This is why I built the Nextcloud MCP server, so that you can talk with your own data. Obviously this is Nextcloud-specific, but if you're using it already then this is possible now.
The default MCP server deployment supports simple CRUD operations on your data, but if you enable vector search the MCP server will begin embedding docs/notes/etc. Currently ollama and openai are supporting embeddings providers.
The MCP server then exposes tools you can use to search your docs based on semantic search and/or bm25 (via qdrant fusion) as well as generate responses using MCP sampling.
Importantly, rather than generating responses itself, the server relies on MCP sampling so that you can use any LLM/MCP client. This MCP sampling/RAG pattern is extremely powerful and it wouldn't surprise me if there was something open source that generalizes this across other data sources.
This is related to the removal of 'de minimis' rule that exempts parcels under $800 to ship duty free. This has caused some European postal services to stop/delay shipping some packages to the US [0]. The Dutch postal service for instance has stopped shipping to the US [1]
> Most language models face a fundamental tradeoff where powerful capabilities require substantial computational resources. We shatter this constraint with Jan-nano, a 4B parameter language model that redefines efficiency through radical specialization: instead of trying to know everything, it masters the art of finding anything instantly. Fine-tuned from Qwen3-4B using our novel multi-stage Reinforcement Learning with Verifiable Rewards (RLVR) system that completely eliminates reliance on next token prediction training (SFT), Jan-nano achieves 83.2% on SimpleQA benchmark with MCP integration while running on consumer hardware. With 128K context length, Jan-nano proves that intelligence isn't about scale, it's about strategy.
> For our MCP evaluation, we used mcp-server-serper which provides google search and scrape tools
At some point, we have to admit we're asking too much from our tools.
I know nothing about your context, but in what context would a single model need to support so many permutations of a data structure? Just because software can, doesn't mean it should.
Anything multi-tenant? There's a reason Salesforce is used for so many large organisations. The multi-nesting lets you account for all the descrepancies that come with scale.
Just tracking payments through multiple tax regions will explode the places where things need to be tweaked.
I dove into using LLMs together with MCP servers for the first time this weekend. Absolutely incredible.
In addition to the code assistant, I configured a Grafana's MCP server with Cline, so that I can chat with an LLM while having real-time metrics and logs.
For context, I self host grafana in addition to a bunch of services on a raspberry pi. Simple prompts such as "why has CPU been increasing this week?" resulted in a deep analysis of logs/metrics that uncovered correlations I had never been aware of.
Incredible. I can only imagine what this will all look like in a few years
Yes, usage metrics are set via a configuration file, which you can also check into git. Changes to resources as well as usage estimates contribute to the forecasted costs
The six week cycle with down time in between chunks can be very effective. The crux is to make sure those blocks are properly scoped to make the best use of everyone's time.
This reminds me of the excellent book 'Shape up' by the team from Basecamp. They also champion upfront 'scoping' to make best use of those six weeks. Highly inspirational book for technically mature organizations.
I've been on openSUSE for 10+ years, and it is truly the gift that keeps on giving. The community super knowledgeable and responsive, and the distro is stable.
The biggest advantage openSUSE has compared to other mainline distributions is the openSUSE Build service (OBS). Contributing patches to existing packages is simple, and the build service also hosts custom packages in a personal rep - this let's me keep any custom packages up to date across all my systems. I believe it works with other distros as well so you don't even need to use openSUSE to utilise the service
Maybe the initial learning is not easy. But once you understood how it works it's an unbelievably valuable resource to build packages, slightly modified or not all for any major distro.
I recently needed a package in Fedora 37. They had dropped it after Fedora 36 because of lack of time. I am not a Fedora user, so I had no build environment handy. With OBS it took me minutes to get the package built.
Electric trucking will come online in the EU in the next decade. Long haul trucking is a major contributor to EU energy demand, and will see serious EV attention over the coming years, both in terms of EV trucks, as well as charging infrastructure
Water and food scarcity in the Middle East, as a consequence of the changing climate and its reliance on the global food supply chain (wheat imports), is IMO one of the most important contributors of political uproar that lead to the Arab Spring, particularly in Egypt.
Edit: possibly a better source describing some of the correlations between extreme climate events and recent global food shortages, and its adverse impact on Middle East and Northern Africa.
https://github.com/cbcoutinho/nextcloud-mcp-server
The default MCP server deployment supports simple CRUD operations on your data, but if you enable vector search the MCP server will begin embedding docs/notes/etc. Currently ollama and openai are supporting embeddings providers.
The MCP server then exposes tools you can use to search your docs based on semantic search and/or bm25 (via qdrant fusion) as well as generate responses using MCP sampling.
Importantly, rather than generating responses itself, the server relies on MCP sampling so that you can use any LLM/MCP client. This MCP sampling/RAG pattern is extremely powerful and it wouldn't surprise me if there was something open source that generalizes this across other data sources.