AI Blindspot
AI blindspot is a podcast that explores the uncharted territories of AI by focusing on its cutting-edge research and frontiers This podcast is for researchers, developers, curious minds, and anyone fascinated by the quest to close the gap between human intelligence and machines. As AI is advancing at Godspeed, it has become increasingly difficult to keep up with the progress. This is a human-in-loop AI-hosted podcast.
Episodes
This episode covers AIE World's Fair Recap of Day 2 focusing on Keynotes & SWE Agents.
🧠 Key Takeaways:
- Moore’s Law for AI Agents: Capability is doubling every 70 days—yes, you read that right.
- Specifications = “New Code”: Aligning human intentions/values directly with model behavior—beyond old-school code artifacts.
- Evals: Absolutely critical for shipping AI, enabling rapid experimentation and tight feedback loops.
- Dagger...
This episode covers the AI Engineer World's Fair 2025, the largest and most impactful edition yet. With over 3,000 attendees and 250+ speakers from around the globe, the event brought together leading voices in AI to explore the future of agentic workflows, model development, and human-AI collaboration.
https://www.ai.engineer/
https://www.youtube.com/watch?v=z4zXicOAF28&t=917s&ab_channel=AIEngineer
The AI Engineer World's Fai...
Aentic workflows are processes where AI agents dynamically plan, execute, and reflect on steps to achieve a goal, differentiating them from static, predefined workflows.
Augmented LLMs, which serve as a base building block, are enhanced with capabilities like tool use and memory, enabling the creation of these more complex agents. This episode also distinguish between an agentic workflow, the sequence of steps, and the agentic arch...
In this episode, we discuss strategies for building effective AI agents, emphasizing simplicity and composable patterns over complex frameworks.
It distinguishes between workflows, which use predefined code paths, and agents, where LLMs dynamically direct their own processes, noting that simpler solutions are often sufficient.
To build effective AI Agents, start simple and composable building blocks, designed tools carefully and l...
DeepSeek-V3, is a open-weights large language model. DeepSeek-V3's key features include its remarkably low development cost, achieved through innovative techniques like inference-time computing and an auxiliary-loss-free load balancing strategy.
The model's architecture utilizes Mixture-of-Experts (MoE) and Multi-head Latent Attention (MLA) for efficiency. Extensive testing on various benchmarks demonstrates strong performance com...
In today's episode, we are discussing two research papers describing the two distinct approaches to building multi-agent collaboration :
MetaGPT is a meta-programming framework using SOPs and defined roles for software development.
https://arxiv.org/pdf/2308.00352
AutoGen uses customizable, conversable agents interacting via natural language or code to build applications.
This episode discusses agentic design pattern Tool Use.
Tool use is essential for enhancing the capabilities of LLMs and allowing them to interact effectively with the real world.
We discuss following papers.
Gorilla: Large Language Model Connected withMassive APIs
https://arxiv.org/pdf/2305.15334
MM-REACT : Prompting ChatGPT for Multimodal Reasoning and Action
https://arxiv.org/pdf/2303.11381
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