The podcast by and for AI Engineers! In 2025, over 10 million readers and listeners came to Latent Space to hear about news, papers and interviews in Software 3.0. We cover Foundation Models changing every domain in Code Generation, Multimodality, AI Agents, GPU Infra and more, directly from the founders, builders, and thinkers involved in pushing the cutting edge. Striving to give you both the definitive take on the Current Thing down to the first introduction to the tech you'll be using in the next 3 months! We break news and exclusive interviews from OpenAI, Anthropic, Gemini, Meta (Soumith Chintala), Sierra (Bret Taylor), tiny (George Hotz), Databricks/MosaicML (Jon Frankle), Modular (Chris Lattner), Answer.ai (Jeremy Howard), et al. Full show notes always on https://latent.space Sponsorship and business inquiries: [email protected] www.latent.space
This podcast episode discusses the AI Engineer Summit, an upcoming conference that brings together AI engineering professionals. It explores the concept of Software 3.0, which represents a paradigm shift in software development enabled by machine learning-based code. The importance of model architectures, foundation mo...
This podcast episode discusses various topics related to AI and language models. The guest, Jerry, shares his experiences at Quora and the challenges involved in information retrieval and ranking techniques. He then talks about starting Llama Index, a project that aims to leverage language models for processing and ana...
This podcast episode features an interview with Dr. Raza Habib, CEO of Human Loop, a platform that focuses on prompt engineering for language models. The discussion covers the early adoption and importance of prompt ops, different types of prompt evaluations and human feedback, and the potential of neural networks and ...
This podcast episode explores the innovative features and capabilities of a podcasting tool that simplifies content creation. The tool allows users to generate scripts, use AI voices, create videos, and supports multiple languages. It also offers features like scheduled publishing and integration with newsletters. The ...
In this episode of the Latent Space Podcast, the host interviews Chris Lanner, an experienced professional in the field of computer science. The discussion revolves around the challenges and developments in the AI research and development space. Chris shares insights into his work experience at Apple, Tesla, and Google...
This podcast episode delves into the journey of Harrison Chase, the founder of LangChain, exploring his transition from sports analytics to shaping a pivotal AI framework that enhances the development of context-aware applications. Harrison highlights how LangChain emerged from his insights into developer needs, provid...
This podcast episode focuses on the RWKV model, a novel architecture that challenges the dominance of transformers in natural language processing. The RWKV model overcomes the scalability and parallelization limitations of transformers, making it suitable for handling large context sizes and complex tasks like website ...
This podcast episode delves into various cutting-edge techniques and challenges in the realm of AI-powered code generation, shedding light on recent advancements and future opportunities in the field. Notable topics discussed include Cursor, an intriguing IDE that aims to fully integrate with GPT-4 and transform softwa...
This podcast episode delves into various aspects of scaling up large language models and training transformer-based models, emphasizing the practical considerations, challenges, and limitations involved. It covers topics such as hardware setup, flops, quantization, distributed training techniques, and emerging research...
This podcast episode features Tianqi Chen, an assistant professor at CMU and chief technologist of AutoML. He shares his passion for coding, detailing how he uses sketchbooks to design and develop open-source projects. The conversation moves to XGBoost, a gradient boosting library, and its surprising success in machine...
This podcast episode delves into the technical developments in AI during July, covering topics such as the release of LAMA2, the growth of AI companions, the evolution of GPT, and ongoing debates around open source. It also explores the challenges and opportunities for startups in the AI landscape, the significance of ...
This podcast episode explored various topics at the intersection of machine learning and systems, including attention mechanisms, memory hierarchies, flash attention, the relationship between academia and industry, evaluation in AI, the hardware lottery, and open-source AI. The discussion highlighted the importance of ...
This podcast episode delves into the significance and implications of Meta's release of LAMA 2, exploring its technical details, open-source nature, potential applications, and the challenges and opportunities it presents for the field of AI. Experts discuss the model's capabilities, the evolving landscape of open-sour...
Machine learning heavily relies on the availability and quality of datasets, which are pivotal for training and evaluating machine learning models. Datasets play a critical role in natural language processing, ranging from tokenization processes to the understanding of scaling laws that govern the effectiveness of larg...
This podcast episode explores various aspects of GPT-4, including its code interpreter feature, its strengths and limitations in different use cases, and potential implications for developers, data journalists, and other users.
This podcast episode discusses the evolution of generative AI, the challenges faced by software engineers and data scientists transitioning into AI engineering roles, and the advancements in MLOps and LLMOps. It also covers the involvement of grassroots organizations in language technology and the importance of data-ce...
This podcast episode explores the development of a small natural language dataset called Tiny Stories, which has led to insights into interpretability, domain-specific models, and the emergence of complex patterns in smaller models. It also delves into the motivation for using synthetic data for language model research...
This podcast episode features GeoHot discussing his journey and the reasons behind his involvement with ML compute. The conversation delves into the challenges faced by existing AI chips and the significance of developing performant ML frameworks. GeoHot introduces TinyGrad, a project aimed at creating a smaller and le...
This podcast episode covers various topics related to large language models (LLMs), including the OpenAI June Update, functions release for ChatGPT, the functions API for LLMs, the role of LLMs in API design, prompt engineering, price reductions for API access to LLMs, selection of functions in OpenAI's API, integratio...
This podcast episode explores the integration of AI into product development, the challenges and opportunities of using LLMs and traditional ML models, the potential of natural language interfaces in analytics, and the ethical considerations surrounding AI training data.
Follow this podcast in Podwise
Sign in to get AI summaries, transcripts and mind maps for any episode, including new ones.
