SUBHANSH

Scientific hypothesis generation is extremely slow. Researchers manually sift through thousands of papers, extract findings, cross-reference contradictions, and form hypotheses over months. Most drug resistance mechanisms (e.g., KRAS G12C) still remain unexplained because no system can rapidly mine contradictions at scale across live literature.

• Literature Ingestion: Queries PubMed databases in real-time, retrieving domain-specific oncology and pharmacology papers.
• Entity Extraction: Uses NER pipelines to identify genes, compounds, pathways, and mutation markers from raw paper text.
• Knowledge Graph: Constructs a dynamic semantic graph (59+ nodes, 40+ relationships) mapping all discovered biological interactions.
• Contradiction Mining Engine: Detects logical conflicts in the graph — e.g., Paper A says X activates Y; Paper B says X inhibits Y. These are the hypothesis seeds.
• Hypothesis Formulation: Synthesizes high-confidence testable hypotheses with validation plans (Western blot / qRT-PCR protocols).

Reduces hypothesis formulation from months to minutes. Demonstrated on KRAS G12C resistance pathways — generated 2 novel, testable hypotheses linking sotorasib resistance to RAC1/PAK1 reactivation and PI3Kγ-AKT bypass. Confidence scores generated by Groq inference provider.

Current AI assistants are stateless and reactive — they forget everything between sessions, can't learn your preferences, don't take autonomous actions, and have no sense of a persistent self. Building complex systems requires orchestrating many AI calls manually with no continuity.

• 15+ Brain Modules: Modular cognitive architecture — neural_memory.py, episodic_memory.py, dreaming.py, self_awareness.py, curiosity.py, global_workspace.py and more run as independent processes.
• Hebbian Memory: Memories are stored with synaptic strength weights that decay over time (72h TTL) — the more a memory is accessed, the stronger it becomes.
• Active Inference: Operates on free-energy minimization — continuously tracks surprise ratios and adjusts behavior to reduce prediction errors.
• Dreaming Cycles: During idle states, FRIDAY runs consolidation loops — pruning weak synaptic links and synthesizing procedural patterns from episodic data.
• Mythos Security: 7-agent autonomous security pipeline — Recon → Hunter → Secrets → DAST → Logic Flaw → Code Quality → Supply Chain scans.
• Smart Routing: Routes queries to the right model based on complexity — flash for reflexive tasks, opus for deep planning.

A fully stateful AI OS that persists knowledge, learns from interactions, audits codebases autonomously, and executes complex multi-step desktop workflows. The first iteration of a genuine cognitive co-pilot — not just a chatbot wrapper.

Security auditing is expensive, manual, and rarely done on personal or indie projects. Most developers ship code with exposed secrets, logical vulnerabilities, and supply chain risks because comprehensive auditing requires multiple paid tools and security expertise.

• Phase 1 — RECON: Maps all file entry points, identifies tech stack, and builds a dependency tree of the target codebase.
• Phase 2 — HUNTER: Scans for logic vulnerabilities, injection points, and unsafe operations using pattern-matching across all source files.
• Phase 3 — SECRETS: Detects hardcoded API keys, tokens, credentials, and environment variable leaks.
• Phase 4 — DAST: Runs dynamic analysis — attempts realistic attack simulations on exposed interfaces.
• Phase 5 — LOGIC FLAW: Audits authentication flows, authorization boundaries, and business logic for exploitable gaps.
• Phase 6 — CODE QUALITY: Flags insecure patterns, deprecated libraries, and unsafe defaults.
• Phase 7 — SUPPLY CHAIN: Checks all dependencies against known vulnerability databases (CVE scoring).

Produces a comprehensive security report with CVSS scores, risk ratings, and remediation guidance — fully automated. Zero vulnerabilities found on the portfolio itself after full Mythos audit. Runs in under 60 seconds on mid-size projects.