u/Different-Turnip3864

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Hey everyone, just wanted to share a massive update with you all!

We just ran a successful trial of our Uracil AI engine.

We actually got it to stretch to 17 Billion parameters running on just a basic 4GB RAM Intel i5 machine with ZERO GPUs. Instead of standard transformers, it ingests raw bytes natively and grows in real-time.

This basically proves our math works on the lowest-end hardware imaginable. If we can do this on a 4GB PC, imagine what we can do with real compute like an H200.

We're currently looking for incubators, hardware partners, and open-source contributors who want to help us scale this up and build a truly sovereign AI ecosystem for India.

Hey everyone, just wanted to share a massive update with you all!

We just ran a successful trial of our Uracil AI engine.

We actually got it to stretch to 17 Billion parameters running on just a basic 4GB RAM Intel i5 machine with ZERO GPUs. Instead of standard transformers, it ingests raw bytes natively and grows in real-time.

This basically proves our math works on the lowest-end hardware imaginable. If we can do this on a 4GB PC, imagine what we can do with real compute like an H200.

We're currently looking for incubators, hardware partners, and open-source contributors who want to help us scale this up and build a truly sovereign AI ecosystem for India. 🇮🇳

Visit us at Trijna Labs .tech

You can check out the trial run here: Search for Trijna Labs on YouTube and watch the latest video on the channel.

(P.S. If you want to see the raw, unedited training footage, I dropped the link in the YouTube video comment section.)

We tested exactly that at Trijna Labs.

We've published a short video showing part of the training process. If you're interested, watch the video and check the YouTube comment link for the unedited raw training footage.

We're looking for honest technical feedback, criticism, and questions from people working on AI systems, model architectures, training infrastructure, or efficient inference.

We're also open to collaborating with researchers, engineers, builders, and anyone interested in helping push the project forward.

Hey everyone,

We wanted to share something we've been pouring our lives into at Trijna Labs.

There's a lot of discussion around "Indian AI" right now, but we noticed that many solutions rely heavily on external AI APIs. We wanted to explore a different path: building the core architecture itself from the ground up—something that can run entirely on sovereign infrastructure without depending on foreign AI services.

Instead of standard autoregressive transformers, we've been experimenting with custom continuous-learning neural topologies that we call the ARS and OSM engines. Our goal is to dramatically reduce compute requirements through topological entropy routing while maintaining strong reasoning performance.

Recently, we reached a milestone that we're genuinely excited about.

We evaluated our architectures using the official EleutherAI lm_eval harness and the LiveBench framework.

Some of the early results:

LiveBench (ARS Engine)
Achieved an 87.5 overall average, including a 93.9 score in abstract reasoning. On several reasoning and language categories, the architecture surpassed the baseline performance of GPT-4o and Claude 3.5 Sonnet.

GSM8K (OSM Engine)
Achieved 85.06% Exact Match accuracy on 5-shot mathematical reasoning tasks.

We know there's still an enormous amount of work ahead, but seeing a homegrown architecture produce results like these on local hardware is a meaningful moment for our team.

For anyone interested in the technical details, we've published our methodology, dataset hashes, benchmark configurations, and reproducibility commands here:

https://trijnalabs.tech/news

We're still a small team tackling a very ambitious problem, and we'd genuinely appreciate feedback from the community—whether that's criticism, architectural suggestions, questions, or challenges to our assumptions.

Thanks for reading.

Hey everyone,

I wanted to share something my team and I have been pouring our lives into at Trijna Labs.

There's a lot of talk lately about "Indian AI," but we noticed that most solutions are just wrappers around OpenAI or Anthropic APIs. We wanted to see if we could actually build the core architecture from the ground up—something that could run entirely locally on sovereign hardware without bleeding data to foreign servers.

Instead of standard autoregressive transformers, we’ve been experimenting with custom continuous-learning neural topologies (we call them the ARS and OSM engines). The goal is to drastically reduce GPU compute costs through topological entropy routing.

We finally got our engines stable enough to run through the official EleutherAI lm_eval harness for GSM8K and the LiveBench framework.

Honestly, we were nervous, but the early numbers are really encouraging:

• LiveBench (ARS Engine): Achieved an 87.5 overall average, hitting an incredible 93.9 in abstract reasoning. For context on pure logic and language tasks, this actually pushes it past the baseline of GPT-4o and Claude 3.5 Sonnet.
• GSM8k (OSM Engine): Peaked at an 85.06% Exact Match on 5-shot math word problems.

We know we still have a massive mountain to climb to scale this globally, but seeing a homegrown architecture hit these numbers on local hardware feels like a huge win for us.

We uploaded our full methodology, exact dataset hashes, and reproducibility commands to our dev log here if any of you want to dig into the math or rip it apart: https://trijnalabs.tech/news

We are a small team trying to do something insanely difficult, so we’d honestly love any brutal feedback, architectural advice, or questions you guys have.

Thanks for reading!

Hey everyone,

We're Trijna Labs — an AI research lab out of India building novel neural architectures from scratch (not fine-tunes, not wrappers)

We just published our baseline benchmark results comparing our proprietary topologies (ARS and OSM) against state-of-the-art closed-weight models on three standard evaluations:

https://preview.redd.it/jfa9nhohgr8h1.png?width=1602&format=png&auto=webp&s=e51903d04e406aecc8e96636d9e08297adee1411

• LiveBench — un-gameable, dynamic general reasoning (prevents memorization)

• GSM8K — multi-step mathematical reasoning (5-shot exact match, greedy decoding)

• HumanEval — code generation (0-shot pass@1)

Key details:

— All evaluations run using EleutherAI's lm_eval harness (v0.4.12)

— Greedy decoding, no sampling tricks

— Full reproducibility methodology, dataset hashes, and raw JSON logs are published

— ARS topology set a new high-water mark on LiveBench

— OSM topology achieved matrix stabilization

We're not claiming we beat GPT-5 or anything sensational. We're showing transparent, reproducible results from architectures we built ground-up. The raw evaluation logs are on our site.

Full results and methodology: https://trijnalabs.tech/news

We'd love to hear your thoughts and feedback. If you're interested in AI systems, neural architectures, or research-driven engineering, feel free to reach out—we're always open to meaningful conversations.

More on what we're building: https://trijnalabs.tech