With TechLatest’s pre-configured Dify AI VM, you focus on innovation, not infrastructure. If you’re serious about building production-ready GenAI apps faster, this is worth a read.

Read the full blog here: medium.com/@techlatest....

✅ RAG, AI agents, and multi-LLM support (GPT, Llama, Qwen & more)
✅ Our ready-to-use Dify AI Virtual Machine that lets you start building on day one
✅ Step-by-step guides for AWS, Azure, and GCP

That’s exactly why we explored Dify AI on TechLatest

Blog: Dify AI: The Easiest Way to Build and Launch GenAI Apps — Now Ready to Use on TechLatest

In this post, we break down:
✅ What Dify AI is and why it’s gaining traction
✅ How it simplifies GenAI app development with low-code + LLMOps

All of this runs privately, securely, and locally inside your own cloud environment — perfect for startups, enterprises, and anyone building AI-driven market intelligence.

Read the full guide here: medium.com/@techlatest....

✅ Building a full multi-agent workflow (lead researcher, web search agent, deep reader, synthesizer)
✅ Running your first autonomous competitor research pipeline

In this guide, we walk through:
✅ Setting up a pre-configured RAGFlow VM on AWS/GCP/Azure
✅ Creating a Competitor Intelligence Knowledge Base
✅ Uploading & parsing competitor PDFs, pricing sheets, landing pages, etc.
✅ Using Ollama (Qwen2.5:7B) as the local LLM

Today, we released a step-by-step guide that shows you how to build a fully autonomous Competitor Intelligence Agent capable of:
✅ Monitoring competitor documents
✅ Summarizing strategy & product updates
✅ Extracting pricing & positioning
✅ Running autonomous web research

Whether you're exploring personal AI assistants, RAG systems, voice automation, or memory-driven agents, this guide gives you a production-ready foundation.

Link: medium.com/@techlatest....

✅ Listen to your voice in real time
✅ Transcribe speech using Whisper
✅ Store past conversations as embeddings
✅ Retrieve memories using Milvus vector search
✅ Respond intelligently using Ollama LLMs
✅ Run in a browser UI via Gradio

We just published a step-by-step guide that walks you through building a Voice-Based AI Memory Assistant that can:

This is your own private, local Deep Research engine—production-ready and customizable.

Read the full guide here: medium.com/@techlatest....

What you’ll learn:

✅ Creating a knowledge base

✅ Parsing + chunking PDFs

✅ Connecting Ollama models

✅ Building a multi-agent research workflow

✅ Running deep research prompts with structured outputs

In this guide, we walk you step-by-step through building a Deep PDF Investigator Agent powered by:

✅ RAGFlow for orchestrating multi-agent workflows

✅ Ollama for running fast, local open-source LLMs like Qwen2.5, Llama, Gemma, DeepSeek, and more

✅ A full multi-agent architecture

This environment runs fully isolated on your cloud instance (AWS / Azure / GCP), giving you a fast, clean, secure vector DB setup — with zero installation required.

Read the full guide: medium.com/@techlatest....

What’s inside the guide:
✅ How the VM bundles ChromaDB + JupyterHub
✅ Storing & querying embeddings
✅ Running semantic similarity search
✅ Building RAG prototypes in notebooks
✅ Using the built-in Generative Benchmarking App
✅ Ideal workflows for research, prototyping & AI app development

Whether you're building AI search, RAG apps, or internal POCs — this guide gets you started fast.

Read the full guide here: medium.com/towardsdev/m...

What’s inside:
✅ Milvus setup & configuration
✅ JupyterHub workflows
✅ Ollama for embeddings + LLM inference
✅ Step-by-step RAG pipeline
✅ Code examples for search, indexing & agents

Link: techlatest.net/support/dify...