Hacker News Reader

tifa2upon Show HN: vLLM Studio – A macOS app for using vLLM models

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

tifa2upon Ask HN: What are you working on? (February 2026)

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

tifa2upon Ask HN: Share your personal website

https://abdellatif.io

tifa2upon 28M Hacker News comments as vector embedding search dataset

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

tifa2upon Ask HN: What Are You Working On? (Nov 2025)

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

tifa2upon Ask HN: What Are You Working On? (Nov 2025)

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

tifa2upon Developers are choosing older AI models

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

tifa2upon Developers are choosing older AI models

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

tifa2upon Developers are choosing older AI models

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.

tifa2upon Ask HN: How to boost Gemini transcription accuracy for company names?

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.

tifa2upon Hacker News Front Page: what 26 hours of traffic got us

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

tifa2upon Production RAG: what I learned from processing 5M+ documents

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

tifa2upon Production RAG: what I learned from processing 5M+ documents

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

tifa2upon Production RAG: what I learned from processing 5M+ documents

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.

tifa2upon Production RAG: what I learned from processing 5M+ documents

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.

tifa2upon Production RAG: what I learned from processing 5M+ documents

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

tifa2upon Production RAG: what I learned from processing 5M+ documents

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

tifa2upon Production RAG: what I learned from processing 5M+ documents

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.

tifa2upon Production RAG: what I learned from processing 5M+ documents

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

tifa2upon SWE-Grep and SWE-Grep-Mini: RL for Fast Multi-Turn Context Retrieval

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

tifa2upon Ask HN: What are you working on? (July 2025)

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

tifa2upon iPhone 11 emulation done in QEMU

Noob question: can you install iOS apps using this?

tifa2upon Ask HN: How are you acquiring your first hundred users?

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.

tifa2upon Notion Mail Is Out

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

tifa2upon SwiftData

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

tifa2upon Tarteel – AI-powered Quran companion

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

tifa2upon Twitter Blew

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.

tifa2upon Levels.fyi 2022 Pay Report

How can you explain the drop of salaries despite inflation?

tifa2upon Show HN: Comp.fyi – Open compensation data by company and level

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

tifa2upon Ask HN: Who is hiring? (March 2018)

Alcatraz AI | https://alcatraz.ai | Palo Alto, CA | ONSITE, VISA FULLTIME

We're a venture-backed, Series A startup developing a new method for physical access point authentication. Similar to FaceID on iPhone X, the technology unlocks spaces only when it identifies the person in front of it has access. To achieve this, we use facial detection and recognition, 3D sensing and artificial intelligence to enable highly secure and frictionless entry into physical locations.

We're hiring full-stack, machine learning, and firmware engineers! You can find the job descriptions at https://www.alcatraz.ai/jobs

Benefits & Perks: - Competitive pay and equity - Flexible hours - Unlimited vacation - Fully-stocked kitchen - Team offsites - Premium medical, dental and vision insurance

If you find any of the positions interesting, drop me a line at ab@alcatraz.ai