1. Perplexity + Gemini gather (with citations)
Current sources and long documents are captured.
For solid research you combine Perplexity (current, cited web answers), Claude (synthesis of long sources + long context), NotebookLM (source-grounded notes) and Gemini (multimodal, very long context) — facts with sources instead of hallucination.
For solid research you combine Perplexity (current, cited web answers), Claude (synthesis of long sources + long context), NotebookLM (source-grounded notes) and Gemini (multimodal, very long context).
The result is facts with sources instead of hallucination. The ground rule: never trust a single AI blindly — always ≥2 sources, and for drift-prone facts (prices, model IDs, numbers) check the primary source.
Cada jueves a las 23:00 Asia/Ho_Chi_Minh, el formato combina filtro de mercado, casos prácticos, preguntas y siguientes pasos claros.
Próxima sesión: jueves, 9 de julio de 2026, 23:00 · hora de Vietnam. Después la serie sigue con ritmo semanal.

The stack for sourced reports with a citation requirement. Prices as a ballpark, as of July 2026, vendor page authoritative.
| Task | Tool (recommended) | Why | Price |
|---|---|---|---|
| Live web research | Perplexity | Current answers with live source citations | € |
| Synthesis / report | Claude | Long context, synthesis of many sources | €€ |
| Source grounding | NotebookLM | Notes strictly on uploaded sources | Free/€ |
| Multimodal / long docs | Gemini | Very long context, multimodal | € |
| Data analysis | Claude / code interpreter | Computes and checks data instead of guessing | €€ |
From gathering with citations to the final, sourced report.
Current sources and long documents are captured.
Answers stay strictly tied to the source material.
Many sources become a solid, sourced write-up.
What makes research results unusable.
A single LLM working from memory hallucinates on current and drift-prone facts. The stack deliberately separates: Perplexity/Gemini fetch current sources with citations, NotebookLM grounds on uploaded material, Claude synthesizes. The result is a sourced report instead of a plausible but unverified answer.
NotebookLM answers questions strictly based on the sources you upload — rather than from general model knowledge. That's ideal when statements must come only from defined documents (e.g. contracts, studies, internal reports).
Matching stacks for other roles — each with a stack table, workflow and common mistakes.
The mid-market starts GDPR-aware with Claude/Copilot (everyday), Notion (knowledge/processes), n8n self-hosted (automation on your own server) and Mistral as an EU-native LLM for sensitive data — AI in the company without handing customer data to unclear third countries.
The strongest AI marketing stack in 2026 combines Claude (strategy, copy, orchestration), Higgsfield/Seedance (cinematic video), HeyGen + fal OmniHuman (talking avatars), ElevenLabs (voiceover) + Suno (music), Canva (design) and n8n (automation + publishing) — full campaigns with no shoot, studio or agency overhead.
Back to the hub with the stack overview and all 7 role-based stacks.
We build you a research workflow with a citation requirement.
Si quiere priorizar un proceso real, unas pocas entradas claras bastan para una primera evaluación sólida.