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Stop writing tests. Start describing them. KaneAI converts natural language into end-to-end tests in seconds. ✔ JIRA / PDF / video uploads ✔ Self-healing UI tests ✔ GitHub PR-based test creation ✔ API & backend validation Modern testing, powered by GenAI Give it a try : Follow #AITesting #KaneAI...

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How to Use KaneAI — Best Gen AI Native (FULL GUIDE) 🔥” QA Engineers & DevOps: Stop wasting 80% of your time on manual tests. KaneAI converts natural language into executable End to End tests instantly: ✅ Upload JIRA issues, PDFs, or videos → Auto-generate test cases ✅ Self-healing locators for resilient Ul tests ✅ Analyze GitHub PRs to create/update tests automatically ✅ Seamless API calls, database queries, and backend validation Transform your testing workflow today - create, debug, and evolve tests in plain English. No coding required. Ship faster with confidence! Give it a try : #AITesting #KaneAI #DevOps #QualityEngineering
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How to Use KaneAI — Best Gen AI Native (FULL GUIDE) 🔥” QA Engineers & DevOps: Stop wasting 80% of your time on manual tests. KaneAI converts natural language into executable End to End tests instantly: ✅ Upload JIRA issues, PDFs, or videos → Auto-generate test cases ✅ Self-healing locators for resilient Ul tests ✅ Analyze GitHub PRs to create/update tests automatically ✅ Seamless API calls, database queries, and backend validation Transform your testing workflow today - create, debug, and evolve tests in plain English. No coding required. Ship faster with confidence! Give it a try : #AITesting #KaneAI #DevOps #QualityEngineering

Erina | AI Tools & News

45,902 görüntüleme • 5 ay önce

🚀 Meet KaneAI TestMu AI the next-generation AI-powered testing assistant for modern QA teams. KaneAI helps teams plan, author, and evolve end-to-end tests using natural language, turning requirements directly into executable code. Built for enterprise-scale complexity, it handles sophisticated workflows across all major programming languages and frameworks. QA teams often face hurdles like rapid development cycles, test case debt, and complex framework management. KaneAI addresses these challenges by: ✅ Generating structured test cases from text, JIRA, PDFs, images, audio, video, or spreadsheets ✅ Enabling complex testing workflows with natural language ✅ Supporting multi-language code exports and deep integrations with JIRA and GitHub ✅ Automating API and database testing, smart versioning, and auto bug detection ✅ Self-healing tests for UI changes and flexible manual interaction KaneAI augments QA teams, boosting productivity, reducing errors, and helping teams focus on outcomes rather than repetitive tasks. Learn more about KaneAI here: Connect with TestMu AI : LinkedIn: Twitter: #QA #TestAutomation #AI #GenAI #KaneAI #TestMu
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🚀 Meet KaneAI TestMu AI the next-generation AI-powered testing assistant for modern QA teams. KaneAI helps teams plan, author, and evolve end-to-end tests using natural language, turning requirements directly into executable code. Built for enterprise-scale complexity, it handles sophisticated workflows across all major programming languages and frameworks. QA teams often face hurdles like rapid development cycles, test case debt, and complex framework management. KaneAI addresses these challenges by: ✅ Generating structured test cases from text, JIRA, PDFs, images, audio, video, or spreadsheets ✅ Enabling complex testing workflows with natural language ✅ Supporting multi-language code exports and deep integrations with JIRA and GitHub ✅ Automating API and database testing, smart versioning, and auto bug detection ✅ Self-healing tests for UI changes and flexible manual interaction KaneAI augments QA teams, boosting productivity, reducing errors, and helping teams focus on outcomes rather than repetitive tasks. Learn more about KaneAI here: Connect with TestMu AI : LinkedIn: Twitter: #QA #TestAutomation #AI #GenAI #KaneAI #TestMu

Mohini Shewale

16,809 görüntüleme • 5 ay önce

Anyone who’s worked in QA knows the gap. Requirements live in one place and tests live somewhere else. Every change creates another round of rewriting. Kane AI by TestMu AI (formerly LambdaTest) approaches that gap differently. Instead of forcing teams to rewrite tests in a new abstraction, it lets you describe intent in natural language and turns that into structured, executable tests that can be exported into your existing frameworks. During exploration, the most practical part wasn’t just test generation, it was how expressive the language layer is. Complex assertions, branching conditions, and validations can be described without switching mental modes into code, but the resulting output still integrates cleanly with real pipelines. There’s also a strong emphasis on API coverage and auto-bug detection, which matters when UI tests aren’t enough. Kane AI by TestMu AI (formerly LambdaTest) can detect failures during execution and attempt self-healing based on original intent, while still requiring review and validation from the team. It doesn’t pretend QA disappears. It acknowledges QA is hard, contextual, and constantly changing. That honesty makes it useful. If you’re dealing with test maintenance and curious how natural-language testing fits into real QA work, Kane AI by TestMu AI (formerly LambdaTest) is available to try. See it in action: TestMu AI
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Anyone who’s worked in QA knows the gap. Requirements live in one place and tests live somewhere else. Every change creates another round of rewriting. Kane AI by TestMu AI (formerly LambdaTest) approaches that gap differently. Instead of forcing teams to rewrite tests in a new abstraction, it lets you describe intent in natural language and turns that into structured, executable tests that can be exported into your existing frameworks. During exploration, the most practical part wasn’t just test generation, it was how expressive the language layer is. Complex assertions, branching conditions, and validations can be described without switching mental modes into code, but the resulting output still integrates cleanly with real pipelines. There’s also a strong emphasis on API coverage and auto-bug detection, which matters when UI tests aren’t enough. Kane AI by TestMu AI (formerly LambdaTest) can detect failures during execution and attempt self-healing based on original intent, while still requiring review and validation from the team. It doesn’t pretend QA disappears. It acknowledges QA is hard, contextual, and constantly changing. That honesty makes it useful. If you’re dealing with test maintenance and curious how natural-language testing fits into real QA work, Kane AI by TestMu AI (formerly LambdaTest) is available to try. See it in action: TestMu AI

AI Highlight

61,494 görüntüleme • 5 ay önce

I'm thrilled to announce Bug0 Studio - a self-serve, AI-native browser testing tool. We're in private beta. DM me your email for access + $250/mo in credits for 3 months. What you can do today: - Create tests from video recordings - Natural language test steps - Self-healing tests (adapts to UI changes) - Live browser preview - CI/CD integration (Vercel, Netlify, GitHub Actions) - Scheduled runs + reports via Slack, email, or PRs - Auto-extract emails (OTPs, magic links) - Team collaboration Most QA tools hide behind "contact us for demo." But I believe powerful QA shouldn't be gatekept behind sales calls and enterprise pricing. Help us build the most user-friendly and powerful AI QA tool that is open and accessible to all🙏 Today it's private beta. Soon, it's open to everyone.
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I'm thrilled to announce Bug0 Studio - a self-serve, AI-native browser testing tool. We're in private beta. DM me your email for access + $250/mo in credits for 3 months. What you can do today: - Create tests from video recordings - Natural language test steps - Self-healing tests (adapts to UI changes) - Live browser preview - CI/CD integration (Vercel, Netlify, GitHub Actions) - Scheduled runs + reports via Slack, email, or PRs - Auto-extract emails (OTPs, magic links) - Team collaboration Most QA tools hide behind "contact us for demo." But I believe powerful QA shouldn't be gatekept behind sales calls and enterprise pricing. Help us build the most user-friendly and powerful AI QA tool that is open and accessible to all🙏 Today it's private beta. Soon, it's open to everyone.

Sandeep

114,592 görüntüleme • 5 ay önce

just dropped a 24-min walkthrough showing how I turn raw n8n automations into actual front-end apps that your team can use no clunky JSON files, no ugly interfaces, and no dev team required I built a clean UI for one of my n8n flows - start to finish - all in one session it’s not a polished SaaS - but it is a working MVP you can test, share, or hand off step-by-step: – mapped the UX in ChatGPT – built the UI live in Bolt – connected it to the backend automation by the end, I had a usable internal tool that felt like a real product — all without writing a single line of frontend code if you're building powerful automations but not wrapping them in UI… you’re missing half the value also dropped my full prompt stack for planning and generating UIs with Claude or ChatGPT want the prompts? comment “UI”, follow + repost and I’ll send them over (must be following so I can DM)
24:30

just dropped a 24-min walkthrough showing how I turn raw n8n automations into actual front-end apps that your team can use no clunky JSON files, no ugly interfaces, and no dev team required I built a clean UI for one of my n8n flows - start to finish - all in one session it’s not a polished SaaS - but it is a working MVP you can test, share, or hand off step-by-step: – mapped the UX in ChatGPT – built the UI live in Bolt – connected it to the backend automation by the end, I had a usable internal tool that felt like a real product — all without writing a single line of frontend code if you're building powerful automations but not wrapping them in UI… you’re missing half the value also dropped my full prompt stack for planning and generating UIs with Claude or ChatGPT want the prompts? comment “UI”, follow + repost and I’ll send them over (must be following so I can DM)

J.B.

58,205 görüntüleme • 1 yıl önce

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.
1:24

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

45,966 görüntüleme • 1 yıl önce

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!
2:52

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

Andrew Ng

340,724 görüntüleme • 1 yıl önce

I cut Fable 5 token usage 2.5x with just one change! - Before: 5.5 M tokens · 7 errors · $8.94 - After: 2.3 M tokens · 0 errors · $4.17 The final build was the same for both, but the path the agent took wildly differed. In both runs, the agent started with the same thing, i.e., it understood the backend before building anything, like: - Permission policies - Available storage buckets - Auth providers configured - How edge functions are deployed The first run used Firebase, which was built for a human dev using a dashboard. While the dev can read the above state by clicking through tabs, an agent has no dashboard. So it gathered the same info through API calls. And there's no single Firebase call that returned this info. The agent required to query multiple times, and each query over-returned. For instance, when the agent asked how sign-in is configured, Firebase also returned the entire auth surface and every method it supported. This was far more context than what it needed. And it repeated across every part of the backend it inspected. Some states (like which auth providers are active) weren't queryable at all. I provided it myself. Otherwise, the agent would have guessed. Errors further compounded the token usage. When a dev sees "permission denied," they can look at the console and figure out whether it's a rule, a path, or an unauthenticated request. Firebase returned the same string to the agent as well, and it had none of that surrounding context to debug. So it guessed again, picked the most likely cause, and rewrote code, utilizing more tokens. This Firebase setup cost me 5.5M tokens and 7 manual interventions during errors on a full-stack RAG app. But I brought that down to 2.3M tokens and 0 manual interventions by using InsForge as the backend context engineering layer (open-source and self-hostable via Docker). It provides the same primitives as Supabase/Firebase, but structures the entire information layer for agents, instead of dashboards. In one CLI call that consumed ~500 tokens, the agent saw the full backend topology before writing a single line of code. This included auth, database, storage, edge functions, model gateway, micro VMs, and deployment. Also, instead of loading the entire product surface into context on every task, four narrowly scoped skills activated only when relevant to keep cognitive load minimal. And to ensure efficient retries if needed, every CLI operation returned structured JSON with meaningful exit codes, so the agent never guessed what to do next. Here's the InsForge GitHub Repo: (don't forget to star it ⭐) The video below depicts the final build, comparing Firebase and InsForge. To dive deeper, I recently published a full walkthrough building the same RAG app on both backends and inspected them end-to-end. Read it below.
0:29

I cut Fable 5 token usage 2.5x with just one change! - Before: 5.5 M tokens · 7 errors · $8.94 - After: 2.3 M tokens · 0 errors · $4.17 The final build was the same for both, but the path the agent took wildly differed. In both runs, the agent started with the same thing, i.e., it understood the backend before building anything, like: - Permission policies - Available storage buckets - Auth providers configured - How edge functions are deployed The first run used Firebase, which was built for a human dev using a dashboard. While the dev can read the above state by clicking through tabs, an agent has no dashboard. So it gathered the same info through API calls. And there's no single Firebase call that returned this info. The agent required to query multiple times, and each query over-returned. For instance, when the agent asked how sign-in is configured, Firebase also returned the entire auth surface and every method it supported. This was far more context than what it needed. And it repeated across every part of the backend it inspected. Some states (like which auth providers are active) weren't queryable at all. I provided it myself. Otherwise, the agent would have guessed. Errors further compounded the token usage. When a dev sees "permission denied," they can look at the console and figure out whether it's a rule, a path, or an unauthenticated request. Firebase returned the same string to the agent as well, and it had none of that surrounding context to debug. So it guessed again, picked the most likely cause, and rewrote code, utilizing more tokens. This Firebase setup cost me 5.5M tokens and 7 manual interventions during errors on a full-stack RAG app. But I brought that down to 2.3M tokens and 0 manual interventions by using InsForge as the backend context engineering layer (open-source and self-hostable via Docker). It provides the same primitives as Supabase/Firebase, but structures the entire information layer for agents, instead of dashboards. In one CLI call that consumed ~500 tokens, the agent saw the full backend topology before writing a single line of code. This included auth, database, storage, edge functions, model gateway, micro VMs, and deployment. Also, instead of loading the entire product surface into context on every task, four narrowly scoped skills activated only when relevant to keep cognitive load minimal. And to ensure efficient retries if needed, every CLI operation returned structured JSON with meaningful exit codes, so the agent never guessed what to do next. Here's the InsForge GitHub Repo: (don't forget to star it ⭐) The video below depicts the final build, comparing Firebase and InsForge. To dive deeper, I recently published a full walkthrough building the same RAG app on both backends and inspected them end-to-end. Read it below.

Avi Chawla

112,879 görüntüleme • 1 ay önce

Detailed overview of the most important patent that you will see to date.. Smartphone and app for personal pathogen status verification at point of entry into an area of congregation 📱 General Overview This patent describes a system combining a smartphone (or computing device) and an app to verify a user’s pathogen status (infection, immunity, vaccination) and communicate it at entry points to areas where people gather—such as buildings, venues, or transit hubs . --- 🧬 Background & Purpose Developed during the COVID‑19 pandemic, the patent addresses the need for systems that can confirm individuals are infection-free or vaccinated before entering shared spaces. With social distancing hindering many businesses economically, this verification system aimed to restore consumer confidence and ease entry management . --- 🔧 Key Components 1. User Device & App The app stores the user’s health record, including test results or vaccination proof. It can generate scannable codes (e.g. QR codes) for sharing status at entry points. Optional biometric capture ensures the user’s identity . 2. Testing/Vaccination Facilities Authorized facilities input results into a backend database accessible by the app. 3. Safe Entry Monitoring System Installed at entry points (e.g., door scanners, kiosks), this system verifies received codes, checks status criteria, and grants or denies access. 4. Communication Protocols The app securely communicates status codes to the monitoring system, often with two-way verification. It may also interface with other applications or standards-based systems . 📝 Operational Workflow Scheduling & Testing: Users schedule tests/vaccinations via the app, and results are linked to their digital record. Status Display & Verification: At entry, the app shows a QR or similar code representing verified status. Biometric checks may link the code to the actual user. Code Validation: Entry systems receive and authenticate the code—ensuring it matches what they issued for that user and facility . Feedback Provided: If entry is approved, the app can display permitted status and relevant entry criteria. 📚 Example Claims & Features Some representative claims include: Claim 1: An app that retrieves pathogen-test results, stores them, and communicates them to an entry-point computer system to determine user access. Claim 5–8: Covering QR-based status code generation, app-received validation codes, and matching logic between mobile device and entry system. Claim 10: App includes collection of biometric indicators for identity verification. Claim 12: Acknowledges vaccination proof as a form of pathogen status. Claim 13–24: Encompass the broader system architecture, device setups, communication methods, app use cases (mobile/web), and identity verification methods. 🏛️ System Architecture Client Side: The user's smartphone runs the app to manage health records, generate QR codes, and capture biometrics. Server Side: Facilities store test/vaccination info; entry-point systems validate incoming codes in real-time. Interoperability: The architecture allows communication among apps, facility systems, and entry-point systems—facilitating a unified, scalable ecosystem . 🔄 Utility & Impact Public Health: Enables safe reopening of businesses and public spaces by minimizing infection risk. Economic Recovery: Builds customer trust through verifiable health assurance—potentially restoring consumer traffic in sectors hit by distancing rules. Scalability: Designed to adapt beyond COVID‑19, applying its framework to future pathogens or pandemics . ✅ Summary US 11,961,346 B2 outlines a comprehensive digital entry-control system, integrating smartphones, test/vaccination data, biometric identity checks, and entry-point verification to regulate access based on verified pathogen status. Originally driven by COVID‑19 needs, it's positioned for broader health verification contexts
9:50

Detailed overview of the most important patent that you will see to date.. Smartphone and app for personal pathogen status verification at point of entry into an area of congregation 📱 General Overview This patent describes a system combining a smartphone (or computing device) and an app to verify a user’s pathogen status (infection, immunity, vaccination) and communicate it at entry points to areas where people gather—such as buildings, venues, or transit hubs . --- 🧬 Background & Purpose Developed during the COVID‑19 pandemic, the patent addresses the need for systems that can confirm individuals are infection-free or vaccinated before entering shared spaces. With social distancing hindering many businesses economically, this verification system aimed to restore consumer confidence and ease entry management . --- 🔧 Key Components 1. User Device & App The app stores the user’s health record, including test results or vaccination proof. It can generate scannable codes (e.g. QR codes) for sharing status at entry points. Optional biometric capture ensures the user’s identity . 2. Testing/Vaccination Facilities Authorized facilities input results into a backend database accessible by the app. 3. Safe Entry Monitoring System Installed at entry points (e.g., door scanners, kiosks), this system verifies received codes, checks status criteria, and grants or denies access. 4. Communication Protocols The app securely communicates status codes to the monitoring system, often with two-way verification. It may also interface with other applications or standards-based systems . 📝 Operational Workflow Scheduling & Testing: Users schedule tests/vaccinations via the app, and results are linked to their digital record. Status Display & Verification: At entry, the app shows a QR or similar code representing verified status. Biometric checks may link the code to the actual user. Code Validation: Entry systems receive and authenticate the code—ensuring it matches what they issued for that user and facility . Feedback Provided: If entry is approved, the app can display permitted status and relevant entry criteria. 📚 Example Claims & Features Some representative claims include: Claim 1: An app that retrieves pathogen-test results, stores them, and communicates them to an entry-point computer system to determine user access. Claim 5–8: Covering QR-based status code generation, app-received validation codes, and matching logic between mobile device and entry system. Claim 10: App includes collection of biometric indicators for identity verification. Claim 12: Acknowledges vaccination proof as a form of pathogen status. Claim 13–24: Encompass the broader system architecture, device setups, communication methods, app use cases (mobile/web), and identity verification methods. 🏛️ System Architecture Client Side: The user's smartphone runs the app to manage health records, generate QR codes, and capture biometrics. Server Side: Facilities store test/vaccination info; entry-point systems validate incoming codes in real-time. Interoperability: The architecture allows communication among apps, facility systems, and entry-point systems—facilitating a unified, scalable ecosystem . 🔄 Utility & Impact Public Health: Enables safe reopening of businesses and public spaces by minimizing infection risk. Economic Recovery: Builds customer trust through verifiable health assurance—potentially restoring consumer traffic in sectors hit by distancing rules. Scalability: Designed to adapt beyond COVID‑19, applying its framework to future pathogens or pandemics . ✅ Summary US 11,961,346 B2 outlines a comprehensive digital entry-control system, integrating smartphones, test/vaccination data, biometric identity checks, and entry-point verification to regulate access based on verified pathogen status. Originally driven by COVID‑19 needs, it's positioned for broader health verification contexts

“Sudden And Unexpected”

92,503 görüntüleme • 1 yıl önce

Andrew Wilkinson owns 40+ businesses. He just showed me how he's using OpenClaw, Claude Code and AI agents to run latest business, start new ones, and automate everything. Here's what I learned: 1. In December 2025, something clicked. He started waking up at 3AM with a smile, sitting in terminal with 10 Claude Code tabs open. He hasn't stopped since. He calls it chasing the dragon. 2.He built a full SaaS product called Deep Personality. A 40-minute personality test that generates a 100-page report written like Robert Greene. $20 000 in revenue. Zero employees. The entire business runs on AI agents. 3. He has agents for support, marketing, and dev. When a support ticket comes in, the agent either handles it or sends it to the dev agent. If it's critical, the agent fixes the bug and merges the PR before he wakes up. Then it emails the customer back. 4. His marketing agent is connected to PostHog, manages Meta and Reddit ads, creates ad creative, runs multivariate tests, and sets budgets. He's about to give it a $100 k/month ad budget and see what happens. 5. He forgot his laptop on a trip to Arizona. He ran his entire business from the back of Ubers using OpenClaw. Nobody picked up that every single email was written by AI. 6. His take on vibe coding: the worst part about business is people. Between your vision and execution are 100 people you have to convince. Vibe coding removes all of them. For the first time he can do every part of building a product himself. 7. He was trying to build OpenClaw before OpenClaw existed. Now he uses a tool called Harbor, which is basically a GUI for managing multiple agents. You can see all your agents, their status, knowledge bases, and databases in one place. 8. He built a custom AI for his relationship. He and his girlfriend took 15 psychological tests, put the results into ChatGPT, and asked it to analyze their relationship. It nailed every fight they've ever had. That became the product idea for Deep Personality. 9. His honest take: he spends 50% of his time debugging, 30% improving the setup, and 20% being productive. It's a treadmill. But the 20% that works is so powerful he can't stop. 10. His prediction: we're 3-6 months from being able to hand basic businesses off to AI to run entirely. And pretty soon Anthropic and OpenAI are going to launch AI CEOs. This is an inside look at how a serious operator Andrew Wilkinson is using AI agents in the real world. The good, the bad, the debugging, all of it. Most people don't show you this. Episode is live on The Startup Ideas Podcast (SIP) 🧃 watch
47:23

Andrew Wilkinson owns 40+ businesses. He just showed me how he's using OpenClaw, Claude Code and AI agents to run latest business, start new ones, and automate everything. Here's what I learned: 1. In December 2025, something clicked. He started waking up at 3AM with a smile, sitting in terminal with 10 Claude Code tabs open. He hasn't stopped since. He calls it chasing the dragon. 2.He built a full SaaS product called Deep Personality. A 40-minute personality test that generates a 100-page report written like Robert Greene. $20 000 in revenue. Zero employees. The entire business runs on AI agents. 3. He has agents for support, marketing, and dev. When a support ticket comes in, the agent either handles it or sends it to the dev agent. If it's critical, the agent fixes the bug and merges the PR before he wakes up. Then it emails the customer back. 4. His marketing agent is connected to PostHog, manages Meta and Reddit ads, creates ad creative, runs multivariate tests, and sets budgets. He's about to give it a $100 k/month ad budget and see what happens. 5. He forgot his laptop on a trip to Arizona. He ran his entire business from the back of Ubers using OpenClaw. Nobody picked up that every single email was written by AI. 6. His take on vibe coding: the worst part about business is people. Between your vision and execution are 100 people you have to convince. Vibe coding removes all of them. For the first time he can do every part of building a product himself. 7. He was trying to build OpenClaw before OpenClaw existed. Now he uses a tool called Harbor, which is basically a GUI for managing multiple agents. You can see all your agents, their status, knowledge bases, and databases in one place. 8. He built a custom AI for his relationship. He and his girlfriend took 15 psychological tests, put the results into ChatGPT, and asked it to analyze their relationship. It nailed every fight they've ever had. That became the product idea for Deep Personality. 9. His honest take: he spends 50% of his time debugging, 30% improving the setup, and 20% being productive. It's a treadmill. But the 20% that works is so powerful he can't stop. 10. His prediction: we're 3-6 months from being able to hand basic businesses off to AI to run entirely. And pretty soon Anthropic and OpenAI are going to launch AI CEOs. This is an inside look at how a serious operator Andrew Wilkinson is using AI agents in the real world. The good, the bad, the debugging, all of it. Most people don't show you this. Episode is live on The Startup Ideas Podcast (SIP) 🧃 watch

GREG ISENBERG

144,005 görüntüleme • 2 ay önce

Dean Koontz has published more than 140 novels, 74 works of short fiction, and sold more than 500 million books. Simply put, he’s one of the most prolific writers alive today. Some highlights from our chat: 1. Dare to love the English language. 2. Characters come alive when they're given free will. Instead of constraining them in an outline, let them go where they want. You know they’re alive once they start surprising you. He says: “I give the characters free will like God gave it to us.” 3. Everything a writer believes about life and death, culture and society, relationships and the self, God and nature will wind up in their books. A writer’s body of work, therefore, reveals the intellectual and emotional progress of its creator, and over time, becomes a map of their soul. 4. To think you understand the world is to be foolish in the extreme. The world is too complex for us to understand it. To see reality clearly is to be utterly enchanted by its staggering complexity. 5. Where should you look? Well, the supernatural enters the world in mundane ways, and rarely the great and glorious flashes of drama. 6. Dean writes his novels page-by-page, and doesn’t move onto the next page until he nails the existing one. There’s no messy first draft. Because of that, he’s basically done with his novels once he finishes the final page. 7. Where does a unique writing voice come from? Three places: style, perspective, and a philosophy of life. 8. Be skeptical of conventional wisdom. There’s an encyclopedia of common wisdom in publishing. All of it is common and none of it is wise. You have to become aware of that, go your own way, and just stick with it because there are so many ways you can be sent wrong based on "that's the way we always do it." 9. The aesthetic plainness of contemporary writing (and culture at large) is crushing our souls. 10. Contemporary fiction is suffering from plainness in particular. It started when writers started imitating Hemingway (who stripped his prose down but kept the mystery and underlying strangeness of the world by implication). But the imitations that came later stripped the prose down while also removing the underlying depth that made Hemingway so great. 11. Koontz Law of Writing #1: Never go inside more than one character's mind in a scene. Each one should come from a singular viewpoint. 12. Koontz Law of Writing #2: Metaphors aren't meant to dazzle readers, but to seduce them into a more intimate relationship with the story. 13. Koontz Law of Writing #3: Metaphors and similes describe a scene more colorfully than a chain of adjectives — while reinforcing the mood. The point is that you can create depth by describing things metaphorically instead of using blunt adjectives. That’s what poetry does: it uses words to say more than the word itself says, which creates a mood. 14. Great prose doesn't come from piling on adjectives. It comes from finding the perfect metaphor that does triple duty: describes the scene, reinforces the mood, and reveals something about the character. 15. The goal is for metaphors not to pop out like showmanship, but to flow into the music of the language. 16. Develop an ear for the musicality of language. 17. A book can succeed with a mediocre plot if the characters are compelling. Character is the center of good fiction. If the characters work, the story works. 18. From the afterword of his book, Watchers: “We have within us the ability to change for the better and to find dignity as individuals rather than as drones in one mass movement or another. We have the ability to love, the need to be loved, and the willingness to put our own lives on the line to protect those we love, and it is in these aspects of ourselves that we can glimpse the face of God; and through the exercise of these qualities, we come closest to a Godlike state.” I've shared the full conversation with Dean Koontz below. The YouTube video link is in the replies, and so are the links to Apple and Spotify.
1:47:18

Dean Koontz has published more than 140 novels, 74 works of short fiction, and sold more than 500 million books. Simply put, he’s one of the most prolific writers alive today. Some highlights from our chat: 1. Dare to love the English language. 2. Characters come alive when they're given free will. Instead of constraining them in an outline, let them go where they want. You know they’re alive once they start surprising you. He says: “I give the characters free will like God gave it to us.” 3. Everything a writer believes about life and death, culture and society, relationships and the self, God and nature will wind up in their books. A writer’s body of work, therefore, reveals the intellectual and emotional progress of its creator, and over time, becomes a map of their soul. 4. To think you understand the world is to be foolish in the extreme. The world is too complex for us to understand it. To see reality clearly is to be utterly enchanted by its staggering complexity. 5. Where should you look? Well, the supernatural enters the world in mundane ways, and rarely the great and glorious flashes of drama. 6. Dean writes his novels page-by-page, and doesn’t move onto the next page until he nails the existing one. There’s no messy first draft. Because of that, he’s basically done with his novels once he finishes the final page. 7. Where does a unique writing voice come from? Three places: style, perspective, and a philosophy of life. 8. Be skeptical of conventional wisdom. There’s an encyclopedia of common wisdom in publishing. All of it is common and none of it is wise. You have to become aware of that, go your own way, and just stick with it because there are so many ways you can be sent wrong based on "that's the way we always do it." 9. The aesthetic plainness of contemporary writing (and culture at large) is crushing our souls. 10. Contemporary fiction is suffering from plainness in particular. It started when writers started imitating Hemingway (who stripped his prose down but kept the mystery and underlying strangeness of the world by implication). But the imitations that came later stripped the prose down while also removing the underlying depth that made Hemingway so great. 11. Koontz Law of Writing #1: Never go inside more than one character's mind in a scene. Each one should come from a singular viewpoint. 12. Koontz Law of Writing #2: Metaphors aren't meant to dazzle readers, but to seduce them into a more intimate relationship with the story. 13. Koontz Law of Writing #3: Metaphors and similes describe a scene more colorfully than a chain of adjectives — while reinforcing the mood. The point is that you can create depth by describing things metaphorically instead of using blunt adjectives. That’s what poetry does: it uses words to say more than the word itself says, which creates a mood. 14. Great prose doesn't come from piling on adjectives. It comes from finding the perfect metaphor that does triple duty: describes the scene, reinforces the mood, and reveals something about the character. 15. The goal is for metaphors not to pop out like showmanship, but to flow into the music of the language. 16. Develop an ear for the musicality of language. 17. A book can succeed with a mediocre plot if the characters are compelling. Character is the center of good fiction. If the characters work, the story works. 18. From the afterword of his book, Watchers: “We have within us the ability to change for the better and to find dignity as individuals rather than as drones in one mass movement or another. We have the ability to love, the need to be loved, and the willingness to put our own lives on the line to protect those we love, and it is in these aspects of ourselves that we can glimpse the face of God; and through the exercise of these qualities, we come closest to a Godlike state.” I've shared the full conversation with Dean Koontz below. The YouTube video link is in the replies, and so are the links to Apple and Spotify.

David Perell

74,956 görüntüleme • 1 yıl önce

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.
0:28

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,735 görüntüleme • 1 yıl önce

I Cracked Polymarket Using Claude Opus 4.6: The 96,000 Dollar Script For 5 Minute High Leverage Windows most traders are currently sitting at their desks fighting a losing battle against a digital wall because they do not realize the house always wins against human emotion. while the crowd is busy chasing the next meme coin or getting washed out in a single wick an automated agent just pulled nearly a hundred thousand dollars out of thin air using nothing but raw logic. i have seen people blow their life savings in these five minute windows because they treated a high leverage prediction market like a playground instead of a laboratory i am moon dev and i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading. in the past i spent hundreds of thousands on devs for apps thinking i would not be able to code myself which was a massive waste of my time and resources. with bots you must iterate to success so i decided to learn live on youtube and now we are here with fully automated systems trading for me instead of getting liquidated by the market the five minute markets on polymarket are essentially a high speed game of musical chairs where the person left standing is usually the one with the fastest script. leverage makes these markets extremely dangerous because it amplifies every mistake you make until your account is completely empty. the only way to survive this environment is to stop trading based on a gut feeling and start trading based on a stress tested mathematical edge the real breakthrough happened when i started using claude opus four point six to write the execution code for these specific five minute windows. having an ai agent that can analyze microstructure data means you can find trends that are completely invisible to the naked eye. it is essentially like having a team of twenty engineers working for you around the clock without the communication lag or the massive overhead costs some of our back tests show a sixty four percent win rate which sounds like a dream to anyone who has ever spent a night staring at a red screen. however the return on these tests varies wildly based on a few specific changes to the strategy parameters and histogram filters. i found that a return of forty one thousand dollars can jump to nearly double that just by adjusting how the bot handles the macd histogram threshold the trap that most people fall into is thinking that a good back test is a license to print money immediately without any further validation. this is a dangerous lie that leads to huge losses because the market in the past is not a perfect mirror of what is going to happen today. that is why i never launch a bot with full size until it has survived the incubation phase where it trades with ten dollars at a time incubation is the ultimate reality check for any trading strategy regardless of how good the numbers look on a computer screen. it is nerve wracking to watch a bot enter its first real trade even if the size is small because that is the moment theory meets reality. most of the bots that pass a back test will fail during the first forty eight hours of incubation and that is exactly why this step is non negotiable the data i use to build these systems covers over two hundred weeks of historical one minute candles to ensure the results are robust and not just luck. we are currently moving toward a machine learning approach where the system can adapt to changing market conditions without me having to intervene. this means the bot will eventually be able to recognize when a high leverage window is too risky and simply wait for a better entry the strategy itself relies heavily on macd variations which is a well known indicator but it is used here with a very specific and proprietary twist. by filtering for trades that hit a specific threshold we can ignore the random price noise that usually liquidates manual traders. we look for an edge of at least six percent which is enough to cover all platform fees and still leave a significant profit on the table i used to think that being a successful trader meant being a genius who could predict the future with a magical crystal ball. the truth is far more boring because success is just about researching an idea and testing it until the data proves it works in the past. then you just let the bot do the work while you go live your life instead of being a slave to the candle sticks and charts this world is changing fast and the people who learn to leverage ai to automate their thinking are going to be the ones who win the next decade. i am not asking you to trust a back test or a screenshot from a website because i want you to trust the process of testing it yourself. code allows you to take your life back from the screens and finally stop the cycle of over trading and emotional liquidations the difference between the traders who make it and the people who blow up is simply the willingness to iterate on their ideas daily. you might fail on your first ten bots but the eleventh one might be the script that changes your entire financial trajectory forever. it is about staying in the game long enough for the math to finally work in your favor and removing the human heart from the execution every day i am back testing and researching new ideas to see if they can survive the stress of real market data. i launch these live bots and let them run for seventy two hours to see if they can handle the pressure of the current market trend. while everyone else is coping and complaining about market volatility we are just adjusting our parameters and letting the ai find the next profitable window vibe coding with claude opus four point six has changed the speed at which i can deploy a new strategy from weeks down to just a few minutes. you can give the ai a general strategy idea and it builds the entire trading infrastructure for you while you focus on the logic. this speed is the ultimate advantage in a market that moves as fast as a five minute prediction window on the blockchain the future of trading is not found in a chat room or a paid signal group but in the code you write and the data you process. i believe that everyone has the ability to become an automated trader if they are willing to put in the work to learn the scripts. it is the only way to escape the trap of the nine to five and the anxiety of manual hand trading in a manipulated market i want you to understand that the ninety six thousand dollar returns i see are the result of hundreds of failed tests that never saw the light of day. you have to be willing to look at a failing bot and kill it without emotion so you can move on to the next research project. that is the quantitative mindset that separates the winners from the people who are just gambling with their savings if you are ready to stop being the liquidity for the big players then it is time to start building your own automated army of bots. for the cost of a few cups of coffee you can get access to the road map and the scripts that are driving these results. i am here every day showing you the process because i want to see more people use code to find their financial freedom and beat the house at its own game
11:04

I Cracked Polymarket Using Claude Opus 4.6: The 96,000 Dollar Script For 5 Minute High Leverage Windows most traders are currently sitting at their desks fighting a losing battle against a digital wall because they do not realize the house always wins against human emotion. while the crowd is busy chasing the next meme coin or getting washed out in a single wick an automated agent just pulled nearly a hundred thousand dollars out of thin air using nothing but raw logic. i have seen people blow their life savings in these five minute windows because they treated a high leverage prediction market like a playground instead of a laboratory i am moon dev and i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading. in the past i spent hundreds of thousands on devs for apps thinking i would not be able to code myself which was a massive waste of my time and resources. with bots you must iterate to success so i decided to learn live on youtube and now we are here with fully automated systems trading for me instead of getting liquidated by the market the five minute markets on polymarket are essentially a high speed game of musical chairs where the person left standing is usually the one with the fastest script. leverage makes these markets extremely dangerous because it amplifies every mistake you make until your account is completely empty. the only way to survive this environment is to stop trading based on a gut feeling and start trading based on a stress tested mathematical edge the real breakthrough happened when i started using claude opus four point six to write the execution code for these specific five minute windows. having an ai agent that can analyze microstructure data means you can find trends that are completely invisible to the naked eye. it is essentially like having a team of twenty engineers working for you around the clock without the communication lag or the massive overhead costs some of our back tests show a sixty four percent win rate which sounds like a dream to anyone who has ever spent a night staring at a red screen. however the return on these tests varies wildly based on a few specific changes to the strategy parameters and histogram filters. i found that a return of forty one thousand dollars can jump to nearly double that just by adjusting how the bot handles the macd histogram threshold the trap that most people fall into is thinking that a good back test is a license to print money immediately without any further validation. this is a dangerous lie that leads to huge losses because the market in the past is not a perfect mirror of what is going to happen today. that is why i never launch a bot with full size until it has survived the incubation phase where it trades with ten dollars at a time incubation is the ultimate reality check for any trading strategy regardless of how good the numbers look on a computer screen. it is nerve wracking to watch a bot enter its first real trade even if the size is small because that is the moment theory meets reality. most of the bots that pass a back test will fail during the first forty eight hours of incubation and that is exactly why this step is non negotiable the data i use to build these systems covers over two hundred weeks of historical one minute candles to ensure the results are robust and not just luck. we are currently moving toward a machine learning approach where the system can adapt to changing market conditions without me having to intervene. this means the bot will eventually be able to recognize when a high leverage window is too risky and simply wait for a better entry the strategy itself relies heavily on macd variations which is a well known indicator but it is used here with a very specific and proprietary twist. by filtering for trades that hit a specific threshold we can ignore the random price noise that usually liquidates manual traders. we look for an edge of at least six percent which is enough to cover all platform fees and still leave a significant profit on the table i used to think that being a successful trader meant being a genius who could predict the future with a magical crystal ball. the truth is far more boring because success is just about researching an idea and testing it until the data proves it works in the past. then you just let the bot do the work while you go live your life instead of being a slave to the candle sticks and charts this world is changing fast and the people who learn to leverage ai to automate their thinking are going to be the ones who win the next decade. i am not asking you to trust a back test or a screenshot from a website because i want you to trust the process of testing it yourself. code allows you to take your life back from the screens and finally stop the cycle of over trading and emotional liquidations the difference between the traders who make it and the people who blow up is simply the willingness to iterate on their ideas daily. you might fail on your first ten bots but the eleventh one might be the script that changes your entire financial trajectory forever. it is about staying in the game long enough for the math to finally work in your favor and removing the human heart from the execution every day i am back testing and researching new ideas to see if they can survive the stress of real market data. i launch these live bots and let them run for seventy two hours to see if they can handle the pressure of the current market trend. while everyone else is coping and complaining about market volatility we are just adjusting our parameters and letting the ai find the next profitable window vibe coding with claude opus four point six has changed the speed at which i can deploy a new strategy from weeks down to just a few minutes. you can give the ai a general strategy idea and it builds the entire trading infrastructure for you while you focus on the logic. this speed is the ultimate advantage in a market that moves as fast as a five minute prediction window on the blockchain the future of trading is not found in a chat room or a paid signal group but in the code you write and the data you process. i believe that everyone has the ability to become an automated trader if they are willing to put in the work to learn the scripts. it is the only way to escape the trap of the nine to five and the anxiety of manual hand trading in a manipulated market i want you to understand that the ninety six thousand dollar returns i see are the result of hundreds of failed tests that never saw the light of day. you have to be willing to look at a failing bot and kill it without emotion so you can move on to the next research project. that is the quantitative mindset that separates the winners from the people who are just gambling with their savings if you are ready to stop being the liquidity for the big players then it is time to start building your own automated army of bots. for the cost of a few cups of coffee you can get access to the road map and the scripts that are driving these results. i am here every day showing you the process because i want to see more people use code to find their financial freedom and beat the house at its own game

Moon Dev

10,921 görüntüleme • 3 ay önce

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:
0:58

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:

Owen Gregorian

51,638 görüntüleme • 11 ay önce

Westerners Flocking to Play Stunning New Chinese Video Game—and Skipping ‘Spyware’ Warnings . CHINESE DESIGNERS JUST LAUNCHED another video game which is a hit worldwide. Where Winds Meet has some spectacular battle scenes on screen -- but the conflicts off-stage (west-east culture wars, psyops, business battles) are just as interesting. People in the game industry in the west are concerned—for several good reasons. Before we get there, take 20 seconds to check out the great-looking visuals, which come from the same studio that created Black Myth Wukong, a globally popular game launched in August 2024, based on the story of the Monkey King. Where Winds Meet also celebrates classic Chinese culture. It is set in the city of Kaifeng in 10th century China – and is filled with gorgeous images of that place. . CULTURAL DEPTH It has cultural depth, too. For example, everyone knows fireworks are an ancient Chinese art, but the game features molten metal spark showers, a popular art form pretty unknown outside China. In one scene you can see the flying apsaras, those Indian-influenced airborne women whose images adorn the ancient Dunhuang Caves on the silk road. Where Winds Meet is already among the favorites on Steam, the world’s biggest online game center. It includes many battles, of course. But there are related conflicts off line, in real life, too. . NUMBER ONE: CULTURE WARS. In many games released in the west, it is considered wrong to give players the choice of having a male or female character, because it implies there are just two sexes, which is a big no-no in the west. In Where the Winds Meet, players who opt for English language follow the western model, and are given the choice of Body Type I or Body Type II, no mention of forbidden words “male” and “female”. But players who opt for the Chinese version are given the choice of male or female. Trying to keep everyone happy! . NUMBER 2. HYBRID WARS, OR PSYOPS. From the moment the game came out, mysterious persons “revealed” that the game was made by Chinese people so it was a security threat, riddled with spyware. You can imagine them thinking that their gameplay information was being transmitted straight to Xi Jinping’s office! “Hmm. This incel sitting in a basement in his mother’s house prefers body type 2! Write that down, comrades.” But here’s the twist. Everybody ignored the warnings. By the end of the first day, people had played the game two million times, and that’s just the new version, the non-Chinese one. . NUMBER THREE: THE BUSINESS BATTLE The US groups which normally dominate the field, like Ubisoft, have repeatedly reported disappointing results recently. And some of the biggest recent hits have been from outside America. Black Myth Wukong was from China’s NetEase, Expedition 33 was from France, and this new game, Where Winds Meet, is also from NetEase. A popular video game reviewer called Hypnotic lamented the poor quality of games from the west—with the players blaming the designers and the designers blaming the players. “The east ends up releasing video games that end up blowing the doors down whenever they release,” he said. “And then you end up getting developers from the west that'll make up all kinds of excuses as to why these games are successful. They'll call it slop. They'll call it Chinese spyware. They'll call it whatever they want. But at the end of the day, at least somebody is trying to put out video games that players actually want, that isn't just indie developers.” . FIELD IS OPEN Now this does NOT mean that games from the east are uniformly taking over the global game industry. The west is still putting out some great games, with Expedition 33 being a good example. What it does mean is that China is catching up fast. We’ve seen this happening in many sectors. And as long as there’s a level playing field, it ultimately means that people get more choice. . A FAIRER, SAFER WORLD But more importantly than that, it gives people around the world a different image of China. The western mainstream media tends to create an image of China as an evil tech dystopia, sometimes literally describing it as a giant gulag. Games like this one show it to be community of creative people, producing fun products, and a place with a rich culture and an amazing history. The result, we hope, will be a fairer world – and a fairer world is a safer world.
4:57

Westerners Flocking to Play Stunning New Chinese Video Game—and Skipping ‘Spyware’ Warnings . CHINESE DESIGNERS JUST LAUNCHED another video game which is a hit worldwide. Where Winds Meet has some spectacular battle scenes on screen -- but the conflicts off-stage (west-east culture wars, psyops, business battles) are just as interesting. People in the game industry in the west are concerned—for several good reasons. Before we get there, take 20 seconds to check out the great-looking visuals, which come from the same studio that created Black Myth Wukong, a globally popular game launched in August 2024, based on the story of the Monkey King. Where Winds Meet also celebrates classic Chinese culture. It is set in the city of Kaifeng in 10th century China – and is filled with gorgeous images of that place. . CULTURAL DEPTH It has cultural depth, too. For example, everyone knows fireworks are an ancient Chinese art, but the game features molten metal spark showers, a popular art form pretty unknown outside China. In one scene you can see the flying apsaras, those Indian-influenced airborne women whose images adorn the ancient Dunhuang Caves on the silk road. Where Winds Meet is already among the favorites on Steam, the world’s biggest online game center. It includes many battles, of course. But there are related conflicts off line, in real life, too. . NUMBER ONE: CULTURE WARS. In many games released in the west, it is considered wrong to give players the choice of having a male or female character, because it implies there are just two sexes, which is a big no-no in the west. In Where the Winds Meet, players who opt for English language follow the western model, and are given the choice of Body Type I or Body Type II, no mention of forbidden words “male” and “female”. But players who opt for the Chinese version are given the choice of male or female. Trying to keep everyone happy! . NUMBER 2. HYBRID WARS, OR PSYOPS. From the moment the game came out, mysterious persons “revealed” that the game was made by Chinese people so it was a security threat, riddled with spyware. You can imagine them thinking that their gameplay information was being transmitted straight to Xi Jinping’s office! “Hmm. This incel sitting in a basement in his mother’s house prefers body type 2! Write that down, comrades.” But here’s the twist. Everybody ignored the warnings. By the end of the first day, people had played the game two million times, and that’s just the new version, the non-Chinese one. . NUMBER THREE: THE BUSINESS BATTLE The US groups which normally dominate the field, like Ubisoft, have repeatedly reported disappointing results recently. And some of the biggest recent hits have been from outside America. Black Myth Wukong was from China’s NetEase, Expedition 33 was from France, and this new game, Where Winds Meet, is also from NetEase. A popular video game reviewer called Hypnotic lamented the poor quality of games from the west—with the players blaming the designers and the designers blaming the players. “The east ends up releasing video games that end up blowing the doors down whenever they release,” he said. “And then you end up getting developers from the west that'll make up all kinds of excuses as to why these games are successful. They'll call it slop. They'll call it Chinese spyware. They'll call it whatever they want. But at the end of the day, at least somebody is trying to put out video games that players actually want, that isn't just indie developers.” . FIELD IS OPEN Now this does NOT mean that games from the east are uniformly taking over the global game industry. The west is still putting out some great games, with Expedition 33 being a good example. What it does mean is that China is catching up fast. We’ve seen this happening in many sectors. And as long as there’s a level playing field, it ultimately means that people get more choice. . A FAIRER, SAFER WORLD But more importantly than that, it gives people around the world a different image of China. The western mainstream media tends to create an image of China as an evil tech dystopia, sometimes literally describing it as a giant gulag. Games like this one show it to be community of creative people, producing fun products, and a place with a rich culture and an amazing history. The result, we hope, will be a fairer world – and a fairer world is a safer world.

Nury Vittachi

49,389 görüntüleme • 7 ay önce

In the 1920s, a Stanford psychologist tracked genius children for 50 years. Malcolm Gladwell breaks down what he discovered: Rich families → successful. Poor families → failures. Not average. Failures. Genius-level IQs that produced nothing. He spent 60 minutes at Microsoft explaining why we're wrong about success: The psychologist was named Terman. He gave IQ tests to 250,000 California schoolchildren. He identified the top 0.1%. Kids with IQs of 140 and above. His hypothesis: these children would become the leaders of academia, industry, and politics. He tracked them. And tracked them. For decades. The results split into three groups. The top 15% achieved real prominence. The middle group had average, moderately successful professional lives. And the bottom group? By any measure, failures. The difference wasn't personality. Wasn't habits. Wasn't work ethic. It was simple: the successful geniuses came from wealthy households. The failures came from poor families. Poverty is such a powerful constraint that it can reduce a one-in-a-billion brain to a lifetime of worse than mediocrity. There's a concept called "capitalization rate." It asks a simple question: what percentage of people who are capable of doing something actually end up doing that thing? In inner city Memphis, only 1 in 6 kids with athletic scholarships actually go to college. If our capitalization rate for sports in the inner city is 16%, imagine how low it must be for everything else. Here's something stranger. Gladwell read the birth dates of the 2007 Czech Junior Hockey Team: January 3rd. January 3rd. January 12th. February 8th. February 10th. February 17th. February 20th. February 24th. March 5th. March 10th. March 26th... 11 of the 20 players were born in January, February, or March. This isn't unique to the Czechs. Every elite hockey team in the world shows the same pattern. Every elite soccer team too. Why? The eligibility cutoff for youth leagues is January 1st. When you're 10 years old, a kid born in January has 10 months of maturity on a kid born in October. That's 3 or 4 inches of height. The difference between clumsy and coordinated. So we look at a group of 10 year olds, pick the "best" ones, give them special coaching, extra practice, more games. We think we're identifying talent. We're just identifying the oldest. Then we give the oldest more opportunities, and 10 years later they really are the best. Self-fulfilling prophecy. The capitalization rate for hockey talent born in the second half of the year? Close to zero. We're leaving half of all potential hockey players on the table because of an arbitrary date on a calendar. Kids born in the youngest cohort of their school class are 11% less likely to go to college. 11% of human potential squandered because we organize elementary school without reference to biological maturity. Now here's the part about math. Asian kids dramatically outperform Western kids in mathematics. The gap is enormous and consistent across decades of testing. Some people say it's genetic. It's not. It's attitudinal. When Asian kids face a math problem, they believe effort will solve it. When Western kids face a math problem, they believe the answer depends on innate ability they either have or don't. Here's the proof. The international math tests include a 120-question survey. It asks about study habits, parental support, attitudes. It's so long most kids don't finish it. A researcher named Erling Boe decided to rank countries by what percentage of survey questions their kids completed. Then he compared it to the ranking of countries by math performance. The correlation was 0.98. In the history of social science, there has never been a correlation that high. If you want to know how good a country is at math, you don't need to ask any math questions. Just make kids sit down and focus on a task for an extended period of time. If they can do it, they're good at math. Why do Asian cultures have this attitude? Gladwell's theory: rice farming. His European ancestors in medieval England worked about 1,000 hours a year. Dawn to noon, five days a week. Winters off. Lots of holidays. A peasant in South China or Japan in the same period worked 3,000 hours a year. Rice farming isn't just harder than wheat farming. It's a completely different relationship with work. There's a Chinese proverb: "A man who works dawn to dusk 360 days a year will not go hungry." His English ancestors would have said: "A man who works 175 days a year, dawn to 11, may or may not be hungry." If your culture does that for a thousand years, it becomes part of your makeup. When your kids sit down to face a calculus problem, that legacy of persistence translates perfectly. Now consider distance running. In Kenya, there are roughly a million schoolboys between 10 and 17 running 10 to 12 miles a day. In the United States, that number is probably 5,000. Our capitalization rate for distance running is less than 1%. Kenya's is probably 95%. The difference isn't genetic. The difference is what the culture values and where it spends its attention. Here's the most fascinating finding. 30% of American entrepreneurs have been diagnosed with a profound learning disability. Richard Branson is dyslexic. Charles Schwab is dyslexic. John Chambers can barely read his own email. This isn't coincidence. Their entrepreneurialism is a direct function of their disability. How do you succeed if you can't read or write from early childhood? You learn to delegate. You become a great oral communicator. You become a problem solver because your entire life is one big problem. You learn to lead. 80% of dyslexic entrepreneurs were captain of a high school sports team. Versus 30% of non-dyslexic entrepreneurs. By the time they enter the real world, they've spent their whole life practicing the four skills at the core of entrepreneurial success: delegation, oral communication, problem solving, and leadership. Ask them what role dyslexia played in their success and they don't say it was an obstacle. They say it's the reason they succeeded. A disadvantage that became an advantage. Here's what Gladwell wants you to understand: When we see differences in success, our default explanation is differences in ability. We forget how much poverty, stupidity, and attitude constrain what people can become. We refuse to admit that our own arbitrary rules are leaving talent on the table. We cling to naive beliefs that our meritocracies are fair. The capitalization argument is liberating. It says you don't look at a struggling group and conclude they're incapable. It says problems that look genetic or innate are often just failures of exploitation. It says we can make a profound difference in how well people turn out. If we choose to pay attention. This 60 minute Microsoft talk will teach you more about success than every self-help book you've ever read combined. Bookmark this & give it an hour today, no matter what.
1:14:47

In the 1920s, a Stanford psychologist tracked genius children for 50 years. Malcolm Gladwell breaks down what he discovered: Rich families → successful. Poor families → failures. Not average. Failures. Genius-level IQs that produced nothing. He spent 60 minutes at Microsoft explaining why we're wrong about success: The psychologist was named Terman. He gave IQ tests to 250,000 California schoolchildren. He identified the top 0.1%. Kids with IQs of 140 and above. His hypothesis: these children would become the leaders of academia, industry, and politics. He tracked them. And tracked them. For decades. The results split into three groups. The top 15% achieved real prominence. The middle group had average, moderately successful professional lives. And the bottom group? By any measure, failures. The difference wasn't personality. Wasn't habits. Wasn't work ethic. It was simple: the successful geniuses came from wealthy households. The failures came from poor families. Poverty is such a powerful constraint that it can reduce a one-in-a-billion brain to a lifetime of worse than mediocrity. There's a concept called "capitalization rate." It asks a simple question: what percentage of people who are capable of doing something actually end up doing that thing? In inner city Memphis, only 1 in 6 kids with athletic scholarships actually go to college. If our capitalization rate for sports in the inner city is 16%, imagine how low it must be for everything else. Here's something stranger. Gladwell read the birth dates of the 2007 Czech Junior Hockey Team: January 3rd. January 3rd. January 12th. February 8th. February 10th. February 17th. February 20th. February 24th. March 5th. March 10th. March 26th... 11 of the 20 players were born in January, February, or March. This isn't unique to the Czechs. Every elite hockey team in the world shows the same pattern. Every elite soccer team too. Why? The eligibility cutoff for youth leagues is January 1st. When you're 10 years old, a kid born in January has 10 months of maturity on a kid born in October. That's 3 or 4 inches of height. The difference between clumsy and coordinated. So we look at a group of 10 year olds, pick the "best" ones, give them special coaching, extra practice, more games. We think we're identifying talent. We're just identifying the oldest. Then we give the oldest more opportunities, and 10 years later they really are the best. Self-fulfilling prophecy. The capitalization rate for hockey talent born in the second half of the year? Close to zero. We're leaving half of all potential hockey players on the table because of an arbitrary date on a calendar. Kids born in the youngest cohort of their school class are 11% less likely to go to college. 11% of human potential squandered because we organize elementary school without reference to biological maturity. Now here's the part about math. Asian kids dramatically outperform Western kids in mathematics. The gap is enormous and consistent across decades of testing. Some people say it's genetic. It's not. It's attitudinal. When Asian kids face a math problem, they believe effort will solve it. When Western kids face a math problem, they believe the answer depends on innate ability they either have or don't. Here's the proof. The international math tests include a 120-question survey. It asks about study habits, parental support, attitudes. It's so long most kids don't finish it. A researcher named Erling Boe decided to rank countries by what percentage of survey questions their kids completed. Then he compared it to the ranking of countries by math performance. The correlation was 0.98. In the history of social science, there has never been a correlation that high. If you want to know how good a country is at math, you don't need to ask any math questions. Just make kids sit down and focus on a task for an extended period of time. If they can do it, they're good at math. Why do Asian cultures have this attitude? Gladwell's theory: rice farming. His European ancestors in medieval England worked about 1,000 hours a year. Dawn to noon, five days a week. Winters off. Lots of holidays. A peasant in South China or Japan in the same period worked 3,000 hours a year. Rice farming isn't just harder than wheat farming. It's a completely different relationship with work. There's a Chinese proverb: "A man who works dawn to dusk 360 days a year will not go hungry." His English ancestors would have said: "A man who works 175 days a year, dawn to 11, may or may not be hungry." If your culture does that for a thousand years, it becomes part of your makeup. When your kids sit down to face a calculus problem, that legacy of persistence translates perfectly. Now consider distance running. In Kenya, there are roughly a million schoolboys between 10 and 17 running 10 to 12 miles a day. In the United States, that number is probably 5,000. Our capitalization rate for distance running is less than 1%. Kenya's is probably 95%. The difference isn't genetic. The difference is what the culture values and where it spends its attention. Here's the most fascinating finding. 30% of American entrepreneurs have been diagnosed with a profound learning disability. Richard Branson is dyslexic. Charles Schwab is dyslexic. John Chambers can barely read his own email. This isn't coincidence. Their entrepreneurialism is a direct function of their disability. How do you succeed if you can't read or write from early childhood? You learn to delegate. You become a great oral communicator. You become a problem solver because your entire life is one big problem. You learn to lead. 80% of dyslexic entrepreneurs were captain of a high school sports team. Versus 30% of non-dyslexic entrepreneurs. By the time they enter the real world, they've spent their whole life practicing the four skills at the core of entrepreneurial success: delegation, oral communication, problem solving, and leadership. Ask them what role dyslexia played in their success and they don't say it was an obstacle. They say it's the reason they succeeded. A disadvantage that became an advantage. Here's what Gladwell wants you to understand: When we see differences in success, our default explanation is differences in ability. We forget how much poverty, stupidity, and attitude constrain what people can become. We refuse to admit that our own arbitrary rules are leaving talent on the table. We cling to naive beliefs that our meritocracies are fair. The capitalization argument is liberating. It says you don't look at a struggling group and conclude they're incapable. It says problems that look genetic or innate are often just failures of exploitation. It says we can make a profound difference in how well people turn out. If we choose to pay attention. This 60 minute Microsoft talk will teach you more about success than every self-help book you've ever read combined. Bookmark this & give it an hour today, no matter what.

Jaynit

1,582,766 görüntüleme • 3 ay önce

EVERYTHING JAPANESE IS REALLY CHINESE. Seriously? Well, yes. I mean, this title is clearly a generalization, so what it means is that the main cultural items that are widely assumed across the world to be Japanese actually come from China. . 1. Ramen Raman is a Chinese noodle dish, not Japanese. It was brought to Japan by Chinese travellers in the late 1800s. The original Japanese name for it was “shina soba” meaning Chinese noodles. . 2. Japanese writing The Japanese writing system known as Kanji has a name formed of two elements. Kanji literally means Han Writing. Han is the biggest ethnic group in China. . 3. The Kimono This was an adaptation of the type of draped silk clothing from China known as Hanfu. After the start of the Heian period in Japan in 794 AD, it became popular in Japan and started to acquire unique Japanese elements. . 4. Blossom trees Japan’s famous cherry blossoms actually come a tree indigenous not to Japan, but to the Himalayas, which border southwest China—in fact a long way from Japan. The practice of celebrating the annual blossoming of beautiful trees such as the cherry blossom and the plum blossom spread from southwest China to Korea and Japan, taking several centuries to make that journey. . 5. Sushi Sushi is fish with vinegared rice. It is vinegared to give it a pickled taste which is a clue towards its origins: a Chinese pickled dish in which salted fish was wrapped in fermented rice. Even today in Japan, a sushi kitchen is called a tsuke-ba, which means ‘pickling place’. . 6. The Japanese language Japan has its own language, sure – but statisticians say that at least 60 per cent of Japanese vocabulary comes from Chinese origins. . 7. Being ruled by an Emperor In the early 7th century, the Japanese adopted the title of Emperor from China for their own leaders. They used the same Chinese characters, and the same concept that the leader, by definition, was the Son of Heaven. This new rank was then retroactively applied to all past Japanese rulers, who all become emperors and sons of heaven. . 8. Bonsai. The practice of growing miniature trees and creating tiny landscapes developed in China, more than 1,500 years ago. It was known as penjing. The technique was introduced to Japan in the 7th century. . 9. Sashimi It was the Chinese rather than the Japanese who realized more than 1800 years ago that the flesh of certain fish was delicious eaten raw – no cooking necessary. Food historian Jacqueline Newman says that raw fish consumption began in southern China before 200 AD. It came to Japan about 11 hundred years later. . 10. The tea ceremony The tea ceremony originated in China during the Tang Dynasty, which is 600 to 900 AD, as a meditative health practice, before evolving into artistic ritual by the time of the Song Dynasty, which started in 960 AD. It was then taken to Japan by monks, like many Chinese cultural practices. . 11. The board game called GO In western culture, Go is a game associated with Japan. In reality, it is the world’s oldest known board game, originating in China about 4,000 years ago, according to Encyclopedia Britannica. The game was taken from China to Japan in 500 AD, which is “comparatively recently” in Chinese terms. . 12. Green tea Green tea, or matcha, comes from China and has been consumed for probably 4,000 years. It was brought to Japan by monks during the 8th and 9th centuries—very recently by Chinese standards! Two holy men, Saichō and Kukai, are credited with bringing it over the waters to Japan. . 13. Architecture The Japanese admired China’s extraordinarily long-lasting structures made of interlocking wooden elements and there was a lot of temple-building based on Chinese innovations in sixth century Japan. The Japanese capital, Nara, copied the checkerboard street layout of the Chinese capital, Chang’an. But, it must be said, the Japanese afterwards developed many of their own unique architectural elements. . 14. The Asian super-sword It was actually Tang dynasty scientists in China who revolutionized sword-making in the 7th century with a blade called the baogangfa – they fused high carbon steel (which enable sharpness) with low-carbon iron (which provided strength). The result was a blade that could slice through samurai armor. Japanese envoys brought Chinese swords back to Japan in the 700s. To give them due credit, the Japanese improved both the recipe and the design, to eventually come up with the curved katana sword. . 15. Cultural figures One of the most famous pop culture items from Japan is Dragonball. The creator of Dragonball fully admitted that his story was simply a retelling of the Chinese classic Journey to the West. Pokemon, another Japanese modern classic, has many ancient Chinese elements, too – research the nine-tailed fox for example. . 16. Zen An Indian monk named Bodhidharma came to China around 520 AD. His teachings became combined with Chinese philosophies such as Daoism. That led to the development of a practice called Chan, which is how the word Zen was originally pronounced. Records indicate that Chinese Chan Buddhism reached Japan in the 7th century but was not firmly established in that country until the 12th century. . CONCLUSIONS? Okay, there are 16 items – things assumed to be Japanese by many people, but which are rooted in Chinese culture. But there are others. So feel free to add more. Or shoot down the ones I have listed, if you prefer. Why do so many Japanese items come from China? Well, for much of history, China was the cool place, the happening place, the creative place, the dominant cultural center of the most highly populated part of the world. So naturally people wanted to visit it. One ancient record show that between AD 603 and AD 839, the Japanese alone sent at least 17 diplomatic missions to the Tang Dynasty royal courts in China. And I am going to add one more item to my list. Do the Japanese people themselves come from China? A short discussion of that somewhat controversial question comes at the end of the video. Read widely. Peace.
9:27

EVERYTHING JAPANESE IS REALLY CHINESE. Seriously? Well, yes. I mean, this title is clearly a generalization, so what it means is that the main cultural items that are widely assumed across the world to be Japanese actually come from China. . 1. Ramen Raman is a Chinese noodle dish, not Japanese. It was brought to Japan by Chinese travellers in the late 1800s. The original Japanese name for it was “shina soba” meaning Chinese noodles. . 2. Japanese writing The Japanese writing system known as Kanji has a name formed of two elements. Kanji literally means Han Writing. Han is the biggest ethnic group in China. . 3. The Kimono This was an adaptation of the type of draped silk clothing from China known as Hanfu. After the start of the Heian period in Japan in 794 AD, it became popular in Japan and started to acquire unique Japanese elements. . 4. Blossom trees Japan’s famous cherry blossoms actually come a tree indigenous not to Japan, but to the Himalayas, which border southwest China—in fact a long way from Japan. The practice of celebrating the annual blossoming of beautiful trees such as the cherry blossom and the plum blossom spread from southwest China to Korea and Japan, taking several centuries to make that journey. . 5. Sushi Sushi is fish with vinegared rice. It is vinegared to give it a pickled taste which is a clue towards its origins: a Chinese pickled dish in which salted fish was wrapped in fermented rice. Even today in Japan, a sushi kitchen is called a tsuke-ba, which means ‘pickling place’. . 6. The Japanese language Japan has its own language, sure – but statisticians say that at least 60 per cent of Japanese vocabulary comes from Chinese origins. . 7. Being ruled by an Emperor In the early 7th century, the Japanese adopted the title of Emperor from China for their own leaders. They used the same Chinese characters, and the same concept that the leader, by definition, was the Son of Heaven. This new rank was then retroactively applied to all past Japanese rulers, who all become emperors and sons of heaven. . 8. Bonsai. The practice of growing miniature trees and creating tiny landscapes developed in China, more than 1,500 years ago. It was known as penjing. The technique was introduced to Japan in the 7th century. . 9. Sashimi It was the Chinese rather than the Japanese who realized more than 1800 years ago that the flesh of certain fish was delicious eaten raw – no cooking necessary. Food historian Jacqueline Newman says that raw fish consumption began in southern China before 200 AD. It came to Japan about 11 hundred years later. . 10. The tea ceremony The tea ceremony originated in China during the Tang Dynasty, which is 600 to 900 AD, as a meditative health practice, before evolving into artistic ritual by the time of the Song Dynasty, which started in 960 AD. It was then taken to Japan by monks, like many Chinese cultural practices. . 11. The board game called GO In western culture, Go is a game associated with Japan. In reality, it is the world’s oldest known board game, originating in China about 4,000 years ago, according to Encyclopedia Britannica. The game was taken from China to Japan in 500 AD, which is “comparatively recently” in Chinese terms. . 12. Green tea Green tea, or matcha, comes from China and has been consumed for probably 4,000 years. It was brought to Japan by monks during the 8th and 9th centuries—very recently by Chinese standards! Two holy men, Saichō and Kukai, are credited with bringing it over the waters to Japan. . 13. Architecture The Japanese admired China’s extraordinarily long-lasting structures made of interlocking wooden elements and there was a lot of temple-building based on Chinese innovations in sixth century Japan. The Japanese capital, Nara, copied the checkerboard street layout of the Chinese capital, Chang’an. But, it must be said, the Japanese afterwards developed many of their own unique architectural elements. . 14. The Asian super-sword It was actually Tang dynasty scientists in China who revolutionized sword-making in the 7th century with a blade called the baogangfa – they fused high carbon steel (which enable sharpness) with low-carbon iron (which provided strength). The result was a blade that could slice through samurai armor. Japanese envoys brought Chinese swords back to Japan in the 700s. To give them due credit, the Japanese improved both the recipe and the design, to eventually come up with the curved katana sword. . 15. Cultural figures One of the most famous pop culture items from Japan is Dragonball. The creator of Dragonball fully admitted that his story was simply a retelling of the Chinese classic Journey to the West. Pokemon, another Japanese modern classic, has many ancient Chinese elements, too – research the nine-tailed fox for example. . 16. Zen An Indian monk named Bodhidharma came to China around 520 AD. His teachings became combined with Chinese philosophies such as Daoism. That led to the development of a practice called Chan, which is how the word Zen was originally pronounced. Records indicate that Chinese Chan Buddhism reached Japan in the 7th century but was not firmly established in that country until the 12th century. . CONCLUSIONS? Okay, there are 16 items – things assumed to be Japanese by many people, but which are rooted in Chinese culture. But there are others. So feel free to add more. Or shoot down the ones I have listed, if you prefer. Why do so many Japanese items come from China? Well, for much of history, China was the cool place, the happening place, the creative place, the dominant cultural center of the most highly populated part of the world. So naturally people wanted to visit it. One ancient record show that between AD 603 and AD 839, the Japanese alone sent at least 17 diplomatic missions to the Tang Dynasty royal courts in China. And I am going to add one more item to my list. Do the Japanese people themselves come from China? A short discussion of that somewhat controversial question comes at the end of the video. Read widely. Peace.

Nury Vittachi

1,198,252 görüntüleme • 8 ay önce

Make Art Not War: The Battle for Creativity It's Adobe's annual Max event in London today and scott belsky's spotlight on AI paints a clear picture: AI isn't just on Adobe's agenda, it is the agenda. Adobe has already scored a home run with generative fill in Photoshop, a feature now spawning entire categories of memes - including my own video, which surprisingly garnered a million views. However, Adobe's ambitions extend beyond still imagery. The Tanker Charges Towards Video At Max, Adobe's Chief Product Officer put an emphasis on AI video generation with Firefly Video. The tech tanker is charging full steam ahead to the next obvious modality for creation, leaving a wake of disruption for any upstarts bold enough to challenge it. The announcement isn't new, but it showcases the emphasis on new product development with a marked increase in the velocity we can expect from the creative tech behemoth. The company that defined the norms of video editing with Premiere, and motion graphics with After Effects, has now entered the realm of AI-powered creation. The wake-up call is clear - the tankers are moving fast. Goliath vs. The Upstarts This development spells a daunting challenge for the numerous start-ups that dared to dethrone Adobe in recent years. For plenty of use cases people have been asking: Why use Photoshop when you have MidJourney? Why use Premiere when you have Descript? Why use After Effects when you have Runway? These aspiring disruptors sought to chip away at Adobe's dominance by offering more specialized, user-friendly solutions - a process that can be characterized as the 'unbundling' of Adobe. Now, they face a head-to-head collision with the very Goliath they sought to topple. Creators' Toolkit: A New Addition But this imminent clash isn't just a tale of corporate competition. This is a story about the tools of creation and their impact on creators themselves and the very canvas of creation. The advent of Firefly, Adobe's AI-driven offering, reflects a broadening recognition of artificial intelligence as an integral part of the creator's toolkit. In other words, Adobe's massive ecosystem of creators needn't wade out into new waters to acquire AI capabilities -- they will simply be infused into the products they already know and (mostly) love, but more critically -- need to use every day to get creative stuff done. The Increasing Stickiness of Adobe's Tools The intersection of AI and creative tools like Photoshop's generative fill is transforming how creators perceive and interact with AI. When they encounter the innovative features of generative fill, they're not primarily thinking about the AI technology that powers it. Instead, they're marveling at the cool new tool that's now part of their beloved Photoshop. This immediate affinity for "Photoshop" masks the sophisticated technology behind it, essentially furthering Adobe's stronghold on the creative industry. Layer in Adobe's stance to training their AI models with sources like Adobe Stock that promise rock-solid data provenance, and you can see Adobe clearly wants to seem like the responsible adults in the room. After all Adobe elected not to put the Behance catalog to work, perhaps rightly so given the ethical backlash to the scraping Artstation imagery. Adobe's Thirty Something Conundrum But it's not all rainbows and sunshine. While Adobe sails ahead full steam, there's an intriguing conundrum waiting in the wings. With 30-year-old codebases forming the foundation of its most popular tools, Adobe faces a significant challenge: its software has back pain. But it's not just a technical problem -- it's also a philosophical one, akin to the ship of Theseus. Can Adobe modernize and refactor its code bases without sacrificing the essence that made these tools indispensable to creators? Can they innovate without alienating their long-time users who've grown accustomed to the 'Adobe way' of doing things? An Unexpected Solution? Interestingly, solutions might emerge from unexpected quarters. Perhaps it'll take an army of developers armed with GitHub Co-Pilot to alleviate Adobe's refactoring nightmare. By automating parts of the refactoring process, it could accelerate the evolution of Adobe's legacy tools, making them more adaptable to the rapidly progressing tech landscape while preserving their core functionality. In a twist of irony, the AI that's reshaping Adobe's offerings might just come to the rescue of its own legacy. As Adobe navigates these murky waters, opportunities are emerging for new entrants in the field. Startups might also find their moment to shine in the midst of Adobe's strategic and technological shifts. With their innovative approaches and less-encumbered platforms, they have the chance to offer alternative solutions to creators seeking novel, efficient, and intuitive tools. The Battle for Creativity The tech giant's journey through a massive transformation at a previously unfathomable speed will set the course for the next era of creative technology. Given the sheer ubiquity of Adobe tools today, it's by far the most common way creators will experience AI. But let's be honest -- this transformation will not be easy. The future of creative tech isn't written yet and as a growing line up of new entrants vie for the prize, one thing's for sure: it's going to be a darn good fight. Make Art Not War In the end, it is the creators who stand to gain the most. As Adobe and its competitors lock horns, they'll strive to deliver increasingly powerful, intuitive, and efficient tools. But, it's up to the creators themselves to harness these innovations. Only by embracing and mastering these new tools can they unlock their full creative potential. So what are you waiting for? Wield these new tools at your disposal and turn your imagination into reality. We are the architects of a new era of creative self expression. If you enjoyed this, drop a like and retweet. Follow Bilawal Sidhu for more writing on creative tech and AI.
1:16

Make Art Not War: The Battle for Creativity It's Adobe's annual Max event in London today and scott belsky's spotlight on AI paints a clear picture: AI isn't just on Adobe's agenda, it is the agenda. Adobe has already scored a home run with generative fill in Photoshop, a feature now spawning entire categories of memes - including my own video, which surprisingly garnered a million views. However, Adobe's ambitions extend beyond still imagery. The Tanker Charges Towards Video At Max, Adobe's Chief Product Officer put an emphasis on AI video generation with Firefly Video. The tech tanker is charging full steam ahead to the next obvious modality for creation, leaving a wake of disruption for any upstarts bold enough to challenge it. The announcement isn't new, but it showcases the emphasis on new product development with a marked increase in the velocity we can expect from the creative tech behemoth. The company that defined the norms of video editing with Premiere, and motion graphics with After Effects, has now entered the realm of AI-powered creation. The wake-up call is clear - the tankers are moving fast. Goliath vs. The Upstarts This development spells a daunting challenge for the numerous start-ups that dared to dethrone Adobe in recent years. For plenty of use cases people have been asking: Why use Photoshop when you have MidJourney? Why use Premiere when you have Descript? Why use After Effects when you have Runway? These aspiring disruptors sought to chip away at Adobe's dominance by offering more specialized, user-friendly solutions - a process that can be characterized as the 'unbundling' of Adobe. Now, they face a head-to-head collision with the very Goliath they sought to topple. Creators' Toolkit: A New Addition But this imminent clash isn't just a tale of corporate competition. This is a story about the tools of creation and their impact on creators themselves and the very canvas of creation. The advent of Firefly, Adobe's AI-driven offering, reflects a broadening recognition of artificial intelligence as an integral part of the creator's toolkit. In other words, Adobe's massive ecosystem of creators needn't wade out into new waters to acquire AI capabilities -- they will simply be infused into the products they already know and (mostly) love, but more critically -- need to use every day to get creative stuff done. The Increasing Stickiness of Adobe's Tools The intersection of AI and creative tools like Photoshop's generative fill is transforming how creators perceive and interact with AI. When they encounter the innovative features of generative fill, they're not primarily thinking about the AI technology that powers it. Instead, they're marveling at the cool new tool that's now part of their beloved Photoshop. This immediate affinity for "Photoshop" masks the sophisticated technology behind it, essentially furthering Adobe's stronghold on the creative industry. Layer in Adobe's stance to training their AI models with sources like Adobe Stock that promise rock-solid data provenance, and you can see Adobe clearly wants to seem like the responsible adults in the room. After all Adobe elected not to put the Behance catalog to work, perhaps rightly so given the ethical backlash to the scraping Artstation imagery. Adobe's Thirty Something Conundrum But it's not all rainbows and sunshine. While Adobe sails ahead full steam, there's an intriguing conundrum waiting in the wings. With 30-year-old codebases forming the foundation of its most popular tools, Adobe faces a significant challenge: its software has back pain. But it's not just a technical problem -- it's also a philosophical one, akin to the ship of Theseus. Can Adobe modernize and refactor its code bases without sacrificing the essence that made these tools indispensable to creators? Can they innovate without alienating their long-time users who've grown accustomed to the 'Adobe way' of doing things? An Unexpected Solution? Interestingly, solutions might emerge from unexpected quarters. Perhaps it'll take an army of developers armed with GitHub Co-Pilot to alleviate Adobe's refactoring nightmare. By automating parts of the refactoring process, it could accelerate the evolution of Adobe's legacy tools, making them more adaptable to the rapidly progressing tech landscape while preserving their core functionality. In a twist of irony, the AI that's reshaping Adobe's offerings might just come to the rescue of its own legacy. As Adobe navigates these murky waters, opportunities are emerging for new entrants in the field. Startups might also find their moment to shine in the midst of Adobe's strategic and technological shifts. With their innovative approaches and less-encumbered platforms, they have the chance to offer alternative solutions to creators seeking novel, efficient, and intuitive tools. The Battle for Creativity The tech giant's journey through a massive transformation at a previously unfathomable speed will set the course for the next era of creative technology. Given the sheer ubiquity of Adobe tools today, it's by far the most common way creators will experience AI. But let's be honest -- this transformation will not be easy. The future of creative tech isn't written yet and as a growing line up of new entrants vie for the prize, one thing's for sure: it's going to be a darn good fight. Make Art Not War In the end, it is the creators who stand to gain the most. As Adobe and its competitors lock horns, they'll strive to deliver increasingly powerful, intuitive, and efficient tools. But, it's up to the creators themselves to harness these innovations. Only by embracing and mastering these new tools can they unlock their full creative potential. So what are you waiting for? Wield these new tools at your disposal and turn your imagination into reality. We are the architects of a new era of creative self expression. If you enjoyed this, drop a like and retweet. Follow Bilawal Sidhu for more writing on creative tech and AI.

Bilawal Sidhu

72,192 görüntüleme • 3 yıl önce