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🛫 A complete Airbus-class turbofan — fully parametric, animated, built entirely in the browser. Created in confBuild with Claude Fable 5: ⚙️ Real internals — 7-stage compressor, annular combustor, 4 turbine stages 🌀 Two-spool animation: HP & LP shafts at real differential speed 🔥 Flow lines & exhaust react...

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Daniel

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Have a look at one of the greatest examples of industrial engineering art ever created. This is the rotor assembly of the Ansaldo Energia GT36, one of the most advanced heavy-duty gas turbines ever developed. What looks like a collection of polished metal blades is actually the result of 3.7 million hours of engineering, combining decades of research in aerodynamics, combustion, metallurgy, cooling systems and precision manufacturing. A gas turbine works by compressing enormous volumes of air, mixing it with fuel, burning it at extreme temperatures, and extracting energy from the expanding gases through multiple turbine stages. That is why no two blade rows look the same. Across this rotor assembly, the colours, shapes and surface finishes constantly change because each section is solving a different problem. Some blades are designed to move and control massive airflow volumes, while others must survive the most extreme environment inside the machine. The most advanced turbine blades contain microscopic internal cooling channels. The cooling does not come from room-temperature air. Compressed air extracted from the compressor section already heated to 650 degrees Celsius, is redirected through passages inside the blade. It then exits through thousands of tiny holes, creating a thin protective cooling film in real time 24/7 over the surface of the blades while the surrounding combustion gases exceed 1,500°C all while rotating at 3000 RPM. A blade is not surviving because the metal alone can withstand the heat. It survives because engineers created a controlled thermal environment around it. The blades rely on advanced nickel-based superalloys containing elements such as rhenium, tungsten, cobalt and chromium, protected by metallic bond coats and ceramic thermal barrier coatings such as yttria-stabilised zirconia. These coatings are one of the most closely guarded proprietary technologies in turbine manufacturing. Every blade requires precision casting, advanced machining, laser drilling and microscopic inspection. A manufacturing defect measured in fractions of a millimetre can affect a rotor weighing around 150 tonnes and spinning at 3,000 RPM. The complete GT36 turbine system weighs around 520 tonnes and, in its most efficient combined-cycle configuration, can produce approximately 800 MW of electricity at around 64% efficiency enough to supply roughly 500,000+ homes. A complete power plant built around a machine like this can cost around $500-600 million, but the true value is not the steel and turbine machinery. It is the industrial capability and know how required to build a machine designed to operate for 30+ years and more than 100,000 equivalent operating hours, while repeatedly surviving one of the harshest environments humans have ever engineered. This is what the peak of industrial engineering looks like before it starts moving, this is what powers the world. Engineering is Art Video by AnsaldoEnergia
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Have a look at one of the greatest examples of industrial engineering art ever created. This is the rotor assembly of the Ansaldo Energia GT36, one of the most advanced heavy-duty gas turbines ever developed. What looks like a collection of polished metal blades is actually the result of 3.7 million hours of engineering, combining decades of research in aerodynamics, combustion, metallurgy, cooling systems and precision manufacturing. A gas turbine works by compressing enormous volumes of air, mixing it with fuel, burning it at extreme temperatures, and extracting energy from the expanding gases through multiple turbine stages. That is why no two blade rows look the same. Across this rotor assembly, the colours, shapes and surface finishes constantly change because each section is solving a different problem. Some blades are designed to move and control massive airflow volumes, while others must survive the most extreme environment inside the machine. The most advanced turbine blades contain microscopic internal cooling channels. The cooling does not come from room-temperature air. Compressed air extracted from the compressor section already heated to 650 degrees Celsius, is redirected through passages inside the blade. It then exits through thousands of tiny holes, creating a thin protective cooling film in real time 24/7 over the surface of the blades while the surrounding combustion gases exceed 1,500°C all while rotating at 3000 RPM. A blade is not surviving because the metal alone can withstand the heat. It survives because engineers created a controlled thermal environment around it. The blades rely on advanced nickel-based superalloys containing elements such as rhenium, tungsten, cobalt and chromium, protected by metallic bond coats and ceramic thermal barrier coatings such as yttria-stabilised zirconia. These coatings are one of the most closely guarded proprietary technologies in turbine manufacturing. Every blade requires precision casting, advanced machining, laser drilling and microscopic inspection. A manufacturing defect measured in fractions of a millimetre can affect a rotor weighing around 150 tonnes and spinning at 3,000 RPM. The complete GT36 turbine system weighs around 520 tonnes and, in its most efficient combined-cycle configuration, can produce approximately 800 MW of electricity at around 64% efficiency enough to supply roughly 500,000+ homes. A complete power plant built around a machine like this can cost around $500-600 million, but the true value is not the steel and turbine machinery. It is the industrial capability and know how required to build a machine designed to operate for 30+ years and more than 100,000 equivalent operating hours, while repeatedly surviving one of the harshest environments humans have ever engineered. This is what the peak of industrial engineering looks like before it starts moving, this is what powers the world. Engineering is Art Video by AnsaldoEnergia

Ammanichanda

101,294 Aufrufe • vor 2 Monaten

I'm making $8,000 a month building Claude skills for other people's businesses. I call it the AI Concierge. I have 5 clients paying $1,200 to $2,000 a month each. My effective rate is about $1,200 an hour. Every engagement runs on one framework I call AOA: Audit, Optimize, Automate. Here's the entire model: 1) Most people skip straight to Automate. That's the fun part. It's also how you end up automating a broken process. 2) Audit first. The client shares their screen and walks the process in painful detail while I take notes in a Google Doc and interrupt with a million questions. 3) Capture the plumbing, not just the steps. Who owns each step, which tool it runs in, where the data lives, and whether that tool can connect to Claude. 4) Hand the audit doc to Claude and ask it to find a better way. Then push back. Claude's first synopsis is usually partly wrong, and the client catches it. 5) Cut the fat. Most processes are 15 steps and only need to be 11. Most clients use 3 tools where 2 would do. 6) One home per fact. One client stored customer data in ClickUp and the CRM. We killed the ClickUp copy. Duplicates are just human error waiting to happen. 7) Have Claude write the build plan. It splits the process into skills, scaffolds each one, and lists next steps. Then you lock the doc. 8) A skill is a markdown file plus reference files. A reference file is just an example of what good looks like. Claude builds all of it. 9) Build one skill at a time from the build plan. If the audit was done right, Claude one shots it. Test on a real run, let Claude interview you, iterate. Version 2 is usually the keeper. 10) Draft first, never auto send. A speed to lead skill drafts replies for review on day one. It earns auto send later. Two things that make this work: 1) Audit and Optimize are 80% of the work. Automate is the 20%. You have to earn the right to build. 2) Skills do one thing well. Split at every point a human or a wait interrupts the chain, then let an orchestrator run them back to back. Full step-by-step playbook for starting a profitable AI Concierge business (free): Full breakdown below. Enjoy!
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I'm making $8,000 a month building Claude skills for other people's businesses. I call it the AI Concierge. I have 5 clients paying $1,200 to $2,000 a month each. My effective rate is about $1,200 an hour. Every engagement runs on one framework I call AOA: Audit, Optimize, Automate. Here's the entire model: 1) Most people skip straight to Automate. That's the fun part. It's also how you end up automating a broken process. 2) Audit first. The client shares their screen and walks the process in painful detail while I take notes in a Google Doc and interrupt with a million questions. 3) Capture the plumbing, not just the steps. Who owns each step, which tool it runs in, where the data lives, and whether that tool can connect to Claude. 4) Hand the audit doc to Claude and ask it to find a better way. Then push back. Claude's first synopsis is usually partly wrong, and the client catches it. 5) Cut the fat. Most processes are 15 steps and only need to be 11. Most clients use 3 tools where 2 would do. 6) One home per fact. One client stored customer data in ClickUp and the CRM. We killed the ClickUp copy. Duplicates are just human error waiting to happen. 7) Have Claude write the build plan. It splits the process into skills, scaffolds each one, and lists next steps. Then you lock the doc. 8) A skill is a markdown file plus reference files. A reference file is just an example of what good looks like. Claude builds all of it. 9) Build one skill at a time from the build plan. If the audit was done right, Claude one shots it. Test on a real run, let Claude interview you, iterate. Version 2 is usually the keeper. 10) Draft first, never auto send. A speed to lead skill drafts replies for review on day one. It earns auto send later. Two things that make this work: 1) Audit and Optimize are 80% of the work. Automate is the 20%. You have to earn the right to build. 2) Skills do one thing well. Split at every point a human or a wait interrupts the chain, then let an orchestrator run them back to back. Full step-by-step playbook for starting a profitable AI Concierge business (free): Full breakdown below. Enjoy!

Corey Ganim

70,760 Aufrufe • vor 20 Tagen

Everyone's building AI agents that run on someone else's server, store memory in someone else's database, and can be shut down by someone else's terms of service. I built one that can't be. FlowClaw is an AI agent that runs on a decentralized distributed computer. Your agent, your conversations, your memory, your tools — all stored onchain on Flow, a distributed network of validator nodes across the world. Not a centralized cloud. Not someone's S3 bucket. A blockchain that functions as censorship-resistant compute and storage for your AI. This isn't a wrapper. Your agent is a Resource — a first-class programmable object in Cadence (Flow's smart contract language) that physically lives in your account's on-chain storage. It can't be duplicated, seized, or deleted by anyone except you. Your encrypted messages, your cognitive memory, your scheduled tasks — they persist on a global distributed ledger that no single entity controls. It's an alpha build. It will break. But it works today on mainnet and I want people to push it this weekend. What it does: You go to authenticate with a passkey (Face ID, Touch ID), and you have a blockchain account in seconds. No wallet. No seed phrase. No tokens needed — gas is sponsored. You're immediately chatting with an AI agent that has real tool execution: live web data, token prices, on-chain balances, Cadence script execution, FLOW transfers. Every message is encrypted client-side before it touches the chain. The agent has a cognitive memory system — it doesn't just remember your last message, it builds molecular memory clusters where related knowledge bonds together for contextual retrieval across sessions. You can spawn sub-agents from a visual canvas to run parallel research. The memory tab shows you exactly what your agent knows. Everything is transparent and everything is yours. 11 smart contracts. No external dependencies. No keeper networks. No account abstraction hacks. Here's the part that matters for the censorship-resistance crowd: FlowClaw supports BYOK — bring your own key. You can plug in any LLM provider. But pair it with Venice and you get the full stack: a censorship-resistant AI model running inference with no content filtering, connected to an agent whose state lives on a decentralized network that no company can shut down, with end-to-end encrypted conversations that nobody can read — not the relay operator, not the LLM provider, not the blockchain validators. Venice doesn't log prompts. Flow can't read your encrypted storage. The relay never sees your plaintext. That's not a privacy policy. That's architecture. You can also use OpenAI, Anthropic, or any OpenAI-compatible provider. The agent platform doesn't care — it's model-agnostic. But the Venice pairing is the one that closes every gap in the stack. For the people tinkering with OpenClaw and the broader open-source agent ecosystem — FlowClaw is exploring what happens when you take the agent off the cloud entirely. Not just open-sourcing the code (though it is), but putting the actual runtime state on a distributed computer. Your agent's memory isn't in a SQLite file on your laptop or a Pinecone index on someone's cluster. It's on-chain, encrypted, and replicated across every validator node on Flow. You own it the way you own a private key — mathematically, not contractually. The blockchain here isn't a gimmick bolted onto an agent for token speculation. It's functioning as the infrastructure layer that replaces AWS. Flow accounts are programmable containers with their own storage, keys, and security capabilities. Passkey authentication works natively because Flow supports P-256 keys at the protocol level — the same curve your phone uses for biometrics. Gas sponsorship works natively because Flow transactions have separate proposer, authorizer, and payer roles built into the protocol. No proxy contracts. No relayers. No ERC-4337. Now here's the part that interests me economically. Every FlowClaw interaction is an on-chain transaction. Every message stored, every memory committed, every session created, every sub-agent spawned. An active user might generate dozens of transactions in a single conversation. Scale that and FlowClaw becomes a real contributor to Flow's transaction volume. Flow.com becomes deflationary at 250 TPS. Applications like FlowClaw that generate high-frequency, storage-heavy transactions are exactly what moves the needle. Every encrypted message uses account storage, which requires FLOW balance to back it. Every transaction burns fees. The more agents running, the more demand for $FLOW — not because of a tokenomics gimmick, but because the protocol literally requires it for compute and storage. FlowClaw doesn't have its own token. The token is $FLOW. The entire platform runs natively on the network — using Flow storage, paying Flow transaction fees, backed by Flow account balances. If FlowClaw succeeds, FLOW captures that value directly. I'm sharing this early because the AI agent space is moving fast and I think the decentralized infrastructure angle is underexplored. Most "crypto AI" projects are tokens with a chatbot attached. FlowClaw is the opposite — it's an agent platform that happens to use a blockchain because the blockchain solves real engineering problems that centralized infrastructure can't. Try it: Github: Create an agent, ask it something, spawn a sub-agent, check your memory tab, pair it with Venice for the full censorship-resistant stack. Break it and tell me what broke. If you think this direction matters, the best thing you can do is use it and give feedback. Your AI agent should be yours. Not your provider's. Not your platform's. Yours.
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Everyone's building AI agents that run on someone else's server, store memory in someone else's database, and can be shut down by someone else's terms of service. I built one that can't be. FlowClaw is an AI agent that runs on a decentralized distributed computer. Your agent, your conversations, your memory, your tools — all stored onchain on Flow, a distributed network of validator nodes across the world. Not a centralized cloud. Not someone's S3 bucket. A blockchain that functions as censorship-resistant compute and storage for your AI. This isn't a wrapper. Your agent is a Resource — a first-class programmable object in Cadence (Flow's smart contract language) that physically lives in your account's on-chain storage. It can't be duplicated, seized, or deleted by anyone except you. Your encrypted messages, your cognitive memory, your scheduled tasks — they persist on a global distributed ledger that no single entity controls. It's an alpha build. It will break. But it works today on mainnet and I want people to push it this weekend. What it does: You go to authenticate with a passkey (Face ID, Touch ID), and you have a blockchain account in seconds. No wallet. No seed phrase. No tokens needed — gas is sponsored. You're immediately chatting with an AI agent that has real tool execution: live web data, token prices, on-chain balances, Cadence script execution, FLOW transfers. Every message is encrypted client-side before it touches the chain. The agent has a cognitive memory system — it doesn't just remember your last message, it builds molecular memory clusters where related knowledge bonds together for contextual retrieval across sessions. You can spawn sub-agents from a visual canvas to run parallel research. The memory tab shows you exactly what your agent knows. Everything is transparent and everything is yours. 11 smart contracts. No external dependencies. No keeper networks. No account abstraction hacks. Here's the part that matters for the censorship-resistance crowd: FlowClaw supports BYOK — bring your own key. You can plug in any LLM provider. But pair it with Venice and you get the full stack: a censorship-resistant AI model running inference with no content filtering, connected to an agent whose state lives on a decentralized network that no company can shut down, with end-to-end encrypted conversations that nobody can read — not the relay operator, not the LLM provider, not the blockchain validators. Venice doesn't log prompts. Flow can't read your encrypted storage. The relay never sees your plaintext. That's not a privacy policy. That's architecture. You can also use OpenAI, Anthropic, or any OpenAI-compatible provider. The agent platform doesn't care — it's model-agnostic. But the Venice pairing is the one that closes every gap in the stack. For the people tinkering with OpenClaw and the broader open-source agent ecosystem — FlowClaw is exploring what happens when you take the agent off the cloud entirely. Not just open-sourcing the code (though it is), but putting the actual runtime state on a distributed computer. Your agent's memory isn't in a SQLite file on your laptop or a Pinecone index on someone's cluster. It's on-chain, encrypted, and replicated across every validator node on Flow. You own it the way you own a private key — mathematically, not contractually. The blockchain here isn't a gimmick bolted onto an agent for token speculation. It's functioning as the infrastructure layer that replaces AWS. Flow accounts are programmable containers with their own storage, keys, and security capabilities. Passkey authentication works natively because Flow supports P-256 keys at the protocol level — the same curve your phone uses for biometrics. Gas sponsorship works natively because Flow transactions have separate proposer, authorizer, and payer roles built into the protocol. No proxy contracts. No relayers. No ERC-4337. Now here's the part that interests me economically. Every FlowClaw interaction is an on-chain transaction. Every message stored, every memory committed, every session created, every sub-agent spawned. An active user might generate dozens of transactions in a single conversation. Scale that and FlowClaw becomes a real contributor to Flow's transaction volume. Flow.com becomes deflationary at 250 TPS. Applications like FlowClaw that generate high-frequency, storage-heavy transactions are exactly what moves the needle. Every encrypted message uses account storage, which requires FLOW balance to back it. Every transaction burns fees. The more agents running, the more demand for $FLOW — not because of a tokenomics gimmick, but because the protocol literally requires it for compute and storage. FlowClaw doesn't have its own token. The token is $FLOW. The entire platform runs natively on the network — using Flow storage, paying Flow transaction fees, backed by Flow account balances. If FlowClaw succeeds, FLOW captures that value directly. I'm sharing this early because the AI agent space is moving fast and I think the decentralized infrastructure angle is underexplored. Most "crypto AI" projects are tokens with a chatbot attached. FlowClaw is the opposite — it's an agent platform that happens to use a blockchain because the blockchain solves real engineering problems that centralized infrastructure can't. Try it: Github: Create an agent, ask it something, spawn a sub-agent, check your memory tab, pair it with Venice for the full censorship-resistant stack. Break it and tell me what broke. If you think this direction matters, the best thing you can do is use it and give feedback. Your AI agent should be yours. Not your provider's. Not your platform's. Yours.

doodlifts

12,172 Aufrufe • vor 7 Monaten

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.
0:33

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.

Blaze

55,926 Aufrufe • vor 5 Monaten

ACTFUN V2 is live on Arc tesnet A complete redesign and the most ambitious version of the platform we've shipped yet. 🧵 Let break down exactly what ACTFUN is, how it works, and why V2 is a big deal. ACTFUN is a Mine-to-Launch token launchpad built on Arc testnet. The idea is simple: anyone can create a token. But instead of a presale or a bonding curve you buy into the community mines it by writing funny posts. No VCs. No insider allocations. Just the community earning supply by participating. Every token launched on ACTFUN has a fixed tokenomic structure: 5% mints instantly to the creator's wallet on launch, 60% is community-mineable (write a funny post, pay a small USDC fee, earn tokens), and 35% is locked as LP reserve. When the full 60% is mined out, the contract auto-graduates no manual step, no admin key, no intervention. Pure code. On graduation, ACTFUN seeds liquidity on up to 4 AMMs simultaneously: UNITFLOW V3 (a Uniswap V3 fork), Uniswap V2, Curve Finance (StableSwap), and Synthra V3 DEX. The 35% LP reserve + all accumulated USDC becomes real onchain liquidity the moment the last token is mined. That's the whole mechanism mine it, graduate it, trade it. V2 also brought a full visual overhaul. New hub landing with dedicated product slabs, Cinzel typography across the entire site, a cleaner Minepad grid, and ecosystem logos representing the infra stack powering the platform: Arc Goldsky ☀️ , Uniswap, Curve, Synthra, Neon, LiveKit, OpenZeppelin, Hardhat. Built to look as serious as the contracts underneath it. The infra running underneath ACTFUN V2: smart contracts in Solidity 0.8.24 with OpenZeppelin 5.x deployed on Arc Testnet (Chain ID 5042002), event indexing via @goldsky_io Turbo pipeline streaming into Neon Postgres with sub-1s lag and zero RPC fanout on the browser, live voice rooms via starnews2.com per token (creators broadcast video + mic, miners join by voice), and a React 18 + Vite + wagmi v2 + Dynamic Labs frontend. Everything is live on Arc Testnet right now. Mint testnet USDC, deploy a token, mine it with your community, and watch it graduate to a live DEX all onchain, all open,
0:20

ACTFUN V2 is live on Arc tesnet A complete redesign and the most ambitious version of the platform we've shipped yet. 🧵 Let break down exactly what ACTFUN is, how it works, and why V2 is a big deal. ACTFUN is a Mine-to-Launch token launchpad built on Arc testnet. The idea is simple: anyone can create a token. But instead of a presale or a bonding curve you buy into the community mines it by writing funny posts. No VCs. No insider allocations. Just the community earning supply by participating. Every token launched on ACTFUN has a fixed tokenomic structure: 5% mints instantly to the creator's wallet on launch, 60% is community-mineable (write a funny post, pay a small USDC fee, earn tokens), and 35% is locked as LP reserve. When the full 60% is mined out, the contract auto-graduates no manual step, no admin key, no intervention. Pure code. On graduation, ACTFUN seeds liquidity on up to 4 AMMs simultaneously: UNITFLOW V3 (a Uniswap V3 fork), Uniswap V2, Curve Finance (StableSwap), and Synthra V3 DEX. The 35% LP reserve + all accumulated USDC becomes real onchain liquidity the moment the last token is mined. That's the whole mechanism mine it, graduate it, trade it. V2 also brought a full visual overhaul. New hub landing with dedicated product slabs, Cinzel typography across the entire site, a cleaner Minepad grid, and ecosystem logos representing the infra stack powering the platform: Arc Goldsky ☀️ , Uniswap, Curve, Synthra, Neon, LiveKit, OpenZeppelin, Hardhat. Built to look as serious as the contracts underneath it. The infra running underneath ACTFUN V2: smart contracts in Solidity 0.8.24 with OpenZeppelin 5.x deployed on Arc Testnet (Chain ID 5042002), event indexing via @goldsky_io Turbo pipeline streaming into Neon Postgres with sub-1s lag and zero RPC fanout on the browser, live voice rooms via starnews2.com per token (creators broadcast video + mic, miners join by voice), and a React 18 + Vite + wagmi v2 + Dynamic Labs frontend. Everything is live on Arc Testnet right now. Mint testnet USDC, deploy a token, mine it with your community, and watch it graduate to a live DEX all onchain, all open,

ACT FUN

22,327 Aufrufe • vor 2 Monaten

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.
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CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

101,793 Aufrufe • vor 6 Monaten

I spent 20 years architecting AAA game engines, going back to the PlayStation 1. I wrote all of that by hand. This one I did not. It is a stylized real time digital twin of the SF Bay Area, built from scratch in C++ and Vulkan. The geometry is real, the look is not. It is meant to read as a painted model of the Bay rather than a photo of it, because a photoreal copy of a place you can already see from a satellite is not worth building. The world is an 80 km square, about 6,400 km², and all of it is built from open data. USGS 3DEP 1 m lidar for the terrain. NOAA CoNED for the bay floor. USGS NHD for water. ESA WorldCover for land cover. OpenStreetMap for footprints and roads. That comes to 13 GB of streamed tiles, and 9.8 GB of it is terrain. Past the edge there is a 420 km coarse ring on Copernicus GLO-30, so the horizon does not end at a flat edge. Distant ridgelines bend down below it. There are 424,787 real building footprints in there, extruded procedurally, with heights off OSM tags and a plausibility gate that drops anything whose tagged height will not fit its own footprint. Across the whole 80 km box it rejects exactly 8 buildings, four of them towers carrying a bad height tag that used to extrude as needles over a derelict pier at Hunters Point. On top sit 41 landmarks at their real coordinates, authored by Fable as parametric Blender scripts rather than sculpted: Golden Gate, Transamerica, Coit, Sutro, Salesforce, the Ferry Building. Each one switches off the generated building underneath it. The simulation is the actual point. A 24 hour recording of real feeds plays back on a clock inside the app, at 1x up to 2000x. Caltrans PeMS for highway flow, 511 for Caltrain, OpenSky for flights, AIS for boats, METAR at SFO, OAK and SJC, and HRRR for weather. 165,356 samples across those 8 feeds. Cars spawn at the volume PeMS measured on that segment. Trains run the delays 511 reported. Planes fly the tracks OpenSky recorded. It is replay, not a live stream. Live polling is next and feeds the same clock, so it is a source swap rather than a rewrite. There is also a globe. You pick the Bay from orbit, fly down to the surface, and back out again. Reversed Z depth, HDR with a filmic tonemap, dual filter bloom, a time of day grade, and a volumetric marine layer for Karl the fog. AI agents write the issues, set the constraints, make the art direction calls, and review and validate every line of the roughly 235,000 lines of C++, GLSL, Python and PowerShell. What I built is the orchestration that holds it together, and the judgement on top of it: what to build, what to cut, and when to accept the result. The language choice is doing real work here. The hard part of AI written software is not producing the code. It is proving the code is right. C++ and Vulkan are unusually good at that, because nothing is hidden: every resource, every frame, every buffer you can read back. 28 golden image scenes compared by SSIM. An MCP server that drives the running app, so an agent can move the camera, set a sky preset, take a screenshot, and read the streaming counters back out. It validates against a live process instead of against its own diff. And a reviewer agent whose only job is to work out what the issue asked for before it reads the summary the PR wrote about itself. So I think C++ and Vulkan stay the heavy duty layer through the AI era. Not in spite of being low level. Because being performant, deterministic and checkable is exactly what an agent loop needs to push against. A managed engine hides the state that verification depends on. This is a hobby project, and it has nothing to do with my day job. Clips are below. More as I go. Map data © OpenStreetMap contributors (ODbL). Land cover: ESA WorldCover 2021 (CC BY 4.0). Terrain: USGS 3DEP and NOAA. Flight data: OpenSky Network. #Vulkan #cpp #gamedev #digitaltwin #OpenStreetMap
0:49

I spent 20 years architecting AAA game engines, going back to the PlayStation 1. I wrote all of that by hand. This one I did not. It is a stylized real time digital twin of the SF Bay Area, built from scratch in C++ and Vulkan. The geometry is real, the look is not. It is meant to read as a painted model of the Bay rather than a photo of it, because a photoreal copy of a place you can already see from a satellite is not worth building. The world is an 80 km square, about 6,400 km², and all of it is built from open data. USGS 3DEP 1 m lidar for the terrain. NOAA CoNED for the bay floor. USGS NHD for water. ESA WorldCover for land cover. OpenStreetMap for footprints and roads. That comes to 13 GB of streamed tiles, and 9.8 GB of it is terrain. Past the edge there is a 420 km coarse ring on Copernicus GLO-30, so the horizon does not end at a flat edge. Distant ridgelines bend down below it. There are 424,787 real building footprints in there, extruded procedurally, with heights off OSM tags and a plausibility gate that drops anything whose tagged height will not fit its own footprint. Across the whole 80 km box it rejects exactly 8 buildings, four of them towers carrying a bad height tag that used to extrude as needles over a derelict pier at Hunters Point. On top sit 41 landmarks at their real coordinates, authored by Fable as parametric Blender scripts rather than sculpted: Golden Gate, Transamerica, Coit, Sutro, Salesforce, the Ferry Building. Each one switches off the generated building underneath it. The simulation is the actual point. A 24 hour recording of real feeds plays back on a clock inside the app, at 1x up to 2000x. Caltrans PeMS for highway flow, 511 for Caltrain, OpenSky for flights, AIS for boats, METAR at SFO, OAK and SJC, and HRRR for weather. 165,356 samples across those 8 feeds. Cars spawn at the volume PeMS measured on that segment. Trains run the delays 511 reported. Planes fly the tracks OpenSky recorded. It is replay, not a live stream. Live polling is next and feeds the same clock, so it is a source swap rather than a rewrite. There is also a globe. You pick the Bay from orbit, fly down to the surface, and back out again. Reversed Z depth, HDR with a filmic tonemap, dual filter bloom, a time of day grade, and a volumetric marine layer for Karl the fog. AI agents write the issues, set the constraints, make the art direction calls, and review and validate every line of the roughly 235,000 lines of C++, GLSL, Python and PowerShell. What I built is the orchestration that holds it together, and the judgement on top of it: what to build, what to cut, and when to accept the result. The language choice is doing real work here. The hard part of AI written software is not producing the code. It is proving the code is right. C++ and Vulkan are unusually good at that, because nothing is hidden: every resource, every frame, every buffer you can read back. 28 golden image scenes compared by SSIM. An MCP server that drives the running app, so an agent can move the camera, set a sky preset, take a screenshot, and read the streaming counters back out. It validates against a live process instead of against its own diff. And a reviewer agent whose only job is to work out what the issue asked for before it reads the summary the PR wrote about itself. So I think C++ and Vulkan stay the heavy duty layer through the AI era. Not in spite of being low level. Because being performant, deterministic and checkable is exactly what an agent loop needs to push against. A managed engine hides the state that verification depends on. This is a hobby project, and it has nothing to do with my day job. Clips are below. More as I go. Map data © OpenStreetMap contributors (ODbL). Land cover: ESA WorldCover 2021 (CC BY 4.0). Terrain: USGS 3DEP and NOAA. Flight data: OpenSky Network. #Vulkan #cpp #gamedev #digitaltwin #OpenStreetMap

Xiang Wei

79,499 Aufrufe • vor 1 Monat

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.
35:48

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

85,668 Aufrufe • vor 5 Monaten

My brother is 19. A sophomore majoring in Software Engineering. I’m the older brother. I have a career. And for two years, I’ve been covering everything tuition, rent, food every single month. Recently, my patience finally snapped. It’s not that I’m heartless. It’s just that every time I saw him, he was glued to his laptop, and to me, it looked like nothing but gaming and endless YouTube loops. I called him and didn't hold back: “David , I honestly don’t see what I’ve been paying for these last two years. Can you show me anything you’ve actually built? Because if not, we need to have a serious talk about whether we should keep doing this.” Silence on the other end. Then, he said quietly: “Okay. Give me one week.” I’ll be honest I wasn’t expecting much. I thought he’d show me a basic landing page or a simple calculator. Typical student stuff. But seven days later, he showed up at my place. He opened his laptop and revealed a live trading terminal. I didn't even realize what I was looking at at first. On the screen: a real-time equity curve. Black background. A steady white line climbing upward. Numbers flickering every few seconds. On the left complex math formulas. On the right a live trade log from Polymarket. He started explaining: “I mapped out the logic for Claude AI, and it wrote the code. The bot trades markets like 'Bitcoin Up or Down' across 5-minute, 15-minute, and 1-hour intervals. It’s built on three core pillars:” - The Kelly Criterion a mathematical formula from 1956 that calculates exactly what percentage of the bankroll to risk on each trade. No more, no less. Growth without the blow-up. - Bayesian Updating the system recalculates probabilities after every single trade. It learns on the fly like a human brain, but without the ego or emotions. - Expected Value (EV) Filter the bot doesn’t even enter a trade unless the math shows a clear edge. If the numbers aren't there, it simply waits for the next opportunity. As I listened, one thing became crystal clear: He hadn’t just "learned to code." He had learned how to think. The Stats: > Initial Deposit: $100 > 7-Day Result: $3,000 > Win Rate: ~80% I asked him: “David, did you come up with all of this yourself?” He just shrugged: “I knew what needed to happen. Claude knew how to write it. We built it together.” That evening, I transferred the money for his next month of tuition. For the first time in two years, I did it with a smile. If you want the code it’s open-source. Like&Follow&Retweet I’ll DM the link to everyone who does all three.
0:51

My brother is 19. A sophomore majoring in Software Engineering. I’m the older brother. I have a career. And for two years, I’ve been covering everything tuition, rent, food every single month. Recently, my patience finally snapped. It’s not that I’m heartless. It’s just that every time I saw him, he was glued to his laptop, and to me, it looked like nothing but gaming and endless YouTube loops. I called him and didn't hold back: “David , I honestly don’t see what I’ve been paying for these last two years. Can you show me anything you’ve actually built? Because if not, we need to have a serious talk about whether we should keep doing this.” Silence on the other end. Then, he said quietly: “Okay. Give me one week.” I’ll be honest I wasn’t expecting much. I thought he’d show me a basic landing page or a simple calculator. Typical student stuff. But seven days later, he showed up at my place. He opened his laptop and revealed a live trading terminal. I didn't even realize what I was looking at at first. On the screen: a real-time equity curve. Black background. A steady white line climbing upward. Numbers flickering every few seconds. On the left complex math formulas. On the right a live trade log from Polymarket. He started explaining: “I mapped out the logic for Claude AI, and it wrote the code. The bot trades markets like 'Bitcoin Up or Down' across 5-minute, 15-minute, and 1-hour intervals. It’s built on three core pillars:” - The Kelly Criterion a mathematical formula from 1956 that calculates exactly what percentage of the bankroll to risk on each trade. No more, no less. Growth without the blow-up. - Bayesian Updating the system recalculates probabilities after every single trade. It learns on the fly like a human brain, but without the ego or emotions. - Expected Value (EV) Filter the bot doesn’t even enter a trade unless the math shows a clear edge. If the numbers aren't there, it simply waits for the next opportunity. As I listened, one thing became crystal clear: He hadn’t just "learned to code." He had learned how to think. The Stats: > Initial Deposit: $100 > 7-Day Result: $3,000 > Win Rate: ~80% I asked him: “David, did you come up with all of this yourself?” He just shrugged: “I knew what needed to happen. Claude knew how to write it. We built it together.” That evening, I transferred the money for his next month of tuition. For the first time in two years, I did it with a smile. If you want the code it’s open-source. Like&Follow&Retweet I’ll DM the link to everyone who does all three.

AdiiX

162,290 Aufrufe • vor 7 Monaten

Using Claude Fable 5, I built a model that predicts the entire 2026 FIFA world cup.. every single game, not just the final.. so let me break the whole thing down. what it does, how it works, and exactly how i built it.. #1 First what it does: it predicts all 104 games of the tournament. not just who lifts the trophy, but every group match, every knockout, the full path from the round of 32 to the final.. everything lands in one dashboard: > group stage, every match with each team's win % and the chance of a draw > standings, how all 12 groups are projected to finish > bracket, the full knockout tree with each team's odds of advancing > champion odds, who's most likely to actually win it all and it doesn't freeze after one prediction. the moment a real game is played, it locks that result in and re-runs everything around it. so the odds move live as the tournament goes, week by week you watch favorites rise and contenders collapse. #2. How it works: the core idea is simple. the model only ever predicts one thing, a single match. the real trick is the repetition. it learns from decades of match history, then plays the whole tournament out from the first game to the final, tens of thousands of times. each run it records who advanced and who won. do that enough and you stop getting one guess and start getting real odds, one team lifts the trophy in maybe 14% of the runs, another in 9%, and so on. #3. So, how i built it ? i didn't hand-write most of the code. i broke the project into 4 pieces, described each one to fable, and let it build while i focused on getting the football logic exactly right. - The data every international match going back over a century, around 50,000 games, plus each team's elo rating, which is the truest measure of strength, and the official 2026 schedule. garbage data means garbage predictions, so this part mattered most. - The features i turned that raw history into signals the model can learn from, the elo gap between the two teams, recent form, goals scored and conceded, and a home boost for the hosts, usa, canada and mexico. - The model for each match it predicts the expected goals for both sides, then turns that into win, draw and loss probabilities plus a likely scoreline. that's what feeds the simulation. - The tournament engine this was the hard part. the 2026 world cup is brand new, 48 teams, 12 groups, a round of 32 that's never existed before, and 8 "best third-placed" teams that slot into the bracket by a fixed fifa table. even the group tiebreakers changed this year, head to head now counts before goal difference. get any of it wrong and the whole bracket falls apart, so i built it carefully and tested the format until it was exact, then wrapped it in a simulation loop that plays the tournament out tens of thousands of times. and the last piece, the live part. as real results come in, they get locked, and only the unplayed games get re-simulated. that's what makes it a living model instead of a one-time prediction. all of it outputs to a clean dashboard you can actually read and screenshot.. right now, before kickoff, it already has a clear favorite to lift the trophy.. 👀 btw who's your pick to win the 2026 world cup?
0:20

Using Claude Fable 5, I built a model that predicts the entire 2026 FIFA world cup.. every single game, not just the final.. so let me break the whole thing down. what it does, how it works, and exactly how i built it.. #1 First what it does: it predicts all 104 games of the tournament. not just who lifts the trophy, but every group match, every knockout, the full path from the round of 32 to the final.. everything lands in one dashboard: > group stage, every match with each team's win % and the chance of a draw > standings, how all 12 groups are projected to finish > bracket, the full knockout tree with each team's odds of advancing > champion odds, who's most likely to actually win it all and it doesn't freeze after one prediction. the moment a real game is played, it locks that result in and re-runs everything around it. so the odds move live as the tournament goes, week by week you watch favorites rise and contenders collapse. #2. How it works: the core idea is simple. the model only ever predicts one thing, a single match. the real trick is the repetition. it learns from decades of match history, then plays the whole tournament out from the first game to the final, tens of thousands of times. each run it records who advanced and who won. do that enough and you stop getting one guess and start getting real odds, one team lifts the trophy in maybe 14% of the runs, another in 9%, and so on. #3. So, how i built it ? i didn't hand-write most of the code. i broke the project into 4 pieces, described each one to fable, and let it build while i focused on getting the football logic exactly right. - The data every international match going back over a century, around 50,000 games, plus each team's elo rating, which is the truest measure of strength, and the official 2026 schedule. garbage data means garbage predictions, so this part mattered most. - The features i turned that raw history into signals the model can learn from, the elo gap between the two teams, recent form, goals scored and conceded, and a home boost for the hosts, usa, canada and mexico. - The model for each match it predicts the expected goals for both sides, then turns that into win, draw and loss probabilities plus a likely scoreline. that's what feeds the simulation. - The tournament engine this was the hard part. the 2026 world cup is brand new, 48 teams, 12 groups, a round of 32 that's never existed before, and 8 "best third-placed" teams that slot into the bracket by a fixed fifa table. even the group tiebreakers changed this year, head to head now counts before goal difference. get any of it wrong and the whole bracket falls apart, so i built it carefully and tested the format until it was exact, then wrapped it in a simulation loop that plays the tournament out tens of thousands of times. and the last piece, the live part. as real results come in, they get locked, and only the unplayed games get re-simulated. that's what makes it a living model instead of a one-time prediction. all of it outputs to a clean dashboard you can actually read and screenshot.. right now, before kickoff, it already has a clear favorite to lift the trophy.. 👀 btw who's your pick to win the 2026 world cup?

Axel Bitblaze 🪓

63,796 Aufrufe • vor 3 Monaten

Introducing Open Source AI CRM, that runs on your OpenClaw. A few weeks ago, we launched Ironclaw (An Open Source OpenClaw CRM Framework) which now has around 1.4k stars. A lot of people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw. OpenClaw today feels a lot like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode wasn’t just React itself, but the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt. That is how I think about DenchClaw. We are not just building on top of OpenClaw; we are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world. We are an OpenClaw Framework, we are aiming to be the most correct way to use OpenClaw. We entered Y Combinator with Merse (AI Audio Comic), it was an app that I personally never used. Michael Seibel confronted us on it, and said, “if you aren’t the best user of your consumer app, then who is?”. I now use DenchClaw daily for everything I do, it also works as a coding agent like Cursor, DenchClaw built DenchClaw. I am addicted to DenchClaw now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter. On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill. The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. It creates a new OpenClaw🦞 profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA. Think of it as Cursor for your Mac which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do LinkedIn outreach using MY browser. DenchClaw sees what you see, does what you do. It’s the everything app, that sits locally on your mac. All yours. Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use. P.S. It comes with Garry Tan's GStack built in.
2:39

Introducing Open Source AI CRM, that runs on your OpenClaw. A few weeks ago, we launched Ironclaw (An Open Source OpenClaw CRM Framework) which now has around 1.4k stars. A lot of people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw. OpenClaw today feels a lot like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode wasn’t just React itself, but the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt. That is how I think about DenchClaw. We are not just building on top of OpenClaw; we are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world. We are an OpenClaw Framework, we are aiming to be the most correct way to use OpenClaw. We entered Y Combinator with Merse (AI Audio Comic), it was an app that I personally never used. Michael Seibel confronted us on it, and said, “if you aren’t the best user of your consumer app, then who is?”. I now use DenchClaw daily for everything I do, it also works as a coding agent like Cursor, DenchClaw built DenchClaw. I am addicted to DenchClaw now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter. On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill. The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. It creates a new OpenClaw🦞 profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA. Think of it as Cursor for your Mac which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do LinkedIn outreach using MY browser. DenchClaw sees what you see, does what you do. It’s the everything app, that sits locally on your mac. All yours. Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use. P.S. It comes with Garry Tan's GStack built in.

Mark Rachapoom

19,616 Aufrufe • vor 6 Monaten

Elon Musk shipped Grok Bot as an AI teammate that sorts your email and watches your competitors. I gave mine a harder brief: build a trading floor that runs itself, and then run it 24/7. GROKSTREET. Fourteen agents. Eleven desks, three chiefs, three offices, one vault. It lives in a browser tab and it does not stop. WHY GROK BOT MAKES THIS WORK Most people misread the architecture. It is not one sandbox per agent. It is ONE persistent cloud computer per account, and every Bot gets its own screen on it. Shared filesystem at /workspace, shared browser sessions, shared credentials. That is the whole unlock. One Bot picks up exactly where another left off. No re-auth, no copy-paste between agents, no state lost at the handoff. A floor is only possible because the desks share a building. The head-of-desk role is not something I invented either. "Manages your other Bots and pulls you in for decisions" ships as a first-class Bot template. Routing and escalation are built in. Skills carry the how. Routines carry the when on a schedule, or off a Slack or GitHub event. Subagents fan out as child sessions with their own context and hand a summary back. Fifty routines per Bot. THE DESKS Every desk holds its own book, hit rate and drawdown, and sizes its own risk: Kelly, with the guard rails written in f* = W − (1−W)/R clamped to [0.01, 0.25]. Under 12 samples it does not pretend to know it defaults to 0.05. Volatility is RiskMetrics EWMA σ²_t = λσ²_{t-1} + (1−λ)r²_t, λ = 0.94 Hit rates carry Wilson 95% intervals, z = 1.96. A desk is only flagged for edge decay when the UPPER bound falls below 0.5, never on raw small-sample noise. Three-for-three is not sixty-for-ninety. Capital routes between desks by softmax on measured Sharpe. THE FLOOR Three chiefs meet in the war room every 3.5 minutes and argue. One reports, another presses him, he concedes or proposes. 14,208 sentence shapes before live figures fill the slots and every slot is filled from that chief's own desks. The crypto chief cannot quote meme numbers. A desk holding $25 gets up, walks to the vault and banks it. The transfer is atomic on arrival, so an interrupted walk cannot destroy cash. Every desk wears its last trade above its head. Green with a tick, red with a cross. You see who the chief is about to praise and who he is about to hit before he gets there. BTC, ETH and SOL come off Kraken server-side. The tape is real. THE BUILD 16 modules. 7,572 lines. Zero npm dependencies. No React. No bundler. No game engine. Vanilla JavaScript on Canvas 2D and a Go binary with the entire world embedded in it. Ships as one 2.3 MB HTML file. 60fps on every floor, zero dropped frames. WHAT I FOUND BY MEASURING INSTEAD OF LOOKING The roster read one number for a desk and the chief's card read another, so a desk could show +$16 in the list and $0 when the chief walked over. Every desk was crediting itself twice. Money could vanish if a walk to the vault got interrupted. Three money bugs, none of them visible by watching. All fixed and instrumented. DIRECTOR MODE Open it with?show and the camera runs itself. It cuts to the war room while a chief is mid-sentence, to whichever office just cleared money, and never cuts away in the middle of a line. Point a screen recorder at it and walk away. The math is real, the feed is real, the engineering is real. The flex is the engine, not a P&L screenshot.
0:39

Elon Musk shipped Grok Bot as an AI teammate that sorts your email and watches your competitors. I gave mine a harder brief: build a trading floor that runs itself, and then run it 24/7. GROKSTREET. Fourteen agents. Eleven desks, three chiefs, three offices, one vault. It lives in a browser tab and it does not stop. WHY GROK BOT MAKES THIS WORK Most people misread the architecture. It is not one sandbox per agent. It is ONE persistent cloud computer per account, and every Bot gets its own screen on it. Shared filesystem at /workspace, shared browser sessions, shared credentials. That is the whole unlock. One Bot picks up exactly where another left off. No re-auth, no copy-paste between agents, no state lost at the handoff. A floor is only possible because the desks share a building. The head-of-desk role is not something I invented either. "Manages your other Bots and pulls you in for decisions" ships as a first-class Bot template. Routing and escalation are built in. Skills carry the how. Routines carry the when on a schedule, or off a Slack or GitHub event. Subagents fan out as child sessions with their own context and hand a summary back. Fifty routines per Bot. THE DESKS Every desk holds its own book, hit rate and drawdown, and sizes its own risk: Kelly, with the guard rails written in f* = W − (1−W)/R clamped to [0.01, 0.25]. Under 12 samples it does not pretend to know it defaults to 0.05. Volatility is RiskMetrics EWMA σ²_t = λσ²_{t-1} + (1−λ)r²_t, λ = 0.94 Hit rates carry Wilson 95% intervals, z = 1.96. A desk is only flagged for edge decay when the UPPER bound falls below 0.5, never on raw small-sample noise. Three-for-three is not sixty-for-ninety. Capital routes between desks by softmax on measured Sharpe. THE FLOOR Three chiefs meet in the war room every 3.5 minutes and argue. One reports, another presses him, he concedes or proposes. 14,208 sentence shapes before live figures fill the slots and every slot is filled from that chief's own desks. The crypto chief cannot quote meme numbers. A desk holding $25 gets up, walks to the vault and banks it. The transfer is atomic on arrival, so an interrupted walk cannot destroy cash. Every desk wears its last trade above its head. Green with a tick, red with a cross. You see who the chief is about to praise and who he is about to hit before he gets there. BTC, ETH and SOL come off Kraken server-side. The tape is real. THE BUILD 16 modules. 7,572 lines. Zero npm dependencies. No React. No bundler. No game engine. Vanilla JavaScript on Canvas 2D and a Go binary with the entire world embedded in it. Ships as one 2.3 MB HTML file. 60fps on every floor, zero dropped frames. WHAT I FOUND BY MEASURING INSTEAD OF LOOKING The roster read one number for a desk and the chief's card read another, so a desk could show +$16 in the list and $0 when the chief walked over. Every desk was crediting itself twice. Money could vanish if a walk to the vault got interrupted. Three money bugs, none of them visible by watching. All fixed and instrumented. DIRECTOR MODE Open it with?show and the camera runs itself. It cuts to the war room while a chief is mid-sentence, to whichever office just cleared money, and never cuts away in the middle of a line. Point a screen recorder at it and walk away. The math is real, the feed is real, the engineering is real. The flex is the engine, not a P&L screenshot.

AdiiX

51,085 Aufrufe • vor 1 Monat

This guy built a $5,000 passive income stream off one Claude Code SEO workflow. The whole thing runs on skill files that turn YouTube videos into ranking blog posts, fully hands off. One prompt, and Claude writes the post, pulls screenshots from the video, sets the meta data, and publishes the draft to WordPress. He's also vibe coding entire local business websites that hit page one of Google in 2 weeks. Ryan Doser came on the pod to walk us through it. Here's what I learned: 1. He repurposes every YouTube video into an SEO blog post with one prompt. Claude Code grabs the transcript, writes the article, takes screenshots from the video, compresses them, uploads to WordPress, sets the title, slug, meta description, and category. Zero manual work. 2. The secret is skill markdown files. Think of them as SOPs for AI. His SEO writer skill encodes every best practice, past example, and formatting rule. The prompt is 5 lines because the skill does 99% of the work. 3. "Triple check this" is a cheat code. Tell Claude Code to triple check anything and it spins up parallel agents to verify the work. Better output, one extra sentence. 4. The money math: $5,000+ in passive sales from a $99 digital product. 80% attributed to blog traffic from this workflow. Site started at zero authority in February. Took off by mid April. 5. He runs the same workflow for a real client at several thousand a month. A national dental IT provider. The client's inbound leads are up and the retainer keeps renewing. 6. AI search impressions are the new currency. His client shows up #1 in Google's AI Overview for their money keyword. That's AI literally telling the searcher "this company is the best." Worth more than a blue link. 7. He vibe coded a fake septic tank website in 3-4 hours. Astro framework, GitHub, Cloudflare. Two weeks later it's on page one for "septic tank pumping near me," beating Better Business Bureau and HomeAdvisor. 8. The stack is basically free. GitHub: free. Cloudflare: free. Astro: free. The only cost is a Claude subscription. $100/month can realistically support 2-3 clients paying $2,000-4,000 each. 9. Target boring local businesses PE hasn't touched. Skip HVAC, plumbing, and roofing. Go after septic, junk removal, dumpster rental, funeral homes. Million-dollar businesses with websites from 2002. 10. Walk in the front door. Cold email and cold calls are drowning in AI spam. Go talk to 10 owners in person. You could be the worst salesperson alive and still close one. His 2 key takeaways: 1. Be your own case study. Spin up a demo site in a weekend, show it ranking in 2 weeks, then pitch: "imagine this on your established domain." 2. SEO is an evergreen asset. A tweet dies in 48 hours. A ranking blog post pays the client back for years, even after you stop working together. That's the pitch that justifies the retainer. Ryan is a non-technical marketer doing this at a level most developers are not. Go follow Ryan Doser. Full video below. (Also available on the Build With AI podcast wherever you get your pods)
37:00

This guy built a $5,000 passive income stream off one Claude Code SEO workflow. The whole thing runs on skill files that turn YouTube videos into ranking blog posts, fully hands off. One prompt, and Claude writes the post, pulls screenshots from the video, sets the meta data, and publishes the draft to WordPress. He's also vibe coding entire local business websites that hit page one of Google in 2 weeks. Ryan Doser came on the pod to walk us through it. Here's what I learned: 1. He repurposes every YouTube video into an SEO blog post with one prompt. Claude Code grabs the transcript, writes the article, takes screenshots from the video, compresses them, uploads to WordPress, sets the title, slug, meta description, and category. Zero manual work. 2. The secret is skill markdown files. Think of them as SOPs for AI. His SEO writer skill encodes every best practice, past example, and formatting rule. The prompt is 5 lines because the skill does 99% of the work. 3. "Triple check this" is a cheat code. Tell Claude Code to triple check anything and it spins up parallel agents to verify the work. Better output, one extra sentence. 4. The money math: $5,000+ in passive sales from a $99 digital product. 80% attributed to blog traffic from this workflow. Site started at zero authority in February. Took off by mid April. 5. He runs the same workflow for a real client at several thousand a month. A national dental IT provider. The client's inbound leads are up and the retainer keeps renewing. 6. AI search impressions are the new currency. His client shows up #1 in Google's AI Overview for their money keyword. That's AI literally telling the searcher "this company is the best." Worth more than a blue link. 7. He vibe coded a fake septic tank website in 3-4 hours. Astro framework, GitHub, Cloudflare. Two weeks later it's on page one for "septic tank pumping near me," beating Better Business Bureau and HomeAdvisor. 8. The stack is basically free. GitHub: free. Cloudflare: free. Astro: free. The only cost is a Claude subscription. $100/month can realistically support 2-3 clients paying $2,000-4,000 each. 9. Target boring local businesses PE hasn't touched. Skip HVAC, plumbing, and roofing. Go after septic, junk removal, dumpster rental, funeral homes. Million-dollar businesses with websites from 2002. 10. Walk in the front door. Cold email and cold calls are drowning in AI spam. Go talk to 10 owners in person. You could be the worst salesperson alive and still close one. His 2 key takeaways: 1. Be your own case study. Spin up a demo site in a weekend, show it ranking in 2 weeks, then pitch: "imagine this on your established domain." 2. SEO is an evergreen asset. A tweet dies in 48 hours. A ranking blog post pays the client back for years, even after you stop working together. That's the pitch that justifies the retainer. Ryan is a non-technical marketer doing this at a level most developers are not. Go follow Ryan Doser. Full video below. (Also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

13,900 Aufrufe • vor 3 Monaten

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.
30:12

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

229,429 Aufrufe • vor 5 Monaten

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns
1:01

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

135,556 Aufrufe • vor 2 Monaten

kimi k3 vs gpt 5.6 sol vs fable 5 vs grok 4.5 Kimi.ai just dropped kimi k3 – a 2.8t param native multimodal model, the first open 3t-class release. key facts: • 1m token context. stable latentmoe activating 16 of 896 experts, built on kimi delta attention (kda) and attention residuals • quantization-aware training from the sft stage onward – mxfp4 weights, mxfp8 activations. moonshot claims ~2.5x scaling efficiency over k2 • max thinking effort by default. low- and high-effort modes are "coming in updates" – there is no way to turn the thinking down today, and you feel it in every run • pricing: $0.30/mtok cache-hit input, $3.00/mtok cache-miss, $15.00/mtok output. claims >90% cache hit rate on coding workloads • benchmarks: swe marathon 42.0 (1st – fable 5: 35.0, sol: 39.0, opus 4.8: 40.0), terminal bench 2.1 88.3, browsecomp 91.2 (1st), program bench 77.8 (1st), gpqa-diamond 93.5. loses frontierswe 81.2 vs fable's 86.6, and deepswe 67.5 vs sol's 73.0 our test – 3 prompts, single-file html, Three.js, fully procedural, no assets: 1. photorealistic european roulette wheel – 37 pockets in the real sequence, mahogany clearcoat bowl, chrome turret, diamond deflectors, flick-to-spin, ball that spirals inward and settles on a mathematically real number 2. las vegas slot machine – 3 reels behind transmissive glass, drag the chrome lever to play, mechanical odometer counters modelled in 3d, coin physics on win 3. full pinball table – 6.5° tilted playfield, flipper impulse physics, spline ramps, drop targets, 6 bumpers, mechanical score reels in the backbox we ran the test on AI/ML API platform results: - cost #1 grok 4.5 – $0.30 #2 kimi k3 – $0.71 #3 gpt 5.6 sol – $2.05 #4 fable 5 – $7.69 - tokens #1 grok 4.5 – 34,241 #2 gpt 5.6 sol – 51,748 #3 fable 5 – 144,126 #4 kimi k3 – 157,999 - lines of code #1 gpt 5.6 sol – 3,054 #2 grok 4.5 – 3,047 #3 kimi k3 – 2,255 #4 fable 5 – 1,950 - generation time #1 grok 4.5 – 5.1 min #2 gpt 5.6 sol – 22.0 min #3 fable 5 – 31.5 min #4 kimi k3 – 75.6 min observations: • kimi k3 is cheap and it is slow. 75.6 minutes across three prompts against grok's 5.1. it is 2.4x grok's price and 15x grok's wall clock. the roulette took 15 min, the slot 18, the pinball 42 • it failed 2 of 3. only the roulette works. the slot machine has reel cutouts on both faces of the cabinet and the symbols face backwards – you can only read your spin by walking around to the rear of the machine. the pinball table stands vertically on its edge with the legs floating detached beside it. • 81% of kimi's output tokens are reasoning, not code. grok: 22%. you are not paying for a bigger answer, you are paying for a longer argument with itself • price per 100 shipped lines – grok $0.010, kimi $0.031, sol $0.067, fable $0.394. a 39x spread for the same three files kimi k3's code quality: upsides: • the roulette is genuinely good – procedural wood grain with real specular breakup, correct european sequence (0-32-15-19-4...), chrome turret, diamond deflectors, clean console • the pinball artwork is the best in the test – a synthwave "nova strike / deep space" field with six individually coloured neon bumper rings, a retro sun on a grid horizon, a nova burst, and a scoring legend printed on the apron. no other model printed the rules on the machine. it is a beautiful texture on a broken object • physics reasoning is real – it derived a 480hz substep for the collider, worked out ball settle conditions and termination guarantees, and checked every ramp exit vector by hand before writing any of it • it is the only model that saw the importmap trap coming. sol shipped a blank white page twice because three.js addons import the bare specifier 'three' and die without an import map downsides: • it dodged that trap on the slot by loading three.js r128 through classic script tags – a 2021 build with no working transmission. its slot glass rendered fully opaque and buried all three reels behind a white pane. the code asks for transmission: 0.93, ior: 1.5 – correct, and silently ignored by a renderer that predates the feature • after 42 minutes and 212k characters of reasoning, the pinball cabinet is not assembled. the table stands vertically on its edge like a wardrobe – the prompt asked for 6.5° from horizontal, it delivered 90°. the legs float detached in the void beside it. head-on it photographs beautifully; orbit ten degrees and it is a painted slab with four chrome rods hovering nearby • the playfield z-fights with the glass – hard black banding across the whole field as soon as you pull the camera back a note on the pinball, in fairness to kimi: nobody passed it. every model shipped broken ball physics and controls you cannot trust. it is the hardest prompt we have run and the whole field failed it, each in its own way kimi k3 reasons better than anything else here and it shows exactly where reasoning pays – physics constants, sequences, edge cases, traps the others walked into follow thehype. for 24/7 ai news, analysis and breakdowns
0:36

kimi k3 vs gpt 5.6 sol vs fable 5 vs grok 4.5 Kimi.ai just dropped kimi k3 – a 2.8t param native multimodal model, the first open 3t-class release. key facts: • 1m token context. stable latentmoe activating 16 of 896 experts, built on kimi delta attention (kda) and attention residuals • quantization-aware training from the sft stage onward – mxfp4 weights, mxfp8 activations. moonshot claims ~2.5x scaling efficiency over k2 • max thinking effort by default. low- and high-effort modes are "coming in updates" – there is no way to turn the thinking down today, and you feel it in every run • pricing: $0.30/mtok cache-hit input, $3.00/mtok cache-miss, $15.00/mtok output. claims >90% cache hit rate on coding workloads • benchmarks: swe marathon 42.0 (1st – fable 5: 35.0, sol: 39.0, opus 4.8: 40.0), terminal bench 2.1 88.3, browsecomp 91.2 (1st), program bench 77.8 (1st), gpqa-diamond 93.5. loses frontierswe 81.2 vs fable's 86.6, and deepswe 67.5 vs sol's 73.0 our test – 3 prompts, single-file html, Three.js, fully procedural, no assets: 1. photorealistic european roulette wheel – 37 pockets in the real sequence, mahogany clearcoat bowl, chrome turret, diamond deflectors, flick-to-spin, ball that spirals inward and settles on a mathematically real number 2. las vegas slot machine – 3 reels behind transmissive glass, drag the chrome lever to play, mechanical odometer counters modelled in 3d, coin physics on win 3. full pinball table – 6.5° tilted playfield, flipper impulse physics, spline ramps, drop targets, 6 bumpers, mechanical score reels in the backbox we ran the test on AI/ML API platform results: - cost #1 grok 4.5 – $0.30 #2 kimi k3 – $0.71 #3 gpt 5.6 sol – $2.05 #4 fable 5 – $7.69 - tokens #1 grok 4.5 – 34,241 #2 gpt 5.6 sol – 51,748 #3 fable 5 – 144,126 #4 kimi k3 – 157,999 - lines of code #1 gpt 5.6 sol – 3,054 #2 grok 4.5 – 3,047 #3 kimi k3 – 2,255 #4 fable 5 – 1,950 - generation time #1 grok 4.5 – 5.1 min #2 gpt 5.6 sol – 22.0 min #3 fable 5 – 31.5 min #4 kimi k3 – 75.6 min observations: • kimi k3 is cheap and it is slow. 75.6 minutes across three prompts against grok's 5.1. it is 2.4x grok's price and 15x grok's wall clock. the roulette took 15 min, the slot 18, the pinball 42 • it failed 2 of 3. only the roulette works. the slot machine has reel cutouts on both faces of the cabinet and the symbols face backwards – you can only read your spin by walking around to the rear of the machine. the pinball table stands vertically on its edge with the legs floating detached beside it. • 81% of kimi's output tokens are reasoning, not code. grok: 22%. you are not paying for a bigger answer, you are paying for a longer argument with itself • price per 100 shipped lines – grok $0.010, kimi $0.031, sol $0.067, fable $0.394. a 39x spread for the same three files kimi k3's code quality: upsides: • the roulette is genuinely good – procedural wood grain with real specular breakup, correct european sequence (0-32-15-19-4...), chrome turret, diamond deflectors, clean console • the pinball artwork is the best in the test – a synthwave "nova strike / deep space" field with six individually coloured neon bumper rings, a retro sun on a grid horizon, a nova burst, and a scoring legend printed on the apron. no other model printed the rules on the machine. it is a beautiful texture on a broken object • physics reasoning is real – it derived a 480hz substep for the collider, worked out ball settle conditions and termination guarantees, and checked every ramp exit vector by hand before writing any of it • it is the only model that saw the importmap trap coming. sol shipped a blank white page twice because three.js addons import the bare specifier 'three' and die without an import map downsides: • it dodged that trap on the slot by loading three.js r128 through classic script tags – a 2021 build with no working transmission. its slot glass rendered fully opaque and buried all three reels behind a white pane. the code asks for transmission: 0.93, ior: 1.5 – correct, and silently ignored by a renderer that predates the feature • after 42 minutes and 212k characters of reasoning, the pinball cabinet is not assembled. the table stands vertically on its edge like a wardrobe – the prompt asked for 6.5° from horizontal, it delivered 90°. the legs float detached in the void beside it. head-on it photographs beautifully; orbit ten degrees and it is a painted slab with four chrome rods hovering nearby • the playfield z-fights with the glass – hard black banding across the whole field as soon as you pull the camera back a note on the pinball, in fairness to kimi: nobody passed it. every model shipped broken ball physics and controls you cannot trust. it is the hardest prompt we have run and the whole field failed it, each in its own way kimi k3 reasons better than anything else here and it shows exactly where reasoning pays – physics constants, sequences, edge cases, traps the others walked into follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

2,187,777 Aufrufe • vor 2 Monaten

how to produce long form documentaries with claude this is how creators are producing long-form youtube documentaries in the sleep niche for about a low cost. you'll spend most of your effort building the workflow once, then every script after that runs through the same pipeline for cents. the format that works in this niche is different from normal youtube. your viewers are actively trying to fall asleep. that's the entire point. so people leave for two reasons: they got bored, or it worked and they're out. the ones who fall asleep come back later and keep listening. that repeat listening is a huge part of why the niche prints. which means the script is 90% of the whole thing. average view duration on my channels sits close to 25 min. that number does not come from cinematic visuals or fancy editing. it comes from narrative structure. if the script gets repetitive, drifts off topic, or loses momentum halfway, people stop listening. better footage cannot rescue a weak story here. the problem is the format does not scale on its own. one video needs a 15k-20k word script, hours of narration, hundreds of visual changes, music, and final assembly. writing that manually takes forever. editing every scene takes even longer. here's the workflow i set up: claude api (NOT the chat app. HIGHLY RECOMMENDED to not skip this. in the chat interface you end up typing "continue.. write chapter 4.. don't repeat yourself.. you forgot what happened in chapter 2" and by the halfway point it's contradicting earlier sections and drifting from the outline. you spend more time babysitting than writing. the api sends every request automatically and you pay per actual usage instead of another monthly subscription) google sheets connected to the claude api. this is the whole engine. you don't need to be a dev. the sheet does two things: first it generates the full documentary structure/outline. then it writes ONE chapter at a time instead of trying to produce the entire 20k words in a single response (which is where models fall apart). before each chapter, it passes claude three things: the outline, the instructions for that specific section, and a running summary of everything already written. that running summary is the trick. it's why chapter 8 never contradicts chapter 2. capcut ai video maker for the first edit. it generates voiceover, subtitles, and an initial visual sequence from auto-matched stock footage. the stock matching is not perfect, but it gets you a 90% first draft way faster than manually searching for hundreds of clips. note: capcut caps at 3000 words, so you split the script into sections, generate each one, export, and combine into the final video. HERE'S HOW THE PRODUCTION ACTUALLY RUNS: step 1 —> topic + title + thumbnail. do NOT skip this. ai cannot tell you which topic has demand or whether a title creates curiosity. this is where most of the value still is. figure this out before you touch any automation. step 2 —> run the sheet. it builds the outline first, then writes chapter by chapter, feeding itself the running summary each time so it stays consistent. cost for a full script usually lands around $0.30-0.40 depending on the model, input length, and number of revisions. step 3 —> paste script into capcut in sub-3000 word chunks. generate voiceover + subtitles + auto-matched visuals for each. export each section. step 4 —> combine sections into the final 2-3 hour video. then you handle the parts ai can't: pacing check, misleading visuals, final editorial judgment. the reason this matters is repeatability. every script moves through the exact same production structure, but you can still change the topic, tone, evidence, pacing, and narrative direction each time. so it stops being random one-off videos and starts being a system. the math: capcut is ~$20/mo and allows many exports. claude api is a little above thirty cents per script. at 30-40 documentaries a month that works out to roughly $1 in direct software cost per finished video. that figure does NOT include your time, research, thumbnails, subscriptions, failed ideas, or the cost of building the workflow itself. it is not the full cost of the business, it's the direct software cost. one more thing worth knowing: mixing real historical/stock footage alongside ai assets is the best defense i've found against the "reused/inauthentic content" flags that destroy fully automated channels. that's from experience, not a rule youtube publishes. this is not passive income and it's not a one-click youtube machine. it's a production system that makes experimentation cheaper. ai removes the repetitive work. it does not remove the need for taste.
1:07

how to produce long form documentaries with claude this is how creators are producing long-form youtube documentaries in the sleep niche for about a low cost. you'll spend most of your effort building the workflow once, then every script after that runs through the same pipeline for cents. the format that works in this niche is different from normal youtube. your viewers are actively trying to fall asleep. that's the entire point. so people leave for two reasons: they got bored, or it worked and they're out. the ones who fall asleep come back later and keep listening. that repeat listening is a huge part of why the niche prints. which means the script is 90% of the whole thing. average view duration on my channels sits close to 25 min. that number does not come from cinematic visuals or fancy editing. it comes from narrative structure. if the script gets repetitive, drifts off topic, or loses momentum halfway, people stop listening. better footage cannot rescue a weak story here. the problem is the format does not scale on its own. one video needs a 15k-20k word script, hours of narration, hundreds of visual changes, music, and final assembly. writing that manually takes forever. editing every scene takes even longer. here's the workflow i set up: claude api (NOT the chat app. HIGHLY RECOMMENDED to not skip this. in the chat interface you end up typing "continue.. write chapter 4.. don't repeat yourself.. you forgot what happened in chapter 2" and by the halfway point it's contradicting earlier sections and drifting from the outline. you spend more time babysitting than writing. the api sends every request automatically and you pay per actual usage instead of another monthly subscription) google sheets connected to the claude api. this is the whole engine. you don't need to be a dev. the sheet does two things: first it generates the full documentary structure/outline. then it writes ONE chapter at a time instead of trying to produce the entire 20k words in a single response (which is where models fall apart). before each chapter, it passes claude three things: the outline, the instructions for that specific section, and a running summary of everything already written. that running summary is the trick. it's why chapter 8 never contradicts chapter 2. capcut ai video maker for the first edit. it generates voiceover, subtitles, and an initial visual sequence from auto-matched stock footage. the stock matching is not perfect, but it gets you a 90% first draft way faster than manually searching for hundreds of clips. note: capcut caps at 3000 words, so you split the script into sections, generate each one, export, and combine into the final video. HERE'S HOW THE PRODUCTION ACTUALLY RUNS: step 1 —> topic + title + thumbnail. do NOT skip this. ai cannot tell you which topic has demand or whether a title creates curiosity. this is where most of the value still is. figure this out before you touch any automation. step 2 —> run the sheet. it builds the outline first, then writes chapter by chapter, feeding itself the running summary each time so it stays consistent. cost for a full script usually lands around $0.30-0.40 depending on the model, input length, and number of revisions. step 3 —> paste script into capcut in sub-3000 word chunks. generate voiceover + subtitles + auto-matched visuals for each. export each section. step 4 —> combine sections into the final 2-3 hour video. then you handle the parts ai can't: pacing check, misleading visuals, final editorial judgment. the reason this matters is repeatability. every script moves through the exact same production structure, but you can still change the topic, tone, evidence, pacing, and narrative direction each time. so it stops being random one-off videos and starts being a system. the math: capcut is ~$20/mo and allows many exports. claude api is a little above thirty cents per script. at 30-40 documentaries a month that works out to roughly $1 in direct software cost per finished video. that figure does NOT include your time, research, thumbnails, subscriptions, failed ideas, or the cost of building the workflow itself. it is not the full cost of the business, it's the direct software cost. one more thing worth knowing: mixing real historical/stock footage alongside ai assets is the best defense i've found against the "reused/inauthentic content" flags that destroy fully automated channels. that's from experience, not a rule youtube publishes. this is not passive income and it's not a one-click youtube machine. it's a production system that makes experimentation cheaper. ai removes the repetitive work. it does not remove the need for taste.

Sulfur

25,540 Aufrufe • vor 2 Monaten

I've built a multiplayer survival game for the browser in 30 days and I didnt write a single line of code 🤯 So I'd like to write my notes about it once the deadline is done now. The last 20% of a project is indeed the hardest part of it all. In the last 2 days i've been working on the final version of Hollowlands for @levelsio's 2026 #vibejam I worked a lot! I fixed a lot of bugs, tested a lot playing with my wife. Fixed more bugs... But now, thank God, I have the final result! The game looks good, yes. I've spent half of the jam just tweaking every single detail of the procedural world generation. And It was worth it! The game still runs well on most devices and still has less than 20MB in size. Which is pretty impressive to me. The process of building was very straighforward: 1. I used Three.js + React to create the game. Before I started I created a huge document defining every rule i'd like the codebase to be implemented upon. It had clean code rules, archtecture decisions, ECS principles (good for games), and so on. This helped a lot constrain the AI to maintain the code maintainable and separated into clear domains (systems). This document was made partially by me given my previous experience building web games (I've made a lot of mistakes in the past and made sure they will not repeat again) and partially AI suggesting best practices. 2. For every feature I prompted the AI, I was very specific on details that I'd like implemented. If I knew what I was doing, I was more specific on the "how" I wanted to be done. If I didnt know what was doing I first asked the AI to brainstorm possibilities with me, based on industry standards, pros and cons, and so on. Then I would decide what path to go. 3. Every single feature I used Leva for tweaking the values. So for example, the size of the trees, colors, distance between objects, animation speed, etc. Everything I have a slider that I can adjust. 4. To make the game look good I used Tripo to create the models. To be honest it's not 100% perfect. If you check the character in game you can notice a few issues here and there (the arms lol), but I believe that it will keep getting better over time. 1 Year ago I tried and it wasnt even close to what it's now. Then I used a few post-processing + shaders + particles techniques to bring the "wow" factor. 5. I did only 1 feature at a time. And every change it was a commit with a clear message of what was done. This helped the AI fetch previous commit to understand what changed and know exactly what happened in the past. If I opened multiple terminals at once and blasted prompts the code and commit history would be a mess in a few days. And for every change the AI did, I asked it to do a Code Review of everything that was changed and look for violations on the document I wrote in the beggining defining the codebase guidelines. I realized that I pretty much applied software engineer principles using AI: Code, review and merge, code, review and merge... 6. On the last week I implemented the multiplayer using Colyseus ⚔️ which was pretty nice. I used AI to fetch the whole documentation and create a skill.md file that helped a lot. The creator of the framework Endel also did a extensive research on how real multiplayer games are implemented. It's open source and it was a gold mine for the AI to come up with the implementation for my game. You can ask him the link :) And that was it I think. I'm extremely tired now. The last 2 days was insane. The game's final version is not what I had in mind in the beginning of the jam. I had to cut a lot of cool features. But given the 30 days deadline I think it was a huge success. Hands down my best game ever made. Now I'll take a few days to rest and come back with more updates on the game. Maybe it can become a big deal in the future with more updates. Who knows. And in the event I win first place in this game jam, I'll use the money to fund my own game studio + cover the expenses of my child that is going to be born in September :) Cheers guys! If you have any question, feel free to reach out. I'll answer every non-bot comment in here haha Note: The link to play is in my profile
1:06

I've built a multiplayer survival game for the browser in 30 days and I didnt write a single line of code 🤯 So I'd like to write my notes about it once the deadline is done now. The last 20% of a project is indeed the hardest part of it all. In the last 2 days i've been working on the final version of Hollowlands for @levelsio's 2026 #vibejam I worked a lot! I fixed a lot of bugs, tested a lot playing with my wife. Fixed more bugs... But now, thank God, I have the final result! The game looks good, yes. I've spent half of the jam just tweaking every single detail of the procedural world generation. And It was worth it! The game still runs well on most devices and still has less than 20MB in size. Which is pretty impressive to me. The process of building was very straighforward: 1. I used Three.js + React to create the game. Before I started I created a huge document defining every rule i'd like the codebase to be implemented upon. It had clean code rules, archtecture decisions, ECS principles (good for games), and so on. This helped a lot constrain the AI to maintain the code maintainable and separated into clear domains (systems). This document was made partially by me given my previous experience building web games (I've made a lot of mistakes in the past and made sure they will not repeat again) and partially AI suggesting best practices. 2. For every feature I prompted the AI, I was very specific on details that I'd like implemented. If I knew what I was doing, I was more specific on the "how" I wanted to be done. If I didnt know what was doing I first asked the AI to brainstorm possibilities with me, based on industry standards, pros and cons, and so on. Then I would decide what path to go. 3. Every single feature I used Leva for tweaking the values. So for example, the size of the trees, colors, distance between objects, animation speed, etc. Everything I have a slider that I can adjust. 4. To make the game look good I used Tripo to create the models. To be honest it's not 100% perfect. If you check the character in game you can notice a few issues here and there (the arms lol), but I believe that it will keep getting better over time. 1 Year ago I tried and it wasnt even close to what it's now. Then I used a few post-processing + shaders + particles techniques to bring the "wow" factor. 5. I did only 1 feature at a time. And every change it was a commit with a clear message of what was done. This helped the AI fetch previous commit to understand what changed and know exactly what happened in the past. If I opened multiple terminals at once and blasted prompts the code and commit history would be a mess in a few days. And for every change the AI did, I asked it to do a Code Review of everything that was changed and look for violations on the document I wrote in the beggining defining the codebase guidelines. I realized that I pretty much applied software engineer principles using AI: Code, review and merge, code, review and merge... 6. On the last week I implemented the multiplayer using Colyseus ⚔️ which was pretty nice. I used AI to fetch the whole documentation and create a skill.md file that helped a lot. The creator of the framework Endel also did a extensive research on how real multiplayer games are implemented. It's open source and it was a gold mine for the AI to come up with the implementation for my game. You can ask him the link :) And that was it I think. I'm extremely tired now. The last 2 days was insane. The game's final version is not what I had in mind in the beginning of the jam. I had to cut a lot of cool features. But given the 30 days deadline I think it was a huge success. Hands down my best game ever made. Now I'll take a few days to rest and come back with more updates on the game. Maybe it can become a big deal in the future with more updates. Who knows. And in the event I win first place in this game jam, I'll use the money to fund my own game studio + cover the expenses of my child that is going to be born in September :) Cheers guys! If you have any question, feel free to reach out. I'll answer every non-bot comment in here haha Note: The link to play is in my profile

André → andreelias.dev

18,190 Aufrufe • vor 5 Monaten

📖SEEDANCE 2.0 JUST MADE EVERY FILM SCHOOL IRRELEVANT FOR SOLO CREATORS Solo creators with the right workflow are closing clients that used to require a full production studio. Seedance 2.0 inside Dreamina holds character consistency across scenes in a way no other tool at this price point comes close to. Same face, same costume, same lighting logic — frame after frame after frame. That's the feature that turns a single prompt session into a short film. The lava demon materializing inside a gothic cathedral. The girl in black holding her ground while everything burns around her. Two characters with completely different visual languages sharing the same atmospheric world — and Seedance holds both of them consistent across every cut. That's not a generation. That's a production. Here's the 7-step workflow that produced this: • Step 1 — Define the character before you define the scene. Write a complete physical description — face structure, hair, clothing, skin, posture. This becomes the anchor every future generation references. • Step 2 — Build the world separately from the character. Gothic cathedral, candlelight, fog, cracked stone, scattered bodies. Define the atmosphere as its own entity before you place anyone inside it. • Step 3 — Generate the reference frame. One image that establishes the visual language, the color grade, the lighting temperature. Lock this as your style reference before generating any video. • Step 4 — Feed the reference into Seedance's image-to-video pipeline with a motion prompt. Camera behavior only — slow push, hold, circle. The image handles the subject. The prompt handles the direction. • Step 5 — Generate four variations per scene. Delete the two that look generated. Keep the one where the character's face holds and the atmosphere feels physical rather than rendered. • Step 6 — Edit in CapCut or Premiere Pro. Add music that matches the emotional temperature of the grade — the visual already tells you what the sound should feel like. Dark orchestral, slow tempo, single instrument carrying the melody. • Step 7 — Save the character description and reference frame as a template. The next episode starts from the same character in the same world. Series content becomes a system, not a restart. How a freelancer sells this: Dark fantasy content for game studios, music artists, and fantasy brands is a real market with real budgets. A musician dropping an album needs a visual world. A game studio needs promotional cinematics. A fantasy brand needs a story. Where to find clients: • Music artists on SoundCloud and Spotify releasing dark, gothic, or cinematic albums — search by genre, find artists with 1k–50k listeners who have no visual content. They have the audience and the need but no production budget for traditional video. • Indie game studios on Itch and Steam launching fantasy or horror titles — they need promotional cinematics and trailers but can't afford a production company. A single free scene built from their game's character art opens every conversation. • Dark fantasy and gothic brands on Instagram and TikTok with strong photo content but zero video presence — jewelry brands, clothing labels, occult lifestyle brands. They have the aesthetic already built. You just add motion to it. • Fantasy and horror fiction authors on Instagram and Substack launching new books — they need visual teasers, trailers, and world-building content to build pre-launch audiences. Most have no idea this kind of production is accessible at this price point. • Tabletop RPG creators and Dungeon Masters on Patreon and Kickstarter — they build entire fantasy universes and need cinematic content for campaigns, promotional videos, and subscriber rewards. The niche is underserved and the creators inside it spend consistently on content tools and services. 📥Tomorrow I'll show you what's sitting right next to this opportunity. 🔖Save this if you are looking for practical AI methods that actually pay.
0:36

📖SEEDANCE 2.0 JUST MADE EVERY FILM SCHOOL IRRELEVANT FOR SOLO CREATORS Solo creators with the right workflow are closing clients that used to require a full production studio. Seedance 2.0 inside Dreamina holds character consistency across scenes in a way no other tool at this price point comes close to. Same face, same costume, same lighting logic — frame after frame after frame. That's the feature that turns a single prompt session into a short film. The lava demon materializing inside a gothic cathedral. The girl in black holding her ground while everything burns around her. Two characters with completely different visual languages sharing the same atmospheric world — and Seedance holds both of them consistent across every cut. That's not a generation. That's a production. Here's the 7-step workflow that produced this: • Step 1 — Define the character before you define the scene. Write a complete physical description — face structure, hair, clothing, skin, posture. This becomes the anchor every future generation references. • Step 2 — Build the world separately from the character. Gothic cathedral, candlelight, fog, cracked stone, scattered bodies. Define the atmosphere as its own entity before you place anyone inside it. • Step 3 — Generate the reference frame. One image that establishes the visual language, the color grade, the lighting temperature. Lock this as your style reference before generating any video. • Step 4 — Feed the reference into Seedance's image-to-video pipeline with a motion prompt. Camera behavior only — slow push, hold, circle. The image handles the subject. The prompt handles the direction. • Step 5 — Generate four variations per scene. Delete the two that look generated. Keep the one where the character's face holds and the atmosphere feels physical rather than rendered. • Step 6 — Edit in CapCut or Premiere Pro. Add music that matches the emotional temperature of the grade — the visual already tells you what the sound should feel like. Dark orchestral, slow tempo, single instrument carrying the melody. • Step 7 — Save the character description and reference frame as a template. The next episode starts from the same character in the same world. Series content becomes a system, not a restart. How a freelancer sells this: Dark fantasy content for game studios, music artists, and fantasy brands is a real market with real budgets. A musician dropping an album needs a visual world. A game studio needs promotional cinematics. A fantasy brand needs a story. Where to find clients: • Music artists on SoundCloud and Spotify releasing dark, gothic, or cinematic albums — search by genre, find artists with 1k–50k listeners who have no visual content. They have the audience and the need but no production budget for traditional video. • Indie game studios on Itch and Steam launching fantasy or horror titles — they need promotional cinematics and trailers but can't afford a production company. A single free scene built from their game's character art opens every conversation. • Dark fantasy and gothic brands on Instagram and TikTok with strong photo content but zero video presence — jewelry brands, clothing labels, occult lifestyle brands. They have the aesthetic already built. You just add motion to it. • Fantasy and horror fiction authors on Instagram and Substack launching new books — they need visual teasers, trailers, and world-building content to build pre-launch audiences. Most have no idea this kind of production is accessible at this price point. • Tabletop RPG creators and Dungeon Masters on Patreon and Kickstarter — they build entire fantasy universes and need cinematic content for campaigns, promotional videos, and subscriber rewards. The niche is underserved and the creators inside it spend consistently on content tools and services. 📥Tomorrow I'll show you what's sitting right next to this opportunity. 🔖Save this if you are looking for practical AI methods that actually pay.

Zentrix⌚️

50,098 Aufrufe • vor 3 Monaten

Amazon is the BEST stock in the Mag 7 and people are genuinely sleeping on it (Save this). Everyone knows Amazon but most people still think of it as the company that delivers their packages in two days and somehow also runs Netflix's servers. That mental model is about 5 years out of date. CEO, Andy Jassy dropped the annual shareholder letter today and it's worth actually reading instead of skimming the headlines because the numbers are wild. AWS AI revenue is running above $15 billion annually and that number is accelerating because every major enterprise on earth needs cloud infrastructure to run their AI ambitions and Amazon built the rails before anyone else knew what the train looked like. Their custom silicon play is the part the market still hasn't fully priced in. Graviton, Trainium, and Nitro are now at a $20B+ run rate together. Trainium chips are already heavily reserved across multiple generations. They're not just selling shovels for the AI gold rush, they're the ones who made the shovels, own the mine, and built the roads leading to it. The capex commitment alone should tell you everything about where this is going. $200 billion in 2026, almost entirely pointed at AI infrastructure. That is a company that knows exactly what it's building toward. On the physical side, grocery gross sales surpassed $150 billion in 2025. Project Leo already has 200+ satellites in orbit before the service has even launched. Over $4 billion is going into rural delivery expansion and 1 million robots are now deployed across their fulfillment network with AI making each one significantly more capable than the last. The advertising business quietly became a monster too. $70 billion annual run rate, rivaling YouTube in scale, but sitting on top of purchase intent data that no social platform can touch. When someone searches on Amazon, they are ready to buy and that is the most valuable real estate in digital advertising and Amazon owns it. 250 million Prime members who are deeply embedded in the ecosystem across shopping, streaming, grocery, pharmacy, and now healthcare. The switching cost is basically your entire life. Now here's where it gets interesting for us specifically. While the market was in full meltdown mode and everyone was panic selling anything with a ticker, our analyst at Milk Road made the call to buy Amazon. That position is now up over 10%. And every PRO member gets the alert the second it happens, the exact trade, the price, and the full rationale behind it. If you're already a PRO member, turn on trade notifications in your account settings so you never miss another one. If you're not a member yet, come join us, link below!
0:31

Amazon is the BEST stock in the Mag 7 and people are genuinely sleeping on it (Save this). Everyone knows Amazon but most people still think of it as the company that delivers their packages in two days and somehow also runs Netflix's servers. That mental model is about 5 years out of date. CEO, Andy Jassy dropped the annual shareholder letter today and it's worth actually reading instead of skimming the headlines because the numbers are wild. AWS AI revenue is running above $15 billion annually and that number is accelerating because every major enterprise on earth needs cloud infrastructure to run their AI ambitions and Amazon built the rails before anyone else knew what the train looked like. Their custom silicon play is the part the market still hasn't fully priced in. Graviton, Trainium, and Nitro are now at a $20B+ run rate together. Trainium chips are already heavily reserved across multiple generations. They're not just selling shovels for the AI gold rush, they're the ones who made the shovels, own the mine, and built the roads leading to it. The capex commitment alone should tell you everything about where this is going. $200 billion in 2026, almost entirely pointed at AI infrastructure. That is a company that knows exactly what it's building toward. On the physical side, grocery gross sales surpassed $150 billion in 2025. Project Leo already has 200+ satellites in orbit before the service has even launched. Over $4 billion is going into rural delivery expansion and 1 million robots are now deployed across their fulfillment network with AI making each one significantly more capable than the last. The advertising business quietly became a monster too. $70 billion annual run rate, rivaling YouTube in scale, but sitting on top of purchase intent data that no social platform can touch. When someone searches on Amazon, they are ready to buy and that is the most valuable real estate in digital advertising and Amazon owns it. 250 million Prime members who are deeply embedded in the ecosystem across shopping, streaming, grocery, pharmacy, and now healthcare. The switching cost is basically your entire life. Now here's where it gets interesting for us specifically. While the market was in full meltdown mode and everyone was panic selling anything with a ticker, our analyst at Milk Road made the call to buy Amazon. That position is now up over 10%. And every PRO member gets the alert the second it happens, the exact trade, the price, and the full rationale behind it. If you're already a PRO member, turn on trade notifications in your account settings so you never miss another one. If you're not a member yet, come join us, link below!

Milk Road AI

12,238 Aufrufe • vor 5 Monaten