Gemini 3 Pro

Gemini 3 Pro is Google DeepMind's flagship frontier AI model, representing the pinnacle of their multimodal understanding and reasoning capabilities. Designed for complex, high-stakes tasks, Gemini 3 Pro pushes the boundaries of artificial intelligence, offering state-of-the-art performance across various data types and problem domains.

General
Author	Google DeepMind
Release Date	2025
Website	https://deepmind.google/
Documentation	https://aistudio.google.com/models/gemini-3
Technology Type	LLM

Key Features

State-of-the-Art Performance: Delivers industry-leading results across a broad spectrum of benchmarks in multimodal understanding and reasoning.
Multimodal Capabilities: Seamlessly processes and integrates information from text, images, audio, and video for holistic understanding.
Advanced Reasoning: Excels in complex reasoning, problem-solving, and abstract thinking tasks.
Frontier Model: Represents the cutting edge of AI development, designed for the most challenging applications.
Scalable and Versatile: Capable of handling diverse workloads, from intricate scientific research to advanced creative generation.

Start Building with Gemini 3 Pro

Gemini 3 Pro offers developers access to Google's most advanced AI model, enabling the creation of applications that require sophisticated multimodal understanding and reasoning. Whether for scientific discovery, complex data analysis, or highly creative tasks, Gemini 3 Pro provides unparalleled capabilities. Explore the overview and documentation to begin integrating this frontier model into your projects.

👉 Gemini 3 Overview 👉 Google DeepMind Research

AI workflow automatic agent

Executive Summary An autonomous big-data processing engine designed to re-render and remaster legacy video courses into modern, publication-grade digital documents (.docx) using 100% AI. Whenever clients request a visual and quality upgrade for their educational catalogs, this platform ingests raw lecture videos and fully reconstructs them into structured, high-resolution, branded training manuals—completely eliminating manual authoring overhead. Smart Ingestion & Voice Preservation Automatically connects to cloud storage via headless browsers, forcing high-definition streams ( ≥ 720 p / 1080 p ≥720p/1080p) while preserving 100% lossless raw audio (captured.audio / .m4a) as permanent assets. AI Speech-to-Text & Knowledge Structuring Transcribes spoken lectures into grammatically correct text (.txt) and time-stamped cue files (.srt) using Whisper AI models, structuring raw speech into pedagogical chapters. 100% AI Visual Remastering & Inpainting Clutter Removal: Erases distracting webcams, legacy logos, and watermarks via AI inpainting and smart cropping. 4x AI Super-Resolution: Upscales blurry software UI screenshots with Real-ESRGAN ONNX neural networks so menus, buttons, and code snippets become crystal sharp. Automated Layout & Document Synthesis Re-renders content into modern, executive-grade templates (dark navy & gold styling) with numbered step-by-step markers (①②③④) and compiles everything into final, formatted .docx handbooks ready for immediate distribution.

Restock Copilot Lite

Restock Copilot Lite is an inventory forecasting tool designed to tell small shops what to reorder and why, without relying on external cloud APIs or expensive GPU clusters. The system tests three forecasting methods (4-week baseline, seasonal, and smoothed trend) on 105 weeks of UK retail data to predict demand for 30 products over an 8-week pre-Christmas simulation. The simulation selected the smoothed method based on a strict pre-agreed rule of lowest forecast error (0.5077), though unmet demand results were mixed. To meet the AMD hardware requirement securely, we eliminated live API dependencies. Plain-language explanations for all restocking actions were generated offline using the open-source Qwen/Qwen2.5-1.5B-Instruct model running via PyTorch purely on the CPU of an AMD Ryzen 7 7735HS laptop. The FastAPI backend serves these static, pre-computed outputs, falling back to a factual template if a check fails. The language model does not forecast, and the stock mentioned in explanations is strictly simulated.

Dukaan Dost: AI Stock Forecast for Kiryana Stores

# Inspiration & Problem Neighborhood grocery (kiryana) shops in emerging markets operate on thin margins, high cash-flow constraints, and informal supplier delivery cadences. Most shopkeepers restock based on gut feel or basic static averages, resulting in frequent stockouts of high-velocity staples like milk, tea, and flour, or excess dead capital tied up in slow-moving inventory. # What Dukaan Dost Does Dukaan Dost turns daily sales logs into actionable inventory intelligence: - **Statistical Demand Forecasting:** Models seasonal weekday demand variations and level-smoothed trends across products over a rolling 7-day horizon. - **Dynamic Stockout Prediction:** Evaluates supplier visit cycles and delivery lead times to compute precise restock triggers, unit counts, and estimated restock costs. - **Rigorous Verification & Simulation:** Features a transparent 28-day historical replay simulator that proves stockout reduction versus traditional baseline ordering rules without data leakage. - **Accessible, Multilingual AI Ordering:** Uses AMD Token Factory open models to convert mathematical forecasts into human explanations and supplier WhatsApp restock messages in both English and Roman Urdu, backed by a strict anti-hallucination guardrail that blocks any draft containing unverified numbers. # Architecture & Tech Stack - **Frontend & Interface:** Lightweight, responsive vanilla web architecture with custom canvas-based data visualizations, printable distributor restock slips, and bilingual localization toggles. - **Forecasting Engine:** Deterministic level-smoothing and weekday factor algorithm executed client-side. - **AI & Acceleration:** Serverless API integration powered by AMD Token Factory running open open-weights LLMs for contextual explanation and verified supplier message generation.

RoboProof: Motion-Learning Lab

RoboProof is a physical-AI research prototype for improving how a VEX V5 robot moves. Instead of asking a language model to control motors directly, it combines a Guiding Vector Field (GVF), which provides a structured path-following reference, with a TD3 reinforcement-learning policy that learns residual adjustments from simulated sensor feedback. The system uses the project’s original Simulator/iraLIB so candidate policies can be compared with the existing controller and an untrained policy under repeatable conditions. Our primary learned candidate is a 512-wide, 10-hidden-layer network. We are training it on an AMD Instinct MI300X and preserving run data, checkpoints, and evaluation evidence. The goal is to determine whether learning improves movement reliability and efficiency—not to assume that a larger network or more GPU utilization guarantees better results. RoboProof is still experimental. Training is underway, and the learned policy has not yet passed the independent qualification gate. We are looking for robotics, controls, reinforcement-learning, and simulator contributors to help valid

CareAuth-ROCm: Autonomous Clinical Prior-Auth

CareAuth-ROCm is an autonomous, audit-grade Clinical Prior Authorization platform engineered for the AMD AI ecosystem and Track 2 of the AMD Hackathon. In modern healthcare, physicians lose over 14 hours every week navigating opaque insurance portals, while patients suffer critical 3 to 7 day delays for surgeries and vital biologic therapies. CareAuth-ROCm fundamentally disrupts this administrative crisis by fusing the massive compute capability of AMD Instinct MI300X accelerators with the agentic orchestration of Evolus. By harnessing AMD ROCm 6.2, HIP-accelerated kernels, FlashAttention-2, and 192GB of ultra-fast HBM3 memory, our local LLM inference pipeline evaluates complex, unstructured Electronic Health Records (EHR) against dynamic payer policies in under 35 milliseconds. Because inference runs entirely on-premise, patient health information never leaks to external proprietary APIs, ensuring full HIPAA sovereignty. The platform delivers a role-based, end-to-end clinical workflow: 1. Prior-Auth Coordinator Cockpit: Evaluates multi-tier clinical cases with real-time confidence scores and diagnostic radar criteria. 2. Clinical Uncertainty Gap Detection: Automatically flags missing step-therapy evidence rather than issuing arbitrary denials. 3. Doctor Certification Station: Equips attending physicians with 1-click electronic attestation backed by EHR notes, embedded DICOM PACS MRI imaging, and transparent policy citations. 4. Lab Official Station: Streamlines high-resolution imaging and diagnostic report verification. 5. MyCare Patient Portal: Translates complex medical decisions into accessible 6th-grade language across multiple languages with transparent out-of-pocket co-pay estimates. CareAuth-ROCm proves that sovereign AMD hardware and intelligent agentic workflows can eradicate bureaucratic friction, protect clinical outcomes, and save billions in healthcare waste.

Turno

Italian regions ban outdoor work from 12:30 to 16:00 on days rated ALTO for heat risk. A supervisor still has to plan the other hours, prove the ban was kept, and brief the crew. On ERA5 (June to August 2026, five sites), 59 to 66% of heat-restricted working hours fall outside the ban window. Turno plans those hours. How it works. ECMWF's 10-member open-data ensemble gives WBGT for every member and hour (Liljegren, thermofeel). Fixed NIOSH limits set 60, 45, 30, 15 or 0 minutes of work per hour, planned on the hottest member. Hours where members are 30 minutes of work apart go to the supervisor with the reason. The supervisor confirms the Worklimate rating; until then the ban is assumed. Output: a day strip, a bilingual crew briefing, a compliance log. On AMD. AMD Token Factory (AMD Radeon Cloud) runs the ordinance reader: MinerU2.5-Pro reads each region's signed PDF and DeepSeek-V4-Flash proposes every rule field with a verbatim quote. Code accepts a field only if the quote is in the document and contains the value. DeepSeek-V4-Flash also explains each hand-off. Off AMD: Gemma 4 writes the crew briefing through Google's Gemini API, and the forecast is ECMWF open data. No GPU instance was available, so the planned Aurora run on an MI300X did not happen. Pre-registered results: settings chosen on June dates; July and August (10 dates, 5 sites) untouched. One day ahead, a single ECMWF forecast over-planned work in 6.6% of heat-restricted hours; Turno in 1.1%, handing 3.0 to 17.0% of working hours to the supervisor. The reader accepted 0 wrong fields across two runs, and a live Lombardia re-read verified 7 of 7. Limits. Truth is ERA5 (0.25 degrees); there is no thermometer on site. ECMWF open data is 3-hourly. The number check proves every number came from the plan; it does not prove each sentence is right. Not tried with a real crew; no substitute for the employer's risk assessment. Live app: turno.aashanjaved.com. Code (MIT): github.com/Aashan47/turno