badmonster

What makes Solidis more performant than other Redis clients like ioredis or redis

interesting project

Are there any plans to add authentication for the HTTP API when running in TCP mode? It looks like the Unix socket mode relies on system-level permissions (which is great), but TCP mode seems wide open unless wrapped externally (e.g. behind a reverse proxy or firewall).

Is there any plan to support nested region collapsing/expanding directly from the editor, similar to how VSCode handles folding for functions or classes?

This was a fantastic read — thanks for putting in the work to script and visualize the evolution of Rust's compiler errors

curious if you're planning any Dockerized or hosted versions for easier deployment in production pipelines?

I had a question about dynamic query fragments: is there a recommended way to compose larger queries from smaller pieces while preserving placeholder safety and avoiding manual string concatenation? For example, conditionally building WHERE clauses or joins from pre-defined fragments?

I'm curious — does rv support or plan to support per-project isolation of system-level dependencies (e.g., gfortran, libxml2, etc.) like what renv sometimes indirectly requires users to manage outside R? If not, do you have recommendations for managing these in a reproducible way alongside rv?

I'm curious: is it possible to apply differentiated KV quantization (like K8V4) to models after they're already converted to .gguf format, or does this require rebuilding the model with special support? If it's compatible with any .gguf file, are there any limitations on model types (e.g. Mistral, Phi-3, etc.) or tokenizer configs?

What is the expected frame rate and latency when running on a typical setup with one Realsense camera and an RTX 3060?

Very cool project — love the offline-first, Git-native approach. One question:

How do you handle conflict resolution when multiple users modify the same issue or thread concurrently across remotes? Is it purely Git's merge mechanics, or do you apply any domain-specific heuristics?

+1...

this is cool How does the MVM avoid paradoxes with Types as Values and Expressions as Values?

having been work with both json and pb, and i'd say pb is pretty solid.

how is this better than PB?

i need this

indeed interesting

Why do LLMs struggle so much with recovering from early wrong turns in multi-turn conversations — even when all prior context is available and tokenized?

Is it due to the model's training distribution (mostly single-shot completions), the way context windows are encoded, or an architectural bottleneck?

Feels like there's no dynamic internal state that evolves over the conversation — only a repeated re-parsing of static history. Has anyone seen work on integrating memory/state mechanisms that allow belief revision within a session, not just regurgitation of past tokens?

could you clarify a bit how to - Split the knowledge into smaller knowledge domains - in this case? does that inform some semantic extraction or more manually?

This is such a cool idea, would love to hack a project sometime with multi-media types and map to knowledge graphs and feed that to agents :)

btw, Neo4j supports vector properties and building vector index out of it. It's supported in cocoindex to configure a vector index: https://cocoindex.io/docs/ops/storages#neo4j

Neo4j also supports building embedding leveraging more information in the graph in addition to single node's property: https://neo4j.com/docs/graph-data-science/current/machine-le... (It's hard to incrementally compute them, but users can still compute them after the graph is built)

looking forward to learn your thoughts :)

Entity resolution is a great topic and there’s lots of research in this area. This is something I’m looking into next. I’m thinking about

- Metadata based match (I’ve done that with search system in the past)

- Embedding base match (false positive is definite consideration

- Using knowledge graph itself to do entity resolution before feeding the entities to graph next

Add human in the loop to guide entity resolutions.

What do you think? Would love to learn your thoughts:)

In my understanding, there are two kinds of use cases potentially can be explored with knowledge graph.

- Structured data - this is probably more close to the use case you mention

- Unstructure data and extract relationship and build KG with natural language understanding - which is this article trying to explore. Here is a paper discussing about this https://arxiv.org/abs/2409.13731

In general it is an alternative way to establish connections with entities easily. And these relationships could help with discovery, recommendation and retrieval. Thanks @alexchantavy for sharing use-cases in security.

Would love to learn more from the community :)

nice article

this is super interesting would love to give it a try!

i'm on cursor, performance has gone done. thinking about windsurf

congrats on the launch!!

How does Mercury achieve near-conventional execution efficiency while preserving the declarative semantics of logic/functional programming? Curious how its mode and determinism system feeds into optimization.

nice!

How does GhostHub implement real-time synchronization between clients during media playback, and how is this affected by the current limitation where sync mode bypasses session password enforcement?

cool name

that's cute