AI/AIZENME
🎧 RESEARCH · Deep Research Level: Beginner ⏱ 15 MIN Freemium

Build Deep-Research Podcast Guest Dossiers & Non-Obvious Follow-Up Question Trees

Stop asking guests the same canned questions they have answered ten times. Synthesize their past talks and articles to surface fresh angles and specific turning points.

CORE TOOLS DeepSeek (Web Search) / Kimi / Claude
BEST FOR Podcast hosts, journalists, biographers, editorial directors
REQUIREMENTS Guest public articles/transcripts or web-enabled LLM
✦ WHY THIS WORKFLOW

Great guests light up when asked something nobody has noticed before. Deep research turns a routine promo chat into a landmark episode.

✦ Interactive Workflow Animation Preview

Step-by-Step Action Checklist

0 / 4 Completed
READY-TO-USE PROMPT TEMPLATE
Open Official Tool / Repo ↗ 
I am hosting a deep-dive podcast interview with [Guest Name/Role]. Here are their past articles, talks, and bio notes:
[Paste materials]

Create a **Non-Obvious Interview Dossier**:
1) [Overused Tropes to Skip]: 3 stories/talking points they have already repeated on other shows;
2) [3 Tension & Evolution Angles]: Shifts or contradictions in their thinking over time;
3) [8 Scene-Driven Questions]: Zero generic "What do you think of X?" questions—anchor every question in a specific moment, trade-off, or turning point;
4) [Second-Order Probes]: Follow-up nudges to move past rehearsed PR answers.
Use Cases & Tags:
Podcast guest dossier preparationFounder & executive interview guidesPanel moderator question treesDocumentary story excavation
MORE

Related AI Workflows

← Describe your idea on Home
RESEARCH · Deep Research
20 MIN

Deploy WorldMonitor Open-Source Real-Time Global OSINT & Geopolitical AI Dashboard

An open-source global intelligence command center with nearly 90k stars on GitHub! WorldMonitor brings military-grade situational awareness to your desktop. It fuses satellite feeds, maritime tracking, commodity supply chain alerts, geopolitical breaking news, and global infrastructure monitors into a unified interface—powered by local LLMs and MCP for autonomous debriefing and anomaly alerts.

WorldMonitor (Open Source)Python / DockerOpen-Source OSINT FeedsDeepSeek / Claude
Best for: Macro hedge fund analysts, cross-border trade executives, international relations researchers, journalists, geopolitical risk directors
Full guide ↗
RESEARCH · Deep Research
20 MIN

Deploy TradingAgents Multi-Agent LLM Framework for Collaborative Financial Quant Research & Risk Hedging

A viral financial multi-agent framework with 110k+ stars on GitHub! TradingAgents simulates an elite Wall Street quantitative hedge fund desk. It orchestrates Fundamental, Sentiment/News, Technical, and Risk Management Agents in structured debates and adversarial cross-examination—delivering institutional-grade research, DCF valuations, and VaR stress tests without emotional bias.

TradingAgents (Open Source)Python 3.10+DeepSeek-R1 / Claude 3.5 Sonnetyfinance
Best for: Quantitative traders, equity research analysts, portfolio managers, family office analysts, FinTech developers
Full guide ↗
RESEARCH · Deep Research
15 MIN

Extract Complex Dual-Column Papers, Tables & LaTeX Math into Clean Markdown with MinerU

The ultimate antidote to messy PDF extraction! MinerU (Magic-PDF) by Shanghai AI Lab is an open-source powerhouse with 23k+ stars. Engineered for complex dual-column academic layouts, merged tables, and intricate LaTeX mathematical formulas where standard OCR engines fail, it reconstructs natural reading flows and exports pristine Markdown ready for RAG pipelines.

MinerU / Magic-PDF (Open Source)PythonPyTorchCUDA
Best for: Academic researchers, patent attorneys, biotech analysts, enterprise knowledge architects, digital archivists
Full guide ↗
RESEARCH · Deep Research
15 MIN

Dual-Level Knowledge Graph RAG with HKU Open-Source LightRAG at 1% Cost of GraphRAG

Break free from shallow vector RAG and the crushing expense of Microsoft GraphRAG! Developed by HKU, LightRAG introduces dual-level graph retrieval (low-level specific entities + high-level thematic relationships). It speeds up indexing by 10x, slashes API token costs to 1%, and supports incremental document updates for nuanced multi-hop Q&A over complex domains.

LightRAG (Open Source)PythonOllama / DeepSeek APINetworkX
Best for: Financial analysts, legal compliance teams, enterprise knowledge managers, RAG developers, academic researchers
Full guide ↗
RESEARCH · Deep Research
15 MIN

Deploy Local DeepSeek-R1 for Autonomous `<think>` Chain-of-Thought Reasoning & Self-Reflection

DeepSeek-R1 is a global milestone in open-source AI, unlocking autonomous deep reasoning via pure reinforcement learning. Free from rigid prompt engineering, the model reflects, hypothesizes, and self-corrects inside `<think>` tags before answering. Deploy distilled models locally via Ollama or vLLM to solve complex code algorithms, legal loophole cross-auditing, and deep logic problems at zero cost.

DeepSeek-R1 (Open Source)Ollama / vLLMPythonOpen WebUI
Best for: System architects, algorithm engineers, corporate attorneys, academic researchers, strategy analysts, open-source AI geeks
Full guide ↗
RESEARCH · Deep Research
15 MIN

Long-Audio Meeting Transcription with Overlapped Speech Diarization via Open-Source MOSS-TD

MOSS-TD by Fudan University is an open-source long-form speech transcription and diarization system engineered for messy multi-speaker scenarios. Even during intense debates where people interrupt and talk over each other, MOSS-TD cleanly isolates overlapping audio tracks and assigns timestamps to every unique speaker.

MOSS-TD (Open Source)PyTorchONNX RuntimePython
Best for: Executive assistants, legal paralegals, investigative journalists, investment analysts, consultants, conference scribes
Full guide ↗