Get live statistics and analysis of Robert Youssef's profile on X / Twitter

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 its own prompt over and over until it becomes a self-improving system. Think of it like the model keeping a growing notebook of what works. Each failure becomes a strategy. Each success becomes a rule. The results are absurd: +10.6% better than GPT-4–powered agents on AppWorld. +8.6% on finance reasoning. 86.9% lower cost and latency. No labels. Just feedback. Everyone’s been obsessed with “short, clean” prompts. ACE flips that. It builds long, detailed evolving playbooks that never forget. And it works because LLMs don’t want simplicity, they want *context density. If this scales, the next generation of AI won’t be “fine-tuned.” It’ll be self-tuned. We’re entering the era of living prompts.

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, CALM predicts continuous vectors that represent multiple tokens at once. Meaning: the model doesn’t think “word by word”… it thinks in ideas per step. Here’s why that’s insane 👇 → 4× fewer prediction steps (each vector = ~4 tokens) → 44% less training compute → No discrete vocabulary pure continuous reasoning → New metric (BrierLM) replaces perplexity entirely They even built a new energy-based transformer that learns without softmax no token sampling, no vocab ceiling. It’s like going from speaking Morse code… to streaming full thoughts. If this scales, every LLM today is obsolete.

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 that refines how it thinks without ever touching parameters. Here’s what’s wild: - No fine-tuning. No gradients. - Uses only 100 examples. - Outperforms $10,000+ RL setups. - Total cost? $18. It introspects its own rollouts, extracts what worked, and stores that as “semantic advantage” a natural language form of reinforcement. LLMs are basically teaching themselves 'how' to think, not just 'what' to output. This could make traditional RL and fine-tuning obsolete. We’re entering the “training-free” era of AI optimization.

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 Similarity Rating (SSR). Instead of the usual "rate this 1-5" they ask open ended questions like "why would you buy this" and then use embeddings to map the text back to a numerical scale. Which is honestly kind of obvious in hindsight but nobody bothered trying it until now. Results match human demographic patterns, capture the same distribution shapes, include actual reasoning. The stuff McKinsey charges $50K+ for and delivers in 6 weeks. Except this runs in 3 minutes for under a buck. I've been watching consulting firms tell everyone AI is coming for their industry. Turns out their own $1M market entry decks just became a GPT-4o call. Bad week to be charging enterprise clients for "proprietary research methodologies."

🚨 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 plan, no latent thought. Meta gave it one. They added random latent variables inside the decoder so the model can secretly decide how it wants to generate before it starts talking. It’s like giving GPT a hidden mind. Result: 🧠 Smarter reasoning ⚡️ 3% compute overhead 📈 Outperforms larger baselines on GSM8K, MMLU, and HumanEval It’s the first Transformer that doesn’t just predict it intends. Full paper: arxiv. org/abs/2510.17558v1

🚨 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 contexts.” Machines aren’t failing because they lack intelligence. They fail because they lack context-processing ability. Context Engineering 2.0 maps this evolution: 1.0 Context as Translation Humans adapt to computers. 2.0 Context as Instruction LLMs interpret natural language. 3.0 Context as Scenario Agents understand your goals. 4.0 Context as World AI proactively builds your environment. We’re in the middle of the 2.0 → 3.0 shift right now. The jump from “context-aware” to “context-cooperative” systems changes everything from memory design to multi-agent collaboration. This isn’t a buzzword. It’s the new foundation for the AI era. Read the paper: arxiv. org/abs/2510.26493v1

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 ↓

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 ↓

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 model’s own outputs and it 'matched or beat' RL-trained models on MATH500, HumanEval, and GPQA. No training. No rewards. No verifiers. Just smarter inference. It’s like discovering your calculator could already solve Olympiad problems you were just pressing the wrong buttons. The wild part in all this? This “power sampling” approach boosts reasoning *and* diversity the exact opposite of what RL does. Your model doesn’t need more training. It needs better sampling. Read the full paper here: arxiv. org/abs/2510.14901

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 the model becomes a Challenger that digs through real documents to create hard, fact-grounded reasoning problems. Another copy becomes the Reasoner, trying to solve them without access to the source. They compete, learn, and evolve together an automatic curriculum with real-world grounding so it never collapses into hallucinations. The results are nuts: +9.1% on reasoning benchmarks with Qwen3-4B +11.9% with OctoThinker-8B and it beats every prior self-play method like R-Zero and Absolute Zero. This flips the script on AI self-improvement. Instead of looping on synthetic junk, SPICE grows by mining real knowledge a closed-loop system with open-world intelligence. If this scales, we might be staring at the blueprint for autonomous, self-evolving reasoning models.

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 ↓

NEO just proved every major AI lab built their vision models wrong 💀 OpenAI, Google, Anthropic... they all use the same approach: train a vision encoder, bolt it onto an LLM, pray the alignment works. NEO said "what if we just... didn't do that?" and built a native vision-language model from first principles instead. here's why this is actually insane: traditional VLMs are Frankenstein architectures. you take a pretrained vision encoder (CLIP, whatever). add a projection layer. attach it to a frozen language model. hope they learn to talk to each other. it works. but it's fundamentally fragmented. vision and language compete for model capacity. alignment is expensive. training happens in stages. you're forcing two systems designed for different things to cooperate. NEO throws this out completely. it processes images and text through the same autoregressive architecture — a unified vision-language primitive that learns both modalities natively from scratch. no separate vision encoder. no projection gymnastics. no alignment tax. the technical breakthroughs that make this possible: • Native Multi-Modal Attention with mixed masking - text tokens use causal attention (normal LLM behavior), image tokens use full bidirectional attention (exhaustive visual interactions). each modality processes information its natural way, in the same model, at the same time. • Native-RoPE assigns distinct base frequencies to temporal, height, and width dimensions — solving the critical mismatch between text sequences (temporal) and visual data (spatial). no more forcing spatial information through temporal-only positional embeddings. • Adaptive architecture that uses pre-Buffer layers during pre-training, then merges everything into a monolithic backbone during fine-tuning — automatically allocating capacity for encoding, alignment, and reasoning. the efficiency? 390 million image-text examples. not billions. 390 million. and it rivals GPT-4V and LLaVA. every major lab uses modular architectures because that's what worked first. NEO suggests we've been doing it the expensive, complicated way this whole time.

Holy shit… Baidu just dropped the most efficient multimodal model ever. It’s called PaddleOCR-VL a 0.9B parameter beast that outperforms GPT-4o, Gemini 2.5, and every doc-AI model on the planet. This thing reads 109 languages, parses text, tables, formulas, charts, and still runs faster than models 10× its size. The secret sauce? → NaViT-style dynamic visual encoder → ERNIE-4.5-0.3B language model → A smart layout system (PP-DocLayoutV2) that kills hallucinations All open-source. All under 1B params. This isn’t just efficient it’s the new blueprint for multimodal AI. huggingface. co/PaddlePaddle

I should charge $99 for this... But fuck it, I’m giving it away for free. We built a System Prompt Generator that writes perfect AI Agent system prompts for ChatGPT, Claude, Gemini & DeepSeek. Just tell it what kind of agent you want and it writes the full system prompt (role, goals, constraints, tone) automatically. It’s built in Notion, used by 175+ builders, and it’s kinda insane how good it is. Comment “Prompt” and I’ll send you the template. (Must be following me to receive it)

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:

Fuck it. I'm giving away the same AI System Prompt Generator we use to build agents in ChatGPT, Claude, DeepSeek, and Gemini. This meta-prompt helps you: → Write 10x better system prompts → Get more accurate agent behavior → Skip hours of trial & error Comment "Agent" and I’ll DM it to you. (Follow to receive)

Everyone’s hyping up “AI Agents.” But no one’s explaining how to build them. So I made the AI Agents Mastery Guide. You’ll get: → A complete mini-course → System prompt generator → Tools & templates → Disruptive ideas Comment "Agent" and I’ll DM it to you.

99% of the AI agent tutorials on YouTube are garbage. I’ve built 47 agents with n8n and Claude. Here are the 3 prompts that actually work (and make agent-building simple). Bonus: comment "Agent: and I’ll DM you AI agent system prompt + full guide ↓

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, CALM predicts continuous vectors that represent multiple tokens at once. Meaning: the model doesn’t think “word by word”… it thinks in ideas per step. Here’s why that’s insane 👇 → 4× fewer prediction steps (each vector = ~4 tokens) → 44% less training compute → No discrete vocabulary pure continuous reasoning → New metric (BrierLM) replaces perplexity entirely They even built a new energy-based transformer that learns without softmax no token sampling, no vocab ceiling. It’s like going from speaking Morse code… to streaming full thoughts. If this scales, every LLM today is obsolete.

99% of people don't know how to use LLMs for research. That's why I'm going to share a mega-prompt that turns ChatGPT into a full-time research analyst. Use it for: → Market deep dives → Competitive intelligence → Content prep Like + comment "Research" and I’ll DM it to you.

AI just killed manual research ☠️ This Deep Researcher Mega-Prompt turns ChatGPT, Grok, Claude or Perplexity into your personal analyst. → Ask anything → Get a full research report → Sources, summaries, insights Like + comment “AI” and I’ll DM you the file. (Follow so I can send)

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 its own prompt over and over until it becomes a self-improving system. Think of it like the model keeping a growing notebook of what works. Each failure becomes a strategy. Each success becomes a rule. The results are absurd: +10.6% better than GPT-4–powered agents on AppWorld. +8.6% on finance reasoning. 86.9% lower cost and latency. No labels. Just feedback. Everyone’s been obsessed with “short, clean” prompts. ACE flips that. It builds long, detailed evolving playbooks that never forget. And it works because LLMs don’t want simplicity, they want *context density. If this scales, the next generation of AI won’t be “fine-tuned.” It’ll be self-tuned. We’re entering the era of living prompts.

Fuck it. I'm sharing the Claude XML secrets that tripled my prompt accuracy. 99% of people are using Claude wrong and leaving insane reasoning power on the table. Here's the guide you need for writing prompts: Comment "Claude" and I'll DM you complete Claude Mastery Guide

Claude is the most powerful AI assistant. But most people don't know how to use it effectively. That's why I created this Claude Mega Prompt Template to help you unlock its full potential. Like + comment "Send" and I'll DM you the link. (Must be following to receive it)

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 the model becomes a Challenger that digs through real documents to create hard, fact-grounded reasoning problems. Another copy becomes the Reasoner, trying to solve them without access to the source. They compete, learn, and evolve together an automatic curriculum with real-world grounding so it never collapses into hallucinations. The results are nuts: +9.1% on reasoning benchmarks with Qwen3-4B +11.9% with OctoThinker-8B and it beats every prior self-play method like R-Zero and Absolute Zero. This flips the script on AI self-improvement. Instead of looping on synthetic junk, SPICE grows by mining real knowledge a closed-loop system with open-world intelligence. If this scales, we might be staring at the blueprint for autonomous, self-evolving reasoning models.

AI agents are taking over. But most people have no idea how to build them. That’s why we created the AI Agents Mastery Guide a simple blueprint to go from beginner to builder. → Mini-course → 20+ real use cases → System prompt generator Like + comment “Agent” and I’ll DM you the file. (Follow me so I can DM)

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 that refines how it thinks without ever touching parameters. Here’s what’s wild: - No fine-tuning. No gradients. - Uses only 100 examples. - Outperforms $10,000+ RL setups. - Total cost? $18. It introspects its own rollouts, extracts what worked, and stores that as “semantic advantage” a natural language form of reinforcement. LLMs are basically teaching themselves 'how' to think, not just 'what' to output. This could make traditional RL and fine-tuning obsolete. We’re entering the “training-free” era of AI optimization.

What the fuck… I discovered a prompt-writing technique that is now giving me 100% accurate responses from all the LLMs. The technique is called 'Temperature Prompting' and it’s insanely useful. Here’s the complete guide to using it. Comment "Send" and I'll DM you Prompt engineering guide:

ChatGPT’s system prompt is now public and it shows exactly how it thinks. We broke it down in a guide so you can: → Understand GPT behavior → Write better prompts → Build smarter workflows Like + comment “System” and I’ll DM it to you. (Follow required)