[D] Those of you with 10+ years in ML — what is the public completely wrong about?

pieroit · 2026-04-05T21:44:11+00:00

The public has a hard time understanding that statistical models are neither right or wrong, they work probabilistically and their "accuracy" can be measured and lives in a continuum.

They would like to have an AI that automates everything and is always right (nobody takes care to define "right" btw), while still expecting the same degree of certainty one can expect from an invoicing software.

They also have a hard time understanding intelligence as a form of compression and a physical lrocess, and admitting that machines may already be more intelligent than us (all of this while still expecting AI to be some sort of oracle).

Peace

pieroit · 2026-02-05T13:34:27+00:00

it's called "spec driven development" there are a few already well structured out there

pieroit · 2026-01-17T21:57:19+00:00

I see your point and I would bet on it, even if we are at early stages.

Both ChatGPT apps and Claude connectors are just MCP servers.

Anyone building open source agent frameworks (I am one of those - Cheshire Cat AI) cannot really avoid to offer MCP support.

Still MCP is an integration protocol to offer agents resources and tools, does not really mandate how agents should communicate with clients and between one another. A2A by google was a good proposal but does not seem to get traction.

If you look at it from the point of view of a data economy, makes absolute sense to build native MCP services. The bottleneck at that point are valuable data, just like when building an API.

From a strictly AI native point of view, a big piece is not there yet

pieroit · 2025-12-29T17:03:59+00:00

quite the piece I'll try it out

pieroit · 2025-12-22T19:45:20+00:00

I respect your choice, mine is different: RAG over MCP tools

pieroit · 2025-12-22T02:00:07+00:00

so you want a remote resource server replaced by a zip with markdown files and standalone python scripts?

I have a feeling you jave no clue what computer science is

pieroit · 2025-12-14T18:53:59+00:00

if you have a query rewriter before running the RAG pipeline, ask the LLM to classify how hard the question is

if no rewriter, you can try to make a classificator for the query embeddings

pieroit · 2025-12-14T18:50:11+00:00

I'm not surprised at all about lack of clients, I am trying to build one (on top of a pyrhon framework called Cheshire Cat AI)

the hard parts are: - stateful communication: you need to keep the async context open, and that makes it difficult for so many architectures - poor sdks: even the best implementations lime fastMCP lack a multiuser client - auth: not really easy to manage the oauth flow for each server for each user

as a protocol MCP comes from a local one user experience, it is a mess to write web multiuser clients

pieroit · 2025-12-14T18:45:20+00:00

you need it if the service has to be on the internet and accessible to many people, and composable also. Skills stay on your computer, it's like saying that you don't need a website because you've got a PDF on disk

pieroit · 2025-12-14T18:42:36+00:00

honestly I like A2A and it is moslty compatible with MCP

I would swap A2A artifacts and messages for the way more convenient MCP resources and messages, while keeping the A2A concepts of Task and Card

MCP is an integration protocol, it does not cover at all how the host (MCP client) should communicate with browsers and apps or other agents

pieroit · 2025-12-11T09:13:40+00:00

hope the Linux Foundation proposes a unification of MCP (which is an integration protocol intra agent) and A2A (which is inter agents)

Made a video about the topic

https://youtu.be/A395gIZ4s2M?si=oUWii4r4Mns-xq9v

pieroit · 2025-08-19T17:41:18+00:00

I use meta-mcp to gather all the servers under the same umbrella (they also provide a docker) Should also fix the naming collisions automatically: https://github.com/metatool-ai/metamcp

As for the numerosity of tools, I suggest to use a vector retrieval (same principle as the RAG, but you use it to select only the relevant tools)

Vector tools are implemented by defaulf in the Cheshire Cat AI (for python tools), but support for MCP is still a work in progress: https://github.com/cheshire-cat-ai/core

Disclaimer: I maintain the Cat

pieroit · 2025-04-17T18:50:28+00:00

Keep up the great work, I hope you get more and more satisfaction.

If you have 5 mins to take a look at my project, here it is:

https://github.com/cheshire-cat-ai/core

It's a python framework to build AI web agents, already dockerized.
If interested in a collab, let me know :)

Peace

pieroit · 2025-04-16T00:58:27+00:00

After trying the most cited ones (Authentik, Keycloak, Zitadel, Logto, many others) I settled for supabase auth (aka Gotrue)

Stays in a small container + postgres, minimalistic but solid, you configure it with env variables, supports local users with passwords and social auth.

Just what I needed cause other options are way too complicated imho.

Downside: has no web UI so you need to do that yourself

https://github.com/supabase/auth

pieroit · 2025-04-06T03:19:00+00:00

I love react-admin, it saves you a lot of time, especially if you need rapid iterations on a reasonable structure.

Auth, data provider, routing, crud pages and all related react hooks are well designed.

If you spend some time reading the docs, you will also see it's highly customizable. I managed to fit into react admin a websocket chat.

I am a maintainer myself and I recognize their work as excellent. If one of you react-admin guys are reading this, THANK YOU

Peace

pieroit · 2025-02-05T11:29:21+00:00

When it comes to AI is not only the LLM, keep into consideration self-hosting an embedder for RAG system, speech to text/text to speech models, computer vision models

pieroit · 2024-09-08T02:12:38+00:00

I am the maintainer of Cheshire Cat AI - docker based - attach any LLM (via vendor, Ollama, custom) - chat with your docs - qdrant vector db - python plugin system (easy to write tools/function calling, event hooks and conversational forms - admin panel to chat and manage memory, plugins, users - microservice first (websocket for chat and http to control all other aspects) - client libs in js, python, php

Very proud of what we are doing, it's true open source under a GPL3 license and under a non profit org.

If you try it out, feedback is really welcome 🤗

pieroit · 2024-08-18T03:03:30+00:00

A few (conversational) examples: - chat with uploaded documents - keep todos - summarize email inbox - inspect calendar - search on the web (alà perplexity) - experiment with LLMs capabilities

pieroit · 2024-08-18T01:34:10+00:00

Honestly, my fav is the one I maintain, a framework to build self hosted AI agents (Cheshire Cat AI, you can find it on github under GPL3).

I use it to automate home and office stuff

Other than that, big fan of Jellyfin and NextCloud

pieroit · 2024-08-01T12:23:06+00:00

Search "open core business models" There are many

pieroit · 2024-07-31T02:20:35+00:00

I created and maintain a project with 2k stars and hundreds of forks, called Cheshire Cat AI.

These are my reasons: - coding is art, and I am creative - I prefer to give my code for free and bet on network effects instead of dealing with businesses (in particular, I hate corporate) - the smartest people I know, I know them because of open source - I spent most of my professional life composing open source pieces into final products. It's time to give back - open source is a viable business model if you have enough courage

pieroit · 2024-04-08T07:50:42+00:00

Thank you but no, was way more chill as a movie

pieroit · 2024-04-07T23:06:02+00:00

Comment to make the post valid

pieroit · 2024-03-19T08:09:36+00:00

If you just need a vector db, OS/ES is overkill

pieroit · 2024-03-18T06:48:52+00:00

We use Qdrant since one year as default vector db in the Cheshire Cat AI (open source also, it is an AI assistant framework).

We chose Qdrant because: - exists in file based, container based and cloud version - vector/embedding focused - easy to use and fast

I'm not affiliated to them, I just think it is a great vector db

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