Myttle

he is not opening an AI app he is texting an agent like a coworker. that is the important part. Hermes lives outside the chat tab. Telegram becomes the interface. the cloud box keeps running. the agent remembers what happened before. most agents die when you close the window. Hermes is built around the opposite idea: - keep the conversation alive - run background tasks - save useful patterns - turn repeated work into skills - clean old skills when they rot that is why the messenger UI matters. it makes the agent feel less like software and more like someone on the team who never logs off. the next useful AI product will not be another empty chat screen. it will be the worker sitting behind the message bubble.

he asked his home assistant what it can do and the answer sounded less like Google Home and more like a small operator living in the house. > lights > music > weather > memory > Home Assistant > local voice > no cloud brain required this is the part people miss about local LLMs. the win is not private chatbot at home. the win is when your house stops waiting for Google’s servers to understand a sentence. “do i need a coat today?” - it checks the weather sensor. “turn off everything except the kitchen.” - it understands the room context. “play something in the living room.” - it touches the media stack. Google Home made the smart home feel easy. Home Assistant + a local LLM makes it feel owned. harder to build, yes. but once it works, the assistant is not a product anymore. it is part of the house.

one guy opens Bookmap and the chart stops being the chart the candles are the toy version. the real game is sitting underneath: - bid - ask - spread - queue - hidden inventory - liquidity that moves before you click retail thinks price is fair value. market makers treat price like an answer to a math problem. where should i quote? how wide should i quote? how much inventory am i carrying? how likely am i to get picked off? that is why the fill feels instant. you did not find a price. a machine solved for one, posted it, waited for someone impatient enough to cross the spread, and collected the edge. the chart shows where price went. the book shows who got paid to send it there.

THE $600 MAC MINI IS NOT THE AI BUSINESS it is the box that keeps the business awake. that is the part people miss. the Mac mini does not need to run a 70B model locally. it just needs to sit there 24/7, quietly moving work between tools. Kimi thinks. n8n routes. the Mac mini stays online. that is enough to sell a real service. > client messages the bot > n8n catches the request > Kimi understands the intent > calendar gets checked > CRM gets updated > email gets sent > owner wakes up to booked work this is not AI assistant content. this is local business infrastructure. the old version was: - AWS setup - VPS bills - DevOps work - cloud automations - $500/month before revenue the new version is uglier: - $600 Mac mini - $3-$8/month power - Kimi API as needed - n8n self-hosted - Telegram or WhatsApp as the front door sell it to 10 appointment businesses at $200/month. salons, clinics, repair shops, cleaners, tutors. they do not care what model is behind it. they care that every lead gets answered before a human opens the phone.

THIS IS WHY A ROCKET COMPANY WANTS THE CODE EDITOR the stage says Cursor, but the deal is not really about an IDE. the IDE is the surface. the valuable part is the distribution layer sitting inside the developer’s day: > repo open > file tree loaded > prompts tied to real code > diffs reviewed in place > agents running tests instead of just talking that is the part SpaceXAI wants near Colossus. a million-H100-equivalent training cluster is useless if the output never reaches the people who actually ship software. Cursor already owns the doorway. developers open it before the dashboard, before the docs, before the meeting notes. the model does not need to convince them to change workflows. it just needs to get better inside the window they already trust. the play is simple: - train stronger coding models on Colossus - ship them through Cursor - watch how builders use them - feed that workflow back into the next model $60B sounds insane if you think Cursor is a VS Code fork. it makes more sense if you think the next AI lab is buying the place where code gets written.

THIS IS HOW CLAUDE CODE STOPS HANDING YOU ITS BUGS the old workflow was stupid. > Claude writes code > tests fail > you read the error > you paste it back > Claude tries again that is not an agent. that is you being the debugger with an AI keyboard. the better setup is a loop: - implement the task - run the test suite - read the failure - classify the error - fix the root cause - run it again - stop only when green the important part is not run npm test. the important part is forcing Claude to say what broke before it edits. assertion failure means logic. type error means shape mismatch. timeout means async or state. module not found means path or dependency. without that step, the agent guesses until the red disappears. with it, the agent starts debugging like an engineer. one slash command. one PostToolUse hook. one failure-reading protocol in CLAUDE.md. now every edit gets checked automatically, and every failed test becomes input for the next attempt. you stop relaying errors. Claude reads its own red and works until green.

THIS IS THE MOMENT THE AI BILL STOPS LOOKING NORMAL a tiny AMD box is running the kind of model people used to rent. that is the only thing that matters. not the chassis. not the AI PC label. the fact that 128GB of unified memory lets a local machine hold models that used to force you into subscriptions. the old dev stack looked normal until you wrote it down: - Claude Code Max: $200/month - ChatGPT Pro: $200/month - Cursor: $20/month - extra API spend when agents run too long $420/month. $5,040/year. and the meter resets every 30 days. the new stack is brutally simple: > install Linux > run Ollama > keep the local coding model warm > expose the endpoint > point the agent loop at your own machine same habit. different economics. this will not replace frontier reasoning. but daily coding, doc search, local assistants, embeddings, Whisper, overnight agents? that work does not need a subscription forever. the cloud becomes the emergency room. the box becomes the workshop.

THIS IS HOW A BRAND GETS BUILT BEFORE THE PRODUCT EVEN EXISTS Claude Design is building the whole brand kit: > colors > typography > mockups > banners > visual language > assets that all look like they came from the same company that is where most founders lose weeks. they do the landing page first. then the deck looks different. then the mobile screens look different. then the ads look different. then the brand is just 12 disconnected files in a folder. the better workflow is backwards: > write the brand brief > define the audience > choose the positioning > build the design system > then generate the landing page, deck, app screens and ads from the same source that is how one person turns an idea into something that looks funded. not because Claude magically replaces taste. because it stops rebuilding the same visual decisions from zero every time. the article said the brand ended up making $8.4K/month.

THIS IS THE PART OF AI MUSIC PEOPLE KEEP IGNORING the song is not the business. the system around the song is. the video shows the useful layer: Claude is running the spreadsheet behind a Suno music workflow. not writing one cute prompt. not making one random track. organizing the machine around it. that is where the money is if this ever becomes real: - track ideas - titles - metadata - release folders - Spotify descriptions - distributor sheets - promo copy - stream reports - what to make next Suno gives you the music. Claude turns the mess around the music into an operating system. the amateur workflow is: > make a song > upload it > hope Spotify finds it the operator workflow is: > generate in a narrow niche > package every release cleanly > ship through a distributor > track streams > double down on what gets saves > repeat until the catalog compounds that is the difference between AI song and an automated music business. most people will flood Spotify with one-off slop. the winners will treat every track like inventory.

THIS IS HOW A $10K WORLD-BUILDING OFFER STOPS LOOKING LIKE A GOOGLE DOC HY-World 2.0 does not just make a pretty AI clip. it turns an input into a 3D world you can actually move through. that is the shift. most clients do not need “an idea”. they need the thing a team can build from: - characters - factions - locations - rules - timelines - visual direction - a world that stays consistent after page one the old version took a studio room. writers, artists, lore designers, concept people, months of back-and-forth. the new version looks more like this: > map the lore into a knowledge graph > turn the graph into a World Bible > generate the playable 3D direction > populate it with agents that have goals > hand the client a universe their team can use that is why this becomes a real offer. a complete world system for an indie game, TTRPG, author, or film team. $5K-$15K for the bible. more if the world becomes interactive. the creator who wins here is not the one with the weirdest prompt. it is the one who can turn a messy idea into a universe other people can actually build inside.

THIS IS WHAT LOCAL AI LOOKS LIKE WHEN IT STOPS BEING A TOY he is holding a 128GB AI mini PC built around AMD Strix Halo. that is the whole shift. for years, the answer to large models was simple: rent the cloud. pay the API. wait for the GPU. watch the meter. now the model starts fitting on the desk. > Ryzen AI Max+ 395 > 128GB unified memory > 4TB NVMe > configurable VRAM > local agents > LLMs running without sending the work out the EVO-X2 is the same category, just pushed harder. up to ~110GB usable VRAM on Linux. Qwen3-235B in the actually fits range. DeepSeek and Llama 70B without asking a cloud provider for permission. that does not kill Claude or OpenAI. it kills the habit of renting every boring run. code agents internal docs private assistants overnight loops tests you would never spend API credits on the cloud becomes the expensive specialist. the box becomes the daily worker.

THIS IS HOW A WORLD CUP SHORTS PIPELINE GETS BUILT WITH ONE CLAUDE CONNECTOR i’m not talking about football clipping. that’s the lazy version. the clip shows the real play: > open YouTube > find a World Cup clip already pulling attention > connect Higgsfield MCP inside Claude > paste the link > turn the format into a new vertical Short > post before the match cycle cools off this is vibe coding for content. Claude becomes the director. Higgsfield becomes the video layer. World Cup demand becomes the distribution engine. no Premiere timeline no CapCut hunting no scrubbing 90 minutes for one hook 2026 is different: - 48 teams - 104 matches - 3 host countries - YouTube as FIFA’s preferred platform - US + Canada bringing the highest-RPM audience in sports the obvious play is stealing goals. the better play is copying the structure. a match creates the attention. a viral clip reveals the format. Claude turns it into a script. AI tools turn it into a new Short. the money is not in being first to upload the goal. it is in being first to package the moment while everyone is searching.

@0xVishnya nihuya 🫣

Rain just launched what crypto cards were missing 👇 The infrastructure behind nearly $2.4B in stablecoin card volume just shipped native Rewards - Points minted onchain, every swipe - Cash back, flights, hotels - Partners set their own rates, fully white-label - Built into the issuing stack, no third-party vendor - Avalanche Card already saw 25% more daily spend Now projects don't have to print and dump their own token just to fund rewards This is how crypto cards start to actually compete with traditional banks on retention and engagement Bullish?

THIS TINY AMD BOX IS WHY THE $200/MONTH AI STACK IS STARTING TO LOOK OLD the clip looks like another AI PC demo. it is not. Lisa Su is holding the part that actually matters: - 128GB unified memory - Ryzen AI Max+ 395 - local 70B / 200B-class models - Ollama on the machine - no API meter running in the background that changes the math for Claude Code people. most builders are still renting their workflow every month: - Claude Max - ChatGPT Pro - Cursor - API credits - cloud GPUs when the model gets too heavy then a box like this shows up and the question gets uncomfortable. why am i paying forever for work that can sit under my desk? the local version will not beat the cloud on every task. but for boring daily agent work, code review, doc search, local inference and overnight loops, “good enough” is the whole business model. $1,800 once. a few dollars of power. the cloud becomes the premium layer. the box becomes the workbench.

my dad runs a small plumbing business. he does not understand crypto. he does understand leaks. he saw the dashboard and asked why some markets had slow red numbers. i told him that was edge decay. he nodded like i said something normal. then explained it back better than i could. a small leak is cheap if you catch it early. ignore it long enough and the whole wall becomes expensive. same with Polymarket. a misprice is useful when it is fresh. then wallets find it. then odds react. then CT notices. then liquidity gets weird. then everyone wants the same exit. the trade did not become wrong. it became expensive to fix. his rule was simple: fresh leak only. if the market already smells like wet drywall, skip. the dashboard now treats old signals like damage. not impossible. just costly. they need more proof to survive. stronger depth. cleaner exit. new wallet motion. less crowd noise. start here: t.me/poly_parlay_bo… he said most traders call the plumber after the ceiling collapses.

@maqxbt thanks for quote

Fable 5 is the most powerful public model ever released and it's the weakest version of itself same base as Mythos 5, different ceiling 80.3% on SWE-Bench Pro. first model to break 90% on Hex analytics but here's what keeps me up at night Mythos 5 - the real one - stays locked behind Project Glasswing, uncovering thousands of critical vulnerabilities. Firefox, OpenBSD, infrastructure you use every day Anthropic just made the two-speed AI release permanent one model for the public, one for the trusted few we got the car, they kept the engine

@Nekt_0 Amazing setup

THIS IS HOW A $500 BACKEND CHORE GETS COMPRESSED INTO A 45-MINUTE AI WORKFLOW The video is not about "AI building an app" by magic. It is a clean handoff chain: sketch the database schema, put it in Google Sheets, feed the app idea and data structure into ChatGPT, let Claude Code turn it into SQL, then use PlanetScale CLI to make the tables real. That is the part beginners miss. They ask a chatbot to "make my database" and get toy output. The better move is separating the work: human decides the product logic, ChatGPT pressure-tests the structure, Claude writes the boring files, the CLI executes. The article is the same idea with fewer moving parts: AI leverage is not one perfect prompt. It is connecting models to the actual production path until a vague app idea becomes shipped infrastructure. No backend contractor, no weekend schema rabbit hole, no copy-pasting random SQL, no pretending a chat response is a database. Just schema, review, generated SQL and execution. The database took 45 minutes, and most of that was learning the CLI. That is the real point. The tool did not replace thinking. It removed the dead labor around it.