Companies are employing AI agents and co-pilots to help their teams increase efficiency and accuracy, but developing apps that are trained properly can require a skill set many enterprise teams don’t have. This week on No Priors, Sarah and Elad are joined by Harrison Chase, the CEO and co-founder of LangChain, an open-source framework and developer toolkit that helps developers build LLM applications. In this conversation they talk about the gaps in open source app development, what it will take to keep up with private companies, the importance of creating prompts that can be compatible with many API models, and why memory is so undeveloped in this space. 

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Show Notes: 
(0:00) Introduction to LangChain
(1:45) Managing an open source environment
(4:30) Developing useful AI agents
(10:03) Sophistication and limitations of AI app development
(14:17) Switching between model APIs
(17:10) Context windows, fine-tuning and functionality
(21:37) Evolution of AI open source environment
(23:53) The next big breakthroughs

Open sourcing AI app development with Harrison Chase from LangChain

At this moment of inflection in technology, co-hosts Elad Gil and Sarah Guo talk to the world's leading AI engineers, researchers and founders about the biggest questions: How far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What’s happening in state-of-the-art in research? “No Priors” is your guide to the AI revolution. Email feedback to show@no-priors.com.
Sarah Guo is a startup investor and the founder of Conviction, an investment firm purpose-built to serve intelligent software, or "Software 3.0" companies. She spent nearly a decade incubating and investing at venture firm Greylock Partners.
Elad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.

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Mikey Shulman, the CEO and co-founder of Suno, can see a future where the Venn diagram of music creators and consumers becomes one big circle. The AI music generation tool trying to democratize music has been making waves in the AI community ever since they came out of stealth mode last year. Suno users can make a song complete with lyrics, just by entering a text prompt, for example, “koto boom bap lofi intricate beats.” You can hear it in action as Mikey, Sarah, and Elad create a song live in this episode. 

In this episode, Elad, Sarah, And Mikey talk about how the Suno team took their experience making at transcription tool and applied it to music generation, how the Suno team evaluates aesthetics and taste because there is no standardized test you can give an AI model for music, and why Mikey doesn’t think AI-generated music will affect people’s consumption of human made music. 

Listen to the full songs played and created in this episode:

Whispers of Sakura

Stone 

Statistical Paradise

Statistical Paradise 2


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Show Notes: 
(0:00) Mikey’s background
(3:48) Bark and music generation
(5:33) Architecture for music generation AI
(6:57) Assessing music quality
(8:20) Mikey’s music background as an asset
(10:02) Challenges in generative music AI
(11:30) Business model
(14:38) Surprising use cases of Suno
(18:43) Creating a song on Suno live
(21:44) Ratio of creators to consumers
(25:00) The digitization of music
(27:20) Mikey’s favorite song on Suno
(29:35) Suno is hiring

Music consumers are becoming the creators with Suno CEO Mikey Shulman

This week on No Priors hosts, Sarah and Elad are catching up on the latest AI news. They discuss the recent developments in AI like Meta’s new AI assistant and the latest in music generation, and if you’re interested in generative AI music, stay tuned for next week’s interview! Sarah and Elad also get into device-resident models, AI hardware, and ask just how smart smaller models can really get. These hardware constraints were compared to the hurdles AI platforms are continuing to face including computing constraints, energy consumption, context windows, and how to best integrate these products in apps that users are familiar with.

Have a question for our next host-only episode or feedback for our team? Reach out to show@no-priors.com

Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil 

Show Notes: 
(0:00) Intro
(1:25) Music AI generation
(4:02) Apple’s LLM
(11:39) The role of AI-specific hardware
(15:25) AI platform updates
(18:01) Forward thinking in investing in AI
(20:33) Unlimited context
(23:03) Energy constraints

Context windows, computer constraints, and energy consumption with Sarah and Elad

Scott Wu loves code. He grew up competing in the International Olympiad in Informatics (IOI) and is a world class coder, and now he's building an AI agent designed to create more, not fewer, human engineers. This week on No Priors, Sarah and Elad talk to Scott, the co-founder and CEO of Cognition, an AI lab focusing on reasoning. Recently, the Cognition team released a demo of Devin, an AI software engineer that can increasingly handle entire tasks end to end.

In this episode, they talk about why the team built Devin with a UI that mimics looking over another engineer’s shoulder as they work and how this transparency makes for a better result. Scott discusses why he thinks Devin will make it possible for there to be more human engineers in the world, and what will be important for software engineers to focus on as these roles evolve. They also get into how Scott thinks about building the Cognition team and that they’re just getting started. 

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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @ScottWu46

Show Notes: 
(0:00) Introduction
(1:12) IOI training and community
(6:39) Cognition’s founding team
(8:20) Meet Devin
(9:17) The discourse around Devin
(12:14) Building Devin’s UI
(14:28) Devin’s strengths and weakness 
(18:44) The evolution of coding agents
(22:43) Tips for human engineers
(26:48) Hiring at Cognition

Cognition’s Scott Wu on how Devin, the AI software engineer, will work for you

AI-generated videos are not just leveled-up image generators. But rather, they could be a big step forward on the path to AGI. This week on No Priors, the team from Sora is here to discuss OpenAI’s recently announced generative video model, which can take a text prompt and create realistic, visually coherent, high-definition clips that are up to a minute long.

Sora team leads, Aditya Ramesh, Tim Brooks, and Bill Peebles join Elad and Sarah to talk about developing Sora. The generative video model isn’t yet available for public use but the examples of its work are very impressive. However, they believe we’re still in the GPT-1 era of AI video models and are focused on a slow rollout to ensure the model is in the best place possible to offer value to the user and more importantly they’ve applied all the safety measures possible to avoid deep fakes and misinformation. They also discuss what they’re learning from implementing diffusion transformers, why they believe video generation is taking us one step closer to AGI, and why entertainment may not be the main use case for this tool in the future. 

Show Links:

Bling Zoo video

Man eating a burger video

Tokyo Walk video


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Show Notes: 
(0:00) Sora team Introduction
(1:05) Simulating the world with Sora
(2:25) Building the most valuable consumer product
(5:50) Alternative use cases and simulation capabilities
(8:41) Diffusion transformers explanation
(10:15) Scaling laws for video
(13:08) Applying end-to-end deep learning to video
(15:30) Tuning the visual aesthetic of Sora
(17:08) The road to “desktop Pixar” for everyone
(20:12) Safety for visual models
(22:34) Limitations of Sora
(25:04) Learning from how Sora is learning
(29:32) The biggest misconceptions about video models

OpenAI’s Sora team thinks we’ve only seen the "GPT-1 of video models"

Multimodal models are making it possible to create AI art and augment creativity across artistic mediums. This week on No Priors, Sarah and Elad talk with Suhail Doshi, the founder of Playground AI, an image generator and editor. Playground AI has been open-sourcing foundation diffusion models, most recently releasing Playground V2.5. 

In this episode, Suhail talks with Sarah and Elad about how the integration of language and vision models enhances the multimodal capabilities, how the Playground team thought about creating a user-friendly interface to make AI-generated content more accessible, and the future of AI-powered image generation and editing.

Sign up for new podcasts every week. Email feedback to show@no-priors.com
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Suhail

Show Notes: 
(0:00) Introduction
(0:52) Focusing on image generation
(3:01) Differentiating from other AI creative tools
(5:58) Training a Stable Diffusion model
(8:31) Long term vision for Playground AI
(15:00) Evolution of AI architecture
(17:21) Capabilities of multimodal models
(22:30) Parallels between audio AI tools and image-generation

Open sourcing AI app development with Harrison Chase from LangChain

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