Abraham Mathews

🌉We reveal a previously hidden yet tight connection between Drifting models and score-based (diffusion) models! 👉 The mean-shift direction in Drifting is actually the score function itself (exact match with Gaussian kernel) 👉 Even with Laplace kernel (the one used in practice), the directions are almost identical in high dimensions 👉 Drifting can be positioned as a "non-parametric score-based model" Title: "A Unified View of Drifting and Score-Based Models" PDF: arxiv.org/abs/2603.07514

@abhijithneil @iScienceLuvr Whoa 🤩

Met @iScienceLuvr at Oss @ GTC!

If you want to be an innovator, you have to be comfortable looking stupid for a long time. You’re going to piss some people off and you’re going to get a lot of nos. That’s the only way to start having valuable breakthroughs.

260307 pick me🫶🏻 #가을 #김가을 #ガウル #GAEUL #IVE #아이브

Kiiikiii presented IVE with a cake and celebrated their Music Show win with them after the encore 🩵

I've been helping people to learn how to build audio plugins for the past 9 years. Recently, I watched AI do what would take me an entire day in 5 minutes. I'm a little late to vibe coding, and I'll tell you why - I LOVE coding. The thought process. The problem solving. The craft of it. But I've seen winds change before. And as an industry, we have to face that this is happening. Here's the thing though. I don't think developers are finished. Not even close! In fact, I actually think developers will become MORE important. The way I see it, this is like DAWs coming to bedroom producers. Did it kill professional engineers? No. It caused a creative explosion. But the role shifted. Producers needed mixing and mastering engineers more than ever to take their ideas to a professional level. That's what's happening with code. AI will let more people create. Developers become the ones who make it market ready. But for this to happen, developers will need to embrace the tools or risk getting left behind. Scary? A little. Exciting? Absolutely. Here was my first experiment. How are you feeling about these tools? Are you embracing them, or rejecting them? Let me know! youtu.be/qeCYi0pwi3I

The talks tonight at the Boston AI Music Meetup made me think about where this field is going. Here’s what I learned: Jatin Chowdhury is an expert in getting machine learning models to run in real-time audio systems. He laid out the very real challenges of taking models out of research environments and putting them into actual audio applications. There are many inference solutions out there such as ONNX, TFLite, and libtorch, but none of them really treat audio as a first-class citizen. Real-time audio has strict latency constraints, tiny buffers, and highly variable consumer hardware. You can't just import your model and hope for the best. That is what motivated Jatin to build RTNeural, an open source C++ library designed specifically for audio neural networks. It enables building production-level audio plugins with neural networks that actually run in real time. He shared several real plugins built using this technology. If I were working on neural nets inside audio plugins, this is where I would start. That said, he was clear about the remaining challenges. Scaling to larger models is still difficult. Consumer hardware varies widely. Communication between CPU and GPU adds overhead. Meanwhile, models are getting heavier and more capable. There is a real tension here. Musicians do not want latency to stand in the way of their creativity, but AI models keep pushing toward greater compute requirements. The research field has challenges to address here. Ethan Manilow, a researcher at Google DeepMind, zoomed out completely. His talk traced how music technology has shaped and reshaped our relationship with sound. He focused on how Edison’s phonograph transformed music from an ephemeral experience into something that could be replayed and transported. But it also made people uncomfortable. Early listeners were unsettled by hearing a human voice without a human present. Generative music may be producing a similar reaction today. When we hear the inflection of a generated vocalist, there can be that same uncanny feeling. History gives us perspective, but not a prescription for the future. Ethan’s broader point was that no technology is guaranteed to succeed. History is often told as a linear progression, but many technologies faded while others persisted. We are still in the process of determining what role AI will ultimately play in music. The most exciting part of AI and music today is that there is still so much to figure out. Even the best audio models are still lacking in some capabilities and their latency is far beyond what many musicians desire. If you want to take part in more discussions like this, join us at our next event in March. Details are on our meetup page.

Today I'll be at @CTmagazine 's Mini-Summit on Creativity & Vocation in Nashville -- not on the AI panel, just going to meet people. If you're around, HMU.

Shop floor + Indian flag = 🤌🇮🇳 Indian deeptech in action. 1. @SkyrootA 2. @vectortechnics 3. @sarlaaviation 4. @etherealmachine

The primary reason people don't achieve big goals is because they don't realize that the only source of truth is making mistakes. They soak up advice and theory, expecting it to be an exact match to their mind, experience, and situation just to fail once and give up completely.

Announcing Datatune: Open source Cursor for Data github.com/vitalops/datat… Datatune lets LLMs and Agents access your entire data without context length limits for more accurate data transformations.

i hate how social media makes us forget that life has stages. it’s normal to be broke, to have broke friends or partners and yes it’s even normal to be unemployed at times. these are phases we all go through. some people are lucky enough to find good jobs at a young age and afford a certain lifestyle. others take longer and that’s perfectly okay. we need to stop comparing ourselves and start accepting our journey. i just want all of us to be at peace with where we are in life while still working and striving for better.

He was $900M in debt but he believed in himself;

I promise it's a good blog. Come have a read! Exciting math and fun commentaries written by world-famous mathematicians like Ken Ono and Evan Chen and Lean gurus like Kenny Lau and Jujian Zhang. Proofs by AxiomProver, an engineering effort built fast and executed relentlessly.

this is the most adorable video you’ll see on the internet today 😍

Sales is only a bad thing when you refuse to learn it because you think it's bad. You are always selling your ideas, your skillset, your personality, your product, your boss's product. If you don't like sales, you are probably unconsciously manipulating people every single day.

Learn both sales *and* coding. Sales teaches you human psychology and communication, while coding teaches you hard logic and thinking precisely. Master both, and you become a killer.