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AI Podcasts

January 12, 2026

OpenAI + @Temporalio : Building Durable, Production Ready Agents - Cornelia Davis, Temporal

AI Engineer

AI
Key Takeaways:
  1. The industry is moving from "Agent as a Script" to "Agent as a Durable Service" where state management is handled by the infrastructure.
  2. Wrap your existing API tools in the `activity_as_tool` function to gain automatic retries and execution history.
  3. Reliability is the only moat in the agentic economy. If your agent cannot survive a server restart during a three-day task, it is not ready for the enterprise.
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January 13, 2026

How Ladder Became #1 Strength Training App

Invest Like The Best

AI
Key Takeaways:
  1. The Macro Trend: The move from fragmented content libraries to integrated health systems where AI synthesizes biomarkers and movement.
  2. The Tactical Edge: Construct internal LLM tools to categorize qualitative feedback. This turns thousands of raw reviews into a precise roadmap.
  3. The Bottom Line: Building a $100M ARR consumer app requires mastery of both growth loops and product retention. Solve for the daily habit to win the long game.
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January 13, 2026

Ben Horowitz on Investing in AI: AI Bubbles, Economic Impact, and VC Acceleration

a16z

AI
Key Takeaways:
  1. The transition from general-purpose AI to specialized application layers. As foundation models commoditize, value migrates to the "fat tail" of human-centric complexity.
  2. Prioritize building or investing in "DNA of the future" companies that incumbents must eventually acquire to survive. Focus on winning the "point of attack" by staying deep in the technical details.
  3. We are in a unique market where demand growth justifies high valuations. Success over the next year depends on identifying founders who are the absolute best in the world at one specific thing.
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January 13, 2026

It's Time To Build

a16z

AI
Key Takeaways:
  1. The Macro Shift: Infrastructure Invisibility. As core technologies become background noise, value moves from the pipes to the unique experiences built on top of them.
  2. The Tactical Edge: Reject Mediocrity. Audit your product for average features and replace them with high-conviction improvements that competitors are too lazy to attempt.
  3. The Bottom Line: Building is the only way to ensure the future happens. If you do not create the next version of reality, you are stuck living in an outdated vision.
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January 13, 2026

Nothing’s Carl Pei on Building a $1B Smartphone Company and Why He Thinks About Death Every Week

The Generalist

AI
Key Takeaways:
  1. The transition from hardware specs to emotional hardware where brand identity and OS-native AI become the primary moats.
  2. Prioritize arbitrage opportunities in marketing by finding underpriced attention on platforms like TikTok before they become crowded.
  3. Success in mature markets requires a Genghis Khan method: be a talent scout, stay open-minded to global supply chains, and use design to win the emotional battle for the consumer's pocket.
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January 12, 2026

Nvidia CES 2026

Semi Doped

AI
Key Takeaways:
  1. The transition from centralized cloud training to distributed local inference creates a massive demand for high-bandwidth storage and custom CPUs.
  2. Audit your technical roadmap to prioritize local agentic workflows that reduce latency and data privacy risks.
  3. The next 12 months will favor hardware that enables physical AI and local autonomy. Owning the compute stack is becoming a competitive necessity for builders who want to move faster than the cloud allows.
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January 9, 2026

Spec-Driven Development: Sharpening your AI toolbox - Al Harris, Amazon Kiro

AI Engineer

AI
Key Takeaways:
  1. The transition from "vibe coding" to "spec-driven" engineering.
  2. Implement EARS-formatted requirements in your AI prompts.
  3. Determinism is the ultimate feature for AI-assisted development.
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January 8, 2026

Post-training best-in-class models in 2025

Weights & Biases

AI
Key Takeaways:
  1. Intelligence is decoupling from scale. As reasoning becomes a commodity, the value moves from the size of the model to the proprietary nature of the training data.
  2. Use TRL or Unsloth for single-GPU fine-tuning. Prioritize cleaning your instruction sets over increasing your training iterations.
  3. The future belongs to those who own their data pipelines. If you can distill elite reasoning into a 350M parameter model, you win on latency, cost, and privacy.
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January 8, 2026

Intelligent Robots in 2026: Are We There Yet? [Nikita Rudin] - 760

The TWIML AI Podcast with Sam Charrington

AI
Key Takeaways:
  1. Moving from Blind Locomotion to Semantic Navigation defines the next frontier.
  2. Prioritize modular architectures that use off-the-shelf VLMs for task orchestration.
  3. Expect the first value-positive humanoid deployments in late 2026.
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Crypto Podcasts

February 25, 2025

The State Of Crypto & AI | Illia Polosukhin & Bowen Wang

Lightspeed

Crypto
AI
Infrastructure

Key Takeaways:

  • :
  • 1. NEAR is pioneering a unified blockchain infrastructure integrating AI, eliminating the need for multiple chains and enhancing user experience.
  • 2. The launch of NEAR 2.0 with fully sharded architecture and reduced block times positions NEAR as a scalable and high-performance blockchain platform.
  • 3. NEAR’s focus on chain abstraction and Trusted Execution Environments sets it apart from other blockchain and Layer 2 solutions, offering a more seamless and secure user experience.
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February 25, 2025

Futarchy Deep Dive: Can Markets Make Better Decisions? | Proph3t

Bell Curve

Crypto
AI
Others

Key Takeaways:

  • :
  • 1. Futarchy harnesses market efficiency to potentially outperform traditional governance in decision-making.
  • 2. Crypto’s regulatory resistance is essential for implementing innovative governance models like futarchy.
  • 3. Enhanced liquidity and decentralized capital formation are critical for the scalability and success of futarchy-based organizations.
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February 24, 2025

Where Does Crypto Go From Here? | EP 71

Good Game Podcast

Crypto
AI
Infrastructure

Key Takeaways:

  • 1. Focus on Financial Utility: Crypto's strongest and most sustainable applications remain within the financial sector, emphasizing the need for robust, revenue-generating projects over speculative tokens.
  • 2. Leverage AI for Innovation: Startups that effectively integrate AI to solve real-world problems, particularly in personalized applications, are poised for significant growth and competitive advantage.
  • 3. Embrace Tokenization: The future of equity and capital formation lies in tokenizing shares and streamlining IPO processes on-chain, presenting a transformative opportunity for startups and investors alike.
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February 24, 2025

Solana’s Vibe Shift, Restaking, and Yapping About Kaito | Ian Unsworth

0xResearch

Crypto
DeFi
AI

Key Takeaways:

  • :
  • 1. Solana’s Dependence on Meme Coins: While meme coins drive substantial revenue for Solana, they also introduce significant vulnerabilities amid changing market sentiments and regulatory pressures.
  • 2. Staking Yield Dynamics: Proposed reductions in staking yields are unlikely to trigger mass unstaking but will push the ecosystem towards more liquid and innovative staking solutions.
  • 3. Kaido’s Tokenomics Potential: Emerging platforms like Kaido offer novel tokenomics and AI integration, presenting new opportunities and challenges in monetizing user engagement and attention.
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February 24, 2025

Memes are Dead, Long Live the Memes | Nick Tomaino

Empire

Crypto
DeFi

Key Takeaways:

  • :
  • 1. Meme coins, while initially promising, often exploit retail investors through pump and dump schemes, necessitating a wary approach.
  • 2. Investing in crypto requires a long-term vision, focusing on meaningful projects and founders committed to sustained growth over fleeting gains.
  • 3. Stablecoins are pivotal in maintaining the US dollar's global influence and are set to grow with increasing adoption and regulatory support.
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February 24, 2025

How Sapien Lets Anyone Earn by Creating Datasets

Outpost | Crypto AI

AI
Crypto
Infrastructure

Key Takeaways:

  • 1. Decentralized data labeling can significantly reduce costs while enhancing data quality through global expert networks.
  • 2. The synergy between crypto and AI unlocks new possibilities for scalable and efficient AI model training.
  • 3. Proprietary, purpose-built datasets are becoming essential for enterprises to maintain a competitive edge in AI development.
See full notes