
RaghuVaran
1.2K posts

RaghuVaran
@RaghuModupalli
Building Agents for Enterprises | Learning Inference engineering | Mechanical Engineer


On several coding and knowledge work evaluations, Opus 5 is the new state-of-the-art:






we had a significant security incident during evaluation of our models. we are sharing what we have learned so far. thanks to @huggingface for the partnership on this. openai.com/index/hugging-…


Gemini 3.6 Flash Leaks: Coming This Month - Google is targeting a late July launch for Gemini 3.6 Flash. - Gemini 3.6 Flash, ID gemini-3.6-flash-tiered, appeared in Antigravity a few minutes ago.

Today we’re launching Ramp Router. 3 years ago, we built an internal LLM router at @tryramp that powers AI products for 70,000 customers. Back then it was mostly about saving money. Now it feels obvious: the best model changes constantly. GPT, Claude, Gemini, Grok, Qwen, DeepSeek, Kimi, GLM - prices and capabilities move every week. So we’re opening up access to everyone. One OpenAI-compatible endpoint. The right model for every request. Lower cost without rewriting your app. Reserve access to use it.






I'm glad people still understand the importance of building high-quality retrieval systems in 2026, especially as the outer models/harnesses are getting better every day. Making agentic retrieval work in production doesn't necessarily require groundbreaking new techniques around retrieval or planning. This article shows that you do need to spend engineering time tuning chunking, synchronization, reranking, tool API design, permissioning, and more. Some interesting tidbits: * Concatenating onto existing Slack threads as one contiguous chunk with heuristics for determining additional relevant context * Real-time updates for Slack * Separate chunking/updates for codebases * The discovery that hybrid search works well (not like this was a super new realization, but always good to validate and understand the specific parameters they tweaked) * Having natural guardrails on which data sources are relevant for which projects Building simple retrieval is easy, building production retrieval is hard. We had to deal with a lot of these challenges when productionizing our own Index feature within LlamaParse.

Call me a factory worker the way I code my singletons













