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"Confession: I've abandoned more AI-generated apps than I’ve deployed. Until last week." Let’s be real e’ve all chased the "wow" of AI dev tools that generate code in seconds. But what happens when real users break everything? You’re left debugging blindfolded, drowning in spaghetti code, and praying it doesn’t...

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Shruti Codes

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27,005 Aufrufe • vor 1 Jahr •via X (Twitter)

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Profilbild von AIEvolutionHub - AI | Tech | Entrepreneurship
AIEvolutionHub - AI | Tech | Entrepreneurshipvor 1 Jahr

another day another amazing tool!

Profilbild von Shruti Codes
Shruti Codesvor 1 Jahr

yeah

Profilbild von Mobile Scanner
Mobile Scannervor 1 Jahr

Scan any documents, convert images into text, PDF files, etc. 👍

Profilbild von Komal Prajapati
Komal Prajapativor 1 Jahr

Sound like good

Profilbild von Shruti Codes
Shruti Codesvor 1 Jahr

yeah

Profilbild von Jubayer
Jubayervor 1 Jahr

great

Profilbild von Shruti Codes
Shruti Codesvor 1 Jahr

glad u liked it

Profilbild von Tabiawith_AI
Tabiawith_AIvor 1 Jahr

Great share

Profilbild von Tanjina Islam
Tanjina Islamvor 1 Jahr

Great share

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pushkar sonivor 1 Jahr

sounds interesting

Profilbild von Extra Time Indonesia
Extra Time Indonesiavor 1 Jahr

wow wow amazing tool

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REPORT: Lawmakers want to make it a felony to fix your own 3D printer—because the government might not “approve” of what you’re making. That’s not a headline from China. It’s happening right here in the United States. Starting in 2027, every 3D printer sold in Washington state would be required to include government-approved software that scans what you’re printing. And if it even might be a gun part, the machine shuts it down. No workarounds. If you try to modify the printer—or if a company sells one that can be modified—they could be hit with felony charges, prison time, and massive fines. They say it’s about “safety.” But let’s be real—this is about control. It’s the same model we’re seeing with smart cars and appliance restrictions. One update, and your machine stops working. One “unauthorized” project, and you’re locked out. Today it’s your printer. Tomorrow it’s your car, your tools, even your right to build or fix anything at all. The solution? Own your tools. Say no to smart restrictions. Because if you can’t control your own machines… what can you control? Watch ZeeeMediaOfficial's full report to see where this is really heading. 👇
12:28

REPORT: Lawmakers want to make it a felony to fix your own 3D printer—because the government might not “approve” of what you’re making. That’s not a headline from China. It’s happening right here in the United States. Starting in 2027, every 3D printer sold in Washington state would be required to include government-approved software that scans what you’re printing. And if it even might be a gun part, the machine shuts it down. No workarounds. If you try to modify the printer—or if a company sells one that can be modified—they could be hit with felony charges, prison time, and massive fines. They say it’s about “safety.” But let’s be real—this is about control. It’s the same model we’re seeing with smart cars and appliance restrictions. One update, and your machine stops working. One “unauthorized” project, and you’re locked out. Today it’s your printer. Tomorrow it’s your car, your tools, even your right to build or fix anything at all. The solution? Own your tools. Say no to smart restrictions. Because if you can’t control your own machines… what can you control? Watch ZeeeMediaOfficial's full report to see where this is really heading. 👇

The Vigilant Fox 🦊

133,738 Aufrufe • vor 6 Monaten

I built a full app with AI - frontend, backend, auth, everything. It ran clean. I almost deployed it. Then I discovered it was missing over half the core features I asked for. Quietly. Silently. No errors. AI agents only see what fits in their context window. They give you code that looks complete but forgets what features were supposed to do. You ask for a shopping cart? You get UI. Password reset? Good luck getting the actual logic. It all looks fine. No crashes. Just logic that silently fails - which is dangerous. Recently I tried TestSprite, which doesn't just test code — it understands your product intent. Reads your repo and prompt history, figures out what your app should do, then creates smart test plans. The numbers that made me freeze: Typical AI apps hit ~40% of target features With TestSprite: 90%+ feature coverage Took a side project I'd given up on. Plugged in TestSprite. In 10 minutes: 7 missing features found, 3 broken edge cases, 1 API error I missed. Also, they are offering a FREE one month starter pack to all new users. If AI handles both coding AND testing... what does development look like next?
10:00

I built a full app with AI - frontend, backend, auth, everything. It ran clean. I almost deployed it. Then I discovered it was missing over half the core features I asked for. Quietly. Silently. No errors. AI agents only see what fits in their context window. They give you code that looks complete but forgets what features were supposed to do. You ask for a shopping cart? You get UI. Password reset? Good luck getting the actual logic. It all looks fine. No crashes. Just logic that silently fails - which is dangerous. Recently I tried TestSprite, which doesn't just test code — it understands your product intent. Reads your repo and prompt history, figures out what your app should do, then creates smart test plans. The numbers that made me freeze: Typical AI apps hit ~40% of target features With TestSprite: 90%+ feature coverage Took a side project I'd given up on. Plugged in TestSprite. In 10 minutes: 7 missing features found, 3 broken edge cases, 1 API error I missed. Also, they are offering a FREE one month starter pack to all new users. If AI handles both coding AND testing... what does development look like next?

Florida

33,991 Aufrufe • vor 11 Monaten

"This will replace 80% of the apps that you have on your phone." Here's my new episode with Peter Steinberger 🦞 where he showed me: ✅ His personal OpenClaw use cases - flight check-in, home security, and much more ✅ His counterintuitive AI coding workflow - no plan mode, no MCPs, and no fancy prompts ✅ Practical advice for other builders and how to build product taste Some quotes from Peter: "It's like having a new weird friend that is also really smart and resourceful that lives on your computer." "Why should I use MyFitnessPal when I have an infinitely resourceful assistant that already knows I'm making bad decisions at KFC?" "I don't use MCPs or any of that crap. Just because you can build everything doesn't mean you should." 📌 Watch now: Thanks to our sponsors: Granola - The best AI meeting notes app I've ever used: Replit ⠕ - Create beautiful prototypes and full stack apps:
0:47

"This will replace 80% of the apps that you have on your phone." Here's my new episode with Peter Steinberger 🦞 where he showed me: ✅ His personal OpenClaw use cases - flight check-in, home security, and much more ✅ His counterintuitive AI coding workflow - no plan mode, no MCPs, and no fancy prompts ✅ Practical advice for other builders and how to build product taste Some quotes from Peter: "It's like having a new weird friend that is also really smart and resourceful that lives on your computer." "Why should I use MyFitnessPal when I have an infinitely resourceful assistant that already knows I'm making bad decisions at KFC?" "I don't use MCPs or any of that crap. Just because you can build everything doesn't mean you should." 📌 Watch now: Thanks to our sponsors: Granola - The best AI meeting notes app I've ever used: Replit ⠕ - Create beautiful prototypes and full stack apps:

Peter Yang

666,628 Aufrufe • vor 6 Monaten

Introducing Lobstack: Simplifying AI Deployment and Management For the past month, I’ve been building something I kept running into myself. Everone knows by now that OpenClaw is powerful. BUT, deploying it shouldn’t require DevOps. Lobstack provisions a dedicated server for you and installs OpenClaw automatically — one click, and your agent is live within minutes. No Docker setup. No terminal. No infrastructure babysitting. Not one line of code. You can choose your dedicated server location, select your LLM (our universal API or your own API), and interact with your agent through the custom web UI or Telegram. Discord, Slack, and more coming. Under the hood, it’s persistent, guarded, and capped — built to run 24/7 like real software. We’re also building a skill + API marketplace and exploring desktop and mobile apps to make agent communication seamless. It’s early. I’d genuinely value feedback from builders, operators, and anyone thinking about autonomous AI infrastructure. Or even any business owner that wants to implement AI into their operations. Try it out: Follow Lobstack for updates. Thanks OpenClaw🦞 community and Peter Steinberger 🦞
3:53

Introducing Lobstack: Simplifying AI Deployment and Management For the past month, I’ve been building something I kept running into myself. Everone knows by now that OpenClaw is powerful. BUT, deploying it shouldn’t require DevOps. Lobstack provisions a dedicated server for you and installs OpenClaw automatically — one click, and your agent is live within minutes. No Docker setup. No terminal. No infrastructure babysitting. Not one line of code. You can choose your dedicated server location, select your LLM (our universal API or your own API), and interact with your agent through the custom web UI or Telegram. Discord, Slack, and more coming. Under the hood, it’s persistent, guarded, and capped — built to run 24/7 like real software. We’re also building a skill + API marketplace and exploring desktop and mobile apps to make agent communication seamless. It’s early. I’d genuinely value feedback from builders, operators, and anyone thinking about autonomous AI infrastructure. Or even any business owner that wants to implement AI into their operations. Try it out: Follow Lobstack for updates. Thanks OpenClaw🦞 community and Peter Steinberger 🦞

Brandon M. Crenshaw

128,003 Aufrufe • vor 5 Monaten

AI music tools are fun… Until every song sounds like it came from the same robot. Same voice. Same vibe. No emotion. If you’re a real artist, that’s a dealbreaker. I’ve been testing this new one - It actually lets you upload your own voice. Clone it. Sing in different languages. Even control tone and emotion. I used the same melody with 3 different voices. One sounded like a studio demo. One like a global pop artist. One like me, but better. It’s not perfect but it’s the first one that feels built for musicians, not memes. If you’ve ever felt boxed in by Suno or other tools, worth a try: Upload your voice. Pick a vibe. Get a full song in seconds. 👉 #AIMusic #VoiceCloning #MusicProduction #MurekaAI
1:49

AI music tools are fun… Until every song sounds like it came from the same robot. Same voice. Same vibe. No emotion. If you’re a real artist, that’s a dealbreaker. I’ve been testing this new one - It actually lets you upload your own voice. Clone it. Sing in different languages. Even control tone and emotion. I used the same melody with 3 different voices. One sounded like a studio demo. One like a global pop artist. One like me, but better. It’s not perfect but it’s the first one that feels built for musicians, not memes. If you’ve ever felt boxed in by Suno or other tools, worth a try: Upload your voice. Pick a vibe. Get a full song in seconds. 👉 #AIMusic #VoiceCloning #MusicProduction #MurekaAI

Shruti

250,806 Aufrufe • vor 1 Jahr

Last week I saw the future of how we'll all work with AI. Logan (Google's AI Studio lead) was showing me Gemini's new“real time streaming” feature. He had his code editor open, and casually said via voice 'hey, should I change this function?' The clip below is wild - you have to see it to believe it. The AI was watching his screen. Like literally watching - seeing his cursor move, understanding his code, giving real-time feedback. Like pair programming with an AI that never gets tired. I've been playing with Claude, ChatGPT, building with v0/Bolt. They're all powerful but this was different. This was like having an AI co-pilot actually seeing your screen, understanding context, and helping in real-time. Different use-case but really bent my mind. Think what this means: • Coding – Ask about any line you're looking at • Debugging – It sees the error in real-time • Learning new tools – It watches you struggle and helps • Writing – It sees what you're typing and suggests edits The tech behind it is really cool: • Processes entire screen in real-time • Understands spatial context • Can handle 500K+ tokens (like reading a book in seconds) • Remembers your entire session Google's giving this away free in AI Studio. Yes, they're competing with OpenAI. But for makers, this is massive. Thanks to Logan Kilpatrick for the time and the demo. It really blew my mind. This is a clip from the full episode of the Startup Ideas Pod which I’ll link below. In that episode he gives 2 more demos so it's worth watching. Happy building.
8:15

Last week I saw the future of how we'll all work with AI. Logan (Google's AI Studio lead) was showing me Gemini's new“real time streaming” feature. He had his code editor open, and casually said via voice 'hey, should I change this function?' The clip below is wild - you have to see it to believe it. The AI was watching his screen. Like literally watching - seeing his cursor move, understanding his code, giving real-time feedback. Like pair programming with an AI that never gets tired. I've been playing with Claude, ChatGPT, building with v0/Bolt. They're all powerful but this was different. This was like having an AI co-pilot actually seeing your screen, understanding context, and helping in real-time. Different use-case but really bent my mind. Think what this means: • Coding – Ask about any line you're looking at • Debugging – It sees the error in real-time • Learning new tools – It watches you struggle and helps • Writing – It sees what you're typing and suggests edits The tech behind it is really cool: • Processes entire screen in real-time • Understands spatial context • Can handle 500K+ tokens (like reading a book in seconds) • Remembers your entire session Google's giving this away free in AI Studio. Yes, they're competing with OpenAI. But for makers, this is massive. Thanks to Logan Kilpatrick for the time and the demo. It really blew my mind. This is a clip from the full episode of the Startup Ideas Pod which I’ll link below. In that episode he gives 2 more demos so it's worth watching. Happy building.

GREG ISENBERG

284,034 Aufrufe • vor 1 Jahr

Alright, this one’s worth your attention if you’re building or deploying agents. Future AGI just open-sourced their entire platform and i don’t mean a trimmed-down version. this is the full stack: UI, backend, simulation engine, evals, optimization loop, observability, guardrails, gateway, docs. all in one repo. Apache 2.0. I’ve been putting it through its paces on production agents, and what stands out isn’t just the breadth it’s the architecture. Most of the current “agent reliability” stack is fragmented. tracing lives in one tool, evals in another, guardrails somewhere else. you end up manually connecting dots, and the agent itself doesn’t really improve you just keep patching prompts and hoping for the best. This flips that model. It’s built as a closed feedback loop: simulate failures → evaluate in real time → detect production issues → learn from them → generate fixes → validate against real traffic → check regressions → redeploy → monitor again And when something new breaks, the loop just runs again. no manual glue. The simulation piece is especially strong. instead of static test cases, it generates adversarial, multi-turn conversations based on how your agent actually behaves basically hunting for the exact scenarios where your system fails confidently. ran a few thousand simulations on our side… caught things we definitely would’ve missed. Evals run fast (sub-50ms) across modalities. not LLM-as-judge trained classifiers. guardrails are built-in, not layered on top. observability gives you step-level visibility into reasoning, cost, latency, quality. But the real shift is the optimization loop. Most tools tell you *what* broke. this system actually fixes it, validates the fix, and ensures nothing else regresses. That’s the missing layer. It’s clearly built with production in mind not a research demo. and the fact that it’s self-hostable makes it even more relevant if you’re running serious workloads. If you’ve been duct-taping together infra around your agents, this is probably the closest thing to a unified system i’ve seen so far. Worth checking out. If you're serious about deploying reliable AI agents, this is worth a look: 👉 You can also try it instantly (no setup) via their cloud version:
2:20

Alright, this one’s worth your attention if you’re building or deploying agents. Future AGI just open-sourced their entire platform and i don’t mean a trimmed-down version. this is the full stack: UI, backend, simulation engine, evals, optimization loop, observability, guardrails, gateway, docs. all in one repo. Apache 2.0. I’ve been putting it through its paces on production agents, and what stands out isn’t just the breadth it’s the architecture. Most of the current “agent reliability” stack is fragmented. tracing lives in one tool, evals in another, guardrails somewhere else. you end up manually connecting dots, and the agent itself doesn’t really improve you just keep patching prompts and hoping for the best. This flips that model. It’s built as a closed feedback loop: simulate failures → evaluate in real time → detect production issues → learn from them → generate fixes → validate against real traffic → check regressions → redeploy → monitor again And when something new breaks, the loop just runs again. no manual glue. The simulation piece is especially strong. instead of static test cases, it generates adversarial, multi-turn conversations based on how your agent actually behaves basically hunting for the exact scenarios where your system fails confidently. ran a few thousand simulations on our side… caught things we definitely would’ve missed. Evals run fast (sub-50ms) across modalities. not LLM-as-judge trained classifiers. guardrails are built-in, not layered on top. observability gives you step-level visibility into reasoning, cost, latency, quality. But the real shift is the optimization loop. Most tools tell you *what* broke. this system actually fixes it, validates the fix, and ensures nothing else regresses. That’s the missing layer. It’s clearly built with production in mind not a research demo. and the fact that it’s self-hostable makes it even more relevant if you’re running serious workloads. If you’ve been duct-taping together infra around your agents, this is probably the closest thing to a unified system i’ve seen so far. Worth checking out. If you're serious about deploying reliable AI agents, this is worth a look: 👉 You can also try it instantly (no setup) via their cloud version:

Aakash Verma

22,910 Aufrufe • vor 3 Monaten