How Grok 4 and Twitter enhance classroom research

How X (fka Twitter)’s “For You” Feed Really Works — 500M tweets a day. One personalized feed. 150 milliseconds. After digging through X(fka Twitter)’s open-source codebase, here’s how your “For You” timeline is actually built — and what drives virality. The 3-Stage Funnel ⭐ Stage 1: Candidate Sourcing (500M → 1.5K) Search Index – Tweets from people you follow CR Mixer – Collaborative filtering via SimClusters UTEG – Topic / entity-based discovery FRS – Accounts you should follow ⭐Stage 2: Heavy Ranker (1.5K → 500) Neural model assigns an engagement-likelihood score: 0.5×Like + 1.0×Retweet + 0.3×Reply – 1.5×Report – 3.0×Block Retweets count twice as much as likes. Reports destroy reach. ⭐Stage 3: Filters → Final Feed Heuristics enforce author diversity, content balance, and Trust & Safety rules. ⭐ Core Findings: 👉For Creators Engagement in the first 30 minutes is the primary growth signal. Media (images / videos) gains higher base ranking. Interaction from high-credibility accounts magnifies exposure. 👉For Engineers Multi-stage ranking enables real-time scaling. GraphJet + TwHIN process billions of relationships per second. Negative feedback carries stronger weight than positive signals. 👉Full deep-technical analysis with infrastructure, feature pipelines, and bias review: https://lnkd.in/ecJ_iNSM The key revelation: the system doesn’t just promote what users love — it systematically suppresses what they reject. #AI #TwitterAlgorithm #MachineLearning #RecommendationSystems #DeepLearning #DataEngineering #GraphAI #SimClusters #TwHIN #GraphJet #OpenSource #ElonMusk #ForYouFeed #RankingSystems #AIInfrastructure #ContentDiscovery #RecommenderSystems #TechDeepDive #MediumArticle #AIExplained

Automating Twitter Content Creation with No-Code Tools Over the past week, I built a complete Twitter post automation system that integrates Telegram, Google Sheets, AI (Gemini), and Twitter Here’s how it works ->Workflow Overview: Telegram Trigger – I send a message or keyword to start the flow. AI-Powered Post Generation – The system uses Gemini AI to generate multiple post options based on the topic. Validation & Logging – Posts are parsed, verified, and logged automatically in Google Sheets. Tweet Publishing – The best post is instantly published to Twitter via API. Feedback Loop – Logs and results are sent back to Telegram for confirmation. -> Key Benefits: Zero manual work — everything runs automatically. Centralized data tracking via Google Sheets. Reliable AI-generated content at scale. Tools Used: Self Hosted N8N for workflow automation Google Sheets for tracking Gemini AI API for text generation Telegram for user interaction Twitter API for publishing Here’s a quick look at the automation flow -> Building this automation taught me how powerful no-code + AI integrations can be in scaling content creation. Would you be interested if I turned this into a tutorial or template for others to use? #Automation #NoCode #AI #Productivity #TwitterAutomation #Make #GeminiAI #WorkflowAutomation

Hey 👋 I recently created an AI-powered automatic Tweet posting workflow using n8n, and it’s a total game-changer! 🤖💬 Here’s what it does ⬇️ 🕒 Every 2 hours, the workflow automatically: 1️⃣ Fetches the latest tweets from selected creators in your niche (you can define who your inspiration is 👤). 2️⃣ Keeps that data rolling for 6 hours, so the content stays fresh and updated 🔄. 3️⃣ Stores the data neatly inside Google Sheets for tracking and reuse 📊. 4️⃣ Before posting, it uses AI magic (GPT + Image Generator) to: ✍️ Rewrite the tweet in a creative, engaging style — always under 280 characters.🎨 Generate a brand-new image that visually represents the rewritten tweet (no reused visuals!). 5️⃣ Finally… it auto-posts the tweet + image directly to Twitter (X) 🐦🔥 The result? 👉 Your Twitter feed stays active 👉 The content is always original 👉 No more manual curation or scheduling hassles I’m super excited about how this automation blends content curation, AI creativity, and workflow automation into one seamless loop 🤩 💡 Tech Stack Used: 🔹 n8n (workflow automation) 🔹 Google Sheets (data storage) 🔹 OpenAI (text rewriting) 🔹 DALL·E / Stability.ai (image generation) 🔹 Twitter API (posting) If you’re into content automation, social media growth, or AI workflows, this setup might just inspire your next project! ⚡ #n8n #Automation #OpenAI #AIAutomation #TwitterAutomation #NoCode #ContentCreation #AIForSocialMedia

As #Linkedin keeps since few days pestering me with questions about how AI improved something that I do ... ... look at th post on Facebook 😂my own private multi-team concurrent hackatons Experiment that repeated few times, and reduced my pile of backlog concepts to explore while increasing reuse Only critical component: gradually, became more proficient and more structured But hit the paywall barrier more often (I use the free versions in these initial phases to avoid surprises, only what passes the filter but require more will be eventually carefully used on paid versions) Reason? Older prompts got lame and shorter versions,and took few rounds to extract answers Now that probably both the models and (mainly) I improved our interactions, usually models hit the ground running with "gusto", and get forward toward the stated aim under the provided conditions often in just one step https://lnkd.in/dxaPnUUK Disclosure: between since the late 1980s I was few times in few countries involved in those software selections for business users where actually vendors were asked to carry out similar activities- hence, I was not re-inventing the wheel, just adapting to a different context...

These past days, twitter is buzzing about the interview with Andrej Karpathy. To me, it's more of a reaction to the whole hated rally - the endless scam, pr circus, selling of thin air, all these never-ending talks about “deals” that are pure farce. Plus, it feels like an aha-moment regarding the very idea of so-called AI — especially because it comes from a high-calibre developer, someone from inside the industry. Of course, for those who actually think, even a little, none of this is news, not some revelation. But since people still don’t think, and just keep buying all of that hype and bs from an endless army of conmen, air-sellers, influencers, and bullsh*t artists, we get these periodic “sudden awakenings.” But about the interview itself - there were interesting points, in my view. Even though the interviewer constantly drifted into abstract fantasies — AGI, evolution, comparing AI with the animal world, that typical media hype like: “ChatGPT is smarter than a 4-year-old child or a dog,” and similar nonsense, having zero understanding of what the intelligence of a dog or a child even is. Despite all those detours and technical depth, a few moments resonated, especially since yesterday we were talking about similar things — and I’ve been writing about this for a long time. For instance on modelling — not even modelling, but mirroring a human — and at the output, it’s not a human, not an animal, but a phantom that writes in a human-like way. That’s exactly why it triggered such hype — because it seems anthropomorphic, at least in the imagination of those on this side of the monitor. That’s why it took off. Also, the theme of training models on the internet as if it were some inherently reliable, default resource — when in reality, the internet is pure garbage, an ocean of trash with only tiny drops of actual articles or books that maybe make up a percent — or fractions of a percent — of the whole. And this is what they train on… And most importantly, at least for me: the issue of today’s reinforcement learning and collapsed data — memorisation or storage of data in the “head” versus actual thinking. These are completely different systems conceptually. Then there’s the matter of diversity of thought versus identical answers, and so on. These are the key things, in particular. Especially considering, as I’ve said for a long time, that the entire educational system has long collapsed into a kind of reinforcement learning — where they don’t teach you to think, don’t help you from school — they only demand “the correct answer,” in lockstep with the Party. If you guessed — you’re “right.” If you missed, lost concentration, forgot to notice a “not,” as they love in those idiotic tests built not on understanding but on catching you — you get a zero, even if your reasoning was correct but you ticked the wrong box… Conceptually, this all goes in the exact opposite direction; same with ideas, thoughts, just slips into gulag of cloned soldiers…

"From Twitter Turmoil to AI Triumph: Parag Agrawal’s Parallel Web Systems Redefines Web Automation" Parag Agrawal, the ex-CEO of Twitter, was terminated abruptly and unceremoniously following Elon Musk’s takeover of the company, and reportedly escorted out of the San Francisco headquarters after being publicly mocked by Musk, who accused him and others of misleading stakeholders regarding Twitter’s metrics. Instead of fading after this humiliation, Agrawal channeled his energies into founding Parallel Web Systems, an artificial intelligence startup, which he has built from the ground up and positioned at the forefront of the AI sector by 2025. How Parag Agrawal Was Terminated Agrawal was fired immediately after Musk completed his $44 billion acquisition of Twitter in late 2022.He and other top executives were physically escorted out of Twitter’s HQ, rather than being allowed a more dignified transition. Musk publicly criticized Agrawal’s leadership and the two exchanged terse messages before the firing.The abruptness and Musk’s public handling of the situation have been widely interpreted as humiliating, with details later revealed in biographies and media reports. Growth and Position of Parallel Web Systems Founded in 2023, Parallel Web Systems attracted prominent investors and raised close to $30 million in seed funding, quickly scaling its workforce and influence.Agrawal built a foundational team from leading tech companies such as Google, Stripe, Airbnb, and Twitter, working intensely out of Palo Alto coffee shops and driving grassroots innovation. Parallel’s vision is to create infrastructure allowing AI agents to perform web research and automate workflows, outperforming conventional models—including OpenAI’s GPT-5—on several key benchmarks by as much as 10-15% in accuracy.The company’s Deep Research API now powers millions of tasks in startups and major enterprises, with applications in coding, insurance, and automation. By August 2025, Parallel was valued at about $450 million and is recognized as a leading AI innovator, focusing on building tools for the “web’s second user”—AI agents instead of humans.Where Parallel Web Systems Stands Now The startup is hailed as one of the most important AI companies of 2025, frequently outperforming top existing models and expanding the capabilities of digital agents in real-world tasks.It is at the cutting edge of research in real-time web data access and agent-driven automation—enabling enterprises to automate complex tasks, and already integrated by a public company for workflow automation exceeding human-level accuracy. Parallel Web Systems is actively developing even more advanced agent architectures and programmable web interfaces for next-generation AI applications.Parag Agrawal’s journey from ousted Twitter CEO to leading AI entrepreneur is now seen as a case study in rebounding from corporate disgrace and building visionary companies from the ground up

🚀 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐓𝐰𝐞𝐞𝐭𝐂𝐨𝐩𝐢𝐥𝐨𝐭.𝐚𝐢 — Your AI-Powered Twitter & Chat Companion. 𝘖𝘷𝘦𝘳 𝘵𝘩𝘦 𝘱𝘢𝘴𝘵 𝘧𝘦𝘸 days, 𝘐’𝘷𝘦 𝘣𝘦𝘦𝘯 𝘦𝘹𝘱𝘭𝘰𝘳𝘪𝘯𝘨 𝘩𝘰𝘸 𝘮𝘶𝘭𝘵𝘪-𝘢𝘨𝘦𝘯𝘵 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘤𝘢𝘯 𝘮𝘢𝘬𝘦 𝘴𝘰𝘤𝘪𝘢𝘭 𝘮𝘦𝘥𝘪𝘢 𝘢𝘯𝘥 𝘈𝘐 𝘪𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘰𝘯𝘴 𝘮𝘰𝘳𝘦 𝘪𝘯𝘵𝘶𝘪𝘵𝘪𝘷𝘦. The result? 𝐓𝐰𝐞𝐞𝐭𝐂𝐨𝐩𝐢𝐥𝐨𝐭.𝐚𝐢 — 𝘢𝘯 𝘈𝘐-𝘥𝘳𝘪𝘷𝘦𝘯 𝘴𝘺𝘴𝘵𝘦𝘮 𝘵𝘩𝘢𝘵 𝘯𝘰𝘵 𝘰𝘯𝘭𝘺 𝘮𝘢𝘯𝘢𝘨𝘦𝘴 𝘺𝘰𝘶𝘳 𝘛𝘸𝘪𝘵𝘵𝘦𝘳 𝘱𝘳𝘦𝘴𝘦𝘯𝘤𝘦 𝘣𝘶𝘵 𝘢𝘭𝘴𝘰 𝘤𝘩𝘢𝘵𝘴 𝘸𝘪𝘵𝘩 𝘺𝘰𝘶 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵𝘭𝘺 𝘪𝘯 𝘳𝘦𝘢𝘭 𝘵𝘪𝘮𝘦. 🧭 𝐃𝐮𝐚𝐥-𝐑𝐨𝐮𝐭𝐞𝐫 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 At the core of 𝐓𝐰𝐞𝐞𝐭𝐂𝐨𝐩𝐢𝐥𝐨𝐭.𝐚𝐢 lies a 𝐭𝐰𝐨-𝐥𝐚𝐲𝐞𝐫 𝐫𝐨𝐮𝐭𝐞𝐫 𝐬𝐲𝐬𝐭𝐞𝐦 powered by 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡: 1️⃣ 𝐏𝐫𝐢𝐦𝐚𝐫𝐲 𝐑𝐨𝐮𝐭𝐞𝐫: Determines whether the user’s message is a 𝐠𝐞𝐧𝐞𝐫𝐚𝐥 𝐜𝐡𝐚𝐭 or a 𝐓𝐰𝐢𝐭𝐭𝐞𝐫-𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐫𝐞𝐪𝐮𝐞𝐬𝐭. — If it’s general, the system responds naturally as an AI chat companion. — If it’s tweet-related, the request is passed to the 𝘛𝘸𝘪𝘵𝘵𝘦𝘳 𝘙𝘰𝘶𝘵𝘦𝘳. 2️⃣ 𝐓𝐰𝐢𝐭𝐭𝐞𝐫 𝐑𝐨𝐮𝐭𝐞𝐫: Delegates tasks to specialized agents: 💬 𝐓𝐰𝐞𝐞𝐭 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐀𝐠𝐞𝐧𝐭 — crafts context-aware tweets from any topic. ⚡ 𝐃𝐢𝐫𝐞𝐜𝐭 𝐏𝐨𝐬𝐭 𝐀𝐠𝐞𝐧𝐭 — publishes tweets on X/Tweet in real time. 🛠️ 𝐓𝐰𝐞𝐞𝐭 𝐔𝐩𝐝𝐚𝐭𝐞 𝐀𝐠𝐞𝐧𝐭 — refreshes existing posts for clarity and reach. 🗑️𝐏𝐨𝐬𝐭 𝐃𝐞𝐥𝐞𝐭𝐢𝐨𝐧 𝐀𝐠𝐞𝐧𝐭 — safely removes outdated or unwanted tweets. Together, these agents form a dynamic ecosystem that automates the entire content lifecycle — from 𝐢𝐝𝐞𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐩𝐮𝐛𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧. 🧩 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 🔹 𝐁𝐚𝐜𝐤𝐞𝐧𝐝: 𝘍𝘢𝘴𝘵𝘈𝘗𝘐 + 𝘗𝘺𝘥𝘢𝘯𝘵𝘪𝘤 + 𝘓𝘢𝘯𝘨𝘊𝘩𝘢𝘪𝘯 + 𝘓𝘢𝘯𝘨𝘎𝘳𝘢𝘱𝘩 🔹 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: 𝘋𝘰𝘤𝘬𝘦𝘳𝘪𝘻𝘦𝘥 & 𝘩𝘰𝘴𝘵𝘦𝘥 𝘰𝘯 𝐑𝐞𝐧𝐝𝐞𝐫 🔹 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝: React + Tailwind CSS 🔹 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Managed through 𝐂𝐥𝐞𝐫𝐤 for secure access 𝘌𝘷𝘦𝘳𝘺 𝘱𝘢𝘳𝘵 𝘪𝘴 𝘮𝘰𝘥𝘶𝘭𝘢𝘳, 𝘢𝘴𝘺𝘯𝘤, 𝘢𝘯𝘥 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘦𝘥 𝘧𝘰𝘳 𝘳𝘦𝘢𝘭-𝘵𝘪𝘮𝘦 𝘪𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘰𝘯𝘴. 💡 𝐕𝐢𝐬𝐢𝐨𝐧: To blend 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧 and 𝐜𝐫𝐞𝐚𝐭𝐢𝐨𝐧 — allowing you to talk, plan, and post effortlessly. Whether you’re chatting casually or managing your Twitter presence, 𝐓𝐰𝐞𝐞𝐭𝐂𝐨𝐩𝐢𝐥𝐨𝐭.𝐚𝐢 handles it all intelligently behind the scenes. If you’d like to explore the 𝐬𝐨𝐮𝐫𝐜𝐞 𝐜𝐨𝐝𝐞, just 𝐃𝐌 𝐦𝐞 — I’ll share the GitHub repo link. 💬 #AI #LangChain #LangGraph #FastAPI #React #TwitterAutomation #Docker #Render #Clerk #OpenSource #AIagents #DevTools #Innovation

✨ Excited to share my latest automation project! ✨ I built a workflow that tracks Twitter mentions of Kalshi in real-time, runs sentiment analysis, and then: ✅ Separates tweet text, usernames & timestamps into structured data ✅ Pushes everything into Google Sheets for trend analysis ✅ Sends instant Telegram alerts for negative mentions ⚠️ This setup gives me a powerful, automated pipeline for monitoring brand perception with zero manual effort. 💡 What excites me most is how this can scale beyond Kalshi—any company or individual can use a similar system to track sentiment, monitor reputational risks, and gain actionable insights in real-time. Here’s a short demo video 👇 Ads: guidriven.com wwwzz.com #Automation #SentimentAnalysis #DataAnalytics #Kalshi #Nocode #WorkflowAutomation #SocialListening #RealtimeData #TelegramBots #GoogleSheets #Innovation #DivVerse Labs #Loubby AI

Everyone’s asking how to use Reddit to rank in LLMs. Our Reddit strategy got 755K total views in the last month. The thinking is simple: → Reddit content gets scraped heavily by AI. → Valuable answers in the right threads can shape what people see when they ask tools like ChatGPT for product or tool recs. So at Alpha Trade Ai we started testing it. Month 1 has been all signal, no pitching. No product drops. No forced mentions. Just value-first posts and comments, and learning how different subs react. And the results have been pretty impressive. On a single account, here’s what we’ve seen so far: 755K total views 675K on posts 80K on comments 775 karma earned But this is just the foundation. Here’s the roadmap we’re following next: 1️⃣ Build trust first Keep contributing to high-impact subs (like r/Entrepreneur, r/SaaS, r/Trading). If you don’t have trust, your post gets nuked. 2️⃣ Write for LLMs Use clear, structured answers: Q&A format, step-by-step breakdowns, subtle product mentions that feel natural. 3️⃣ Start comparing “Here’s what we tried. Here’s what worked.” LLMs love tradeoffs and specific use cases, not hype. 4️⃣ Stay consistent We’re moving to a rhythm of 2–3 original posts a month, plus weekly comments in tool-related threads. Just enough to stay indexed. 5️⃣ Amplify beyond Reddit When a post hits, we share it on LinkedIn and X. If it gets backlinks from blogs or forums? Even better,, it increases the weight LLMs give it. This is the long game. It’s not about virality. It’s about presence. If you want to shape how people discover your product before they hit your site, this is how you lay the groundwork. We’ll keep testing and sharing what works.

The internet isn't just changing. It's dying. Reddit cofounder Alexis Ohanian just said what many of us have been thinking: "Much of the internet is now dead." Botted. Quasi-AI. Even Sam Altman admitted he's seeing more LLM-run accounts than real people on Twitter now. Here's what this means for marketers: We're entering the era of "Proof of Life" marketing. The future isn't about who can generate the most content fastest. It's about who can create the most human connections. While everyone's racing to automate everything, the real competitive advantage will be authenticity at scale. Think about it: - Where do you get your best information now? Probably group chats with real people you trust. - Where do you actually engage? Content that feels genuinely human. - What cuts through the noise? A real voice, real experience, real insight. This shifts how we should be thinking about growth: Stop optimizing for volume. Start optimizing for verifiable humanity. The brands that win won't be the ones with the most AI-generated posts. They'll be the ones that build real communities, foster actual conversations, and create content that could only come from lived experience. Your competitors are all using the same AI tools. Your differentiation isn't in the tech—it's in the human insight behind it. The "dead internet theory" isn't a theory anymore. It's a warning. And it's also the biggest opportunity for marketers who understand that people buy from people, not from algorithms. What do you think? Are we heading toward a more human internet, or deeper into the bot-verse? Source: https://lnkd.in/g9Wtd9J3