Yamini Kamalpuria

Full-Stack Developer

Devi Ahilya Vishwavidyalayafull_time, internship, freelance

Open to roles

JavaScriptPythonData Structures & AlgorithmsOOP

From Resumes to Results: A Proof-of-Work GTM Playbook for Wooble

Companies are drowning in applicants — volume has doubled since 2022, yet only ~3% reach an interview and recruiting teams now handle 93% more applications with 14% smaller teams. Nearly 70% have moved to skills-based hiring, but over half say their biggest obstacle is verifying skill claims. That gap is Wooble's opportunity: instead of trusting a resume, companies see the actual work. My job in this sprint: find the companies where this pain is sharpest, prove the fit, and start the conversation. Process I refused to list 25 famous names at random. First I defined the Ideal Customer Profile — who proof-of-work hiring genuinely helps — then scored every account 1–5 on four factors: hiring volume, skill-screening difficulty, employer-brand sensitivity, and live buying signal. I layered in June 2026 funding news so the list is a pipeline, not a directory. What didn't work: my first cold email opened with Wooble's features and felt like every other vendor pitch — I rewrote it to open with the prospect's specific pain (screening AI hires after a raise) and the product only appears as the answer. Results 25 scored accounts in three tiers, a Top 3 chosen as a deliberate portfolio (Sarvam AI = momentum, CRED = lighthouse reference, Pepper Content = quick win), a 3-part outreach sequence for Sarvam, and three objection-handling scripts. The framework is repeatable: any new account can be scored and slotted in minutes. Reflection Verify each decision-maker's exact name and title via LinkedIn Sales Navigator before outreach rather than targeting the role. A/B test two subject lines per account. Build a one-page proof-of-work explainer per vertical (AI, content, sales) so the pitch is pre-tailored. And set up a nurture track for the 80 accounts I'd not target this week — today's "not now" becomes "perfect timing" at their next funding round.

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Queue Cure

76% of India's clinics run on paper token slips. Patients wait 2–3 hours with zero visibility — they don't know if they're 3rd or 30th in line. Receptionists manage everything from memory. One distraction and the queue breaks. There's no screen, no estimate, no way to check without walking up to the desk. Process Started by breaking the problem into two roles: receptionist and patient. The receptionist needs speed — adding a patient should take under 10 seconds, no friction. The patient needs visibility — current token, how many ahead, estimated wait. The core technical challenge was keeping both screens in sync instantly without either side refreshing. I chose Socket.IO for real-time bidirectional sync over a simple polling approach — any state change on the server broadcasts to all connected clients immediately. Built the queue as a server-side state machine so all logic lives in one place and both clients always see the same truth. Results Both screens sync live with zero page refresh. A receptionist can add a patient and assign a token in under 10 seconds. Wait time updates dynamically as the queue moves — calculated from real data, not a fixed guess. Deployed on Vercel + Railway with the live app accessible from any phone on the waiting room screen. Reflection I'd add a QR code on the receptionist screen that patients scan to open the waiting room view directly — removes the need to tell every patient the URL. I'd also persist queue state to a database so a browser refresh or server restart doesn't wipe the queue mid-day, which matters in a real clinic setting.

5 media files · queue-cure-gamma.vercel.appView

This is Yamini’s work on Wooble.

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