tifa2up

8 ms·

1.
by tifa2up 6mo ago
Interesting project. Curious why Electrobun over Tauri here? Tauri has a much larger ecosystem and rust based for improved performance.
2.
Gemini 2 Is the Top Model for Embeddings(agentset.ai)
5 points by tifa2up 6mo ago|0 comments
3.
The Next Coding Interface Is a Canvas(blog.abdellatif.io)
2 points by tifa2up 7mo ago|0 comments
4.
Hackable Software(blog.abdellatif.io)
1 points by tifa2up 7mo ago|0 comments
5.
by tifa2up 7mo ago
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 Hotp
6.
Do things like Oh My OpenCode work?(github.com)
1 points by tifa2up 8mo ago|0 comments
7.
Show HN: EasyClaw – Seamless Installation for OpenClaw(easyclaw.com)
3 points by tifa2up 8mo ago|3 comments
8.
by tifa2up 8mo ago
https://abdellatif.io
9.
Semantic Thesaurus(thesaurus.abdellatif.io)
2 points by tifa2up 9mo ago|0 comments
10.
Vector DB Comparison(agentset.ai)
1 points by tifa2up 9mo ago|0 comments
11.
Always Do a Work Trial(blog.abdellatif.io)
2 points by tifa2up 10mo ago|0 comments
12.
by tifa2up 10mo ago
https://agentset.ai/leaderboard/embeddings good rundown of other open-source embedding models
13.
Opus 4.5 is the best model for RAG(agentset.ai)
2 points by tifa2up 10mo ago|0 comments
14.
Gemini 3 vs. GPT 5.1 for RAG(agentset.ai)
4 points by tifa2up 10mo ago|0 comments
15.
Embedding Model Leaderboard(agentset.ai)
1 points by tifa2up 10mo ago|0 comments
16.
by tifa2up 10mo ago
Right now it's single shot, we're looking into building an "Agentic Retrieval" based on Claude ADK. tbd how it'll work
17.
by tifa2up 10mo ago
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
18.
Best Reranker for RAG: We tested the top models(agentset.ai)
1 points by tifa2up 10mo ago|0 comments
19.
by tifa2up 11mo ago
Think it varies by use case. It didn't do well with long context
20.
by tifa2up 11mo ago
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 extre
21.
by tifa2up 11mo ago
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.
22.
Reranker Leaderboard(agentset.ai)
1 points by tifa2up 11mo ago|0 comments
23.
by tifa2up 11mo ago
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.
24.
by tifa2up 11mo ago
Yes, we got 187 self-serve users (all on the free plan). And are in talks with an enterprise now.
25.
Hacker News Front Page: what 26 hours of traffic got us(blog.abdellatif.io)
7 points by tifa2up 11mo ago|2 comments
26.
by tifa2up 11mo ago
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
27.
by tifa2up 11mo ago
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
28.
by tifa2up 11mo ago
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.
29.
by tifa2up 11mo ago
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
30.
by tifa2up 11mo ago
text similarity finds items that closely match. Reranking my select items that are less semantically "similar" but are more relevant to the query.

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