- AI Engineering
- Posts
- Build Full-Stack LLM Apps in Python
In today’s newsletter:
Reflex Build: Full-Stack LLM apps in Python
Mito AI: Enhance your Jupyter Notebooks
Fire Enrich: Turn a simple list of emails into a structured dataset
Reading time: 3 minutes.
Reflex is a Python library that lets you create full-stack web apps entirely in Python.
With the new Reflex Build, you can integrate Python libraries or external APIs directly into your app using a single prompt.
Describe your app in a prompt, and Reflex generates the frontend and backend in Python.
Live preview shows changes immediately.
Supports Python libraries and APIs for dashboards, data analysis tools, or AI applications.
This allows data scientists, ML engineers, and AI engineers to build end-to-end applications in Python
Mito AI is an open-source toolkit that integrates directly into Jupyter Notebooks, offering intelligent assistance for data analysis tasks.
Key Features:
Automated Workflow Generation: Create complete data pipelines—from import to modeling—using natural language prompts.
Context-Aware Debugging: Identify and resolve errors within your notebook without losing context.
Excel to Python Code: Convert Excel data manipulations into clean Python code seamlessly.
Natural Language Database Queries: Interact with your databases using plain English queries.
It’s 100% Open Source.
FireEnrich, is a Multi-Agent AI system that transforms emails into rich datasets with company profiles, tech stacks, and more.
Here’s how it works:
It first extracts the company domain from each email, then launches an orchestrator that runs agents in sequence:
Discovery Agent – Finds basic company info
Company Profile Agent – Identifies industry and market segment
Financial Intel Agent – Gathers funding and investor data
Tech Stack Agent – Detects technologies from GitHub and docs
General Agent – Finds leadership details like the CEO
Finally, a synthesis layer combines all agent outputs into a clean, verified dataset ready for use.
You can run this with any LLM
It’s Open Source
That’s a Wrap
That’s all for today. Thank you for reading today’s edition. See you in the next issue with more AI Engineering insights.
PS: We curate this AI Engineering content for free, and your support means everything. If you find value in what you read, consider sharing it with a friend or two.
Your feedback is valuable: If there’s a topic you’re stuck on or curious about, reply to this email. We’re building this for you, and your feedback helps shape what we send.
WORK WITH US
Looking to promote your company, product, or service to 150K+ AI developers? Get in touch today by replying to this email.