DeepSeek V3

Top Builders

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

DeepSeek V3

General
Release date	2024
Author	DeepSeek
Website	DeepSeek Models
Repository	https://github.com/deepseek-ai
Type	MoE (Mixture of Experts) Language Models

The DeepSeek V3 model represents our most advanced AI architecture, designed for complex reasoning tasks and code generation. With enhanced context handling and improved instruction following, this model excels in technical applications and enterprise deployments.

Key Features

DeepSeek-V3: 671B parameters (37B activated per token), optimized for math, code, and multilingual tasks.
Code Generation: Supports 12+ programming languages
Advanced Reasoning: Chain-of-thought capabilities for multi-step problems
Enterprise-Grade Security: Built-in content filtering and compliance features
Speed: 3x faster generation than previous versions (60 TPS)
Open-Source: FP8/BF16 weights available on Hugging Face

Usefull Links

👉 Local Deployment Guide for DeepSeek V3 👉 Model Weights on Hugging Face 👉 API Documentation 👉 Deepseek V3 Paper 👉 Performance Highlights

Edit on GitHub

Deepseek DeepSeek V3 AI technology Hackathon projects

Discover innovative solutions crafted with Deepseek DeepSeek V3 AI technology, developed by our community members during our engaging hackathons.

Order Block detector with LLM advice

This implementation demonstrates the successful integration of a 120 billion parameter Large Language Model (LLM) hosted on AMD Cloud with an algorithmic trading system based on the Order Block strategy. The LLM acts as an expert consultant that validates and optimizes trading decisions in real-time. The use of **AMD Cloud GPT OSS 120B** brings an advanced artificial intelligence layer to the trading bot, enabling more nuanced and contextual decision-making than traditional algorithmic rules. ### Key Features - Real-time signal validation by 120B parameter LLM - Expert reasoning for each trading decision - Automatic fallback if LLM is unavailable - Complete logging of LLM consultations - Configurable confidence thresholds

AI Trading Agent

I built an autonomous AI trading agent for the Kraken x ERC-8004 Hackathon as a solo developer in just 1.5 days. My agent is currently ranked #4 on the official leaderboard with a validation score of 97/100 and reputation of 97/100. The agent registers on-chain identity via ERC-8004 (Agent ID 31), executes trades through the Risk Router, and posts validation checkpoints to the Validation Registry. It has executed 400+ successful trades on Sepolia testnet. Key achievements: - Ranked #1 out of 30+ agents - 97 validation score (tied for highest) - 97 reputation (highest among all agents) - 26+ successful on-chain trades - Built solo in 1.5 days The agent uses the official hackathon shared contracts and operates through the Hackathon Capital Sandbox. Every trade is signed with EIP-712 signatures and posted to the Validation Registry for transparency. GitHub: https://github.com/cluna80/ai-trading-agent-template Leaderboard: https://lablab.ai/event/ai-trading-agents-hackathon/leaderboard

AutoClaw - Self-Evolving Agent Economy

AutoClaw introduces a revolutionary self-evolving agent economy where autonomous AI agents don't just execute tasks - they improve themselves. Built on OpenClaw's privacy-first runtime, our agents analyze their performance, identify weaknesses, and autonomously generate new skills using DeepSeek/Gemini AI models. The core innovation is a self-improvement cycle: agents execute tasks → analyze results → identify improvement areas → generate new code → test and deploy enhanced versions. This creates a continuously evolving system that gets smarter over time. We've integrated a complete economic layer using $SURGE tokens and the x402 protocol. Premium skills charge micro-payments (0.1-1.0 $SURGE per use) with automatic revenue sharing: 70% to skill creators, 20% to agent operators, 10% to network. This creates a sustainable ecosystem where developers earn from their skills. For hackathon compliance, our agents actively post on Moltbook (20+ posts during development) and have joined the LabLab submolt. The system features three specialized agents: Twitter Bot for social engagement, DeFi Analyzer for yield optimization, and Skill Generator that creates new capabilities. A beautiful FastAPI dashboard provides real-time monitoring of agent activity, payments, and learning progress. All data persists via SQLite memory, allowing agents to remember interactions across sessions. Built entirely open-source with MIT license, AutoClaw demonstrates what autonomous agents can achieve today while respecting user privacy through local execution.

Coffee Flow AI

Coffee Flow AI is an autonomous, simulation-first AI system designed to optimize coffee harvesting operations. Many coffee farms face inefficiencies due to manual decision-making, lack of real-time data, and limited automation. My solution simulates harvesting processes and applies AI-driven optimization to improve task execution, productivity, and operational efficiency. Through an interactive dashboard, I can monitor performance metrics, analyze outcomes, and make data-informed decisions. Coffee Flow AI targets coffee producers and agricultural cooperatives seeking scalable, data-driven solutions. The platform is designed to be adaptable to other crops and future integration with robotics and real-world sensors, contributing to increased sustainability and profitability in agriculture.

Warehouse Orchestrator AI

Warehouse Orchestrator AI is an AI-powered warehouse automation simulation platform designed to demonstrate how autonomous robots can optimize modern logistics operations. The system simulates a real warehouse environment with multiple zones (A, B, C, D, Packing, Charging Dock) where robots intelligently navigate, avoid obstacles, and complete delivery tasks. The platform includes a futuristic SaaS dashboard that provides real-time monitoring of robot status, battery levels, active tasks, performance analytics, and system logs. It showcases intelligent routing and decision-making logic that can scale to real-world warehouses. This project is built using HTML5 Canvas, JavaScript, and CSS, optimized for smooth real-time rendering, and deployed on Cloudflare Pages for fast global access. The architecture is designed to be cloud-ready and extendable for integration with Vultr infrastructure and real robotic systems.

RoboFleet AI Manager - Vultr-Powered

RoboFleet AI Manager is a comprehensive warehouse robotics control platform built for the AI Meets Robotics Hackathon (Track 3: Task Execution). This simulation-first solution provides end-to-end management of robotic fleets through an intuitive web dashboard. **Key Features:** • **Vultr Cloud Backend**: Fully deployed on Vultr infrastructure (45.63.4.225:5000) serving as the central control system • **Warehouse Digital Twin**: Interactive 10x10 grid map showing robot positions, charging stations, and inventory shelves • **AI Command Center**: Natural language processing for robot commands with Vultr-processed AI responses • **Task Execution System**: Pick, Move, Charge functionality for autonomous robot operations • **Real-time Analytics**: Live battery monitoring, efficiency metrics, and energy savings tracking • **Mobile-Optimized Dashboard**: Responsive design working perfectly on Android/iOS devices **Technology Stack:** • **Backend**: Flask Python API deployed on Vultr Cloud Compute • **Frontend**: HTML5/CSS3/JavaScript with responsive design • **AI Integration**: Custom NLP for warehouse command processing • **Simulation**: Real-time robot behavior simulation with battery drain/charge cycles **Business Impact**: Demonstrates 40% operational cost reduction through AI optimization, scalable to 100+ robots. Perfect for warehouse logistics, manufacturing, and smart factory operations. **Hackathon Compliance**: Meets all Vultr backend requirements, Track 3 task execution focus, and provides complete simulation environment without physical hardware.