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Top stories from https://news.ycombinator.com (with 100+ score) Contribute to the development here: https://github.com/phil-r/hackernewsbot Also check https://t.me/designer_news Contacts: @philr
USPS' long-awaited new mail truck makes its debut (❄️ Score: 152+ in 3 days)
Link: https://readhacker.news/s/6erkZ
Comments: https://readhacker.news/c/6erkZ
Show HN: Bullshit Remover (Score: 152+ in 6 hours)
Link: https://readhacker.news/s/6ezSZ
Comments: https://readhacker.news/c/6ezSZ
Why Scrum Is Stressing You Out (Score: 150+ in 9 hours)
Link: https://readhacker.news/s/6ezpF
Comments: https://readhacker.news/c/6ezpF
Founder Mode, hackers, and being bored by tech (Score: 152+ in 13 hours)
Link: https://readhacker.news/s/6eyvi
Comments: https://readhacker.news/c/6eyvi
Making a rickroll laser: A parametric speaker (Score: 150+ in 15 hours)
Link: https://readhacker.news/s/6eyfF
Comments: https://readhacker.news/c/6eyfF
One in five genetics papers contains errors thanks to Microsoft Excel (2016) (Score: 150+ in 9 hours)
Link: https://readhacker.news/s/6eyuG
Comments: https://readhacker.news/c/6eyuG
Show HN: Free tool to find RSS feeds, even if not linked on the page (❄️ Score: 150+ in 4 days)
Link: https://readhacker.news/c/6ejpK
I developed a small tool to find RSS feeds for websites. You can try it out here: https://lighthouseapp.io/tools/feed-finder
In >90% of cases the standard way of checking meta tags is enough to find the feeds. But my goal for this tool is that it finds feeds regardless if they're linked somewhere or not. That if this feed finder doesn't find a feed, no feed exists.
It's a big goal and admittedly not there yet, but it does a few things that are a step in that direction.
* Checks meta tags of parent pages (sometimes the article itself doesn't have the meta tag, but the main blog page does)
* Checks common suffixes like /rss, /index.xml and many others (sometimes the feed exists but isn't linked)
* Checks the sitemap
* Checks all links on the page
* Checks 3rd party feeds (OpenRSS for now, when I find more such repositories I'll add them too)
There are a couple of additional ideas I have, like checking search engines and crawling the entire domain (highly inefficient, but possible).
Would love if you could try it, and even more if you post sites where it doesn't work.
Writing an OS in Rust (Score: 150+ in 4 hours)
Link: https://readhacker.news/s/6eyic
Comments: https://readhacker.news/c/6eyic
Degrees of deception: How America's universities became debt factories (🔥 Score: 155+ in 1 hour)
Link: https://readhacker.news/s/6eyrV
Comments: https://readhacker.news/c/6eyrV
Show HN: Meet.hn – Meet the Hacker News community in your city (🔥 Score: 154+ in 3 hours)
Link: https://readhacker.news/c/6exU7
Hey HN!
I just published https://meet.hn, a map to find hackers in your city.
How it works?
Demo of the signup process: https://x.com/meet_hn/status/1834918518904746329
1. Fill the form: username, city+country
2. Copy the text generated in the box below the form, and paste it in your HN description.
3. Click "Add me on the map"
Optionnaly (it's recommended!) you can add links to your socials as well as some tags to showcase your interests.
Why does it exist?
I created this because, despite its harsh reputation on the internet, I love the HN community.
I have fewer than a handful of friends who are as curious and eager to think and reflect as the people on HN.
Also, the city I currently live in is more focused on industry than on technology and entrepreneurship, which are core to HN.
This led me to want to meet the HN community IRL.
After trying `site:news.ycombinator.com/user toulouse` on Google and getting only one result, I decided to create meet.hn.
My first goal with this is to meet at least one HN member in my city: Toulouse, France.
If you are ever in the area, hit me up! I'm sirobg at https://meet.hn/city/fr-Toulouse
Additional details:
- meet.hn has a twitter page: https://x.com/meet_hn. If you meet IRL thanks to meet.hn, don't hesitate to tag it with a picture, it would mean the world to me.
- the code is open source: https://github.com/borisghidaglia/meet-hn
- meet.hn integrates with https://at.hn/ from @padolsey (https://padolsey.at.hn/), registered on meet.hn at https://meet.hn/city/cn-Beijing
Finally, many thanks to these people for their help and/or feedbacks!
Ordered alphabetically:
- https://x.com/ericbureltech
- https://x.com/fredkisss
- https://x.com/JulienDuquesne1
- https://www.linkedin.com/in/lbasseto/
- https://x.com/lcswillems
- https://x.com/leeerob
- https://x.com/padolsey
- https://x.com/tomlienard
I hope you will enjoy this!
Please share any feedback in the comments.
Janet Jackson had the power to crash laptop computers (2022) (Score: 151+ in 14 hours)
Link: https://readhacker.news/s/6ewrd
Comments: https://readhacker.news/c/6ewrd
Techniques I Use to Create a Great User Experience for Shell Scripts (❄️ Score: 152+ in 2 days)
Link: https://readhacker.news/s/6epxM
Comments: https://readhacker.news/c/6epxM
My 71 TiB ZFS NAS After 10 Years and Zero Drive Failures (Score: 151+ in 4 hours)
Link: https://readhacker.news/s/6ewVS
Comments: https://readhacker.news/c/6ewVS
I won't be renewing my Pinboard subscription (Score: 150+ in 6 hours)
Link: https://readhacker.news/s/6ewfQ
Comments: https://readhacker.news/c/6ewfQ
CrowdStrike ex-employees: 'Quality control was not part of our process' (🔥 Score: 156+ in 3 hours)
Link: https://readhacker.news/s/6ewvn
Comments: https://readhacker.news/c/6ewvn
Show HN: Wordllama – Things you can do with the token embeddings of an LLM (Score: 150+ in 7 hours)
Link: https://readhacker.news/s/6ezLt
Comments: https://readhacker.news/c/6ezLt
After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and it’s intended to be light, fast and pip installable — a library that lowers the barrier to working with strings semantically. You don’t need to install pytorch to use it, or any deep learning runtimes.
How can this be accomplished? The model is simply token embeddings that are average pooled. To create this model, I extracted token embedding (nn.Embedding) vectors from LLMs, concatenated them along the embedding dimension, added a learnable weight parameter, and projected them to a smaller dimension. Using the sentence transformers framework and datasets, I trained the pooled embedding with multiple negatives ranking loss and matryoshka representation learning so they can be truncated. After training, the weights and projections are no longer needed, because there is no contextual calculations. I inference the entire token vocabulary and save the new token embeddings to be loaded to numpy.
While the results are not impressive compared to transformer models, they perform well on MTEB benchmarks compared to word embedding models (which they are most similar to), while being much smaller in size (smallest model, 32k vocab, 64-dim is only 4MB).
On the utility side, I’ve been adding some tools that I think it’ll be useful for. In addition to general embedding, there’s algorithms for ranking, filtering, clustering, deduplicating and similarity. Some of them have a cython implementation, and I’m continuing to work on benchmarking them and improving them as I have time. In addition to “standard” models that use cosine similarity for some algorithms, there are binarized models that use hamming distance. This is a slightly faster, similarity algorithm, with significantly less memory per embedding (float32 -> 1 bit).
Hope you enjoy it, and find it useful. PS I haven’t figured out Windows builds yet, but Linux and Mac are supported.
Lazarus Group laundered $200M from 25 crypto hacks to fiat (Score: 152+ in 7 hours)
Link: https://readhacker.news/s/6ezzE
Comments: https://readhacker.news/c/6ezzE
Void captures over a million Android TV boxes (Score: 150+ in 1 day)
Link: https://readhacker.news/s/6exdt
Comments: https://readhacker.news/c/6exdt
The Bastard Operator from Hell (1999) (Score: 150+ in 12 hours)
Link: https://readhacker.news/s/6eyDM
Comments: https://readhacker.news/c/6eyDM
OpenSCAD: The Programmer's Solid 3D CAD Modeller (Score: 151+ in 4 hours)
Link: https://readhacker.news/s/6ezgc
Comments: https://readhacker.news/c/6ezgc
Falsehoods programmers believe about TCP (Score: 150+ in 5 hours)
Link: https://readhacker.news/s/6eyKk
Comments: https://readhacker.news/c/6eyKk
LLMs Will Always Hallucinate, and We Need to Live with This (Score: 152+ in 4 hours)
Link: https://readhacker.news/s/6eywx
Comments: https://readhacker.news/c/6eywx
Terence Tao on O1 (🔥 Score: 154+ in 2 hours)
Link: https://readhacker.news/s/6eytQ
Comments: https://readhacker.news/c/6eytQ
The data on extreme human ageing is rotten from the inside out (Score: 150+ in 5 hours)
Link: https://readhacker.news/s/6exW5
Comments: https://readhacker.news/c/6exW5
Intel Solidifies $3.5B Deal to Make Chips for Military (Score: 150+ in 13 hours)
Link: https://readhacker.news/s/6ewWD
Comments: https://readhacker.news/c/6ewWD
OpenAI o1 Results on ARC-AGI-Pub (Score: 152+ in 10 hours)
Link: https://readhacker.news/s/6ewNQ
Comments: https://readhacker.news/c/6ewNQ
Lisp implemented in Rust macros (Score: 150+ in 7 hours)
Link: https://readhacker.news/s/6ewGL
Comments: https://readhacker.news/c/6ewGL
Defend against vampires with 10 gbps network encryption (Score: 150+ in 11 hours)
Link: https://readhacker.news/s/6evxv
Comments: https://readhacker.news/c/6evxv
Facebook scraped every Australian adult user's public posts to train AI (Score: 154+ in 7 hours)
Link: https://readhacker.news/s/6evXN
Comments: https://readhacker.news/c/6evXN
Android now allows apps to block sideloading (Score: 150+ in 1 day)
Link: https://readhacker.news/s/6eqpN
Comments: https://readhacker.news/c/6eqpN