Top Builders

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

IBM

IBM (International Business Machines Corporation), founded in 1911, is a global leader in technology and consulting. With headquarters in Armonk, New York, IBM has a rich history of pioneering advancements in computing and information technology, continually transforming industries with its innovative solutions.

General
CompanyIBM
Founded1911
Repositoryhttps://github.com/IBM

Start building with IBM's products

IBM offers a wide range of innovative products and services that drive digital transformation for businesses of all sizes. From cloud computing and AI to quantum computing and blockchain, IBM's technologies empower developers and organizations to create and deploy powerful applications. Explore the possibilities with IBM's solutions and see what you can create during lablab.ai hackathons.

watsonx.ai

This is an enterprise AI studio that supports the entire AI lifecycle, allowing users to train, validate, tune, and deploy AI models. It includes tools for generative AI, machine learning, and foundation models like IBM Granite and third-party models from Hugging Face and Meta’s Llama 3. Watsonx.ai also offers capabilities such as the Prompt Lab for prompt engineering, Tuning Studio for model adaptation, and a Flows engine for seamless AI deployment.

watsonx.data

This component provides a robust data store built on an open lake house architecture, supporting both on-premises and multi-cloud environments. It facilitates data engineering, data virtualization, and cost optimization for data warehouses, allowing businesses to modernize their data lakes and streamline data pipelines.

watsonx.governance

A toolkit for AI governance, ensuring transparency, accountability, and ethical AI practices throughout the AI model lifecycle. It helps manage risks, monitor model performance, and ensure compliance with regulatory standards, making AI deployments more responsible and explainable.

  • watsonx Assistant: A conversational AI application for creating chatbots and virtual agents, enhancing customer service with natural language processing.

  • watsonx Orchestrate: An automation solution that uses AI to streamline workflows and automate repetitive tasks across various business domains like HR, sales, and procurement.

  • watsonx Code Assistant: A tool to aid developers by generating code based on natural language inputs, improving productivity and reducing coding complexity.

Granite Models

Granite is IBM's flagship series of LLM foundation models based on decoder-only transformer architecture. Granite language models are trained on trusted enterprise data spanning internet, academic, code, legal and finance.

👉 Try Granite on watsonx.ai

AI Models:

  • Granite 13b chat

  • Granite 13b instruct

  • Granite multilingual

  • Granite Japanese

Embedding Models:

The slate.125m.english.rtrvr and slate.30m.english.rtrvr models are bi-encoder sentence transformers that generate embeddings for various inputs like queries, passages, or documents. Both models are trained to maximize the cosine similarity between pairs of texts (e.g., a query and a passage), producing sentence embeddings that can be compared using cosine similarity.

Edit on GitHub

IBM AI Technologies Hackathon projects

Discover innovative solutions crafted with IBM AI Technologies, developed by our community members during our engaging hackathons.

👤 Shadow-Orchestrator-Ransom-Worm-🪱

👤 Shadow-Orchestrator-Ransom-Worm-🪱

An autonomous repository restoration system designed to recover, analyze, and modernize abandoned software projects at scale. Instead of generating new code from scratch, the platform focuses on preserving digital infrastructure that would otherwise be lost to dependency drift, broken builds, outdated frameworks, missing documentation, and institutional knowledge decay. At its core is a swarm architecture composed of specialized agents that operate as a coordinated restoration pipeline. Each agent performs a specific function: repository discovery, dependency analysis, architecture mapping, build reconstruction, code translation, provenance tracking, security verification, and pull request generation. The swarm collectively reconstructs the intent of a project, identifies failure points, and proposes auditable improvements. Unlike traditional automation tools, every action performed by the system is cryptographically witnessed. Analysis results, build artifacts, dependency graphs, remediation plans, and generated patches are sealed into an append-only WORM (Write Once Read Many) ledger. This creates a permanent chain of provenance that allows every modification to be traced, verified, and reproduced. The governance layer is implemented through deterministic execution receipts. Agents cannot execute independently; each stage must produce a verifiable cryptographic proof before the next stage can proceed. This transforms repository restoration into a governed workflow rather than a collection of disconnected scripts. The result is a platform that combines AI-driven software archaeology, autonomous maintenance, and cryptographic accountability. RANSOM.WORM turns forgotten repositories into living assets, preserving open-source knowledge while creating a transparent, auditable record of every transformation. ╭─ SNAPKITTY SHADOW SEAL ─╮ │ 🪱 WORM-SEALED • APPEND-ONLY │ │ 🌙 GRAVEYARD AGENT VERIFIED │ │ 🔐 SHA-256 PROVENANCE LOCKED │ ╰─ SHADOW//RANSOM.WORM ─╯

Bellwether

Bellwether

Problem. Mid-market procurement teams cover 200–2,000 active suppliers on $50M–$500M of annual spend, and review them once a year. By the time a supplier blows up — layoffs, lawsuit, CFO churn, sanctions hit — the buyer finds out from a missed delivery, not from a monitoring tool. One avoided supplier blowup pays for 35 years of Bellwether on a 200-supplier list. What it does. Every morning at 06:00 local, Bellwether wakes up and per-supplier: 1. CrewAI swarm of 4 agents (Researcher / Compliance / Analyst / Writer) fans out 2. Bright Data pulls SERP, LinkedIn, Web Unlocker evidence with provenance per record 3. OFAC SDN list fetched directly from Treasury — deterministic match, never LLM-judged 4. IBM Granite 3.1 8B Instruct on AMD MI300X (vLLM, JSON-mode) extracts structured risk signals from the evidence 5. Deterministic Python scorer (~40 lines, unit-tested) weights signals into a 0–10 score with a 7-day delta 6. Markdown memo written with every score hyperlinked to its source URL + fetch timestamp 7. Perplexity Comet drives the buyer's HubSpot tenant in-browser to file the Supplier Review ticket and assign the account owner — HubSpot REST as fallback if no Comet session token MCP-native. Bellwether ships a FastMCP server exposing `query_supplier_risk(supplier_id)` and `list_suppliers()` — a buyer's CFO can ask Claude Desktop "what's the current risk on Acme?" and get the cited memo back without leaving their tool. Auditable by design. The model extracts; deterministic Python decides. Sanctions hits pin the score at 10 via exact string match against the official OFAC list — Granite is never allowed to decide a regulatory verdict, only to describe one. Every claim in every memo carries `source_url`, `fetched_at`, `scraper_id`. Cost envelope. ~$6/month per supplier all-in (Bright Data + MI300X). One hour of an analyst is $95. Live demo (judge-touchable artifact): https://bellwether-demo.vercel.app/acme

Autonomous AI Agents for CRISPR Diagnostics

Autonomous AI Agents for CRISPR Diagnostics

Our project addresses the critical gap in healthcare accessibility across Africa by leveraging advanced autonomous AI agents to automate and streamline CRISPR diagnostics and complex clinical workflows. Powered by state-of-the-art large language models like Meta-Llama-3-8B-Instruct, the system acts as an intelligent layer capable of parsing complex genetic data, validating clinical outputs, and providing real-time decision support for healthcare providers. By optimizing laboratory diagnostics and handling administrative clinical structures autonomously, the platform dramatically reduces latency times, eliminates manual errors, and guarantees high-precision medical analysis. This ensures that cutting-edge precision healthcare becomes fast, affordable, and scalable for underserved populations.