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1.
SubQ 1.1 Small (news.ycombinator.com)
2.
PixelRAG beats text parsers on accuracy and cuts AI agent token costs 10x (venturebeat.com)
3.
Rethinking search as code generation (news.ycombinator.com)
4.
Enforce AI at the Intelligence Layer — or Expect Your AI Agents to Go Rogue (feeds.feedburner.com)
5.
MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26% (venturebeat.com)
6.
MIT's MeMo lets teams swap in a better LLM without retraining — and performance jumps 26% (venturebeat.com)
7.
Why prompt debt, retrieval debt, and evaluation debt are quietly reshaping enterprise AI risk (venturebeat.com)
8.
Your AI agents need a terminal, not just a vector database (venturebeat.com)
9.
A 0.12% parameter add-on gives AI agents the working memory RAG can't (venturebeat.com)
10.
Context architecture is replacing RAG as agentic AI pushes enterprise retrieval to its limits (venturebeat.com)
11.
New agents.txt file found on DreamHost (news.ycombinator.com)
12.
Gemini API File Search is now multimodal (news.ycombinator.com)
13.
The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next (venturebeat.com)
14.
The AI scaffolding layer is collapsing. LlamaIndex's CEO explains what survives. (venturebeat.com)
15.
The retrieval rebuild: Why hybrid retrieval intent tripled as enterprise RAG programs hit the scale wall (venturebeat.com)
16.
I Won a Championship That Doesn't Exist (news.ycombinator.com)
17.
RAG precision tuning can quietly cut retrieval accuracy by 40%, putting agentic pipelines at risk (venturebeat.com)
18.
Watch the Lego ‘Project Hail Mary’ Set (Almost) Go to Space (gizmodo.com)
19.
Show HN: Continual Learning with .md (news.ycombinator.com)
20.
Chroma Context-1: Training a Self-Editing Search Agent (news.ycombinator.com)
21.
I built an AI receptionist for a mechanic shop (news.ycombinator.com)
22.
How LinkedIn replaced five feed retrieval systems with one LLM model, at 1.3 billion-user scale (venturebeat.com)
23.
Agents need vector search more than RAG ever did (venturebeat.com)
24.
Enterprises are measuring the wrong part of RAG (venturebeat.com)
25.
This tree search framework hits 98.7% on documents where vector search fails (venturebeat.com)
26.
Why MongoDB thinks better retrieval — not bigger models — is the key to trustworthy enterprise AI (venturebeat.com)
27.
Databricks' Instructed Retriever beats traditional RAG data retrieval by 70% — enterprise metadata was the missing link (venturebeat.com)
28.
GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs (venturebeat.com)
29.
From shiny object to sober reality: The vector database story, two years later (venturebeat.com)
30.
Energy and memory: A new neural network paradigm (sciencedaily.com)
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