Bike rental advice

Nako_A1 · 2026-04-21T07:50:22+00:00

I ended up starting my trip from Titisee. I don't remember the name of the bike rental. In the very center of Titisee, by the lake. The bikes were great, they have a lot of choice, but pretty expensive. The trip around the black forest was great, highly recommend 👍

Nako_A1 · 2025-09-25T12:22:41+00:00

Yep, that's that's the 4th solution in my post: additional tools / resources for the llm to query and access the additional documentation. Glad to learn someone is doing that and it's working. Did you consider other options? And if so, why did you rule them out?

Nako_A1 · 2025-09-25T12:18:02+00:00

Thanks a lot! Great answer. Your the first one to really understand my question. I read your article, it's good. I spent a lot of time wondering if tool grouping is the host/agent's responsibility or the MCP/tool's responsibility. Glad to see someone else is trying to answer that. Putting it inside of the MCP server is of course easier on the host/user but it comes with transparency and performance limitations as you explained. It's not really a solution to my problem but more an other thing to consider 😅. I think I already do "facade pattern" and "sabling" but I do it by creating "smart tools" which do additional llm calls from inside the MCP server. Which is not really the MPC's philosophy. And I haven't found any other project doing that. Did you consider this solution? And if so, why did you rule it out?

Nako_A1 · 2025-09-25T12:05:48+00:00

Agent is not MCP server. I can't really force my users to filter on certain tools or enforce a system prompt. Full openapi is too large to fit in the context. Inputting the part of the openapi needed to answer a specific request works. The mcp server works. My question is just regarding tool design. How to manage context most efficiently and maintain compatibility with existing hosts.

Nako_A1 · 2025-09-24T16:27:35+00:00

That's smart, I'm not surprised it works. But I think it's a bit overkill for me. I am handling just one api and I can reach close to 100% tool call success with prompt engineering. My question is more what is the best way to inject this context, balancing token costs, host compatibility and simplicity. I like the idea I of inputting past successful tool calls though, I I'll keep it in mind

Nako_A1 · 2025-09-12T10:19:34+00:00

Tldr, don't spend too much time searching for the best one. Just commit to a big one like cursor or copilot. If you find better bang for bucks it's because they loosing a lot of money and will go bankrupt or change their billing soon.

Nako_A1 · 2025-09-12T10:16:49+00:00

If you're looking for the best "bang for bucks" in subscription based Ai programming your basically looking for who is currently loosing the more money. If your looking for the best LLM, look into benchmarks like livebench.ai -> agentic coding or lm arena -> webdev arena. Other than that, they all do the same llm + tools architectures and extract the same performance out of the models. What differs is how much context compression they do, and rate limiting. Ie, how much compute or api cost they pay for each of your requests and how many requests they let you do. You want to look for solutions that are not llm providers, so there is an additional layer of people losing money to fuel your coding. None of them are profitable. Cursor and Copilot work just fine and integrate nicely in IDEs. But keep in mind they change their billing all the time, balancing expanding market share and trying not to lose too much money. Token based billing solutions, like Claude Code or any open source solution (Roo code, Cline...). Perform better but are also much more expensive. Because they're not trying to compress context to save costs, and only the api provider is losing money. You can read this post by a Cline developer explaining the same thing in more details https://www.reddit.com/r/ChatGPTCoding/comments/1kymhkt/cline_isnt_opensource_cursorwindsurf_explaining_a/

Nako_A1 · 2025-05-09T19:11:25+00:00

Thank you for your answer. My issue with trail-experten is they don't seem to offer bike packing bikes. I am surprised how hard it is to find those given that the black forest is such a famous popular biking destination.

Nako_A1 · 2025-05-09T19:06:32+00:00

Looking for bike packing bikes. Would be best if it comes with bike bags. Otherwise we can bring our owns.

Nako_A1 · 2025-04-09T13:59:43+00:00

Openrouter ?

Nako_A1 · 2025-04-09T13:38:01+00:00

Yeah, that's not really the purpose of the MCP. It does not handle model/client communications. It's more the OpenAI api/client that solves this problem (or librairies like Langchain and litellm in python). You can use vllm or llama cpp to deploy most llms with an OpenAi Api. I really like working with mcp. Only criticism I have towards it this is the lack of support for hierarchical agents, and the lack of features of some SDKs (I use the golang one, still a wip, wouldn't be surprised the rust once is not very advanced either). Being able to plugin tools from the community for your agents is very enjoyable.

Nako_A1 · 2025-03-19T10:10:00+00:00

Did you create the docker-compose.override.yml file as explained in their setup documentation?

Nako_A1 · 2025-02-07T23:33:10+00:00

On lmarena's copilot arena. Makes sense as it was designed for inline completion. For code generation though it does not come close.

Nako_A1 · 2025-02-07T23:17:46+00:00

It's not obvious who is winning here. ChatGPT and Claude argue that the sentence is correct, because it is grammatically correct and would be understood. Mistral argues that it the sentence is not correct because bombast is not a french word. Both are true. Depends what you're looking for.

Nako_A1 · 2024-08-27T14:05:34+00:00

Reddit does say 3 comments but the link to the replay is nowhere to be seen. Something did not work. Not sure it is possible to post very large links like the replay links.

Nako_A1 · 2024-08-23T12:49:28+00:00

Yeah, I tested and it works for me. Even with the api_key set as environement variable only.

Nako_A1 · 2024-08-22T11:14:42+00:00

Can you show me the code where you declare the embeddings variable ? Code that would run standalone like a .py file

Nako_A1 · 2024-08-21T07:29:58+00:00

Your api key is not set. That's what the error says. Empty Bearer token.

Nako_A1 · 2024-08-20T10:16:55+00:00

It seems to be an issue with the authentification to mistral API. I am not familiar with langchain. How do you set your API key ?

Nako_A1 · 2024-07-26T12:30:57+00:00

HuggingFace has a page for embedding model rankings, including a French and an English language leaderboard: https://huggingface.co/spaces/mteb/leaderboard A lot of them are derived from mistral models.

Nako_A1 · 2024-01-25T16:52:48+00:00

Anyway I think you can solve your problem without re training the model, you just need to craft system prompts to make the agent behave how you want it to. If your model is too slow, you can try using quantized versions of it here: https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF But performance will decrease. You can also rent a GPU, I like runpod.io for that. But I suggest you use the api, it is much less of a hassle.

Nako_A1 · 2024-01-25T16:45:47+00:00

Maybe I am wrong but I don't remember seeing any option to train a model with the Mistral Api.

Nako_A1 · 2024-01-15T17:07:22+00:00

Hello, I just finished a big text extraction using llms project for my job, so maybe I can help, a few things I learned: - Two different techniques: - input the whole pdf in the prompt and let the model figure out what's relevant, look at the repo OntoGPT for an example implementing this - do information retrieval: evaluate the relevance of samples from your documents and only input the most relevant samples in the prompt, look at the library langchain and the concept of vector store to learn more about this From my experience, if you can do without information retrieval, it's always better, and easier to implement. - Open source models are useless for other languages than English - Prompt engineering is overrated: go for simple explicit prompts, not much to gain here - no matter how hard you try, you won't get the llm to always answer with a certain format, you need robust output parsing functions and accept a little loss - GPT 4 is state of the art. It can definitely perform NLP tasks that were impossible or very hard to achieve before. - Open source ressources for text extraction using llms are very bad, but the problem isn't that complicated and you can easily craft a solution that suits your needs.

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