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2025-Changelog-Interviews-Transcripts

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• The introduction of Amp and Thorsten Ball's blog post on building an agent
• The inspiration behind the blog post and the "AGI" experience of Thorsten Ball
• The capabilities of modern language models and their ability to accomplish complex tasks with minimal code
• The democratization of AI and the accessibility of building agents for non-experts
• The discussion of AGI and whether the capabilities of Amp constitute true artificial general intelligence
• The conversation is about a language model (LLM) that can interact with users in a way that seems like it's using other tools and commands
• The LLM is trained on completing conversations and can use "tool calling" to execute external commands
• Tool calling involves the user giving the LLM a specific syntax to use when calling a tool, and the LLM will respond accordingly
• The process involves the LLM sending a request to a provider (such as Anthropic, Google, or OpenAI) to execute a tool, and then sending the result back to the user
• The simplicity of the algorithm used by the LLM is what makes it effective, allowing it to "loop until it works" and try different approaches to solve a problem
• The LLM's ability to access a vast corpus of ideas and tools makes it a powerful tool for developers and users.
• The evolution of tool calling in AI models, from requiring explicit instructions to being able to understand and use external tools on its own.
• The analogy of tool calling being like "shelling out" in programming languages, where the model runs external commands and waits for results.
• The training of models to use tools and shell out, which has improved their capabilities and reduced hallucinations.
• The comparison of different AI models, including Amp, Codex AI, Claude Code, and Gemini CLI, and their strengths and limitations.
• Sourcegraph's perspective on Amp, including its assumption that the model should be given access to tools and tokens, and its potential applications for both individual developers and enterprises.
• Amp's purpose is to provide a powerful AI agent for coding, with the option to spend tokens for advanced capabilities
• Amp is a CLI application that can be used in various code editors, including VS Code, Cursor, and Codium
• The platform has a server component that allows teams to share conversations, links, and other features
• Amp's design is centered around adapting to rapid changes in AI technology, with a focus on simplicity and flexibility
• The team's approach to AI development is to build light scaffolding around the model, allowing it to evolve and improve over time
• The platform's web copy, written by Thorsten Ball, reflects this philosophy and acknowledges the rapid changes happening in AI development
• Discussion of the limitations of Amp's UI and its intended audience
• Analogy of using Amp to a Burning Man journey, releasing restrictions and exploring new possibilities
• Explanation of the "Come with us" slogan, emphasizing curiosity and excitement about AI tools
• Critique of other AI software copy, labeling it as "magic" and emphasizing the power and capabilities of Amp
• Explanation of how Amp works, including the use of web stack and Svelte for UI
• Example of using Amp to automate tasks, such as formatting files and creating new components
• Discussion of the importance of setting "rails" and guiding agents to achieve desired results
• Mention of a podcast series on Ampcode.com, where the hosts share their excitement and experiences with Amp
• AI capabilities and limitations
• Enterprise adoption and skepticism
• Misconceptions about AI and its capabilities
• Different skill levels and comfort with AI
• High expectations and the need for effort and practice
• Anthropomorphism and personification of AI
• The importance of understanding and learning about AI
• The limitations of large language models, including the need to know what context to provide and what not to include to avoid derailing them.
• The concept of a "learning curve" and how it's often downplayed in software development, but is actually a necessary step in using these models.
• The comparison of using AI-powered tools like CursorTab to traditional text editing skills, such as those using Vim.
• The idea that AI-powered tools can make traditional text editing skills, like Vim macros, obsolete.
• The changing landscape of developer tooling and the potential for AI-powered tools to replace traditional text editors and key bindings.
• The emergence of a new era of developer tooling where efficiency and speed are prioritized over traditional skills and key bindings.
• The discussion centers around the idea that the way people approach problems and tasks changes over time and across generations.
• The speakers draw parallels between the evolution of technology, such as vehicles and phones, and the way people use tools for programming.
• A generational divide is noted, with younger people being more open to using AI and newer tools, while older people may be resistant to change.
• The concept of "baggage" is discussed, referring to the emotional attachment people have to certain tools or ways of doing things.
• Rational skepticism is also mentioned, with the speakers noting that it's natural to be skeptical of new ideas and technologies, especially after seeing many fads come and go.
• Identity and attachment to specific skills and tools
• The value of skills shifting due to new technologies, such as AI
• The importance of recognizing that skills are not useless, but have lost value
• Adapting to new tools and leveraging their benefits
• The distinction between using AI to augment thought versus replacing it
• The need for humans to engage in decision-making and judgment despite AI assistance
• The potential for AI to free humans from mundane tasks and enable more complex and creative work
• AI and programming: the impact of AI on the programming profession and the types of programmers that will be most affected
• Evolution of programmer roles: from traditional coding to more high-level, "babysitting" roles
• Changes in programming tools and technologies: the rise of Kubernetes and agents, and how they are changing the way code is written and maintained
• Job market changes: how AI and automation are affecting the job market for programmers and other technical professionals
• Emerging trends in coding: "paint by numbers" programming, where the focus is on specifying what the code should do rather than writing it line by line
• Impact of AI on code generation: how AI is being used to generate test suites, storybooks, and other types of code
• Future of programming: speculation on how the field will continue to evolve and change in the coming years.
• Automating mundane tasks through AI-powered tools
• Reducing the amount of typing and coding required for tasks
• Creating tools to aid in testing and debugging software
• Using AI to generate code and automate complex tasks
• Changing the approach to software engineering by making previously unaffordable tasks now achievable with AI
• The potential for AI to unlock new possibilities and reduce the effort required for tasks such as testing, debugging, and analysis
• Impact of AI on codebase adaptation and evolution
• Changes in engineering practices and code generation
• Shift in open source contribution and usage
• Diminishing value of code duplication and shared libraries
• Impact on coding standards and practices, including the DRY principle
• Potential for AI-generated code to replace human-written code in certain contexts
• Future of code discovery and reuse with AI-powered code generation
• Efficiency of code and tools in relation to changing capabilities
• Changing nature of how people consume and interact with code
• Impact of AI and LLMs on traditional notions of structure and organization
• Potential shift away from traditional open-source models
• Rethinking the value of source code in the era of automation and AI
• The impact of open source on the world in the future
• The difference between source code and tools in AI development
• The potential diminishing value of repetitive or generic code
• The increasing value of unique and creative contributions
• The importance of perspective and zooming in/out on specific problems or broad trends
• The role of AI agents in augmenting human capabilities and improving outcomes
Jerod Santo: Today we're joined by Thorsten Ball from Sourcegraph, working on Amp... Excited to dig into this with you.

Thorsten Ball: Hi. Nice to be here, guys. Thanks for having me.

Jerod Santo: I was very impressed by your blog post back in April on ampcode.com, "How to build an agent, or the emperor has no clothes", in which you walk us through, kind of line by line, a pretty -- a basic, but functional coding agent written in Go. And it really did a good job of demystifying it for myself. Ca...

Thorsten Ball: Yeah, the reason why I wrote the blog post is I had my mind blown so much that I couldn't shut up about it, and I had to get it out there... And the blog post ended up resonating with a lot of people. That's the most likes I've ever had on a tweet, I think, and the most visits, surely... But it start...

Jerod Santo: "I felt the AGI." Okay...

Thorsten Ball: Yeah. And I felt the AGI, because what I had running was a super-tiny prototype. It was Claude 3.7... And I gave it a read file tool, so it could access files, I gave it a list directory tool, and a run terminal tool, so it can run Bash commands. And I was playing around with it and I was like "Oh, i...

\[07:44\] So to spread the message inside of Sourcegraph, I wrote this blog post about how to build an agent, which is basically a modification of what I've just described to you... Like, Claude 3.7, three, four tools, and then off it goes. And pretty well received... And then still, I saw more and more people talking ...

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2025 Changelog Interviews Transcripts

Complete transcripts from the 2025 episodes of the Changelog Interviews podcast.

Generated from this GitHub repository.

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