Discover Best Apps according to The Community!

Discover the most voted AI applications that have been built during lablab.build hackathons. Explore our AI Hackathon projects and get inspired to create your own!

Sight beyond Sight

Sight beyond Sight

Our website enhances online content accessibility for the visually impaired with a cost-effective text-to-speech service using contemporary AI tools. Current market solutions lack necessary amenities and are costly. Working on the website: > once the website loads, the user inputs the URL of the website to be analyzed > this website is parsed using Beautiful Soup to gather the meaningful text content available on the page > this content is passed to the OpenAI text-davinci-003 model as a prompt and a summary is generated for the same > this summary is read out to the user using Azure in natural human tone > next, the website is again parsed using Beautiful Soup with the aim to download relevant images on that website > these images are then analyzed using Google Cloud Vision API and feature labels describing the prominent objects/contents of that image are generated > these labels are passed as a prompt to the OpenAI text-davinci-003 model and a meaningful sentence is generated which describes the images > the prompt already includes a set of sample labels and outputs that the model can use to understand the format of the desired output. > the image description generated in the above step is then read aloud using Azure. For Redis: Redis caches URL results for up to 3 hours, if URL exists in cache, output is displayed/read aloud. Otherwise, website is processed for new output. Results are removed after 3 hours for possible content changes. It allows for fast data access making it suitable for high performance use cases. For voice control: > using space bar, user can ask queries regarding summary through available chatbot > above query is converted to text via speech recognition library of python > this text and the summary are given to the OpenAI text-davinci-003 model as a prompt and the query is resolved > the result is spoken out and if speech unrecognized, an error message stating to retry is read aloud

Galacticos
RedisGPT-3
LegacyLink AI

LegacyLink AI

LegacyLink AI is an AI-assisted legacy database modernization tool built for the IBM Bob Hackathon. It helps developers convert messy legacy SQL schemas into clean Python SQLAlchemy ORM project scaffolds, reducing the time needed to understand old database structures and generate boilerplate code. The system lets users upload a legacy SQL file, then automatically parses database objects such as tables, columns, data types, indexes, constraints, views, and functions. It normalizes cryptic names into readable conventions, such as converting tbl_CUST_MSTR_2012_v2 into Customer, fk_str_id into store_id, and dt_upd_dt into updated_at. LegacyLink AI generates a downloadable project containing SQLAlchemy 2.0 ORM models, database setup code, pytest test files, requirements.txt, README documentation, and a modernization report explaining the transformation. The generated project can be tested immediately using pytest. The project also includes an optional IBM watsonx.ai AI Assistant powered by IBM Granite. The assistant helps users explain schemas, summarize modernization reports, identify possible foreign-key-like fields, and understand generated ORM code in simple English. For enterprise traceability, AI Assistant interactions can be logged into PostgreSQL. IBM Bob can then connect through MCP to inspect the audit logs and summarize recent assistant activity, including the user question, model used, SQL file processed, table count, and status. IBM Bob was used as the development partner for planning, coding, debugging, testing, documentation, and MCP workflow validation. The final result combines Streamlit, SQLAlchemy, pytest, IBM watsonx.ai, PostgreSQL, MCP, and IBM Bob into a practical modernization workflow for legacy database teams.

Try-Tri-Hard
Streamlit
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IBM watsonx AssistantIBM GraniteIBM+2
RepoMind : AI Onboarding Copilot

RepoMind : AI Onboarding Copilot

RepoMind solves one of the biggest pain points in software development: onboarding into large and complex codebases. Developers joining new projects often spend weeks trying to understand system architecture, dependencies, and workflows. This slows productivity, delays delivery, and creates frustration across engineering teams. RepoMind is an AI-powered onboarding copilot built using IBM watsonx and IBM Granite. Users paste a GitHub repository URL and instantly receive an architecture summary, module breakdowns, execution flow explanations, and a conversational AI assistant that answers questions like “How does authentication work?” or “Where is the payment logic handled?” The platform uses IBM Granite 3 8B Instruct through watsonx.ai to deliver contextual understanding of entire repositories. The backend is powered by FastAPI and GitPython for repository analysis, while the frontend runs on Streamlit for a simple and interactive experience. RepoMind was developed in collaboration with IBM Bob and is designed for junior developers, onboarding engineers, open-source contributors, and teams managing legacy systems. The business opportunity is significant. The global developer tools market exceeds $26 billion, while AI-powered coding and onboarding tools continue to grow rapidly. RepoMind follows a scalable SaaS model with free access for public repositories and premium plans for private repositories, team dashboards, and enterprise integrations with GitHub, GitLab, and Bitbucket. Unlike tools focused on autocomplete or code search, RepoMind specializes in full codebase comprehension through natural-language interaction, helping developers understand systems faster and reduce onboarding time from weeks to minutes.

Kernel Panic
Streamlit
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IBM watsonx AssistantStreamlitIBM Granite+1
CampusFix AI – From Complaint to Campus Care

CampusFix AI – From Complaint to Campus Care

CampusFix AI is an intelligent campus infrastructure management platform designed to close the gap between identifying a campus problem and getting it resolved. In a typical campus environment, issues such as water leakage, electrical faults, damaged furniture, sanitation problems, and facility breakdowns may be reported through different channels, making them difficult to track and prioritize. CampusFix AI creates a single digital pathway for these problems. A student can submit an issue with its description, location, category, priority, and supporting information. The system converts the complaint into a trackable ticket and provides administrators with a centralized Operations Center. The AI layer assists in understanding reported issues and organizing them according to their nature and urgency. This allows campus teams to focus attention on issues that require faster action instead of treating every complaint in the same way. Administrators can monitor tickets, view issue categories, assign problems to relevant departments, track progress, and keep users informed through notifications. The platform also provides analytics, issue heatmaps, and campus-level monitoring to help identify recurring infrastructure problems. The key idea behind CampusFix AI is simple: a complaint should not disappear after it is submitted. It should become an accountable, trackable action until resolution. By connecting students, administrators, departments, and AI in one workflow, CampusFix AI aims to make campus maintenance more transparent, responsive, and data-driven.

HexaForge
AI StudioGemini 3 FlashGemini 3 pro+5
DevPartner AI

DevPartner AI

The Problem AI coding assistants are increasingly trusted to modify production code — but most of them work as a "black box." A developer asks for a change, the AI edits files, and the developer is left hoping nothing breaks. There's no proof the change is safe, no record of what rules were checked, and no easy way to undo damage if something goes wrong. This is especially dangerous in sensitive areas like authentication, payments, and user data, where a single unverified AI edit could open a serious security hole. **The Solution** DevPartner AI is a **Developer Verification Agent** that sits between a developer's request and the actual code change — acting as a safety layer that never lets AI blindly modify production code. Instead of just generating a fix, it walks every change through a transparent, auditable pipeline: 1. **Project Scan** — indexes the entire codebase and profiles its structure, languages, and complexity. 2. **Change Request & Intent** — the developer describes what they want in plain English, and the AI interprets the true intent behind it. 3. **Invariants** — the system automatically extracts the rules that must never break (e.g., "only verified sessions can delete a user"). 4. **Impact Analysis** — calculates the "blast radius" of the change, showing exactly how much of the codebase it touches. 5. **Risk Assessment** — scores how dangerous the change could be, based on impact and any existing security findings. 6. **Change Plan** — shows the developer a clear, step-by-step plan before any code is touched. 7. **Approval** — a human-in-the-loop checkpoint, so nothing merges without explicit developer sign-off. 8. **Snapshot & Verify** — the current state is saved, then the change is tested through property checks, mutation testing, and adversarial counterexample hunting. 9. **Evidence** — a cryptographic SHA-256 hash is generated as tamper-proof, auditable proof that the verification actually happened. 10. **Rollback** — if anything is wrong, the developer can revert instantly with one click. Along the way, DevPartner AI also runs automated **security scanning**, catching real issues like hardcoded credentials or authorization logic that wrongly trusts client-supplied data — flagging them by severity (HIGH, CRITICAL) with the exact file and explanation. **Target Users** Software development teams, DevOps engineers, and security-conscious startups who want to safely adopt AI-assisted coding without risking silent, unverified changes reaching production. **What Makes It Creative and Unique** Most AI coding tools optimize for *speed* — generating code fast and trusting it. DevPartner AI flips that priority to **trust and proof**. It's not just an AI code editor; it's a verification layer that treats every AI-driven change like a claim that must be proven, not assumed. The combination of extracted invariants, blast-radius mapping, adversarial mutation testing, and cryptographic evidence generation is unusual for a hackathon project — it borrows ideas from formal verification and security auditing, areas normally reserved for large enterprise tools, and makes them accessible in a lightweight, interactive interface. The one-click rollback and in-memory sandbox isolation also mean developers can experiment fearlessly, knowing nothing is permanently at risk until they explicitly approve it. --- **Word count: ~480 words** — fits within your limit with a little room to spare. A quick honest note: I wrote this based on the UI and workflow you've shown me, but I haven't seen your actual code or confirmed every step truly works as described (e.g., whether mutation testing and counterexample hunting are fully implemented or partially simulated). Please adjust anything that overstates what's actually functional — judges may check claims against your live demo and repo.

Team Rookies
Vercel
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AntigravityIBM GraniteIBM+1
CodeAtlas

CodeAtlas

CodeAtlas is an AI-powered engineering intelligence platform designed to help developers and engineering teams understand, audit, and modernize complex codebases. Modern software systems are increasingly difficult to reason about. As repositories grow, teams lose visibility into architecture, dependencies, technical debt, and change impact. Refactoring becomes risky, onboarding slows down, and modernization projects become expensive and unpredictable. CodeAtlas solves this problem by transforming GitHub repositories into interactive engineering intelligence. Users can import a repository directly from GitHub, and CodeAtlas automatically scans the codebase, analyzes dependencies, maps architecture relationships, identifies high-risk modules, and generates modernization insights. The platform includes: Interactive dependency graph visualization Risk-based module analysis Blast-radius simulation Technical debt estimation Architecture health scoring Dependency confidence analysis Engineering findings and onboarding guidance Suggested modernization sequences Exportable engineering reports One of the key goals of CodeAtlas is transparency. Instead of pretending the analysis is perfect, the system exposes dependency confidence limitations caused by unresolved imports, framework aliases, or dynamic imports. This makes the platform more trustworthy and operationally useful for real engineering teams. The frontend was built with Next.js, TypeScript, and React Flow, while the backend uses FastAPI with a custom repository scanner, dependency graph engine, and risk analysis system. CodeAtlas was built during the IBM Bob Hackathon to demonstrate how AI can assist engineering organizations with architecture visibility, modernization planning, and repository intelligence.

CodeAtlas
Vercel
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ChatGPTIBMIBM watsonx Assistant
Trellis: The Knowledge Fabric for Law Firms

Trellis: The Knowledge Fabric for Law Firms

Every law firm sits on an unmined fortune: the experiential wisdom of senior partners. Yet, when a veteran attorney retires or departs, decades of strategic instinct permanently evaporate. This institutional knowledge loss costs mid-to-large firms $15M to $40M annually. The barrier to documentation is economic. Lawyers bill in six-minute increments; spending time writing down lessons represents an immediate loss of billable revenue. While generative AI should solve this, strict obligations toward attorney-client privilege block public LLM tools. Meanwhile, vertical legal tools like Harvey provide generic legal advice but lack a firm's specific internal wisdom. Trellis solves this with a secure, two-tier system architecture. The first layer is the Personal Second Brain. This is a private, on-device edge environment running Gemini Nano where attorneys quickly capture unstructured thoughts via voice memos, notes, or image OCR. Because data stays local, capture is entirely unredacted and factual. The second layer is the Team-Managed Knowledge Graph. This shared ledger is built through an automated, dual-pass sanitization pipeline. First, Microsoft Presidio strips explicit PII like names, dates, and entities. Second, Gemini Pro abstracts the specific case details into generalized strategic principles. The lawyer reviews a side-by-side diff before approving publication to the firm's shared memory. Using a hybrid vector-graph RAG model, team members can query this collective intelligence using natural language. To eliminate legal risk, Trellis enforces a hard deterministic guardrail: if the retrieved context score falls below a strict threshold, the system executes a strict refusal rather than risk a hallucination. Finally, Trellis exposes a Model Context Protocol (MCP) endpoint. This allows third-party tools like Harvey, CoCounsel, or Copilot to securely plug directly into the substrate, grounding generic legal AI capabilities in the firm's actual historical wisdom.

Pan sa Manila
medal
Vercel
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Gemini AIGemini 3 proGemini 3 Flash+2
Shift Handover Pro

Shift Handover Pro

Software maintenance becomes difficult when a development request is left partially implemented. The next engineer must reconstruct what was requested, understand what has already changed, identify what remains incomplete, and determine which parts of the application could be affected. This manual re-orientation can slow maintenance and increase the risk of changing existing behavior unintentionally. Shift Handover Pro is an AI-assisted maintenance resume tool designed to help engineers safely continue unfinished development work. Our demonstration uses an ATC (air traffic control) shift-handover application with a pending equipment-status change. The requested change was to simplify a legacy fault equipment state into three states: Operational, Maintenance, and Offline. Instead of treating the change as an isolated code edit, Shift Handover Pro demonstrates how IBM Bob 2.0 Agent mode can work with full-repository context to understand the application and identify the areas affected by the request. Bob identified four key touch points before implementation: the CSS selector, Supabase migration, dashboard logic, and handover form UI. The change was then implemented with backward compatibility in mind, including handling legacy status data without requiring manual database work. The target users are software developers, maintenance engineers, technical teams, and organizations working with existing or legacy applications where development tasks may be paused, transferred, or resumed by another engineer. Users interact with the workflow by providing or continuing a maintenance request, reviewing the repository-aware analysis, examining the affected components, and validating the resulting application behavior. The live demonstration shows the updated equipment-status workflow and the corresponding IBM Bob development evidence. What makes Shift Handover Pro different is its focus on context recovery rather than simple code generation. The goal is not merely to ask AI to write code, but to understand the relationship between an unfinished request and the existing repository before making a change. The workflow is: Change Request → Full Repository Context → Impact Analysis → Planning → Implementation → Verification This approach demonstrates how repository-aware AI can reduce repetitive re-orientation during maintenance while keeping the requested change connected to the existing application behavior.

Dhola MInds
AgentOpsAntigravityChatGPT+2
ARCA SENTRY

ARCA SENTRY

Thousands of companies are racing to ship AI — chatbots, voice agents, automated customer service. But who audits them? The EU AI Act takes full effect in August 2026, with fines reaching up to 7% of global revenue. A single non-compliant chatbot response can trigger an investigation. Most teams have no defense. ARCA SENTRY is the compliance brain for enterprise AI. A council of five specialised auditor agents — running on DeepSeek through Featherless — audits every interaction your AI produces, in real time, against the full EU regulatory stack: EU AI Act, GDPR, DORA, PII leakage and prompt injection. It integrates with any AI channel: drop-in proxy mode (OpenAI / Anthropic / Gemini), raw HTTP endpoints, WhatsApp Business, Facebook Messenger. Zero code changes beyond the base URL. Before you ship, run the Red Team: an automated pen-test suite that probes your agents against every European framework and exports forensic reports in PDF, Markdown and HTML — showing exactly how the model can be broken. In production, the gateway intercepts violations mid-flight, blocking the response before it reaches the user. Every incident auto-generates a remediation ticket with estimated fine, suggested fix (generated by Gemini Pro), and a tamper-evident SHA-256 hash chain for forensic reproducibility. Auto-detects five languages: Italian, Portuguese, Spanish, Chinese, English. Don't let AI become your most expensive line item. Take back control of your agents. The council of auditors guards your AI — and your business. ARCA SENTRY. Don't pay the fine. Pay attention.

AI Society - B Drive
Vercel
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Claude CodeGemini AIFeatherless+6
BiteBuddy Meal Planner

BiteBuddy Meal Planner

BiteBuddy is a meal-planning tool that is prized for its customization. What this entails is that for every different user, a different meal plan is generated based on the user's preferences, needs, and dislikes. A tool to create a meal plan needs to understand what the customer needs. Therefore, the tool asks the user about their body characteristics to help organize the facts needed to create a balanced diet. Precisely, the user's Body Mass Index (BMI) is calculated to give an idea about the possible goals and hence, display an adequate diet. After that, the tool asks the user about any allergies, if any exist, so as not to include these foods in the plan. Moreover, they are asked to input any preferences they might have to include in their diet. The user input is then processed to generate the data. After which, the data is sent via the APIs through properly formatted prompts. The generated meal plan is then displayed in table format for every day of the week and at least three meals per day. Each meal has its nutritional values displayed, including carbohydrates, calories, fats, and proteins; the tool also suggests the right portions and the recipe with the ingredients that aid in preparing that meal. The recipe sub-window also illustrates the meal by providing a photo. Sorting is a feature that can be used to sort the meals based on their nutritional facts. The main advantage of this website is that it groups everything that the user might need to plan a healthy diet, considering their allergies and any food preferences they might have for the week. The tool replaces the need to visit nutritionists and explore different food regimes. It also replaces searching and exploring various recipes among multiple cooking platforms.

The DeCoders
ChatGPTDALL-E-2GPT-3
MediCast: Autonomous Surgical Intelligence

MediCast: Autonomous Surgical Intelligence

Medical error is the third leading cause of death globally, and surgical complications account for a massive percentage of hospital liability. MediCast is an autonomous, computer vision-driven SaaS co-pilot engineered to fundamentally de-risk the operating room. Built exclusively for the AMD Developer Hackathon, it transforms opaque surgical video feeds into structured, actionable clinical intelligence in real-time. The Unicorn Pitch: Why MediCast Wins Proactive Risk Mitigation: A concurrent multi-agent swarm (Safety, Anatomy, and Phase Agents) autonomously monitors live surgical feeds, identifying critical safety deviations and anatomical hazards before they become fatal errors. Zero-Footprint Scalability: Hospitals refuse to install massive, expensive GPU servers in sterile environments. MediCast circumvents this entirely. By leveraging AMD Instinct™ GPUs in the cloud via the Fireworks AI API, we deliver blazing-fast inference directly to the OR with zero local hardware required. Gemma-Powered Clinical Debriefing (Bonus Track): We engineered our post-op AI Arbiter exclusively on Google’s Gemma 2 (gemma2-9b-it). It mines the structured surgical data to generate automated clinical reports and allows surgeons to interactively "chat" with their own procedures for unprecedented post-op quality assurance. Commercial Readiness: Complete with a secure, HIPAA-styled SSO gateway, real-time WebSockets, and a premium React dashboard, MediCast isn't a prototype it’s a deployable product ready for clinical pilot. By fusing AMD's massive compute throughput with the Fireworks AI API and Gemma 2, MediCast doesn't just analyze surgery it makes it safer, trackable, and instantly reviewable

The CheckInTech
AMD Developer CloudKraken Websocket apiAntigravity+5
SPECIES - Evolutionary Quant Research

SPECIES - Evolutionary Quant Research

SPECIES is an evolutionary quantitative research platform designed to discover, evaluate, and validate trading strategies as populations rather than optimizing a single strategy in isolation. A research run begins with a configurable population of strategy genomes. Across multiple generations, SPECIES evaluates each candidate using historical market data, calculates deterministic fitness, applies selection pressure, and creates new descendants through bounded mutation and crossover. Every surviving strategy keeps a traceable lineage, making the evolutionary process inspectable rather than a black box. Evolution alone is not enough to qualify a strategy. Provisional champions must pass separate validation, walk-forward, regime-robustness, and qualification gates before they can move toward execution. Qualified strategies can be connected to Alpaca Paper Trading, but deployment never forces an order. Current market signals are evaluated deterministically, and execution remains protected by explicit approval and RiskGuard boundaries. SPECIES also includes an AI research copilot that explains persisted evidence, fitness, validation results, lineage, and experiment outcomes. The AI does not decide fitness, qualification, risk, or execution; those decisions remain owned by deterministic application logic. The goal is to make quantitative strategy discovery evolutionary, reproducible, explainable, and safer to move from research toward paper execution.

Origin Labs
AlpacaGroqOpenAI+1
InsightStream: AI Customer Analytics

InsightStream: AI Customer Analytics

InsightStream is an AI-powered customer business analytics dashboard designed to help businesses transform raw customer data into meaningful insights and smarter decisions. Many organizations collect large amounts of customer data but struggle to analyze it effectively due to the complexity of traditional analytics tools. InsightStream solves this problem by providing an interactive and user-friendly platform that makes customer analytics accessible to both technical and non-technical users. The platform enables users to upload customer datasets, explore interactive dashboards, perform RFM (Recency, Frequency, Monetary) customer segmentation, identify churn risks, and generate actionable business recommendations to improve customer retention and customer lifetime value. It also supports SQL-based data exploration, CSV uploads, SQLite database integration, and exportable visual reports for better decision-making. Built using Python, Streamlit, SQLite, Pandas, and Plotly, InsightStream focuses on scalability, modular development, and rapid prototyping. Throughout the development process, IBM Bob served as an AI-powered development partner by assisting with code generation, debugging, architecture planning, workflow optimization, and repository understanding. InsightStream demonstrates how AI-assisted development can accelerate the creation of practical, business-focused software solutions with real-world value.

Token Overflow
Streamlit
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IBM
ClipContext

ClipContext

ClipContext takes a short creator video and turns it into platform-ready metadata: ten candidate titles, ten candidate descriptions, and ten candidate hashtag sets are generated per video, each pool independently ranked by an AI discriminator, with the top 5 of each pool surfaced to the creator — plus, optionally, a direct upload of the analysed video to the creator's own YouTube channel with whichever candidates they picked. Every candidate is written in a style pulled from real trending videos — not a generic "make it punchy" instruction. ClipContext always analyzes worldwide YouTube trends in the video's own niche; if the creator gives their channel handle, it uses that specific creator's own top-performing videos instead. Local-first preprocessing: Video validation, audio extraction, 1 FPS frame scanning, visual-quality scoring, and perceptual-diversity frame selection all run locally before any paid AI call. Evidence-grounded generation: Titles/descriptions/hashtags are required to trace back to the video's actual transcript and visuals — not free-associated from a topic string. Genuine diversity, not ten rewordings. Each of the 10 candidates per pool is generated against a distinct strategy (question, bold claim, curiosity gap, number-led, story, technical, emotional, SEO-minimal, creator-voice, and more). Two trend sources: ClipContext always mines worldwide YouTube trends in the video's own niche for a real, data-derived style profile (typical title/description/hashtag structure, SEO vocabulary, tone) — and, if a creator hands over their channel, it swaps in that specific creator's own top-performing videos instead, so the output sounds like them, not a generic trending-video template. Independent AI ranking: A second model scores and ranks each candidate pool against the video's ground truth and real trend benchmarks, with a stated reason per score — the top 5 per pool are what reach the results page.

Kira
Vercel
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AMD Developer CloudWhisperAI/ML API
Novus - Research Intelligence Assistant

Novus - Research Intelligence Assistant

Novus is a multi-agent AI platform that transforms enterprise R&D workflows by automating the entire research intelligence pipeline — from duplication detection to grant proposal generation — in under 3 minutes. The Problem: Fortune 500 companies lose $28 billion annually funding research that already exists. R&D teams spend months writing grant proposals across disconnected tools with no unified system to check duplication, find grants, and ensure compliance simultaneously. Our Solution: Novus deploys 6 specialized AI agents coordinated through Band SDK: 1. Intake Agent — Parses R&D proposals and extracts competitive intelligence signals 2. Duplication Scout — Scans 200M+ papers across Semantic Scholar, OpenAlex and arXiv in real time 3. Relevance Agent — Scores proposals against current industry funding trends 4. Eligibility Agent — Matches to live grants from Grants.gov and NIH Reporter, surfaces already-funded competing research 5. Proposal Writer — Drafts complete, funder-aligned grant proposals automatically 6. Compliance Agent — Reviews against grant requirements before submission Key Features: - Real academic paper search with authors, years and source links - Live grant matching with direct Apply Now links - Already-funded similar research discovery with award amounts - International funder recommendations including EU Horizon, Wellcome Trust and Gates Foundation - Full audit trail via AgentOps - Beautiful React Flow live agent pipeline visualization Tech Stack: Band SDK, Anthropic Claude, AI/ML API, Featherless AI, Semantic Scholar, OpenAlex, arXiv, Grants.gov, NIH Reporter, Next.js, React Flow, AgentOps, Qdrant

Novus
AgentOpsAI/ML APIAnthropic Claude+3
Alpha Hunter — Autonomous AI Trading Scientist

Alpha Hunter — Autonomous AI Trading Scientist

Alpha Hunter is an autonomous AI trading scientist built to close the loop between quantitative research, strategy validation, portfolio allocation, and execution. Instead of simply asking an AI what to buy, Alpha Hunter first asks whether a trading strategy actually has a robust edge. The system discovers strategy hypotheses and evaluates them using market data, historical performance, out-of-sample validation, robustness analysis, and market-regime compatibility. An adversarial AI layer then attempts to break promising strategies by looking for weaknesses such as overfitting, instability, regime dependence, concentration, and poor generalization. Surviving strategies receive an Edge Score and can compete for capital through Alpha Hunter's adaptive allocation engine. Allocation considers strategy quality, risk, volatility, portfolio exposure, correlation, market regime, and current opportunity rather than blindly increasing every position. Alpha Hunter supports equities, ETFs, and crypto. Crypto is not permanently excluded; when its risk-adjusted opportunity deteriorates, the system can pause new allocation while continuing to monitor existing positions. A deterministic risk engine acts as a safety layer between AI recommendations and execution. It enforces portfolio and position constraints while allowing profitable positions to use adaptive trailing protection designed to protect accumulated gains and let strong trades continue running. The execution layer connects to Alpaca Paper Trading. Current positions, quantities, orders, P&L, and broker execution states are reconciled with Alpaca rather than being simulated in the UI. The result is a complete autonomous trading research loop: Discover → Test → Challenge → Score → Allocate → Execute → Monitor → Learn. Alpha Hunter is designed not merely to generate trades, but to continuously determine which strategies deserve trust and where capital has the best risk-adjusted opportunity.

crazyxyz
AlpacaAntigravityChatGPT
Autonomous Agents from APIs - Zero Code Builder

Autonomous Agents from APIs - Zero Code Builder

Using Vultr, Groq, Llama, Coral, and Fetch.ai, our project introduces a transformative way to generate and deploy Autonomous AI Agents from APIs—a no-code, prompt-driven AI Agent Builder and conversational interface to manage intelligent agents across enterprise systems in real time. From Prompt ➜ Plan ➜ Code ➜ Execute ➜ Iterate ➜ Deploy ➜ Use (on Coral, Custom, and Fetch AI) User explains their goal in natural language. Our AI locates relevant APIs, generates step-by-step agent plans, writes the code, deploys tools to Vultr-hosted MCP servers, and registers them with Coral and Fetch.ai. All agents are accessed via a multi-modal chat interface—text or voice—enabling users to test, refine, and operate agents live. These agents interact with real-world APIs via the MCP servers created through the Agent Builder. No code. Just intent. From developers and marketers to support and operations teams—anyone can turn apps into automations, tools, or digital workers. Enterprise-ready and built for the future of work, Vortex IQ enables users to go from idea to working agent in minutes. All AI inference runs on Groq’s ultra-low-latency hardware using Llama 3 models, making it fast, private, and cost-efficient. Agents are also composable via Fetch.ai’s Agentverse and ASI:One. Future-Ready with Scalable Impact: a. 3,000+ Retailers: Rolling out AI agents across BigCommerce, Adobe Commerce, Shopify, and Salesforce Commerce Cloud. b. Composable Agent Hub: 100s of reusable agents and templates for plug-and-play use across any API-powered system. c. Cloud Partnerships: Vultr, GCP, and Azure hosting for global agentic compute-as-a-service access. Tech Stack: Hosted on Vultr, using Groq, Llama 3, Coral Protocol, and Fetch.ai for compute, orchestration, and autonomy. Other tech: Google ADK, Next.js From prompt to production—this is how the future of work is built.

Autonomous Agents from APIs - Vultr Track
medal
GroqLlama 3.2Llama 3.1+4
TradePilot AI - Autonomous AI Trading Agent

TradePilot AI - Autonomous AI Trading Agent

TradePilot AI TradePilot AI is an autonomous AI-powered trading platform that transforms real-time market intelligence into disciplined, executable trading decisions. Unlike traditional trading platforms that leave users to monitor markets, interpret signals, manage risk, and execute trades manually, TradePilot AI combines these processes through a multi-agent architecture. The Market Agent analyzes live market data and identifies market conditions and opportunities. The Strategy Agent interprets this analysis and generates BUY, SELL, or HOLD decisions. The Risk Agent evaluates proposed trades against predefined risk and exposure limits. The Supervisor and Autonomous Agent coordinate the entire trading cycle, while the Execution Layer connects directly to Alpaca for order submission and monitoring. TradePilot is designed with risk aware autonomous execution. Each trade is subject to deterministic controls, including a maximum 5% allocation of account equity per trade and 50% maximum portfolio exposure. Once a position is opened, automated position management can apply stop-loss, take-profit, and breakeven protection while monitoring order and position states. Built with Alpaca, TradePilot connects AI reasoning directly to real trading infrastructure, including market data, account information, positions, and order execution. The complete pipeline is: Market Data → AI Analysis → Strategy Decision → Risk Evaluation → Alpaca Execution → Position Management Our goal is not simply to build another trading dashboard or market prediction tool. TradePilot AI is designed as an autonomous trading agent that can continuously observe markets, reason about opportunities, make decisions, execute when conditions are met, and actively manage positions while remaining constrained by predefined risk controls.

TradePilot
Vercel
application badge
VercelOpenAIAlpaca+4
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