My Courses
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Observing and Tracing Multi-Modal Multi-Agent Systems through Portkey
Portkey enables observability and tracing in multi-modal, multi-agent systems for enhanced understanding and development.
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A Practioner’s Guide to PydanticAI Agents
PydanticAI Agents leverage Pydantic’s validation to build reliable, type-safe AI decision-making systems.
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A Practitioner’s Guide to Nexus – A Scalable Multi-Agent Framework
Nexus is a lightweight Python framework for building scalable, reusable LLM-based multi-agent systems.
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The DRAMA Framework Explained – From Large LLMs to Efficient Small Dense Retrievers
DRAMA enhances dense retrieval by leveraging LLM-based data augmentation and pruning to create efficient, high-performance retrievers with multilingual and long-context capabilities.
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AI Co-Scientist Systems – A Multi Agent System for Research
AI co-scientists powered by Gemini 2.0 accelerate scientific discovery by generating and ranking hypotheses using a multi-agent system.
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Benchmarking AI on Software Tasks with OpenAI SWE-Lancer
SWE-Lancer benchmarks AI models on 1,400+ real freelance software engineering tasks worth $1M, evaluating their coding and management capabilities in full-stack development.
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Mixture-of-Mamba for Enhancing Multi-Modal State Space Models
Mixture-of-Mamba enhances State Space Models for efficient multi-modal data processing across text, images, and speech.
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Step-Video-T2V for Text to Video Generation
Step-Video-T2V, a cutting-edge text-to-video model with 30B parameters, enhances video quality using Video-VAE, Video-DPO, and 3D-attention.
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Advancing Multilingual Text Embeddings through Nomic Embed Text V2
Nomic Embed Text V2 revolutionizes text embeddings with Mixture-of-Experts (MoE), enhancing efficiency, multilingual support, and scalability
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How DeepSearch Accelerates Question-Answering in LLMs?
DeepSearch revolutionizes question-answering in LLMs, enhancing precision, completeness, and efficiency in information retrieval.
