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Tired of rewriting the same backend integrations every time? Auth flows. Database connections. Notifications. Webhooks. Rate limiting. Retries. Provider switches. You build it once, then copy-paste-modify it across every new project like it’s 2015. Ductape ends that cycle

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25,752 просмотров • 4 месяцев назад •via X (Twitter)

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.Packy McCormick: “What is the competitive advantage that you're building over time?” benahorowitz.eth: “Reputation.” “We build reputation. Every relationship matters.” “And that compounds and compounds and compounds across entrepreneurs, across industries, across sectors.” “It takes a long time to build a reputation. But once you do, it’s the most powerful thing.” Marc Andreessen 🇺🇸

.Packy McCormick: “What is the competitive advantage that you're building over time?” benahorowitz.eth: “Reputation.” “We build reputation. Every relationship matters.” “And that compounds and compounds and compounds across entrepreneurs, across industries, across sectors.” “It takes a long time to build a reputation. But once you do, it’s the most powerful thing.” Marc Andreessen 🇺🇸

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Yarchi

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The hardest part of AI storytelling has always been this: the same character sounding like a different person every time you generate a new line. Seed Audio 1.0 fixes that. Feed it reference audio once and the voice identity holds, → same tone → same energy → across a full scene or even a multi part story. This is the first time consistency actually feels solved.

The hardest part of AI storytelling has always been this: the same character sounding like a different person every time you generate a new line. Seed Audio 1.0 fixes that. Feed it reference audio once and the voice identity holds, → same tone → same energy → across a full scene or even a multi part story. This is the first time consistency actually feels solved.

AI Panda

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Two highly requested features just landed in Stitch! You can now generate custom images like logos right on the canvas and copy screens across projects. ✨ Native Image Generation: Create custom logos or hero graphics, and ask the agent to drop them directly into your UI. ✨ Copy & Paste Across Projects: Grab that perfect screen you made last week and paste it into a new project. Perfect for combining styles and merging content. Your vibe design partner just leveled up. Let’s build! 👇

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Cartesia

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leopardracer

231,513 просмотров • 3 месяцев назад

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Press Trust of India

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I got tired of explaining myself to AI every single day. Same context. Same workflow. Same instructions. Copy-pasted into every new session like it’s 2022. So I tried Panofy. Uploaded my own scripts and documents to train an agent — then gave it a single instruction. 🔥 It ran the task. Got it right. First time. No prompt loops. No setup. It just… knew. The thing that surprised me most? It actually improves the more you use it. Learns from mistakes. Builds memory. Adapts to how you think. Fewer tokens, better results. I’ve used a lot of AI tools. This one made me feel like I finally own mine. Panofy isn’t just another tool — it’s the best AI agent for expert-level tasks. If you’re serious about building with AI, this is worth trying. Sign up and get 10,000 free credits + 7-day Panofy Plus free trial 👉 #AI #AIAgents #Productivity

Aryan Rakib

33,551 просмотров • 4 месяцев назад

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.

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

113,219 просмотров • 2 месяцев назад

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

122,548 просмотров • 2 месяцев назад

How to create Farcaster mini app without coding - Step 1: > Go to ChatGPT > Just type: "I would like to create a Farcaster mini app, can you give me some ideas?" > Choose any one of the ideas - Note: - You can also type your own idea to ChatGPT and alter it - Step 2: - Go to ChatGPT > Paste the first prompt (shared in my channel) > Include the project you chose and tell ChatGPT to alter the prompt according to the idea below - Step 3 - Go to: > Sign up using Gmail or GitHub > Projects -> New Project -> Type the name of your project >Just paste the prompt we created earlier > It will start creating your app > If you need to alter anything, just go to GPT and type: [ I need a prompt to alter "your issue (like changing colour, etc.)" in my app already created in v0app by Vercel ] > Copy the prompt and paste it in v0 > No need to worry about errors , it will rectify them for you > If your project is finished, just click "Publish" on your top right > It will automatically host your app in Vercel > Click "Visit site" to see your app on another device and copy the website URL - Step 4 - Go to: v0 app > Just paste Prompt 2 from the file I provided > Click Publish once it’s completed - Step 5 - Go to: v0 app > Just paste the prompt 3 from the file I provided - Step 6 (Important) > Just paste Prompt 4 from the file I provided - Step 7 > Just paste Prompt 4 from the file I provided - Step 8 > Just paste Prompt 6 from the file I provided > It’s an important step to do — you need to create a manifest inside your app so that you can host it on Farcaster - Step 9 - Go to: > Sign up or Login > Settings → Developers → Mini Apps → Create Manifest (New) > Paste your website URL (remove https) > It should be like: > If you find any error while doing this, just copy and paste it in the v0 app > Once everything is finished, you need to create Account Association > Scan the QR on your mobile and tap and hold the Yes button > It will show an error — just copy and paste those errors into the v0 app > Click Publish again, and then click Refresh in Manifest > Click Open your app > for the main prompt file check our tg channel link in our bio

How to create Farcaster mini app without coding - Step 1: > Go to ChatGPT > Just type: "I would like to create a Farcaster mini app, can you give me some ideas?" > Choose any one of the ideas - Note: - You can also type your own idea to ChatGPT and alter it - Step 2: - Go to ChatGPT > Paste the first prompt (shared in my channel) > Include the project you chose and tell ChatGPT to alter the prompt according to the idea below - Step 3 - Go to: > Sign up using Gmail or GitHub > Projects -> New Project -> Type the name of your project >Just paste the prompt we created earlier > It will start creating your app > If you need to alter anything, just go to GPT and type: [ I need a prompt to alter "your issue (like changing colour, etc.)" in my app already created in v0app by Vercel ] > Copy the prompt and paste it in v0 > No need to worry about errors , it will rectify them for you > If your project is finished, just click "Publish" on your top right > It will automatically host your app in Vercel > Click "Visit site" to see your app on another device and copy the website URL - Step 4 - Go to: v0 app > Just paste Prompt 2 from the file I provided > Click Publish once it’s completed - Step 5 - Go to: v0 app > Just paste the prompt 3 from the file I provided - Step 6 (Important) > Just paste Prompt 4 from the file I provided - Step 7 > Just paste Prompt 4 from the file I provided - Step 8 > Just paste Prompt 6 from the file I provided > It’s an important step to do — you need to create a manifest inside your app so that you can host it on Farcaster - Step 9 - Go to: > Sign up or Login > Settings → Developers → Mini Apps → Create Manifest (New) > Paste your website URL (remove https) > It should be like: > If you find any error while doing this, just copy and paste it in the v0 app > Once everything is finished, you need to create Account Association > Scan the QR on your mobile and tap and hold the Yes button > It will show an error — just copy and paste those errors into the v0 app > Click Publish again, and then click Refresh in Manifest > Click Open your app > for the main prompt file check our tg channel link in our bio

Maran

48,106 просмотров • 9 месяцев назад

THE BITCOIN CLOCK NEVER LIES AND IT POINTS TO OCTOBER 9 Over a decade of cycles and this rhythm has never once broken: Bull phase: 1,064 days Bear phase: 364 days 2015-2017 → 1,064 days up 2017-2018 → 364 days down 2018-2021 → 1,064 days up 2021-2022 → 364 days down 2022-2025 → 1,064 days up 2025-2026 → 364 days down Run the math on every cycle and the same date emerges October 9, 2026 Not a prediction a pattern that has repeated without a single exception across every cycle $BTC has ever completed

THE BITCOIN CLOCK NEVER LIES AND IT POINTS TO OCTOBER 9 Over a decade of cycles and this rhythm has never once broken: Bull phase: 1,064 days Bear phase: 364 days 2015-2017 → 1,064 days up 2017-2018 → 364 days down 2018-2021 → 1,064 days up 2021-2022 → 364 days down 2022-2025 → 1,064 days up 2025-2026 → 364 days down Run the math on every cycle and the same date emerges October 9, 2026 Not a prediction a pattern that has repeated without a single exception across every cycle $BTC has ever completed

ᴛʀᴀᴄᴇʀ

161,590 просмотров • 1 месяц назад

🚨 AI grading isn’t the future it’s happening right now. Another AI grading company just scanned and graded cards that were already graded by PSAcard … and the results matched almost perfectly. Same subs. Same flaws detected. Same final grades. This is the proof people have been ignoring: AI can grade with zero human error, zero bias, and full consistency every single time. Collectors will fight it at first. They’ll say it’ll “ruin the hobby.” Then they’ll try it… and they’ll realize the truth: ✔ Faster turnaround ✔ More accurate grading ✔ No subjectivity ✔ No missed flaws ✔ Lower costs ✔ Fairer prices ✔ Instant, on-demand grading AI doesn’t guess. AI doesn’t get tired. AI doesn’t have “bad days.” AI sees every pixel, every edge, every micron of surface detail the same way, every time. Once people experience perfect consistency, they won’t want anything else. Human grading built the hobby. AI grading will perfect it. It’s coming whether the hobby is ready or not.

🚨 AI grading isn’t the future it’s happening right now. Another AI grading company just scanned and graded cards that were already graded by PSAcard … and the results matched almost perfectly. Same subs. Same flaws detected. Same final grades. This is the proof people have been ignoring: AI can grade with zero human error, zero bias, and full consistency every single time. Collectors will fight it at first. They’ll say it’ll “ruin the hobby.” Then they’ll try it… and they’ll realize the truth: ✔ Faster turnaround ✔ More accurate grading ✔ No subjectivity ✔ No missed flaws ✔ Lower costs ✔ Fairer prices ✔ Instant, on-demand grading AI doesn’t guess. AI doesn’t get tired. AI doesn’t have “bad days.” AI sees every pixel, every edge, every micron of surface detail the same way, every time. Once people experience perfect consistency, they won’t want anything else. Human grading built the hobby. AI grading will perfect it. It’s coming whether the hobby is ready or not.

MD

81,619 просмотров • 9 месяцев назад

Introducing Connectors—a new way to build apps and automations that integrate seamlessly with the tools you already use every day 🚀 🧩 Connectors make it effortless to extend your apps with services like Google , Dropbox , HubSpot , Linear , Notion , asana, Slack and more—without the hassle of managing credentials or reinventing integrations from scratch. More details in 🧵

Introducing Connectors—a new way to build apps and automations that integrate seamlessly with the tools you already use every day 🚀 🧩 Connectors make it effortless to extend your apps with services like Google , Dropbox , HubSpot , Linear , Notion , asana, Slack and more—without the hassle of managing credentials or reinventing integrations from scratch. More details in 🧵

Replit ⠕

171,389 просмотров • 11 месяцев назад

🚨 LEONARDO DICAPRIO REFUSES TO DATE WOMEN OVER THE AGE OF 25 — AND THE PATTERN IS IMPOSSIBLE TO IGNORE A breakdown is going viral after this man lined up every relationship from Leonardo DiCaprio’s last 20+ years… and it’s the same outcome every time. • Gisele Bündchen — dumped at 23 • Bar Refaeli — dumped at 25 • Blake Lively — dumped at 23 • Toni Garrn — dumped at 21 • Kelly Rohrbach — cut off before 26 • Nina Agdal — dumped at 25 • Camila Morrone — gone right after 25 Not once. Not twice. Every single time the age creeps past mid-20s… the relationship ends. Same cycle. Same cutoff. Same result. Now a viral graph is making it impossible to ignore and people aren’t calling it a “preference” anymore. They’re calling it his rule. So what is it about women over the age of 25 that Leonardo DiCaprio refuses to deal with?

🚨 LEONARDO DICAPRIO REFUSES TO DATE WOMEN OVER THE AGE OF 25 — AND THE PATTERN IS IMPOSSIBLE TO IGNORE A breakdown is going viral after this man lined up every relationship from Leonardo DiCaprio’s last 20+ years… and it’s the same outcome every time. • Gisele Bündchen — dumped at 23 • Bar Refaeli — dumped at 25 • Blake Lively — dumped at 23 • Toni Garrn — dumped at 21 • Kelly Rohrbach — cut off before 26 • Nina Agdal — dumped at 25 • Camila Morrone — gone right after 25 Not once. Not twice. Every single time the age creeps past mid-20s… the relationship ends. Same cycle. Same cutoff. Same result. Now a viral graph is making it impossible to ignore and people aren’t calling it a “preference” anymore. They’re calling it his rule. So what is it about women over the age of 25 that Leonardo DiCaprio refuses to deal with?

HustleBitch

12,806 просмотров • 4 месяцев назад

The brand tax is costing your team more than you think. Every team that makes video at scale is paying it. The hours spent fixing fonts, swapping colors, changing typography. Every video. One element at a time. That ends today. Introducing Brand Systems. Paste your company URL and HeyGen automatically extracts your logo, fonts, and colors. No uploads. No manual setup. Just a URL. From there, it works across everything you already use in HeyGen: → Templates: apply your brand in one click. Colors swap, logo drops in, fonts update. → AI Studio: every element you insert defaults to your brand automatically. → Video Agent: generate fully on-brand videos from a single prompt. Set it once. Stay on-brand across every video. No more brand tax. Try Brand Systems today on HeyGen.

HeyGen

220,460 просмотров • 5 месяцев назад

Qullamaggie on The Only Way to Build Confidence in Trading “It was a combination of studying the patterns. What I did was I built a database in Evernote, which is pretty much a note-taking software. I took screenshots of all the setups before and after, both on the daily and intraday time frames, just to look at what a good setup looks like—at the start of the move or before the move, when it starts breaking out, and then a few weeks or a few months after. How does it act? I looked at all these variations, and that’s how I built the confidence. I started trading the setup myself too, and I saw some success. It felt like I was always improving. I was always learning a new variation or something new, so that’s really how I build the confidence. I say it on my stream too—like everyone should do it. Whatever setup you stumble upon and you want to trade it, backtest it. Look through hundreds if not thousands of examples of that setup, build a database, and go through that database once in a while. Just scroll through it. That’s how you memorize this. It’s all about pattern recognition really. Trading is all about pattern recognition. It could be just purely technical, and you can also combine it with fundamentals. Like I do, I also look at the theme—what’s the theme, what’s the earnings, revenues. I look at the news overall, like what’s driving the stock. So I found some similarities there too. It’s the same things that have worked for 100 years really. That’s how you build the confidence. That’s how you do it—there’s no other way.”

Qullamaggie on The Only Way to Build Confidence in Trading “It was a combination of studying the patterns. What I did was I built a database in Evernote, which is pretty much a note-taking software. I took screenshots of all the setups before and after, both on the daily and intraday time frames, just to look at what a good setup looks like—at the start of the move or before the move, when it starts breaking out, and then a few weeks or a few months after. How does it act? I looked at all these variations, and that’s how I built the confidence. I started trading the setup myself too, and I saw some success. It felt like I was always improving. I was always learning a new variation or something new, so that’s really how I build the confidence. I say it on my stream too—like everyone should do it. Whatever setup you stumble upon and you want to trade it, backtest it. Look through hundreds if not thousands of examples of that setup, build a database, and go through that database once in a while. Just scroll through it. That’s how you memorize this. It’s all about pattern recognition really. Trading is all about pattern recognition. It could be just purely technical, and you can also combine it with fundamentals. Like I do, I also look at the theme—what’s the theme, what’s the earnings, revenues. I look at the news overall, like what’s driving the stock. So I found some similarities there too. It’s the same things that have worked for 100 years really. That’s how you build the confidence. That’s how you do it—there’s no other way.”

Lone

18,136 просмотров • 3 месяцев назад