Today, we have Or Weis, Co-Founder and CEO at Permit.io, on the show! Or has spent over two decades in software engineering and is now building tools to help developers manage authorization and access control as AI agents become an increasingly important part of software systems.
In this episode, we explore how AI agents are changing the way we think about identity, authentication, and authorization. Or explains why agents can’t simply be treated like human users or traditional machine identities, and how their unpredictable and autonomous behavior introduces new security challenges, from prompt injection attacks to the confused deputy problem.
We also dive into the Model Context Protocol (MCP), why delegated access requires a different approach to security, and the shift from session-based permissions to action-level authorization. Or walks us through Permit.io’s MCP Gateway and introduces the concept of agentic intent—how organizations can track changes in an agent’s intended actions and intervene when its behavior starts drifting from the original task.
Finally, we discuss how enterprises are navigating the transition to agentic workflows, the challenges of connecting AI agents to sensitive data and production systems, and why existing security and compliance practices need to evolve as AI adoption accelerates.
Tune in for a deep dive into AI agent security, authorization, MCP, and what it takes to build secure and reliable agentic systems.
Visit permit.io to learn more
1:00:09
Today, we have Or Weis, Co-Founder and CEO at Permit.io, on the show! Or has spent over two decades in software engineering and is now building tools to help developers manage authorization and access control as AI agents become an increasingly important part of software systems.
In this episode, we explore how AI agents are changing the way we think about identity, authentication, and authorization. Or explains why agents can’t simply be treated like human users or traditional machine identities, and how their unpredictable and autonomous behavior introduces new security challenges, from prompt injection attacks to the confused deputy problem.
We also dive into the Model Context Protocol (MCP), why delegated access requires a different approach to security, and the shift from session-based permissions to action-level authorization. Or walks us through Permit.io’s MCP Gateway and introduces the concept of agentic intent—how organizations can track changes in an agent’s intended actions and intervene when its behavior starts drifting from the original task.
Finally, we discuss how enterprises are navigating the transition to agentic workflows, the challenges of connecting AI agents to sensitive data and production systems, and why existing security and compliance practices need to evolve as AI adoption accelerates.
Tune in for a deep dive into AI agent security, authorization, MCP, and what it takes to build secure and reliable agentic systems.
Visit permit.io to learn more
Today, we have Dan Manges, Co-Founder and CEO at RWX, on the show! Dan previously served as CTO at Braintree and co-founded Root Insurance, and is now building a CI/CD platform designed for the agentic era.
In this episode, we explore how AI coding agents are changing software development and why the bottleneck is shifting from writing code to validating it. Dan explains why traditional CI/CD workflows are struggling to keep up, and how RWX uses content-based caching, graph-based execution, and a CLI-first approach to make builds faster and more efficient.
We also dive into how engineering teams can rethink their development workflows around AI agents, from validating code changes before opening pull requests to using sandboxes and distributed compute. Dan shares his thoughts on harness engineering, why developer tooling needs to evolve alongside AI models, and what it takes to build reliable software when code is becoming increasingly cheap to produce.
Finally, we discuss how teams should measure engineering productivity beyond build times and costs, and why Dan believes the shift to agentic development is creating opportunities to rethink how software gets built and shipped.
Tune in for a deep dive into CI/CD, AI coding agents, and the future of software engineering.
Visit https://www.rwx.com/blog/series-a for more info.
43:13
Today, we have Dan Manges, Co-Founder and CEO at RWX, on the show! Dan previously served as CTO at Braintree and co-founded Root Insurance, and is now building a CI/CD platform designed for the agentic era.
In this episode, we explore how AI coding agents are changing software development and why the bottleneck is shifting from writing code to validating it. Dan explains why traditional CI/CD workflows are struggling to keep up, and how RWX uses content-based caching, graph-based execution, and a CLI-first approach to make builds faster and more efficient.
We also dive into how engineering teams can rethink their development workflows around AI agents, from validating code changes before opening pull requests to using sandboxes and distributed compute. Dan shares his thoughts on harness engineering, why developer tooling needs to evolve alongside AI models, and what it takes to build reliable software when code is becoming increasingly cheap to produce.
Finally, we discuss how teams should measure engineering productivity beyond build times and costs, and why Dan believes the shift to agentic development is creating opportunities to rethink how software gets built and shipped.
Tune in for a deep dive into CI/CD, AI coding agents, and the future of software engineering.
Visit https://www.rwx.com/blog/series-a for more info.
AI coding tools are changing how software is built, but understanding what happens under the hood still matters.
Robert Brennan, Co-Founder and CEO at OpenHands, explains why new developers should embrace AI tools without neglecting the fundamentals of software architecture, databases, and security.
Even if you’re no longer writing every line of code, you still need to understand what good software looks like.
1:16
AI coding tools are changing how software is built, but understanding what happens under the hood still matters.
Robert Brennan, Co-Founder and CEO at OpenHands, explains why new developers should embrace AI tools without neglecting the fundamentals of software architecture, databases, and security.
Even if you’re no longer writing every line of code, you still need to understand what good software looks like.
Today, we have Robert Brennan, Co-Founder and CEO at OpenHands, on the show! Robert previously worked at Google and Fairwinds, and is now building an open-source platform for AI-powered software development.
In this episode, we explore how AI agents are changing the way developers work, from automating code reviews and debugging to proactively monitoring production systems and fixing issues. Robert explains what makes an AI agent reliable, why agent harnesses are becoming commoditized, and how teams can continuously improve agent performance.
We also dive into how software teams should prepare for agentic development, including the trade-offs between monorepos and microservices, trunk-based development, and traditional code review workflows. Robert also walks us through OpenHands’ SDK and Agent Canvas, and how they enable developers to build and manage autonomous workflows.
Finally, we discuss the idea of an AI “dark factory,” where agents handle everything from writing code to testing and deployment. Robert shares his prediction that source code could become an antiquated abstraction layer within the next few years, and what that means for the future of software engineering.
Tune in for a deep dive into AI coding agents, developer tools, and the future of autonomous software development.
Visit https://www.openhands.dev/ to learn more.
38:43
Today, we have Robert Brennan, Co-Founder and CEO at OpenHands, on the show! Robert previously worked at Google and Fairwinds, and is now building an open-source platform for AI-powered software development.
In this episode, we explore how AI agents are changing the way developers work, from automating code reviews and debugging to proactively monitoring production systems and fixing issues. Robert explains what makes an AI agent reliable, why agent harnesses are becoming commoditized, and how teams can continuously improve agent performance.
We also dive into how software teams should prepare for agentic development, including the trade-offs between monorepos and microservices, trunk-based development, and traditional code review workflows. Robert also walks us through OpenHands’ SDK and Agent Canvas, and how they enable developers to build and manage autonomous workflows.
Finally, we discuss the idea of an AI “dark factory,” where agents handle everything from writing code to testing and deployment. Robert shares his prediction that source code could become an antiquated abstraction layer within the next few years, and what that means for the future of software engineering.
Tune in for a deep dive into AI coding agents, developer tools, and the future of autonomous software development.
Visit https://www.openhands.dev/ to learn more.
Today we are talking with Vignesh Baskaran, the CTO and co-founder of Hexo Labs, about teaching AI agents to improve themselves. Vignesh has been training neural networks since 2012, back when he was still called a data scientist.
Then he became an ML engineer and now an AI engineer, though he says the underlying work has never really changed. It's to figure out how to make a system behave the way you intend it to. He built the litigation search engine that Google itself became a customer of, and now he's chasing something new, agents that rewrite and retrain other agents without a human in the loop.
We dig into Sia, the meta-agent at the center of Hexo's research, and why improving an agent means touching both its harness and its actual model weights, not just one or the other. We talk about proxy evals for when you don't have much to ground truth. The Darwin-Gödel machine and why formal verification is too strict a bar for anything commercial.
How Hexo's work echoes DeepMind's Alpha lineage from AlphaGo to AlphaEvolve, and the spectrum from clearly verifiable to totally subjective tasks? Why VAE evals are quietly wrecking agent quality across the industry, and a great story about an agent that discovered a customer's own eval file was silently corrupted, something buried in hundreds of thousands of traces that no human would have caught.
Visit https://hexolabs.com/
52:49
Today we are talking with Vignesh Baskaran, the CTO and co-founder of Hexo Labs, about teaching AI agents to improve themselves. Vignesh has been training neural networks since 2012, back when he was still called a data scientist.
Then he became an ML engineer and now an AI engineer, though he says the underlying work has never really changed. It's to figure out how to make a system behave the way you intend it to. He built the litigation search engine that Google itself became a customer of, and now he's chasing something new, agents that rewrite and retrain other agents without a human in the loop.
We dig into Sia, the meta-agent at the center of Hexo's research, and why improving an agent means touching both its harness and its actual model weights, not just one or the other. We talk about proxy evals for when you don't have much to ground truth. The Darwin-Gödel machine and why formal verification is too strict a bar for anything commercial.
How Hexo's work echoes DeepMind's Alpha lineage from AlphaGo to AlphaEvolve, and the spectrum from clearly verifiable to totally subjective tasks? Why VAE evals are quietly wrecking agent quality across the industry, and a great story about an agent that discovered a customer's own eval file was silently corrupted, something buried in hundreds of thousands of traces that no human would have caught.
Visit https://hexolabs.com/
Today on the show, we have a special guest — Ashmeet Sidana, the founder of Engineering Capital.
Ashmeet started his career as an engineer at some great companies like Hewlett-Packard and Silicon Graphics before founding his own company, getting it acquired, and eventually starting his venture capital firm, Engineering Capital.
With his strong engineering background, Ashmeet looks for startups that have a technical insight — something unique that gives them an edge over their competitors. This focus on technical insight sets Engineering Capital apart from other VC firms that often emphasize market insight or distribution insight or some other kind of advantage.
We talked about AI, Exponential Engineers, Entrepreneurship, and had a lot of fun.
53:21
Today on the show, we have a special guest — Ashmeet Sidana, the founder of Engineering Capital.
Ashmeet started his career as an engineer at some great companies like Hewlett-Packard and Silicon Graphics before founding his own company, getting it acquired, and eventually starting his venture capital firm, Engineering Capital.
With his strong engineering background, Ashmeet looks for startups that have a technical insight — something unique that gives them an edge over their competitors. This focus on technical insight sets Engineering Capital apart from other VC firms that often emphasize market insight or distribution insight or some other kind of advantage.
We talked about AI, Exponential Engineers, Entrepreneurship, and had a lot of fun.
Today we have Dr. Ewelina Kurtys on the show. Ewelina has a background in Neuroscience and is currently working at FinalSpark.
FinalSpark is using live Neurons for computations instead of traditional electric CPUs. The advantage is that live Neurons are significantly more energy efficient than traditional computing, and given all the energy concerns right now with regards to running AI workloads and data centers, this seems quite relevant, even though bioprocessors are still very much in the research phase.
42:34
Today we have Dr. Ewelina Kurtys on the show. Ewelina has a background in Neuroscience and is currently working at FinalSpark.
FinalSpark is using live Neurons for computations instead of traditional electric CPUs. The advantage is that live Neurons are significantly more energy efficient than traditional computing, and given all the energy concerns right now with regards to running AI workloads and data centers, this seems quite relevant, even though bioprocessors are still very much in the research phase.
Today we're talking with one of our favorite engineers, Rafal Wilinski. Rafal has been on the cutting edge of AI development in the last few years as he has led AI teams at Zapier and Vendr.
Rafal walks us through the hard-won lessons about actually integrating AI tools into the applications you're building. One of the hardest things in integrating these AI tools is how to ensure you're getting better and not regressing as you improve your prompts and upgrade your models. He shows how using evals is one part of the story along with deeply investigating customer signals to see how they are or aren't succeeding with AI.
Along the way, we also talk about RAG, his favorite models, his AI development toolset, and why Poland has been killing it lately. Check it out and be sure to follow Rafal if you want to learn more on building with AI.
*Timestamps*
01:10 Start
08:05 Zapier Agents
09:41 How does it differ from previous workflows?
13:10 Does text transform into a workflow?
14:31 Using Zapier's existing tools to power Agents?
15:48 Agents are given a only set of actions from which they can choose
17:45 Browser related stuff?
21:16 Choosing a model
25:28 Different prompts for different models?
40:36 Most often used models
41:45 TypeScript or Python
44:19 Cost
45:35 Less talk about non AI stuff
47:32 On regular devs building with AI
52:18 Protecting against user behaviour
54:25 Future of RAG
56:26 Is AI progress slowing down?
01:00:43 Personal AI tools
01:02:41 Losing understanding of repos?
01:04:33 Would you need Software Background in a few years?
01:06:08 GitHub Actions
01:07:31 Poland
1:08:52
Today we're talking with one of our favorite engineers, Rafal Wilinski. Rafal has been on the cutting edge of AI development in the last few years as he has led AI teams at Zapier and Vendr.
Rafal walks us through the hard-won lessons about actually integrating AI tools into the applications you're building. One of the hardest things in integrating these AI tools is how to ensure you're getting better and not regressing as you improve your prompts and upgrade your models. He shows how using evals is one part of the story along with deeply investigating customer signals to see how they are or aren't succeeding with AI.
Along the way, we also talk about RAG, his favorite models, his AI development toolset, and why Poland has been killing it lately. Check it out and be sure to follow Rafal if you want to learn more on building with AI.
*Timestamps*
01:10 Start
08:05 Zapier Agents
09:41 How does it differ from previous workflows?
13:10 Does text transform into a workflow?
14:31 Using Zapier's existing tools to power Agents?
15:48 Agents are given a only set of actions from which they can choose
17:45 Browser related stuff?
21:16 Choosing a model
25:28 Different prompts for different models?
40:36 Most often used models
41:45 TypeScript or Python
44:19 Cost
45:35 Less talk about non AI stuff
47:32 On regular devs building with AI
52:18 Protecting against user behaviour
54:25 Future of RAG
56:26 Is AI progress slowing down?
01:00:43 Personal AI tools
01:02:41 Losing understanding of repos?
01:04:33 Would you need Software Background in a few years?
01:06:08 GitHub Actions
01:07:31 Poland
If you’ve ever felt like engineering teams are stuck in execution mode—heads down, building what they’re told—then today’s episode is for you. We're talking about what it really takes to build high ownership engineering cultures where devs aren't simply just shipping code, but they're helping shape the product.
And our guest this week is Matt Watson. He's a long time founder, engineer, and now the CEO of Full Scale, a company that helps startups and scale ups, grow their engineering teams with top talent from the Philippines. Matt's also the author of a book called Product Driven that shows how engineers can build with more clarity, purpose, customer focus and we get into some of the details in that book during this podcast.
So in this episode, we get into everything from the downsides of specialization to the importance of empathy, to why code shipped isn't the same as value delivered. We hope you enjoy it.
51:38
If you’ve ever felt like engineering teams are stuck in execution mode—heads down, building what they’re told—then today’s episode is for you. We're talking about what it really takes to build high ownership engineering cultures where devs aren't simply just shipping code, but they're helping shape the product.
And our guest this week is Matt Watson. He's a long time founder, engineer, and now the CEO of Full Scale, a company that helps startups and scale ups, grow their engineering teams with top talent from the Philippines. Matt's also the author of a book called Product Driven that shows how engineers can build with more clarity, purpose, customer focus and we get into some of the details in that book during this podcast.
So in this episode, we get into everything from the downsides of specialization to the importance of empathy, to why code shipped isn't the same as value delivered. We hope you enjoy it.
Today's episode is with Aayush Shah. Aayush is one of the co-founders of Blacksmith, which is a CI compute platform. Basically, Blacksmith will run your GitHub Actions jobs faster and with more visibility with the standard GitHub Actions CI runners.
The founding team has a fun background doing systems work at Cockroach and Faire, and they're taking on a big problem in running this massive CI fleet.
The explosion in AI agents has really changed the CI world. CI is more useful than ever, as you want to be sure the changes from your agents aren't breaking your existing functionality. At the same time, there's a huge increase in demand and spikiness of CI workloads as developers can fire off multiple agents to work in parallel, each needing to run the CI suite before merging. Aayush talked about how they're handling this load and facilitating visibility into test failures.
We also covered cloud economics. Aayush said the traditional cloud-based storage options don't work for them -- EBS and locally attached SSDs are too expensive for their workloads where they don't need the standard durability guarantees. He walks us through building their own fleet outside the hyperscalers and the plans going forward, along with some of the economics of multi-tenancy that Blacksmith has previously written about.
*Blacksmith will be at KubeCon | CloudNativeCon North America 2025. Register Here*
https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/register/
01:09 Start
08:38 Github Actions
23:07 Cockroach
27:22 Forced bundling
31:11 Compute Locations
39:32 Network I/O Costs
40:51 Multi tenancy
46:04 Pricing Model
52:42 Seed Round
54:13 YC Experience
56:27 Office Locations
59:06 Codebase
01:01:24 AI Workflow
1:04:02
Today's episode is with Aayush Shah. Aayush is one of the co-founders of Blacksmith, which is a CI compute platform. Basically, Blacksmith will run your GitHub Actions jobs faster and with more visibility with the standard GitHub Actions CI runners.
The founding team has a fun background doing systems work at Cockroach and Faire, and they're taking on a big problem in running this massive CI fleet.
The explosion in AI agents has really changed the CI world. CI is more useful than ever, as you want to be sure the changes from your agents aren't breaking your existing functionality. At the same time, there's a huge increase in demand and spikiness of CI workloads as developers can fire off multiple agents to work in parallel, each needing to run the CI suite before merging. Aayush talked about how they're handling this load and facilitating visibility into test failures.
We also covered cloud economics. Aayush said the traditional cloud-based storage options don't work for them -- EBS and locally attached SSDs are too expensive for their workloads where they don't need the standard durability guarantees. He walks us through building their own fleet outside the hyperscalers and the plans going forward, along with some of the economics of multi-tenancy that Blacksmith has previously written about.
*Blacksmith will be at KubeCon | CloudNativeCon North America 2025. Register Here*
https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/register/
01:09 Start
08:38 Github Actions
23:07 Cockroach
27:22 Forced bundling
31:11 Compute Locations
39:32 Network I/O Costs
40:51 Multi tenancy
46:04 Pricing Model
52:42 Seed Round
54:13 YC Experience
56:27 Office Locations
59:06 Codebase
01:01:24 AI Workflow
Today, Sam Lambert from Planetscale is back for a third time. Planetscale just announced Planetscale Postgres, so we had to get Sam back to tell us how and why they decided to add support for Postgres. It's always great to have Sam on -- he brings great stories about real customers and honest insight about the state of the database industry.
In this episode, we talk about the road to Postgres and how operational excellence is the only true advantage in database providers. Sam walks us through the current Planetscale Postgres offering, along with details on Nova, a new sharded Postgres project that Planetscale is working on. Along the way, we get updates on Planetscale Metal, how demand has been for Planetscale Postgres, and future plans for Planetscale.
*Timestamps*
01:16 Start
06:37 The Timeline
15:15 Not Much IP in the Database Market
21:48 PSBouncer
24:17 Zonal affinity
27:38 Query Insights
29:34 How to sign up
32:02 Convex
34:37 Other data stores?
56:18 Acquisitions
1:06:13
Today, Sam Lambert from Planetscale is back for a third time. Planetscale just announced Planetscale Postgres, so we had to get Sam back to tell us how and why they decided to add support for Postgres. It's always great to have Sam on -- he brings great stories about real customers and honest insight about the state of the database industry.
In this episode, we talk about the road to Postgres and how operational excellence is the only true advantage in database providers. Sam walks us through the current Planetscale Postgres offering, along with details on Nova, a new sharded Postgres project that Planetscale is working on. Along the way, we get updates on Planetscale Metal, how demand has been for Planetscale Postgres, and future plans for Planetscale.
*Timestamps*
01:16 Start
06:37 The Timeline
15:15 Not Much IP in the Database Market
21:48 PSBouncer
24:17 Zonal affinity
27:38 Query Insights
29:34 How to sign up
32:02 Convex
34:37 Other data stores?
56:18 Acquisitions
Today, we're talking Valkey, Redis, and all things caching. Our guest is Madelyn Olson, who is a principal engineer at AWS working on Elasticache and is one of the most well-known people in the caching community. She was a core maintainer of Redis prior to the fork and was one of the creators of Valkey, an open-source fork of Redis.
In this episode, we talk about Madelyn's road to becoming a Redis maintainer and how she found out about the March 2024 license change. Then, Madelyn shares the story of Valkey being created, philosophical differences between the projects, and her reaction to re-relicensing of Redis in May 2025.
Next, we dive into the performance improvements of recent Valkey releases, including the I/O threads improvements and the new hash table layout. Along the way, Madelyn dispels the notion that the single-threaded nature of Redis / Valkey is that big of a hindrance for most workloads. Finally, she compares some of the Valkey improvements to some of the other recent cache competitors in the space.
Check it out!
*Timestamps*
01:10 Start
07:52 TLS for Redis
12:44 Core Maintainer
15:03 Maintaining a Fork
17:38 License Change
19:14 How quick was the decision on fork?
21:33 When was the first Valkey release?
25:53 How does compatibility look right now?
27:18 Improvements
51:23 Valkey written in C
57:32 Tradeoffs?
01:02:49 KeyDB
01:04:35 MemoryDB
01:11:31 Roadmap?
01:13:14 Major Bumps vs Minor Bumps
01:14:48 Finding Good Maintainers
01:16:16 ChatGPT for specialized stuff?
1:21:15
Today, we're talking Valkey, Redis, and all things caching. Our guest is Madelyn Olson, who is a principal engineer at AWS working on Elasticache and is one of the most well-known people in the caching community. She was a core maintainer of Redis prior to the fork and was one of the creators of Valkey, an open-source fork of Redis.
In this episode, we talk about Madelyn's road to becoming a Redis maintainer and how she found out about the March 2024 license change. Then, Madelyn shares the story of Valkey being created, philosophical differences between the projects, and her reaction to re-relicensing of Redis in May 2025.
Next, we dive into the performance improvements of recent Valkey releases, including the I/O threads improvements and the new hash table layout. Along the way, Madelyn dispels the notion that the single-threaded nature of Redis / Valkey is that big of a hindrance for most workloads. Finally, she compares some of the Valkey improvements to some of the other recent cache competitors in the space.
Check it out!
*Timestamps*
01:10 Start
07:52 TLS for Redis
12:44 Core Maintainer
15:03 Maintaining a Fork
17:38 License Change
19:14 How quick was the decision on fork?
21:33 When was the first Valkey release?
25:53 How does compatibility look right now?
27:18 Improvements
51:23 Valkey written in C
57:32 Tradeoffs?
01:02:49 KeyDB
01:04:35 MemoryDB
01:11:31 Roadmap?
01:13:14 Major Bumps vs Minor Bumps
01:14:48 Finding Good Maintainers
01:16:16 ChatGPT for specialized stuff?
