Blitzy and Autonomous Software Development for Enterprise
Codebases in enterprise organizations often span hundreds of thousands, or even millions, of lines of code. In many cases, changes must follow established patterns precisely, or risk introducing costly security or operational issues. A common challenge with AI coding tools is that they struggle at this scale: LLM context windows cover only a fraction of a large codebase, and output quality degrades the longer an agent runs.
Blitzy is an autonomous software development platform for the enterprise. It starts by understanding your entire codebase, building a knowledge graph that acts as a shared source of truth. From a well-defined spec that engineers review and approve, it orchestrates specialized agents that work in parallel to build, review, and test code autonomously at scale. The result is a pull request that engineers can then review and merge
Sid Pardeshi (https://x.com/sidppar) is the CTO and co-founder of Blitzy (https://blitzy.com/), and he previously worked at NVIDIA. In this episode, he joins Kevin Ball to discuss why large codebases break today’s coding agents, how a knowledge graph extends what models can reason about, how a reviewable plan keeps specialized agents aligned, and where autonomous software development is headed.
Full Disclosure: This episode is sponsored by Blitzy
Sponsorship inquiries:
sponsor@softwareengineeringdaily.com (mailto:sponsor@softwareengineeringdaily.com)
The post Blitzy and Autonomous Software Development for Enterprise (https://softwareengineeringdaily.com/podcasts/blitzy-and-autonomous-software-development-for-enterprise/) appeared first on Software Engineering Daily (https://softwareengineeringdaily.com).
20 Oct 2026
Physical AI at NVIDIA
In recent years, the main impact of AI has been on the digital world, and most notably software engineering. However, its longer-term effect on the physical world may prove even more profound, reshaping how cars drive themselves, how factories run, and how robots move through homes and warehouses.
World models are at the center of the movement to bring AI into the physical space. These models take in observations of the current state of the world and predict what comes next, which lets a robot plan its next move or a car anticipate the road ahead. NVIDIA has been building a family of these models called Cosmos, and sits at the frontier of physical AI and robotics.
Amit Goel (https://www.linkedin.com/in/amit-goel-759146a)has been at NVIDIA (https://www.nvidia.com/) for fifteen years, and he is currently the Head of Robotics Ecosystem and Edge AI Product. In this episode, Amit joins Matt Merrill to discuss what separates physical AI from digital AI, how world models plan and predict, the two-tower architecture that lets a robot reason and act at once, why training on video demanded a new data pipeline, and how any developer can start building in this space.
Free Physical AI Courses: https://nvda.ws/4xqCfo9 (https://nvda.ws/4xqCfo9)
Cosmos: https://nvda.ws/4zJ4iRj (https://nvda.ws/4zJ4iRj)
GR00T Teleop + LeRobot: https://huggingface.co/blog/nvidia/nvidia-isaac-teleop-and-gr00t17-in-lerobot (https://huggingface.co/blog/nvidia/nvidia-isaac-teleop-and-gr00t17-in-lerobot)
Sponsorship inquiries:
sponsor@softwareengineeringdaily.com (mailto:sponsor@softwareengineeringdaily.com)
The post Physical AI at NVIDIA (https://softwareengineeringdaily.com/podcasts/physical-ai-at-nvidia/) appeared first on Software Engineering Daily (https://softwareengineeringdaily.com).
15 Oct 2026
Software Verification in the Age of Coding Agents
Software testing is widely understood as essential to building reliable systems, and it’s one of the hardest things to get right. A fundamental issue is that tests are good at confirming the behavior a team expects, but the most damaging failures tend to hide in the edge cases no one thought to check during development. Those blind spots are only growing as AI produces more code that no human has closely reviewed.
Antithesis (https://antithesis.com/) is a software verification company built around an approach called deterministic simulation testing. The platform runs a customer’s software in a fully controlled environment, exploring thousands of different paths through it at runtime. Crucially, the paths aren’t pre-specified and they’re not merely the expected cases. In this way, it can surface rare, damaging bugs that normal testing would never catch.
Carl Sverre (https://x.com/carlsverre) is on the Red Team at Antithesis and he also leads much of the company’s AI strategy. In this episode, Carl joins Sean Falconer to discuss the Antithesis approach to software verification, why reliability tooling has been chronically underinvested, what makes stateful and distributed systems so hard to test, how deterministic simulation testing works under the hood, and much more.
Full Disclosure: This episode is sponsored by Antithesis
Sponsorship inquiries:
sponsor@softwareengineeringdaily.com (mailto:sponsor@softwareengineeringdaily.com)
The post Software Verification in the Age of Coding Agents (https://softwareengineeringdaily.com/podcasts/software-verification-in-the-age-of-coding-agents/) appeared first on Software Engineering Daily (https://softwareengineeringdaily.com).
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