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🤓 Latest Submissions
Querypex — AI Data Analyst with Full Transparency
Querypex is an AI-powered data analyst built for students, small business owners, and lean teams who have large datasets and no dedicated analyst to make sense of them. Instead of requiring SQL or BI expertise, Querypex automatically profiles a connected dataset, surfaces the most important insights, and lets users ask follow-up questions in plain English — with every answer grounded in a real, executed SQL query, not a guess. The app connects to data four ways: CSV/Excel upload, a live Supabase database, a generic API connector, and Bright Data for live real-world pricing. Its AI chat, powered by Claude, always shows the exact query behind every answer, along with a confidence indicator, so nothing is a black box. Querypex also runs real machine learning — linear regression, logistic regression, and PCA — computed server-side rather than approximated by an LLM, with each model honestly reporting its own accuracy and limitations. The demo dataset is real: 1.5 million genuine, anonymized grocery transactions from the public Instacart Market Basket dataset, independently verified before loading. Users can also upload their own CSV, and the Dashboard and AI Chat adapt to it dynamically. Additional features include a searchable data explorer, CSV/PDF/PowerPoint export, and a downloadable Jupyter notebook that reproduces the full analysis pipeline in Python. Querypex was built end-to-end using native.builder's agent workflow: a Product Architect scoped the project into a formal PRD, a Task Planner broke it into ordered build tasks, and a Builder agent implemented each one — including real infrastructure problem-solving, like resolving a Supabase quota limit and fixing repeated query timeouts with materialized views. The result is a genuine application, not a thin wrapper around a chatbot — with a real data layer, a scale strategy, and complete transparency at every step.
10 Aug 2026
TalentPulse
GTM and recruiting teams are flying blind on talent market shifts. TalentPulse solves this by combining Bright Data's LinkedIn Scraper with Claude AI to deliver enterprise hiring intelligence in seconds. BUILT SOLO IN 3 DAYS: - Scraped 16,611 live LinkedIn jobs across 9 AI role categories using 15 Bright Data snapshots (USA + India) - Extracted 63 skills using NLP across all job descriptions - Trained Random Forest salary predictor (R²=0.5139) - T-tests + ANOVA proving PyTorch = +$25K premium (p<0.001) - GTM company scoring — 6,716 companies ranked by buying intent - Claude Sonnet 4.6 agentic loop with 4 autonomous tool calls - Real-time delta detection — NVIDIA added 129 net new roles in one week, detected automatically - Live search — select any role + location, Bright Data fetches fresh jobs instantly KEY FINDINGS: - NVIDIA is #1 GTM target (score 107) — 163 open AI roles - Capital One is #2 (score 105) — finance competing with Big Tech for AI talent - LLMs/GenAI in 26.7% of all postings — fastest growing skill - San Francisco median salary: $200,000 - Market shifting from BUILD to DEPLOY & GOVERN Bright Data is the core infrastructure — without it, reliable access to live LinkedIn data at scale is simply not possible. Built on Bright Data. Powered by Claude Sonnet 4.6.
31 May 2026
