ElevenLabs

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

ElevenLabs

ElevenLabs is a voice technology research company, developing the most compelling AI speech software for publishers and creators. The goal is to instantly convert spoken audio between languages. ElevenLabs was founded in 2022 by best friends: Piotr, an ex-Google machine learning engineer, and Mati, an ex-Palantir deployment strategist. It's backed by Credo Ventures, Concept Ventures and other angel investors, founders, strategic operators and former executives from the industry.

General
Release date	2022
Author	ElevenLabs
Type	Voice technology research

Products

Speech Synthesis

Speech Synthesis tool lets you convert any writing to professional audio. Powered by a deep learning model, Speech Synthesis lets you voice anything from a single sentence to a whole book in top quality, at a fraction of the time and resources traditionally involved in recording.

VoiceLab

Design entirely new synthetic voices or clone your own voice. The generative AI model lets you create completely new voices from scratch, while the voice cloning model learns any speech profile from just a minute of audio.

Resources

Useful resources on how to build with ElevenLabs

👉 Discover more ElevenLabs Tutorials on lablab.ai

ElevenLabs - Helpful Resources

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Education How to use ElevenLabs technology
Help Center Advice and answers from ElevenLabs team
Tutorials Learn how to make the most of ElevenLabs
API Docs API docs for developers
ElevenLabs GitHub ElevenLabs's open-source repositories

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ElevenLabs AI technology page Hackathon projects

Discover innovative solutions crafted with ElevenLabs AI technology page, developed by our community members during our engaging hackathons.

WritenDraw Flight Simulator for Software Developer

WritenDraw is an agentic AI simulation platform that puts junior developers through realistic production incidents to bridge the gap between learning to code and working in a real team. The core innovation is the agentic workflow: Google Gemini 2.0 Flash orchestrates the entire simulation through three autonomous agents: AGENTIC EVALUATION: Every step requires free-text responses (no multiple choice). Gemini evaluates each response against per-step rubrics, scoring reasoning 0-15. The AI adapts feedback based on accumulated performance. AGENTIC MENTORING: The AI mentor maintains persistent context, tracking understanding level, chat count, and time pressure. Early messages: patient, asks "what do you think?" By message 7+: "just write it up." The agent autonomously decides how much help to give. AGENTIC AUDIT: The system logs every response, chat message, code submission, and score — creating a complete picture of how a developer thinks through a crisis. The AI continuously assesses and adapts. The simulation drops you into a P1 incident at ShopRight (fictional UK supermarket). You join a standup, read a Jira ticket, investigate messy code with no hints, chat with the AI mentor, write a fix, respond to code review, create a deployment plan, and contribute to a retro. Paste is disabled — Key insight: explanation scores higher than code (10 vs 5 points). Wrong code with a great explanation beats perfect code with no explanation — because in real teams, communication matters as much as code. Built on the author's published research — "TrueSkills: AI-Resistant Assessment Through Personalized Understanding Validation" (SSRN, 2025, DOI: 10.2139/ssrn.5674130) — which demonstrated that AI-resistant assessment requires evaluating understanding rather than recall. WritenDraw takes this further: testing how developers think under realistic production pressure. Built with Python/Flask, Google Gemini 2.0 Flash, CodeMirror, Pyodide, and Docker.

Based Codey

Vibe coding alone wasn’t enough. I wanted to become a real, hands-on coder without losing creativity or momentum. I was frustrated by tools that assume perfect conditions: a laptop, stable internet, long focus windows, and formal education paths. Real life doesn’t work like that. So I built Based Codey to let me learn, build, and ship from anywhere, even from my phone. The deeper inspiration was democratizing access to real software development—so learning to code isn’t locked behind privilege, hardware, or gatekeeping. What it does Based Codey is an AI-powered, production-oriented coding environment that allows the user to build real applications end-to-end in a conversational, hands-on way, with the option for education. It combines: – Various AI agents with differing reasoning levels (from snippet repair to project level reasoning) – A live editor and preview environment – Tasking, annotations, and iterative workflows – Mobile-first usability so I can ship from anywhere – It’s not just “generate code”—it’s learning by building, with real files, real structure, and real ownership. – Still, if you want just "generate code" it can do that.

Partenit Gate - AI That Makes Robots Accountable

For the “AI Meets Robotics” Hackathon, we built a live, interactive warehouse simulation where operators manage multiple robots in real time. In our system, AI is not the final decision-maker. It acts as a translator and planner — generating tasks and interpreting high-level goals. The actual execution is controlled by Partenit Gate, a deterministic reasoning layer that verifies every proposed action against real-world constraints: safety zones, speed limits, human proximity, and operational rules. The Gate can ALLOW, MODIFY, or BLOCK actions. If a robot attempts something unsafe, the system does not fail open — it enforces constraints physically and provides a clear reasoning trace explaining why. The result is a hybrid architecture where AI adds flexibility, but deterministic logic guarantees safety, auditability, and operator trust. This project demonstrates how AI and robotics can work together responsibly — not through vibes, but through verified decisions.

Ghetto-Bird Ai Pet

Ghetto-Bird AI Pet is an AI-powered virtual companion inspired by the creativity, resilience, and culture of urban communities. The project reimagines the digital pet experience by combining conversational AI, personality modeling, and adaptive learning to create a dynamic companion that evolves with its user. Unlike traditional virtual pets, Ghetto-Bird AI Pet is emotionally responsive and culturally aware. It learns from interactions, develops preferences, remembers conversations, and adapts its tone and personality over time. The AI pet can offer motivation, storytelling, humor, life advice, and entertainment, creating a personalized experience for each user. The project aims to make AI companionship more relatable and expressive by blending culture, technology, and emotional intelligence. Whether users are looking for a friend, a hype partner, or a daily check-in companion, Ghetto-Bird AI Pet grows alongside them — becoming smarter, more intuitive, and more uniquely theirs over time.

Restaurant Phone Assistant

This project introduces Ayaan, a professional AI Phone Assistant engineered for Urban Flames. It serves as a high-speed, zero-latency receptionist that handles customer inquiries and order placement with the precision of a seasoned host. By automating the most frequent phone-based tasks, Ayaan streamlines restaurant operations and ensures that no customer is left on hold. The system is governed by a strict conciseness protocol. Every response is direct and punchy, ensuring guests receive information quickly without unnecessary fluff. This significantly improves the user experience and reduces call duration during busy hours. Key Functional Capabilities: 1. Real-Time Menu Access: Ayaan provides instant, accurate information about dishes and pricing. It is programmed to provide only verified details, ensuring customers receive up-to-date information without any guesswork. 2. Precision Ordering: The system manages the full order-taking lifecycle, capturing Name, Phone, Email, and specific order details. It confirms all data with the guest before finalization to ensure accuracy. 3. Automated Management: Once an order is placed, the system automatically processes and logs the data into a centralized management system. It formats timestamps for human readability and sets a "Pending" status for immediate kitchen fulfillment. 4. Operational Efficiency: By handling front-of-house calls 24/7, Ayaan allows staff to focus on food quality and in-person service, ensuring Urban Flames never misses a revenue opportunity.

RoboGripAI

This project presents a simulation-first robotic system designed to perform structured physical tasks through reliable interaction with objects and its environment. The system focuses on practical task execution rather than complex physics modeling, ensuring repeatability, robustness, and measurable performance across varied simulated conditions. Simulation-first robotic system performing structured physical tasks such as pick-and-place, sorting, and simple assembly. Designed for repeatable execution under varied conditions, with basic failure handling, environmental interaction, and measurable performance metrics. A key emphasis of the system is reliability under dynamic conditions. The simulation introduces variations such as object position changes, minor environmental disturbances, and task sequence modifications. The robot is designed to adapt to these variations while maintaining consistent task success rates. Basic failure handling mechanisms are implemented, including reattempt strategies for failed grasps, collision avoidance corrections, and task state recovery protocols. The framework incorporates structured task sequencing and state-based control logic to ensure deterministic and repeatable behavior. Performance is evaluated using clear metrics such as task completion rate, execution time, grasp accuracy, recovery success rate, and system stability across multiple trials. The modular system design allows scalability for additional tasks or integration with advanced planning algorithms. By prioritizing repeatability, robustness, and measurable outcomes, this solution demonstrates practical robotic task automation in a controlled simulated environment, aligning with real-world industrial and research use cases. Overall, the project showcases a dependable robotic manipulation framework that bridges perception, decision-making, and action in a simulation-first setting, delivering consistent and benchmark-driven task execution.