Hangar DX Podcast

“AI is producing more than we could ever imagine before, but we’re still defining confidence in the same old way that we’ve been doing it for decades. And it’s causing a lot of problems.” 
On HangarDX, Ankit Jain talks with Jay Paulson, Director of Engineering at Symetra, about engineering confidence in the AI era and why the business still sees engineering as a black box. They talk about: 
Why AI raised expectations while the confidence behind delivery is still based on belief, not evidence, and
Why a team told it’s going too fast should slow down, and what to do with the extra capacity, from juniors writing code by hand to giving people time back, and
How asking for delivery evidence and forward visibility changes the conversation with leadershipChapters
00:00 Introduction to the episode and guest
01:24 Jay Paulson's background and focus on engineering confidence
03:25 What is engineering confidence? Definitions and examples
05:01 The role of metrics and belief in confidence
06:20 AI's influence on productivity and confidence
08:48 Grounded confidence versus belief-based confidence
11:15 When does engineering confidence meet expectations?
14:52 The importance of evidence in planning and estimation
17:40 The pressure to do more with AI and its effects
20:23 The idea of slowing down to improve quality
26:52 The risks of belief-based planning and confidence
28:36 Building more grounded confidence through evidence
32:18 Using visibility to manage delivery and expectations

Replace code reviews with verified intent
AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/
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"I feel this is a non-solution to a non-problem that is being given so much credence for very dubious reasons, and in general, it will be harmful.”
On HangarDX, Ankit Jain talks with Ray Myers, a software developer and coding-agent expert, about why the push toward dark software factories, where agents write the code and no one reads it, is the wrong goal. They talk about:
- Why dark factories are a reboot of vibe coding that still breaks down under real scale, risk, and change over time, and
- Why you can get most of the value of dark factories from continuous delivery practices teams have taught for a decade, and
- Why people who are telling you not to read code are actually reading the code
Chapters
00:00 Introduction to Ray Myers and the concept of dark factories
04:09 What is a dark factory and its hypothetical nature
07:12 Scale, risk, and change in enterprise software
11:48 Why Ray Myers is anti-dark factory
13:27 The importance of reading code and guardrails
17:23 Leveraging guardrails and automated checks
22:25 The role of verification systems and revealing code
33:21 The limits of automation and the analogy of self-driving cars
36:51 The future of AI, automation, and human oversight in software development
Replace code reviews with verified intent
AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/
📫 Sign up for our email list for more podcasts, articles, events, and other updates: https://www.aviator.co/podcast
✏️ Subscribe for more videos: @Aviator-Co
🙌 Join a curated community of senior engineers and engineering leaders focused on developer experience and solving productivity challenges at scale! Check out our upcoming off-the-record online sessions where vetted, experienced professionals can exchange ideas and share hard-earned wisdom: https://dx.community/

"We just pretend to do code reviews. I'm wading through this large PR, and I try to understand what's going on and be smart, but the fact is that I don't really know what's going on."
In this episode of the HangarDX podcast, Ankit Jain, co-founder and CEO of Aviator, talks to Dr. Michaela Greiler, software engineering researcher and consultant, about:
What code review surrender looks like on teams that never actually decided to stop reviewing
How her SCOPE model moves oversight earlier, into planning, and scales it by risk, alignment, and understanding, and
Why an engineer saying "I'm shipping code I don't understand" is a green flag for a team?
Replace code reviews with verified intent
AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/
📫 Sign up for our email list for more podcasts, articles, events, and other updates: https://www.aviator.co/podcast
✏️ Subscribe for more videos: @Aviator-Co
🙌 Join a curated community of senior engineers and engineering leaders focused on developer experience and solving productivity challenges at scale! Check out our upcoming off-the-record online sessions where vetted, experienced professionals can exchange ideas and share hard-earned wisdom: https://dx.community/

"My working hypothesis is that human beings are too slow when dealing with code. If we're going to move at the speed of the agents, we're all going to have to back off a little bit and maintain high-level supervision."
On this episode of HangarDX, Ankit Jain sits down with Robert C. Martin (Uncle Bob), co-author of the Agile Manifesto and author of Clean Code, to talk about:
Why he stopped reading code, and the constraints he uses instead: unit tests, CRAP scores, mutation testing, and Gherkin,
Why code review is slowing teams down, and why only humans can be responsible for what agents produce, and
How a single engineer may now do the work of a team, and why Clean Code principles still apply.
Note: At 17:05 Uncle Bob starts sharing his screen, so we recommend watching a video version of this episode to tget the full experience: https://youtu.be/5kCISBJwoZo
Chapters
00:00 Introduction
01:48 How Uncle Bob codes today
03:12 What changed his mind about AI coding
04:52 Three constraints: unit tests, CRAP, and mutation testing
08:06 Aligning tests with intent
09:38 Gherkin and human-authored specs
11:00 Human judgment as the final arbiter
12:52 Small functions, readability, and complexity
13:20 Relaxing constraints as agents improve
13:54 Code written for agents, not humans
15:10 The future of code review and software teams
17:55 A dynamic UML tool for reviewing agent output
21:09 Architecture diagrams as a review surface
23:05 Disciplines every team needs
25:25 Why harness engineering is overrated
26:37 From harness to a single trusted agent
27:59 Are software factories doomed?
28:30 The AI exponential curve and its physical limits
29:30 Future optimizations and quantum computing
29:58 Responsibility and ownership of AI-generated code
32:26 Rewriting Clean Code for the AI era
33:30 Why Clean Code principles still apply
📫 Sign up for our email list for more podcasts, articles, events, and other updates: https://www.aviator.co/podcast
✏️ Subscribe for more videos: @Aviator-Co
🙌 Join a curated community of senior engineers and engineering leaders focused on developer experience and solving productivity challenges at scale! Check out our upcoming off-the-record online sessions where vetted, experienced professionals can exchange ideas and share hard-earned wisdom: https://dx.community/
Replace code reviews with verified intent
AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/

David Curlewis, Senior Engineering Manager at Canva, joins Ankit Jain on HangarDX to talk about what happens to platform teams when AI gives every developer a bulldozer.
Why platform teams should go down a layer and offer primitives instead of full highways, and
How inner sourcing and a graduation model help platform teams scale without fighting their own developers, about
What happens when vibe-coded dashboards go mission-critical overnight, and why outcome metrics beat volume?
Chapters
00:00 Welcome to Hangar DX
01:03 David’s platform engineering background
02:13 Why AI is changing platform teams
04:04 The golden path versus AI-built roads
07:40 Platform primitives: observability, security, and MCPs
11:46 Why platform engineering is becoming a core company function
14:56 Centralization, autonomy, and maturity
20:51 Inner source as a model for shared ownership
23:17 Designing contribution guidelines for AI agents
26:50 Intent debt, cognitive debt, and keeping specifications aligned
31:00 Why companies are building their own AI platforms
32:25 Moving from code review to reliable validation
34:50 Measuring AI productivity beyond lines of code
40:06 The return of generalist engineers
📫 Sign up for our email list for more podcasts, articles, events, and other updates: https://www.aviator.co/podcast
✏️ Subscribe for more videos: @Aviator-Co
🙌 Join a curated community of senior engineers and engineering leaders focused on developer experience and solving productivity challenges at scale! Check out our upcoming off-the-record online sessions where vetted, experienced professionals can exchange ideas and share hard-earned wisdom: https://dx.community/
Replace code reviews with verified intent
AI writes code faster than humans can review it. Aviator Verify provides compliance-grade verification through spec-driven development. Ship faster with complete audit trails. https://verify.aviator.co/