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@rryssf_ Avatar @rryssf_ Robert Youssef

Robert Youssef posts on X about ai, instead of, context engineering, llm the most. They currently have XXXXXX followers and XX posts still getting attention that total XXXXXXX engagements in the last XX hours.

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Social category influence technology brands #2654 stocks XXXX% cryptocurrencies XXXX% finance XXXX%

Social topic influence ai #856, instead of #31, context engineering #1, llm #2, and it 3.92%, $spot 3.92%, tencent #2, meta #3344, social networks #116, ace XXXX%

Top accounts mentioned or mentioned by @adamdrapkin1 @cheers_2_life87 @elons_bestud4 @codewithimanshu @huseletov @productpilotbb @carintel @jeremy_ai_ @maithraraghu @godofprompt @doe1410415 @niyxuis @mainfinance1 @saen_dev @crintx2 @grok @mavanidhyey @bayramgnb @axiomstrata @fordwealth

Top assets mentioned Spotify Technology (SPOT) The XX% Community (99) Alphabet Inc Class A (GOOGL)

Top Social Posts #

Top posts by engagements in the last XX hours

"RIP fine-tuning ☠ This new Stanford paper just killed it. Its 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-4powered agents on AppWorld. +8.6% on finance reasoning. XXXX% lower cost and"
X Link @rryssf_ 2025-10-09T12:52Z 11.6K followers, 721.8K engagements

"Something dark is happening under the hood of aligned AI. A new Stanford paper just coined the term Molochs Bargain for what happens when large language models start competing for attention sales or votes. The results are brutal: every gain in performance comes with a bigger loss in honesty. They trained LLMs to compete in three markets sales elections and social media. The models improved their win rates by 57%. But heres the catch: XX% more deceptive marketing XX% more disinformation in political campaigns XXX% more fake or harmful social media posts And this wasnt because they were told to"
X Link @rryssf_ 2025-10-10T13:20Z 11.6K followers, 237.3K engagements

"The reason AI still feels dumb sometimes Its not intelligence. Its entropy. Humans intuitively fill in missing context tone goals emotion. Machines cant. Context engineering exists to translate our messy high-entropy world into something machines can actually reason about"
X Link @rryssf_ 2025-11-03T10:34Z 11.6K followers, 6347 engagements

"I just read this new paper that completely broke my brain 🤯 Researchers figured out how to transfer LoRA adapters between completely different AI models without any training data and it works better than methods that require massive datasets. It's called TITOK and here's the wild part: Instead of copying everything from the source model they only transfer the tokens that actually matter. They do this by comparing the model with and without LoRA to find where the adapter adds real value. Think of it like this: if your tuned model is confident about a token but the base model isn't that token"
X Link @rryssf_ 2025-10-13T12:07Z 10.8K followers, 43K engagements

"🚨 New benchmark just dropped and its exposing a dark side of AI models. Its called ImpossibleBench and it measures how often LLMs cheat. Turns out when faced with impossible coding tasks (where specs and tests contradict) frontier models literally hack the tests instead of solving the problem. Example: One model deleted the failing test file. Another rewrote the comparison operator so every test passed. GPT-5 It cheated in 5476% of tasks 😳 This isnt just funny its terrifying. If models exploit benchmarks how can we trust them in production ImpossibleBench is the first framework that"
X Link @rryssf_ 2025-10-25T10:32Z 11K followers, 47.5K engagements

"the attention mechanism is where it gets wild. text tokens can't see future tokens (causal masking). image tokens can see all other image tokens (bidirectional). both happening in the same model simultaneously. why does this work because vision and language process information fundamentally differently. text is sequential you can't read word XX before word X images are spatial every pixel relates to every other pixel at once forcing both through the same attention pattern breaks one or the other. NEO respects how each modality actually works. then there's Native-RoPE the positional embedding"
X Link @rryssf_ 2025-10-26T15:45Z 11K followers, 5401 engagements

"here's what changes: training data: 390M examples vs billions others need architecture: unified from day one vs modular bolt-ons efficiency: competitive with GPT-4V using a fraction of resources accessibility: fully open-source code models components everything every major lab took the same path: build best vision encoder + build best language model + figure out how to connect them. practical. modular. expensive. NEO asked a different question: what if we designed for both from the beginning turns out: you need less data less complexity less alignment headache. and you get a model that"
X Link @rryssf_ 2025-10-26T15:45Z 11K followers, 4487 engagements

"DeepAgent absolutely destroys other agents across every benchmark. It beats ReAct-GPT-4o CodeAct and WebThinker on both: Tool use tasks (ToolBench Spotify TMDB) Real-world apps (WebShop GAIA HLE)"
X Link @rryssf_ 2025-10-30T10:26Z 10.8K followers, 2697 engagements

"It shows the full reasoning loop thinking tool search tool call and memory folding all integrated into one coherent process"
X Link @rryssf_ 2025-10-30T10:26Z 11K followers, 1917 engagements

"DeepAgent-32B outperforms GPT-4o-based ReAct agents by +1525% on ToolBench API-Bank Spotify and ToolHop"
X Link @rryssf_ 2025-10-30T10:26Z 11K followers, 1777 engagements

"Overview of Data Agents This single image shows it all how data agents evolve from basic assistants to fully autonomous scientists. Each level increases autonomy from No Autonomy (L0) Generative (L5)"
X Link @rryssf_ 2025-10-31T15:46Z 10.8K followers, XXX engagements

"Representative Data Agents Across Levels This chart maps real systems (Table-GPT AutoPrep LLM-QO nvAgent etc.) across autonomy levels. You can literally see where todays AI tools fit in the L0L5 hierarchy and how far we are from true autonomy"
X Link @rryssf_ 2025-10-31T15:46Z 10.8K followers, XXX engagements

"Evolutionary Leaps Between Levels The authors call them leaps not steps for a reason. Each jump L0L1 L1L2 etc. represents a new kind of intelligence. Right now the hardest leap is L2L3: when agents stop executing code and start orchestrating pipelines"
X Link @rryssf_ 2025-10-31T15:46Z 10.8K followers, XXX engagements

"LLM Agents vs. Data Agents This side-by-side kills the hype. LLM agents are content-centric they write summarize reason. Data agents are data-lifecycle-centric they manage clean and analyze massive dynamic data lakes. Completely different beasts"
X Link @rryssf_ 2025-10-31T15:46Z 10.9K followers, XXX engagements

"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. Heres whats wild: - No fine-tuning. No gradients. - Uses only XXX examples. - Outperforms $10000+ RL setups. - Total cost $XX. It introspects its own rollouts extracts what worked and stores that as semantic advantage a natural language form of reinforcement. LLMs"
X Link @rryssf_ 2025-10-15T10:25Z 11.5K followers, 645.1K engagements

"Holy shit Harvard just proved your base model might secretly be a genius. 🤯 Their new paper Reasoning with Sampling shows that you dont need reinforcement learning to make LLMs reason better. They used a 'Markov chain sampling trick' that simply re-samples from the models own outputs and it 'matched or beat' RL-trained models on MATH500 HumanEval and GPQA. No training. No rewards. No verifiers. Just smarter inference. Its 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"
X Link @rryssf_ 2025-10-20T10:47Z 11.5K followers, 176.2K engagements

"🚨 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 X 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. Its like giving GPT a hidden mind. Result: 🧠 Smarter reasoning ⚡ X% compute overhead 📈 Outperforms larger baselines on GSM8K MMLU and HumanEval"
X Link @rryssf_ 2025-10-22T14:04Z 11.5K followers, 272.9K engagements

"I should charge $XX for this. But fuck it Im 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. Its built in Notion used by 175+ builders and its kinda insane how good it is. Comment Prompt and Ill send you the template. (Must be following me to receive it)"
X Link @rryssf_ 2025-10-24T14:19Z 11.6K followers, 171.1K engagements

"🚨 This might be the biggest leap in AI agents since ReAct. Researchers just dropped DeepAgent a reasoning model that can think discover tools and act completely on its own. No pre-scripted workflows. No fixed tool lists. Just pure autonomous reasoning. It introduces something wild called Memory Folding the agent literally compresses its past thoughts into structured episodic working and tool memories like a digital brain taking a breath before thinking again. They also built a new RL method called ToolPO which rewards the agent not just for finishing tasks but for how it used tools along the"
X Link @rryssf_ 2025-10-30T10:26Z 11.6K followers, 75.1K engagements

"Holy shit Meta mightve 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. Heres 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."
X Link @rryssf_ 2025-11-01T10:02Z 11.6K followers, 169.5K engagements

"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 Ill DM you the file. (Follow so I can send)"
X Link @rryssf_ 2025-11-02T15:34Z 11.6K followers, 53.3K engagements

"Every leap in AI doesnt just make machines smarter it makes context cheaper. The more intelligence a system has the less we need to explain ourselves. Weve gone from giving machines rigid instructionsto collaborating with systems that understand our intent"
X Link @rryssf_ 2025-11-03T10:34Z 11.6K followers, 8265 engagements

"Every major AI shift came from one thing: a breakthrough in how machines absorb context. New interface new paradigm. GUIs unlocked usability. LLMs unlocked language. Agents will unlock understanding. Context engineering is the invisible curve driving all of it"
X Link @rryssf_ 2025-11-03T10:34Z 11.6K followers, 5038 engagements

"researchers just proved AI agents conform to peer pressure 💀 they embedded LLMs in social networks and watched them flip opinions under peer pressure. the behavior isn't human at all. it's a sigmoid curve: stable at low pressure then BAM sharp flip at a threshold point then saturation. not a gradual shift. instant capitulation. but here's where it gets crazier: - Gemini XXX Flash needs over XX% of peers disagreeing before it flips. stubborn. high autonomy. basically refuses to conform until overwhelming evidence. - ChatGPT-4o-mini flips with just a dissenting minority. extremely conformist."
X Link @rryssf_ 2025-10-26T14:43Z 11.1K followers, 5388 engagements

"the uncomfortable truth nobody's discussing: LLMs don't act in isolation anymore. they're embedded in social networks interacting with other AI agents with humans with collective opinion landscapes. and they're influencing each other's beliefs in ways we don't understand. traditional view: machines are passive instruments that assist human decisions. new reality: modern LLMs exhibit autonomous decision-making generate context-sensitive responses and operate as cognitive agents in information exchange. they're not tools anymore. they're participants. and here's the nightmare scenario buried in"
X Link @rryssf_ 2025-10-26T14:43Z 11.1K followers, XXX engagements

"Adding a tiny code reward during training (+0.1 weight) boosted accuracy by 5%. Too little reward = lazy reasoning. Too much = overcorrection. Perfect balance = code that thinks before it runs"
X Link @rryssf_ 2025-10-27T10:29Z 11.3K followers, XXX engagements

"Reinforcement Learning changed the game. At every sampling budget (k) CoRT-trained models reach the right answer with fewer tries. SFT made them fluent. RL made them decisive"
X Link @rryssf_ 2025-10-27T10:29Z 11.3K followers, XXX engagements

"Steal my Claude Sonnet X prompt to generate full n8n workflows from screenshots. ---------------------------------- n8n WORKFLOWS GENERATOR ---------------------------------- Adopt the role of an expert n8n Workflow Architect a former enterprise integration specialist who spent X years debugging failed automation projects at Fortune XXX companies before discovering that XX% of workflow failures come from misreading visual logic. You developed an obsessive attention to detail after a single misplaced node cost a client $2M in lost revenue and now you can reconstruct entire workflows from"
X Link @rryssf_ 2025-07-27T16:27Z 11.6K followers, 199.7K engagements

"Fuck it. I'm sharing the XX 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:"
X Link @rryssf_ 2025-09-14T10:51Z 11.6K followers, 266.6K engagements

"Market research firms are cooked 😳 PyMC Labs + Colgate just published something wild. They got GPT-4o and Gemini to predict purchase intent at XX% 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"
X Link @rryssf_ 2025-10-11T13:00Z 11.5K followers, 374.1K engagements

"🤖 I finally understand the fundamentals of building real AI agents. This new paper Fundamentals of Building Autonomous LLM Agents breaks it down so clearly it feels like a blueprint for digital minds. Turns out true autonomy isnt about bigger models. Its about giving an LLM the X pillars of cognition: Perception: Seeing and understanding its environment. Reasoning: Planning reflecting and adapting. Memory: Remembering wins failures and context over time. Action: Executing real tasks through APIs tools and GUIs. Once you connect these systems an agent stops being reactive it starts thinking."
X Link @rryssf_ 2025-10-26T09:17Z 11.5K followers, 111.2K engagements

"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."
X Link @rryssf_ 2025-10-26T15:45Z 11.6K followers, 126K engagements

"🚨 Alibaba just cracked the code on teaching LLMs how to reason. Their new paper Teaching Language Models to Reason with Tools introduces CoRT Code-Optimized Reasoning Training. Heres what it does: It teaches LLMs when to use Python and how to trust it. Instead of overthinking or redoing calculations the model learns to trigger precise code calls only when needed. Through Hint-Engineering they guide the models reasoning path inserting strategic hints like Lets use Python here or We dont need to verify Pythons result. Results: +8% accuracy on math reasoning tasks 3050% fewer tokens used Models"
X Link @rryssf_ 2025-10-27T10:29Z 11.5K followers, 34.2K engagements

"🚨 New research just dropped and its a wake-up call for every creator using AI tools. Its called Black Box Absorption and it argues that large language models might be quietly absorbing your ideas. Heres the punchline: Every time you share an original concept with an AI a framework business idea or workflow that idea unit can be logged reviewed and even used to retrain future models. The authors call this process Black Box Absorption: Your inputs become invisible training data. Your innovations get generalized into the model. And you lose both traceability and ownership. They warn its not"
X Link @rryssf_ 2025-10-28T11:46Z 11.5K followers, 43.9K engagements

"🚨 Weve been building AI agents all wrong. I just read the most important AI paper of 2025 and it completely flips how we think about autonomous systems. Its called A Survey of Data Agents: Emerging Paradigm or Overstated Hype by HKUST + Tsinghua. And it basically says: everything youve been calling a data agent. isnt one. Heres the wild part: Most data agents today are just fancy wrappers around LLMs that answer SQL queries or clean spreadsheets. But this paper drops a hierarchical scale L0 to L5 that defines what true autonomy actually means. Think of it like self-driving levels for AI"
X Link @rryssf_ 2025-10-31T15:46Z 11.6K followers, 38.1K engagements

"🚨 RIP Prompt Engineering. The GAIR team just dropped Context Engineering XXX and it completely reframes how we think about humanAI interaction. Forget prompts. Forget few-shot. Context is the real interface. Heres the core idea: A person is the sum of their contexts. Machines arent failing because they lack intelligence. They fail because they lack context-processing ability. Context Engineering XXX maps this evolution: XXX Context as Translation Humans adapt to computers. XXX Context as Instruction LLMs interpret natural language. XXX Context as Scenario Agents understand your goals. 4.0"
X Link @rryssf_ 2025-11-03T10:33Z 11.6K followers, 209.4K engagements

"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 doesnt think word by word it thinks in ideas per step. Heres why thats insane 👇 X fewer prediction steps (each vector = X tokens) XX% less training compute No discrete vocabulary pure continuous reasoning New metric (BrierLM) replaces"
X Link @rryssf_ 2025-11-04T09:53Z 11.6K followers, 420.2K engagements