2. Context Engineering 2.0 Traces the 20+ year evolution of context engineering, reframing it as a fundamental challenge in human-machine communication spanning from primitive computing (Era 1.0) to today’s intelligent agents (Era 2.0) and beyond.
LLMS
-
Top AI Research Papers: Agents, LLMs, and Mathematical Exploration
By
–
Top AI Papers of the Week (Nov 3 – 9): – Tool-to-Agent Retrieval
– Context Engineering 2.0
– Mathematical Exploration at Scale
– Petri Dish Neural Cellular Automata
– Enhancing Long-Term Memory in LLMs
– Diffusion LMs are Super Data Learners
– Towards Robust Mathematical -
LlamaIndex Summary Index Framework API Reference
By
–
Check out the LlamaIndex summary index: https://
developers.llamaindex.ai/python/framewo
rk-api-reference/indices/summary/
… -
RAG Systems: Indexing and Retrieval Are Not the Same
By
–
Here's a common misconception about RAG! Most people think RAG works like this: index a document → retrieve that same document. But indexing ≠ retrieval. What you index doesn't have to be what you feed the LLM. Once you understand this, you can build RAG systems that
-
Complete n8n Guide: JSON, Nodes, AI Integrations
By
–
By far the best n8n guide I’ve seen. Nate’s worked with 1,000s of users and just wrapped everything he’s learned into this 36-page guide! → Clear lessons on JSON, nodes and debugging
→ Cloud vs self-host setup
→ AI integrations & LLM chains Totally free. Link in ↓ -
Share insights on LLMs, AI Agents and Machine Learning
By
–
If you found it insightful, reshare with your network.
— Akshay 🚀 (@akshay_pachaar) 9 novembre 2025
Find me → @akshay_pachaar✔️
For more insights and tutorials on LLMs, AI Agents, and Machine Learning!https://t.co/iYLe5uKmVfIf you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
-
Chinese AI Models Optimized for Cheaper GPU Hardware
By
–
Side effect of blocking Chinese firms from buying the best NVIDIA cards: top models are now explicitly being trained to work well on older/cheaper GPUs. The new SoTA model from @Kimi_Moonshot uses plain old BF16 ops (after dequant from INT4); no need for expensive FP4 support.
-
Cheaper access to best AI models for everyone now available
By
–
This is great news for the rest of us. It means cheaper access to the best available models for everyone. 非常感谢 @Kimi_Moonshot
! 😀 -
PhysToolBench: Testing MLLMs’ Physical Tool Understanding
By
–
Can AI truly understand tools like humans do? Researchers introduce PhysToolBench, the first benchmark testing MLLMs’ grasp of physical tools—from recognizing and explaining how they work to creatively inventing new ones when none are available. Tests on 32 leading models show
-
DLER Method Cuts AI Output Length by 70% While Improving Accuracy
By
–
Can shorter answers be smarter? Researchers say yes.
— 机器之心 JIQIZHIXIN (@jiqizhixin) 9 novembre 2025
New method DLER pairs simple truncation with better RL tricks to cut output length by >70% while improving accuracy over prior baselines, such as batch reward normalization, higher clipping, dynamic sampling.
It also scales… pic.twitter.com/75UXfQ0a6KCan shorter answers be smarter? Researchers say yes. New method DLER pairs simple truncation with better RL tricks to cut output length by >70% while improving accuracy over prior baselines, such as batch reward normalization, higher clipping, dynamic sampling. It also scales