Atharva Arya

EIR at @join_ef / Resident @lossfunk / Open Source @tardis_sn

[Yashraj Shukla @whynesspower]

Stats which made YC give us an interview this time:
(on our 3 month old product)
>$200k cARR
>$20k revenue
closed two F500 global, four more in pipeline

The 10 min call went like bullet and they loved the idea. Too bad could not show enough US traction
Untill next time? 💪

[Atharva Arya @2001_atharva]

@biraj21_ youre done when the overlords arrive

[Atharva Arya @2001_atharva]

@niraj_munot 💸💸

[Atharva Arya @2001_atharva]

@HariAyapps @naveen_venk Congrats!!

[hari_haran @HariAyapps]

Our paper on Privacy Preserving Load Forecasting via Federated Learning just got published!!🥳🥳

Personalized AI for energy is finally leaving the centralized silo.

Your home’s power data doesn't have to live in a utility’s database to be useful.

We show how to train high-accuracy load models across thousands of smart meters without ever moving the raw usage data. Instead, it ships "noisy" model updates and keeps the personal habits local.

It's not just keeping data private, it’s making the model actually better by letting it learn your specific routine.

When a utility predicts power demand, it usually needs to see exactly when you turn on your dryer or AC.

That is a massive privacy risk. High-res meter data is basically a map of your life.

The conventional fix is Federated Learning (FL), where the model goes to the data. But standard FL has a "heterogeneity" problem, it struggles because every house uses power differently.

Our paper introduces DP-SA-FedPer: an architecture that solves the privacy-utility-efficiency trilemma.

Differential Privacy (DP): It adds mathematical "noise" to model updates so no one can reverse-engineer your habits from the weights.

Secure Aggregation (SA): The server only sees the total sum of updates, never an individual household’s contribution.

Lightweight Personalization: The model is split. A global "base" learns general grid trends, while a local "head" stays on your meter to learn your specific 2 AM laundry habit.

> 95.2% Accuracy (outperforming standard centralized models).

> 12% lower communication cost than baseline FL.

> 6.5% boost specifically from the personalization layer.

Privacy isn't a tax on performance anymore. By combining secure aggregation with local "heads," we can build a grid that is more efficient, more scalable, and respects the front door.

@naveen_venk @archgen_

[Atharva Arya @2001_atharva]

@harshactually Thanks for trying it out. We're working on making it way better soon!

[Atharva Arya @2001_atharva]

I vibe coded a product which gets public rental listings from Facebook and puts them on a map.

So you can ask questions like-
"Show me 2BHKs close to Embassy Golf Links Business Park, furnished and under 30k. "

And it will put those listings on a map, give you travel times, nearby places and so much more.

I have always wanted something like this, so built it.

Try it out at https://t.co/RGbXQE7fZZ

No paywall for now.

#rent #Bengaluru #VibeCoding #BuildInPublic #RealEstate #rental #flathunters

[Atharva Arya @2001_atharva]

@GaurangGujrati Glad you liked it!

[Atharva Arya @2001_atharva]

@VRuoam We're closed source at the moment. Not planning open source any soon.

[Atharva Arya @2001_atharva]

@nkcbuilds Yes, we're soon expanding to other cities too. Will announce soon!

[Atharva Arya @2001_atharva]

@7Psychon Thank you!

[Atharva Arya @2001_atharva]

@dhairya_dhiru99 Thank you!

[Atharva Arya @2001_atharva]

@hello_0029 Glad you liked it!

[Atharva Arya @2001_atharva]

@0xastro98 Thank you!

[Atharva Arya @2001_atharva]

@followmaymay Looking into it!

[Atharva Arya @2001_atharva]

@AnshulGains https://t.co/mlMmr4t6aL should work now

[Atharva Arya @2001_atharva]

@AnshulGains @ankit_pfc Apologies! Should now!

[Atharva Arya @2001_atharva]

@IceCreamChai Will soon!

[Atharva Arya @2001_atharva]

@gargsumit Thank you

[Atharva Arya @2001_atharva]

@jagadeeshaX Apologies. Should work now!

[Atharva Arya @2001_atharva]

@AnshulGains Apologies. I think it should be back now?