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• The importance of human input and judgment in the development process |
• The freedom to choose tools and AI systems, such as Claude Code or Copilot, in software development |
• The focus on employee satisfaction and well-being, such as providing high-quality keyboards |
• The need for "babysitters" or humans with taste and judgment to guide the development process and ensure high-quality outcomes. |
• The concept of "babysitting" in AI development, where humans monitor and correct AI tools, may be a temporary necessity but not a long-term requirement. |
• The flow state of working with AI tools is different from traditional coding, involving a loop of conversation, source code, and decision-making. |
• As AI tools become more capable, the need for babysitting decreases, and humans can focus on directing the work and making high-level decisions. |
• The volume of parallelism that AI tools enable is increasing, requiring a change in how systems are constructed and designed. |
• A new programming language that is AI-native and designed to work with LLMs could revolutionize the way code is written and maintained. |
• The ideal programming language would have a feedback loop that allows for immediate correction and evaluation of code, rather than waiting for compile or run-time errors. |
• Examples of languages like Unison that use databases to synchronize and evaluate code could be a model for future AI-native languages. |
• The current state of technology allows for building AI systems without AGI or new breakthroughs |
• The "agent is glue" concept, where agents are used to orchestrate and connect different systems and tools, rather than being a proprietary and closed system |
• The idea that the current AI landscape is leading to a "tollbooth" effect, where companies like Anthropic and OpenAI are becoming gatekeepers of AI technology and development |
• The potential for a hybrid inference model that combines local resources with cloud-based inference |
• The shift from building custom agents to using pre-built agents like Claude Code and Anthropic's SDK |
• The importance of thinking about agents as glue, allowing for more flexibility and customization in AI development and integration. |
• The importance of not relying solely on the LLM for complex tasks, but instead using deterministic systems to help orchestration |
• The value of using a strict modeling language to correct hallucinations in the LLM |
• The ability to create custom models from scratch using API documents and other sources |
• The need for change control and human oversight in the automation loop |
• The use of a chatbot interface to interact with the LLM and automate tasks |
• The importance of earning the right to autonomy through consistent performance in human observation |
• The desire for autonomy with guardrails and clear parameters to mitigate risk |
• Development process and document-driven development |
• Agent flow and PEPs (Python Enhancement Proposals) |
• Use of automation and tools to streamline development |
• Collaboration and delegation of tasks |
• Current state of software engineering and tooling, comparing to the early days of Windows and DOS |
• Building and integrating new tools and skills into agents and workflows |
• Discussion of the limitations of current computing infrastructure and the potential for new innovations |
• Rewriting of System Initiative to be AI-native, allowing for more efficient interaction with AI systems |
• Introduction of a new interface for interacting with System Initiative, including agents, web UI, and public API |
• Explanation of how the new interface allows for different levels of interaction with System Initiative |
• Discussion of System Initiative's potential as a cloud operating system that can be used on top of any hardware, public or private |
• Explanation of the generic design of System Initiative and its ability to interact with custom applications and APIs |
• The concept of a "cloud operating system" and the need for a simplified abstraction layer for infrastructure management |
• The potential for AI-powered infrastructure management to reduce the need for human intervention and intermediate abstraction layers |
• The current support for AWS, GCP, and custom hardware, with plans to expand to Azure and other cloud providers |
• The importance of architecture migration and mobility in the cloud, with a focus on minimizing application downtime and maximizing efficiency |
• The growing demand for on-premises infrastructure management and the company's plans to address this need |
• The role of user adoption and feedback in driving product development and growth |
• The challenges of going to market in 2025, including the need to differentiate AI-powered products from existing solutions and managing customer expectations around AI adoption. |
• Practitioner challenge of staying engaged with skeptical customers |
• Enterprise customers' "existential crisis" about adopting AI technology |
• Top-down approach to sales and adoption in large enterprises |
• Practical examples and documentation as key to turning AI technology into practical solutions |
• Bottoms-up approach vs. top-down approach to sales and adoption |
• Sales strategy and organizational challenges in selling AI technology |
• Developing a sales playbook and messaging for a company |
• The importance of understanding the sales motion and having enough certainty to grow a sales force |
• The risks of hiring sales reps too early and not being able to give them clear direction |
• The value of founder-led sales and being present in every deal to learn about the market and product |
• The benefits of in-person sales and building relationships with customers to learn about their needs and pain points |
• The need to be in the trenches with customers and understand their problems to effectively sell a solution |
• Remote work vs in-person interaction |
• Importance of face-to-face communication for complex sales and infrastructure automation |
• Challenges of replicating in-person experience on Zoom |
• Value of hands-on experience and dedicated time with clients |
• Adam Jacob's willingness to help clients with infrastructure problems |
**Adam Stacoviak:** We're back with our good friend, Adam Jacob. And Adam, there's always something, some sort of outage around our conversations, some sort of big event... There's something in open source, there's a debacle, there's an outage in the case of AWS recently... And I actually was at my son's ninja training... |
**Jerod Santo:** Yeah. It's got big. |
**Adam Stacoviak:** What's the juice? What are your thoughts on this? Is it just one machine in Ashburn, Virginia that's running this thing? What's the state of our cloud? |
**Adam Jacob:** Yeah, that's a good question. This was an interesting one to watch as an old guy. |
**Adam Stacoviak:** Okay... |
**Adam Jacob:** You know, I feel an old guy, in that I helped build the early internet, and then... You know, I went through these waves... And we spent a long time in the sort of early part of the DevOps movement trying to figure out how people should react when these outages happen, and sort of the HugOps movement, a... |
**Adam Stacoviak:** "Someone must die..." |
**Adam Jacob:** Yeah. It was we were playing Mortal Kombat, man... |
**Adam Stacoviak:** "Finish him!" |
**Adam Jacob:** Yeah, totally. Corey Quinn wrote this article that was "Basically, this happened because all the smart people left." That was my TL;DR of his article. |
**Jerod Santo:** Oh, yeah... |
**Adam Jacob:** And I was like, a) I couldn't imagine writing that article. All love to Corey as a person or whatever, but I was just like, if I worked at AWS and it was "All the smart people left, and so now the outages are coming..." |
**Jerod Santo:** That one hurts. |
**Adam Jacob:** You're "Oh. Uh. Oh. Brutal." Also, who do you think built those old systems that are failing? It was the smart people that you're lamenting having left, It's not the new guy... The new guy didn't put that together, It was the old timers who put that together, and that's what failed you now. |
**Adam Stacoviak:** That's right. They're duct-taping. |
**Adam Jacob:** Yeah, they're just trying to keep it going, you know? |
**Jerod Santo:** The other take was they're cutting over to their AI SREs, or whatever. |
**Adam Jacob:** Sure... \[laughs\] |
**Jerod Santo:** "It's AI's fault" was the other take. |
**Adam Jacob:** "It was all AI slop", or whatever. And I don't know, all of that feels crazy to me. Obviously, we have no idea. Not really. I mean, they've said some things, I think -- |
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