Diwas Bhattarai

I like to build stuffs | CS PhD | ML, Music, Photography

[Diwas Bhattarai @di_was]

Traditional Nepali music generated using AI
🇳🇵🇳🇵
Dataset consists of songs by one of my favorite music ensembles @KutumbaOfficial. The audio sample below was generated from 43rd epoch. #Nepal #nepali #nepalimusic

[Diwas Bhattarai @di_was]

Why worry about thinking levels when you can just

[Diwas Bhattarai @di_was]

@KarolCodes No

[Diwas Bhattarai @di_was]

Cerebras

[OpenAI @OpenAI]

5.4 sooner than you Think.

[Diwas Bhattarai @di_was]

ReClaw

[Diwas Bhattarai @di_was]

@jerryjliu0 2026: RLM

[Diwas Bhattarai @di_was]

@giffmana > successfully updated nvidia drivers

CC is agi

[Diwas Bhattarai @di_was]

This changes a lot of things 🤯

[AI at Meta @AIatMeta]

🔉 Introducing SAM Audio, the first unified model that isolates any sound from complex audio mixtures using text, visual, or span prompts.

We’re sharing SAM Audio with the community, along with a perception encoder model, benchmarks and research papers, to empower others to explore new forms of expression and build applications that were previously out of reach.

🔗 Learn more: https://t.co/FPnfv66UCP

[Diwas Bhattarai @di_was]

@ianyu93 @DSPyOSS I wish this blog had been around when I first started with DSPy!

DSPy is great (Signature + optimization = ❤️). Not a huge fan of dspy.context and global dspy.configure though.

[Diwas Bhattarai @di_was]

@nearcyan Claude Away 😂

[Diwas Bhattarai @di_was]

wen @cursor_ai wrapped?

[Diwas Bhattarai @di_was]

GIL

[Diwas Bhattarai @di_was]

@MaximeRivest OpenAI literally released an optimizer and its going to blow your mind 🤯🚀

A bread 🍞:

[Diwas Bhattarai @di_was]

This is great!

[Alexandr Wang @alexandr_wang]

1/ Today we’re proud to announce a partnership with @midjourney, to license their aesthetic technology for our future models and products, bringing beauty to billions.

[Sukjun (June) Hwang @sukjun_hwang]

Tokenization has been the final barrier to truly end-to-end language models.

We developed the H-Net: a hierarchical network that replaces tokenization with a dynamic chunking process directly inside the model, automatically discovering and operating over meaningful units of data

[Riley Goodside @goodside]

Does does it require reasoning to answer this question, given I require the first letter of each word in your response to spell in words the count of letters in its own SHA1 hash?

[Adam Roberts @ada_rob]

On the occasion of returning to Magenta's roots at @sonarplusd, we're dusting off the blog to share news and insights about what we're working on at @GoogleDeepMind on the Lyria Team.

https://t.co/6C11gOH5vX

Our latest post is about the Lyria RealTime API, providing access to our "live music model" that powers MusicFX DJ. Lyria RealTime enables unprecedented interactivity with a generative audio model for music, and has been stretching our imagination with the ways it can be integrated into physical and virtual instruments, spaces, and applications.

We've included a few open source interfaces for you to fork and build with, including the one @ToroyMoi used during the I/O preshow controlled by a MIDI Fighter. We are so excited to see what you will do with the technology yourself.

Enjoy!

[Diwas Bhattarai @di_was]

Haskell being Nepal is actually wild—both are beautiful, complex, and misunderstood, but only one gets used in production (spoiler: it’s neither).

Brought to you by—ChatGPT

[Diwas Bhattarai @di_was]

Haskell is clearly Nepal. Nobody really knows what either of them are.

[Nic Barker @nicbarkeragain]

Programming languages as countries. I’ll start.
C is obviously the USA.

[Jack Morris @jxmnop]

excited to finally share on arxiv what we've known for a while now:

All Embedding Models Learn The Same Thing

embeddings from different models are SO similar that we can map between them based on structure alone. without *any* paired data

feels like magic, but it's real:🧵

[dadabots @dadabots]

🥳 Announcing Stable Audio Open Small
- Fastest text2audio evarrr?!
- Introducing ARC: adversarial-relativistic-contrastive training for accelerating diffusion models
- 75ms on H100

Weights https://t.co/azeQiDRU7q
Blog https://t.co/guUFZ1muvq
Paper https://t.co/GoIJ6OfvLy