jascha_eng
Joined 2 years ago
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1,072
Submissions
260
Member since
Jan 23
Recent submissions
Comment · 3 days ago
What makes me most excited is that we probably get fable 5.5 soon with the IPO. Which is going to surpass this again. But cool to see progress!
Comment · 4 days ago
huh? autosave, bsod, security the client side hacks of gaming
how is any of this a hardware improvement. Software got a lot better.
Of course hardware also got better but software also got a let better at working around the hardwares quirks.
Comment · 8 days ago
+1 for the omniscience benchmark!
Although even this one needs to be taken with a grain of salt. Fable/Opus still act very differently to Astra and Argon imo. But maybe it's just because I am used to these. I wouldn't clearly put them above each other though, although I still prefer anthropics models, but that could be simply out of comfort.
Comment · 10 days ago
yeh running some automatic scripts against public urls to check if https is enabled and scanning if there are public s3 buckets is barely any better than a security linter.
There are some teams that are better than this, but they are rare.
Comment · 17 days ago
Comment · 17 days ago
Btw, I do work for Tiger Data/timescale, but am not at all associated with this team. I just found this release really exciting.
I sent the thread to Erik (the main contributor) though, so if you have feedback or questions, good chance that he'll see it.
Comment · 17 days ago
Well you can always move to tiger cloud if you want it managed :)
And this is PostgreSQL licensed just like our pg_textsearch which has been adopted to Googles AlloyDB and microsofts HorizonDB already, so there is hope!
Comment · 17 days ago
benchmarks are in the blog post and the benchmarking suite is also open sourced alongside it.
Comment · 17 days ago
from the accompanying blog post: https://www.tigerdata.com/blog/announcing-pg_vectorsearch-sc...
> TLDR: We are launching pg_vectorsearch, a new open-source PostgreSQL extension that makes vector search dramatically faster and more cost-efficient. Its new PRISM index uses partitioning and compression instead of HNSW's graph-based approach, delivering faster queries with a fraction of the memory and significantly faster index builds. At 100 million vectors, PRISM achieved 6 ms query latency on a machine with 4× less memory than HNSW, with index builds over 20× faster.
Comment · 25 days ago
Imo omniscience correlates better to how useful the model is in practice than the intelligence index. But you have to use both together of course.
Comment · 25 days ago
-5 on omniscience? https://artificialanalysis.ai/evaluations/omniscience
That's not particularly great.
That said I love that they don't seem to restrict cyber capabilities to any degree and even lean into it.
If the best model for cyber attacks is open for everyone to use it just makes us all safer I think. Of course you then also HAVE to use it or otherwise you're vulnerable, which is a great distribution play.
Comment · 1 month ago
Yeh it's absurd that people claim this all the time. It's some crazy conspiracy theory and when you ask for examples nothing ever shows up.
It would be economical suicide from anthropic and OpenAI to actually need models intentionally.
But hey I guess it's hard with technology that truly seems like magic. People say if you'd bring electricity to the middle ages you'd be called a witch and burned. The same is happening to the model labs here because they are bringing tech that the world isn't ready for yet.
Comment · 1 month ago
I played around a bit with pencil.devs mcp integration before: https://www.pen.dev/
Which was at least fun for a bit although I failed to really see the use after models became better at designing themselves.
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