Taz

388 posts

Taz

Taz

@taz_ca

wiggling parameters

เข้าร่วม Kasım 2025
231 กำลังติดตาม12 ผู้ติดตาม
Taz
Taz@taz_ca·
Taz tweet media
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Ron Paul
Ron Paul@RonPualS·
@thsottiaux Just publish your off-peak hours and 10x the Codex quota then. I’ll move my entire circadian rhythm to match your server capacity. You don’t even need to fix the limits — just tell me when to sleep.
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Tibo
Tibo@thsottiaux·
With Codex the there is quite the gulf in load between peak and off-peak times, and we would like to achieve more of a smoother traffic pattern as that would be a more optimal use of our compute. We have ideas, but curious what you all think we should do? Would more usage during off-peak and surge multiplier during peak times make sense?
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Taz
Taz@taz_ca·
@latkins dont jinx it shh
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Rohan Paul
Rohan Paul@rohanpaul_ai·
Anthropic top researcher Nicolas Carlini (67.2k citations on Google Scholar) says Claude is a better security researcher than him, made $3.7 mn from exploiting smart contracts, and found vulnerabilities in Ghost (a 52K+ Github star project).
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Taz
Taz@taz_ca·
@jxnlco you're the funniest mf at oai Jason
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Taz
Taz@taz_ca·
@HessianFree congrats! did you guys specifically train it for some robotics tasks or is it more that the fast inference on edge enables it to be deployed for it?
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Omead Pooladzandi
Omead Pooladzandi@HessianFree·
your spotify cache is bigger than our largest AI model. Bonsai: 1-bit weights. 1.7B to 8B params. 14x compression vs bf16. 8x faster on edge. 256 MB to 1.2GB. Based on Qwen 3. we just came out of stealth. intelligence belongs at the edge and we're going to put it there. Apache 2.0. we compressed intelligence. more coming. @PrismML
Omead Pooladzandi tweet media
PrismML@PrismML

Today, we are emerging from stealth and launching PrismML, an AI lab with Caltech origins that is centered on building the most concentrated form of intelligence. At PrismML, we believe that the next major leaps in AI will be driven by order-of-magnitude improvements in intelligence density, not just sheer parameter count. Our first proof point is the 1-bit Bonsai 8B, a 1-bit weight model that fits into 1.15 GBs of memory and delivers over 10x the intelligence density of its full-precision counterparts. It is 14x smaller, 8x faster, and 5x more energy efficient on edge hardware while remaining competitive with other models in its parameter-class. We are open-sourcing the model under Apache 2.0 license, along with Bonsai 4B and 1.7B models. When advanced models become small, fast, and efficient enough to run locally, the design space for AI changes immediately. We believe in a future of on-device agents, real-time robotics, offline intelligence and entirely new products that were previously impossible. We are excited to share our vision with you and keep working in the future to push the frontier of intelligence to the edge.

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Cailyn Y.
Cailyn Y.@cailynyongyong·
Aaas
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Yuchen Jin
Yuchen Jin@Yuchenj_UW·
hey friends! 👋 Only cool people are allowed to reply to this tweet obviously.
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Google Research
Google Research@GoogleResearch·
Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI
GIF
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ErvistheGreat
ErvistheGreat@ErvistheGreat·
@taz_ca @gnukeith Fuk yeah. Rockin out with the full weights of your kock out.. or something like that
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Keith
Keith@gnukeith·
I don't quite get it, is the 27B model smarter than the 35-A3B model?
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Taz
Taz@taz_ca·
@bbarski @gnukeith its different on a case by case basis since some labs know how to train MoE well enough so they can still push the performance I'm pretty sure nvidia doesn't do dense models anymore though, they're pushing that efficiency frontier and MoE's are the way to go for that
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RM
RM@bbarski·
@taz_ca @gnukeith The same applies to nvidia nemotron models ?
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Taz
Taz@taz_ca·
@bnjmn_marie Yuppp thanks Ben, noticed that 27B is dense so it's got the full force of that running on each pass
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Benjamin Marie
Benjamin Marie@bnjmn_marie·
@taz_ca The 35B is an MoE. Faster but fewer active parameters.
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Benjamin Marie
Benjamin Marie@bnjmn_marie·
For OpenClaw, just use Qwen3.5 27B! Q4 GGUFs match the original's model accuracy You don't need expensive hardware or models
Benjamin Marie tweet mediaBenjamin Marie tweet media
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Taz
Taz@taz_ca·
@PuterOnX @TheAhmadOsman internal sources went silent for almost 2 minute during a presentation for more when someone asked during a qna 2 days ago though when I say silent I mean literally mic muted silent
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Puter
Puter@PuterOnX·
@taz_ca @TheAhmadOsman Yeah very dodgy this time around literally no interactions around it which is all the more irritating but I guess ahmad is confident? I think it can be an internal source
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Taz
Taz@taz_ca·
@Hangsiin How much API equivalent usage does the pro sub get U in a week? More than Claude?
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NomoreID
NomoreID@Hangsiin·
To be honest, if you're a power user, I'd say you absolutely shouldn't do it. Unless you're planning to subscribe to multiple Pro accounts. Even without using Fast, GPT-5.4 starts eating into your usage pretty quickly. Part of it is probably that I've gotten more and more used to running things in parallel. If I had used Fast, I probably would have burned through it all in a single day.
Sherwin Wu@sherwinwu

Set Codex to this and don't ever look back

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Taz
Taz@taz_ca·
@fynnso what did U start bro 😭
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Taz
Taz@taz_ca·
@MillionInt still can't wait to see what u do next Jerry, u gotta give us a teaser soon
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Jerry Tworek
Jerry Tworek@MillionInt·
Every team has its own project Hail Mary. I have one very close to my heart
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