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bakulbadwal/README.md

Hi, I'm Bakul

  • πŸŽ“ MBA @ UVA Darden '27 - applied AI builder
  • πŸ› οΈ Bio/longevity, blockchain, VC and PE, AI-native services, e-learning, etc.
  • πŸ“« Badwalb27@darden.virginia.edu

Projects

Bio/Longevity

LiveForever (live, private) β€” personal health control tower unifying wearables (Oura Ring, Apple Health, Whoop), 10+ years of clinical bloodwork & daily self-logged training/habits, and personal genomics (DNA via 23andMe) into one owned data layer.

OpenAI Build Week 2026: Public version - Live β†’ Β· Repo

Benchmarks PhenoAge biological-age, N-of-1 correlation analysis quantifying what each input does to HRV, recovery, resilience scores + 28-day analysis on interventions.

LiveForever β€” personal evidence lab

PhaseSignal (Live β†’) β€” scores live ClinicalTrials.gov data against a base rate, reweighted across four factors.

AI Γ— Blockchain

TraceHound (Live β†’) β€” agentic crypto hack tracer: live hop-by-hop tracing and narration from compromised wallet via Etherscan API, cross-references OFAC sanctioned addresses from U.S. Treasury's SDN watchlist. Built from experience with federal law enforcement on crypto crime.

Also runs as a read-only MCP server β€” any agent can run a trace and get back cited evidence.

TraceHound

Tranche AI (Live β†’) β€” condition-gated capital release for VC deals, designed for AI-agent milestone review; smart contract + dispute flow live on Base Sepolia (EAS attestations). Solidity.

Tranche AI β€” capital that releases on proof, not trust

AI Γ— VC & PE

Deal Docket (Live β†’) β€” deal-screening dashboard built around an AI-enabled service-roll-up thesis; adjustable five-box scoring framework.

AI Stack (Live β†’) β€” interactive map of the AI industry from silicon to application layer, value accrual, token cost calculator.

AI Frontier Dispatch β€” personalizable AI + markets briefing: track frontier builders (Grok API for live X read), GitHub/Hugging Face, deals/careers signals. One-line plugin install for any harness.

Orbit (private) β€” relationship intelligence engine scores tie strength from live email and LinkedIn records, auto-schedules follow-ups. Obsidian graph + dashboard.

E-Learning

IB Technicals Fluency Trainer β€” merger-model cockpit, purchase-price allocator, DCF sensitivity heatmap - live playgrounds.

Consulting Case Prep Trainer β€” profit-diagnosis game, market-sizing builder, exhibit reader. Includes a designed eval harness: golden set, two-axis LLM judge, calibration plan.

The Operator's P&L Room β€” eight-quarter run-the-business simulator under leverage & covenants, 13-week cash-crisis room for distress-operator decision making.

Product Case Studies

Short product write-ups β€” problem Β· users Β· product decisions & tradeoffs Β· how I'd measure success Β· roadmap:

Project The product-thinking angle
TraceHound β†’ Agentic AI for underserved users; rigor about a tool's limits
AI Frontier Dispatch β†’ Generalizing a personal tool for others' setups; borrow vs. build
Tranche AI β†’ Scoping a frontier problem in VC
OpenAI Build Week '26: LiveForever β†’ Separating deterministic evidence from model interpretation
The AI Stack β†’ Mapping where value accrues across AI
Recruiting Trainer Suite β†’ Framing three tools as one product line; retention-first design
PhaseSignal β†’ Transparency vs. black-box incumbents
Deal Docket β†’ Making assumptions visible; build-vs-backend judgment

Evaluation discipline

LLM products shipped with tests: golden set Β· deterministic policy gate Β· LLM-judge rubric Β· calibration plan (TPR/TNR, bias correction) β€” each states what's validated vs. designed.

  • TraceHound evals β€” executable deterministic gate (npm run eval) over a golden set: catches invented hops, false and unsafe claims, missing evidence citations. The MCP server returns cited evidence records with stable IDs to a consuming agent.
  • Consulting Trainer evals β€” 12-scenario golden set with deliberate tempting-wrong-answers, two-axis LLM judge with anti-halo instructions, calibration plan.

How these are built

Most are self-contained apps β€” vanilla HTML/CSS/JS, no framework, no build step, no dependencies β€” designed, built, and shipped solo end-to-end. Some split data from rendering (data.json + app.js) for content updates. Some use a real Python data pipeline (data/build_dataset.py) that pulls and scores live data. TraceHound is a Next.js app with server-side API keys.

HTML5 CSS3 JavaScript Python Solidity Next.js

Connect

LinkedIn Β· Email

Pinned Loading

  1. aistack aistack Public

    Interactive field atlas of the AI industry stack β€” seven layers, ~20 company profiles, money-flow traces, and a token-economics calculator.

    CSS

  2. ibtrainer ibtrainer Public

    Interactive M&A and valuation fluency trainer with live model playgrounds, generative drills, and spaced repetition. No LBO content by design.

    HTML

  3. phasesignal phasesignal Public

    Public-data clinical trial success screener β€” live ClinicalTrials.gov data, cited historical base rates, and an explainable five-factor scoring engine with live reweighting.

    JavaScript

  4. tranche-ai tranche-ai Public

    Agent-attested, condition-gated capital release for VC deals β€” on-chain via EAS

    TypeScript

  5. tracehound tracehound Public

    Agentic on-chain hack tracer with executable evals, exposed as a read-only MCP server β€” live transaction tracing, real OFAC sanctions-list matching, evidence-cited LLM narration, and demand-letter …

    TypeScript 1

  6. dealdocket dealdocket Public

    A deal-sourcing dashboard for an AI-enabled service-roll-up thesis β€” five-box scoring framework with live-reweighting and a 30-deal illustrative pipeline.

    JavaScript