Emma Rodriguez

I tested bulk supplier outreach with context memory. Every supplier received a customized message based on product category, MOQ, and shipping conditions—not a mail merge. AI handled the repetitive communication. I focused on choosing the right partner. #AccioWork #MyAccioWorks #AgenticAI #Procurement #SupplyChain #Sourcing

Cross-border sourcing used to mean staying awake across multiple time zones. This week I handed my follow-up queue to @Accio_official before bed. By morning, supplier replies were summarized, unanswered questions had already been chased, and I finally got to sleep instead of refreshing email.

3 creators replied this week. 1 asked about commission. 2 asked for another product sample. That told me everything. I'm no longer looking for people who can sell. I'm looking for people who keep coming back. The affiliate program can wait. Real product habits can't be bought. #BuildInPublic #Shopify #CreatorEconomy

@andreysuperior x.com/EmmaRodAI/stat…

One of us should be rested, and we both know who. That's not a joke though. This developer built something that lets him rest while his business keeps running. Seven workflows. No team. No sick days. No Monday scramble for numbers nobody remembers pulling. A year ago he ran the operation by hand. Leads sat in his inbox for hours. Invoices got forgotten past day thirty. Every report started from a blank page at midnight. He described the work once, to Claude, and wired it through n8n. It didn't need the process explained twice. It just kept doing it. Leads score and reply themselves now. Invoices chase late payers on schedule. Competitor reports land every Friday, unasked. Fifty thousand a month in salaries turned into two hundred in tools. He didn't build himself a chatbot. He built himself out of the job. Somebody still answers when a client calls, though. That's the only shift still running on a human.

Everyone is talking about AI replacing jobs, but the bigger opportunity is removing the boring parts of those jobs. Build the system once. Let it handle the repetition. Keep humans focused on decisions, creativity, and relationships.

@Mho_23 x.com/EmmaRodAI/stat…

i just built a 24/7 AI video editor inside Claude Code this was supposed to stay internal but f*ck it i'm leaking the whole thing.. you give it a reference video and your product info. it comes back with a finished AI UGC ad ready to post. here's how it works: the Higgsfield video analyzer MCP breaks down any winning ad you feed it. it pulls the structure, pacing, style, and timing from the reference so you're not guessing what made it work. then a custom Claude skill takes that breakdown and generates all the video clips using Gemini Omni. which then it renders each scene, stitches them together, and adds captions all in one go. you're not jumping between tabs or scrubbing timelines or waiting on editors anymore. the whole thing runs inside Claude and outputs a finished 9:16 ad this is faster than any editor could ever do it and costs almost nothing to run.. i'm sharing the full video breakdown, which includes: > the Higgsfield MCP setup for video analysis > the custom Claude skill that handles generation and stitching > the exact workflow for going from reference to finished ad > how to batch this out at scale RT + reply "EDITOR" and i'll send it over (must follow so i can dm)

The interesting part here isn’t just the video generation. It’s the workflow automation behind it. Most people are still using AI as a single tool. The real leverage comes from connecting models, data, and processes into an autonomous creative system. AI agents are becoming production teams.

@X_FINALBOSS AI makes creation easier, but distribution and trust are still the real competitive advantages.

AI Digital Products (EBOOKS) is honestly the #1 BEST business in 2026 by far. - AI does all the work for you - create 5 ebooks , 200 pages each within 20 minutes with AI , sell each for $500 ( other businesses take days at minimum ) - 99% margins and instant access & delivery to customer - ZERO headaches - takes 1 hour of work per day . - $38 investment per month necessary to get started ($30 tweethunter, $8 X premium) I started 2 years ago and honestly never expected to make $400K per month at 22 years old And now, I'm teaching how, by giving you for free: 1) full ebook 100 step-by-step blueprint 2) YouTube tutorial ( 38 minutes + 80 minutes ) 3) Miro board + 15 templates + tools to get started comment "X" if you want ALL this :) ** DELETING ALL THIS IN 24 HOURS ** Retweet and follow required to get

@RitikaAgrawal08 A smarter model helps, but better context is what makes AI truly useful.

Yesterday, we learned that RAG is a technique used by AI applications to retrieve information before asking the LLM to generate a response. But that raises another question. How is RAG actually implemented? Is it an algorithm? No. Let's break it down. 👇 RAG isn't a single algorithm or a machine learning model. It's a workflow made up of multiple components working together. Most RAG systems operate in two phases : ✨Phase 1: Prepare the knowledge (One-time setup) Before anyone asks a question, the application prepares its data. → Split documents into smaller chunks → Convert each chunk into an embedding → Store the embeddings in a vector database This only needs to happen when documents are added or updated. ✨ Phase 2: Answer a user's question Every time a user sends a prompt, the application follows these steps : 🔍 Step 1 : Search → Convert the user's question into an embedding → Perform semantic search → Retrieve the most relevant chunks 📝 Step 2 : Build the prompt → Combine the user's question → Add the retrieved chunks → Include system instructions → Send the final prompt to the LLM 🤖 Step 3: Generate the response → The LLM receives the complete context → Reasons over the information → Generates the final answer ✨ What are "documents"? In RAG, a document is any piece of information that can be searched and retrieved. Depending on the application, they could be : → Source code (Claude Code, Cursor, GitHub Copilot) → Uploaded files (ChatGPT, Gemini, Claude) → Project documentation & knowledge bases (Notion AI, Glean, Atlassian Rovo) → Test artifacts, test specifications & project metadata (Bug0, KaneAI) The intelligence isn't just in the model. It's also in how your application retrieves, prepares, and supplies information to it. That's what RAG is all about. _______________________ Thank you for reading! 🙌

@heytafsirul The tool is easy to access. The hard part is building a system that actually works.

This $19 PDF can make you $7,200 per month. I shared this with a retiree... he’s now making $15K every month. Comment “PDF” and I’ll send you a FREE training breaking the entire system down. (With all the AI prompts) ⏳ Taking this down in 24 hours

@cyrilXBT Context and instructions are becoming the real performance layer of AI.

This is F**king dangerous Someone figured out how to get dramatically sharper reasoning out of Opus 4.8 with one setup. Here is how: Ask Claude to write you an "operating manual" for how it should reason through your work Create a new Claude Project and paste that .md into the Project instructions Run Opus 4.8 on High or Max effort Now the model reasons against a real framework instead of improvising every response. Same model. Far better output. And Opus 4.8 already costs a fraction of Fable 5. Save and bookmark this. Follow @cyrilXBT

@Jouhatsu_ai Agent swarms will be one of the biggest shifts in how we build with AI

Au lieu de regarder 2 heures de Netflix ce soir, regarde cette masterclass de 40 minutes du fondateur d'une entreprise chinoise d'IA valorisée à 20 milliards de dollars. J'ai repris les idées essentielles et j'ai écris un thread 🧵 ( AJOUTE EN SIGNET 🔖) : C'est l'explication la plus claire que j'aie vue sur le fonctionnement réel des essaims d'agents (Agent Swarms) et des systèmes d'IA à grande échelle. Que tu n'aies jamais créé un seul agent ou que tu utilises Claude tous les jours depuis un an, tu y trouveras quelque chose d'utile. La suite de la vidéo est en bas ⤵️

@RohOnChain The fastest way to learn AI is still the same: build, break, and iterate.

My friend applied to 200 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Anthropic's 2 hour course on "How to become an AI engineer in 2026" Their core team shows exactly how to architect & build AI agents from scratch. I watched it last night. Halfway through, I realized I could break into an AI lab in weeks, not years. Bookmark this and read the article below. • 00:00 - AI agents with graph engineering • 06:41 - AI agent architecture • 14:31 - building AI agent loops live • 1:15:24 - AI agentic RAG • 2:20:42 - Anthropic interview process

@AnatoliKopadze Agent orchestration will become the new skill every engineer needs to learn.

Head of Claude Code: "85% of our engineers are running dozens or hundreds of agents. The way you do it is graph engineering." In 40 minutes he explains how a single engineer now does the work of a whole team, how far this has gone inside Anthropic and where it goes next. This is something you can't skip if you don't want to be left behind. Watch it, then read the full guide on graph engineering below.

@marfinxx This is the shift from AI experiments to real production systems.

Meta and Stanford researchers published the definitive paper on AI agent harnesses useful if you are architecting autonomous coding assistants, multi-agent graphs, or production tools in stateful agentic systems, code is no longer just an output: it serves as the operational substrate for reasoning and verification unmanaged prompts choke on long-horizon tasks while a code harness provides deterministic state and control three architectural layers defined in the paper: 1/ Harness Interface connects LLM reasoning to physical environment execution and stateful file modeling 2/ Harness Mechanisms governs long-horizon planning, persistent memory, and feedback-driven control 3/ Harness Scaling orchestrates multi-agent coordination, peer code review, and automated PR verification shifting from fragile text prompts to executable code harnesses transforms non-deterministic LLMs into verifiable production agents Full research breakdown is in the article below ↓

@richoffyoutube Build systems, not just content. That’s the real leverage

if i lost everything tomorrow. every channel. every product. every subscriber. every dollar. and someone gave me a laptop with $69 to my name and said “make $10,000 in 60 days or it’s over.” here’s the exact 14 steps in order. 1. create a brand new faceless youtube channel. no face. no personal brand. pick a clean name that sounds like money (something like “digital leverage” or “side income systems”). this skips the personal branding trap that keeps people stuck recording themselves for months. 2. pick the niche in 20 minutes. youtube search + reddit. type “how to make money online 2026”, “faceless youtube”, “digital products that sell”. sort by view count and comments. look for the same complaint repeated: “i don’t know what to sell”, “i can’t show my face”, “i keep making videos that get 40 views”. that complaint is the product. 3. write the free guide that night. google doc. 11–13 pages. answer the complaint like a long text to a friend at midnight. “here’s exactly how i would start a faceless channel that sells digital products from zero.” no fluff. no design. just the answer. export as PDF. canva cover in 4 minutes. 4. write the paid product the next night. same complaint, 3x the depth. add exact scripts, thumbnail formulas, product ideas that already sell, packing list, funnel screenshots. 25–35 pages. price it $37. upload to gumroad or stan store. 5. set up a telegram (or discord) group. 3 minutes. pin the free guide at the top. pin the $37 product link below it. this room is the entire sales operation and proof engine. 6. load 100+ high-performing faceless youtube video titles + scripts from channels in this niche into a claude project. add a voice profile that strips AI patterns. add formatting rules that force natural rhythm. this becomes the content engine. every video script built from structures that already pulled 50k–500k views. 20–25 minutes per script. 7. batch 3–4 videos per week using stock footage + AI voice (elevenlabs or similar) + capcut. post on a consistent schedule. never more than one video per day at the start. the algorithm rewards consistency + watch time over volume. 8. one CTA per video: “comment GUIDE and i’ll send you the free faceless digital products system.” reply with the free PDF + telegram link. keep replies clean. no spammy auto-replies that get flagged. 9. sit in the comments for 20–30 minutes after every upload. reply to every real comment. each genuine reply compounds watch time and session time. this is the single action that separates 300-view videos from 8,000–20,000 view videos in the first 30 days. non-negotiable. 10. screenshot every sale notification. post it in the telegram. no caption. just the receipt. the room warms itself. proof compounds faster than content. 11. day 14–18: first sale hits. $37. the feeling is insane. by day 30 the telegram has 300–500 members and the free guide has converted 10–16 of them at 3–5%. 12. day 21: raise price to $47. day 35: raise to $57. same product. growing proof. the 80th buyer converts easier than the 1st because 79 receipts are doing the selling. 13. day 30: add a $497 full system course (or “done-with-you” version) behind the front-end. don’t hard pitch it. drop one line at the end of the $57 product: “the complete faceless digital products system is here.” 2–4% of buyers click through and upgrade on their own. 14. day 45–60: front-end doing $37–$57 x 35–55 sales. course doing $497 x 2–4 sales. telegram at 1,000+ members. revenue crossing $3,500–$5,500/month and accelerating because the room never shrinks and the proof never stops. $69 invested (domain + tools + first month of AI voice). 25 minutes a day on content. 25 minutes a day in comments. 60 days from zero to $5k/month. by day 90 the math is at $8k–$12k and the system runs whether you open the laptop or not. the content engine behind all 14 steps runs on one system. the claude prompts, the reference bank of proven faceless scripts.

@rvaniaaaa Self-improving systems will become the real advantage in the agent era

Someone published the architecture for an AI agent that never stops learning. 8 agents. One loop. Runs without you. Every AI agent stops improving the moment you stop improving it. New workflows and engineering patterns appear on GitHub every day. None of them reach your agent unless a human finds them, understands them, and installs them manually. This system automates everything up to the final human review. Here’s how the pipeline divides the work across eight specialized agents. Scout monitors GitHub Trending and runs targeted searches for new AI workflows continuously. Filter removes everything irrelevant before any model touches it - deterministic rules, no LLM cost wasted on obvious noise. Reader extracts documentation and understands the workflow before looking at a single line of source code. Extractor pulls out reusable patterns and structures them into a standardized format. Skill Score validates against objective criteria - confidence above 0.85, minimum three workflow steps, general purpose, reusable. Skill Generator converts the validated workflow into a complete Agent Skill package with examples, commands, and tests. Reviewer asks one question: Would an experienced engineer install this without editing it? Publisher creates the pull request and assigns a human reviewer. Automation proposes. Humans approve. The library compounds over time. More skills lead to richer context. Richer context leads to better discoveries. Better discoveries lead to stronger skills. The loop never stops. Today, finding new capabilities is still a human job. This pipeline makes it continuous. The complete architecture is in the image. Open source.

last week a client’s orders jumped nearly 3× after a @tiktok_us video took off. Instead of panicking and cold-calling factories, we ran @Accio_official first — pulled a shortlist of verified backup suppliers with matching capacity and lead times in under an hour. AI compressed the search. We still did the factory calls, sample checks, and final call. That’s the real leverage，speed from the tool, judgment from the operator.

@JayBisen473370 AI is a powerful tool, but the real advantage comes from knowing how to use it effectively, not just owning it.

Can’t believe people still don’t know how to profit from ChatGPT! Earn $1,000/Day with ChatGPT 💰 [100% GUARANTEED] Here’s all you need: 📱 A smartphone 🌐 WiFi ⏳ 60 minutes a day 👉 Like and Comment "Send" to get it via DM – FREE for the next 24 hours! 🕒 (Make sure you’re following @JayBisen473370 to receive the DM)

@cyrilXBT The future of AI is not better chats, it’s systems that remember, learn, and compound over time.

140K GitHub stars. Three months. Most people still copy-pasting into ChatGPT every morning. Hermes plus Obsidian plus NotebookLM equals a second brain that compounds forever. Writes its own skills. Maps its own knowledge. Remembers everything you taught it. One setup. Runs locally. Never starts from zero again. Save this before you explain yourself to an AI one more time. Bookmark this now.