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AI Podcasts

January 6, 2026

Building durable Agents with Workflow DevKit & AI SDK - Peter Wielander, Vercel

AI Engineer

AI
Key Takeaways:
  1. The Macro Transition: We are moving from "fire-and-forget" prompts to durable execution environments where state is as important as the model itself.
  2. The Tactical Edge: Wrap your existing tool calls in the `useStep` function to gain instant retry logic and execution history.
  3. The Bottom Line: Reliability is the primary moat in the agent market. Builders who adopt durable workflows will move to production while others are still debugging local scripts.
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January 6, 2026

Build a Prompt Learning Loop - SallyAnn DeLucia & Fuad Ali, Arize

AI Engineer

AI
Key Takeaways:
  1. The move from manual prompt engineering to automated prompt learning. As models become commodities, the proprietary loop that refines them becomes the moat.
  2. Implement a Train-Test Split for your prompts. Use a subset of failure data to generate new rules and validate them against a separate holdout set to ensure the logic holds.
  3. Reliability is the only metric that matters for agent adoption. If you are not using a feedback loop to update your system instructions, you are building on sand.
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January 6, 2026

The Netflix Culture Code That Changed Entertainment Forever | Reed Hastings Interview

Invest Like The Best

AI
Key Takeaways:
  1. The move from industrial management to creative inspiration. As AI automates routine tasks, the only remaining value is high-variance human creativity.
  2. Apply the Keeper Test today. Ask your leads which team members they would fight for and provide generous exits for the rest to reset your talent bar.
  3. Scaling doesn't require more rules. It requires better people. If you can maintain talent density, you can run fast while your competitors choke on their own handbooks.
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January 5, 2026

Welcome to AIE CODE - Jed Borovik, Google DeepMind

AI Engineer

AI
Key Takeaways:
  1. The transition from general-purpose LLMs to specialized coding agents that operate on the entire codebase rather than isolated snippets.
  2. Audit your current stack for agentic readiness. Prioritize tools that integrate with Gemini 3 or similar high-reasoning models to automate repetitive pull requests.
  3. Code is the substrate of the digital world. If you control the means of AI code generation, you control the speed of innovation for every other industry.
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January 5, 2026

Claude Agent SDK [Full Workshop] — Thariq Shihipar, Anthropic

AI Engineer

AI
Key Takeaways:
  1. The industry is moving from "Chat-as-Interface" to "Computer-as-Interface" where models operate directly on file systems.
  2. Replace your complex list of fifty tools with a single Bash tool and a secure sandbox.
  3. The winners in the agent race will not build the best prompts. They will build the best infrastructure for models to execute code.
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December 31, 2025

Infinity, Paradoxes, Gödel Incompleteness & the Mathematical Multiverse | Lex Fridman Podcast #488

Lex Fridman

AI
Key Takeaways:
  1. The move from a singular "Universe" view to a "Multiverse" perspective mirrors the transition from centralized monoliths to fragmented, interoperable ecosystems.
  2. Build systems that fail gracefully when hitting Gödelian limits.
  3. Truth is a vast ocean while proof is a small boat. Your roadmap must account for the reality that your system will eventually encounter truths it cannot verify.
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December 31, 2025

AI in 2026: 3 Predictions For What’s To Come (a16z Big Ideas)

a16z

AI
Key Takeaways:
  1. The Macro Pivot: Outcome-Based Intelligence. We are moving from AI as a Service to Results as a Service where software value is tied to revenue generation rather than seat licenses.
  2. The Tactical Edge: Verticalize the Data. Build in sectors with non-public outcome data to create a compounding moat that resists commoditization by foundation models.
  3. The winners of 2026 will be those who use AI to solve core human needs for connection and discovery while building defensible, data-rich business models.
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December 31, 2025

AutoGrad Changed Everything (Not Transformers) [Dr. Jeff Beck]

Machine Learning Street Talk

AI
Key Takeaways:
  1. The Macro Transition: Moving from "Big Model" monoliths to "Lots of Little Models" where distributed Bayesian assets represent specific physical objects.
  2. The Tactical Edge: Prioritize "Object-Centered" architectures that track uncertainty. This allows robots to "phone a friend" when encountering novel data.
  3. The LLM era is hitting a wall of implicit representation. The next 12 months belong to those building explicit, causal world models grounded in physics rather than language.
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December 31, 2025

[State of Post-Training] From GPT-4.1 to 5.1: RLVR, Agent & Token Efficiency — Josh McGrath, OpenAI

Latent Space

AI
Key Takeaways:
  1. The move from "bigger is better" to "smarter is cheaper" as token efficiency becomes the primary metric for agentic success.
  2. Prioritize building on models that demonstrate high performance on "graph walk" evals to ensure your long-context applications actually work.
  3. Utilitarian and efficient models that prioritize task completion over cheery personality will dominate the developer market.
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Crypto Podcasts

January 24, 2026

Gold Sets the Bar, But Bitcoin Can Catch Up. Here’s How: Bits + Bips

Unchained

Crypto
Key Takeaways:
  1. The institutionalization of Bitcoin has temporarily sacrificed its digital gold status for liquidity, creating a massive opportunity for those who can stomach the volatility before the next decoupling.
  2. Monitor Japanese government bond yields as a leading indicator for global risk tolerance.
  3. Bitcoin is currently a liquidity sponge, not a bunker. Expect it to follow the Trump Put and tech earnings until its volatility profile mirrors a currency rather than a speculative stock.
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January 23, 2026

The Intersection of AI and Crypto: What Worked, What Didn’t, and What’s Next | Roundup

Bell Curve

Crypto
Key Takeaways:
  1. The market is moving from the "Compute Layer" to the "Agentic Layer." Owning the GPU is less valuable than owning the agent that controls the wallet.
  2. Build agent-first interfaces. Stop designing for human clicks and start structuring your data so an LLM can execute transactions on your behalf.
  3. The next 12 months belong to on-chain agents that handle treasury ops and commerce. The "decentralized GPU" narrative is dead. The "AI Agent with a bank account" narrative is just beginning.
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January 23, 2026

The “Quantum Threat” Behind Bitcoin’s Sudden Sell-Off

Bankless

Crypto
Key Takeaways:
  1. The transition from global cooperation to regional protectionism is driving a capital outflow loop that favors hard assets over sovereign debt.
  2. Monitor the development of quantum-resistant signatures on alternative L1s to hedge against Bitcoin’s potential cryptographic obsolescence.
  3. The next year will be defined by the race to tokenize real-world assets and the struggle to maintain protocol relevance as TradFi giants enter the arena.
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January 22, 2026

DePIN’s Biggest New Deal: Valeo x NATIX | Alireza Ghods

Proof of Coverage Media

Crypto
Key Takeaways:
  1. The transition from digital-only AI to Physical AI requires a massive bridge of high-fidelity video data.
  2. Monitor DePIN projects that move from "map-to-earn" to "train-to-earn" for foundational models.
  3. NATIX is no longer just a mapping company; it is the data refinery for the next generation of autonomous machines.
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January 21, 2026

Markets Are Entering A Wartime Economy | Cem Karsan

Forward Guidance

Crypto
Key Takeaways:
  1. The transition from a supply-side model to a populist-driven wartime economy makes inflation a permanent feature rather than a bug.
  2. Rotate out of traditional portfolios into non-correlated volatility strategies and hard assets.
  3. The next decade belongs to those who recognize that the rules-based order has been replaced by a raw competition for strategic resources.
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January 21, 2026

How Nansen’s New Trading Agent Makes It Easier to Follow the Smart Money Onchain

Unchained

Crypto
Key Takeaways:
  1. The commoditization of technical infrastructure means alpha moves from who has the data to who has the best prompts.
  2. Test agentic workflows with small capital amounts to identify where natural language outperforms manual execution.
  3. The next 12 months will see a transition from manual click-and-sign trading to intent-based portfolio management.
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