tifa2up

Interesting project. Curious why Electrobun over Tauri here? Tauri has a much larger ecosystem and rust based for improved performance.

https://agentset.ai/

Open-source RAG infrastructure.Every team I talk to has the same experience: RAG works in the demo, breaks in production.

We handle ingestion through retrieval with optimizations baked in. 97.9% on HotpotQA vs 88.8% for standard RAG. Model-agnostic, 22+ file types, built-in citations, MCP server. MIT licensed.

https://github.com/agentset-ai/agentset

https://abdellatif.io

https://agentset.ai/leaderboard/embeddings good rundown of other open-source embedding models

Right now it's single shot, we're looking into building an "Agentic Retrieval" based on Claude ADK. tbd how it'll work

I'm building https://github.com/agentset-ai/agentset, RAG as a service that works quite well out of the box.

We achieve this performance by baking in the best practices before any tweaking

Think it varies by use case. It didn't do well with long context

For large context (up to 100K tokens in some cases). We found that GPT-5: a) has worse instruction following; doesn't follow the system prompt b) produces very long answers which resulted in a bad ux c) has 125K context window so extreme cases resulted in an error

We tried GPT-5 for a RAG use case, and found that it performs worse than 4.1. We reverted and didn't look back.

Don't solve it on the STT level. Get the raw transcription from Gemini then pass the output to an LLM to fix company names and other modifications.

Happy to share more details if helpful.

Yes, we got 187 self-serve users (all on the free plan). And are in talks with an enterprise now.

You typically add a lot of metadata with each chunk text to be able to filter it, and do to include in the citations. Injecting metadata means that you see what metadata adds helpful context to the LLM, and when you pass the results to the LLM you pass them in a format like this:

Title: ... Author: ... Text: ...

for each chunk, instead of just passing the text

Quite a decent hit. Local models don't perform very well in long contexts. We're planning to support a local-only offline set-up for people to host w/o additional dependencies

OP. The way you improve it is move away from single shot semantic/keyword search and have an agentic system that can evaluate results and do follow-up queries.

OP. We migrated to GPT-5 when it came out but found that it performs worse than 4.1 when you pass lots of context (up to 100K tokens in some cases). We found that it:

a) has worse instruction following; doesn't follow the system prompt b) produces very long answers which resulted in a bad ux c) has 125K context window so extreme cases resulted in an error

Again, these were only observed in RAG when you pass lots of chunks, GPT-5 is probably a better model for other taks.

text similarity finds items that closely match. Reranking my select items that are less semantically "similar" but are more relevant to the query.

OP. Reranking is a specialized LLM that takes the user query, and a list of candidate results, then re-sets the order based on which ones are more relevant to the query.

Here's sample code: https://docs.cohere.com/reference/rerank

OP here. We've been working on agentset.ai full-time for 2 months. The product now gets you something working quite well out of the box. Better than most people with no experience in RAG (I'd say so with confidence).

Ingestion + Agentic Search are two areas that we're focused on in the short term.

OP here. rerankers are finetuned small models, they're cheap and very fast compared to an additional GPT-5 call.

Searched for 'hi' and it took 166s to return a response using this model: https://pasteboard.co/oB4VqVC5FGkl.png

Claude Code took 0.1s, Cursor CLI 19s

Working on an open source RAG-as-a-service platform that bakes in the best practices and continues to evolve them so that customers get the best retrieval without having to go deep or stay up to date.

Our flagship feature is Agentic RAG, which is quite difficult to build from scratch.

https://agentset.ai https://github.com/agentset-ai/agentset

Noob question: can you install iOS apps using this?

Founder of https://agentset.ai here. We found lots of success posting on the r/RAG subreddit. We've been working with RAG for sometime so have enough experience to answer other people's questions and establish credibility by dropping our link.

For anyone who used both, how does this compare to superhuman?

Noob here, how does SwiftData compare to Redux (as a paradigm)?

It uses Nvidia's Riva and NeMo: https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31...

ex-Twitter engineer here. +1 to this, having a bunch of bot followers wouldn't get you verified. There are internal metrics that show what % of followers are likely bots and other metrics are taken into consideration.

How can you explain the drop of salaries despite inflation?

Love the visualization you're doing with how salary scales at different companies. It's often overlooked

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