LLMs

large language models, foundation models, transformers

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Scoured 205 posts in 7.1 ms

DataXflowGen for GenAI-driven model generation

 🤖AI  Content type: Academic
nature.com·

The Transformer Architecture: A Step-by-Step Guide

 🤖AI  Content type: Blog

Show HN: LLM memory without context bleed; 100% precision vs. <10% vector search

 🟢OpenAI

4DO-DETR for otitis media detection

 🤖AI  Content type: Academic
nature.com·

Melanie Mitchell: What We Get Wrong About AI

 🤖AI

Causal Semantic Alignment for LLM-based Time Series Forecasting

 📈Prediction Markets  Content type: Academic
arxiv.org·

Issue #390 - The ML Engineer 🤖

 🤖AI  Content type: News  Content type: Blog

NVIDIA's Cosmos 3: The World's First Fully Open AI Omnimodel

 🤖AI  Content type: News
aimagazine.com·

Prompt Engineering Is Dead. Process Engineering Is the New AI Skill.

 🧠AGI  Content type: Blog
medium.com
·

New comment by aldoakhanov in "Ask HN: Who wants to be hired? (June 2026)"

 🇨🇳Qwen

Mixture-of-Experts (MoE), Explained: Why “Active Parameters” Decide What Runs on Your Machine

 🇨🇳Qwen

Research Is Not Engineering at a Slower Speed

 🤖AI

Why LLMs hallucinate?

 🤖AI  Content type: Blog
medium.com
·

Context engineering for AI agents: the infrastructure behind every decision

 🕵️AI Agents  Content type: Blog
redis.io·

Automated doubt 🤔, open code review 📝, how LLMs really work 🔨

 🇨🇳Qwen
tldr.tech·

The Bill Arrives: How to Manage Agentic AI Costs at Scale

 🤖AI  Content type: Blog
cockroachlabs.com·

A wild idea: Abstract reality using ontology

 🕵️AI Agents  Content type: Discussion

Prompt Engineering is Dead? Not Really. Here’s What Every AI Engineer Must Learn Next.

 🕵️AI Agents  Content type: Blog
medium.com
·

CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching

 🤖AI  Content type: Academic
arxiv.org·

Context Engineering Is Eating Prompt Engineering

 🕵️AI Agents  Content type: Blog
medium.com
·
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