Gemini 3 Flash

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Gemini 3 Flash

Gemini 3 Flash is a highly efficient and speed-optimized multimodal AI model developed by Google DeepMind. As part of the next generation of Gemini models, Flash is designed to excel in agentic tasks, offering advanced reasoning and thinking capabilities with a focus on high throughput and low latency. This model is ideal for applications requiring rapid responses and complex processing across various data modalities.

General
Author	Google DeepMind
Release Date	2025
Website	https://deepmind.google/
Documentation	https://ai.google.dev/gemini-api/docs/gemini-3
Technology Type	LLM

Key Features

Speed-Optimized: Engineered for fast inference, making it suitable for real-time applications and high-volume workloads.
Multimodal Capabilities: Processes and understands information from various modalities, including text, images, and potentially audio/video.
Advanced Reasoning: Supports sophisticated reasoning and problem-solving for complex agentic tasks.
Agentic Workflows: Designed to power autonomous AI agents, enabling them to plan, act, and interact intelligently.
Scalable Performance: Balances high performance with resource efficiency for broad deployment.

Start Building with Gemini 3 Flash

Gemini 3 Flash provides developers with a powerful, speed-optimized model for building responsive and intelligent AI applications, especially those focused on agentic workflows. Its multimodal capabilities and advanced reasoning make it a versatile tool for integrating cutting-edge AI into products and services. Explore the developer guide to harness the full potential of Gemini 3 Flash.

👉 Gemini 3 Developer Guide 👉 Google DeepMind Research

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Google Gemini 3 Flash AI technology Hackathon projects

Discover innovative solutions crafted with Google Gemini 3 Flash AI technology, developed by our community members during our engaging hackathons.

CIPHER AI

Cipher is an intelligent workflow orchestration platform designed to simplify how people build, automate, and optimize complex processes. Instead of relying on rigid automation rules, Cipher combines the power of AI-driven agents, real-time collaboration, and adaptive decision-making to create workflows that can think, respond, and evolve. Users can visually design workflows, connect specialized AI agents, and monitor every step of execution through a unified interface. Cipher not only automates repetitive tasks but also detects errors, provides contextual assistance, and recommends improvements to enhance efficiency and reliability. Whether it's streamlining business operations, accelerating software development, managing content pipelines, or coordinating data-driven tasks, Cipher adapts to diverse use cases without requiring extensive technical expertise. Built with a focus on security, scalability, and usability, Cipher empowers individuals and teams to transform ideas into intelligent processes that deliver meaningful outcomes. By bridging the gap between traditional automation and autonomous AI collaboration, Cipher enables organizations to work faster, make better decisions, and unlock new levels of productivity in an increasingly complex digital world.

HOLY-UNDERWRITERS

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TELMED AI DOCTOR

Telmed AI Doctor is an AI-powered telemedicine platform built to close Africa's healthcare access crisis. Nigeria has only 0.4 doctors per 1,000 people. Millions suffer from treatable conditions simply because qualified medical guidance is out of reach. Telmed deploys AI as the first line of medical response — available 24/7, on any device, at zero cost to the patient. Users interact via text, voice, or image. They describe symptoms naturally or photograph affected areas and receive a structured clinical interpretation, severity classification — Low, Moderate, or Urgent — a personalised medication schedule, and clear escalation triggers for when a human doctor is immediately required. The AI engine runs on a multi-model cross-validation architecture. Google Gemini handles primary diagnostic reasoning while DeepSeek independently validates the same input — eliminating hallucination risk on clinical outputs. Google Cloud Vision powers image-based symptom analysis. Google Places connects users to the nearest verified pharmacy. Built on Node.js and Express.js with a vanilla JavaScript frontend optimised for low-bandwidth African mobile networks. Recognised as a Top 100 project out of 3,000+ submissions at the lablab.ai AI Agent Olympics Hackathon, May 2026. Telmed is not a product. It is infrastructure. The kind Africa should have built a decade ago.

HazardMind AI

HazardMind AI is a multi-agent disaster response system built for the Band of Agents Hackathon. When a disaster is reported, four specialized AI agents collaborate through Band to deliver a complete situational analysis in under 60 seconds. Agent 1 — Satellite Agent fetches real Sentinel-1 SAR and Sentinel-2 optical imagery from Copernicus, automatically selecting the optimal satellite based on cloud cover and disaster type. It processes multi-band imagery using NDWI, NDVI, and SAR indices to produce georeferenced classification maps and vectorized risk zones. Agent 2 — Hazard Detection Agent cross-validates satellite findings with GDACS and USGS real-time disaster data to confirm risk zones and severity levels across flood, earthquake, and landslide scenarios. Agent 3 — Impact Assessment Agent overlays WorldPop population density data with OSM infrastructure to calculate affected populations, hospitals at risk, blocked roads, and optimal evacuation routes via OpenRouteService. Agent 4 — Executive Report Agent compiles all findings into an interactive MapLibre dashboard with satellite overlays, risk zone layers, and a downloadable PDF report with AI-generated executive summary. All four agents communicate through Band — confirming findings, escalating critical discoveries, and maintaining a visible discussion log that provides full auditability of every decision made during the response. Built for NDMA, UN agencies, and disaster response organizations worldwide.

Rascue and AI

RescueBand AI is a next-generation multi-agent emergency response platform designed to improve wildlife conservation and disaster rescue operations through collaborative artificial intelligence. When an incident is reported through text, image upload, drone imagery, or field observations, RescueBand AI automatically activates a coordinated network of specialized AI agents. These agents work together to analyze the situation, assess risks, identify affected species, recommend rescue protocols, generate response plans, and communicate actionable intelligence to rescue teams. The platform consists of multiple collaborative agents including a Drone Vision Agent, Environmental Analysis Agent, Research Agent, Mission Planner Agent, Communication Agent, Validation Agent, and Biodiversity Intelligence Agent. Each agent performs a specialized task and shares information with the others to create a complete rescue workflow. One of the platform's core innovations is Guardian Vision, an AI-powered image analysis system capable of identifying wildlife species, detecting injuries, estimating severity levels, and automatically triggering the rescue workflow. The system can generate incident severity scores, recommended equipment lists, emergency action plans, and downloadable SOS rescue reports. Beyond emergency response, RescueBand AI focuses on prevention and conservation. The system transforms fragmented rescue efforts into a coordinated intelligence network where AI agents collaborate in real time. This reduces response delays, improves decision-making, supports conservation organizations, and helps protect vulnerable wildlife populations. RescueBand AI demonstrates how multi-agent AI can move beyond simple automation and become an active partner in wildlife protection, environmental monitoring, and disaster response. Our vision is to create a future where AI not only helps rescue lives but also preserves biodiversity and strengthens ecosystem resilience worldwide.

M.A.S.H: Multi Agent System for Hospitals

M.A.S.H (Multi Agent System for Hospitals) is a decentralized healthcare orchestration platform comprising a Desktop Interface for doctors/pharmacists, and a Mobile Interface for patients. The system is designed with a minimal, jargon-free UI to streamline clinical workflows and remove patient administrative friction. Upon system boot, M.A.S.H spools up 6 specialized AI agents. Using the Band of Agents SDK (BandSDK), these agents are distributed across multiple virtual, secure rooms (Reception & Location Room, Clinical Consultation Room, and Pharmacy & Inventory Room) to coordinate patient visits while protecting event traffic. 1. For Patients: Patients can book appointments from anywhere via the Mobile Interface. During booking, they interact with a chatbot regarding their symptoms. The Registration Agent automatically assigns the most suitable doctor by matching the patient’s issue with doctors’ specialties and availability. On arrival, the Patient Navigation Agent guides the patient directly to the doctor’s room. 2. For Doctors: In the Consultation Room, the Patient Summary Agent aggregates medical histories, lab results, and surgeries from Supabase. Written prescriptions trigger the Medicine/Prescription Management Agent. 3. For Pharmacy & Stock: In the Pharmacy Room, the Medicine/Prescription Management Agent audits stock level. If available, it routes the order to the pharmacy. If out-of-stock, it requests human-in- the-loop validation for alternate drug comments. Concurrently, the Stock Management Agent tracks usage trends to automate reorders. This multi-room agent mesh completely automates administrative handoffs in real-time.