Spark. This feature is coming to EXO 1.0 which will be open-sourced soonTM.
storus•3mo ago
Excellent! Good luck!
musicale•3mo ago
But you could also just get two DGX Spark and get 2 * 1.9x = 3.8x total throughput for two query streams.
rcarmo•3mo ago
This is very nicely done. I wonder what the values will look like a year from now with M5 Macs, though.