OpenAI GPT-3.5 AI technology Top Builders

Explore the top contributors showcasing the highest number of OpenAI GPT-3.5 AI technology app submissions within our community.

OpenAI GPT-3.5

GPT-3.5 is a set of models that improve on GPT-3 and can understand as well as generate natural language or code. It is an autoregressive language model (LLM) from OpenAI that uses deep learning to produce human-like text. It is a fine-tuned version of GPT-3, the third-generation language prediction model in the GPT series created by OpenAI. GPT-3.5 has garnered significant attention and acclaim for its unparalleled ability to understand and generate human-like text. With an astounding 175 billion parameters at its disposal, GPT-3.5 stands as one of the most expansive and powerful language models ever constructed at the time of its release.

GPT-3.5's exceptional performance is not only limited to its sheer size but also stems from its highly refined architecture. Harnessing the power of deep learning, GPT-3.5 delivers consistently accurate and relevant results, elevating the standard for language models and establishing itself as a trailblazer in the field of artificial intelligence.

General
Relese date	March 15, 2022
Author	OpenAI
Type	Autoregressive, Transformer, Language model

Start building with OpenAI GPT-3.5

OpenAI GPT-3 has a rich ecosystem of libraries and resources to help you get started. We have collected the best GPT-3.5 libraries and resources to help you get started to build with GPT-3 today. To see what others are building with GPT-3, check out the community built GPT-3 Use Cases and Applications.

OpenAI GPT-3.5 Tutorials

Chroma Tutorial: How to give GPT-3.5 chatbot memory-like capability

👉 Discover more GPT-3.5 Tutorials on lablab.ai

OpenAI GPT-3.5 Boilerplates

Kickstart your development with a GPT-3.5 based boilerplate. Boilerplates is a great way to headstart when building your next project with GPT-3.

OpenAI GPT-3.5 Libraries

A curated list of libraries and technologies to help you build great projects with GPT-3.5.

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OpenAI GPT-3.5 AI technology Hackathon projects

Discover innovative solutions crafted with OpenAI GPT-3.5 AI technology, developed by our community members during our engaging hackathons.

VHeal AI

VHeal AI is an Agentic AI-powered hospital discharge copilot designed to streamline patient discharges by autonomously coordinating between hospital staff, systems, and workflows. In modern hospitals, discharging a patient is a multi-step, often delayed process involving doctors, nurses, transport, billing, and pharmacy. VHeal AI detects discharge triggers, reasons through medical workflows, completes checklists, and notifies stakeholders through messaging APIs like WhatsApp and SMS. It also generates discharge summaries using LLMs such as GPT-4o and Claude 3. The project uses a LangChain-based agentic framework, integrated with FastAPI, Streamlit/React, and real-time dashboards for staff. VHeal AI mimics a real hospital staff member, offering timely, autonomous, and safe discharge experiences—helping hospitals save beds, time, and lives.

SafeBridge AI toolchain C SmartContract Audit Tool

SafeBridge: AI-Powered Sentinel for Qubic Cross-Chain Security SafeBridge is a revolutionary AI-driven security auditor built for the Qubic ecosystem, specifically targeting the critical vulnerabilities plaguing cross-chain bridges. It transforms slow, manual audits into a real-time, automated defense system. Leveraging Qubic's unmatched speed (40M TPS) and C++ smart contract environment, SafeBridge proactively hunts for exploits like reentrancy attacks, logic flaws, and consensus exploits in bridge contracts. SafeBridge is an automated security sentinel for Qubic-to-EVM blockchain bridges, using AI-augmented static/dynamic analysis to hunt critical vulnerabilities like reentrancy attacks, logic flaws, and consensus exploits in C++ smart contracts. It scans contract code (e.g., detecting unsafe patterns like untrusted external calls before state updates via Clang AST parsing) while cross-referencing 500+ historical exploit signatures. Core Innovation: AI-Powered Analysis: Advanced ML models scan C++ bytecode (non-EVM), cross-referencing 500+ historical exploit signatures and simulating live attacks. Qubic-Optimized Architecture: Utilizes direct memory scanning, Qubic's tick-based system for replay protection, and CPU-oriented processing for deep vulnerability detection. Radical Efficiency: Employs a novel 64-bit vulnerability bitmask compression, reducing audit report storage by over 90%. Real-Time Protection: Continuous monitoring provides immediate alerts on threats, while exploit simulation allows safe testing of fixes. Impact: SafeBridge prevents catastrophic financial losses (historically $2.8B+ annually from bridge hacks) by identifying critical vulnerabilities before exploitation. It generates plain-English risk reports (e.g., "Critical: Unprotected withdraw()"), transforming complex audits into actionable insights. This fosters trust, accelerates secure DeFi development on Qubic, and establishes a new standard for proactive cross-chain security.

Enerflux

Enerflux is a real-time energy analytics and visualization platform designed to provide intuitive insight into the behavior of distributed energy systems. It fetches and displays live energy data from multiple zones (e.g., Lusaka, Shanghai), processes dynamic node statuses such as current draw, forecasts, and operational health, and visualizes this information on an interactive SVG circuit canvas. Enerflux also detects and animates rerouted energy flows, helping operators identify surpluses, shortages, and emergency rerouting paths. Designed with both technical clarity and user experience in mind, Enerflux serves as a powerful dashboard for smart grid operators, researchers, and energy system analysts.

JIN-IND-lablab PROSUS

JIN-IND is an AI-powered gig platform designed to uplift underpaid, unemployed, and informal workers in India by connecting them to verified, dignified, and flexible job opportunities. In a country where over 90% of the workforce is in the unorganized sector, millions are left without fair pay, job security, or digital identity in the employment space. Our solution uses Ollama for local LLM integration, enabling AI assistance even in low-resource settings, and GROQ’s ultra-fast inference for real-time, intelligent job matching and chatbot support. We ensure Aadhaar-based worker verification, secure UPI payments, and community-driven peer reviews to build trust and transparency in every transaction. Aligned with the Prosus Track’s focus on digital inclusion and financial empowerment, JIN-IND bridges the gap between Bharat and India—bringing tech to the grassroots. Employers, NGOs, and admins gain powerful dashboards to measure social impact, workforce growth, and regional employment trends. Built with Next.js, Tailwind, and ShadCN UI, JIN-IND is not just a gig marketplace—it’s an economic booster for India’s future. We aim to reduce exploitation, increase financial stability, and enable millions to participate in the digital economy with dignity and autonomy.

EQenhance

EQenhance is a Streamlit-based AI assistant designed to help users reframe emotionally loaded workplace communication with confidence and clarity. Using a multi-agent system grounded in: 1. Critical thinking using: RED (Recognize, Evaluate, Draw) 2. GPI (Gaze Pressure Index) to measure confidence - Self research in Arxiv Preprint and published on Zenodo: https://zenodo.org/records/15824808?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImU5YzZiYTk1LTQ3YjMtNGJlYS05NTVjLTIxZTE4N2VmNzMzMiIsImRhdGEiOnt9LCJyYW5kb20iOiIyYjVhMmY3NjdhYTMxZTE0NDBhNTg3NWY0NTEyY2E5MCJ9.l4UNanBD-LBjvShyyeyW6EBiq8qXqnSDrL4LyMMPRfRSflY7IMVVT7cw8GPrUZdJog60ZSuZpViQBnYpPIVYVQ, 3. DBT (Dialectical Behavioral Therapy) skills, the app provides rewrites and feedback that reduce emotional masking and increase assertiveness. It logs user interactions and emotional pressure scores locally in SQLite for HR or future analytic uses. EQenhance is designed to integrate seamlessly into enterprise platforms like Microsoft Teams and Slack. It is trauma-informed, data-savvy, and extensible — empowering users to communicate clearly without compromise.

QubiScan

QubiScan is an AI-powered smart contract auditing and education platform purpose-built for the Qubic Network. It provides developers with a seamless, frontend-only environment to write, audit, and simulate-deploy C++ smart contracts securely—without needing any backend infrastructure. The platform combines in-browser static analysis with OpenAI-powered AI reviews to detect security issues, explain risks, and suggest safer code alternatives. It also generates downloadable PDF audit reports, making it useful for solo devs, security teams, or hackathon projects looking to document code quality. 🔑 Key Features 🧠 Static Vulnerability Scanner: Detects risky code patterns (e.g., strcpy, unchecked input, logic flaws) in real time. 🤖 OpenAI Integration: Explains vulnerabilities and provides natural-language suggestions for improvements. 📄 PDF Report Generator: Creates professional audit reports for submission or sharing. 🚀 Testnet Deployment Simulation: Users can select a testnet account and simulate a deploy action. 📚 Learning Modules: Interactive tutorials teach best practices in Qubic C++ contract security. 🗂️ Audit History Gallery: Stores previous audit results locally via browser storage. 🧩 Technical Highlights 100% frontend-only: built with React, Tailwind, Monaco Editor, and jsPDF. No server needed — AI integration is handled directly with the OpenAI API. All data is processed client-side, preserving developer privacy and speed. Supports a growing list of predefined Qubic testnet accounts for mock deployment testing. QubiScan bridges the gap between powerful smart contract tooling and developer education—making Qubic more accessible, secure, and developer-friendly. It’s designed to help teams ship faster, safer, and with confidence.