Top Builders

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

Vercel

Vercel is a platform designed for developers, providing speed, reliability, and scalability to create and deploy web applications. With built-in CI/CD, zero configuration, and deep integrations with popular Git providers such as GitHub, GitLab, and Bitbucket, Vercel streamlines the development process, making it easy for teams to collaborate and iterate on their projects.

General
Release date2015
AuthorVercel
TypeDeployment and hosting platform

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Vercel AI technology page Hackathon projects

Discover innovative solutions crafted with Vercel AI technology page, developed by our community members during our engaging hackathons.

OBSERVA

OBSERVA

OBSERVA is a privacy-first AI mobility assistant designed for blind and low-vision users who need safe, reliable navigation support in everyday environments. Unlike many tools that depend on cloud processing, OBSERVA is built to run core safety features fully on-device, including in airplane mode, so users can rely on it in low-connectivity settings and for sensitive private contexts. Our current Android prototype uses a continuous CameraX loop with low-latency audio and haptic alerts, a hazard engine with cooldown and scene memory to avoid repetitive alert spam, and an accessibility-focused interface optimized for non-visual interaction. The product direction includes robust object recognition, hazard detection, contextual navigation assistance, and multilingual interaction/translation pathways while maintaining strict local processing guarantees. We position OBSERVA in a gap between pure OCR/vision apps and infrastructure-dependent systems (e.g., code-tag ecosystems), focusing on independent mobility, comfort, and trust. Our core value proposition is simple: private, offline, safety-oriented assistive intelligence that is usable in the real world, not just in ideal network conditions.

Apohara VOUCH

Apohara VOUCH

Apohara VOUCH turns multi-agent decisions into cryptographically-verifiable offline receipts — signed, hash-chained, timestamped, and audit-ready in under 30 seconds. Built on 3 production LLM sponsors (Band SDK + AI/ML API + Featherless AI) with a deterministic post-LLM gate (BAAAR) that fails-closed on five auditable halt conditions. EU AI Act Art. 12 by construction. **When AI agents make a regulated decision, you can't trust the decision — and you can't prove it either.** Procurement, lending, hiring, and customer escalation are now mediated by multi-agent systems: 5–10 LLMs coordinate through chat rooms, hand off state, vote, and reach a verdict. Three failures follow: 1. **No audit trail.** When a regulator asks "who decided this, and why?", you have a chat log — not an evidence packet. Logs can be edited. Screenshots can be forged. LLM weights are opaque. 2. **No failure mode.** The agents coordinate, but if one hallucinates a vendor ID, the room reaches the wrong verdict anyway. Multi-agent consensus is consensus on the wrong answer. 3. **No offline verifiability.** The regulator asks for proof. You re-run the agents. They produce a different answer. The room is no longer reproducible. The EU AI Act Art. 12 (record-keeping), DORA Art. 16 (ICT incident logs), NIST AI RMF (Manage), and OWASP Agentic all require verifiable, tamper-evident, offline-checkable evidence. None of the existing solutions — vector stores, prompt logs, evals — satisfy all three. **Apohara VOUCH** is the first multi-agent substrate that produces EU AI Act Art. 12 evidence packets by construction, verified offline in under 30 seconds, with no LLM in the critical path. **Apohara VOUCH — vouch for every agent decision.**

RepoMap

RepoMap

Are you a coder? Think back to when you first started... could you just open up a massive GitHub repository and instantly read it? Probably not People always say that open source projects are a developer's playground. A place to explore, tinker, and learn. But let's be honest... when a novice coder opens up a massive, complex repository, they don't see a playground. They just get completely overwhelmed. Well, here is the solution: RepoMap..... RepoMap is powered by a multi-agent pub-sub architecture, orchestrated by the BAND framework. We use four specialized AI agents working in a seamless pipeline: First, the Ingestion Agent clones your repo and reads the files using Llama 3.3. Second, the Graph Agent builds a Neo4j knowledge graph, turning files and imports into nodes and edges. Third, the History Agent injects years of GitHub commit history into the map. And finally, the Maintenance Agent analyzes the graph for vulnerabilities But let's be real... in the current world of AI, a lot of people are just building things without actually understanding how they work. Don't worry—we aren't forcing you to learn how your code works... though you definitely should! If you want to take the easy route, just unleash our Maintenance Agent. It will autonomously help you write better code, clear out legacy dead code, manage your versions, and automatically document the most critical hubs in your architecture. NOTE - AS I AM A STUDENT AND NOT HAVE ANY CARD FOR PAYMENT VERIFICATION I WAS UNABLE TO GET BAND PRO USING THE CODE GIVEN AND WAS NOT ABLE TO HOST AGENTS ON BAND BUT MY ARCHITECTURE IS FULLY BASED ON IT AND JUST HOSTING IS NEEDED.

HireFlow AI

HireFlow AI

Hiring is slow, inconsistent, and opaque. Recruiters juggle resumes, skill assessments, culture signals, and compensation benchmarks in isolation — with no audit trail and no record of why a candidate was hired or rejected. HireFlow AI fixes this with five specialized AI agents that collaborate through a shared Band workspace. A hiring manager creates a job, adds candidates, and triggers an evaluation. Five agents take it from there — each with its own Band identity, its own role, and its own API key. The Resume Analyst opens a Band chat thread and posts its findings. The Technical Evaluator reads that thread, scores technical fit, and posts back. The Culture Evaluator reads both messages and assesses team fit. The Compensation Analyst benchmarks salary range. Finally, the Ranking Agent reads the entire thread and synthesises everything into a HIRE, HOLD, or REJECT with full reasoning. When Technical and Culture scores diverge by more than 1.5 points, HireFlow automatically triggers Debate Mode — the conflict is posted to Band and the Ranking Agent explicitly mediates before reaching a verdict. No AI recommendation is ever final. Every decision sits in a human approval queue with scores, strengths, weaknesses, and agent reasoning surfaced inline. Approve, override, or request more analysis — your call. Every action is written to a timestamped audit log visible in the Band Activity view. Band is not a notification layer here. It is the shared workspace where agents build on each other's work. Context compounds. No agent acts in isolation. The Band thread is the hiring war room.

Parley - the agent that can say no

Parley - the agent that can say no

Parley is the trust primitive missing from cross-org agent collaboration: an agent you can be turned down for. Regulated collaborations stall when raw data legally can't move - two hospitals sharing cohort analytics under HIPAA, or a bank and a fintech exchanging KYC/AML aggregates. Today those deals take weeks of legal/DPO review, or die. Parley fixes this with a recruited agent from the OTHER organization. Across two real orgs in one Band room (four agents - coordinator, modeler, checker on the requester side; a vault on the owner side), the owner's vault uses its own model to: consent-to-join (it can refuse the job), counter-offer a safe alternative ("no raw rows - I'll run it in place and return only k-anonymous aggregates"), and release nothing until a first-party human at the data owner approves. An agent's APPROVE is rejected by construction. Governance is structural code, not prompts, so a hijacked or swapped model can't disable it: every capability exports zero raw rows; a composing differential-privacy budget (Rényi-DP accountant) mechanically forces a decline when exhausted; consent is purpose-bound (GDPR Art. 5(1)(b)); the owner's policy can only tighten the LLM; and every step is Ed25519-signed and hash-chained, so a third party re-attests nine invariants against a pinned key with zero trust — uv run python -m parley.verify exits 0, or 1 if a single byte is flipped. It's heterogeneous by design: any agent in either org can run on any provider (Claude, Groq, OpenRouter, OpenAI, or any /v1) - Claude is the default, not a requirement; the refusal was demonstrated live on Groq and OpenRouter. One kernel ships four domains (clinical/HIPAA, customer data, code review, HR) - deploy your own by editing one scenario file. 124 tests; real runs in proof/.

CodeForge OS

CodeForge OS

CodeForge OS is an AI-powered software planning and development assistant designed to bridge the gap between an idea and execution. While modern AI tools can generate code, teams still spend significant time defining requirements, planning architecture, creating implementation strategies, designing test cases, and organizing releases. It automates this process through a collaborative multi-agent workflow. The platform allows users to input a project idea in natural language. Instead of relying on a single AI response, multiple specialized agents work together, each focusing on a specific stage of the software development lifecycle. The Product Manager Agent analyzes the idea and generates detailed requirements, user stories, feature breakdowns, and project objectives. The Architect Agent designs the system architecture, technology stack recommendations, database structure, APIs, and scalability considerations. The Engineering Agent creates implementation plans, development milestones, and technical workflows. The QA Agent generates testing strategies, edge cases, validation criteria, and quality assurance plans. Finally, the Release Manager Agent produces deployment roadmaps, release strategies, and execution timelines. The platform simplifies project planning, reduces time spent on documentation, improves team collaboration, and helps ensure that important stages of software development are not overlooked. Whether a user is building a startup MVP, preparing a hackathon project, creating a college project, or planning a production-scale application, it acts as an intelligent planning partner. Our vision is to evolve it into a complete AI-powered software operating system that not only plans applications but also assists with development, testing, deployment, and continuous improvement throughout the entire software lifecycle.