HeyJaredAI Odyssey
AI Odyssey is your journey through the vast and evolving world of artificial intelligence. Powered by AI, this podcast breaks down both the foundational concepts and the cutting-edge developments in the field.
- Indexed episodes, last 90 days
- 8
- Latest publication
- Sep 27, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 6, 2026
Latest episodes
Personal AI Agents That Act for You: Hermes, OpenClaw, Grok Bot and Muse (opens the original)
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🎧 Personal AI Agents That Act for You: Hermes, OpenClaw, Grok Bot and Muse AI agents are moving beyond answering questions. You can give them a task, let them work across apps, and step in when needed. This episode looks at four options for individuals. Hermes Agent and OpenClaw offer control on your own devices, with setup required. Grok Bot works in a cloud computer for eligible subscribers. Meta’s Muse is built for everyday tasks: it can use a browser and connected apps, keep working after yo
Jev: What’s Behind the Buzz? (opens the original)
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🎧 Jev: What’s Behind the Buzz? Jev is getting attention with a simple promise: AI that makes a choice instead of writing an answer. But what does that actually mean? In this episode, we unpack TypeSafe AI’s new model: what it is, how software can use it to sort requests or choose a next step, and why its claims of faster, cheaper decisions are attracting interest. We also look at what Jev does not solve. A neatly formatted answer can still be wrong, and the company’s performance claims need inde
Grounding Agent Memory: When AI Must Check What It Remembers (opens the original)
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🎧 Grounding Agent Memory: When AI Must Check What It Remembers An AI assistant that remembers yesterday can repeat yesterday’s mistakes. This episode explores research from Microsoft on checking an agent’s memories against its working environment before saving them for future tasks. A separate curator inspects databases or documents through read-only tools, then corrects, narrows or discards uncertain memories. In one database benchmark, success reached 73%, compared with 70% for memory alone an
Recursive Self-Improvement: AI Must Learn to Improve Its Own Learning (opens the original)
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🎧 Recursive Self-Improvement: AI Must Learn to Improve Its Own Learning An AI that fixes one answer has not necessarily learned anything for tomorrow. Recursive self-improvement asks for something harder: changes that persist across tasks and reshape how the system makes its next improvements. We explore a new research roadmap that separates five levels of autonomy, from executing prescribed updates to revising the mechanisms of improvement itself. The distinction matters for anyone deciding how
AGENTSCOPE: Why Bigger Models Do Not Fix Agent Debugging (opens the original)
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When an AI agent fails after dozens of steps, the final error rarely reveals where the problem began. AGENTSCOPE turns long execution traces into structured reasoning-action graphs, then checks them against ten neural invariants covering reasoning, control flow, and tool use. On the new AgentErrata benchmark, it raised exact failure-step localization from 1.32% to 31.35% with GPT-5.1 and more than doubled failure-type accuracy over a direct LLM judge. Yet the best exact localization score remain
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