xirclev

🚲

[xirclev @xirclev_]

definitely together,
but most importantly, willing to do it alone and gladly.

[VV @visualizevalue]

Everyone, Someone

[Bitcoin for Freedom @BTC_for_Freedom]

People go mental when someone turns off a TV showing a football match, but when they print 80% of all money in history and wipe away most of their wealth they do nothing.

[Alfin @AlfinCodes]

Minimum requirements for Linux

[xirclev @xirclev_]

@chapsmx @CepnikMaciej @TimHortonsMX @StarbucksMex when Maci figures out less traveled mexican coffee tastes better

[rabbitholebot @rabbitholebot]

Voting is the adult version of writing a letter to Santa Claus.

[John Berryhill @Berryhillj]

If you are using a domain name for personal email, a hobby, or some kind of other personal or family use, you NEED to move it out of @GoDaddy immediately:

[Mauricio García 🚴‍♂️ 🚧🎨 @elmaugarcia]

Primero el peatón; SIEMPRE.

[calle @callebtc]

we discovered a great use case for bitchat at a conference: panel moderator on the cypherpunk stage used it to collect questions from the audience. it worked great.

no registration, no accounts, no qr codes, just pure mesh q&a

[calle @callebtc]

Phil Zimmerman, inventor of PGP end-to-end email encryption, 1991.

We're still fighting the exact same fight today.

[Namefi @namefi_io]

https://t.co/18g5rIq82P

[xirclev @xirclev_]

antes de la pandemia ya teníamos algo en el mapa, en su forma primitiva, pero no había la motivación por "la falta de la banca tradicional" https://t.co/pH7zykyAK2

[xirclev @xirclev_]

el efecto inevitable de la automatización sobre la dimensión del cubículo o cómo sobran las labores burocráticas cuando vas a migrar al UBI o todos ya firmaron, mejor evítate broncas para que te paguen antes y me llega más de bono

https://t.co/iXy7m8nf5x

[xirclev @xirclev_]

d̶e̶j̶e̶n̶ ̶t̶o̶d̶o̶ ̶s̶u̶ ̶d̶i̶n̶e̶r̶o̶ ̶e̶n̶ ̶e̶l̶ ̶b̶a̶n̶c̶o̶

[xirclev @xirclev_]

@Porkbun @coinbase @CoinbaseSupport it is on you, for raising the bar so high 🤝

[xirclev @xirclev_]

@DrNickA maybe it has a lot to do with training and invaluable weights, id is many years ahead of implementation and h̶i̶g̶h̶ entropy is beaten by a couple of minutes and the coins in your pocket

https://t.co/eIXyw45Kga

[xirclev @xirclev_]

@AnnaRRose @worldnetwork iridiology works with a naked eye, reading an iris with hi-res cameras and fine tuned LLMs should deliver much more than a proof of humanity.

https://t.co/YJ33pW8xeL
https://t.co/OxgsUvwUO1

[Laprisamata @Laprisamata1]

Antiflesh series
Coming soon✨

[xirclev @xirclev_]

dónde esta tu dios ahora?

[zerohedge @zerohedge]

OPENAI TAPS GOOGLE IN NEW CLOUD DEAL FOR MORE COMPUTE: RTRS

[xirclev @xirclev_]

@LuisBetx9 @kinetic4i @ 20s 👌 🪇

[xirclev @xirclev_]

@isracts @abrahamrivv @bairorb @marcrypted @vicl9403 winning setup 👌

[Adrián Treviño @visionario_btc]

Tu amigo con 0.001 #BTC en su primera cita:

[Andi Marafioti @andimarafioti]

Today, we share the tech report for SmolVLM: Redefining small and efficient multimodal models.
🔥 Explaining how to design a tiny 256M VLM that uses less than 1GB of RAM and outperforms our 80B models from 18 months ago!

Here are the coolest insights from our experiments:
✨ Longer context = Big wins: Increasing the context length from 2K to 16K gave our tiny VLMs a 60% performance boost!
✨ Smaller is smarter with SigLIP: Surprise! Smaller LLMs didn't benefit from the usual large SigLIP (400M). Instead, we use the 80M base SigLIP that performs equally well at just 20% of the original size!
✨ Pixel shuffling magic: Aggressively pixel shuffling helped our compact VLMs "see" better, achieving the same performance with sequences 16x shorter!
✨ Learned positional tokens FTW: For compact models, learned positional tokens significantly outperform raw text tokens, enhancing efficiency and accuracy.
✨ System prompts and special tokens are key: Introducing system prompts and dedicated media intro/outro tokens significantly boosted our compact VLM’s performance—especially for video tasks.
✨ Less CoT, more efficiency: Turns out, too much Chain-of-Thought (CoT) data actually hurts performance in small models. They dumb
✨ Longer videos, better results: Increasing video length during training enhanced performance on both video and image tasks.

🌟 State-of-the-Art Performance, SmolVLM comes in three powerful yet compact sizes—256M, 500M, and 2.2B parameters—each setting new SOTA benchmarks for their hardware constraints in image and video understanding.
📱 Real-world Efficiency: We've created an app using SmolVLM on an iPhone 15 and got real-time inference directly from its camera!
🌐 Browser-based Inference? Yep! We get lightning-fast inference speeds of 40-80 tokens per second directly in a web browser. No tricks, just compact, efficient models!

If you’re into efficient multimodal models, you’ll love this one.