Augment Code

Hello, world! We’re Augment Code, the first developer AI purpose-built for teams. What makes Augment different? Context. Every Augment feature is context-aware. Read more: https://bit.ly/4fdJ1UU

Discover how Augment's lightning-fast responses transformed the coding experience for AMP's robotics team. Read the story: https://lnkd.in/gVV4GzVu

How full codebase context leads to better code completions. Demonstrated with a Fibonacci implementation.

LMNT co-founder, Zach Johnson, explains how context-aware code completions helped their team maintain consistent style while building speech models. See how they use Augment to speed up development without sacrificing code quality.

Thrilled to highlight a breakthrough from our Augment Code research team: When you're working with large repositories, context is everything - A code completion system needs to understand not just what you're typing, but how it fits into your codebase. This is where Augment has always shined, and today we're pushing the boundaries further. Our researcher Xuanyi Dong shares how we've developed a way to learn directly from developer workflows within their open source environment (we never train on customer proprietary code). The results are remarkable: improvements equivalent to doubling our model size or using 10x more training data. But what excites us most is how this advances our ability to combine more intelligent code suggestions with comprehensive awareness of big repositories. Team Augment is excited to continue to push the state of the AI art. Technical details here: https://lnkd.in/getaTJFX.

Why context length is the key to better code generation 👇 Most LLM solutions optimize for chat: - Short prompts - Long responses - Limited context But coding is different... Code generation needs to understand: - Dependencies - Call sites - READMEs - Build files - Third-party libraries - Project context Our research shows: code prediction quality keeps improving well beyond 10k tokens of context. The challenge? Processing all that context while maintaining low latency. The numbers: - Chat: ~100 input tokens → 100s output tokens - Code: 1000s input tokens → 10s output tokens Different workload = different optimization needs Here's how we do it at Augment: https://lnkd.in/gVT5kQRg #AIforDevelopers #SoftwareEngineering #CodeGeneration #AI #DeveloperTools #Programming

Deep context awareness, sophisticated code suggestions, and intuitive user interface. After adopting Augment Code, Webflow, a leading website experience platform, improved developer productivity, reduced onboarding time, and enhanced team collaboration. Read the story: https://lnkd.in/gFQpz8nm

The gap between how developers actually code and how we train AI models has been a massive blind spot in code completion. Why? Because real development is messy: ⚠️ Developers jump between files constantly ⚠️ Code exists in incomplete states ⚠️ Functions are half-written ⚠️ Imports are unresolved ⚠️ Variables are undefined But AI models are trained on polished repository code - the final, perfect version. It's like teaching someone to cook by only showing them finished dishes, never the actual cooking process. Today, we're introducing RLDB - a breakthrough that closes this gap by learning from real development patterns. Instead of disrupting developers for feedback, we learn from their natural workflow. Key innovations: ✔️ Learning from incomplete code states ✔️Substantial improvement similar to 20x more fine-tuning data ✔️Zero manual labeling needed Dive into the technical details: https://lnkd.in/g8gV4txw H/t to author Barry Dong and many thanks to the team: Jeffrey Shen Yuri Volkov Carl Case Michiel de Jong Guy Gur-Ari #ArtificialIntelligence #Programming #Innovation #Technology

Augment's chat is multi-turn. This means it can add upon responses from the context of previous responses.

Context-aware AI is transforming how businesses operate by delivering more personalized, relevant, and efficient responses based on user behavior and environmental context. These startups are at the forefront of this innovation: Duckbill - Meghan Verena Joyce, Emi Gonzalez : AI that learns from user interactions to handle 75+ task categories, offering seamless, round-the-clock support. You.com - Richard Socher, Bryan McCann: Customizable AI agents that integrate with enterprise systems, enhancing workflows and boosting productivity. Augment Code - Igor Ostrovsky: AI agents designed for developers, providing personalized code assistance and intelligent task management. Cassidy - Justin Fineberg, Ian Woodfill: A platform for creating hyper-personalized AI assistants that integrate with over 100 knowledge tools for real-time data processing and advanced workflows. Bardeen - Artem H.: A browser-based AI agent that learns user actions, automating complex, multi-step tasks with ease. Rezolve AI - Saurabh Kumar: A context-aware AI that ensures accurate, secure responses with its unique "context injection" method. Lyzr AI - Siva Surendira, Anirudh Narayan: Privacy-focused AI agents designed to automate business functions while ensuring data security and contextual relevance. Alan AI - Ramu Sunkara, Andrey Ryabov: Industry-specific AI that delivers personalized, context-aware responses, ensuring seamless and efficient user interactions. Read more here: https://lnkd.in/g2Sta7ry