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Quick correction, from what I understand Dax and Adam started from scratch for the new version.


I'm currently building an internal tool using SurrealDB directly, but I'm curious to use Morphik since it implement features I hadn't the time to figure out yet. (For example, I started with hardcoded schemas and I like how you support both).

Minor nitpick, but the README for your ui-component project under ee says:

"License This project is part of Morphik and is licensed under the MIT License."

However, your ee folder has an "enterprise" license, not the MIT license.


Thanks for pointing that out! Fixed it.

For the metadata extraction, we save these as Column(JSONB) for each documents which allows it to be changed on the fly.

Although, I keep wondering if it would have been better to use something like mongodb for this part, just because it's more natural.

Please let me know if you have questions and how it works out for you.


In which client and with which LLM are you using it?

I use it in Claude Desktop for the right use case, it's much better than thinking mode.

But, I admit, I haven't tried it in Cursor or with other LLMs yet.


Are you using Typesense for the search server? If so, are you using hybrid search with embeddings, or just regular search?


I wrote a Go-based search engine that indexes full words and prefixes for the foods and their alternate names because I was dissatisfied with how slow OpenSearch was given the very specific needs I had. I have Cloudflare in front to act as a cache.


Goose from Block is also a client that supports MCP. It can be used both with UI or as a CLI: https://block.github.io/goose/


On my end, I use https://github.com/skydeckai/mcp-server-aidd, which has

- File manipulation - Directory manipulation - tree-sitter integration

and more.

I also installed Tavily Search, sequential thinking, and Playwright.

I still use Cursor for development, and I use Claude Desktop for higher-level documentation, testing, etc.

For example, I'll check out a new repo that is lacking in documentation. I'll get the app running, then explain to Claude where the code lives, how to access the real app, and how I want the features documented.

Then Claude will happily scan the codebase, take screenshots of the running app, etc., all by himself, and then create a report (through the artifact system) with visualizations, graphs, etc.


I'm new to the world of graph, and I just started building with SurrealDB in embedded mode.

If you don't mind taking a few minutes, what are the main reasons to use Kuzu instead?


Hi, glad to help! I'm a DevRel advocate at Kuzu, and have spent a decent amount of time in other database paradigms thinking about these things. I'm familiar with SurrealDB too.

Although I cannot comment too much SurrealDB's exact capabilities and performance at this point, I can definitely highlight that at the data modeling and query language-level: Kuzu's data model is a property graph model (so an actual "graph" model rather than a multi-model database) and Kuzu implements Cypher as its query language, which is already widely adopted in the industry and is very intuitive to write (for both humans and LLMs).

Although Surreal DB does indeed offer an embedded mode, Kuzu is by design 100% embedded, is super-lightweight and can run natively in many environments, such as browsers, Android applications, AWS Lambda (serverless) and we're especially designed to be a VERY Python-friendly graph database that integrates with pretty much all well-known Python libraries. Because of its columnar storage layer, Kuzu can seamlessly read and write different data formats, such as Panda/Polars DataFrame, Arrow tables, Iceberg or Delta Lake tables and seamlessly move data between advanced graph analytics libraries like NetworkX. For anything related to graph computation, Kuzu is likely to have all the right tools and utilities to help you solve the problem at hand.

In my opinion, it's a myth that databases are heavy, monolithic pieces of software, and hopefully, using Kuzu will demonstrate that it's totally possible to have data in your primary store but seamlessly move it to a performant graph storage layer when required, and move the results back with minimum friction and cost. Hope that helps!


Haven't had time to try it out, but I've built myself a tool to tag my bookmarks and it uses 3.5 Haiku. Here is what it said about the official article content:

I apologize, but the URL and page description you provided appear to be fictional. There is no current announcement of a Claude 3.7 Sonnet model on Anthropic's website. The most recent Claude 3 models are Claude 3 Haiku, Sonnet, and Opus, released in March 2024. I cannot generate a description for a non-existent product announcement.

I appreciate their stance on safety, but that still made me laugh.


Thanks for taking the time to explain your process. I'm just starting out, and so far I was planning to:

- Radar: just cool categorized articles, libraries, apps, etc. that I found during the week and organized in weekly drops.

- Guides: In my mind, those were closer to TIL than real guides, though I couldn't figure out how to name them. I love TIL for that.

- Playground: Forcing myself to share prototypes, snippets, etc.

I also have tons of LLM chats that could become content. Instead of trying to rewrite them, I will just share them as they are (while being transparent).

The main idea, as you said, is to find a modality that works well for me and force myself to write more.


1. My personal website. -> I spent so much time ingesting content, I finally realized I need to produce some too.

2. A AI assisted brief generator -> clients often have a hard time articulating their requirements for new projects.

3. Prototyping the UX of "my" version of the perfect "process aware" editor. More organized than a Wiki, more flexible than tools like Jira, Aha and all. Not ready to share a public link yet. My goal is share my mockup in a week or two.


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