Hey DDS Fam! 👋 I’m officially checking in for the DDS AI Application Challenge, and I’m taking a big swing with FirstHire 2.0. The Pivot: We all know the recruitment market is flooded. But in 2026, the problem isn't finding people—it’s filtering the "AI noise." Recruiters are drowning in AI-inflated resumes and generic bot-outreach. I’m spending the next 8 days building FirstHire.ai to move past the "GPT-wrapper" stage and into a high-signal, agentic workflow. What I’m building during this challenge: 1. 🎯 The Intent Engine: Moving from "Cold Search" to "Warm Timing." I'm building logic to identify candidates hitting tenure milestones and professional churn triggers. 2. ✅ The Authenticity Layer: This is the big one. An agent that cross-references LinkedIn claims against live "Proof-of-Work" (GitHub, portfolios, technical commits). If the agent can't verify the skill, the candidate doesn't make the list. The Tech Stack: • Frontend: React (Moving from my "TalentGPT" legacy code) • Backend: Python / FastAPI • Automation: Make.com / Zapier for agent orchestration • AI: GPT-4o / Claude 3.5 for contextual verification My 8-Day Roadmap: • Day 1: Strategy & Rebrand (Done! ✅) • Day 2-3: Architecting the Intent Scraper logic. • Day 4-5: Building the Authenticity Verification agent. • Day 6: Integrating the "Outcome-Based" outreach engine. • Day 7: Testing & Refinement. • Day 8: Final Demo & Launch. The goal is to reduce the Source-to-Interview time from 14 days to 48 hours. I’ll be sharing my wins (and the bugs) every day. If you’re building an agentic workflow for this challenge, let’s connect in the comments. I’d love to see what everyone else is working on! Let’s get to work! #BuildInPublic #FirstHireAI #AgenticAI #DDSChallenge #AIInnovation

Ever noticed how AI becomes 10x smarter the moment you ask: "Are you sure?" It’s a classic trap. LLMs are programmed to be helpful, which means they sometimes prioritize "completing the task" over absolute accuracy. They act like a brilliant but overconfident intern - they’d rather make up an answer than admit they don't know. When you challenge the first response, you force the model to switch from "PREDICTIVE MODE" to "AUDIT MODE." How to stop taking the first (and often wrong) answer: • Interrogate the Logic: Don't just accept a fact; ask the AI to explain how it reached that conclusion. • The "Are You Sure?" Button: If a piece of data feels off, call it out. The model will often "apologize" and provide the correct version it skipped the first time. • Ask for Counter-Arguments: Tell it to find the flaws in its own previous answer. Bottom line: Accuracy lives 2-3 questions deep. If you’re only using the first output, you’re only using 50% of the AI's actual intelligence. What’s the wildest hallucination an AI has ever given you before you called it out? Let's hear from you - comment below! 👇