In this episode, Shimin and Dan explore the latest advancements in AI coding, including NVIDIA's new models, the implications of AI-generated code, and the outcome of Anthropic's project Vend, AI management of vending machines. They also discuss the significance of multi-agent systems in coding, the concept of vibe coding, and delve into the research on hallucination neurons in large language models. The episode concludes with a year-end review reflecting on the rapid developments in AI technology throughout 2025.

Takeaways

• AI-generated code has been found to create more problems than human code.
• AI in vending machines has led to humorous and unexpected outcomes.
• Multi-agent systems can enhance the coding process by providing diverse solutions.
• H-neurons in LLMs are linked to hallucination and overcompliance.
• Year-end reflections highlight the rapid adoption of AI in the industry.
• The future of AI coding looks promising with ongoing innovations.

Resources Mentioned
NVIDIA Nemotron 3 Family of Models
GLM-4.7: Advancing the Coding Capability
Our new report: AI code creates 1.7x more problems
Project Vend: Phase two
Claude Code Changelog
One Agent Isn't Enough
Vibe Coding
H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs
2025 LLM Year in Review

Chapters

• (00:00) - Introduction to AI Coding Landscape

• Email us at [email protected] you have any feedback, requests, or just want to say hello! 
• Checkout our website www.adipod.ai

In this episode of "Artificial Developer Intelligence," hosts Shimin Zhang and Dan explore the latest advancements in AI, including the release of GPT 5.2 and its implications for the industry. They discuss the integration of Cloud Code into Slack, Mistral AI's new coding model, and the innovative MindEval framework for assessing AI's clinical competence. The episode also features a deep dive into AI-generated user interfaces and a lively discussion on the evolving role of hackers in the tech industry. 

Takeaways
GPT 5.2 offers incremental improvements and new modes for AI applications

Cloud Code's integration into Slack aims to streamline coding workflows.

Mistral AI's new model targets the coding space with open-weight strategies.

OpenAI's enterprise products show significant adoption, especially in non-coding sectors.

Resources Mentioned
Introducing GPT-5.2
Claude Code is coming to Slack, and that’s a bigger deal than it sounds
Mistral AI surfs vibe-coding tailwinds with new coding models
Introducing MindEval: a new framework to measure LLM clinical competence
AI should only run as fast as we can catch up
Useful patterns for building HTML tools
Ask HN: How can I get better at using AI for programming?
Claude Agent Skills: A First Principles Deep Dive
Generative UI: A rich, custom, visual interactive user experience for any prompt
CoreWeave CEO defends AI circular deals as ‘working together’
OpenAI boasts enterprise win days after internal ‘code red’ on Google threat

Chapters

• (00:00) - Introduction to AI in Software Engineering

• Email us at [email protected] you have any feedback, requests, or just want to say hello! 
• Checkout our website www.adipod.ai

In this episode, Shimin and Dan explore the evolving landscape of AI in software engineering, discussing the implications of the Cloud Opus 4.5 sole document, the ethical considerations of AI models, and the impact of AI on developer productivity. They delve into spec-driven development, the latest advancements in AI models like DeepSeek v3.2, and the intersection of AI and mental health. The conversation also touches on the potential AI bubble and the challenges faced by developers in integrating AI tools effectively.

Takeaways
The Cloud Opus 4.5 sole document reveals insights into AI model training.
Spec-driven development is a promising approach for AI-assisted coding.
DeepSeek v3.2 showcases advancements in reasoning models.
AI models can exhibit traits similar to human emotions and traumas.
Skills in AI may not always resolve context issues effectively.

Resources Mentioned
How AI is transforming work at Anthropic
Claude 4.5 Opus Soul Document
12 Factor Agents
Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl
From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates
When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models
Are we really repeating the telecoms crash with AI datacenters?
Anthropic CEO weighs in on AI bubble talk and risk-taking among competitors
Time until the AI bubble bursts
Microsoft’s Attempts to Sell AI Agents Are Turning Into a Disaster

Chapters
Connect with ADIPod

• Email us at [email protected] you have any feedback, requests, or just want to say hello! 
• Checkout our website www.adipod.ai

In this episode of Artificial Developer Intelligence, hosts Shimin and Dan discuss the evolving landscape of AI in software engineering, touching on topics such as OpenAI's recent challenges, the significance of Google TPUs, and effective techniques for working with large language models. They also delve into a deep dive on general agentic memory, share insights on code quality, and assess the current state of the AI bubble.

Takeaways

• Google's TPUs are designed specifically for AI inference, offering advantages over traditional GPUs.
• Effective use of large language models requires avoiding common anti-patterns.
• AI adoption rates are showing signs of flattening out, particularly among larger firms.
• General agentic memory can enhance the performance of AI models by improving context management.
• Code quality remains crucial, even as AI tools make coding easier and faster.
• Smaller, more frequent code reviews can enhance team communication and project understanding.
• AI models are not infallible; they require careful oversight and validation of generated code.
• The future of AI may hinge on research rather than mere scaling of existing models.

Resources Mentioned
OpenAI Code Red
The chip made for the AI inference era – the Google TPU
Anti-patterns while working with LLMs
Writing a good claude md
Effective harnesses for long-running agents
General Agentic Memory Via Deep Research
AI Adoption Rates Starting to Flatten Out
A trillion dollars is a terrible thing to waste

Chapters
Connect with ADIPod

• Email us at [email protected] you have any feedback, requests, or just want to say hello! 
• Checkout our website www.adipod.ai

In this episode of Artificial Developer Intelligence, hosts Shimin and Dan explore the latest advancements in AI models, including the release of Claude Opus 4.5 and Gemini 3. They discuss the implications of these models on software engineering, the rise of open-source models like Olmo 3, and the enhancements in the Claude Developer Platform. The conversation also delves into the challenges of relying on AI for coding tasks, the potential pitfalls of the AI bubble, and the future of written exams in the age of AI.

Takeaways

•  Claude Opus 4.5 setting benchmarks, enhance usability and reduce token consumption.
• The introduction of open-source models like Olmo 3 is a significant development in AI.
• The future of written exams may be challenged by AI's ability to generate human-like responses.
• Relying too heavily on AI can lead to a lack of critical thinking and problem-solving skills.
• The AI bubble is at 25s to midnight
• Recent research suggests that AI models can improve their performance through emulating query based search.
• The importance of prompt engineering in AI interactions is highlighted.

Resources Mentioned
Introducing Claude Opus 4.5
Build with Nano Banana Pro, our Gemini 3 Pro Image model
Andrej Karpathy's Post about Nano Banana Pro
Olmo 3: Charting a path through the model flow to lead open-source AI
Introducing advanced tool use on the Claude Developer Platform
TiDAR: Think in Diffusion, Talk in Autoregression
SSRL: SELF-SEARCH REINFORCEMENT LEARNING
Mira Murati's Thinking Machines seeks $50 billion valuation in funding talks, Bloomberg News reports
Boom, bubble, bust, boom. Why should AI be different?
Nvidia didn’t save the market. What’s next for the AI trade?

Chapters

• (00:00) - Introduction to Artificial Developer Intelligence

• Email us at [email protected] you have any feedback, requests, or just want to say hello! 
• Checkout our website www.adipod.ai

In this episode of Artificial Developer Intelligence, hosts Shimin and Dan explore the latest developments in AI, including Google's Gemini 3 model and its implications for software engineering. They discuss the rise of AI-driven cybersecurity threats, the concept of world models, and the evolving landscape of software development techniques. The conversation also delves into the ethical considerations of AI compliance and the challenges of running open weight models. Finally, they reflect on the current state of the AI bubble and its potential future.

Takeaways

• The rent for running AI models is too high.
• The AI bubble may burst, but it can still leading to innovation.
• Persuasion techniques can influence AI behavior.
• World models are changing how we understand AI.
• Gemini 3 shows significant improvements over previous models.
• Cybersecurity threats are evolving with AI technology.
• Software development is becoming more meta-focused.

Resources Mentioned
Disrupting the first reported AI-orchestrated cyber espionage campaign
GTIG AI Threat Tracker: Advances in Threat Actor Usage of AI Tools
Why Fei-Fei Li, Yann LeCun and DeepMind Are All Betting on “World Models” — and How Their Bets Differ
Google's new Gemini 3 model arrives in AI Mode and the Gemini app
Code research projects with async coding agents like Claude Code and Codex
ADK architecture: When to use sub-agents versus agents as tools
I have seen the compounding teams
Call Me A Jerk: Persuading AI to Comply with Objectionable Requests
In Search of the AI Bubble’s Economic Fundamentals
The Benefits of Bubbles | Stratechery by Ben Thompson
Is Perplexity the first AI unicorn to fail?

Chapters

• (00:00) - Introduction to Artificial Developer Intelligence

• Email us at [email protected] you have any feedback, requests, or just want to say hello! 
• Checkout our website www.adipod.ai

In this episode of Artificial Developer Intelligence, hosts Shimin and Dan explore the rapidly evolving landscape of AI, discussing recent news, benchmarking challenges, and the implications of AGI as a conspiracy theory. They delve into the latest techniques in AI development, ethical considerations, and the potential impact of AI on human intelligence. The conversation culminates in the latest advancements in LLM architectures, and the ongoing concerns surrounding the AI bubble. 

Takeaways

• Benchmarking AI performance is fraught with challenges and potential biases.
• AGI is increasingly viewed as a conspiracy theory rather than a technical goal.
• New LLM architectures are emerging to address context limitations.
• Ethical dilemmas in AI models raise questions about their decision-making capabilities.
• The AI bubble may lead to significant economic consequences.
• AI's influence on human intelligence is a growing concern among.

Resources Mentioned:
AI benchmarks are a bad joke – and LLM makers are the ones laughing
Technology Radar V33
How I use Every Claude Code Feature 

How AGI became the most consequential conspiracy theory of our time
Beyond Standard LLMs
Stress-testing model specs reveals character differences among language models
Meet Project Suncatcher, Google’s plan to put AI data centers in space
OpenAI CFO Sarah Friar says company isn’t seeking government backstop, clarifying prior comment

Chapters:

• (00:00) - Introduction to Artificial Developer Intelligence

• Check out our website: www.ADIpod.ai