201–210 of 293 posts
These protocols are to put handlers between you and your own data so they can sell it back to you via “search.” Companies who are betting their future on LLMs realized a few years ago that the data they can legally use is the only long term difference between them, aka “moat.” Now that everyone has more or less the same public data access, and a thin compute moat is still there, the goal is to transfer your private t…
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There's a simple reason for that. AI (real AI) is now an engineering problem, not a computer science problem.
so you are telling me that hallucinations (that by definition happen at the model layer) are an engineering problem ? so if we just spin up the right architecture, hallucinations won't be a problem anymore ? I have doubts
Yes.
Hallucinations were a big problem with single shot prompting. No one is seriously doing that anymore. You have an agentic refinement process with an evaluator in the loop that takes in the initial output, quality checks it, and returns a pass/fail to close the loop or try again, using tool calls the whole time to inject verified/real time data into the context for decision making. Allows you to start actually building reliable/reasonable systems on top of LLMs with deterministic outputs.
A key difference between MCP and A2A that is apparent to me after building with MCP and now reading the material on A2A: MCP is solving specific problems people have in practice today. LLMs need access to data that they weren't trained on, but that's really hard because there's a millions different ways you could RAG something. So MCP defines a standard by which LLMs can call APIs through clients. (and more). A2A sol…
LangChain is still around but that doesn't mean much. MCP isn't much better.
The MCP announcement had me excited on day one. Compared to that, this is a miss for me. The capabilities it provides seem to be no more than a system prompt, which was already a mostly solved problem. What “agents” need is not a protocol for operating, they need a protocol for discovery and addressability. How do I find someone’s agent? How do I talk to it and verify its identity? Once I’ve done that, it can just be…
An endpoint implementing this protocol would describe the agent and its capabilities, including examples. So I guess you could index that and create a discovery service.
A key difference between MCP and A2A that is apparent to me after building with MCP and now reading the material on A2A: MCP is solving specific problems people have in practice today. LLMs need access to data that they weren't trained on, but that's really hard because there's a millions different ways you could RAG something. So MCP defines a standard by which LLMs can call APIs through clients. (and more). A2A sol…
> I think I can safely say which one will still be around in 6 months LangChain is still around but that doesn't mean much. MCP isn't much better.
MCP solves a data and API integration problem.
Both are concrete things that people need to do today. AI agents talking to one another is not a concrete problem that organizations building features that integrate AI have today.
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There is no such thing as "safe" data in context of a general system, not in a black-or-white sense. There's only degrees of safety, and a question how much we're willing to spend - in terms of effort, money, or sacrifices in system capabilities - on securing the system, before it stops being worth it, vs. how much an attacker might be willing to spend to compromise it. That is, it turns into regular, physical world…
Are you generating these replies with an LLM? Edit: My apologies then.
The act of writing a comment on HN forces me to think through the opinions and beliefs in it, which is extremely valuable to me :). Half the time, I realize partway through that I'm wrong, and close the window instead of submitting.
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> I think I can safely say which one will still be around in 6 months LangChain is still around but that doesn't mean much. MCP isn't much better.
Langchain has long solved (we can argue on if it's done it well, opinions vary) the problem of needing to orchestrate LLM calls into a coherent workflow. Plus it had a first mover advantage. MCP solves a data and API integration problem. Both are concrete things that people need to do today . AI agents talking to one another is not a concrete problem that organizations building features that integrate AI have today.
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so you are telling me that hallucinations (that by definition happen at the model layer) are an engineering problem ? so if we just spin up the right architecture, hallucinations won't be a problem anymore ? I have doubts
>so you are telling me that hallucinations (that by definition happen at the model layer) are an engineering problem ? Yes. Hallucinations were a big problem with single shot prompting. No one is seriously doing that anymore. You have an agentic refinement process with an evaluator in the loop that takes in the initial output, quality checks it, and returns a pass/fail to close the loop or try again, using tool calls…
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I was in the same boat in regards to trying to find the actual JSON that was going over the wire. I ended up using Charles to capture all the network requests. I haven't finished the post yet, but if you want to see the actual JSON I have all of the request and responses here https://www.catiemcp.com/blog/mcp-transport-layer/
I had never heard of charles ... ( https://www.charlesproxy.com/ ) I basically wrote a simple version of it 20 years ago ( https://github.com/kristopolous/proxy ) that I use because back then, this didn't exist ... I need to remember to toss my old tools aside