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Create your own Ai girlfriend no rules no filter 🎥 #flightattendant #uniform #asianbabe #asiangirl #asianhotties #aiwaifu #aigirl #aiporn #aigenerated #nsfwai #nsfw #porn #adult #pov #blowjob

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DREAM.AI

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26,771 просмотров • 9 дней назад •via X (Twitter)

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POV: You just landed on the Moon... Suit up. Step out. Collect samples. Explore the unknown. What looks like a real space mission? Fully created with AI. As NASA pushes forward with the Artemis Program—and with Artemis II making its return— the future of space feels closer than ever. And now… you can create your own mission. No rocket needed. Just imagination + AI.
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Meta just dropped the biggest Ad Library update ever: you can now sort any brand's ads by highest impressions 🤯 Which means you can see exactly which competitor ads are getting the most views. Daniel ⛰️ from ACTIV said "if someone can create an AI scraper to review the top 10 ads every week, I'd pay for that." So I built it with Claude Code. Here's how it works: → Import any brand with their Ad Library URL → Apify scrapes their top-performing ads automatically → Click into any ad for full breakdown (headline, copy, CTA) → Gemini watches the video or analyzes the image → Returns asset type, visual format, messaging angle, hook tactic, offer type No manual research, no watching videos one by one, no messy spreadsheets. Here's what I built: - 50+ DTC brands already loaded (AG1, Caraway, Chomps, Dr. Squatch, Gruns, Jones Road, Magic Spoon, Ridge) - Weekly auto-scrape to refresh top ads - AI analysis on any ad in one click - Bookmark system to save winners - Filter by brand, category, media type, or AI tags Built 100% in Claude Code, hosted on Replit. I recorded a full step-by-step showing exactly how I built this. Want to build your own version? I'm giving away the prompts I used + the Claude Code starter template. To get access for free: > Like this post > Comment "META" And I'll send it over (must be following so I can DM)
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🚨 California’s Welfare Wonderland: Illegals Qualify for Full Medi-Cal, Housing, Paid Childcare, and $1,200+ Per Anchor Baby—While Citizens Get Denied A former California social services insider just detonated the myth: undocumented adults bypass SSN scrutiny entirely. No income checks. No asset verification. They walk in and qualify for full-scope Medi-Cal (state-funded expansions that covered 1.7+ million before the 2026 adult enrollment freeze), subsidized housing, Migrant Head Start (federally reclassified childcare now under tighter rules but long exploited), and—via U.S.-born citizen children—prorated CalFresh cash aid and food stamps at roughly $1,200 per kid plus $275 per person. State programs like CAPI sweeten the deal for aged or disabled non-citizens ineligible for federal SSI. This isn’t compassion—it’s an engineered inversion of the social contract. Federal law (PRWORA) bars most benefits for undocumented entrants precisely to protect the system. California weaponized state dollars (that they fraudulently bill the Federal government for, right, Gavin? and mixed-status loopholes to create a parallel economy: stay-at-home "moms" in Gucci driving Escalades, while citizen parents grind two jobs and veterans sleep rough. The result? Budget deficits, strained $50 Billion+ Medi-Cal rolls, and a perverse incentive structure that treats lawful residents as second-class in their own country. Scholarly truth: Every dollar funneled here is extracted from working taxpayers who face asset tests, work requirements, and clawbacks. It erodes the rule of law, distorts labor markets, and mocks the Founders’ compact—government exists to secure the blessings of liberty for citizens, not to import dependents who vote the pipeline open. America doesn’t hate immigrants. We reject the replacement racket. Time to enforce borders, end state-funded magnets, and put citizens first—before the system collapses under its own contradictions. #WelfareForIllegals #AnchorBabyBonanza #CaliforniaFraudExposed #TaxpayerTheft #AmericaFirstOrBust #EndTheLoopholeNow (Repost the video. Share if you’ve had enough of your tax dollars funding luxury for lawbreakers.)

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11,007 просмотров • 2 месяцев назад

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SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. 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Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:

Owen Gregorian

70,672 просмотров • 1 год назад

Hello Cfx/FiveM Rockstar Games I am tweeting you today to ask for your assistance. You banned my SevenWands server for no reason, or for the wrong reasons. Please know that I paid a team of developers and mappers for 13 months to create my own magical universe. I created a lore, I created spells, I created factions, all from my own imagination and that of my team. We have been working on this project for over a year, as I said, and it has cost me over $100,000, all for the love of the magical world. We have NOTHING TO DO with Harry Potter. Obviously, as fans of the franchise, we were inspired by it, and I can't hide my love for it. But out of respect for your rules, I decided to create my own magical world, with absolutely everything made from scratch. Everything was homemade. Nothing was stolen from a server or anything else, and I have all the proof. I would like to talk to you privately, to show you that we simply want to open our world to players who want change and new things on FiveM. In two weeks since opening, we have welcomed 10,000 players, and every night we had 500 online (which is the limit) because we only accept a maximum of 500 players, so that players can have fun and there are no bugs. We do everything for the community, and we hope you will quickly resolve this issue, which is undeserved. I even registered the SevenWands trademark internationally to prove my commitment to you... Thank you for reading! ------------------------------- Bonsoir, Je vous tweet aujourd'hui pour vous demander de l'aide. Vous avez banni mon serveur SevenWands sans raison, ou alors de mauvaises raisons. Sachez que j'ai payé une équipe de dev, mappeurs durant 13 mois pour avoir mon propre univers magique, j'ai créé un lore, j'ai créé des sorts, j'ai créé des factions, tout sort de ma tête et celle de mon équipe. Ce projet, on travaille dessus depuis plus d'un an comme dis précédemment, et ça m'a coûté plus de 100.000$, et tout ça pour l'amour du monde magique. Nous n'avons RIEN A VOIR avec Harry Potter, nous nous sommes inspirés bien évidemment étant un fan de cette licence, et je ne peux vous cacher mon amour pour cette licence. Mais par respect de vos règles, j'ai décidé de créer mon monde magique, avec absolument tout fait de A à Z. Tout a été fait maison. Rien n'a été volé d'un serveur ou autre, et j'en ai toutes les preuves. J'aimerais discuter avec vous en privé, vous démontrer que nous voulons simplement ouvrir notre monde aux joueurs qui veulent du changement et de nouvelles choses sur FiveM. En deux semaines d'ouvertures, nous avons accueilli 10.000 joueurs, et chaque soirs nous étions 500 en ligne (qui est la limite) car nous n'acceptons que 500 joueurs maximum, pour que les joueurs aient du plaisir et qu'il n'y ai pas de bugs. Nous faisons tout pour la communauté, et nous espérons que vous allez vite régler ce problème, qui n'est pas mérité. J'ai même déposé la marque "SevenWands" au niveau international pour vous prouver mon engagement.. Merci d'avoir lu !
4:07

Hello Cfx/FiveM Rockstar Games I am tweeting you today to ask for your assistance. You banned my SevenWands server for no reason, or for the wrong reasons. Please know that I paid a team of developers and mappers for 13 months to create my own magical universe. I created a lore, I created spells, I created factions, all from my own imagination and that of my team. We have been working on this project for over a year, as I said, and it has cost me over $100,000, all for the love of the magical world. We have NOTHING TO DO with Harry Potter. Obviously, as fans of the franchise, we were inspired by it, and I can't hide my love for it. But out of respect for your rules, I decided to create my own magical world, with absolutely everything made from scratch. Everything was homemade. Nothing was stolen from a server or anything else, and I have all the proof. I would like to talk to you privately, to show you that we simply want to open our world to players who want change and new things on FiveM. In two weeks since opening, we have welcomed 10,000 players, and every night we had 500 online (which is the limit) because we only accept a maximum of 500 players, so that players can have fun and there are no bugs. We do everything for the community, and we hope you will quickly resolve this issue, which is undeserved. I even registered the SevenWands trademark internationally to prove my commitment to you... Thank you for reading! ------------------------------- Bonsoir, Je vous tweet aujourd'hui pour vous demander de l'aide. Vous avez banni mon serveur SevenWands sans raison, ou alors de mauvaises raisons. Sachez que j'ai payé une équipe de dev, mappeurs durant 13 mois pour avoir mon propre univers magique, j'ai créé un lore, j'ai créé des sorts, j'ai créé des factions, tout sort de ma tête et celle de mon équipe. Ce projet, on travaille dessus depuis plus d'un an comme dis précédemment, et ça m'a coûté plus de 100.000$, et tout ça pour l'amour du monde magique. Nous n'avons RIEN A VOIR avec Harry Potter, nous nous sommes inspirés bien évidemment étant un fan de cette licence, et je ne peux vous cacher mon amour pour cette licence. Mais par respect de vos règles, j'ai décidé de créer mon monde magique, avec absolument tout fait de A à Z. Tout a été fait maison. Rien n'a été volé d'un serveur ou autre, et j'en ai toutes les preuves. J'aimerais discuter avec vous en privé, vous démontrer que nous voulons simplement ouvrir notre monde aux joueurs qui veulent du changement et de nouvelles choses sur FiveM. En deux semaines d'ouvertures, nous avons accueilli 10.000 joueurs, et chaque soirs nous étions 500 en ligne (qui est la limite) car nous n'acceptons que 500 joueurs maximum, pour que les joueurs aient du plaisir et qu'il n'y ai pas de bugs. Nous faisons tout pour la communauté, et nous espérons que vous allez vite régler ce problème, qui n'est pas mérité. J'ai même déposé la marque "SevenWands" au niveau international pour vous prouver mon engagement.. Merci d'avoir lu !

TeufeurS

985,267 просмотров • 8 месяцев назад

Is writing quality subjective or objective? Of course, taste is personal and writers hate rules, but the more you read, the more you see the same patterns everywhere. Michael Dean has scored 100s of essays on 27 different metrics. He says we can use AI not to automate writing, but to elevate it. Here’s what we talked about: 1) You can’t develop good taste without knowing the fundamentals of your genre. These 27 patterns across all essays are not rules, but questions. Once you understand the constraints, you can break them creatively. 2) The best way to get good at writing is to change how you read. Don’t just read for content. Highlight the parts that resonate, and then deconstruct the voice and structure. 3) Storytelling is about stretching the unknown across time. 4) Great titles do triple duty: they create mystery, distill your thesis, and use phonetics that roll off the tongue. 'Pride and Prejudice' succeeds on all counts, while 'English Aristocracy' falls flat. 5) Get feedback by asking readers what they love and hate. Everyone knows to rework the parts people hate, but don't forget about the other invisible enemy: boredom. Beware of the parts people are indifferent to. Forgettable sections are the enemy of good writing, and no reaction is a bad reaction. 6) The ultimate goal of writing advice is to forget it. Good practice brings you fluency. People romanticize the Grateful Dead as an emblem of free-spirited intuition, but nobody talks about their insane work ethic. Young Jerry Garcia practiced banjo scales for 10 hours a day. Phil Lesh studied classical music theory for trumpet. They kicked out Bob Weir for not being rigorous enough and he had to work his way back into the band. By going into your head, you can get out of your head. 7) A good hook is a fractal. A hook isn’t just a clever way to intrigue a reader, it should capture the core dilemma of your essay. Understand the questions your writing answers, and then bake those questions into the subtext of your opening. 8) Personal writing involves the biographical details of your life. Could somebody put their name on your essay and get away with it? If so, you’re not on the page. 9) Personal writing has 3 criteria: biography (what happened), interiority (what you thought), and outlook (what you believe). It’s not about pushing these to their extremes, but having them work together to support your main idea. 10) To find your voice, change your arena. The way we shape language is linked to our environment. You’ll speak differently to a 5,000-person audience than to your five best friends. You’re more likely to experiment when the stakes and visibility are low. Consider an unlisted page on your website. Or consider a pseudonym (Fernando Pessoa had 75 of them). 11) Write about less: A student once asked Umberto Eco for advice about how to write a thesis. He wanted to write about all the volcanoes in the world. Eco told him to narrow it down. How about all the active volcanoes in the world? Nope, there are 48 of them. Get more specific. “Okay, how about the Popocatépetl Volcano in Mexico?” Okay, that works. Pick one volcano and focus on that. 12) We should prepare for AI to get better than we can imagine. If AI gets “extraterrestrially good,” becomes hyper relevant, and can replicate us to uncanny degrees—will you still write your own sentences? If the answer is yes, you don’t need to panic every time a new model comes out. By imagining the extremes, it helps you understand the timeless parts of the practice. 13) AI's ability to help you write is constrained by how much information you give it. Michael says: “The more you write, the more personal and aligned the replica can be.” 14) Write your first draft for yourself, and write your second draft for your reader. 15) Paragraphs are the atomic unit of composition. We are traumatized by The Five Paragraph Essay and need better heuristics. Each paragraph can start with a frame to hook the reader and end with a reward. Do it over and over again, and you'll get a Reader's Trance. 16) By learning to write essays, you become a Writing Generalist. Most genres specialize, but the essay is a medium of fusion. It combines the soul of a memoirist, the rigor of a philosopher, the pen of a poet, the persuasion of a marketer, the research of a journalist, and the creativity of a novelist. I've shared the full conversation with Michael Dean below. Of course, you can watch it here, but if you'd like to watch it on YouTube or listen on Apple or Spotify, I've shared those links below.
1:51:30

Is writing quality subjective or objective? Of course, taste is personal and writers hate rules, but the more you read, the more you see the same patterns everywhere. Michael Dean has scored 100s of essays on 27 different metrics. He says we can use AI not to automate writing, but to elevate it. Here’s what we talked about: 1) You can’t develop good taste without knowing the fundamentals of your genre. These 27 patterns across all essays are not rules, but questions. Once you understand the constraints, you can break them creatively. 2) The best way to get good at writing is to change how you read. Don’t just read for content. Highlight the parts that resonate, and then deconstruct the voice and structure. 3) Storytelling is about stretching the unknown across time. 4) Great titles do triple duty: they create mystery, distill your thesis, and use phonetics that roll off the tongue. 'Pride and Prejudice' succeeds on all counts, while 'English Aristocracy' falls flat. 5) Get feedback by asking readers what they love and hate. Everyone knows to rework the parts people hate, but don't forget about the other invisible enemy: boredom. Beware of the parts people are indifferent to. Forgettable sections are the enemy of good writing, and no reaction is a bad reaction. 6) The ultimate goal of writing advice is to forget it. Good practice brings you fluency. People romanticize the Grateful Dead as an emblem of free-spirited intuition, but nobody talks about their insane work ethic. Young Jerry Garcia practiced banjo scales for 10 hours a day. Phil Lesh studied classical music theory for trumpet. They kicked out Bob Weir for not being rigorous enough and he had to work his way back into the band. By going into your head, you can get out of your head. 7) A good hook is a fractal. A hook isn’t just a clever way to intrigue a reader, it should capture the core dilemma of your essay. Understand the questions your writing answers, and then bake those questions into the subtext of your opening. 8) Personal writing involves the biographical details of your life. Could somebody put their name on your essay and get away with it? If so, you’re not on the page. 9) Personal writing has 3 criteria: biography (what happened), interiority (what you thought), and outlook (what you believe). It’s not about pushing these to their extremes, but having them work together to support your main idea. 10) To find your voice, change your arena. The way we shape language is linked to our environment. You’ll speak differently to a 5,000-person audience than to your five best friends. You’re more likely to experiment when the stakes and visibility are low. Consider an unlisted page on your website. Or consider a pseudonym (Fernando Pessoa had 75 of them). 11) Write about less: A student once asked Umberto Eco for advice about how to write a thesis. He wanted to write about all the volcanoes in the world. Eco told him to narrow it down. How about all the active volcanoes in the world? Nope, there are 48 of them. Get more specific. “Okay, how about the Popocatépetl Volcano in Mexico?” Okay, that works. Pick one volcano and focus on that. 12) We should prepare for AI to get better than we can imagine. If AI gets “extraterrestrially good,” becomes hyper relevant, and can replicate us to uncanny degrees—will you still write your own sentences? If the answer is yes, you don’t need to panic every time a new model comes out. By imagining the extremes, it helps you understand the timeless parts of the practice. 13) AI's ability to help you write is constrained by how much information you give it. Michael says: “The more you write, the more personal and aligned the replica can be.” 14) Write your first draft for yourself, and write your second draft for your reader. 15) Paragraphs are the atomic unit of composition. We are traumatized by The Five Paragraph Essay and need better heuristics. Each paragraph can start with a frame to hook the reader and end with a reward. Do it over and over again, and you'll get a Reader's Trance. 16) By learning to write essays, you become a Writing Generalist. Most genres specialize, but the essay is a medium of fusion. It combines the soul of a memoirist, the rigor of a philosopher, the pen of a poet, the persuasion of a marketer, the research of a journalist, and the creativity of a novelist. I've shared the full conversation with Michael Dean below. Of course, you can watch it here, but if you'd like to watch it on YouTube or listen on Apple or Spotify, I've shared those links below.

David Perell

90,841 просмотров • 1 год назад

I Spent $100k On Developers Before Learning This: Build Your AI Bot Today the blueprint to building your first ai trading bot without a degree or a single clue where to start is hidden in plain sight. most people think you need a stanford degree or some crazy math background to build these systems but i spent ten years in tech scared to code for that exact reason. i thought it was only for the geniuses and the nerds while i was just a guy who played video games and wanted his time back the reality is that code is the great equalizer because it doesn't care who you are or where you came from. i lost hundreds of thousands of dollars hiring developers who did shoddy work and i lost even more through liquidations and over trading because i was too emotional to follow my own rules. i knew i had to automate everything if i wanted to survive this game so i decided to learn live on youtube and iterate my way to success everyone is looking for the holy grail indicator that prints money while they sleep but they are looking in the wrong place. the real secret isn't a magical line on a chart but a process i call the rbi system which stands for research backtest and implement. most traders fail because they try to build a bot before they even know if their strategy worked in the past which is basically just gambling with extra steps you have to start with deep research into a strategy like supply and demand zones where you buy where the banks buy and sell where they sell. once you have a solid idea you must backtest it against years of data to see if it actually has an edge. if it doesn't work in the past it definitely won't work in the future but if it shows promise then you move to the implementation phase with small size there is a hidden cost to automation that can wipe out your profits before you even place a trade if you aren't careful. i found myself overusing api credits and running up a massive bill just to fetch wallet balances and token lists. if your bot is calling the exchange every five seconds just to see how much money you have you are essentially burning cash for no reason you can use ai tools like cursor to help you write the python code even if you are a total beginner. i still use ai to explain complex functions and identify where my code is being inefficient or chewing through credits. i had to refactor my entire dashboard and timer logic to only check balances every thirty minutes instead of every few seconds to save those precious credits the man who made thirty one billion dollars in the markets had one rule he never broke throughout his entire career. jim simons was the greatest algorithmic trader to ever live and he proved that systems will always beat human intuition over a long enough timeline. his secret wasn't some complex formula that no one else could understand but a commitment to a specific way of thinking simons always said you just have to make your systems better and better because that is what everyone else is trying to do. the game never really ends because the markets are always evolving and your edge will eventually decay if you don't iterate. this is why i build in public and show every step of the process because the iteration is where the actual money is made the reason you get liquidated isn't the market or the whales or some conspiracy against your small account. the real reason is the conversation you have with yourself at two in the morning when you are down on a trade and decide to move your stop loss. humans are built for survival not for trading and our emotions like fomo and fear will always sabotage our results when you automate your trading you are essentially signing a non negotiable contract with yourself that the bot will execute without question. if the plan says to sell fifty percent in an uptrend and ninety five percent in a downtrend the bot does it every single time. it doesn't feel the panic when a red candle drops or the greed when a green one spikes it just follows the code i used to spend all day staring at screens chasing bars up and down thinking that more screen time equaled more profit. i got into trading to get my time back but i ended up becoming a slave to the charts until i finally learned to code. now i have fully automated systems trading for me instead of getting liquidated because i removed the weakest link in the system which was me you don't need to spend ten years learning how to code before you can start building your own trading bots. if you spend three to six months getting the gist of python and using ai to bridge the gap you can start building immediately. start with a simple supply and demand bot that looks for major coin trends and only enters when the odds are heavily in your favor by checking the trend of bitcoin ethereum and solana simultaneously you can ensure you aren't fighting the overall market direction. i look for at least two out of those three to be trending before my bot is even allowed to look for an entry. this simple filter alone can save you from thousands of dollars in paper cuts during choppy sideways markets if you can't fly then run and if you can't run then walk but by all means you must keep moving toward automation. the process of taking an idea out of your brain and putting it into a system is the most secretive and valuable skill in the world. don't follow the pack and try to solve the same problems as everyone else but find your own edge and code it into existence the deal you make with yourself at the start of your journey is what determines if you will actually make it or not. i made a contract with myself to learn live and show everything because i believe that transparency is the only way to truly learn this craft. stick to your plan and iterate every single day because the systems you build today are the equalizers that will change your life tomorrow
50:50

I Spent $100k On Developers Before Learning This: Build Your AI Bot Today the blueprint to building your first ai trading bot without a degree or a single clue where to start is hidden in plain sight. most people think you need a stanford degree or some crazy math background to build these systems but i spent ten years in tech scared to code for that exact reason. i thought it was only for the geniuses and the nerds while i was just a guy who played video games and wanted his time back the reality is that code is the great equalizer because it doesn't care who you are or where you came from. i lost hundreds of thousands of dollars hiring developers who did shoddy work and i lost even more through liquidations and over trading because i was too emotional to follow my own rules. i knew i had to automate everything if i wanted to survive this game so i decided to learn live on youtube and iterate my way to success everyone is looking for the holy grail indicator that prints money while they sleep but they are looking in the wrong place. the real secret isn't a magical line on a chart but a process i call the rbi system which stands for research backtest and implement. most traders fail because they try to build a bot before they even know if their strategy worked in the past which is basically just gambling with extra steps you have to start with deep research into a strategy like supply and demand zones where you buy where the banks buy and sell where they sell. once you have a solid idea you must backtest it against years of data to see if it actually has an edge. if it doesn't work in the past it definitely won't work in the future but if it shows promise then you move to the implementation phase with small size there is a hidden cost to automation that can wipe out your profits before you even place a trade if you aren't careful. i found myself overusing api credits and running up a massive bill just to fetch wallet balances and token lists. if your bot is calling the exchange every five seconds just to see how much money you have you are essentially burning cash for no reason you can use ai tools like cursor to help you write the python code even if you are a total beginner. i still use ai to explain complex functions and identify where my code is being inefficient or chewing through credits. i had to refactor my entire dashboard and timer logic to only check balances every thirty minutes instead of every few seconds to save those precious credits the man who made thirty one billion dollars in the markets had one rule he never broke throughout his entire career. jim simons was the greatest algorithmic trader to ever live and he proved that systems will always beat human intuition over a long enough timeline. his secret wasn't some complex formula that no one else could understand but a commitment to a specific way of thinking simons always said you just have to make your systems better and better because that is what everyone else is trying to do. the game never really ends because the markets are always evolving and your edge will eventually decay if you don't iterate. this is why i build in public and show every step of the process because the iteration is where the actual money is made the reason you get liquidated isn't the market or the whales or some conspiracy against your small account. the real reason is the conversation you have with yourself at two in the morning when you are down on a trade and decide to move your stop loss. humans are built for survival not for trading and our emotions like fomo and fear will always sabotage our results when you automate your trading you are essentially signing a non negotiable contract with yourself that the bot will execute without question. if the plan says to sell fifty percent in an uptrend and ninety five percent in a downtrend the bot does it every single time. it doesn't feel the panic when a red candle drops or the greed when a green one spikes it just follows the code i used to spend all day staring at screens chasing bars up and down thinking that more screen time equaled more profit. i got into trading to get my time back but i ended up becoming a slave to the charts until i finally learned to code. now i have fully automated systems trading for me instead of getting liquidated because i removed the weakest link in the system which was me you don't need to spend ten years learning how to code before you can start building your own trading bots. if you spend three to six months getting the gist of python and using ai to bridge the gap you can start building immediately. start with a simple supply and demand bot that looks for major coin trends and only enters when the odds are heavily in your favor by checking the trend of bitcoin ethereum and solana simultaneously you can ensure you aren't fighting the overall market direction. i look for at least two out of those three to be trending before my bot is even allowed to look for an entry. this simple filter alone can save you from thousands of dollars in paper cuts during choppy sideways markets if you can't fly then run and if you can't run then walk but by all means you must keep moving toward automation. the process of taking an idea out of your brain and putting it into a system is the most secretive and valuable skill in the world. don't follow the pack and try to solve the same problems as everyone else but find your own edge and code it into existence the deal you make with yourself at the start of your journey is what determines if you will actually make it or not. i made a contract with myself to learn live and show everything because i believe that transparency is the only way to truly learn this craft. stick to your plan and iterate every single day because the systems you build today are the equalizers that will change your life tomorrow

Moon Dev

11,726 просмотров • 5 месяцев назад

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

280,425 просмотров • 3 месяцев назад

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.
18:53

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Himanshu Kumar

37,638 просмотров • 4 месяцев назад

Use this prompt in OpenClaw to create your own AI agent command center that syncs up your life like Tony Stark's Jarvis in Iron Man. Adapt the specifics (agent names, data sources, branding) below to your own setup. Prompt: Build me a mission control dashboard for my OpenClaw AI agent system. Stack: Next.js 15 (App Router) + Convex (real-time backend) + Tailwind CSS v4 + Framer Motion + ShadCN UI + Lucide icons. TypeScript throughout. This is the command center where I monitor and control my autonomous AI agent(s) running on OpenClaw. The agent operates 24/7 on a Mac Mini, connected to Telegram/Discord, running cron jobs, spawning sub-agents, and reading/writing to a filesystem-based memory and state system. Dark mode only. Ultra-premium aesthetic, think Iron Man's JARVIS HUD meets a Bloomberg terminal. Subtle glass effects (backdrop-blur-xl, bg-white/[0.03]), no heavy gradients or glow. Rounded corners (16-20px on cards). Framer Motion for page transitions, stagger animations on card grids, spring physics on interactions. Mobile-first responsive. Never cookie-cutter. ## Architecture The dashboard reads live data from TWO sources: 1. **Convex**: real-time database for structured data (tasks, contacts, content drafts, calendar events, activity logs) 2. **Local API routes** (`/api/*`): read files from the agent's workspace filesystem at `~/.openclaw/workspace/` and return JSON. This is how live system state flows into the dashboard. ## Pages & Views (8 nav items, some with tab sub-views) ### 1. HOME (`/`) Dashboard overview. Grid of live status cards: - **System Health**: read from `/api/system-state` (parses `state/servers.json`). Show each service with UP/DOWN indicator, port, last check time. - **Agent Status**: read from `/api/agents` (parses `agents/registry.json` + agent workspace files). Show active agent count, healthy/unhealthy ratio, active sub-agent count from OpenClaw sessions API. - **Cron Health**: read from `/api/cron-health` (parses `state/crons.json`). Table of all scheduled jobs with name, schedule, last status (green/red dot), consecutive errors. - **Revenue Tracker**: read from `/api/revenue` (parses `state/revenue.json`). Current revenue, monthly burn, net. - **Content Pipeline**: read from `/api/content-pipeline` (parses `content/queue.md`). Kanban-style: Draft | Review | Approved | Published counts. - **Quick Stats**: total tasks, pending approvals, active sessions, uptime. All panels auto-refresh every 15 seconds. Live indicator dot + "AUTO 15S" badge in header. ### 2. OPS (`/ops`) with 3 tabs: Operations | Tasks | Calendar **Operations tab:** Full operational view. Server health table, branch status (from `state/branch-check.json`), observations feed (from `state/observations.md`), system priorities (from `shared-context/priorities.md`). **Tasks tab:** Strategic task suggestion system. API route `/api/suggested-tasks` reads/writes `state/suggested-tasks.json`. Cards grouped by category (Revenue, Product, Community, Content, Operations, Clients, Trading, Brand) with emoji headers. Each card shows title, reasoning, next action, priority badge, effort badge, approve/reject buttons. Filter bar by status and category. **Calendar tab:** Weekly calendar view from Convex `calendarEvents` table. Drag-to-create, color-coded by type, time slots. ### 3. AGENTS (`/agents`) with 2 tabs: Agents | Models **Agents tab:** Card grid of all registered agents from `/api/agents`. Each card shows name, role, model, level (L1-L4), status. Cards are CLICKABLE: expanding into a detail panel showing: - Agent personality (reads their SOUL .md) - Capabilities and rules (reads their RULES .md) - Sub-agents they can spawn - Recent outputs (reads from `shared-context/agent-outputs/`) **Models tab:** Model inventory table showing all available models, their routing (which tasks go to which model), costs, and failover chains. ### 4. CHAT (`/chat`): 2 tabs: Chat | Command **Chat tab:** Chat interface to communicate with the agent. Left sidebar shows session list (from `/api/chat-history` reading .jsonl transcript files). Main area shows messages with role-aligned bubbles (user right, assistant left), date separators, channel badges (telegram/discord/webchat). Input bar with send button + voice input (Web Speech API with SpeechRecognition). Messages sent via `/api/chat-send` which queues to a file the agent reads. **Command tab:** Quick command interface for common operations. ### 5. CONTENT (`/content`) Content pipeline management. Read from Convex `contentDrafts` table AND `/api/content-pipeline`. Show drafts in kanban columns. Each card shows title, platform target, draft text preview, status, created date. Edit/approve/reject actions. ### 6. COMMS (`/comms`) with 2 tabs: Comms | CRM **Comms tab:** Communication hub showing recent Discord digest, Telegram messages, notification history. **CRM tab:** Client pipeline kanban (Prospect → Contacted → Meeting → Proposal → Active). API route `/api/clients` reads markdown files from `clients/` directory. Each card shows client name, status, contacts, last interaction, next action. ### 7. KNOWLEDGE (`/knowledge`) with 2 tabs: Knowledge | Ecosystem **Knowledge tab:** Searchable knowledge base. Global search across all workspace files using `/api/knowledge` endpoint. **Ecosystem tab:** Product grid showing all products/apps in the ecosystem. Each card shows product name, status (Active/Development/Concept), health indicator, key metrics. Cards link to `/ecosystem/[slug]` detail pages with tabbed views (Overview, Brand, Community, Content, Legal, Product, Website, Actions). Detail pages read from `/api/ecosystem/[slug]` which parses workspace memory files. ### 8. CODE (`/code`) Code pipeline view. Shows repositories from `/api/repos` (scans ~/Desktop/Projects/ for git repos). Each repo card shows name, branch, last commit, dirty file count, language breakdown. Detail view at `/api/repos/detail` shows recent commits, file tree, open PRs. ## Navigation Top horizontal nav bar, NOT sidebar. All 8 items visible at all viewport widths. Use `flex` layout with `flex-1` items. Text size uses `clamp(0.45rem, 0.75vw, 0.6875rem)` for fluid scaling. Active item gets `text-primary bg-primary/[0.06]` static highlight (no sliding animation). Agent/app name visible at md+ breakpoints (`hidden md:inline`). Tab sub-views use a reusable `TabBar` component with pill/glass styling and Framer Motion `layoutId` transitions. Tab state stored in URL via `?tab=` search params. ## API Routes (all under `src/app/api/`) Each API route reads from the agent's workspace filesystem and returns JSON: - `/api/system-state` → reads `state/servers.json`, `state/branch-check.json` - `/api/agents` → reads `agents/registry.json`, agent SOUL .md files - `/api/agents/[id]` → reads specific agent's SOUL .md, RULES .md, outputs - `/api/cron-health` → reads `state/crons.json` - `/api/revenue` → reads `state/revenue.json` - `/api/content-pipeline` → parses `content/queue.md` (markdown with status markers) - `/api/suggested-tasks` → GET (read) / POST (approve/reject) on `state/suggested-tasks.json` - `/api/observations` → reads `state/observations.md` - `/api/priorities` → reads `shared-context/priorities.md` - `/api/chat-history` → reads .jsonl transcript files with pagination/search/channel filter - `/api/chat-send` → writes to queue file - `/api/clients` → reads markdown files from `clients/` directory - `/api/ecosystem/[slug]` → reads memory files for specific ecosystem - `/api/repos` → scans project directories for git repos - `/api/health` → returns status, uptime, memory usage, Convex connectivity All filesystem paths should be configurable via environment variable (default: `~/.openclaw/workspace/`). ## Convex Schema Define tables for: activities, calendarEvents, tasks, contacts, contentDrafts, ecosystemProducts. Include seed scripts (`convex/seed.ts`) to populate initial data. ## Key Design Rules - Mobile-first, test at 320px minimum - Font sizes 10-14px for body text, everything must fit naturally at small viewports - Cards use consistent border radius (16-20px) - Glass cards: `bg-white/[0.03] backdrop-blur-xl border border-white/[0.06]` - No heavy blur blobs or grain overlays - Stagger animations on card grids (0.05s delay per item) - Skeleton loading states for all async data - Custom scrollbar styling - Empty states with helpful messaging - All text must use Inter or system font stack - Never mix sharp and rounded corners in the same view - Premium = lighter feel, more whitespace, less visual noise ## File Structure ``` src/ app/ page.tsx, layout.tsx, providers.tsx agents/page.tsx calendar/page.tsx chat/page.tsx code/page.tsx comms/page.tsx content/page.tsx ecosystem/page.tsx, ecosystem/[slug]/page.tsx knowledge/page.tsx ops/page.tsx api/[...all routes above] components/ nav.tsx tab-bar.tsx dashboard-overview.tsx ops-view.tsx, suggested-tasks-view.tsx agents-view.tsx, models-view.tsx chat-center-view.tsx, voice-input.tsx content-view.tsx comms-view.tsx, crm-view.tsx knowledge-base.tsx, ecosystem-view.tsx code-pipeline.tsx activity-feed.tsx, calendar-view.tsx ui/ (ShadCN primitives) hooks/ lib/ convex/ schema.ts functions for each table seed.ts ``` Build the complete application. Every component, every API route, every Convex function. Production-quality code and premium design, not stubs. Dark mode only. Make it look incredibly beautiful and premium, no cookie cutter UI / AI slop.
1:14

Use this prompt in OpenClaw to create your own AI agent command center that syncs up your life like Tony Stark's Jarvis in Iron Man. Adapt the specifics (agent names, data sources, branding) below to your own setup. Prompt: Build me a mission control dashboard for my OpenClaw AI agent system. Stack: Next.js 15 (App Router) + Convex (real-time backend) + Tailwind CSS v4 + Framer Motion + ShadCN UI + Lucide icons. TypeScript throughout. This is the command center where I monitor and control my autonomous AI agent(s) running on OpenClaw. The agent operates 24/7 on a Mac Mini, connected to Telegram/Discord, running cron jobs, spawning sub-agents, and reading/writing to a filesystem-based memory and state system. Dark mode only. Ultra-premium aesthetic, think Iron Man's JARVIS HUD meets a Bloomberg terminal. Subtle glass effects (backdrop-blur-xl, bg-white/[0.03]), no heavy gradients or glow. Rounded corners (16-20px on cards). Framer Motion for page transitions, stagger animations on card grids, spring physics on interactions. Mobile-first responsive. Never cookie-cutter. ## Architecture The dashboard reads live data from TWO sources: 1. **Convex**: real-time database for structured data (tasks, contacts, content drafts, calendar events, activity logs) 2. **Local API routes** (`/api/*`): read files from the agent's workspace filesystem at `~/.openclaw/workspace/` and return JSON. This is how live system state flows into the dashboard. ## Pages & Views (8 nav items, some with tab sub-views) ### 1. HOME (`/`) Dashboard overview. Grid of live status cards: - **System Health**: read from `/api/system-state` (parses `state/servers.json`). Show each service with UP/DOWN indicator, port, last check time. - **Agent Status**: read from `/api/agents` (parses `agents/registry.json` + agent workspace files). Show active agent count, healthy/unhealthy ratio, active sub-agent count from OpenClaw sessions API. - **Cron Health**: read from `/api/cron-health` (parses `state/crons.json`). Table of all scheduled jobs with name, schedule, last status (green/red dot), consecutive errors. - **Revenue Tracker**: read from `/api/revenue` (parses `state/revenue.json`). Current revenue, monthly burn, net. - **Content Pipeline**: read from `/api/content-pipeline` (parses `content/queue.md`). Kanban-style: Draft | Review | Approved | Published counts. - **Quick Stats**: total tasks, pending approvals, active sessions, uptime. All panels auto-refresh every 15 seconds. Live indicator dot + "AUTO 15S" badge in header. ### 2. OPS (`/ops`) with 3 tabs: Operations | Tasks | Calendar **Operations tab:** Full operational view. Server health table, branch status (from `state/branch-check.json`), observations feed (from `state/observations.md`), system priorities (from `shared-context/priorities.md`). **Tasks tab:** Strategic task suggestion system. API route `/api/suggested-tasks` reads/writes `state/suggested-tasks.json`. Cards grouped by category (Revenue, Product, Community, Content, Operations, Clients, Trading, Brand) with emoji headers. Each card shows title, reasoning, next action, priority badge, effort badge, approve/reject buttons. Filter bar by status and category. **Calendar tab:** Weekly calendar view from Convex `calendarEvents` table. Drag-to-create, color-coded by type, time slots. ### 3. AGENTS (`/agents`) with 2 tabs: Agents | Models **Agents tab:** Card grid of all registered agents from `/api/agents`. Each card shows name, role, model, level (L1-L4), status. Cards are CLICKABLE: expanding into a detail panel showing: - Agent personality (reads their SOUL .md) - Capabilities and rules (reads their RULES .md) - Sub-agents they can spawn - Recent outputs (reads from `shared-context/agent-outputs/`) **Models tab:** Model inventory table showing all available models, their routing (which tasks go to which model), costs, and failover chains. ### 4. CHAT (`/chat`): 2 tabs: Chat | Command **Chat tab:** Chat interface to communicate with the agent. Left sidebar shows session list (from `/api/chat-history` reading .jsonl transcript files). Main area shows messages with role-aligned bubbles (user right, assistant left), date separators, channel badges (telegram/discord/webchat). Input bar with send button + voice input (Web Speech API with SpeechRecognition). Messages sent via `/api/chat-send` which queues to a file the agent reads. **Command tab:** Quick command interface for common operations. ### 5. CONTENT (`/content`) Content pipeline management. Read from Convex `contentDrafts` table AND `/api/content-pipeline`. Show drafts in kanban columns. Each card shows title, platform target, draft text preview, status, created date. Edit/approve/reject actions. ### 6. COMMS (`/comms`) with 2 tabs: Comms | CRM **Comms tab:** Communication hub showing recent Discord digest, Telegram messages, notification history. **CRM tab:** Client pipeline kanban (Prospect → Contacted → Meeting → Proposal → Active). API route `/api/clients` reads markdown files from `clients/` directory. Each card shows client name, status, contacts, last interaction, next action. ### 7. KNOWLEDGE (`/knowledge`) with 2 tabs: Knowledge | Ecosystem **Knowledge tab:** Searchable knowledge base. Global search across all workspace files using `/api/knowledge` endpoint. **Ecosystem tab:** Product grid showing all products/apps in the ecosystem. Each card shows product name, status (Active/Development/Concept), health indicator, key metrics. Cards link to `/ecosystem/[slug]` detail pages with tabbed views (Overview, Brand, Community, Content, Legal, Product, Website, Actions). Detail pages read from `/api/ecosystem/[slug]` which parses workspace memory files. ### 8. CODE (`/code`) Code pipeline view. Shows repositories from `/api/repos` (scans ~/Desktop/Projects/ for git repos). Each repo card shows name, branch, last commit, dirty file count, language breakdown. Detail view at `/api/repos/detail` shows recent commits, file tree, open PRs. ## Navigation Top horizontal nav bar, NOT sidebar. All 8 items visible at all viewport widths. Use `flex` layout with `flex-1` items. Text size uses `clamp(0.45rem, 0.75vw, 0.6875rem)` for fluid scaling. Active item gets `text-primary bg-primary/[0.06]` static highlight (no sliding animation). Agent/app name visible at md+ breakpoints (`hidden md:inline`). Tab sub-views use a reusable `TabBar` component with pill/glass styling and Framer Motion `layoutId` transitions. Tab state stored in URL via `?tab=` search params. ## API Routes (all under `src/app/api/`) Each API route reads from the agent's workspace filesystem and returns JSON: - `/api/system-state` → reads `state/servers.json`, `state/branch-check.json` - `/api/agents` → reads `agents/registry.json`, agent SOUL .md files - `/api/agents/[id]` → reads specific agent's SOUL .md, RULES .md, outputs - `/api/cron-health` → reads `state/crons.json` - `/api/revenue` → reads `state/revenue.json` - `/api/content-pipeline` → parses `content/queue.md` (markdown with status markers) - `/api/suggested-tasks` → GET (read) / POST (approve/reject) on `state/suggested-tasks.json` - `/api/observations` → reads `state/observations.md` - `/api/priorities` → reads `shared-context/priorities.md` - `/api/chat-history` → reads .jsonl transcript files with pagination/search/channel filter - `/api/chat-send` → writes to queue file - `/api/clients` → reads markdown files from `clients/` directory - `/api/ecosystem/[slug]` → reads memory files for specific ecosystem - `/api/repos` → scans project directories for git repos - `/api/health` → returns status, uptime, memory usage, Convex connectivity All filesystem paths should be configurable via environment variable (default: `~/.openclaw/workspace/`). ## Convex Schema Define tables for: activities, calendarEvents, tasks, contacts, contentDrafts, ecosystemProducts. Include seed scripts (`convex/seed.ts`) to populate initial data. ## Key Design Rules - Mobile-first, test at 320px minimum - Font sizes 10-14px for body text, everything must fit naturally at small viewports - Cards use consistent border radius (16-20px) - Glass cards: `bg-white/[0.03] backdrop-blur-xl border border-white/[0.06]` - No heavy blur blobs or grain overlays - Stagger animations on card grids (0.05s delay per item) - Skeleton loading states for all async data - Custom scrollbar styling - Empty states with helpful messaging - All text must use Inter or system font stack - Never mix sharp and rounded corners in the same view - Premium = lighter feel, more whitespace, less visual noise ## File Structure ``` src/ app/ page.tsx, layout.tsx, providers.tsx agents/page.tsx calendar/page.tsx chat/page.tsx code/page.tsx comms/page.tsx content/page.tsx ecosystem/page.tsx, ecosystem/[slug]/page.tsx knowledge/page.tsx ops/page.tsx api/[...all routes above] components/ nav.tsx tab-bar.tsx dashboard-overview.tsx ops-view.tsx, suggested-tasks-view.tsx agents-view.tsx, models-view.tsx chat-center-view.tsx, voice-input.tsx content-view.tsx comms-view.tsx, crm-view.tsx knowledge-base.tsx, ecosystem-view.tsx code-pipeline.tsx activity-feed.tsx, calendar-view.tsx ui/ (ShadCN primitives) hooks/ lib/ convex/ schema.ts functions for each table seed.ts ``` Build the complete application. Every component, every API route, every Convex function. Production-quality code and premium design, not stubs. Dark mode only. Make it look incredibly beautiful and premium, no cookie cutter UI / AI slop.

klöss

201,471 просмотров • 5 месяцев назад