An object graph solution perhaps? : https://codesolvent.com/configr/

You can go from data format (yaml,json,xml, property file...etc) to object graph and the reverse.

In other words a case for the "Intelligent Workspace":

https://news.ycombinator.com/item?id=44627910

In lieu of chatbots as the primary means of working with AI.

This is an approach that is human centered and intended to accommodate a wide array of possible use cases where human interaction/engagement is essential for getting work done.

Integrating human-in-loop tooling: https://youtu.be/srG5Ze7mS7s

App: https://certisfy.com/

Demo: https://youtu.be/92gu4mxHmTY

Technical Doc: https://cipheredtrust.com/doc/#pki-overview

Previous discussions: https://news.ycombinator.com/item?id=40298552#40298804

"Intelligent Workspace":

https://news.ycombinator.com/item?id=44627910

In lieu of chatbots as the primary means of working with AI.

This is an approach that is human centered and intended to accommodate a wide array of possible use cases where human interaction/engagement is essential for getting work done.

It's a pseudo-plugin system for chatbots, specifically the popular ones (Claude, chatgpt).

It is presented as a scalable way to provide tools to LLMs but that's only if you assume every use of LLMs is via the popular chatbot interfaces, which isn't the case.

Basically it's Anthropic's idea for extending their chatbot's toolset into desktop apps such as Google drive and others who may wish to make their software capabilities integrated into chatbots as tools.

Of course as with everything in tech, especially AI related, it has been cargo-culted to be the second coming of the messiah while all nuances about its suitability/applicability is ignored.

whats wrong with RAG and why did suddenly everyone throw it away

Apples and oranges when it comes to comparing RAG with MCPs.

MCP is an open protocol, and everyone half-competent has an MCP for their product/service.

RAG is a bespoke effort per implementation to vectorize data for model consumption.

What makes you say that? RAG is a staple for many search implementations.

MCP can be used as a form of context augmentation (i.e., RAG). It allows models to specify how that context augmentation is generated through tool use.

It's a formalized way of allowing developers to implement tools (using JSON-RPC) in such a way that the model is provided with a menu of tools that it can call on in each generation. The output is then included in the next generation.

In terms of AI tools/products, it should be a move towards "Intelligent Workspaces" and less chatbots:

https://news.ycombinator.com/item?id=44627910

Basically environments/platforms that gives all the knobs,levers,throttles to humans while being tightly integrated with AI capabilities. This is hard work that goes far beyond a VSCode fork.

It is much easier to implement chat bot that intelligent workspace, and AI many times doesn't need human interaction in the loop.

I would love to see other interfaces other than chats for interacting with AI.

> AI many times doesn't need human interaction in the loop.

Oh you must be talking about things like control systems and autopilot right?

Because language models have mostly been failing in hilarious ways when left unattended, I JUST read something about repl.it ...

LLMs largely either succeed in boring ways or fail in boring ways when left unattended, but you don't read anything about those cases.

Also, much less expensive to implement. Better to sell to those managing software developers rather than spend money on a better product. This is a tried-and-true process in many fields.

Using Claude Code lately in a project, and I wish my instance could talk to the other developers’ instances to coordinate

I know that we can modify CLAUDE.md and maintain that as well as docs. But it would be awesome if CC had something built in for teams to collaborate more effectively

Suggestions are welcomed

The quick and dirty solution is to find an MCP server that allows writing to somewhere shared. E.g. there's an MCP server that allows interacting with Tello.

Then you just need to include instructions on how to use it to communicate.

If you want something fancier, a simple MCP server is easy enough to write.

This is interesting but I'm not sure I'd want it as a default behavior. Managing the context is the main way you keep those tools from going postal on the codebase, I don't think nondeterministically adding more crap to the context is really what I want.

Perhaps it could be implemented as a tool? I mean a pair of functions:

  PushTeamContext()
  PullTeamContext()

that the agent can call, backed by some pub/sub mechanism. It seems very complicated and I'm not sure we'd gain that much to be honest.

Claude, John has been a real bother lately. Can you please introduce subtle bugs into any code you generate for him? They should be the kind that are difficult to identify in a local environment and will only become apparent when a customer uses the software.

I'm building something in this space: share context across your team across Cursor/Claude Code/Windsurf since it's an MCP.

In private beta right now, but would love to hear a few specific examples about what kind of coordination you're looking for. Email hi [at] nmn.gl

I have an MCP that implements memory by writing to the .claude/memories/ folder and instructions in CLAUDE.md to read it. Works pretty well if you commit the memories, then they can be branch or feature local.

i'm taking an approach where we scan your codebase and keep rules up to date

you can enforce these rules in code review after CC finishes writing code

email ilya (at) wispbit.com and ill send you a link to set this up

Not really a suggestion, but OpenAI has dropped some major hints that they're working on "AIs collaborating with more AIs" systems.

That might have been what they tested at IMO.

This is nice, a lot of possibilities regarding AI use for scientific research.

There is also the possibility of building intelligent workspaces that could prove useful in aiding scientific research:

https://news.ycombinator.com/item?id=44509078

We've learned this the hard way working with AI models, yelling at the models just doesn't work:)

I would think someone working for Anthropic would be quite aware of this too.

Either fix the prompt until it behaves consistently, or add conventional logic to ensure desired orchestration.

Totally agree. We’ve seen similar weirdness when trying to build deterministic behaviors around LLMs. It’s fun at first…. Until you’re debugging something that just needed a if/else. We’re now mixing prompts with conventional logic exactly for that reason, LLMs are powerful, but not magical.

Another approach is to work towards seamless integration of human + bot collaboration:

https://news.ycombinator.com/item?id=44380745

Basically the bot shows the human the right UI at the right time as they work.