LTM-2-mini

LTM-2-mini (Long-Term Memory Mini Model) is a cutting-edge AI model developed by Magic, designed to process extremely large contexts efficiently. Capable of handling up to 100 million tokens (equivalent to 10 million lines of code), this model is revolutionizing industries that require the analysis of vast datasets. Its innovative sequence-dimension algorithm allows for efficient processing, making it approximately 1,000 times more resource-efficient than traditional attention-based models like Llama 3.1. The LTM-2-mini significantly enhances machine comprehension over longer contexts, enabling better reasoning and recall in tasks like software development, legal document analysis, and database management.

Read more about LTM-2-mini in our article 👉 How Magic.dev’s LTM-2-mini is Redefining AI’s Ability to Handle Vast Contexts

General
Author	Magic (Founded by Eric Steinberger and Sebastian De Ro)
Release Date	August 2024
Website	https://magic.dev/
Technology Type	AI Model, Natural Language Processing (NLP)

Key Features

100 Million Token Context Window: The ability to process up to 100 million tokens simultaneously, surpassing the capabilities of existing models.
Sequence-Dimension Algorithm: Reduces computational and memory overhead by 1,000 times compared to traditional attention mechanisms.
HashHop Evaluation: A unique evaluation method that tests the model's ability to perform multi-hop reasoning and skip-step tasks across vast datasets.
Efficient Resource Usage: Despite handling a massive amount of data, the LTM-2-mini requires minimal GPU power, utilizing a small fraction of the resources needed by comparable models.
Multi-hop and Non-linear Reasoning: Capable of understanding complex relationships and dependencies across entire datasets without relying on semantic cues.

Applications

Software Development: Revolutionizes how developers manage and understand large codebases. The model assists in identifying code dependencies, suggesting optimizations, and automating routine tasks like unit testing or documentation.
Legal Document Analysis: Processes entire legal contracts, cases, and documents to ensure no vital details are overlooked, improving the accuracy and speed of legal analysis.
Database and Large Text Analysis: Ideal for analyzing massive datasets and conversation histories, enabling AI to recall and process information holistically without missing critical details.
Real-time Code Generation: Supports coding platforms by providing intelligent, context-aware suggestions and quality assurance in large, complex projects.

Get Started Building with LTM-2-mini:

Ready to harness the power of ultra-large context windows in your projects? With Magic’s LTM-2-mini, you can revolutionize how AI interacts with large datasets and complex codebases. Whether you're developing software, analyzing legal documents, or building conversational agents, LTM-2-mini opens up new possibilities for AI-assisted efficiency.

👉 Read more about LTM-2-mini and start building intelligent solutions that leverage the unmatched context-handling capabilities of LTM-2-mini.

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

Apollo

Token-Efficient Routing Agent - AMD Developer Hackathon ACT II, Track 1. Completes all 8 required task categories (factual knowledge, math, sentiment, summarization, NER, code debugging, logical reasoning, code generation) while minimizing Fireworks AI token spend without sacrificing accuracy. Every routing decision - task category, difficulty, which model answers it - is made by plain rule-based code before any API call. Zero cost, zero latency, nothing hardcoded: models are read fresh from ALLOWED_MODELS every time. Routing is category- and difficulty-aware, not just cheapest-overall: - Code tasks go to a code-specialized model, scaling from cheapest to strongest with difficulty. - Hard math/logic (difficulty >= 0.7) escalates to the most capable model rather than risk the 80% accuracy gate to save tokens. - Everything else - including easy math/logic - prefers the cheapest reachable Gemma model, else a difficulty-proportional pick. Every Fireworks call passes reasoning_effort: "none", suppressing hidden "thinking" tokens some models emit even for trivial tasks - measured to cut one sentiment call from 36 to 2 completion tokens, same correct answer. Each task gets a shared 26-second budget across up to three attempts (selected model, same-model retry, cross-model fallback), with an asymmetric retry policy: fast failures retry immediately, slow failures skip to a different model, so retries can't exceed the 30-second per-request limit. A local-model variant was prototyped for zero-token answers, then deliberately removed after testing showed inconsistent performance under simulated constrained resources - favoring a smaller (195MB), fully-verified submission over an unproven gain this close to the deadline. Verified against hand-written samples, 24 paraphrased variants, and the official practice tasks from the participant guide, run against the actual image pulled fresh from the public registry under the documented 4GB RAM / 2 vCPU grading constraints.

GenAI Giants

GenAI Giants is an AI-powered platform built to transform learning, developer productivity, and innovation at scale. Our vision is to create inclusive, cloud-native solutions that empower users across India by supporting multiple local languages and bridging the gap between cutting-edge technology and accessibility. By integrating seamlessly with modern developer workflows, IDEs, and CI/CD pipelines, the platform enables faster coding, intelligent automation, and effortless scalability for projects of all sizes. At its core, GenAI Giants is more than a productivity tool—it is a movement to democratize AI knowledge and make advanced technology accessible to everyone, regardless of language or background. We emphasize social impact by fostering collaboration, nurturing talent, and building a community where developers can learn, innovate, and grow together. The platform leverages strategic cloud architecture and multilingual AI capabilities to deliver a robust ecosystem that supports diverse use cases—from education and skill development to enterprise-grade automation. By combining scalability, inclusivity, and innovation, GenAI Giants aims to accelerate digital transformation across industries while ensuring that technology remains accessible to all. Ultimately, our mission is to empower developers and learners with tools that not only enhance productivity but also drive meaningful change. GenAI Giants stands as a bridge between advanced AI and real-world impact, creating opportunities for growth, collaboration, and innovation across India and beyond.

Qubic Liquidation Guardian

Qubic Liquidation Guardian is a hybrid Track 1 + Track 2 project built by CrewX that brings real-time liquidation protection, institutional-grade risk analysis, and automated alerting to the Qubic Network. The problem is simple: DeFi liquidations happen instantly, but users do not get instant signals. As a result, borrowers lose capital, protocols lose liquidity, and investors hesitate to adopt new systems without safety infrastructure. Inspired by this gap, Qubic Liquidation Guardian provides a complete safety layer over lending protocols deployed on the Nostromo Launchpad. At its core, the system includes an on-chain event listener and a real-time risk scoring engine, which analyzes: • Health Factor • Liquidation Proximity • Total Debt Exposure • Active Positions These metrics are combined into a 0–100 Risk Score, dynamically updated for each borrower. Based on the score, users are automatically classified into Low, Medium, High, and Critical risk tiers, enabling rapid decision-making. The platform also includes advanced features such as: • Whale Watch: Detect large-value transactions to anticipate market shifts • Smart Alerts: Severity-based notifications connected to any tool • Auto-Airdrop: Rewards for users who resolve high-risk positions • Crash Simulator: A built-in testing environment to simulate -70% market dumps, rebounds, and full resets to verify protocol safety Qubic Liquidation Guardian is designed to strengthen the Nostromo ecosystem by improving investor confidence, increasing protocol safety, and enabling risk-aware liquidity management. With over 35 production-ready API endpoints, an edge-distributed database, and a Next.js 15 architecture, the application is fully deployable and already live for testing. Ultimately, this project delivers exactly what new chains and protocols need: speed, stability, transparency, and automation—making Qubic safer for everyone.