HackStack

Project Info

Laila

Devpost

Inspiration

We all know how important it is to have time for meaningful work and to maintain a balanced life. But when we discovered that 40% of workers spend a quarter of their work week on manual, repetitive tasks—like logging emails, updating spreadsheets, and managing schedules—it really caught our attention. These tasks, while small, add up over time, leading to increased burnout and reduced job satisfaction. We felt there had to be a better way—a way to automate these mundane processes so that people can focus on the work that truly matters to them. This insight drove us to create a solution that not only enhances productivity but also helps individuals reclaim their time and maintain a healthier work-life balance.

What it does

Introducing LAILA, an intelligent digital assistant designed to make your workday easier. LAILA automates repetitive web tasks in real-time, allowing you to focus on what truly matters and boosting your productivity effortlessly. By combining advanced action models, voice technology, and generative AI, LAILA takes your voice commands or input, interprets them, and transforms them into clear, actionable steps. These tasks are then carried out instantly and displayed on your screen, so you can track the progress as it happens—all without lifting a finger. In essence, LAILA turns tedious tasks into simple, automated actions, helping you reclaim your time and work smarter, not harder.

How we built it

We developed a comprehensive system architecture diagram to illustrate the communication flow and interaction between various components of our solution. Here's the tech stack: Front End Next.js and React: For building a responsive, dynamic user interface. Tailwind CSS and Shadcn: For efficient and customizable styling. TypeScript: Ensures type safety and scalability. Back End Twilio: Enables seamless calling capabilities. Deepgram: Utilized for speech-to-text processing. SingleStore: A real-time data management system for fast analytics. Apache Kafka: Manages real-time event streaming. Perplexity and Gemini: For interpreting tasks and generating instructions using AI. Phoenix: Evaluates and observes LLM (Large Language Model) performance. Selenium: Automates browser tasks for efficient web interactions.

Challenges we ran into

Integrating multiple APIs together cohesively Getting the Gemini AI to generate clear, accurate, and actionable instructions Connecting Kafka and SingleStore Ensuring real-time performance

Accomplishments we're proud of

Developed a working MVP that uses an advanced action model to automate web tasks. Integrated a diverse range of technologies—like AI, voice recognition, and real-time data handling—into a cohesive, functioning system.

What we learned

How critical it is to thoroughly read and understand API documentation. The potential of action models for automating more complex web flows and pipelines. Working with a diverse tech stack and multiple APIs taught us the importance of teamwork and adaptability.

What's next

for LAILA Handle more complex web flows Expanding to more complex web flows Integrating more AI models

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • HTMLIn code
  • JavaScriptIn code
  • Next.jsIn code
  • PythonIn code
  • ReactIn code
  • SQLIn code
  • Tailwind CSSIn code
  • TypeScriptIn code
  • Google GeminiClaimed

9 of 10 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

228 KB

Source files

41

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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