Open Source Startup Podcast

This Open Source Startup Podcast episode has our co-hosts Robby and Tim in conversation with Dr. Felipe Huici, CEO of Unikraft - the compute layer for sandboxes, AI agents, or any workload with VM-grade isolation.
Their open source, also called unikraft, has 4K stars on GitHub and provides a next-generation cloud native kernel.
This episode explores how Unikraft is building infrastructure for the next generation of AI agents, arguing that agents should run in virtual machines rather than containers. The conversation focuses on the unique requirements of agentic workloads: fast startup times, the ability to pause and resume state, strong isolation, and efficient resource utilization at massive scale.
Unikraft’s technology enables lightweight virtual machines that can start in under 10 milliseconds, helping companies reduce latency, lower infrastructure costs, and run large numbers of ephemeral agents on minimal hardware. The discussion also covers emerging AI infrastructure needs such as checkpointing, branching, headless browser automation, and GPU access.
The podcast also traces Unikraft’s origins from an academic research project to an open-source Linux Foundation initiative and, eventually, a startup founded in 2022.
The conversation examines customer adoption, the role of Unikraft as foundational infrastructure for AI platforms, competition and collaboration within the agent ecosystem, the future of GPUs and virtualization, and lessons learned from building a company in the rapidly evolving cloud and AI infrastructure market.

This Open Source Startup Podcast episode has our co-hosts Robby and Tim in conversation with James Bayer, Chief Product Officer at software development platform Flox.
Flox's open source, also called flox, provides a software environment platform powered by package manager Nix.
In this episode, James shares lessons from his career across Cloud Foundry, Pivotal, and HashiCorp, where he helped turn widely adopted open-source projects like Terraform into sustainable businesses. His core takeaway is that support-only open source is difficult to scale; successful companies usually monetize the “multiplayer” capabilities that teams and enterprises need while keeping individual usage free.
Now at Flox, James sees a similar opportunity built on top of Nix, a powerful but historically complex technology. He joined because Flox makes Nix dramatically easier to use, helping developers and AI agents manage software environments and dependencies. He also discussed the balance between open-source principles and commercial viability, and why he remains optimistic about the future of software development in the age of AI.

This Open Source Startup Podcast episode has our co-hosts Robby and Tim in conversation with Zohar Einy the Co-Founder of agentic SDLC platform Port.
They have a few open source projects including ocean which allows third-party systems to integrate with their developer portal.
Port is positioning itself as the infrastructure layer for the agentic era, evolving the traditional internal developer portal into what its founders describe as a “system of record for agents.”
The company believes the future belongs not to vertical point solutions, but to flexible platforms that organizations control themselves, enabling anyone, from developers to non-technical employees, to become builders.
Rooted in the founders’ experience of overwhelming developer workflows and ticket volumes, Port aims to centralize engineering context while making both humans and AI agents more self-sufficient. Their hybrid approach combines openness and commercial software, with public roadmaps, community contributions, and open-source integrations helping customers extend the platform while maintaining governance and control.
The conversation also explored how AI is reshaping engineering organizations. Port is focused on creating the infrastructure around agents rather than building the agents themselves, providing visibility, permissions, governance, and a unified “context lake” for agent activity.
As companies deploy increasing numbers of coding, security, SRE, and product agents, leaders need a control plane to understand what agents are doing and ensure they operate safely. The team is already seeing customers use Port to automate large portions of engineering support workflows, and they believe enterprises are adopting AI-driven workflows as quickly as, or faster than, mid-market companies.
Internally, this pace of change requires constant adaptation, particularly across go-to-market teams, where education and flexibility have become more important than rigid playbooks.

The latest Open Source Startup Podcast episode has our co-hosts Robby and Tim in conversation with Neal Swaelens and Oleks Yaremchuk, 2 of the Co-Founders of runtime agent security company Manifold Security.
Manifold recently released Manifest, their open-access, graph-based supply chain intelligence tool for users to scan skills and plugins to uncover any potential supply chain risks.
In this episode, Neal and Oleks explain why AI agents are reshaping cybersecurity - shifting the focus from guardrails to runtime security.
As tools like Claude Code and Codex spread rapidly, companies often have little visibility into the agents, plugins, skills, and external assets employees are using, creating major supply chain and runtime risks.
Drawing on their experience building LLMGuard and leading security teams at Protect AI and Palo Alto Networks, they argue that runtime detection and response is still a wide-open market opportunity.
They also discuss what it takes to build in the crowded AI security space, where buyers now expect real products instead of roadmap promises.
The conversation highlights lessons from open projects like LLMGuard and Manifest, why reducing noise and false positives matters, and how open ecosystems can help establish trust and industry standards for securing AI agents and assets.

The latest Open Source Startup Podcast episode has our co-hosts Robby and Tim in conversation with Batuhan Taskaya, the founding engineer and current Head of Engineering at generative media cloud Fal. Fal is a developer platform that allows builders to develop and fine-tune models with serverless GPUs and on-demand clusters.
This episode explores how a small, highly technical team carved out a unique position in the AI boom by focusing on generative media - images, video, and audio - while most of the industry rushed toward language models.
Early on, they recognized that image and video models operate very differently from LLMs. With no strong API-first players in image generation, they started there and doubled down on building reliable, high-performance infrastructure for running these models in the cloud, leveraging deep expertise in systems and performance engineering.
Their strategy of embracing open-source models, then fine-tuning and optimizing them for real-world use cases, helped them quickly gain traction - growing from zero to $400M of revenue by 2026 and scaling rapidly as demand for generative media surged.
The conversation also dives into how the company evolved into a full-stack generative media platform, expanding from images into video and audio as those markets matured, especially with video seeing explosive growth in 2024–2025.
A key differentiator has been their relentless focus on inference performance, custom kernel optimization, and cost efficiency, which has driven strong customer retention. Rather than betting on a single model, they embrace rapid model turnover and ecosystem fragmentation, ensuring flexibility for developers and enterprises alike.
Looking ahead, the biggest challenges lie in scaling video models and securing enough compute capacity in a supply-constrained GPU market. Throughout, the story highlights the power of small, focused teams with clear strategy and the ability to pivot quickly in a fast-moving AI landscape.