Inference by Turing Post

What if the future of self-driving isn’t one perfect robotaxi – but a stack that can turn almost any car into a self-driving car? In this episode of Inference, we ride through San Francisco – as one of the first to do this test drive – and talk about what’s changing in autonomous driving: cheaper hardware, better models, synthetic data, and a whole new approach to building the software behind the wheel. Ali Kani has been at NVIDIA Automotive for almost 8 years – he’s been through all the ups and downs, and he’s eager to share.
*We talk about:*
Why Level 2 is already possible with a surprisingly cheap sensor setup
What is still missing for Level 4
Why next year could matter for Level 4
How NVIDIA combines an end-to-end driving model with a classical safety stack
​​Why open source matters for the future of autonomous driving
Why synthetic data and simulation may matter as much as real-world driving data
How different cities, laws, and driving cultures change the way autonomous systems behave
Why the goal is bigger than one self-driving car – it’s making many cars autonomous by open sourcing the whole stack (it’s HUGE)
We also experience live what still makes urban driving hard: construction, cyclists, congestion, weird negotiations at stop signs, and all the messy little moments humans barely notice but cars have to handle perfectly.
What I liked about this conversation is that it makes the shift feel very real. *We’re moving from self-driving built inside closed labs to self-driving becoming a shared capability that can spread across the whole car industry.*
This is a conversation about a future that starts tomorrow. It’s open and very exciting.
Chapters:
0:00 The Future of Self-Driving Starts Now
0:19 Open Autonomous Driving Beyond Tesla and Waymo
1:07 Inside NVIDIA’s Low-Cost Level 2 Self-Driving Stack
1:48 From Level 2 to Level 4: Hyperion, Thor, and Redundancy
2:43 How NVIDIA Combines End-to-End AI with Safety Guardrails
3:56 What Changed in AlphaMaio Since GTC
5:12 The Key Technologies Needed to Solve Self-Driving
7:22 Real Data vs Synthetic Data in Autonomous Driving
9:21 Driving Through Real San Francisco Traffic
18:55 AlphaDream and the Next Generation of Simulation
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https://www.turingpost.com/p/av
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*Guest:*
Ali Kani, Vice President and General Manager of Automotive, NVIDIA
https://www.linkedin.com/in/ali-kani-b22198
https://blogs.nvidia.com/blog/author/alikani/
Read more:
https://www.turingpost.com/p/selfdriving
https://thefocus.ai/posts/the-car-wash-test/

In this second part of my conversation with Michael Bolin, lead for open-source Codex at OpenAI, we move from harness engineering to the human side of the story.
What does it mean to be a programmer when you are no longer typing most of the code? Which skills become more important in an agent-driven workflow? Will coding agents eventually take over most software implementation? And if that happens, what is left for the human engineer besides pushing prompts around like a confused project manager with Wi-Fi?
All of it and more in this part – watch it.
*Follow on*: https://www.turingpost.com/
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*Guest:* Michael Bolin, tech lead on Codex, OpenAI
https://www.linkedin.com/in/michael-bolin-7632712/
https://x.com/bolinfest
https://github.com/openai/codex
Chapters:
0:00 — Do You Still Need to Learn Coding?
0:18 — From Systems to Humans: The Future of Programming
0:39 — Switching Mindset: Building for Agents vs Developers
1:13 — What Happens When Agents Consume the Web?
1:27 — Programmer Identity in the Age of AI
2:15 — Are Engineers Building More Than Ever?
2:37 — Key Skills for Engineers Working with AI Agents
3:59 — Will Agents Take Over Coding?
4:57 — Engineering Taste vs AI Decisions
5:10 — From Idea to Product Faster Than Ever
6:01 — Risks: Losing Human Judgment Too Early
6:42 — Do We Still Need Humans in the Loop?
8:06 — Book That Shaped a Builder’s Mindset
📰 Transcript:https://www.turingpost.com/p/bolincodex
https://x.com/TheTuringPost
https://www.linkedin.com/in/ksenia-se
#AI #OpenAI #Codex #MichaelBolin #SoftwareEngineering #Programming #CodingAgents #AIAgents #DeveloperTools #HarnessEngineering #FutureOfWork #Engineering #TuringPost

Regarding the question of what matters most – the model or the harness – Michael Bolin is somewhere in the middle.
Stronger models clearly pushed Codex to new heights. But without the right harness around them, those models would not be able to operate reliably, and – most importantly – safely on a real developer’s machine. At least, not yet.
In this episode of Inference, I talk with Michael Bolin – lead for open source Codex at OpenAI – about the engineering layer that makes coding agents actually function: the agent loop, sandboxing, tool orchestration, and the design decisions that determine how much freedom an agent should have.
In this conversation, we get into:
What a harness actually is and why every coding agent needs one
Can a model be enough for a reliable coding workflow
Why do they build harness as small and tight as possible
How Codex handles sandboxing and security across OS
Why safety and security are not the same thing in agentic systems
How coding agents are changing the daily workflow of developers
Why documentation, tests, repo structure, and agents.md suddenly matter more
Whether too much context can make an agent worse
Why Michael believes the future may involve fewer tools, but more powerful ones
If you’re trying to understand where coding agents are actually going, this episode is for you.
Subscribe to the channel to be notified about Part 2, where we discuss what becomes of the software engineer in the age of agents.
Chapters:
0:00 The New Inner Loop of AI Coding Agents
0:17 Introduction: Michael Bolin and Open Source Codex
1:17 What the “Harness” Is in AI Coding Agents
2:13 Security and Sandboxing for AI Agents
4:33 Codex Launch and Rapid Growth
5:25 The Codex App: A New Interface for Developers
6:36 How Coding Agents Change Developer Workflows
10:04 Writing Codebases and Documentation for AI Agents
12:44 Context Engineering and Prompting for Codex
16:02 Model vs Harness: What Really Matters for Agents
19:23 Multi-Agent Systems, Tools, and the Future of AI Development
*Follow on*: https://www.turingpost.com/
*Did you like the episode? You know the drill:*
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🫶 Thank you for watching and sharing!
*Guest:*
 Michael Bolin, tech lead on Codex, OpenAI
https://www.linkedin.com/in/michael-bolin-7632712/
https://x.com/bolinfest
https://github.com/openai/codex
📰 Transcript: https://www.turingpost.com/bolin1
*Turing Post* – AI stories from labs the Valley doesn't cover.
https://x.com/TheTuringPost
https://www.linkedin.com/in/ksenia-se
Tags: #AI #OpenAI #Codex #CodingAgents #DeveloperTools #AgenticAI #SoftwareEngineering #HarnessEngineering #Harness

In this episode, Ioannis Antonoglou, co-founder and CTO @ReflectionAI (ex-DeepMind, AlphaGo/AlphaZero/MuZero) explains what they are building: a frontier open-weight “general agent model” trained end-to-end with pretraining plus reinforcement learning.
And I’ll be honest: I left this conversation more skeptical than I expected. They raised $2 billion last year. But where the results?
Reflection’s thesis is huge – build the missing Western open base model, then use RL to push it to the frontier. The problem is that this is also the slowest path in the game. “All hands on deck building the model” means no clear wedge product yet, few concrete proof points, and a lot of execution risk while closed labs keep shipping.
Am I missing something? Watch the video and leave your opinion in the comments
Chapters:
0:00 Building AGI and the Mission Behind Reflection
0:25 From AlphaGo to Today: How AI Progress Really Happens
2:11 Breakthroughs vs. Engineering: What Still Matters Most
3:10 Defining AGI and Why It May Not Need Huge Breakthroughs
3:41 Why Reflection Shifted from Coding Agents to Frontier Models
5:15 The New Focus: Open Frontier Models and General Agents
6:33 Bottlenecks in Building Frontier AI: Team, Compute, and Scale
7:48 AI Tools, Internal Workflows, and Model-First Strategy
8:24 Can Open Models Catch Closed Labs?
10:34 Reinforcement Learning, Research Priorities, and Advice for Young Builders
14:01 Joining DeepMind, Open Science, and the Book That Shaped Him
*Follow on*: https://www.turingpost.com/
*Did you like the episode? You know the drill:*
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*Guest:*
Ioannis Antonoglou, Co-Founder, President & CTO at Reflection AI https://x.com/real_ioannis https://www.linkedin.com/in/ioannis-alexandros-antonoglou-45393253 https://reflection.ai/
📰 Transcript: https://www.turingpost.com/nathan
*Turing Post* – AI stories from labs the Valley doesn't cover.
https://x.com/TheTuringPost
https://www.linkedin.com/in/ksenia-se
Tags: #reflectionai #opensource #deepmind #ai #openclaw #aisafety

Ioannis Antonoglou helped build AlphaGo, AlphaZero, and MuZero at DeepMind. Now he’s CTO and co-founder of Reflection AI, betting that frontier models should be open weights, not a black box behind an API.
In Part 1, we talk about openness as an actual strategy: why open models can move faster, why “sovereignty” matters for enterprises and governments, and why safety might improve when the ecosystem can stress-test the system instead of guessing.
We also get into the uncomfortable part: capable open agents can misbehave in public, fast (OpenClaw is the recent reminder). Is that a reason to close everything up, or a reason to make the risks visible and fixable?
Topics covered:
 – Why a former DeepMind builder chose open weights
 – Open models as a commercial engine (and what investors bought)
 – Openness, safety, and “more eyes on the system”
 – Concentration of AI power in closed labs
 – Who open frontier models are really for (research, enterprises, governments)
Subscribe for Part 2: how Reflection plans to compete with closed labs and what they’re building under the hood.
Chapters:
0:00 — “No One Was Sharing This Information”
0:16 — From DeepMind to Reflection AI
0:52 — Why Move from Closed Labs to Open Weights?
2:20 — Pitching Open Models Before the DeepSeek Moment
3:31 — What Changed in the Past Year
4:43 — Why Openness Accelerates Scientific Progress
6:06 — Open Source vs Safety: The Open Claw Case
7:19 — The Real Concern: Concentration of AI Power
8:23 — The Open Source Paradox
9:11 — The Value Proposition of an Open Frontier Model
*Follow on*: https://www.turingpost.com/
*Did you like the episode? You know the drill:*
📌 Subscribe for more conversations with the builders shaping real-world AI.
💬 Leave a comment if this resonated.
👍 Like it if you liked it.
🫶 Thank you for watching and sharing!
*Guest:*
Ioannis Antonoglou, Co-Founder, President & CTO at Reflection AI https://x.com/real_ioannis https://www.linkedin.com/in/ioannis-alexandros-antonoglou-45393253/ https://reflection.ai/
📰 Transcript: https://www.turingpost.com/antonoglou_part1
*Turing Post* – AI stories from labs the Valley doesn't cover.
https://x.com/TheTuringPost
https://www.linkedin.com/in/ksenia-se
Tags: #reflectionai #opensource #deepmind #ai #openclaw #aisafety