Founders Toolkit

• Feynman: Democratizing Elite AI Education

  Rebuilds university-level AI coursework as a Learning Ladder, the same concept met at five heights from a kindergarten spark to the real thing, so every student finds a rung they can stand on. Free for first-generation and low-income students, rolling out first in Jurupa Valley and Montclair.

  AI Education EdTech Digital Equity Learning Ladder
  Launching Announcement ›

• Truth Computing Health: Clinical AI for Vision Care

  A design partnership with Affordable Family Vision Care (Dr. Quintana): human-gated AI for an optometry practice, spanning intake, imaging workflows, and clinical documentation, each reviewed by a clinician before it enters the record.

  Healthcare Computer Vision Optometry Documentation
  Design Partner Website ›

• Steering LLM Behavior Beyond Supervised Limits

  Shapes language model behavior where supervised fine tuning stalls, using reinforcement learning to align outputs with human intent. Combines reward modeling, policy optimization, and preference alignment into a repeatable recipe for more reliable, controllable AI systems.

  Reinforcement Learning Language Models Fine Tuning Jupyter
  Active GitHub ›

• Elo-Driven Curriculum RL for Reasoning Models

  Trains reasoning models faster by serving problems matched to their live skill level instead of random difficulty. Extends RLOO with an Elo rating system and a Gaussian scheduler that ramps difficulty as the model improves, with full Elo metadata logging for reproducibility. Outperforms SFT and vanilla RLOO baselines on pass@k (Stanford CS research with Donnie Raymond).

  Reinforcement Learning Curriculum Learning RLOO Elo Rating LLM Reasoning
  Research GitHub ›

• Swish: Production Backend at Consumer Scale

  The server-side backbone powering a live consumer application, engineered for reliability from day one. Spans API design, data modeling, and service architecture in production Python.

  Python Backend API Design
  Active GitHub ›

• Optimizing Decisions Under Uncertainty with MDPs & POMDPs

  Tackles sequential decision making where outcomes are uncertain and information is incomplete, the core challenge of real-world autonomy and operations. Benchmarks MDPs, POMDPs, and policy search head-to-head on a structured problem to isolate which framework delivers the best solution quality (Stanford CS238 capstone).

  MDPs POMDPs Policy Search Python
  Research GitHub ›

• Benchmarking Medical AI for Strabismus Detection

  Establishes a reproducible machine learning baseline for detecting strabismus from clinical data, giving ophthalmology research a measurable benchmark to build on. This is research code only; it is not a medical device, has not been cleared or approved by the FDA or any regulator, and is not intended for clinical use, diagnosis, or treatment.

  Medical AI Classification Computer Vision Jupyter
  Published GitHub ›

• Data-Driven Player Valuation: Selecting a World XI

  Turns raw player performance data across global leagues into principled valuation, selecting an optimal World XI. An end-to-end pipeline spanning preprocessing, model training, and predictive analytics, with logistic regression driving transparent, defensible selections.

  Predictive Analytics Logistic Regression Sports Analytics Python
  Published GitHub ›

• Probing Quantum Advantage on NP-Hard Optimization

  Applies the Quantum Approximate Optimization Algorithm (QAOA) to the NP-hard Traveling Salesman Problem, the canonical benchmark for real-world routing and logistics. Benchmarks quantum approaches against classical solvers to pinpoint exactly where quantum advantage emerges at scale.

  Quantum Computing QAOA Combinatorial Optimization Python
  Published GitHub ›

• Demystifyd: Launching a Global Career Platform

  Connects students and professionals across borders as they build their careers, taken from concept to live product. Drove discovery, product strategy, and go-to-market for the web platform.

  Product Management MVP Web Platform Go to Market
  Live Visit ›

• EZRecruit: Automating College Athletic Recruiting

  Gives university varsity coaches a single proactive hub to consolidate recruit data, automate updates, and track every interaction. Built end-to-end from discovery through product strategy, design, and financial modeling (Stanford CEE250).

  Product Strategy MVP Web App Financial Modeling
  Product Product Review ›

• Forecasting Fight Outcomes with Hybrid ML Models

  Predicts MMA fight results from fighting style, physical attributes, and experience. Pairs logistic regression with a dropout-regularized feed-forward neural network to balance interpretability against predictive power (Stanford CS221).

  Machine Learning Logistic Regression Neural Networks Python
  Research GitHub ›

• The Complete Pre-Truth Computing Research Record

  The full archive of undergraduate projects and academic papers spanning AI, optimization, applied mathematics, and systems. The complete body of work that laid the foundation for Truth Computing.

  Research AI Optimization Applied Mathematics
  Archive GitHub ›

Project and course references describe academic and personal work by Truth Computing’s founders. References to Stanford University (including course numbers such as CS238) describe coursework and research and do not imply that Truth Computing is affiliated with, sponsored by, or endorsed by Stanford University. Product, project, and technology names are used for identification only and remain the trademarks of their respective owners; their use does not imply affiliation or endorsement. Linked repositories are hosted on individual founders’ accounts and may be governed by their own license terms.