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---
title: Best Google AI posts of 2024 | daily.dev
description: The most upvoted Google AI posts of 2024, curated by the daily.dev community.
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og:description: The most upvoted Google AI posts of 2024, curated by the daily.dev community.
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---

# Best of Google AI — 2024

1. 1  
[](https://daily.dev/posts/how-to-develop-ai-apps-100-for-free--2ozjg9dpp "How To Develop AI Apps 100% For FREE!")  
Video  
![Avatar of jherr](https://media.daily.dev/image/upload/s--Fuk-ONjA--/f_auto/v1718349299/logos/jherr)Jack Herrington · 2y  
How To Develop AI Apps 100% For FREE!  
71
2. 2  
[](https://daily.dev/posts/how-to-use-gemini-in-python-duumcsgrk "How to Use Gemini in Python")  
Article  
![Avatar of planetpython](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/eac3cc584cb043b5a242d13752bd3d0c)Planet Python · 2y  
How to Use Gemini in Python  
Learn how to use Gemini AI model in Python by accessing the Gemini API, installing the required libraries, and setting parameters for generating responses.  
70  
3
3. 3  
[](https://daily.dev/posts/gemma2-tutorial-local-llm-development-with-genkit-djxultpwb "Gemma2 Tutorial: Local LLM Development with Genkit")  
Article  
![Avatar of firebase-developers](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/e13782b61c20475895cc14c209cc0989)Firebase Developers · 2y  
Gemma2 Tutorial: Local LLM Development with Genkit  
Google's open-source LLM, Gemma2, can be run locally using Ollama. This step-by-step guide explains how to install Gemma2, create a Genkit project, and integrate it with Ollama Gemma2\. The process involves installing appropriate packages, configuring Genkit, and writing a sample flow to handle requests. The setup is efficient enough to be accomplished in around 30 lines of code.  
35
4. 4  
[](https://daily.dev/posts/retrieval-interleaved-generation-rig-when-real-time-data-retrieval-meets-response-generation-hdbgpcqvk "Retrieval Interleaved Generation (RIG): When real-time data retrieval meets response generation")  
Article  
![Avatar of tai](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/8b7e3e63e47b4d34b4c1e634b902c12f)Towards AI · 2y  
Retrieval Interleaved Generation (RIG): When real-time data retrieval meets response generation  
Retrieval Interleaved Generation (RIG) is a cutting-edge technique in natural language processing that dynamically combines real-time data retrieval with response generation. Unlike Retrieval-Augmented Generation (RAG), which performs a single retrieval step before generating a response, RIG interleaves multiple retrievals during the response creation process, ensuring up-to-date and accurate information. This method significantly reduces hallucinations and improves accuracy for complex, data-dependent queries. RIG is particularly useful in fields like healthcare, finance, and scientific research, where real-time, precise information is crucial.  
32  
1
5. 5  
[](https://daily.dev/posts/ai-news-uncensored-ai-will-create-anything--skjdko1ga "AI News: Uncensored AI Will Create ANYTHING!")  
Video  
![Avatar of mreflow](https://media.daily.dev/image/upload/s--JWE7fI4Q--/f_auto/v1716024560/logos/mreflow)Matt Wolfe · 2y  
AI News: Uncensored AI Will Create ANYTHING!  
27
6. 6  
[](https://daily.dev/posts/build-ai-powered-angular-apps-with-google-gemini-enahzbuwk "Build AI-Powered Angular Apps with Google Gemini")  
Article  
![Avatar of medium_js](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium)Medium · 3y  
Build AI-Powered Angular Apps with Google Gemini  
Learn how to build AI-powered Angular apps using Google Gemini. This post covers integrating Gemini APIs, obtaining an API key, creating an Angular application, generating text, building multi-turn conversations, and more.  
21
7. 7  
[](https://daily.dev/posts/new-in-dart-3-3-extension-types-javascript-interop-and-more-wdelrkpaw "New in Dart 3.3: Extension Types, JavaScript Interop, and More")  
Article  
![Avatar of medium_js](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium)Medium · 3y  
New in Dart 3.3: Extension Types, JavaScript Interop, and More  
Dart 3.3 brings new features such as extension types, improved JavaScript interop, and WebAssembly support. It also introduces the Google AI Dart SDK.  
15  
2
8. 8  
[](https://daily.dev/posts/building-openai-compatible-api-yrctc9o7k "Building OpenAI-Compatible API")  
Article  
![Avatar of gopenai](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/f34dfd0c312c4a59b897eb64ad28d895)GoPenAI · 2y  
Building OpenAI-Compatible API  
This guide provides step-by-step instructions for building an OpenAI-compatible text generation API using Google Gemini. It includes details on configuring the AI, setting up the FastAPI web application, creating request models, and implementing streaming responses. The guide concludes with example code for testing the API both with and without the streaming feature.  
11
9. 9  
[](https://daily.dev/posts/sentencepiece-bpe-tokenizer-in-go-mpec119iu "SentencePiece BPE Tokenizer in Go")  
Article  
![Avatar of elibendersky](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/ac2e84092d814b68aa34080a3232a3cd)Eli Bendersky · 2y  
SentencePiece BPE Tokenizer in Go  
This post discusses the development of go-sentencepiece, a pure Go implementation of the SentencePiece tokenizer, which is used in Google AI's models like Gemma and Gemini. Unlike the C++ and Python bindings, go-sentencepiece doesn't require a C compiler. It focuses on BPE tokenization and only supports encoding & decoding, not the training phase. The implementation leverages advanced algorithms, which significantly improve performance. A protobuf file configures the tokenizer, and an online demo is available for testing.  
10

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