Satvik Naren

MLE @ Apple  | Entrepreneur | AIRFORCE KID | GATECH | NITW | tweets are of ML opinion

[Satvik Naren @satvikDadie]

https://t.co/XhmsyRLZ1l

Eigenvalues and eigenvectors play particularly in areas like dimensionality reduction, data transformation, and even optimization

https://t.co/SZHf9TPWMF

[Satvik Naren @satvikDadie]

@noumi_priya @shreemastrology And we both are in 10% ?

[Satvik Naren @satvikDadie]

@noumi_priya Don’t watch those debates . Or don’t be a part of it . Keep smiling 🙂

[Satvik Naren @satvikDadie]

@noumi_priya Was in coorg 😶

Could this be linked to the location? I assumed my subconscious was venerating the dream.

[Satvik Naren @satvikDadie]

@noumi_priya I’m being Satvik , can’t either

[Deedy @deedydas]

I've spend 10+ hrs perfecting how to get the best out of Opus 5.5 for video generation workflow:

- Use it with Claude Code, not the app.
- Use OpenRouter API to access all the other models (image gen, video gen, audio gen) with one key
- Use Gemini 3.8 TTS and make it create a skill from the API to put emotion into the voice.
- Use Manim, Hyperframes, Motion Canvas for motion graphics for HTML web grounded video
- Use GPT 2.5 Image Sunburst for keyframes and Veo 3.1 / Seedance 2.5 for generation. Seedance does better with motion shots. Ensure you have reference images for consistency and generate an animatic before full video.
- Add a script writing planning skill. Use OpenTimelineIO to assemble.
- Explicitly tell it to avoid Claudisms like short punchy sentences and a lot of numbers. “Narrate like a university professor.”
- Allow it to use yt-dlp with search to inject clips from real videos (creative commons) in composition
- Use ElevenLabs for music gen in the background
- Add a caption skill which uses any decent ASR model to get word-level timed subtitles on the final video
- Add a critic skill which takes screenshots and uses transcription to validation the audio and video quality

In the prompt, specify a) what you want b) aspect ratio c) length d) style preferences. ffmpeg handles most of the actual video manipulation tasks.

Here's an example of a 4 minute Neocloud business explainer:

[Satvik Naren @satvikDadie]

@Dir_jashwanth Fake news

[Deedy @deedydas]

As cost of coding has plummeted, long builds (CI) has become a bottleneck for many software cos. Three of the biggest startups solving this:

1. Blacksmith (@useblacksmith)
2. Namespace (@namespacelabs)
3. Depot (@depotdev)

[Adi @devsvn2]

I cleared 3 out of 4 remote jobs while I was interviewing. If I had to start my system design learning from scratch again this is where I would start.

1. System design primer

https://t.co/VYVmtd6T9Z

2. System design Interview

https://t.co/TGwvOavJm7

3. System design academy

https://t.co/iuhLV0yZnP

4. System Design 101

https://t.co/aYbaZyJUYL

5. Gkcs youtube

https://t.co/I4gKLqomv1

6. How discord stores mountain of text messages

https://t.co/9UhfEv1zN9

7. Handling double payments in a distributed system (v important)

https://t.co/5tOu8KXW1P

8. Real time messaging

https://t.co/bdGyVoKGGc

9. Hello interview (watch while you have your lunch)

https://t.co/eikb6Gmh1B

[Deedy @deedydas]

The economics of a Neolab.

A neolab is loosely defined as a startup of AI researchers who raises a lot of money pre-production to be able to finance GPU compute to take on a large AI problem.

To buy 1000 GB300s or ~14 NVL72 racks will set you back $125-150M for 3yrs with 15-30% upfront. That’s about ~2-2.5MW.

Thats about enough to do 10^25 flops a quarter and get to a GPT-4 level model which is 1-2 OOMs off frontier for pretraining.

If you post-train on a great open source model, you have a better chance of getting to frontier. The risks are a) you need to spend millions on RL environments too and b) being lapped by another model release while being tied to a base model.

For this to payback, you need to give your customers a better and ideally cheaper inference service than a base model and serve them for long enough to recoup your large investment. Even at 50% margin on inference, to recoup $10M in training means serving ~10T tokens (!) if you price like Fable / Astra given a standard cache read / input / output split ($2/M blended). And you have to justify being better than a release like Opus 5.5 which is even cheaper. Often, you end up charging your customers a huge premium in terms of platform fees and compute fees on top of pure inference.

Meanwhile, every hour you’re not utilizing your GPUs you are burning money so you typically resell this compute back to a broker or run inference for open models / resell spot instances. At below a ~60% utilization on spot, you will still lose money.

Add to that insane cost of talent.

So what can you do with the compute?
- Not play the model game at all.
- Play an entirely different model game (Jev, World Labs) that if big labs played, would either a) cannibalize their business or b) be incrementally not significant revenue c) would cause too much distraction from the main main thing
- Acquire a proprietary data set (Peridodic Labs) in enough volume in a domain of usefulness to eclipse frontier quality. Often happens in robotics, biology, chemistry.

If you do overcome the challenge of building a model that is useful and well priced beyond big labs models, given the huge price of compute, you still need to play in an area where the revenue / compute ratio is signficant and market demand is large enough to payback your compute spend.

It is a difficult game.

[Pramod Goyal @goyal__pramod]

Took the time and wrote down everything I have been learning about GPUs and CUDA. Consider checking it out! https://t.co/mwhzTH8Ars

[Matt Dancho (Business Science) @mdancho84]

FULL GUIDE: HOW TO SCALE YOUR MODEL: https://t.co/OLYJg1RL2W

PART 12: HOW TO THINK ABOUT GPUS: https://t.co/sTRQGbAhAm

I have one more thing before you go.

If you want to become a generative AI data scientist in 2026 ($200,000 career), then I'd like to help:

[Satvik Naren @satvikDadie]

@KTRBRS @NameisNani @odela_srikanth Gone matash . Goram undi anna movie

[Paradis @ParadisLabs]

Recommended reading for the weekend:

1. @alexandr_wang — Why I'm Building Muse
2. @gregisenberg — AI-native services: a $100B opportunity
3. @FranklnTempletn — Macro Views: Growth Holds, Pressure Builds
4. @Google — Behind Project Suncatcher, our moonshot to put AI in space
5. @EpochAIResearch — The plunging price of thought
6. @AnthropicAI — Measurements for
understanding the pace of AI development inside frontier labs

Hope everyone has a great weekend!

[Satvik Naren @satvikDadie]

@noumi_priya Friend had extra premier tickets , he took me in . I thought it’s nani wont be that bad .

[Satvik Naren @satvikDadie]

@rameshlaus @NameisNani Movie is quite bad

[Satvik Naren @satvikDadie]

@noumi_priya Went to premier pre release show ! Got VIP ticket

[Satvik Naren @satvikDadie]

@kairamvaashi Worst movie I ever watched

[Yuri Kushch @YuriKushch]

@neuralkian @Junda_Chen_ Course: https://t.co/CRTl8wWbAv