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Users of most AI coding products are still stuck in the vibe-coding loop: Prompt → Test → Debug → Adjust → Fix Errors → Repeat Bubble Code users take a different path. State the product goal. If the result needs improvement, submit a Bubble Up. Then Bubble Engine takes... show more

DAPPOS

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25,865 次观看 • 1 个月前 •via X (Twitter)

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Code mode is not a one-shot build. If the result does not fully match your product goal, submit a Bubble Up. Bubble Engine will test, evaluate, and iteratively optimize the underlying Coding SOP, moving refinement from the user to the system. State the goal. Build with Bubble Code. Improve with Bubble Up.

Code mode is not a one-shot build. If the result does not fully match your product goal, submit a Bubble Up. Bubble Engine will test, evaluate, and iteratively optimize the underlying Coding SOP, moving refinement from the user to the system. State the goal. Build with Bubble Code. Improve with Bubble Up.

DAPPOS

28,693 次观看 • 25 天前

No setup. No coding workflow. Users state the goal. Bubble Code runs the right Coding SOP. Just switch to Code mode, upload an image, and tell Bubble Code what business you want to build. Enjoy software created from one voice prompt. 🫧

No setup. No coding workflow. Users state the goal. Bubble Code runs the right Coding SOP. Just switch to Code mode, upload an image, and tell Bubble Code what business you want to build. Enjoy software created from one voice prompt. 🫧

DAPPOS

26,374 次观看 • 1 个月前

Users state the goal: build a site that tracks Iran-related Polymarket markets and breaking movements. Bubble Code routes it to a Polymarket Agent SOP, connects live market data, sets monitoring keywords, and builds Iran Pulse: a working dashboard for probabilities, volume, liquidity, abnormal changes, and breaking signals. This is not a normal AI demo or static page. It is a ready-to-use market-monitoring product that can refresh, run, deploy, and be used directly.

Users state the goal: build a site that tracks Iran-related Polymarket markets and breaking movements. Bubble Code routes it to a Polymarket Agent SOP, connects live market data, sets monitoring keywords, and builds Iran Pulse: a working dashboard for probabilities, volume, liquidity, abnormal changes, and breaking signals. This is not a normal AI demo or static page. It is a ready-to-use market-monitoring product that can refresh, run, deploy, and be used directly.

DAPPOS

141,569 次观看 • 1 个月前

UltraCode is running over 100 Opus 4.8 agents right now. This is the future of software development. Not 5 agents. Not 10. Over 100 working simultaneously across the entire BridgeMind ecosystem. This is what vibe coding looks like at scale. One person orchestrating a swarm of AI agents building, debugging, and shipping in parallel. We are so early.

UltraCode is running over 100 Opus 4.8 agents right now. This is the future of software development. Not 5 agents. Not 10. Over 100 working simultaneously across the entire BridgeMind ecosystem. This is what vibe coding looks like at scale. One person orchestrating a swarm of AI agents building, debugging, and shipping in parallel. We are so early.

BridgeMind

108,400 次观看 • 2 个月前

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.

DAPPOS

14,289 次观看 • 2 个月前

Simplicity is at the heart of great software. This is one of the reasons why Claude Code has been sticky for me. As a builder, I love planning and brainstorming, and this is now a key focus of Claude Code. I use Shift + Tab a lot to cycle between brainstorming, planning, and execution. This functionality provides the appropriate interface for me to either be very involved or less involved as I please. This works particularly well when building out new and complex features or entire new projects. This saves a huge amount of time. It allows me to tune Claude Code to execute and build more effectively. It also builds a loop of trust, and I often (surprisingly) find Claude Code asking for clarifications when it's confused. Coding agents don't normally do that. I have shared before on the power of brainstorming with AI for longer times. Try it and you will not be disappointed. Vibe coding is fun, but pair it with intentional development cycles, and you watch how far you can take a project with coding agents today.

Simplicity is at the heart of great software. This is one of the reasons why Claude Code has been sticky for me. As a builder, I love planning and brainstorming, and this is now a key focus of Claude Code. I use Shift + Tab a lot to cycle between brainstorming, planning, and execution. This functionality provides the appropriate interface for me to either be very involved or less involved as I please. This works particularly well when building out new and complex features or entire new projects. This saves a huge amount of time. It allows me to tune Claude Code to execute and build more effectively. It also builds a loop of trust, and I often (surprisingly) find Claude Code asking for clarifications when it's confused. Coding agents don't normally do that. I have shared before on the power of brainstorming with AI for longer times. Try it and you will not be disappointed. Vibe coding is fun, but pair it with intentional development cycles, and you watch how far you can take a project with coding agents today.

elvis

81,765 次观看 • 9 个月前

Building is only the beginning. Managing what you build matters too. With xBubble, every AI website created through Bubble Code is managed in one project workspace. View all deployments, track running or paused status, pause, resume, restart, or decommission projects, and jump back into chat anytime to refine your website through conversation. From creation to deployment to continuous iteration, xBubble gives users a complete lifecycle workspace for AI products.

Building is only the beginning. Managing what you build matters too. With xBubble, every AI website created through Bubble Code is managed in one project workspace. View all deployments, track running or paused status, pause, resume, restart, or decommission projects, and jump back into chat anytime to refine your website through conversation. From creation to deployment to continuous iteration, xBubble gives users a complete lifecycle workspace for AI products.

DAPPOS

30,922 次观看 • 1 个月前

An Oldie but a Goldie. Approach ➜ Power V One of the biggest keys to a consistent swing is what happens from approach to Power V. As the knob turns toward the pitcher and into the path of the pitch, the barrel naturally falls behind your hands. We don’t need to flick it back to have it fall behind..DUH From there, don’t throw the barrel at the ball. Release it in the most direct line along the path of the pitch. Some pitches require a slightly steeper path. Others require a flatter one. But the goal stays the same: ✔️ Keep the barrel below your hands through extension. Approach = The barrel works to the side of the shoulder as the body turns toward the pitcher. Power V = The release of the lag and extension of the barrel through the baseball. Master that section of the swing, and you’ll give yourself a much better chance to create consistent contact, adjust to different pitch planes, and produce real power. Train with an expert, not an influencer.

An Oldie but a Goldie. Approach ➜ Power V One of the biggest keys to a consistent swing is what happens from approach to Power V. As the knob turns toward the pitcher and into the path of the pitch, the barrel naturally falls behind your hands. We don’t need to flick it back to have it fall behind..DUH From there, don’t throw the barrel at the ball. Release it in the most direct line along the path of the pitch. Some pitches require a slightly steeper path. Others require a flatter one. But the goal stays the same: ✔️ Keep the barrel below your hands through extension. Approach = The barrel works to the side of the shoulder as the body turns toward the pitcher. Power V = The release of the lag and extension of the barrel through the baseball. Master that section of the swing, and you’ll give yourself a much better chance to create consistent contact, adjust to different pitch planes, and produce real power. Train with an expert, not an influencer.

Stance Doctor

10,981 次观看 • 1 个月前

THIS STUDENT WAS VIBE CODING AN APP, THEN GOT A $55,444.78 BILL FROM GOOGLE CLOUD All because they accidentally pushed their Gemini API key to GitHub They thought the repository was private It was just a small side project, and they still had $220 in free credits left By the time they checked their email, it was already too late This video shows exactly how things like this happen and why more and more developers are running into the same problem: > One commit turned into a $55k nightmare > API keys were exposed in frontend code and even inside app binaries > People hardcoded secrets into scripts and ended up with hundreds of dollars in charges within hours > One OpenAI key was abused nearly a million times before anyone noticed Never hardcode API keys Never commit them to GitHub, even if the repository is private Never expose them in your frontend Always use environment variables and set up spending alerts Even in the era of vibe coding, security still matters Knowing a few basic best practices can save you from some very expensive mistakes If you’re a vibe coder, make sure to read the article I attached, you’ll find plenty of practical tips that could save you a lot of trouble Save this post so you don’t lose it

THIS STUDENT WAS VIBE CODING AN APP, THEN GOT A $55,444.78 BILL FROM GOOGLE CLOUD All because they accidentally pushed their Gemini API key to GitHub They thought the repository was private It was just a small side project, and they still had $220 in free credits left By the time they checked their email, it was already too late This video shows exactly how things like this happen and why more and more developers are running into the same problem: > One commit turned into a $55k nightmare > API keys were exposed in frontend code and even inside app binaries > People hardcoded secrets into scripts and ended up with hundreds of dollars in charges within hours > One OpenAI key was abused nearly a million times before anyone noticed Never hardcode API keys Never commit them to GitHub, even if the repository is private Never expose them in your frontend Always use environment variables and set up spending alerts Even in the era of vibe coding, security still matters Knowing a few basic best practices can save you from some very expensive mistakes If you’re a vibe coder, make sure to read the article I attached, you’ll find plenty of practical tips that could save you a lot of trouble Save this post so you don’t lose it

Bonsai 🌳

110,437 次观看 • 1 个月前

India should host the biggest vibe coding conference the world has seen 🚀 Everything about Vibe coding(talks, panels, AI product building, hackathon, including the biggest names in the world) come here for an ultimate showdown(think 10k+ people in a Vibecoding conference) 🔥 And I have a plan Why? Because the number of learners, founders and professionals who are building via vibe coding in India and powering the global platforms is testament to the fact that if it should happen anywhere, it is here For the last 4+ years, I have enabled 3000+ people to build and continue to do so in a new avatar(announcement soon) and I believe if the entire ecosystem is willing to come together we can create a real spectacle We have the skills, the access and the experience to pull this off. The only thing it needs is for everyone seeing this to reach out and join hands(partners, sponsors & more) to make this as big as possible I am so hyped about it that the website is set, the name is set(Vibecon) and we can make an incredible run to make it happen. Reply to this post if you think we should do this, if we have >500 responses we will get to work 🥳 Reach out if you have ideas to partner to make the biggest Vibecoding conference a reality ♥️

Prashant Sharma

11,011 次观看 • 1 年前

there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two questions: what starts a run, and what ends it. hand-run, you answer both yourself, every time. 1) turn-based → you prompt, it acts, you review, you prompt again. both jobs stay with you. use when requirements are still forming. 2) goal-based → "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back. use when the outcome is measurable but the path isn't. 3) time-based → a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop. use for recurring work. 4) proactive → no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself. use for standing duties you can't predict. not which one is most advanced. whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. full breakdown in the article below.

there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two questions: what starts a run, and what ends it. hand-run, you answer both yourself, every time. 1) turn-based → you prompt, it acts, you review, you prompt again. both jobs stay with you. use when requirements are still forming. 2) goal-based → "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back. use when the outcome is measurable but the path isn't. 3) time-based → a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop. use for recurring work. 4) proactive → no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself. use for standing duties you can't predict. not which one is most advanced. whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. full breakdown in the article below.

Hanako

475,101 次观看 • 24 天前

At Uber a big problem for design teams and engineering teams was design source of truth. No one knew what the app truly looked like, so weekly a bunch of designers would get into a meeting room and check that the engineers correctly implemented the figma designs that they made Now with coding agents throughput of changes has increased an order of magnitude and it has become impossible to manually keep up. Here we used the Revyl CLI to create a flow of every state in the Uber design, by navigating our mobile use agent on an cloud iOS simulator. This is something that would have taken a team of designers tens of hours to recreate manually; All done in less than an hour asynchronously with a simple prompt. Enable your team to know what your users are actually seeing and empower coding agents to give your users a delightful experience without any blindspots Get started with our new free trial and create a map for your own app 🗺️

At Uber a big problem for design teams and engineering teams was design source of truth. No one knew what the app truly looked like, so weekly a bunch of designers would get into a meeting room and check that the engineers correctly implemented the figma designs that they made Now with coding agents throughput of changes has increased an order of magnitude and it has become impossible to manually keep up. Here we used the Revyl CLI to create a flow of every state in the Uber design, by navigating our mobile use agent on an cloud iOS simulator. This is something that would have taken a team of designers tens of hours to recreate manually; All done in less than an hour asynchronously with a simple prompt. Enable your team to know what your users are actually seeing and empower coding agents to give your users a delightful experience without any blindspots Get started with our new free trial and create a map for your own app 🗺️

Anam Hira

96,508 次观看 • 3 个月前

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Rachel🥥

61,143 次观看 • 1 个月前

THIS 38,000-STAR GITHUB REPO TURNS ONE AI AGENT INTO A REAL TEAM THAT CAN BRANCH, VERIFY ITS WORK AND WAIT FOR YOUR APPROVAL most people still run agents as one long chain where every step waits, one failure kills the run and the full workflow starts again Task → Planner → 5 Researchers in Parallel → Skeptic → Writer → Human Gate LangGraph gives every node one job while a shared state carries the findings, decisions and context through the entire system the skeptic can reject an unsupported finding and route the work back before it contaminates the final report, while independent branches keep moving if the run crashes, durable execution resumes from the saved state instead of rebuilding everything, then human-in-the-loop pauses the graph before anything expensive gets sent or published bookmark this repo and watch one prompt turn into an actual org chart for AI agents

THIS 38,000-STAR GITHUB REPO TURNS ONE AI AGENT INTO A REAL TEAM THAT CAN BRANCH, VERIFY ITS WORK AND WAIT FOR YOUR APPROVAL most people still run agents as one long chain where every step waits, one failure kills the run and the full workflow starts again Task → Planner → 5 Researchers in Parallel → Skeptic → Writer → Human Gate LangGraph gives every node one job while a shared state carries the findings, decisions and context through the entire system the skeptic can reject an unsupported finding and route the work back before it contaminates the final report, while independent branches keep moving if the run crashes, durable execution resumes from the saved state instead of rebuilding everything, then human-in-the-loop pauses the graph before anything expensive gets sent or published bookmark this repo and watch one prompt turn into an actual org chart for AI agents

Gipp 🦅

11,524 次观看 • 15 天前

do you understand what Anthropic just admitted? their engineers haven’t written most of their own code since early 2026. 80% of the code merged into Anthropic’s codebase last month was written by Claude. let that sink in. the company building the most powerful AI in the world is already being built by that AI. and the numbers keep getting wilder: → engineers are shipping 8x more code than in 2024. not because they’re working harder. because Claude is doing most of it. → Claude’s success rate on open-ended coding problems hit 76% in May 2026. up 50 points in just 6 months. → one Anthropic employee said “it’s been 5 months since I last wrote any code myself.” → Claude Mythos Preview achieved 52x speedup on research optimization. a skilled human gets 4x in 4-8 hours. → the length of tasks AI can reliably complete is doubling every 4 months. and here’s the part that keeps me up at night: in April 2026, Claude-powered agents were given an open AI safety problem and left alone to solve it. two human researchers recovered 23% of the performance gap in a week. the agents recovered 97% in 800 hours. Anthropic calls this recursive self-improvement. AI building AI. getting better. building a better version. repeat. they say we’re not there yet. but they also say it could come sooner than most institutions are prepared for.

do you understand what Anthropic just admitted? their engineers haven’t written most of their own code since early 2026. 80% of the code merged into Anthropic’s codebase last month was written by Claude. let that sink in. the company building the most powerful AI in the world is already being built by that AI. and the numbers keep getting wilder: → engineers are shipping 8x more code than in 2024. not because they’re working harder. because Claude is doing most of it. → Claude’s success rate on open-ended coding problems hit 76% in May 2026. up 50 points in just 6 months. → one Anthropic employee said “it’s been 5 months since I last wrote any code myself.” → Claude Mythos Preview achieved 52x speedup on research optimization. a skilled human gets 4x in 4-8 hours. → the length of tasks AI can reliably complete is doubling every 4 months. and here’s the part that keeps me up at night: in April 2026, Claude-powered agents were given an open AI safety problem and left alone to solve it. two human researchers recovered 23% of the performance gap in a week. the agents recovered 97% in 800 hours. Anthropic calls this recursive self-improvement. AI building AI. getting better. building a better version. repeat. they say we’re not there yet. but they also say it could come sooner than most institutions are prepared for.

Poonam Soni

54,556 次观看 • 2 个月前

$80k+/month ad accounts are quietly testing animated object ads not influencers not classic ugc just tiny characters turning everyday problems into visuals you instantly get an aloe cooling down a clam boiling under pressure an avocado stressing on the counter you understand the idea in seconds no long explanations no expert talking the animation does all the selling ai creates the characters ai builds the scenes ai generates dozens of hooks one concept, multiple videos, endless variations to test rt + comment “creatures” and i’ll send the framework (follow for dm)

$80k+/month ad accounts are quietly testing animated object ads not influencers not classic ugc just tiny characters turning everyday problems into visuals you instantly get an aloe cooling down a clam boiling under pressure an avocado stressing on the counter you understand the idea in seconds no long explanations no expert talking the animation does all the selling ai creates the characters ai builds the scenes ai generates dozens of hooks one concept, multiple videos, endless variations to test rt + comment “creatures” and i’ll send the framework (follow for dm)

Alessandro

11,245 次观看 • 4 个月前

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

Atlas

500,090 次观看 • 26 天前

At Uber, design source of truth was a big problem for design and engineering teams. No one knew what the app truly looked like. So every week, a group of designers would sit in a meeting room and check that engineers had correctly implemented the Figma designs. Now coding agents have increased the throughput of changes by an order of magnitude. Keeping up manually has become impossible. Here we used the Revyl to map every state in Ubert (demo uber), navigating our mobile use agent on a cloud iOS simulator. This would have taken a team of designers tens of hours to recreate by hand. We did it in under an hour, asynchronously, with a simple prompt. Let your team see what your users actually see. Empower coding agents to ship a delightful experience with no blindspots. Get started with our free trial and create a map for your own app.

At Uber, design source of truth was a big problem for design and engineering teams. No one knew what the app truly looked like. So every week, a group of designers would sit in a meeting room and check that engineers had correctly implemented the Figma designs. Now coding agents have increased the throughput of changes by an order of magnitude. Keeping up manually has become impossible. Here we used the Revyl to map every state in Ubert (demo uber), navigating our mobile use agent on a cloud iOS simulator. This would have taken a team of designers tens of hours to recreate by hand. We did it in under an hour, asynchronously, with a simple prompt. Let your team see what your users actually see. Empower coding agents to ship a delightful experience with no blindspots. Get started with our free trial and create a map for your own app.

Anam Hira

203,223 次观看 • 1 个月前

Samsung India, this isn’t how you treat your flagship customers. An S24 Ultra user installs the first One UI 8.5 beta… and suddenly a white dot appears on the display. He contacts support, they assume it’s a green/pink line issue and mention replacement (valid up to S23 series). But this case? Completely different. He goes to the service center… and gets blamed instead. A minor dent from one year ago is now suddenly the cause? So what are we saying here, the customer is lying?🤔 Yes, beta comes with risk. Users understand that. But these are also the same users helping you test, improve, and refine your software. This kind of handling? Not acceptable. Samsung India management needs to look into this and ensure proper assistance is provided, along with better guidance on how customer service should respond. Flagship users deserve better support not assumptions.

Samsung India, this isn’t how you treat your flagship customers. An S24 Ultra user installs the first One UI 8.5 beta… and suddenly a white dot appears on the display. He contacts support, they assume it’s a green/pink line issue and mention replacement (valid up to S23 series). But this case? Completely different. He goes to the service center… and gets blamed instead. A minor dent from one year ago is now suddenly the cause? So what are we saying here, the customer is lying?🤔 Yes, beta comes with risk. Users understand that. But these are also the same users helping you test, improve, and refine your software. This kind of handling? Not acceptable. Samsung India management needs to look into this and ensure proper assistance is provided, along with better guidance on how customer service should respond. Flagship users deserve better support not assumptions.

W

85,122 次观看 • 4 个月前

🚨 THIS IS HOW AI BUBBLE WILL CRASH S&P 500 Read this carefully before buying stocks. Three AI and space giants are entering public markets in the same cycle with a combined valuation approaching $4 trillion. 1. THE BIGGEST IPO WAVE IN DECADES → SpaceX has already gone public with $1.7 trillion valuation $SPCX debuted on Nasdaq on June 12 and become one of the largest public listings in history. → OpenAI has already filed a confidential S-1 and is targeting a valuation above $1 trillion. → Anthropic is also considering an IPO at a valuation of around $1 trillion. 2. THE LIQUIDITY DRAIN The S&P 500 is now carried mostly by the Mag 7 and AI stocks. Nvidia, Microsoft, Google, Amazon and others make up roughly 33% to 35% of the entire index. These three listings could pull $200 billion or more from the market. Funds may need to sell existing positions to buy SpaceX, OpenAI and Anthropic. Nvidia, Microsoft and Google could be the first to feel the pressure. The S&P 500 has also resisted fast tracking these unprofitable giants into the index. That means the rotation could hit existing index leaders even harder. 3. HISTORY IS REPEATING At the peak of every major bubble, capital became concentrated in a small group of “can’t lose” companies. → The Roaring Twenties → The Nifty Fifty era → Japan’s 1980s bubble → The Dot-Com Bubble of 1999 to 2000 Today, tech concentration is once again near historical extremes. 4. THE REAL SELLING STARTS AFTER THE IPO After an IPO, early investors finally get the chance to lock in profits. Lockup expirations have historically increased selling pressure. During the Dot Com crash, even the strongest companies got destroyed. → Amazon: 95% dump → Microsoft: 65% dump → Intel: 80% dump → Oracle: 80% dump → Yahoo: 97% dump A great business does NOT protect investors from extreme overvaluation. Massive listings at these valuations, while many AI companies are still deeply unprofitable, are a clear sign of market euphoria. I’ve said this before. And the cycle is still playing out exactly according to plan. Turn notifications on. I’ll post the warning BEFORE it hits the headlines.

🚨 THIS IS HOW AI BUBBLE WILL CRASH S&P 500 Read this carefully before buying stocks. Three AI and space giants are entering public markets in the same cycle with a combined valuation approaching $4 trillion. 1. THE BIGGEST IPO WAVE IN DECADES → SpaceX has already gone public with $1.7 trillion valuation $SPCX debuted on Nasdaq on June 12 and become one of the largest public listings in history. → OpenAI has already filed a confidential S-1 and is targeting a valuation above $1 trillion. → Anthropic is also considering an IPO at a valuation of around $1 trillion. 2. THE LIQUIDITY DRAIN The S&P 500 is now carried mostly by the Mag 7 and AI stocks. Nvidia, Microsoft, Google, Amazon and others make up roughly 33% to 35% of the entire index. These three listings could pull $200 billion or more from the market. Funds may need to sell existing positions to buy SpaceX, OpenAI and Anthropic. Nvidia, Microsoft and Google could be the first to feel the pressure. The S&P 500 has also resisted fast tracking these unprofitable giants into the index. That means the rotation could hit existing index leaders even harder. 3. HISTORY IS REPEATING At the peak of every major bubble, capital became concentrated in a small group of “can’t lose” companies. → The Roaring Twenties → The Nifty Fifty era → Japan’s 1980s bubble → The Dot-Com Bubble of 1999 to 2000 Today, tech concentration is once again near historical extremes. 4. THE REAL SELLING STARTS AFTER THE IPO After an IPO, early investors finally get the chance to lock in profits. Lockup expirations have historically increased selling pressure. During the Dot Com crash, even the strongest companies got destroyed. → Amazon: 95% dump → Microsoft: 65% dump → Intel: 80% dump → Oracle: 80% dump → Yahoo: 97% dump A great business does NOT protect investors from extreme overvaluation. Massive listings at these valuations, while many AI companies are still deeply unprofitable, are a clear sign of market euphoria. I’ve said this before. And the cycle is still playing out exactly according to plan. Turn notifications on. I’ll post the warning BEFORE it hits the headlines.

Wimar.X

54,068 次观看 • 1 个月前