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.

Compliance Guardian - Multi-Agent Compliance

Compliance Guardian automates financial transaction compliance using 3 specialized agents communicating through Band as the collaboration layer. Agent 1 - Risk Analyst (risk_scanner): Scans transactions for suspicious patterns including high-risk jurisdictions (KY, PA), large amounts ($10k+), and new customers. Sends risk assessments via Band to Agent 2. Agent 2 - Compliance Officer (compliance_reviewer): Applies BSA/AML regulations, identifies SAR filing requirements, and determines escalation needed. Escalates high-risk cases via Band to Agent 3. Agent 3 - Human Review Coordinator (review_coordinator): Manages the escalation queue, collects Approve/Reject decisions with comments, and maintains a complete audit trail with timestamps. Business Value: 80% reduction in manual compliance review time, automatic suspicious activity detection, and complete regulatory audit trail. Built for Band of Agents Hackathon 2026 - Track 3: Regulated & High-Stakes Workflows.

FinGuardAI fraud detection

**FinGuardAI** is an AI-powered financial security and fraud detection platform designed to help businesses and individuals manage financial activities with greater security, accuracy, and intelligence. The platform uses advanced artificial intelligence and machine learning to monitor transactions in real time, detect suspicious activities, identify fraudulent patterns, and generate actionable insights to reduce financial risks. FinGuardAI goes beyond traditional fraud prevention by providing smart financial analytics, spending behavior monitoring, and risk assessment tools that support informed decision-making. The system is designed to improve transparency, strengthen cybersecurity, and enhance operational efficiency in financial management. Built with scalability and real-world adaptability in mind, FinGuardAI can support organizations of different sizes while maintaining secure and reliable performance. Our mission is to redefine financial security through intelligent automation, enabling safer transactions, proactive fraud prevention, and smarter financial management for a rapidly evolving digital economy.

KINANTI Well Ops Agent System

KINANTI Well Ops Agent System is a multi-agent collaboration platform designed for oil and gas production operations. The system helps teams diagnose well performance issues, assess ESP health, plan interventions, review HSE requirements, and make operational decisions through a coordinated agent workflow. Instead of treating one agent’s diagnosis as the final answer, KINANTI uses Band as the shared collaboration layer. Each specialized agent reads the existing Band context, contributes its own analysis, and hands off structured findings to the next agent. If the Intervention Planner or HSE Reviewer detects incomplete data, model mismatch, or operational risk, the workflow can trigger a diagnosis pivot and send the case back for reassessment. The goal is not only faster diagnosis, but faster alignment when well behavior changes. Every hypothesis, challenge, revision, and final decision is recorded as part of the workflow, making the process more transparent and auditable for production, engineering, and safety teams.

MediChain AI Prior Auth Review

MediChain AI is a multi-agent healthcare prior authorization review system built for the Band of Agents Hackathon 2026. The Problem: Medical prior authorization costs US hospitals $10B/year. A single case bounces between 5 departments over 2–14 days through manual emails and phone calls. The Solution: Five specialized AI agents collaborate through a shared Band room to complete the full review in under 60 seconds: - IntakeAgent extracts and structures the case (GPT-4o via AI/ML API) - MedicalNecessityAgent reviews clinical evidence (GPT-4o via AI/ML API) - InsurancePolicyAgent checks coverage prerequisites (Mistral-7B via Featherless AI) - ComplianceRiskAgent flags privacy and audit requirements (GPT-4o via AI/ML API) - DecisionCoordinator synthesises all findings into a final packet (GPT-4o via AI/ML API) Band is central — not a wrapper. Every agent reads previous agents' findings from the Band room before acting. The Band room serves as persistent shared context, coordination layer, and HIPAA-compliant audit trail. Demo: A doctor requests MRI approval for chronic lower back pain. Policy requires 6 weeks of conservative treatment. Physical therapy records are missing. System returns: NEEDS MORE INFORMATION — with exact missing document list in under 60 seconds. The system always requires human sign-off. It never makes final medical or insurance decisions. All data is synthetic. Not medical advice.

Supply Chain-sentinel

SupplyChain Sentinel AI Autonomous Multi-Agent Supply Chain Intelligence Platform SupplyChain Sentinel AI is an advanced platform designed to help organizations manage supply chain disruptions, supplier risks, and sourcing decisions. Utilizing specialized AI agents, predictive models, and strategic reasoning, it creates a real-time digital operations center for monitoring and responding to market disruptions. Unlike traditional supply chain management, which relies on manual analysis and reactive decision-making, SupplyChain Sentinel AI enables proactive management through a coordinated ecosystem of AI agents. Key agents include: - Signal Monitoring Agent: Analyzes supply chain events and market indicators to identify risks and opportunities. - Disruption Detection Agent: Assesses the severity and operational impact of disruptions. - Alternative Supplier Agent: Searches for viable sourcing alternatives based on compatibility, capacity, and constraints. - Risk Scoring Agent: Generates risk scores and reliability assessments using AI/ML models. - Strategy Agent: Synthesizes intelligence to create actionable recommendations such as supplier substitutions or logistics adjustments. Band orchestrates communication and coordination among the agents, allowing collective intelligence to drive decision-making. The platform features a real-time dashboard for monitoring disruptions, supplier intelligence, risk analytics, and agent activities, helping organizations transition from reactive to proactive, intelligence-driven operations. Applicable across various sectors like manufacturing, logistics, healthcare, and government, SupplyChain Sentinel AI provides a scalable foundation for autonomous operational intelligence.

PulseSignal: Executive GTM Strategy Engine

PulseSignal is a next-generation Market Intelligence platform built for Go-To-Market (GTM) and Sales teams. The platform solves the problem of 'lagging indicators' by treating unstructured web data—specifically high-velocity hiring and news—as leading indicators of corporate strategy. Our autonomous pipeline executes in three stages: 1. Live Data Intercept: Powered by Bright Data SERP API, we perform targeted crawls of Google Search and career pages. 2. Data Structuring: Google Gemini Flash parses messy HTML into strict JSON, identifying niche skills and business priorities. 3. Executive Synthesis: We implemented a Hybrid Multi-Model Architecture, routing complex reasoning through DeepSeek V4 Flash (via AI/ML API) to generate persona-specific playbooks for Sales, Recruiting, and Investors. Key features include a dynamic 'Manager's Weekly Report' with an Account Prioritization Matrix, an Evidence Ledger for transparency, and a 'Magic, Moment' Gmail integration that takes an AI-drafted email and pre-loads it, directly into the user's browser, ready to send. PulseSignal doesn't just show data; it generates quantifiable ROI.