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EchoPanel — Coordinated AI Interview Panel

Purpose: A voice-based interview platform where multiple distinct AI interviewer personas — Technical, Product/Business, Behavioural, Customer, Hiring Manager — conduct a single, real-time conversation with a candidate, coordinating with each other the way a real human panel would, rather than acting as one generic chatbot with a single voice. The core problem it solves: Most AI interview tools ask a fixed list of questions and treat every answer the same way. A real panel doesn't work like that — a technical interviewer might accept a correct implementation, while a product interviewer independently pushes back because the candidate never explained the business impact. EchoPanel is built to reproduce that dynamic. Goals Adaptive questioning — questions are generated live based on what the candidate actually said, not a predefined script Shared context across personas — every persona reads and writes to the same underlying "Context Graph" of claims, so a later interviewer can challenge something an earlier one accepted, without re-processing the whole transcript Fair turn-taking — a "Turn Arbiter" decides who speaks next based on topic relevance and whether a claim has gone unchallenged, rather than a rigid round-robin Difficulty that adapts to performance — a rolling per-topic competence score pushes questions from recall-level to applied to edge-case as the candidate demonstrates strength Honesty checks — vague or contradictory answers are flagged automatically before being scored Evidence-linked feedback — the final report ties every verdict back to a specific claim and transcript timestamp, not a vague summary Real-time, interruptible voice — powered by Agora's Conversational AI Engine, so the interaction feels like a live conversation, not a form Transparent AI disclosure — the candidate is always clearly told they're speaking with an AI panel, with explicit consent logged before the interview starts

Markdown Viewer — Open & Inspect .md Files Online

Markdown Viewer is a free online tool for opening, reading, and inspecting Markdown files directly in your browser. Drop a .md, .markdown, or .txt file, or paste Markdown from ChatGPT, Claude, Slack, GitHub, or anywhere else. It renders GitHub Flavored Markdown with headings, tables, task lists, code blocks, syntax highlighting, links, images, blockquotes, and more. Unlike basic Markdown viewers, it also provides document diagnostics including word count, character count, heading structure, links, images, code blocks, task-list progress, and estimated reading time. It can also catch structural issues such as skipped heading levels, duplicate headings, broken internal anchors, malformed links, and missing image alt text. Everything is processed locally in your browser. Your Markdown file is never uploaded or stored on a server. No account, signup, or subscription is required.

Interview ai agent

Interview AI Assistant is a voice-native adaptive AI interview panel built for the Agora EchoSphere 2026 Hackathon.   Most AI interview bots ask a fixed list of questions regardless of how the candidate answers. This one adapts — a Interviewer Agent conducts the live conversation while a separate Evaluator Agent silently scores each answer for clarity, relevance, depth, and confidence in real time. That score decides whether the next question goes deeper, moves to a new topic, or offers a supportive follow-up — so no two interviews play out the same way.   Built with: - Lyzr Agent Studio for multi-agent orchestration (Interviewer + Evaluator agents working from shared state) - Agora Conversational AI Engine + RTC SDK for real-time voice, turn-taking, and interruption handling - Structured JSON scoring pipeline feeding a live scorecard and a final evaluation report   Why I built it: early-stage interview screening doesn't scale well for recruiters, and most "voice AI" demos are just chatbots with a microphone bolted on — they don't actually reason about what they hear. This project explores what a genuinely adaptive, multi-agent voice interview looks like, and could extend into real hiring screening, mock-interview practice for candidates, or training tools for junior interviewers.   Team Ultron — Adarsh Sarode & Jigar Kumeriya