Interesting take from @tarunchitra @ 22:04 however, agents being early shouldn't be the point of discussion here
Reliability still ranges from subpar to nonexistent in the vast majority of scenarios, especially in scientific research or any type of research involving financial data and so on...
Benchmark scores for most “frontier” models keep going up but the reliability issues talked about above persist
GPT has web search consisting of web scraping and that doesn’t magically prevent it from outputting degraded results and slop. A browser MCP will not fix that
To expand on MCP: it’s a set of tools that unlocks certain things and we are finalizing our own clients, however simply plugging them into LLMs cannot mask their inherent cognition shortcomings. Meaning that it’s nothing more than a tool that needs to be used properly for max performance extraction.
So were agents too early or are they doomed to be very limited in the current paradigm?
Such opinions have been ours for much longer than the existence of “Chasing AI’s Holy Grail”( )
NEW EP: What Went Wrong with Crypto AI Agents with Tarun Chitra and Wei Dai
Everyone’s talking AI agents, but no one’s asking the hard questions.
In today's episode, @ayyyeandy & @robbie_rollup sit down with @tarunchitra of @gauntlet_xyz and @_weidai of @1kxnetwork to cover:
> What’s next for crypto AI agents post-meme hype
> DeepSeek’s reward model and compute efficiency
> Can MCP become the "HTTP of AI agents"?
> Why are today’s agent protocols so insecure (and can crypto fix that?)
> Reasoning models and long-term AI planning
> Micro-payments and autonomous onchain agents
Full episode links below.
Timestamps:
00:00 Intro
00:49 Magic Ad
01:05 Current Research Landscape
04:01 Types of Quantum Computers
09:58 Quantum Crashing Compute Marketplace?
17:51 Why Run Models Locally?
22:04 The Next Agent Boom
26:29 What is MCP?
39:30 Mantle Ad
40:11 Agent to Agent Communication Trends
49:16 Model Training Rewards?
55:32 Commoditizing Compute
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