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# ![@rryssf_ Avatar](https://lunarcrush.com/gi/w:26/cr:twitter::1666103476945031168.png) @rryssf_ Robert Youssef

Robert Youssef posts on X about ace, instead of, baidu, tencent the most. They currently have XXXXX followers and 1540 posts still getting attention that total XXXXXX engagements in the last XX hours.

### Engagements: XXXXXX [#](/creator/twitter::1666103476945031168/interactions)
![Engagements Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::1666103476945031168/c:line/m:interactions.svg)

- X Week XXXXXXX -XX%
- X Month XXXXXXXXX +269%
- X Months XXXXXXXXX +280,180%
- X Year XXXXXXXXX +13,324,473%

### Mentions: XX [#](/creator/twitter::1666103476945031168/posts_active)
![Mentions Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::1666103476945031168/c:line/m:posts_active.svg)

- X Week XX +2.20%
- X Month XXX -XX%
- X Months XXXXX +3,744%
- X Year XXXXX +153,500%

### Followers: XXXXX [#](/creator/twitter::1666103476945031168/followers)
![Followers Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::1666103476945031168/c:line/m:followers.svg)

- X Week XXXXX +13%
- X Month XXXXX +65%
- X Months XXXXX +6,536%

### CreatorRank: XXXXXXX [#](/creator/twitter::1666103476945031168/influencer_rank)
![CreatorRank Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::1666103476945031168/c:line/m:influencer_rank.svg)

### Social Influence [#](/creator/twitter::1666103476945031168/influence)
---

**Social category influence**
[technology brands](/list/technology-brands)  [stocks](/list/stocks)  [finance](/list/finance)  [social networks](/list/social-networks) 

**Social topic influence**
[ace](/topic/ace) #1668, [instead of](/topic/instead-of) #734, [baidu](/topic/baidu) #15, [tencent](/topic/tencent) #40, [llms](/topic/llms) #11, [integration](/topic/integration), [accuracy](/topic/accuracy), [penn state](/topic/penn-state), [loop](/topic/loop), [delta](/topic/delta)

**Top assets mentioned**
[IBM (IBM)](/topic/ibm) [Alphabet Inc Class A (GOOGL)](/topic/$googl)
### Top Social Posts [#](/creator/twitter::1666103476945031168/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](https://x.com/rryssf_/status/1976269613072843063) [@rryssf_](/creator/x/rryssf_) 2025-10-09T12:52Z 8999 followers, 713.9K 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](https://x.com/rryssf_/status/1949506927207088279) [@rryssf_](/creator/x/rryssf_) 2025-07-27T16:27Z 8998 followers, 193.2K engagements


"This is going to break your brain 🤯 New research just proved that being RUDE to AI makes it smarter. Penn State researchers tested ChatGPT-4o with XXX questions across X politeness levels. The results are wild: - Very polite prompts: XXXX% accuracy - Polite prompts: XXXX% accuracy - Neutral prompts: XXXX% accuracy - Rude prompts: XXXX% accuracy - Very rude prompts: XXXX% accuracy Statistical tests confirmed this isn't random - impolite prompts consistently outperformed polite ones. Here's the kicker: older models like GPT-3.5 behaved the OPPOSITE way. But GPT-4 and beyond They actually"  
[X Link](https://x.com/rryssf_/status/1977638031952892002) [@rryssf_](/creator/x/rryssf_) 2025-10-13T07:30Z 8998 followers, 68.8K 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](https://x.com/rryssf_/status/1977707844251361530) [@rryssf_](/creator/x/rryssf_) 2025-10-13T12:07Z 8998 followers, 42.6K engagements


"Most LLM surveys fail because models regress to the mean. When asked for a direct XX rating GPT-4o replied X almost every time producing KS similarity = XXXX to real human data. Translation: the distribution was basically useless"  
[X Link](https://x.com/rryssf_/status/1976996297636036855) [@rryssf_](/creator/x/rryssf_) 2025-10-11T13:00Z 8897 followers, 13.6K engagements


"@godofprompt Anyone sleeping on prompt engineering is missing the bigger picture. This is how you compress a decade of insight into a week"  
[X Link](https://x.com/rryssf_/status/1976229958914629943) [@rryssf_](/creator/x/rryssf_) 2025-10-09T10:15Z 8900 followers, XXX engagements


"Heres how ACE works 👇 It splits the models brain into X roles: Generator - runs the task Reflector - critiques what went right or wrong Curator - updates the context with only what matters Each loop adds delta updates small context changes that never overwrite old knowledge. Its literally the first agent framework that grows its own prompt"  
[X Link](https://x.com/rryssf_/status/1976269629002801409) [@rryssf_](/creator/x/rryssf_) 2025-10-09T12:52Z 8903 followers, 28.5K engagements


"Fine-tuning updates weights. ACE updates understanding. Its cheaper interpretable and reversible. You can literally watch how your AI learns one context delta at a time. This is the start of agentic self-learning where prompts become the new model weights"  
[X Link](https://x.com/rryssf_/status/1976269676926874015) [@rryssf_](/creator/x/rryssf_) 2025-10-09T12:53Z 8976 followers, 15.9K engagements


"the hidden costs nobody mentions: API costs multiplied: - X agents = X the API calls - coordination messages between agents = another 3-5 calls - my bill went from $200/month to $1400/month - actual productivity gain: maybe XX% maintenance nightmare: - one agent breaks entire chain fails - debugging requires tracing through X different logs - updating one agent's prompt retest entire system - version control becomes a mess latency stacking: - agent X takes X secondspasses to agent 2: +3 seconds - passes to agent 3: +3 seconds - X agents = 21+ seconds minimum - single agent doing same task: 8"  
[X Link](https://x.com/rryssf_/status/1979895779880743200) [@rryssf_](/creator/x/rryssf_) 2025-10-19T13:01Z 8960 followers, XXX engagements


"when multi-agent actually makes sense (rare): scenario 1: truly independent domains - agent A handles customer support - agent B handles accounting - agent C handles content creation - zero overlap zero coordination needed - this works because they're actually separate apps scenario 2: human-in-the-loop workflows - agent A does research human reviews agent B executes - the human becomes the coordinator - removes the coordination complexity - slower but way more reliable scenario 3: embarrassingly parallel tasks - processing 1000 support tickets - each agent takes XXX tickets independently -"  
[X Link](https://x.com/rryssf_/status/1979896020608655862) [@rryssf_](/creator/x/rryssf_) 2025-10-19T13:02Z 8949 followers, XXX engagements


"The numbers are ridiculous. ACE beat every major baseline: +10.6% on AppWorld (agents) +8.6% on FiNER (finance) and matched GPT-4.1powered IBM CUGA using a smaller open-source model. And it cut rollout latency by XXXX% while lowering cost 80%"  
[X Link](https://x.com/rryssf_/status/1976269660778860874) [@rryssf_](/creator/x/rryssf_) 2025-10-09T12:52Z 8990 followers, 18.4K 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](https://x.com/rryssf_/status/1967179508152434801) [@rryssf_](/creator/x/rryssf_) 2025-09-14T10:51Z 8996 followers, 265.4K engagements


"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 Ill DM it to you. (Follow to receive)"  
[X Link](https://x.com/rryssf_/status/1975140243981676566) [@rryssf_](/creator/x/rryssf_) 2025-10-06T10:05Z 8994 followers, 101.7K engagements


"Context Engineering for AI Agents by @AnthropicAI 🔖 Bookmark for later"  
[X Link](https://x.com/rryssf_/status/1975215314678984981) [@rryssf_](/creator/x/rryssf_) 2025-10-06T15:03Z 8999 followers, 69.9K engagements


"I just wrote a prompt and gave it to ChatGPTand now I know exactly how to talk to any LLM. Its wild how fast everything changes when you stop using AI like Google and start treating it like a mind. 👇 Most people prompt like theyre ordering food. I want X. Make it Y. Add Z. Thats not how models work. They dont follow instructions. They follow context. Youre not prompting. Youre programming cognition. Once you understand that the entire job market flips. The valuable skill isnt coding or knowing APIs. Its knowing how to talk to intelligence how to shape reasoning structure goals and debug"  
[X Link](https://x.com/rryssf_/status/1975935648197746709) [@rryssf_](/creator/x/rryssf_) 2025-10-08T14:45Z 8999 followers, 45.6K 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](https://x.com/rryssf_/status/1976639085919064537) [@rryssf_](/creator/x/rryssf_) 2025-10-10T13:20Z 8995 followers, 234.8K 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](https://x.com/rryssf_/status/1976996282033225936) [@rryssf_](/creator/x/rryssf_) 2025-10-11T13:00Z 8995 followers, 371K engagements


"consumer research is about to get weird. a new paper shows you can predict real purchase intent without asking humans. you prompt an LLM to role-play a specific customer (age income etc.) show it a product have it write a short reaction - another AI maps that text to a Likert score. no fine-tuning. XX surveys 9300 humans. XX% of human testretest reliability. the trick isnt the model. its how you ask"  
[X Link](https://x.com/rryssf_/status/1977367685169131879) [@rryssf_](/creator/x/rryssf_) 2025-10-12T13:36Z 8995 followers, 64.6K 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](https://x.com/rryssf_/status/1978406803613561047) [@rryssf_](/creator/x/rryssf_) 2025-10-15T10:25Z 8998 followers, 638.6K engagements


"Today everyones obsessed with fine-tuning and RLHF. But Tencent just showed you can replicate RL effects without touching model weights. Their secret Semantic advantage. Instead of numeric rewards the LLM explains why one output is better and learns from that"  
[X Link](https://x.com/rryssf_/status/1978406820004929699) [@rryssf_](/creator/x/rryssf_) 2025-10-15T10:25Z 8996 followers, 24.5K engagements


"This part blew my mind. It even improves tool use efficiency. After learning the model made fewer calls to the code interpreter but got more right answers. Its literally learning when not to think out loud"  
[X Link](https://x.com/rryssf_/status/1978406867710959978) [@rryssf_](/creator/x/rryssf_) 2025-10-15T10:25Z 8996 followers, 13.5K engagements


"Cross-domain It transfers. Train on math problems better at web search. Train on web search still strong at reasoning. Frozen LLMs are learning generalized behaviors just from contextual experience updates. This isnt fine-tuning. Its inference-time evolution. Read full paper here:"  
[X Link](https://x.com/rryssf_/status/1978406883980710166) [@rryssf_](/creator/x/rryssf_) 2025-10-15T10:25Z 8997 followers, 13.2K engagements


"Holy shit Baidu just dropped the most efficient multimodal model ever. Its called PaddleOCR-VL a 0.9B parameter beast that outperforms GPT-4o Gemini XXX and every doc-AI model on the planet. This thing reads XXX languages parses text tables formulas charts and still runs faster than models XX 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 isnt just efficient its the new blueprint for multimodal AI. huggingface. co/PaddlePaddle"  
[X Link](https://x.com/rryssf_/status/1979193211727024289) [@rryssf_](/creator/x/rryssf_) 2025-10-17T14:30Z 8999 followers, 101.2K engagements


"What is PaddleOCR-VL A Vision-Language model (VLM) built for document parsing it doesnt just read text; it understands layout structure and semantics. Its made up of two parts: X. PP-DocLayoutV2 - handles layout element detection reading order X. PaddleOCR-VL-0.9B - recognizes text tables formulas and charts Basically: it reads PDFs like a human but at lightning speed"  
[X Link](https://x.com/rryssf_/status/1979193227057205442) [@rryssf_](/creator/x/rryssf_) 2025-10-17T14:30Z 8998 followers, 4509 engagements


"Architecture magic: Instead of using massive end-to-end VLMs Baidu built a hybrid pipeline that separates layout understanding and content recognition. Layout: lightweight RT-DETR + Pointer Network Recognition: NaViT dynamic visual encoder + ERNIE-4.5-0.3B LLM This combo avoids hallucinations and cuts inference time dramatically. Smart design brute force scaling"  
[X Link](https://x.com/rryssf_/status/1979193243452797324) [@rryssf_](/creator/x/rryssf_) 2025-10-17T14:30Z 8998 followers, 3393 engagements


"Training is insane: - 30M+ multimodal samples - XXX supported languages Data sources: open datasets web data synthetic data and Baidus in-house OCR corpora It even uses automatic labeling pipelines GPT-style large models clean and validate labels. And for tough examples They built a hard case mining engine that synthesizes new data for the models weak spots"  
[X Link](https://x.com/rryssf_/status/1979193258871001552) [@rryssf_](/creator/x/rryssf_) 2025-10-17T14:30Z 8997 followers, 2853 engagements


"Results speak for themselves: On OmniDocBench v1.5 PaddleOCR-VL hits an overall score of XXXXX beating: GPT-4o (75.02) Qwen2.5-VL (87.02) MinerU2.5 (90.67) Its now SOTA in text formula and table recognition and does it all with under 1B parameters. Efficiency is the new frontier. Baidu just proved it"  
[X Link](https://x.com/rryssf_/status/1979193271256854685) [@rryssf_](/creator/x/rryssf_) 2025-10-17T14:30Z 8994 followers, 2447 engagements


"tried the "multiple specialized agents working together" approach. ended up with X bots arguing in Slack while nothing got done. the AI agent hype is pushing everyone toward multi-agent systems. CrewAI AutoGPT agent swarms. here's what X months of building these taught me: XX% of you are overengineering this. one powerful agent seven mediocre ones coordinating. here's why multi-agent systems are a trap (and when they actually work):"  
[X Link](https://x.com/rryssf_/status/1979895332914733308) [@rryssf_](/creator/x/rryssf_) 2025-10-19T13:00Z 8999 followers, 6871 engagements


"the better alternative: one agent with multiple skills. this is where Claude Skills changes everything. instead of: ❌ X specialized agents passing context around do this: ✅ X agent that loads relevant skills on-demand example: my current setup one agent with X skills: - email-processor skill (how to parse emails) - calendar-management skill (scheduling rules) - crm-update skill (data formatting) - response-templates skill (how to reply) - reporting skill (what to track) the agent: reads incoming email loads email-processor skill identifies it needs scheduling loads calendar-management skill"  
[X Link](https://x.com/rryssf_/status/1979896204797382736) [@rryssf_](/creator/x/rryssf_) 2025-10-19T13:03Z 8998 followers, XXX engagements


"key difference: - single agent = single context window = no information loss - multi-agent = context switching = information hemorrhage the "specialization" you want comes from skills not separate agents"  
[X Link](https://x.com/rryssf_/status/1979896413350994173) [@rryssf_](/creator/x/rryssf_) 2025-10-19T13:04Z 8995 followers, XXX 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](https://x.com/rryssf_/status/1980224308153823701) [@rryssf_](/creator/x/rryssf_) 2025-10-20T10:47Z 8999 followers, 132.6K engagements


"Why it works: they sample from something called a power distribution (p). Instead of random next tokens it biases the model toward high-likelihood reasoning paths it already knows but rarely chooses. Think of it as turning up the focus dial on the models intelligence without retraining it"  
[X Link](https://x.com/rryssf_/status/1980224353443967422) [@rryssf_](/creator/x/rryssf_) 2025-10-20T10:47Z 8999 followers, 3639 engagements


"They even explain why this helps reasoning: 👉 Normal sampling picks many okay paths. 👉 Power sampling picks fewer but deeper reasoning paths that lead to correct outcomes. That subtle shift changes everything its like turning the model into a better planner at inference time"  
[X Link](https://x.com/rryssf_/status/1980224365812990198) [@rryssf_](/creator/x/rryssf_) 2025-10-20T10:47Z 8998 followers, 3102 engagements