Div Agarwal

Never knew this meaning

Any alternates to GitHub?

@hiarun02 Dude crazy apps. Lit UI!!!

Claude Code 4.7 is insane. i know literally NOTHING about coding. ZERO. and i just built 3 fully functioning web apps in 30 minutes. http://localhost:3000/ http://localhost:8000/ http://localhost:5000/ check it out.

I think at this point, fewer LOC should have more value. AI creates sloppy lines, you can feel proud with it as more 'work done' but it's a fallacy.

People with lot of green spots on your github contribution chart, do you prefer quality or quantity?

I see so many on device solutions coming up. Like on device LLM? The whole purpose of cloud was to have complex compute done remotely, cheaper and efficiently. What a UNO reverse on business models.

@beknabdik @claudeai @Bitwarden @vercel @getdelve Making a red team for this might be a good idea

@div__vi @claudeai @Bitwarden @vercel @getdelve voice + memory has its own attack surface worth staying in. ambient prompt injection through any mic is a new class. anyone in range can poison the memory without touching the app.

I am participating in the @claudeai Opus 4.7 Hackathon was building stuff related to Voice AI and memory. But given the crazy amount of security attacks @Bitwarden @vercel etc etc I might want to create something for this. Right @getdelve ?

@divinesturgeon 😂😂

@div__vi Pig Latin translator

Building cool projects with STS realtime audio model. Any requests?

@elonmusk Can you please also buy @LinkedIn and fix it?

@miniapeur Math never lies

@theo I agree with you a lot but no.

Markdown is the new Python

@alexinexxx sick

this is my new nvim setup btw

@yacineMTB checkout @dimensionalos

we are so very early in robotics. so so early

@kapilansh_twt quantum

after AI what's coming next ? seriously what do you think

@vivoplt Add JEPA to it. the future.

Research papers you must read for AI Engineer interviews: 1. Attention is all you need (Transformers) 2. LoRA (Low rank adaption) 3. PEFT ( Parameter Efficient Fine Tuning) 4. VIT (Vision Transformers) 5. VAE (Variational Auto Encoder) 6. GANs ( Generative Adversarial Networks) 7. BERT ( Bidirectional Encoder Representation from Transformers) 8. Diffusion Models (Stable Diffusion) 9. RAG (Retrieval Augment Generation) 10. GPT (Generative Pre-trained Transformers)

@Austen too good to be true?

Engineers! We want to: Fly you to Austin Give you an apartment Clean your room Do your laundry Feed you 3x/day Help you master building with AI And give you a $200k to $1m job. You pay nothing, ever, no matter what. Even if you don’t take the job.

We can’t. That’s the funny part. So basically the past that has been catalogued can be assumed as open to use. Only thing we can do is make things future proof. :)

Apart from LLMs, people need to start taking a look into security, especially cryptography and PQC. No one is going to boast about they made a quantum machine that broke an existing cryptographic protocol or they have the information stored waiting for an advanced machine that can break your encryption. We need to transition to post quantum cryptography faster.

The smartest lobster. @thehousesf