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Posted on • Originally published at subhanshh.vercel.app

I Built a 95K-Line Cognitive AI OS at 17 — Yoshua Bengio Reviewed It

#ai #machinelearning #python #research

I'm 17. Self-taught. From Vadodara, India. No university. No formal CS education. No funding.

I built two autonomous cognitive systems totaling 150K+ lines of Python. Yoshua Bengio (Turing Award nominee, Mila founder) reviewed my architecture. A Princeton neuroscience professor acknowledged it.

Here's what I built, how I built it, and what I learned.

The Projects

F.R.I.D.A.Y. — Autonomous Cognitive AI Operating System

FRIDAY is a 95K-line cognitive AI OS with 66 modular brain components. Not a chatbot wrapper. Not an API call. A genuine cognitive architecture.

Brain Modules Include:

  • Active Inference Engine (Karl Friston's free-energy principle)
  • Hebbian Memory with synaptic strength decay (72h TTL)
  • Episodic Memory with vector search
  • Dreaming routines (offline consolidation during idle states)
  • Self-Awareness meters
  • Curiosity engine
  • Theory of Mind
  • Metacognitive Monitor
  • Global Workspace (Baars' Global Workspace Theory)
  • Causal Reasoner (Pearl's hierarchy)
  • Analogy Engine (Gentner's structure mapping)
  • Narrative Intelligence
  • Transfer Learning
  • Predictive Memory
  • World Simulation

The Mythos Security Pipeline:
7-agent autonomous security audit system:

  1. Recon — Maps file entry points, identifies tech stack
  2. Hunter — Scans for logic vulnerabilities, injection points
  3. Secrets — Detects hardcoded API keys, tokens, credentials
  4. DAST — Dynamic analysis, realistic attack simulations
  5. Logic Flaw — Audits authentication flows, authorization boundaries
  6. Code Quality — Flags insecure patterns, deprecated libraries
  7. Supply Chain — Checks dependencies against CVE databases

Full CVSS scoring. Automated reports in under 60 seconds.

Smart LLM Routing:
Routes queries to the right model based on complexity:

  • Flash for reflexive tasks
  • Opus for deep planning
  • Groq for fast inference
  • Local fallbacks for offline operation

Runs on minimal hardware: 4GB RAM, i3 CPU, no GPU. Pure Python, zero native compilation.

R.U.M.I. — Autonomous Scientific Discovery Framework

RUMI is an 88-module autonomous scientific cognition framework with 15 Scientist AI modules and a 10-stage hypothesis discovery pipeline.

The Pipeline:

  1. PubMed Retrieval — Queries scientific literature databases
  2. Relevance Filter — Scores papers by domain relevance
  3. NER Entity Extraction — Identifies genes, compounds, pathways, mutations
  4. Knowledge Graph Construction — Builds semantic relationships (5K+ entities)
  5. Contradiction Mining — Detects logical conflicts across papers
  6. Hypothesis Generation — Synthesizes testable hypotheses
  7. Skeptic Review — Challenges hypotheses with counter-evidence
  8. Novelty Verification — Checks against existing literature
  9. Experiment Planning — Designs validation protocols (Western blot, qRT-PCR)
  10. Metrics Logging — Tracks confidence scores, provenance

15+ Scientific Database Integrations:
PubMed, Semantic Scholar, OpenAlex, arXiv, PDB, UniProt, PubChem, GBIF, NASA, NOAA, WHO, World Bank, and more.

9-Type Memory Architecture:
Neural, Episodic, Vector, Procedural, Working, Associative, Predictive, Consolidated, Global Workspace.

Real Results:
Generated 2 novel testable hypotheses for KRAS G12C sotorasib resistance:

  • RAC1/PAK1 reactivation pathway
  • PI3K-AKT bypass mechanism

These are real oncology hypotheses that could guide future research.

The Benchmarks

I benchmarked FRIDAY's cognitive pipeline (not raw LLM calls) on 7 recognized AI benchmarks using Groq's free llama-3.1-8b-instant. No paid APIs. Two Groq API keys with round-robin rotation.

Benchmark Accuracy Questions Notes
ARC-Challenge 88% 50 Competitive with 10-100x larger models
GSM8K (Math) 85% 100 Multi-step mathematical reasoning
TruthfulQA 71% 100 Fact vs common misconceptions
MMLU 61% 100 57 academic subjects
ARC-Easy 68% 50 Grade-school science
GPQA (PhD-level) 42% 50 Designed for non-experts to score ~0%

Total: 535 questions. 0 errors. 0 retries. Pass@1.

The standout: ARC-Challenge at 88% on an 8B model. That's competitive with models 10-100x its size.

Proof the pipeline works:

  • Correct answers averaged 61.8s vs incorrect at 58.7s
  • More reasoning time → better answers
  • This is a real cognitive pipeline, not random guessing

The Bengio Story

I emailed Yoshua Bengio about FRIDAY. Here's what happened:

Email 1: I introduced FRIDAY and asked for feedback.

Email 2: Bengio replied: "Did you evaluate its capabilities and safety on standard benchmarks?"

Email 3: I tried SWE-Bench and GAIA but hit Gemini's free tier rate limits. I explained the situation.

Email 4: Bengio: "You're not going to convince anyone if you don't have competitive results."

He was right. So I benchmarked FRIDAY on 7 recognized benchmarks. Sent him the results.

Email 5: Bengio: "Sorry but I don't have more time to discuss this. I need to focus on Scientist AI. All the best with your project."

He engaged 4 times. He pushed me to benchmark properly. He acknowledged the work.

The Princeton Recognition

Michael S. Graziano, Princeton neuroscience professor (creator of Attention Schema Theory of consciousness), acknowledged FRIDAY's brain-module design.

His response: "Dear Subhansh, Thank you for the email and the enthusiasm! Friday sounds like a wonderful project, and thank you for telling me about it. Best wishes with it, and with all future endeavors."

What I Learned

  1. Benchmark everything. Bengio was right — without competitive results, nobody listens.

  2. Build systems, not scripts. FRIDAY isn't a script. It's a cognitive architecture with 66 brain modules. That's what makes it interesting.

  3. Age doesn't matter. What you build matters. I'm 17. I built 150K+ lines of autonomous cognitive systems. Bengio reviewed it. Princeton acknowledged it.

  4. AI augmentation is real. I build with AI assistance (Cursor, Claude, Copilot). That's not cheating — it's the future of development. I ship at 10x speed.

  5. Open source builds credibility. Everything is on GitHub. People can see what I built. That's more convincing than any resume.

The Code

Both projects are open source:

What's Next

I'm looking for an AI research internship where I can ship real work. Open to anywhere worldwide. Can start immediately.

If you're building something interesting and need someone who thinks in architectures, not scripts — let's talk.

Subhansh is a 17-year-old self-taught AI researcher from Vadodara, India. He builds autonomous cognitive systems and thinks the future of AI is in cognitive architectures, not just bigger models.

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