Most Web3 creators aren’t building a brand All you need is a good prompt and AI Meanwhile, companies are dropping thousands on motion design and identity work I’ve been testing different video tools... @MedievalEmpires is one of my best The Result and the Prompt🧵

AI can automate decisions But decisions need rails to execute Payments. Settlement. Trading. Cross-border liquidity Most financial infrastructure wasn't built for 24/7 AI execution That is why I’m paying attention to @KiiChainio Think about what a real autonomous agent actually needs: - A model that reasons ✓ - A system that orchestrates ✓ - Financial rails that move at the same speed ✗ That last one is missing. Digital markets don't close. AI agents don't sleep. But most financial infrastructure still operates like it's 2009. Banks close on weekends Settlement takes days Cross-border payments are still a nightmare You can't build 24/7 autonomous systems on top of 9-to-5 rails Then... KiiChain is building a 24/7 onchain FX layer for cross-border payments and trading Simple version: programmable global value movement So AI agents don't just decide They execute Instantly. Anywhere. The next wave of AI won't just be smarter It'll be financially autonomous Agents that decide, pay, settle, and trade Without waiting for a bank to open on Monday Infrastructure is the unsexy part of every revolution But it's always where the real opportunity hides

I'm not a freak… I promise… AI transforms me into this I bought a replica of what everyone thinks is @elonmusk ‘s head

Amazon just launched Alexa for Shopping Most people will use it like a search bar That is the wrong way The old prompt: "Running shoes." The right prompt: "I need running shoes for 3 times a week, I weigh 78kg, I run on asphalt, I have knee pain, I want something durable, max $130, prioritize comfort over speed, give me 3 options with real pros, cons and differences." That is not a search. That is a delegated buying decision. And Alexa for Shopping is built for exactly that. ▪️It compares products dynamically. ▪️Tracks up to a year of price history. ▪️Creates price alerts. ▪️Pulls your purchase history and preferences. ▪️And with Buy for Me, it can execute the purchase automatically when conditions are met. Stop treating it like Google Treat it like a buyer you are briefing Context => Objective => Restrictions => Decision criteria => Format => Action The more you give, the better the output. There is also a big shift for sellers. @amazonnews SEO is becoming AIO Keywords are no longer enough Your product listing needs to be understandable, comparable and justifiable to an AI agent. Titles, bullets, FAQs, images. All of it needs to answer the questions an agent will ask on behalf of a buyer. If Alexa cannot explain why your product fits, it will recommend the one that can One rule before you use it: Delegate research, comparison and alerts. Review before any important purchase. The agent optimizes for Amazon. You optimize for yourself.

After hundreds of hours with @cursor_ai , I built a setup that actually works Most people skip the hardest part They jump straight to implementation. They type "build me a login system" and let Cursor run. Then they wonder why it drifts, overwrites things it shouldn't, and produces code they can't review. The problem is the missing layer before the model. There are 5 agents that cover the complete development cycle Most setups have one. The best setups have all five, in the right order. Full breakdown in the article

GPT-5.5 Instant is the best model for everyday use right now Not because of benchmarks Because of what actually changed OpenAI just made it the new default layer of ChatGPT And the numbers are real: - 52.5% fewer hallucinated claims vs GPT-5.3 Instant in sensitive prompts like medicine, law and finance. - 37.3% fewer inaccurate claims in difficult conversations flagged by users. Shorter answers Better factuality Better image understanding Better context memory But the feature that changes the most is Memory Sources Before: "Write a proposal for this client." ChatGPT guesses what it doesn't know. Now: it pulls from previous conversations, uploaded documents, personal preferences and connected context. And it shows you exactly what sources it used to answer. That is a transparent, personalized, context-aware response. - For content creators it means less re-explaining your style every session. - For consultants it means proposals that actually remember the client. - For everyday users it means fewer long answers, better image analysis and real continuity between conversations. GPT-5.5 and GPT-5.5 Pro handle the complex stuff. But for daily use, it is the one to have open.

After hundreds of hours with @cursor_ai .. this is the one thing that changed everything It's not a model. Not a rule. It's an agent: The Planning Agent Here's why it's the most important one in your setup: Cursor doesn't fail because it can't write code It fails because it receives vague requests You type: "Create a login system." And Cursor starts building but... that request doesn't define: - What provider to use. - How to manage the session. - What routes exist. - What files can be touched. - What current behavior must be preserved. - What tests are required. - What's out of scope. - What risks exist. - What "done" means. So Cursor infers. And inference creates drift. The Planning Agent fixes this. It converts vague intention into an executable specification. It investigates the codebase, asks clarifying questions, creates a detailed plan with file paths and code references, and waits for approval before building anything. Cursor calls planning "the most impactful change" for working better with agents. I agree. Without a plan, every other agent works on a weak foundation. With a plan, you control scope, direction and risk before a single line of code is written. That's the difference between vibe coding that ships and vibe coding that loops forever.

"Spam" filters for social media are starting to appear... Claw cron Job with GPT to scan X mentions and automatically detect: - AI reply slop - engagement farming - synthetic replies - shill behavior We're slowly entering the era of personal AI firewalls for attention

I run a process optimization company I am not a developer I could design the system. I could map the workflow. I could see exactly what needed to be built. But I couldn't build it. @cursor_ai closed that gap. Not by making me a developer. By making the gap irrelevant. Here's what it actually solves, and why it matters for anyone building with AI. Full breakdown in the article ↓

The most important thing about Cursor's new PR Review is not the review... It's what it connects Cursor just closed the full development cycle inside the IDE And to understand it, you need to see the three pieces together: 1/ Build in Parallel: Cursor breaks a plan into independent subtasks and runs them with async subagents, maintaining order when dependencies exist. 2/ Split PRs: Cursor takes a large diff and proposes splitting it into smaller, mergeable PRs - identifying logical slices before creating them. 3/ PR Review: Inline threads, general comments, file tree, reviewer status and quick actions to move forward without leaving the editor. The old flow looked like this: Idea, code, massive diff, GitHub, review, comments, back to editor, fixes, GitHub, merge. Cursor is turning it into: Plan, parallel execution, logical split, PR, review, fixes, merge. All inside the same environment. But the most interesting read isn't the convenience. It's that Cursor is solving the problem they created. When agents produce more code, a new problem appears: more diffs, more PRs, more reviews, more risk of mixing responsibilities. The underlying message is clear: Cursor doesn't want to be just an AI editor. It wants to be the operational layer over the full software lifecycle. Plan, build, split, review and merge without jumping between editor, GitHub and terminal. That positions them differently from GitHub Copilot, Claude Code or Devin.

Claude Multiagent Sessions is one of the most powerful things they've shipped Not because it's faster... Because it's a completely different architecture One coordinator... Multiple specialists. Each with their own model, tools, and context. Here's what this means for builders 🧵