Hey HN,
I'm a 20yo solo builder from India,
I got frustrated that every capable AI model
assumes you have a GPU, a credit card, or
reliable internet. None of those are true for
most of the world — including me.
So I started digging into the compression literature and ways through which i can solve this problem
What I found:
- DeepSeek distilled 671B reasoning into 1.5B
that runs on a laptop
- TRM (Samsung, 2025) beat DeepSeek R1 on
ARC-AGI with 7M parameters by iterating
instead of scaling
- RWKV runs in constant memory with no
quadratic attention cost
- GRPO lets you specialize a tiny model on a
narrow domain in hours on CPU
The techniques exist. What doesn't exist:
a systematic effort to apply all of them together,
specifically for low-resource languages and
low-end hardware, and give the results away free.
I'm building this. Calling it KIRO.
The goal is simple: take every major open source
frontier model, compress it into domain-specific
versions under 500MB, and deploy them offline
on the cheapest Android hardware available.
Starting with math/physics education
because that's the problem I know personally.
Expanding to healthcare triage, legal aid,
and agricultural advisory.
Currently running my first experiment on my i3
— R1-1.5B vs Qwen-7B on Hindi math problems.
Will post results when training finishes.
Two honest questions for HN:
1. Is anyone else working on this specific
intersection — compression + low-resource
languages + offline deployment?
2. What would make this genuinely useful vs
just technically interesting to you?
Everything will be open source.
I'm a 20yo solo builder from India,
I got frustrated that every capable AI model assumes you have a GPU, a credit card, or reliable internet. None of those are true for most of the world — including me.
So I started digging into the compression literature and ways through which i can solve this problem
What I found: - DeepSeek distilled 671B reasoning into 1.5B that runs on a laptop - TRM (Samsung, 2025) beat DeepSeek R1 on ARC-AGI with 7M parameters by iterating instead of scaling - RWKV runs in constant memory with no quadratic attention cost - GRPO lets you specialize a tiny model on a narrow domain in hours on CPU
The techniques exist. What doesn't exist: a systematic effort to apply all of them together, specifically for low-resource languages and low-end hardware, and give the results away free.
I'm building this. Calling it KIRO.
The goal is simple: take every major open source frontier model, compress it into domain-specific versions under 500MB, and deploy them offline on the cheapest Android hardware available.
Starting with math/physics education because that's the problem I know personally. Expanding to healthcare triage, legal aid, and agricultural advisory.
Currently running my first experiment on my i3 — R1-1.5B vs Qwen-7B on Hindi math problems. Will post results when training finishes.
Two honest questions for HN:
1. Is anyone else working on this specific intersection — compression + low-resource languages + offline deployment?
2. What would make this genuinely useful vs just technically interesting to you?
Everything will be open source.