DeepSeek R1

Top Builders

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

DeepSeek R1

General
Release date	2023
Author	DeepSeek
Website	DeepSeek Models
Repository	https://github.com/deepseek-ai
Type	Foundation Language Model

The DeepSeek R1 model provides a lightweight yet powerful solution for basic natural language processing tasks. Optimized for speed and efficiency, this model delivers reliable performance for text classification, entity recognition, and simple text generation.

Key Features

4K Token Context Window: Handles medium-length documents effectively
Multi-Lingual Support: Base capabilities in 5 major languages
Low Resource Requirements: Runs efficiently on standard hardware
Fine-Tuning Ready: Compatible with common ML frameworks

Useful Links

👉 [Deepseek R1 Paper] (https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf) 👉 [Access on Hugging Face] (https://huggingface.co/deepseek-ai/DeepSeek-R1) 👉 [Try Deepseek] (https://deepseek.com) 👉 [API Documentation] (https://api-docs.deepseek.com/)

Edit on GitHub

Deepseek DeepSeek R1 AI technology Hackathon projects

Discover innovative solutions crafted with Deepseek DeepSeek R1 AI technology, developed by our community members during our engaging hackathons.

Sentinel

Sentinel is an autonomous, multi-agent architecture review system designed for regulated and high-stakes enterprise environments. In industries like finance, healthcare, and insurance, deploying new technical workflows requires rigorous scrutiny across multiple domains—security, compliance, and IT governance. Manual reviews are often massive bottlenecks. Sentinel solves this by orchestrating a team of specialized AI agents through the Band collaboration layer to autonomously debate, audit, and score technical proposals. Built specifically for Track 3 (Regulated & High-Stakes Workflows), Sentinel goes beyond simple linear automation or thin API wrappers by utilizing Band as a true shared interaction layer. The workflow is managed by the Conductor agent, which ingests technical architecture documents and coordinates the room. The Harness agent injects historical company policies and compliance baselines directly into the shared workspace. Operating in parallel, the Adversary aggressively red-teams the architecture for critical security flaws (such as prompt injection vulnerabilities in LLM execution flows), while the Guardian audits for regulatory violations (such as GDPR or SOC2 data handling failures). Because these agents operate within a shared Band chat room, they do not work in silos. Once the specialized audits are complete, the Evaluator reads the room's context to synthesize the distinct flags into a cohesive architectural review. Finally, the RiskScorer processes this evaluation to generate a definitive, quantitative risk matrix and an automated approve/escalate decision payload. By demonstrating real agent-to-agent collaboration, role specialization, complex context exchange, and task handoffs, Sentinel proves that multi-agent systems can handle complex, regulated decision-making safely, transparently, and efficiently.

Senatus AI - Enterprise Investment Committee

Senatus AI is an enterprise multi-agent investment committee platform built natively on Band. When a portfolio manager submits a stock ticker and investment thesis, five specialized AI agents convene in a dedicated Band room and conduct structured adversarial due diligence: a ResearchAgent pulls live market data and financials to open the deliberation, a BullAnalyst constructs the strongest case for the position and files a formal BUY motion, a BearAnalyst directly challenges the bull thesis point-by-point and files an AVOID motion, a ComplianceOfficer screens the full debate for regulatory red flags and can escalate and pause the room at any point mid-deliberation, and a SynthesisChair reads the entire committee transcript, tallies the motions, and delivers a final Buy, Hold, or Avoid verdict with a conviction score. The human chairperson holds final authority throughout, they can intervene at any stage of the debate, interrogate any agent directly in the Band room, and must formally approve, override, or request further analysis before the decision is sealed. Every agent message, motion, rebuttal, escalation, and human action is permanently logged in Band as an immutable audit trail, giving compliance teams a complete chain-of-evidence for every investment decision. Band is not a notification layer in Senatus AI, it is the deliberation chamber itself.

EthicsBoard AI — Multi-Agent IRB Review

Institutional Review Board (IRB) approval is the mandatory gate for any research involving human subjects — and today it takes six to twelve weeks of fragmented email review. A single overlooked consent clause restarts the clock. It isn't a knowledge problem; it's a coordination problem. EthicsBoard AI is a multi-agent IRB review platform built for Track 3 (Regulated & High-Stakes Workflows). Four specialist agents — Protocol, Ethics, Privacy, and a Committee coordinator — collaborate entirely through Band to review a research protocol end to end. Crucially, no agent calls another directly: every analysis, handoff, and decision flows through a single Band room via @mention routing, and that room becomes the tamper-evident, timestamped audit record regulators require. The workflow is genuinely non-linear. The Protocol agent classifies risk and routes the review (expedited vs. full board under 45 CFR 46). Ethics and Privacy then review in parallel, flagging deficiencies with exact citations — for example, missing written assent for minors (45 CFR 46.408) and a missing HIPAA Business Associate Agreement (45 CFR 164.308). Before any human is involved, the Committee challenges the Ethics agent on its most severe finding, and Ethics must defend whether it is blocking or resolvable by minor revision. Only then does the Committee invoke Band's add-participant service to bring a human IRB Chair into the same room for the final, legally binding decision. Each agent runs on a different framework — LangGraph, Pydantic AI, CrewAI, and FastAPI — powered by open and frontier models through Featherless AI and the AI/ML API. The result is a true coordination layer: four agents that communicate, share structured context, disagree, escalate, and defer to a human — making the collaboration visible, useful, and central.

AegisMesh: Multi-Agent Security Remediation

AegisMesh is a Band-powered multi-agent security platform that transforms vulnerability remediation from a manual process into an autonomous, explainable workflow. Traditional security scanners identify vulnerabilities but leave developers responsible for designing fixes, validating those fixes, and determining whether the application is actually secure. AegisMesh addresses this challenge using three specialized AI agents coordinated through Band. The Blue Coder Agent generates remediation patches for vulnerable code. The Red Auditor Agent performs adversarial security analysis using Graph-of-Thought reasoning to challenge those patches and search for exploit paths. The Security Intelligence Agent evaluates the overall security posture and produces a final risk assessment and recommendation. Band serves as the coordination layer between agents, enabling role specialization, task handoffs, shared context, task state management, transcript generation, and workflow orchestration. Vulnerabilities move through a structured lifecycle including triage, patch generation, adversarial validation, and security intelligence reporting. AegisMesh emphasizes explainability and auditability. Users can inspect attack paths evaluated by the auditor, review reasoning behind findings, and explore complete agent transcripts that show how remediation decisions were reached. The platform also includes an AI/ML intelligence dashboard that provides visibility into model usage, token consumption, request volume, and inference costs during execution. The system combines frontier AI models selected for their strengths: Qwen3-Coder for patch generation, DeepSeek for adversarial auditing, and GPT-4o for security intelligence. By combining specialized AI agents coordinated through Band, AegisMesh moves beyond vulnerability detection to deliver autonomous remediation, adversarial validation, explainable reasoning, and actionable security intelligence in a single workflow.

CORDANE

When a large enterprise reviews a vendor contract, the document is typically passed sequentially between Legal, Finance, Risk, and Operations. Everyone has a different perspective and specific constraints, causing negotiations to stall for weeks. We built Cordane to collapse that timeline into seconds. Cordane is a multi-agent consensus engine built entirely on the Band coordination layer. Rather than using a generic agent swarm, we engineered a strict Mixture of Experts (MoE) architecture. We mapped four specific frontier models to specialized corporate roles: Legal (Claude 3.5 Sonnet) analyzes exposure, Finance (GPT-4o) evaluates margin risk, Risk (DeepSeek Reasoner) ensures global compliance, and Operations (Llama 3 70B) checks SLA feasibility. When a contract is uploaded to the Next.js dashboard, the FastAPI orchestrator initializes a Band Shared Room. All four agents read the document simultaneously. What makes Cordane unique is that they do not work in silos—they negotiate. If Legal flags an uncapped indemnity clause, Finance dynamically reads that task state via Band and immediately recalibrates its margin assessment in real-time. To make this enterprise-ready, we built a strict "Consensus Evaluator" algorithm. Cordane does not force hallucinations just to reach an agreement; if the agents hit a mathematical deadlock on risk tolerance, the system safely halts and escalates the decision to a human executive. Finally, every single API call, constraint check, and agent message in the Shared Room is compiled into an Immutable Audit Log on the UI, giving enterprise governance teams complete visibility into how the AI arrived at its verdict.

VoiceHire: AI Recruitment Workspace

Every recruiter knows the pain: dozens of CVs to screen, interview slots to coordinate, notes scattered across tools, and hiring decisions that rely more on memory than evidence. VoiceHire brings it all into a single workspace where AI does the repetitive work so recruiters can focus on what matters. Upload a batch of resumes and the platform extracts structured profiles with skills, experience, and education. Describe a role and it auto-generates a detailed job posting. With one click, the Candidate Matcher ranks every applicant against the requirements scoring on skills, experience, past performance, and culture fit and explains exactly why each candidate is a strong match or where their gaps are. The real breakthrough is the interview itself. When a candidate joins, six AI agents collaborate across three dedicated rooms. One builds a tailored competency rubric from the job and resume. Another generates probing questions targeting specific skill gaps. A voice agent delivers them naturally while transcribing every word. Two evidence extractors one technical, one behavioral analyze responses for genuine signals. A skeptic runs in the background challenging weak claims and flagging inconsistencies. After the interview, three agents deliberate as a committee, weighing evidence from both sides before reaching a hiring recommendation. Recruiters see everything live: competency coverage updating in real time, integrity flags appearing as suspicious behavior is detected, and multiple interviews running side by side. When it's done, there's a complete evidence portfolio with every question, every answer, every signal extracted, and the full deliberation transcript. No more "I think they were good" just auditable, defensible hiring.