How to get your SaaS tool ready within 3 days

Explore Databutton, Google’s Mesop, and Essential GitHub Repos in This Week’s AI Engineering Newsletter

⚠️ This is an archived (1 month old) post from my weekly newsletter called High Signal AI where I aggregate the best of AI Engineering resources to help you build faster.

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Discover Databutton: Revolutionizing SaaS Development with AI - High Signal AI #4

This week’s action-prompting insights:

1. Top find of the week 🏆: Databutton
2. Developer tool of the week
3. GitHub repositories you must check out
4. What people are building with AI
5. AI startups that raised funds
6. Startup Program by Cohere
7. AI tutorials / videos / posts worth your time
8. Reading list for the week
9. Weekly updates from tools, libraries and hubs

Top find of the week 🏆: databutton.com

Databutton is an AI-powered platform that helps users quickly develop and deploy SaaS applications.

It allows users to describe their app idea in natural language, and AI generates the necessary code, including React UIs and Python APIs.

Databutton supports integration with various services and offers one-click deployment, handling security and infrastructure. The platform is designed for a range of users, from individual developers to teams, with various pricing plans based on usage credits.

Dev tool(s) you should check out! 🛠️

UI developement app open sourced by Google — Mesop (a competitor of Streamlit) is a Python-based UI framework developed by Google for rapid internal app development. It enables users to quickly build web apps without needing to learn frontend technologies like JavaScript, CSS, or HTML

GitHub repositories you must check out 📌

• FlashRAG (RUC-NLPIR) — 708 stars: FlashRAG is a Python toolkit designed for efficient research in Retrieval-Augmented Generation (RAG) scenarios, offering 32 benchmark datasets and 12 state-of-the-art algorithms. GitHub — FlashRAG
• PraisonAI (Mervin Praison) — 715 stars: PraisonAI provides various AI models and tools for natural language processing, computer vision, and other AI applications, with pre-trained models and deployment scripts. GitHub — PraisonAI
• AutoGroq (jgravelle) — 979 stars: AutoGroq automates neural network quantization to reduce model size and enhance inference speed without significant accuracy loss. GitHub — AutoGroq

What people are building with AI 🧑‍💻

• FlashRAG: A Python toolkit created by RUC-NLPIR for efficient research in Retrieval-Augmented Generation (RAG). It includes 32 benchmark datasets and 12 state-of-the-art algorithms for flexible and customizable RAG pipelines. GitHub — FlashRAG
• BabyAGI Streamlit App: This GitHub repository by dory111111 showcases the BabyAGI model integrated into a Streamlit application for easy interaction and deployment. GitHub — BabyAGI Streamlit
• EDA GPT: This Streamlit app provides an interface for performing Exploratory Data Analysis (EDA) using GPT models, simplifying the process of data exploration and visualization. EDA GPT
• Carbon Footprint Predictor: A Hugging Face Space by as-cle-bert that predicts carbon footprints using a machine learning model. Users can input various factors to estimate their environmental impact. Carbon Footprint Predictor

Learning resources 📚:

• The “AI Agents in LangGraph” course by DeepLearning.AI teaches you to build and manage AI agents using LangGraph, an extension of LangChain.
• The course covers constructing agents from scratch with Python, implementing them with LangGraph, utilizing agentic search, managing agent state, and incorporating human-in-the-loop systems.
• It is aimed at those with intermediate Python knowledge, featuring instructors Harrison Chase and Rotem Weiss.

AI startups that raised funds 💰

• Pika, an AI video startup, has raised $55 million in a funding round led by Spark Capital, with participation from notable investors including Jared Leto.
• Vercel, a San Francisco-based platform for developing cloud web applications, raised $250 million in a Series E funding round led by Accel, valuing the company at $3.25 billion.
• Saguaro Biosciences, based in Quebec City, raised $3 million in seed funding led by AQC Capital and Anges Québec.
• Caju AI, based in Charlottesville, VA, has raised $3 million in seed funding from Grotech Ventures, Felton Group, and other investors.

Startup Program Announced by Cohere

• Cohere’s startup program launch: Cohere launched a startup program to support early-stage companies solving real-world business challenges with AI.

AI tutorials / videos / posts worth your time

• Building open source LLM agents with Llama3 using LangGraph — The video by Lance from LangChain demonstrates the process of creating large language model (LLM) agents using Llama 3 and LangGraph.
• The OpenAI Cookbook provides a comprehensive guide on preparing and analyzing data for fine-tuning chat models. It covers data preparation steps, error checking, token counting, and cost estimation. The guide is aimed at ensuring high-quality data inputs to improve the performance and efficiency of chat models during fine-tuning.

Reading list for the week

• The Qwen team has announced the release of Qwen2, an evolution from Qwen1.5, featuring five models with sizes ranging from 0.5B to 72B parameters. These models show improved performance in multiple areas, including coding, mathematics, and multilingual tasks, and support extended context lengths up to 128K tokens.
• Scoping Data projects — The article “Scoping Data Projects: Why Technology Alone Isn’t Enough” by Jan Meskens emphasizes the importance of comprehensive project scoping in data science.
• Building LLM applications for production — Huyen Chip’s article on LLM engineering discusses best practices for optimizing prompts, managing costs, and handling latency in large language models. Key strategies include using Chain-of-Thought prompting, generating multiple outputs for better results, and breaking down complex prompts.

Weekly updates from tools, libraries and hubs

• LangChain x Groq integration: @LangChainAI announced an upcoming webinar on building LLM agent apps with LangChain and Groq’s integration.
• Qwen2 models released: Binyan Hui announced the release of Qwen2 models in 5 sizes (0.5B, 1.5B, 7B, 57B-14B MoE, 72B) as Base & Instruct versions. Models are multilingual in 29 languages and achieve SOTA performance on academic and chat benchmarks. Released under Apache 2.0 except 72B.
• Prometheus-2 Evaluates RAG Apps: Prometheus-2 provides an open-source alternative to GPT-4 for evaluating Retrieval-Augmented Generation (RAG) applications. It is appreciated for its cost-effectiveness and transparency, with detailed information available on LlamaIndex Cookbook.

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High Signal AI | Harshit Tyagi | Substack