Map the content path to clarify your streaming architecture.... → Ingest & preparation: Content entry and processing → Storage & availability: Consistent access on demand → Delivery & playback: Player performance AIOZ Stream connects these stages into one media workflow.show more

So James Drake & company kicked Burt for being... content. Here's how I see it, why encourage anyone participating on this trip to stream and punish them when they do? No one is going to donate for them to sit around holding hands. It's the truth. #Fishtankliveshow more

20 days ago, I connected Claude Code to my... newly created instagram handle.. I gained 4.3M views and 6500+ followers in less than a month [ i post Ai generated animated stories ] Full workflow: i let claude study my account before i write another reel.. This is the cleanest content workflow i've built on claude. give it your IG first. 4 prompts handle the rest.. niche research, the reel script, the hook, and the daily automation.. the whole loop is basically, give claude your IG → find what's working → write retention-optimized scripts → engineer the hook → automate the daily output.. ▫️ Setup: give claude your instagram open claude code. claude code has a built-in web tool that browses any public URL. or install any agentic browser like Browser Harness or Firecrawl or Comet browser paste this with your handle filled in: "Browse and pull the last 30 reels and posts. Analyze my recurring topics, top-performing hooks, formats, and engagement patterns. Then map out my actual audience and what they consistently respond to." claude reads your profile, pulls every reel down, and now has the context to personalize every prompt below to YOUR account, not a generic niche. if you're on claude desktop, the same works with firecrawl MCP connected. ▫️ Prompt 1 find what actually goes viral in your niche: "Analyze the highest-performing Instagram Reels, TikToks, and Reddit posts in the [niche] niche from the last 30 days. Identify repeating hooks, visual styles, emotional triggers, and content formats that consistently generate high engagement. Then summarize the 5 strongest content angles optimized for AI-generated content and short-form videos." run this after the setup. you get 5 angles backed by what's already working in your niche, cross-checked against what's already working on YOUR account. ▫️ Prompt 2 write a high-retention reel script "Write a short-form Instagram Reel script about [topic] with an aggressive hook in the first 2 seconds. Create immediate curiosity, tension, or controversy to stop scrolling, then deliver a fast and satisfying payoff. Keep it under 30 seconds and optimize the structure for watch time, replays, comments, and shares. Finish with a subtle CTA." the line that matters: "optimize the structure for watch time, replays, comments, and shares." claude writes for the metrics, not just the word count. ▫️ Prompt 3 engineer better hooks "Study the top-performing Reels in [niche] and break down the hook structure, pacing, and emotional triggers used in the first 3 seconds. Then generate 5 new hook variations that are even more curiosity-driven, emotionally charged, and optimized to stop scrolling instantly. Focus on triggers like surprise, fear, ego, urgency, or desire." most reels die in the first 2 seconds. this prompt has claude reverse-engineer what already works, then give you 5 sharper versions to swap in. ▫️ Prompt 4 automate the whole workflow "Build a complete AI-powered content workflow for Instagram in the [niche] niche. The system should identify trending topics daily, generate high-retention scripts, create matching AI visuals, turn them into short-form videos, and generate optimized captions and hashtags. Structure everything as a repeatable workflow designed for consistent daily posting and growth." once the niche and script structure are validated, this turns it into a daily loop. one prompt that handles topic → script → visual → video → caption. these 4 prompts are the building blocks. the setup is what makes them yours. your real value is in the [niche] you plug in. content workflow built in one weekend, daily posting on autopilot from monday.show more

Sora 2 + Linah AI + n8n is borderline... unfair 🤯 This stack auto-generates UGC ads at scale with OpenAI’s new Sora 2… and routes them through Linah AI for instant product-in-hand content + delivery. Perfect for ecom brands & agencies that need a constant flow of creatives without bleeding $10K/month on influencers. Instead of chasing creators, shipping products, and waiting weeks… → Upload 1 product image → Add your prompt & pick video quantity (1–50) → Sora 2 generates hyper-realistic UGC ads → Linah AI ensures product-in-hand consistency + brand look → n8n delivers polished videos straight to your account Each one costs cents. Each one comes with full commercial rights. And the whole process is fully automated. No waiting. No freelancers. Just infinite UGC on demand. Want the workflow + prompt pack? Comment “SORA” + like this post (you’ll need to follow so I can DM it over)show more

THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN... SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.show more

THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN... SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.show more

My favorite AI workflow lately is my thought-to-post pipeline.... I just go on walks, have a good content idea, ramble it, and have an optimized post in my writing style without typing. It's super simple: 1. Download an AI-powered voice dictation app to your phone (I use Wispr Flow) 2. Go on long walks and let ideas flow - when you get a good one, open Wispr Flow and ramble your thoughts (doesn't need to be perfect) 3. Notes auto-save. These become the core ideas for posts later 4. Open Claude and create a new Project called "Post generator" 5. Use this prompt: "I’m going to provide you with my own written material, and your task will be to understand and mimic its style. You'll start this exercise by saying "BEGIN.” After, I'll present an example text, to which you'll respond, "CONTINUE". The process will continue similarly with another piece of writing and then with further examples. I'll give you unlimited examples. Your response will only be "CONTINUE.” You're only permitted to change your response when I tell you "FINISHED". After this, you'll explore and understand the tone, style, and characteristics of my writing based on the samples I've given. Finally, I'll prompt you to craft a new piece of writing on a specified topic, emulating my distinctive writing style" 6. Now's the fun part: Go to Twitter Analytics and download your top posts (Premium → Analytics → Content → Download button) 7. Paste your best-performing tweets into Claude repeatedly until it says "FINISHED" 8. Take your voice notes, paste them into your trained Claude Project, prompt "make a post in my writing style" 9. Post is ready to go. Polish and edit slightly *if* needed. The AI is trained on how you actually write, not generic content. Your voice notes capture your real, raw thoughts without the friction of typing. I have my best ideas while walking. If I try to write them in my notes app mid-walk, I forget halfway through. Voice dictation captures everything as I ramble. Game changer for turning scattered thoughts into polished posts!show more

What Actually is Sei Network's “Giga” Upgrade? Sei Network’s... (Sei) Giga upgrade is a major overhaul designed to make the network faster, more scalable and better suited for high-performance onchain trading. Put simply, Giga is rebuilding three critical parts of the blockchain: consensus, execution and storage. (1) The first track focuses on consensus, with upgrades such as Autobahn designed to improve how Sei validators agree on the state of the chain. (2) The Ares upgrade targets execution, the part of the blockchain responsible for actually processing transactions. (3) Eidos focuses on storage, which is becoming increasingly important as blockchain throughput rises. Why does storage matter? Every transaction a blockchain processes has to be recorded. If the database cannot write data as quickly as the network executes transactions, higher throughput eventually becomes meaningless. Eidos is designed to solve that bottleneck. (4) Sei plans to replace the traditional Merkle-tree structure used for EVM state with FlatKV, a flat key-value database where updating one piece of state requires essentially one write. A lattice hash, or LtHash, is then used to maintain a verifiable fingerprint of the entire state without repeatedly recalculating an entire hash path. (5) Eidos also separates live EVM state from other blockchain data. This means transactions accessing current state no longer have to compete with historical data for the same database resources. (6) Sei is also introducing LittDB-backed storage for blocks and receipts. These records are written once but queried repeatedly, making them a different workload from constantly changing blockchain state. Older historical data will eventually move away from active nodes into archival storage, allowing nodes to focus their resources on the data needed for real-time operations. The interesting part is how Sei plans to deploy all of this. Instead of shutting down the network and migrating the entire database at once, Eidos is designed to migrate storage while Sei continues producing blocks. The old and new systems can run side by side during the transition, with data moved in batches and integrity checks performed throughout the process. The first phase arrived on Sei mainnet with the v6.6 release in August 2026, beginning the separation of EVM state and introducing improvements to the pruning process. The broader Eidos architecture, including FlatKV, LtHash, the new receipt store and off-node archival storage, is expected to arrive through subsequent releases. Sei’s ultimate Giga target is 200,000 transactions per second. But reaching that kind of execution speed requires more than a faster transaction engine. The blockchain also needs a storage system capable of keeping up. That is essentially what Eidos is trying to build. Giga is not just about making Sei execute transactions faster. It is about rebuilding the infrastructure underneath that speed so the network can actually sustain it.show more

How to stay sane VTubing for a living! (From... a full-time Vtuber and manager) 1 - Let people know you are in it full-time: Let your audience know this relies on their support without holding them hostage; Let them know you value their support, yes also to lurkers, yes also to people who can only be there for a few minutes a day. They are who made you. 2 - Be greatful: Don't whine about failures, celebrate the times you get back up. Nobody likes consistent negativity on their feed. Of course you can express when you are struggling, BUT consider building yourself AND your audience up, tons better for your MH. 3 - Budget properly: Yes, that new model or $300 background looks good, but surviving looks better, staying out of debt feels loads better. Keep aa 50-50 from your goals as a wage and only reinvest 50% of your VTuber stonks into VTubing, especially into projects that make very little back like music, skebs, etc. 4 - Be friendly, not a pushover: Risking your safety, mh, privacy is never worth it. Nobody should be making demands beyond the boundaries and timescale you set. Collab not comfortable for your schedule, don't do it. Friends pushing you to show your irl info, not your friends. Chat asking too much from you, you can give them a friendly no. It's your space, curate it to your needs. 5 - Work hard and reflect: Nobody wants to admit they have been lazy or could have done more, but sometimes it's good to admit to yourself that you were expecting too high a number for the content you put out. It sucks, but looking into your weaknesses as well as strengths is the only way to do better in the future. 6 - Make time: Don't spend 12h a day streaming when you get paid less than minimum wage per hour, stop burning yourself out for zero discoverability. Your time is better spend brainstorming and creating more off-stream content, looking at other streamers content and learning from those that came before you. Your time is so valuable, don't waste it. 7 - Finally: You are a person off-stream!!! Eat, drink, workout, socialize if you can. Have secrets. Have a life, friends and interests outside of stream. You don't need to share everything with your chat and community, and likewise you DON'T have to share your VTubing journey with your family if they don't need to know. If it's full-time, it's a job, there needs to be a step back at the end of the day, for your own sanity. Hope this reaches those that want to go full-time as a VTuber!!!show more

Phase Shift Initiated Since before GTC 2024, NVIDIA GDN... (Graphics Delivery Network) has been a strong catalyst for the enthusiasm we have seen for our innovation, not only among the community but also among the team. NVIDIA’s technology, platforms, and teams have consistently inspired us - with GDN being no exception. Recently, we’ve recalibrated our development efforts, doubling down on bringing our release to GDN’s cutting-edge infrastructure. Five of our developers are now fully focused on GDN integration, and in this week alone, we’ve achieved four major backend milestones, and are quickly closing in on three more. These advancements are propelling Web3 technology directly onto NVIDIA GeForce Servers. By harnessing NVIDIA GDN platform, we’re transforming high-fidelity 3D content into a seamless Web3 experience accessible anywhere—directly in your browser. No downloads. No accounts. Just Blockchain. This breakthrough eliminates the reliance on high-end hardware, redefining accessibility for industries like gaming, manufacturing, and media. With Kondux and GDN, even the most resource-intensive 3D applications can be effortlessly streamed to any device, delivering unmatched performance and interactivity. We’re not just overcoming barriers; we’re creating an entirely new playground for high-fidelity 3D assets.show more

Claude can make your own money printer That is... exactly what happened to me I wrote my own script It took me 6 hours On the very first night the bot made $2,705 profit Copytrade: Wallet: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions scripts and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated the skill executes a predefined multi step process These workflows can include data analysis document generation automation scripts or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions Claude performs tasks using consistent methodology rather than ad hoc prompting Testing and iteration Skills are continuously refined through triggering tests functional validation and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity improves output consistency and scales productivity across thousands of tasksshow more

It is spectacularly easy to get shadowbanned on X... One wrong reply and your reputation can go up in flames Luckily there are a few steps you can take to reduce your odds of getting banned by 10x 🔶First step: Don't reply from notifications Notifications are a minefield because hidden, spammy replies are shown to you as normal replies It's never worth replying from here. You risk replying to low reputation accounts without even knowing it Just reply normally from the posts so you know what's marked as spam 🔶Second step: turn off sensitive content in your settings Engaging with sensitive content could hurt your reputation score Little do you know, but a ton of content that’s not actually sensitive, gets marked as sensitive Turn off sensitive content in your settings, then become shocked when you see how much innocent content is marked as sensitive (tutorial video below) 🔶Third Step: Check for following/follower ratio before hitting reply Easy check to take before every reply This is brought up in the code 100 different times as a reputation destroyer Make sure to hover over people’s name before replying and check for their ratio 60% followers to following is the ratio cut off point 🔶Fourth Step: Don’t feed the trolls There are 100+ labels for toxic behavior in the code Engaging with anyone with these labels hurts your reputation For some of these labels they can be applied just for “insults” Best not to engage with any trolls (also good for your mental health) 🔶Fifth Step: Get the blue checkmark The easiest step of them all It’s coded into the algo that if you have the blue checkmark you can’t be marked as spam Yes it costs money, but if you have any serious interest of growing on this platform, it’s a must have 🔶🔶🔶🔶🔶 I've said this before, but it absolutely stinks you have to take all these steps just to get some reach At times, it can be like walking in a minefield It doesn't exactly make a social media platform feel very 'social' I have high confidence Elon is working to fix this. He's mentioned it before. But in the meantime, take these steps and be careful. It's worth it if you care about being seen on this platform. I'm 100% confident brighter days are ahead. Let me know below if any of these tips helped and what other steps you take to avoid getting banned.show more

For almost a year, I have been building a... simpler and more dependable way for applications to respond to activity on Cardano. Today, I am incredibly proud to release OgmiosDotnet.BlockchainEvents v1.0.0. BlockchainEvents is an open source transaction filtering, pub sub and event delivery layer for Cardano. It connects to Ogmios, evaluates every transaction against completely customisable rules, and emits only the activity an application has chosen to receive as standard CloudEvents. The rule engine can be shaped around almost any requirement. An application or applications can listen for activity involving a particular address, asset, policy, smart contract, governance action, treasury withdrawal, metadata value or DEX transaction. Builders can also create entirely new rules around their own domain logic. A project can operate one BlockchainEvents instance and allow many different applications to subscribe to the specific Cardano events they care about. A wallet could subscribe to address activity. A governance platform could subscribe to proposals and votes. A DEX service could subscribe to swaps involving selected assets or contracts. The blockchain connection, transaction processing and event infrastructure are shared, while each application receives its own relevant stream of events. This can remove considerable duplication across a project, reduce infrastructure costs and give development teams one dependable event layer to build around. Consumers can be as simple as an HTTP endpoint. When a transaction satisfies a rule, BlockchainEvents transforms it into a CloudEvent and automatically posts it to the subscribed application. Events can also be consumed through gRPC and server sent events. This allows applications to use Cardano data exposed through Ogmios without installing an Ogmios SDK, tracking a particular SDK version, maintaining their own WebSocket connection or implementing the chain sync protocol themselves. Delivery is backed by a durable queue. When a consumer becomes unavailable, its events remain waiting. Once the application returns, delivery resumes automatically. Applications can restart, deploy or experience a temporary outage without immediately losing the Cardano activity produced while they were offline. It is also extremely fast. During a full local Docker stress test, the pipeline processed more than 1,100 transactions per second through the rule engine, CloudEvents transformation, pub sub and durable queue, with approximately one millisecond p99 processing latency and zero failed emissions. The entire stack can be deployed through Docker across cloud providers, infrastructure providers and self hosted environments, while the underlying queue and delivery architecture remains abstracted from the applications using it. This project was funded through Catalyst Fund 14 after receiving 197 million ADA in yes votes across 420 votes cast. I am deeply grateful to everybody who voted for the proposal, followed the development, tested the releases, shared feedback or supported both me and this project over the past year. A great deal of work has gone into reaching this point, and I am genuinely excited to see what Cardano builders create with it. Further technical details, reliability results and the Minswap implementation are included below. Repository: Release: The possibilities from here are endless, this brings industry standard event driven architecture right to Cardano.show more

HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES.... AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇show more

How a 22-year-old developer built a full 3D Jet... Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423 He launched a single-prompt generation workflow that built the entire HTML5 project from scratch: Top layer: A Three.js and WebGL rendering pipeline dynamically creates 3D water physics, real-time wave dynamics, dynamic lighting, and jet ski fluid mechanics, all written autonomously inside one output file without external frameworks. Bottom layer: The Claude Opus 5 engine processed a massive 690-million-token context window to generate the complete gameplay logic, collision handling, dynamic sound generation, controls, and UI layout directly from a detailed initial system prompt. The trend of single-prompt 3D game creation is rapidly exploding across media and indie development. The author monetizes this tech stack through three main channels: 1. Viral Content & Media Systems: Short-form breakdown videos driving massive reach, monetized via promo placements, prompt-pack access, and private developer communities. 2. Rapid Hypercasual Prototyping: Testing 10+ WebGL mechanics per day, flipping fully functional browser games on itch io or CodeCanyon, and licensing prototypes directly to casual game portals. 3. Interactive WebGL Client Solutions: Delivering custom 3D promotional browser games and interactive brand experiences for clients in 48 hours instead of weeks. First month results: > WebGL games generated: 24 > Viral impressions generated: 3.8M+ > Total revenue across licensing & content: $21,400 The AI completely automated the core development lifecycle: Claude Opus 5 built the physics engine, rendered 3D graphics in WebGL, hooked up audio controllers, and generated interactive browser logic with zero manual line-by-line coding. Bookmark it and check article 👇show more

A cinematic AI-powered visual experience showcasing how imagination can... be transformed into stunning digital worlds From futuristic environments and creative fashion visuals to seamless character transformations every scene blends technology creativity, and storytelling into one immersive piece. Made with MiniMaxH3 on WeryAI Wery Prompt: A stylish young South Asian woman with long dark hair, natural glamorous makeup and an elegant modern outfit. Keep her face, hairstyle, outfit and appearance consistent throughout the entire video. Scene 1 — 0–4s: She sits at a sleek modern workstation in a futuristic studio. She looks directly into the camera and says naturally: “What if you could turn your ideas into reality in just a few clicks?” Natural facial expressions and hand gestures. Slow cinematic camera push-in. Perfect lip-sync. Scene 2 — 4–7s: She turns to her computer and opens WeryAI. She types a creative idea and says: “That’s exactly what I love about WeryAI.” The interface responds with elegant AI processing animations and glowing digital effects. Scene 3 — 7–11s: Her simple idea transforms into stunning AI-generated visuals: a fashion scene, futuristic city, cinematic character, premium product advertisement and social-media video. She looks impressed and says: “Give it an idea, and watch it come to life.” Use smooth cinematic transitions and dynamic camera movement. Scene 4 — 11–15s: She turns back toward the camera with a confident smile and says: “WeryAI. Your imagination, powered by AI.” The camera slowly pulls back as the WeryAI brand reveal appears with a clean futuristic glow. Audio: Natural confident female voice, warm energetic delivery, subtle futuristic background music and soft cinematic sound effects. Keep dialogue clear and prominent. Lip-sync: The woman must visibly speak every line with accurate lip synchronization, natural mouth movement, blinking and facial expressions. Visual quality: Photorealistic, cinematic 4K, realistic skin, smooth camera movement, premium lighting, shallow depth of field, consistent character. No subtitles, no distorted face, no warped hands, no extra fingers, no flickering, no random text, no watermark.show more

how consumer apps are making money from tiktok slideshows...... most people think app marketing means paid ads or influencer deals. the apps actually printing money right now are doing neither. they're running networks of slideshow accounts that look like normal content pages, and almost nobody notices the machine behind them. here's how it works. you follow what seems like a regular relationship advice account. the posts are relatable, sometimes rude, exactly what the algorithm feeds you. then you hit slide 5 and there's an app mentioned. every single post on that account funnels to the same app. one publisher running this playbook did $40k last month, verified by sensor tower, and that's one publisher. the part people miss: most of these accounts aren't even run by humans making content. the characters are AI. the images are AI. the "person" whose thoughts you're reading doesn't exist. viewers aren't fact-checking, they're reacting to whether the post is relatable, and AI passes that bar daily. myths keeping people out of this: "i need to make original content for my app." false. the fastest path is taking a slideshow that's already proven to convert, keeping the structure and pacing exactly, and swapping only the ad slide. the performance lives in the format, not in your creativity. "i can find my images on google." false. google image search is flooded with stock assets that read as fake instantly. pinterest is where the authentic-looking references live. and you don't post those directly, they're copyrighted, you feed them to AI as reference and generate your own singular images. "every post needs to go viral." false. accounts in this niche post variations constantly and most land under a thousand views. that's the model working. the flopped posts still collect saves and likes, and one breakout carries the month. volume across variations beats one polished post every time. "the ad slide should change with each post." false. everything around it should vary, the pain point, the images, the captions. the ad slide stays identical and polished every single time. you write that caption once, make it perfect, and lock it. consistency on the conversion slide, chaos everywhere else. what actually drives this: you can now clone a proven slideshow into a hundred variations, schedule a month of posts in advance, and have an ai agent handle the posting from your terminal. the production bottleneck is gone. the only real constraint left is picking a post that's already validated and moving before the account farms make this the default playbook for every app on the store. structural notes on what i changed: the original was a chronological tutorial, this reframes it as a reveal (the machine exists, here's how it works) with the tool mechanics compressed into the final paragraph instead of a walkthrough. the myths section absorbs the google/pinterest tip, the ad slide advice, and the volume logic, which were the three strongest insights buried in the tutorial. dropped the ui narration entirely, written format doesn't need it.show more

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This guy built an AI pipeline that generates hyperrealistic... fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.show more