RIP fine-tuning ☠️
This new Stanford paper just killed it.
It’s called 'Agentic Context Engineering (ACE)' and it proves you can make models smarter without touching a single weight.
Instead of retraining, ACE evolves the context itself.
The model writes, reflects, and edits
Holy shit... this might be the next big paradigm shift in AI. 🤯
Tencent + Tsinghua just dropped a paper called Continuous Autoregressive Language Models (CALM) and it basically kills the “next-token” paradigm every LLM is built on.
Instead of predicting one token at a time,
Holy shit... Tencent researchers just killed fine-tuning AND reinforcement learning in one shot 😳
They call it Training-Free GRPO (Group Relative Policy Optimization).
Instead of updating weights, the model literally learns from 'its own experiences' like an evolving memory
Market research firms are cooked 😳
PyMC Labs + Colgate just published something wild. They got GPT-4o and Gemini to predict purchase intent at 90% reliability compared to actual human surveys.
Zero focus groups. No survey panels. Just prompting.
The method is called Semantic
🚨 Holy shit...Meta just rewrote how Transformers think.
They built something called The Free Transformer and it breaks the core rule every GPT model has lived by since 2017.
For 8 years, Transformers have been blindfolded forced to guess the next token one at a time, no inner
🚨 RIP “Prompt Engineering.”
The GAIR team just dropped Context Engineering 2.0 — and it completely reframes how we think about human–AI interaction.
Forget prompts. Forget “few-shot.” Context is the real interface.
Here’s the core idea:
“A person is the sum of their
LLMs are not all created equal.
BERT → The reader
GPT → The writer
LLaMA → The open challenger
PaLM → The few-shot master
Gemini → The multimodal beast
Mistral → The efficient rebel
DeepSeek → The reasoning genius
Here's how to use them in 7 minutes ↓
HuggingFace just dropped 9 free AI courses and they’re insanely good.
LLMs, agents, vision, 3D, audio, gaming all hands-on.
All open-source. All free.
Here’s what’s inside ↓
Holy shit… Harvard just proved your base model might secretly be a genius. 🤯
Their new paper “Reasoning with Sampling” shows that you don’t need reinforcement learning to make LLMs reason better.
They used a 'Markov chain sampling trick' that simply re-samples from the
Fuck it.
I'm sharing the 10 Gemini prompts that built my entire SaaS from scratch.
These prompts literally replaced my CTO, lead dev, and product manager.
Comment 'send' and I'll DM you the complete Gemini guide to master it:
You don’t need a PhD to understand Retrieval-Augmented Generation (RAG).
It’s how AI stops hallucinating and starts thinking with real data.
And if you’ve ever asked ChatGPT to “use context” you’ve wished for RAG.
Let me break it down in plain English (2 min read):
Holy shit… Meta might’ve just solved self-improving AI 🤯
Their new paper SPICE (Self-Play in Corpus Environments) basically turns a language model into its own teacher no humans, no labels, no datasets just the internet as its training ground.
Here’s the twist: one copy of
24 hours ago, xAI launched Grok 4…
And it's SHOCKINGLY good at coding, research, and creativity.
I built 5 insane projects in a day.
Here's what Grok 4 can actually do ↓