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Many engineering requests begin as Slack messages long before they become tickets. 💬⚡ With Cognition's Devin in Slack, teams can investigate issues, answer technical questions, and start development work directly from the conversation, helping work move forward without unnecessary context switching. 🤖🚀 Learn more:

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28,921 views • 2 months ago •via X (Twitter)

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Software development is more than just coding. Introducing Droids -- the world's first software development agents. 🤖 Starting today, Droids are available for general access. Factory integrates with your entire engineering system (GitHub, Slack, Linear, Notion, Sentry) and serves that context to your Droids as they autonomously build production-ready software. Factory is the first platform that allows you to work with agents: local + synchronous and remote + asynchronous.

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Caspian 東澔

10,823 views • 4 months ago

There are many cryptocurrencies, each designed to serve a unique purpose. XRP is designed to help move money quickly and efficiently. It’s often discussed in the context of payments that settle faster and with lower fees than traditional systems. XRP can also act as a bridge between different currencies, helping value move from one place to another without relying on multiple intermediaries. Faster settlement and lower costs can make payments more accessible for businesses, institutions, and everyday users around the world.

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National Cryptocurrency Association

213,087 views • 8 months ago

💬 We get asked What advantages do AI-based tools bring to trading? ❕ Answer from a GT App Top Trader: They help reduce manual work and make trading decisions more structured. Instead of guessing, you start with ready setups and test them faster. 🔸 Faster analysis Less time goes into switching between charts, indicators, and timeframes. AI-generated strategies give you a ready starting point that you can immediately test and evaluate. 🔸 Structured approach Instead of searching for ideas, you work with complete strategy setups. You can backtest them, review performance, and understand how they behave before going live. 🔸 What I do I use the GT AI Trading Agent to generate strategy ideas and test them quickly. It helps me move from an idea to a validated setup without wasting time on manual trial and error. Want to try AI Trading yourself?

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GT Protocol

32,973 views • 5 months ago

We were tracking 50+ campaigns manually. Viktor turned it into one report in minutes. We skipped a project coordinator hire. We're a 12-person team managing 140+ AI and tech creators. For months, campaign tracking meant someone on the team manually checking spreadsheets, chasing creators over DM, and pulling together a status update before every client call. It worked, but it was the kind of work that quietly ate hours nobody had to spare. We tried solving it by splitting the work across the team. That just meant more people spending time on the same repetitive problem instead of one. Then we gave Viktor one job: own the campaign tracker. Pull status across all active campaigns, flag what's overdue, what's pending approval, what needs a payment follow-up, and what's coming up next. Post it directly into our Slack workspace before the week starts. Inside Slack, Viktor flagged overdue tasks, pending approvals, payment follow-ups, and upcoming deadlines. All from one report, posted directly into our workspace. Everything stayed read-only. Nothing was edited or sent without approval. The team stopped spending the first hour of every Monday piecing together what was happening across 50+ campaigns. That time went back to the work that actually moves clients forward. The teams still tracking this in spreadsheets are one missed deadline away from a client conversation they don't want to have. A copilot helps you work. An AI employee works when you don't. Hire Viktor for your team. $100 in credits included, no card. Full link in first comment. #AIemployee #CreatorMarketing #CampaignOps Paid Partnership

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Nova Reid

103,724 views • 1 month ago

Hi! It's me Rina! I’ve received a few questions and requests recently, so I wanted to explain things a little more clearly:) I’ve written the details about the requests I’m currently receiving on my blog. If you’re interested, please check the link in my profile and read the information there first! I’m Rina, 27, and I work at a Japanese izakaya. I spend a lot of time talking with different people through my job, but outside of work, I’m actually quite calm and enjoy having time to myself. I’m not always good at expressing everything directly, so this account is also a place where I can share a slightly more honest and personal side of myself—my everyday thoughts, small moments from my life, and things I normally wouldn’t say at work. I’m reading the messages and requests I receive, and I really appreciate everyone who takes the time to contact me! For more details, please visit the blog through the link in my profile.

Rina

32,909 views • 2 months ago

News alert: It’s electric⚡ GE Aerospace has successfully demonstrated a commercial narrowbody hybrid electric engine system as part of NASA’s Turbofan Engine Power Extraction program. Testing advanced our understanding of turbofan engine and electric system integration and controls, that can work with or without energy storage like batteries, helping pave the way for a more electric future of flight. ✈️ Learn more in the press release about how this architecture could benefit customers and make hybrid electric commercial aviation more viable: #GEAerospace NASA's Glenn Research Center #HybridElectric #NASA #FutureOfFlight

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GE Aerospace

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William Schuman

27,320 views • 1 year ago

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

46,478 views • 6 months ago

🚀 Kibble DeFAI is Live! Smarter. Faster. Simpler. Everything you need in just one chat Meet DeFAI, the all-in-one AI assistant for crypto users: 🤖 Trading Assistant – Ask anything, get real-time trading suggestions 📊 Market Insights – Know what’s moving before it moves 💧Liquidity Management – Optimize your swaps with intelligent routing ⚡️ Smart Order Routing (SOR) – Automatically find the best prices across platforms 💬 And yes… you can swap directly inside the chat, just ask! No more switching tabs or figuring things out alone. Just message the bot and get everything – insights, strategies, and swaps – instantly. 🧪 Try Kibble DeFAI now: 📘 Learn how it works: Welcome to the future of DeFAI #Kibble #DeFAI #AI #DeFi #ChatToSwap

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Kibble

70,837 views • 1 year ago

Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.

Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.

elvis

37,069 views • 3 months ago

🚀Just launched: Amazon Q, the most capable GenAI-powered assistant is generally available today: Customers are using Q to transform how their teams get work done. When employees chat with Amazon Q, it provides immediate, relevant information and advice to help streamline tasks, speedup decision-making, and help spark creativity and innovation at work. . Early indications signal Amazon Q could help our customers’ employees become more than 80% more productive at their jobs; and with the new features we’re planning on introducing in the future, we think this will only continue to grow. 🟠 Amazon Q Developer allows developers to spend more time coding and less time on maintenance and performing other tedious, repetitive tasks. Q assists developers and IT professionals (IT pros) with all of their tasks—from coding, testing, and upgrading applications, to troubleshooting, performing security scanning and fixes, and optimizing AWS resources. Q also comes with Q Developer Agents which can autonomously perform range of tasks and we expect it to be the state of the art accuracy in benchmarks like SWE-Bench. 🟠 Amazon Q Business empowers employees to be more data-driven, and helps customers make better, faster decisions using company knowledge and data. Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in enterprise systems 🟠 Amazon Q Apps, a new and powerful capability of Amazon Q Business, enables employees to use natural language to quickly and securely build their own generative AI applications to automate daily tasks without requiring any prior coding experience. Employees simply describe the type of app they want, in natural language, and Q Apps will quickly generate an app that accomplishes their desired task, helping them streamline and automate their daily work with ease and efficiency.

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Swami Sivasubramanian

25,216 views • 2 years ago

🚀🔮 InterPredict V2: From Beta Testing to Production Readiness 🌐⚡ InterPredict is entering a critical new stage: moving from an early beta product towards a more reliable, scalable, and production-ready community prediction marketplace. 🧠📊 This is not simply a version update. It is a structured transition from testing the concept to strengthening the infrastructure required for real-world use. 🛠️🌍 🧪 What the Beta Phase Achieved The beta phase provided an important testing environment for: • ✅ Validating the core prediction-market concept • 👥 Observing how users interact with the platform • 🐛 Identifying technical issues and improvement areas • 💬 Collecting community feedback • 📈 Understanding how prediction markets perform under real usage conditions Beta testing was about learning what works, discovering what needs improvement, and preparing the foundation for the next phase. 🔍🚧 ⚙️🚀 What InterPredict V2 Brings With V2, the focus is shifting towards a smoother, faster, and more dependable user experience. Key priorities include: ⚡ Improved performance 🛡️ Greater reliability 📈 Better scalability 🔗 Seamless system integration ⚙️ More efficient platform operations 🎨 A clearer and more accessible interface The objective is to connect the platform’s core components into one integrated system that can support broader participation and future growth. 🌱🌐 🛠️📋 Development Progress The current development status is as follows: ✅ Smart contract development: Completed ✅ Backend and server development: Completed 🔄 Frontend development: In progress 🔗 Full platform integration: In progress 🧪 Final testing: Upcoming 🌐🚀 Mainnet launch: Planned after successful testing and review This staged approach is important. Before a Mainnet release, the platform must be tested thoroughly across its technical systems, user flows, security processes, and overall reliability. 🔐✅ 📊🧠 More Than Just Making Predictions InterPredict’s wider purpose extends beyond individual predictions. 🔮 The platform is designed to help transform community knowledge, opinions, and expectations into structured prediction markets. When participation is organised effectively, collective views can produce useful market signals and offer insight into how communities assess future events. 💡📈 This may create value in several areas: • 🗣️ Encouraging informed participation • 📊 Making community sentiment easier to analyse • 🔍 Creating transparent prediction-based markets • 🧠 Supporting data-driven discussion • 🤝 Giving users a more interactive role in the ecosystem Prediction markets do not guarantee correct outcomes, but they can provide a structured way to compare expectations and observe changing sentiment. ⚖️📉📈 🧪🔐 The Importance of the Final Testing Phase The period before Mainnet will be especially significant. It should focus on: • End-to-end testing: checking that all platform components work together correctly • Early-user feedback: identifying practical issues from real user interactions • Performance review: assessing speed, stability, and scalability • Security checks: reviewing smart contracts and supporting infrastructure • User-experience refinement: making the platform easier to understand and use • Operational readiness: ensuring the system can support activity after launch A successful Mainnet launch depends not only on completing development, but also on the quality of testing and review that comes before it. ✅🚀 🔄🌍 From Vision to Real-World Adoption InterPredict’s progress can be viewed as a clear development path: 🔬 Experimentation → 🏗️ Infrastructure → 🔗 Integration → 🔍 Testing → 🌍 Adoption Beta tested the initial vision 🧪 V2 strengthens the technical foundation 🛠️ Integration connects the platform’s main components 🔗 Testing prepares the system for wider use 🔍 Mainnet will measure how effectively the ecosystem performs in practice 🚀 Each stage has a different purpose, and completing them carefully is essential for sustainable growth. 🌱📈 🌟 The Road Ahead InterPredict V2 represents an important step towards building a stronger community prediction marketplace. 🔮🌐 The next milestones will determine how effectively the platform can convert its technical progress into a dependable experience for users. 👥⚙️ The goal is clear: create a system where people can predict, participate, share perspectives, and contribute to meaningful market signals in a transparent and structured way. 💬📊🤝 🔮✨ Final Takeaway → Beta tested the vision. 🧪 → V2 is strengthening the infrastructure. 🛠️ → Mainnet will put the ecosystem to the test. 🌐�* The journey continues: 🎲 Predict. 🤝 Participate. 🌍 Shape Tomorrow. 💜⚡ 🔗 Follow InterPredict: InterPredict 📲 Telegram: InterLink Labs 👤 + 🌐 InterPredict InterLink Foundation KV Reina | InterLink Labs Mira #InterPredict #ITP #InterLink #ITLG #ITL #PredictionMarkets #ITL #ITLG #Web3 #Blockchain #dApp #Mainnet #Tokenomics

🚀🔮 InterPredict V2: From Beta Testing to Production Readiness 🌐⚡ InterPredict is entering a critical new stage: moving from an early beta product towards a more reliable, scalable, and production-ready community prediction marketplace. 🧠📊 This is not simply a version update. It is a structured transition from testing the concept to strengthening the infrastructure required for real-world use. 🛠️🌍 🧪 What the Beta Phase Achieved The beta phase provided an important testing environment for: • ✅ Validating the core prediction-market concept • 👥 Observing how users interact with the platform • 🐛 Identifying technical issues and improvement areas • 💬 Collecting community feedback • 📈 Understanding how prediction markets perform under real usage conditions Beta testing was about learning what works, discovering what needs improvement, and preparing the foundation for the next phase. 🔍🚧 ⚙️🚀 What InterPredict V2 Brings With V2, the focus is shifting towards a smoother, faster, and more dependable user experience. Key priorities include: ⚡ Improved performance 🛡️ Greater reliability 📈 Better scalability 🔗 Seamless system integration ⚙️ More efficient platform operations 🎨 A clearer and more accessible interface The objective is to connect the platform’s core components into one integrated system that can support broader participation and future growth. 🌱🌐 🛠️📋 Development Progress The current development status is as follows: ✅ Smart contract development: Completed ✅ Backend and server development: Completed 🔄 Frontend development: In progress 🔗 Full platform integration: In progress 🧪 Final testing: Upcoming 🌐🚀 Mainnet launch: Planned after successful testing and review This staged approach is important. Before a Mainnet release, the platform must be tested thoroughly across its technical systems, user flows, security processes, and overall reliability. 🔐✅ 📊🧠 More Than Just Making Predictions InterPredict’s wider purpose extends beyond individual predictions. 🔮 The platform is designed to help transform community knowledge, opinions, and expectations into structured prediction markets. When participation is organised effectively, collective views can produce useful market signals and offer insight into how communities assess future events. 💡📈 This may create value in several areas: • 🗣️ Encouraging informed participation • 📊 Making community sentiment easier to analyse • 🔍 Creating transparent prediction-based markets • 🧠 Supporting data-driven discussion • 🤝 Giving users a more interactive role in the ecosystem Prediction markets do not guarantee correct outcomes, but they can provide a structured way to compare expectations and observe changing sentiment. ⚖️📉📈 🧪🔐 The Importance of the Final Testing Phase The period before Mainnet will be especially significant. It should focus on: • End-to-end testing: checking that all platform components work together correctly • Early-user feedback: identifying practical issues from real user interactions • Performance review: assessing speed, stability, and scalability • Security checks: reviewing smart contracts and supporting infrastructure • User-experience refinement: making the platform easier to understand and use • Operational readiness: ensuring the system can support activity after launch A successful Mainnet launch depends not only on completing development, but also on the quality of testing and review that comes before it. ✅🚀 🔄🌍 From Vision to Real-World Adoption InterPredict’s progress can be viewed as a clear development path: 🔬 Experimentation → 🏗️ Infrastructure → 🔗 Integration → 🔍 Testing → 🌍 Adoption Beta tested the initial vision 🧪 V2 strengthens the technical foundation 🛠️ Integration connects the platform’s main components 🔗 Testing prepares the system for wider use 🔍 Mainnet will measure how effectively the ecosystem performs in practice 🚀 Each stage has a different purpose, and completing them carefully is essential for sustainable growth. 🌱📈 🌟 The Road Ahead InterPredict V2 represents an important step towards building a stronger community prediction marketplace. 🔮🌐 The next milestones will determine how effectively the platform can convert its technical progress into a dependable experience for users. 👥⚙️ The goal is clear: create a system where people can predict, participate, share perspectives, and contribute to meaningful market signals in a transparent and structured way. 💬📊🤝 🔮✨ Final Takeaway → Beta tested the vision. 🧪 → V2 is strengthening the infrastructure. 🛠️ → Mainnet will put the ecosystem to the test. 🌐�* The journey continues: 🎲 Predict. 🤝 Participate. 🌍 Shape Tomorrow. 💜⚡ 🔗 Follow InterPredict: InterPredict 📲 Telegram: InterLink Labs 👤 + 🌐 InterPredict InterLink Foundation KV Reina | InterLink Labs Mira #InterPredict #ITP #InterLink #ITLG #ITL #PredictionMarkets #ITL #ITLG #Web3 #Blockchain #dApp #Mainnet #Tokenomics

Tekkaus® | InterLink Global Leader • MOD • OG

146,322 views • 11 days ago

Tanay is building e-trolleys in India. Instead of spending heavily upfront, he imported components from China in small quantities, tested the market and proved there was demand. Once the product started working, he began the next phase: Indianising the product. The exterior, steel work and as many components as possible are now being sourced/manufactured in India. But there was a problem. He still needed to communicate very specific product changes to his Chinese suppliers. And this is where Toplingo came in. Instead of sending instructions through a translator, Tanay communicates directly with the Chinese manufacturers boss in their native language. The supplier even responded saying they appreciated the effort because it made the requirements much easier to understand. When you're manufacturing across countries, small communication gaps can become expensive mistakes. - something gets lost in translation - a specification gets misunderstood - change takes longer than it should And suddenly, you're dealing with delays, rework and unnecessary costs. Direct communication removes one more layer between the person building the product and the person making it. That's the real opportunity with AI translation. Making cross-border business feel more direct. From a founder in India to a factory owner in China - without language becoming the bottleneck. Isn't it cool? Tanay Bassi 🇮🇳 Use Toplingo for FREE here:

Tanay is building e-trolleys in India. Instead of spending heavily upfront, he imported components from China in small quantities, tested the market and proved there was demand. Once the product started working, he began the next phase: Indianising the product. The exterior, steel work and as many components as possible are now being sourced/manufactured in India. But there was a problem. He still needed to communicate very specific product changes to his Chinese suppliers. And this is where Toplingo came in. Instead of sending instructions through a translator, Tanay communicates directly with the Chinese manufacturers boss in their native language. The supplier even responded saying they appreciated the effort because it made the requirements much easier to understand. When you're manufacturing across countries, small communication gaps can become expensive mistakes. - something gets lost in translation - a specification gets misunderstood - change takes longer than it should And suddenly, you're dealing with delays, rework and unnecessary costs. Direct communication removes one more layer between the person building the product and the person making it. That's the real opportunity with AI translation. Making cross-border business feel more direct. From a founder in India to a factory owner in China - without language becoming the bottleneck. Isn't it cool? Tanay Bassi 🇮🇳 Use Toplingo for FREE here:

Prakash Dadlani

19,747 views • 18 days ago

ONE PROMPT WON'T MAKE YOU RICH👀 It won’t change your life overnight. And it won’t solve all your problems. But the right prompt can do something much more important. It can reveal things you hadn’t noticed before. Most people use AI to find answers. But the most useful prompts work differently. They make you ask yourself questions. > What’s really holding you back? > What decisions have you been putting off for months? > What are you wasting your time on, convincing yourself it’s important? Sometimes, one honest answer is more valuable than a hundred motivational videos and dozens of books. That’s exactly why I liked this prompt. It doesn’t try to impress with complexity. It helps you see your situation from the outside. Spend a few minutes on it. Perhaps a single conversation with AI will give you more clarity than you expect.

ONE PROMPT WON'T MAKE YOU RICH👀 It won’t change your life overnight. And it won’t solve all your problems. But the right prompt can do something much more important. It can reveal things you hadn’t noticed before. Most people use AI to find answers. But the most useful prompts work differently. They make you ask yourself questions. > What’s really holding you back? > What decisions have you been putting off for months? > What are you wasting your time on, convincing yourself it’s important? Sometimes, one honest answer is more valuable than a hundred motivational videos and dozens of books. That’s exactly why I liked this prompt. It doesn’t try to impress with complexity. It helps you see your situation from the outside. Spend a few minutes on it. Perhaps a single conversation with AI will give you more clarity than you expect.

kozh ./

24,139 views • 2 months ago