Gaurav Patwardhan

The problem

Moving to a new city is painful. Existing tools are outdated, scattered across 5 tabs, or behind paywalls. You need one place to understand neighborhoods — livability, rentals, amenities, commute times, weather — before signing a lease.

How it works

• —Aggregates 4+ live data sources: NoBroker rentals, Overpass API (OSM), OpenWeatherMap, custom livability scoring
• —Scores 100+ Bengaluru neighborhoods algorithmically: proximity to schools, hospitals, supermarkets, commute zones
• —Renders interactive maps with MapLibre GL — click any neighborhood to drill into rental listings and scores

The problem

Generic fitness apps don't know your programme. Manually tracking sets and weights is tedious, and none of it connects to coaching that actually references what you did last week. There's no tool that combines natural language logging with contextual AI feedback based on your specific history.

How it works

• —Text a keyword on Telegram (chest · back · shoulder · legs) and receive your pre-filled workout template instantly
• —Fill in sets, reps, weight, RPE and reply — a code node parses the log and writes every exercise as a row in Google Sheets
• —GPT-4o-mini fetches your last 4 sessions per exercise from Sheets, detects plateaus and PRs, and sends coaching feedback as Marcus — a direct, data-driven coach persona

The problem

Job hunting in India means manually checking 5+ job boards every day — Naukri, LinkedIn, Indeed, SmartRecruiters, Workday. That's 2–3 hours of repetitive, soul-destroying work before you've even applied to anything.

How it works

• —Scrapes 5+ job boards daily using Selenium (JS-heavy sites) and BeautifulSoup
• —Filters results by resume keywords, location preference (Bengaluru/Remote), and experience level
• —Ranks output: 60% keyword match + 40% GPT-3.5 semantic confidence score

The problem

Claude Code writes detailed session data to local JSON files — but nobody reads them. You don't know your cache hit rate, how much of your 200K context window you're actually using, or whether your session habits are efficient. If you can't measure it, you can't improve it.

How it works

• —Reads 3 local ~/.claude/ files: stats-cache.json, session-meta/*.json, and facets/*.json — no API calls, no auth, nothing leaves your machine
• —Computes cache hit rate (% of tokens served from cache) and capacity utilization (% of 200K session window used on average)
• —Surfaces session statistics: total sessions, min/max/average tokens, and a recent activity breakdown with per-session detail