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OpenAI just leaked a model so powerful it triggered code red. And yes, it’s called Garlic. Here’s the 30-second breakdown so you actually understand why this matters → ✔ Google Gemini 3 crushed OpenAI in benchmarks ✔ ChatGPT usage dropped ✔ Sam Altman paused projects to build “Garlic” ✔... show more

Julian Goldie SEO

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114,491 просмотров • 7 месяцев назад •via X (Twitter)

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Google just killed every paid coding tool. Their new AI Studio destroys Cursor completely. Here's how to build React apps instantly: → Open Google AI Studio Build Mode → Type what you want to build → Watch it code everything automatically → Deploy to GitHub with one click → Share working apps instantly Features that crush the competition: • Angular + React support ✔ • GitHub integration ✔ • Real-time code preview ✔ • Gemini API built-in ✔ • Enterprise AI for $0 ✔ Save this thread, you'll build faster apps. Want the SOP? DM me. 💬

Google just killed every paid coding tool. Their new AI Studio destroys Cursor completely. Here's how to build React apps instantly: → Open Google AI Studio Build Mode → Type what you want to build → Watch it code everything automatically → Deploy to GitHub with one click → Share working apps instantly Features that crush the competition: • Angular + React support ✔ • GitHub integration ✔ • Real-time code preview ✔ • Gemini API built-in ✔ • Enterprise AI for $0 ✔ Save this thread, you'll build faster apps. Want the SOP? DM me. 💬

Julian Goldie SEO

188,249 просмотров • 9 месяцев назад

Google is about to merge two massive AI tools. And this could kill your current coding workflow overnight. Here’s what’s happening with Anti-Gravity + Google AI Studio 👇 Right now: → AI Studio = prototyping playground → Anti-Gravity = autonomous coding agents → You manually move everything → You lose context → You lose time The leak? One-click prototype → production. Shared session tokens. Agents keep your context. No more copying code. No more broken workflows. And Anti-Gravity agents: ✔ Plan your build ✔ Write code ✔ Test in browser ✔ Debug automatically ✔ Deploy Prototype to live app in one flow. If this lands mid-February like insiders say… Coding changes forever. Save this video, you’ll want to prepare before it drops. Want the SOP? DM me. 💬

Google is about to merge two massive AI tools. And this could kill your current coding workflow overnight. Here’s what’s happening with Anti-Gravity + Google AI Studio 👇 Right now: → AI Studio = prototyping playground → Anti-Gravity = autonomous coding agents → You manually move everything → You lose context → You lose time The leak? One-click prototype → production. Shared session tokens. Agents keep your context. No more copying code. No more broken workflows. And Anti-Gravity agents: ✔ Plan your build ✔ Write code ✔ Test in browser ✔ Debug automatically ✔ Deploy Prototype to live app in one flow. If this lands mid-February like insiders say… Coding changes forever. Save this video, you’ll want to prepare before it drops. Want the SOP? DM me. 💬

Julian Goldie SEO

24,257 просмотров • 5 месяцев назад

🚨🇺🇸 OPENAI DECLARES "CODE RED" AS GOOGLE AND ANTHROPIC EAT THEIR LUNCH Sam Altman has sent an internal memo telling OpenAI employees to drop everything and focus on fixing ChatGPT. Jason Calacanis from the All In Podcast: "A Code Red has been called by Sam Altman. He sent a memo on Monday, told employees to stop working on side quests, you know, like ads, et cetera, and focus on the core ChatGPT experience. Make it faster, make it better. And I think we all know why, because Gemini and Claude from Anthropic have been crushing it. ChatGPT 5, let's call it what it is, was a bit of a flop. It didn't perform to expectations. And Anthropic is beating OpenAI in enterprise revenue." For those of you unaware, Google released Gemini 3 last month, which surpassed ChatGPT in a range of benchmark tests and set a new record on "Humanity's Last Exam," a test designed to identify artificial superintelligence. Altman publicly called Gemini 3 a "great model." Privately, he told staff ChatGPT had fallen behind and they're "catching up fast." And then there's Grok. Elon Musk xAI started years behind everyone else and is already nipping at OpenAI's heels. OpenAI was supposed to be the unbeatable first mover. Now they're playing defense while the competition sprints ahead. The AI wars just got real. Source: The Information, Wall Street Journal, The All-In Podcast

🚨🇺🇸 OPENAI DECLARES "CODE RED" AS GOOGLE AND ANTHROPIC EAT THEIR LUNCH Sam Altman has sent an internal memo telling OpenAI employees to drop everything and focus on fixing ChatGPT. Jason Calacanis from the All In Podcast: "A Code Red has been called by Sam Altman. He sent a memo on Monday, told employees to stop working on side quests, you know, like ads, et cetera, and focus on the core ChatGPT experience. Make it faster, make it better. And I think we all know why, because Gemini and Claude from Anthropic have been crushing it. ChatGPT 5, let's call it what it is, was a bit of a flop. It didn't perform to expectations. And Anthropic is beating OpenAI in enterprise revenue." For those of you unaware, Google released Gemini 3 last month, which surpassed ChatGPT in a range of benchmark tests and set a new record on "Humanity's Last Exam," a test designed to identify artificial superintelligence. Altman publicly called Gemini 3 a "great model." Privately, he told staff ChatGPT had fallen behind and they're "catching up fast." And then there's Grok. Elon Musk xAI started years behind everyone else and is already nipping at OpenAI's heels. OpenAI was supposed to be the unbeatable first mover. Now they're playing defense while the competition sprints ahead. The AI wars just got real. Source: The Information, Wall Street Journal, The All-In Podcast

Mario Nawfal

65,725 просмотров • 7 месяцев назад

Websites used to take days to build. Now Gemini 3 builds them in 60 seconds. Here’s the simple breakdown → → It creates full websites from just plain English → It builds CRM dashboards you can actually click and use → It makes internal tools most businesses need → No coding, no design skills, no software → You just type what you want This is perfect for realtors, coaches, consultants, small-biz owners who need content fast — not tech skills. Save this video, you’ll need it. Want the SOP? DM me. 💬

Websites used to take days to build. Now Gemini 3 builds them in 60 seconds. Here’s the simple breakdown → → It creates full websites from just plain English → It builds CRM dashboards you can actually click and use → It makes internal tools most businesses need → No coding, no design skills, no software → You just type what you want This is perfect for realtors, coaches, consultants, small-biz owners who need content fast — not tech skills. Save this video, you’ll need it. Want the SOP? DM me. 💬

Julian Goldie SEO

38,644 просмотров • 7 месяцев назад

🚨 Open-source AI just got serious. Alibaba just released Qwen 3.5-Plus 🤯 397B parameters. Only 17B active per inference. Performance comparable to Gemini 3 Pro — at just $0.8 per million tokens (1/18 the cost). But here’s what makes it different 👇 This isn’t just another benchmark drop. In the demo, Qwen runs directly inside the terminal: ➙ Reviews GitHub PRs ➙ Fetches diffs automatically ➙ Runs CLI & smoke tests ➙ Generates integration tests ➙ Executes /context ➙ Shows real-time token usage breakdown That’s not a chatbot. That’s a real developer workflow. Under the hood: ⚡ 60% lower deployment memory ⚡ Inference throughput increased by 8.6× (32K context) ⚡ Inference throughput increased by up to 19× (256K ultra-long context) ⚡ Native multimodal model ⚡ Supports direct video input (up to 2 hours) And the ecosystem is massive: ✔ 201 languages ✔ 250K vocabulary ✔ 400+ open-sourced models ✔ 1B+ downloads ✔ 200K+ derivative models Available now via Qwen App, MoDa Community, Hugging Face, and Alibaba Cloud Bailian. ✅ The AI race isn’t about who’s biggest anymore. → It’s about who’s usable. 🔥 Watch the demo 👇 #Qwen #OpenSourceAI #AI #DevTools

🚨 Open-source AI just got serious. Alibaba just released Qwen 3.5-Plus 🤯 397B parameters. Only 17B active per inference. Performance comparable to Gemini 3 Pro — at just $0.8 per million tokens (1/18 the cost). But here’s what makes it different 👇 This isn’t just another benchmark drop. In the demo, Qwen runs directly inside the terminal: ➙ Reviews GitHub PRs ➙ Fetches diffs automatically ➙ Runs CLI & smoke tests ➙ Generates integration tests ➙ Executes /context ➙ Shows real-time token usage breakdown That’s not a chatbot. That’s a real developer workflow. Under the hood: ⚡ 60% lower deployment memory ⚡ Inference throughput increased by 8.6× (32K context) ⚡ Inference throughput increased by up to 19× (256K ultra-long context) ⚡ Native multimodal model ⚡ Supports direct video input (up to 2 hours) And the ecosystem is massive: ✔ 201 languages ✔ 250K vocabulary ✔ 400+ open-sourced models ✔ 1B+ downloads ✔ 200K+ derivative models Available now via Qwen App, MoDa Community, Hugging Face, and Alibaba Cloud Bailian. ✅ The AI race isn’t about who’s biggest anymore. → It’s about who’s usable. 🔥 Watch the demo 👇 #Qwen #OpenSourceAI #AI #DevTools

Toha Khan

123,375 просмотров • 4 месяцев назад

You can use a 100% open source and MUCH cheaper/free alternative to Claude Code and Opus 4.5 OpenCode + MiniMax M2.1 can even build a 3D website using pure vibe coding. Steps are really simple: 1. Install OpenCode using the command 'npm i -g opencode-ai' 2. Get your MiniMax API key here: 3. Configure the MiniMax (official) mode Just type "opencode connect minimax" Coding plans start at $2… 10x cheaper than Claude Code (yes). You can also invite friends so they can get 10% off and you’ll get 10% API credits. (You can also use it locally if you have the config) And you're ready to build and iterate almost endlessly since the model is both way faster and cheaper than Opus in CC.

You can use a 100% open source and MUCH cheaper/free alternative to Claude Code and Opus 4.5 OpenCode + MiniMax M2.1 can even build a 3D website using pure vibe coding. Steps are really simple: 1. Install OpenCode using the command 'npm i -g opencode-ai' 2. Get your MiniMax API key here: 3. Configure the MiniMax (official) mode Just type "opencode connect minimax" Coding plans start at $2… 10x cheaper than Claude Code (yes). You can also invite friends so they can get 10% off and you’ll get 10% API credits. (You can also use it locally if you have the config) And you're ready to build and iterate almost endlessly since the model is both way faster and cheaper than Opus in CC.

Paul Couvert

42,443 просмотров • 6 месяцев назад

Try out Gemini Code: a coding assistant in your terminal powered by Gemini 2.5 Pro. 💜 💜 Release Notes: → It's easy to get started, just run `pip install gemini-code` in a virtual environment, and then run `gemini` to initialize it. It requires your own API key, so don't forget to get one from → I built this because I love Claude Code and wanted a similar experience -- I like that I can just use my own API key and Gemini is generally way cheaper as well. → Found a bug? Feel free to fork the code and fix it (lol)! If you want to try to improve it, please do so - repo is public! → Reminder and a disclaimer that I don't work at Google and this is not an official Google project - just a weekend side hack → It is highly likely you'll get throttled by rate limits - I put as much info as I can in the guide so you know what behavior to expect when that happens. You can also set which default model to use if you want to switch to 1.5 Pro.

Try out Gemini Code: a coding assistant in your terminal powered by Gemini 2.5 Pro. 💜 💜 Release Notes: → It's easy to get started, just run `pip install gemini-code` in a virtual environment, and then run `gemini` to initialize it. It requires your own API key, so don't forget to get one from → I built this because I love Claude Code and wanted a similar experience -- I like that I can just use my own API key and Gemini is generally way cheaper as well. → Found a bug? Feel free to fork the code and fix it (lol)! If you want to try to improve it, please do so - repo is public! → Reminder and a disclaimer that I don't work at Google and this is not an official Google project - just a weekend side hack → It is highly likely you'll get throttled by rate limits - I put as much info as I can in the guide so you know what behavior to expect when that happens. You can also set which default model to use if you want to switch to 1.5 Pro.

Raiza Martin

159,723 просмотров • 1 год назад

Google just STOLE Apple away from OpenAI... And it might decide who wins the AI race. Apple announced a multi-year $1 billion deal with Google to power the next generation of Siri using Gemini AI. Not ChatGPT. Gemini. This is the same Apple that 18 months ago announced a partnership with OpenAI to integrate ChatGPT into iPhones. Everyone thought that meant OpenAI won. Turns out it was an audition. And OpenAI failed. Here's why: June 2024: Apple announces OpenAI partnership. Sam Altman tweets: "very happy to be partnering with apple." Media declares OpenAI the winner of the AI race. December 2025: Sam Altman issues "code red" at OpenAI. Tells everyone to pause everything and ship ChatGPT 5.2 faster. Why the panic? Google released Gemini 3 and it was actually good. January 2026: Apple picks Google. ChatGPT stays as an "optional feature" for complicated queries. But Gemini becomes the DEFAULT intelligence layer for 2 billion Apple devices. The financial reality: Apple pays OpenAI $0 But Apple pays Google $1 BILLION per year That's a verdict. The excuse OpenAI gave for why they did it for free was "exposure to millions of iPhone users." Which basically means "we couldn't negotiate worth shit." Meanwhile Google walked away with both the money AND the distribution. Why Google won: Infrastructure ownership. OpenAI runs on Microsoft's Azure cloud. That creates a dependency chain: Apple → OpenAI → Microsoft. 3 companies. 3 points of failure. Google owns its entire stack. One relationship. Zero middlemen. Apple's statement said Google's technology provides "the most capable foundation." Not "most innovative." Not "best partner." Most CAPABLE. In other words, OpenAI's tech couldn't handle the scale. The Alphabet boost: Google's stock hit $4 trillion market cap after the announcement. Up 65% in 2024 on AI momentum alone. This deal validates Google's pivot from "search company" to "AI infrastructure company." Now they power Samsung's Galaxy AI AND Apple's Siri. Billions of mobile devices running on Gemini. OpenAI's big problem here: Still no profit. Ever. Anthropic is stealing enterprise customers. DeepSeek launched a price war forcing ChatGPT to cut prices. GPT-5 was overhyped and underwhelming. Circular financing deals are getting scrutinized. And now Apple just downgraded them from partner to backup option. That "code red" in December? Too little, too late. The reality everyone's missing: This isn't about chatbot quality. It's about who owns the infrastructure to power billions of devices. Google proved it with Samsung. Now Apple. OpenAI proved it can build a viral product but can't scale it profitably. Very different skill sets. Elon called it "unreasonable concentration of power for Google." He's right. But that's exactly why Apple chose them. Apple doesn't want a startup partner. They want a utility provider. Google is now the default AI for Android AND iOS. OpenAI is relegated to opt-in queries for people who specifically request ChatGPT. That's the difference between infrastructure and feature. The next 12 months: Apple launches Gemini-powered Siri in spring 2026. If it works, every iPhone user defaults to Google's AI. ChatGPT becomes the thing people use when Siri can't answer. The backup plan. My takeaway for entrepreneurs watching this: Distribution beats innovation. Google didn't necessarily build a better chatbot. They built better infrastructure and negotiated better terms. OpenAI won the hype race. Google won the business war. What do you think can save OpenAI now?

Google just STOLE Apple away from OpenAI... And it might decide who wins the AI race. Apple announced a multi-year $1 billion deal with Google to power the next generation of Siri using Gemini AI. Not ChatGPT. Gemini. This is the same Apple that 18 months ago announced a partnership with OpenAI to integrate ChatGPT into iPhones. Everyone thought that meant OpenAI won. Turns out it was an audition. And OpenAI failed. Here's why: June 2024: Apple announces OpenAI partnership. Sam Altman tweets: "very happy to be partnering with apple." Media declares OpenAI the winner of the AI race. December 2025: Sam Altman issues "code red" at OpenAI. Tells everyone to pause everything and ship ChatGPT 5.2 faster. Why the panic? Google released Gemini 3 and it was actually good. January 2026: Apple picks Google. ChatGPT stays as an "optional feature" for complicated queries. But Gemini becomes the DEFAULT intelligence layer for 2 billion Apple devices. The financial reality: Apple pays OpenAI $0 But Apple pays Google $1 BILLION per year That's a verdict. The excuse OpenAI gave for why they did it for free was "exposure to millions of iPhone users." Which basically means "we couldn't negotiate worth shit." Meanwhile Google walked away with both the money AND the distribution. Why Google won: Infrastructure ownership. OpenAI runs on Microsoft's Azure cloud. That creates a dependency chain: Apple → OpenAI → Microsoft. 3 companies. 3 points of failure. Google owns its entire stack. One relationship. Zero middlemen. Apple's statement said Google's technology provides "the most capable foundation." Not "most innovative." Not "best partner." Most CAPABLE. In other words, OpenAI's tech couldn't handle the scale. The Alphabet boost: Google's stock hit $4 trillion market cap after the announcement. Up 65% in 2024 on AI momentum alone. This deal validates Google's pivot from "search company" to "AI infrastructure company." Now they power Samsung's Galaxy AI AND Apple's Siri. Billions of mobile devices running on Gemini. OpenAI's big problem here: Still no profit. Ever. Anthropic is stealing enterprise customers. DeepSeek launched a price war forcing ChatGPT to cut prices. GPT-5 was overhyped and underwhelming. Circular financing deals are getting scrutinized. And now Apple just downgraded them from partner to backup option. That "code red" in December? Too little, too late. The reality everyone's missing: This isn't about chatbot quality. It's about who owns the infrastructure to power billions of devices. Google proved it with Samsung. Now Apple. OpenAI proved it can build a viral product but can't scale it profitably. Very different skill sets. Elon called it "unreasonable concentration of power for Google." He's right. But that's exactly why Apple chose them. Apple doesn't want a startup partner. They want a utility provider. Google is now the default AI for Android AND iOS. OpenAI is relegated to opt-in queries for people who specifically request ChatGPT. That's the difference between infrastructure and feature. The next 12 months: Apple launches Gemini-powered Siri in spring 2026. If it works, every iPhone user defaults to Google's AI. ChatGPT becomes the thing people use when Siri can't answer. The backup plan. My takeaway for entrepreneurs watching this: Distribution beats innovation. Google didn't necessarily build a better chatbot. They built better infrastructure and negotiated better terms. OpenAI won the hype race. Google won the business war. What do you think can save OpenAI now?

Ricardo

50,663 просмотров • 6 месяцев назад

Alibaba just dropped a BOMBSHELL on the AI world. Their new Qwen 3 model is FREE and crushing the competition. 🚀 Qwen 3 vs others: ✅ Outperforms GPT-4o by 3.2% on benchmarks ✅ Beats DeepSeek-Coder by 1.8% on coding tasks ✅ 5.4% better at math than GPT-4o ✅ Free API access (unlike competitors) ✅ Video generation built-in (ChatGPT can't do this) I've personally tested all three sizes. The results will shock you. Save this video tutorial - it contains everything you need to integrate this powerhouse into your workflow. 💼 Want the SOP? DM me.

Alibaba just dropped a BOMBSHELL on the AI world. Their new Qwen 3 model is FREE and crushing the competition. 🚀 Qwen 3 vs others: ✅ Outperforms GPT-4o by 3.2% on benchmarks ✅ Beats DeepSeek-Coder by 1.8% on coding tasks ✅ 5.4% better at math than GPT-4o ✅ Free API access (unlike competitors) ✅ Video generation built-in (ChatGPT can't do this) I've personally tested all three sizes. The results will shock you. Save this video tutorial - it contains everything you need to integrate this powerhouse into your workflow. 💼 Want the SOP? DM me.

Julian Goldie SEO

10,977 просмотров • 1 год назад

Google just made websites obsolete. Antigravity 2.0 builds full landing pages while you watch. Here’s how this actually works in real life. → You type one prompt → AI plans the entire app → It writes the code → Tests it in a browser → Fixes errors automatically → Deploys it live No devs. No templates. No monthly fees. Powered by Gemini 3 + parallel AI agents, so design, backend, and testing happen at the same time. This is how people are launching pages in under 5 minutes. Save this video, you’ll use it again. Want the SOP? DM me. 💬

Google just made websites obsolete. Antigravity 2.0 builds full landing pages while you watch. Here’s how this actually works in real life. → You type one prompt → AI plans the entire app → It writes the code → Tests it in a browser → Fixes errors automatically → Deploys it live No devs. No templates. No monthly fees. Powered by Gemini 3 + parallel AI agents, so design, backend, and testing happen at the same time. This is how people are launching pages in under 5 minutes. Save this video, you’ll use it again. Want the SOP? DM me. 💬

Julian Goldie SEO

13,010 просмотров • 5 месяцев назад

AntiGravity just became 10x more powerful. And 99% of users have no idea. Here's what you can actually do with it: → Switch between free APIs (Opus, Sonnet, Claude) → Generate images with Google's Nano Banana → Connect MCP servers directly to your projects → Use Claude Code inside the terminal for better UI → Deploy apps to Netlify in one click → Pull NotebookLM content into your builds No paid APIs. No limits. Just stack free tools. Save this video, you'll never use it the same way again. Want the SOP? DM me. 💬

AntiGravity just became 10x more powerful. And 99% of users have no idea. Here's what you can actually do with it: → Switch between free APIs (Opus, Sonnet, Claude) → Generate images with Google's Nano Banana → Connect MCP servers directly to your projects → Use Claude Code inside the terminal for better UI → Deploy apps to Netlify in one click → Pull NotebookLM content into your builds No paid APIs. No limits. Just stack free tools. Save this video, you'll never use it the same way again. Want the SOP? DM me. 💬

Julian Goldie SEO

98,112 просмотров • 6 месяцев назад

Anthropic just lit a fire 🔥 They pulled Claude Code from the $20 plan… So devs did what devs do: Rebuilt it. Open-source. Better. OpenClaude ↓ • Any model (GPT, Gemini, local) • No subscriptions • No limits • Full coding agent in your terminal But here’s the kicker: You can route tasks across models. Cheap for simple. Powerful for complex. One CLI. Infinite setups. Gatekeep tools → Get replaced. This is going to blow up.

Anthropic just lit a fire 🔥 They pulled Claude Code from the $20 plan… So devs did what devs do: Rebuilt it. Open-source. Better. OpenClaude ↓ • Any model (GPT, Gemini, local) • No subscriptions • No limits • Full coding agent in your terminal But here’s the kicker: You can route tasks across models. Cheap for simple. Powerful for complex. One CLI. Infinite setups. Gatekeep tools → Get replaced. This is going to blow up.

Leonard Rodman

69,034 просмотров • 2 месяцев назад

Which LLM reasons best when it doesn't have all the information? Enter LLM Poker Arena to find out. It's a Poker Playing benchmark where top reasoning models play Texas Hold'em poker against each other. Claude Opus 4.5, GPT-5.2, Gemini 2.5 Pro, and Grok 4 all sit at the same table and play full tournaments to see who finishes with the chips. Poker is very different when it comes to reasoning. It has to balance probabilistic reasoning, opponent modeling and make decisions under uncertainty. Poker is an interesting evaluation because it tests reasoning under incomplete information, something most coding benchmarks do not capture. In this tournaments the rules are: - Each LLM starts with $1,000 chips - Small and big blinds start at $25 / $50 - Blinds double every 3 minutes - All models run in their reasoning or thinking modes After the first 5 tournaments: - Claude Opus 4.5 with Thinking has 3 wins - GPT-5.2 has 2 wins - Grok 4 and Gemini 2.5 Pro have 0 wins Early results suggest Claude performs quite well at poker as well. Also five is a very small sample size. Planning to run many more tournaments, publish the full benchmark data and add a prediction market on top of it. Thanks for the suggestion clipz. Much more coming as part of Poker Cities !! This was built on Replit ⠕ using their AI integrations, which made it straightforward to connect Claude, GPT, and Gemini. What model do you think wins after 100 tournaments?

Which LLM reasons best when it doesn't have all the information? Enter LLM Poker Arena to find out. It's a Poker Playing benchmark where top reasoning models play Texas Hold'em poker against each other. Claude Opus 4.5, GPT-5.2, Gemini 2.5 Pro, and Grok 4 all sit at the same table and play full tournaments to see who finishes with the chips. Poker is very different when it comes to reasoning. It has to balance probabilistic reasoning, opponent modeling and make decisions under uncertainty. Poker is an interesting evaluation because it tests reasoning under incomplete information, something most coding benchmarks do not capture. In this tournaments the rules are: - Each LLM starts with $1,000 chips - Small and big blinds start at $25 / $50 - Blinds double every 3 minutes - All models run in their reasoning or thinking modes After the first 5 tournaments: - Claude Opus 4.5 with Thinking has 3 wins - GPT-5.2 has 2 wins - Grok 4 and Gemini 2.5 Pro have 0 wins Early results suggest Claude performs quite well at poker as well. Also five is a very small sample size. Planning to run many more tournaments, publish the full benchmark data and add a prediction market on top of it. Thanks for the suggestion clipz. Much more coming as part of Poker Cities !! This was built on Replit ⠕ using their AI integrations, which made it straightforward to connect Claude, GPT, and Gemini. What model do you think wins after 100 tournaments?

Anshul Dhawan

32,192 просмотров • 5 месяцев назад

Google just quietly killed copy-paste for devs. This Stitch + Gemini 3.0 update is actually wild. Google Stitch now lets you: → Describe your app in plain text → Generate a full UI with buttons, forms, pages → Hit one export button → Open AI Studio with all HTML, screens and context preloaded → Let Gemini 3 write and debug the code for you You go from “idea in your head” → “working product” in about an hour instead of days. Freelancers, agencies, solopreneurs… this is your unfair advantage if you actually learn how to use it. Save this video, you’ll copy this workflow later. Want the SOP? Send me a DM. 💬

Google just quietly killed copy-paste for devs. This Stitch + Gemini 3.0 update is actually wild. Google Stitch now lets you: → Describe your app in plain text → Generate a full UI with buttons, forms, pages → Hit one export button → Open AI Studio with all HTML, screens and context preloaded → Let Gemini 3 write and debug the code for you You go from “idea in your head” → “working product” in about an hour instead of days. Freelancers, agencies, solopreneurs… this is your unfair advantage if you actually learn how to use it. Save this video, you’ll copy this workflow later. Want the SOP? Send me a DM. 💬

Julian Goldie SEO

32,975 просмотров • 7 месяцев назад

Elon Musk just made one if the biggest moves in taking over the programming industry “SpaceX just bought Cursor for $60 billion. Do you realize how big this is? SpaceX went public — the biggest IPO in history. $75 billion raised, almost a $2 trillion valuation and the first thing to do with that money? Buy the most popular AI coding tool on the planet. Here's why that changes everything. Elon now owns 3 layers: the compute, Colossus data centers, the models, Grok through xAI, and now the tool that developers actually use every day. It's the full stack. And here's what makes Cursor different from Claude Code or Codex. Cursor is model agnostic. You can run Claude in it, GPT, Gemini, whatever model you want. It's not locked to any one company, and now it has SpaceX's resources behind it. Cursor said they were bottlenecked by compute. Well, that bottleneck has just been removed. $4 billion in annual revenue, over half the Fortune 500 already uses it, and now it's backed by a $2 trillion company. OpenAI has Codex, Anthropic has Claude Code, and now Elon has Cursor.” Let me break this down in simple terms Elon Musk now controls more of the full AI picture: - Massive computers, power (data centers like Colossus) - Smart AI models (Grok from xAI) - The actual tool millions of developers use every day (Cursor) For every day users this means Faster and smarter apps and websites in the future. More developers using powerful AI tools means new apps, games, websites, and features get built quicker and cheaper. This means better video games, smoother streaming, smarter phone apps and better programs For Developers they can describe what they want in plain English (“make a feature that does X”) and the AI handles more of the heavy lifting

Elon Musk just made one if the biggest moves in taking over the programming industry “SpaceX just bought Cursor for $60 billion. Do you realize how big this is? SpaceX went public — the biggest IPO in history. $75 billion raised, almost a $2 trillion valuation and the first thing to do with that money? Buy the most popular AI coding tool on the planet. Here's why that changes everything. Elon now owns 3 layers: the compute, Colossus data centers, the models, Grok through xAI, and now the tool that developers actually use every day. It's the full stack. And here's what makes Cursor different from Claude Code or Codex. Cursor is model agnostic. You can run Claude in it, GPT, Gemini, whatever model you want. It's not locked to any one company, and now it has SpaceX's resources behind it. Cursor said they were bottlenecked by compute. Well, that bottleneck has just been removed. $4 billion in annual revenue, over half the Fortune 500 already uses it, and now it's backed by a $2 trillion company. OpenAI has Codex, Anthropic has Claude Code, and now Elon has Cursor.” Let me break this down in simple terms Elon Musk now controls more of the full AI picture: - Massive computers, power (data centers like Colossus) - Smart AI models (Grok from xAI) - The actual tool millions of developers use every day (Cursor) For every day users this means Faster and smarter apps and websites in the future. More developers using powerful AI tools means new apps, games, websites, and features get built quicker and cheaper. This means better video games, smoother streaming, smarter phone apps and better programs For Developers they can describe what they want in plain English (“make a feature that does X”) and the AI handles more of the heavy lifting

Wall Street Apes

212,830 просмотров • 25 дней назад

Gemini 3 Deep Think is free right now. And it changes what “prompting” means. Turn on Deep Think (toggle near the input). Ask for strategy not trivia. Wait 30 to 60 seconds. Read the reasoning not just the conclusion. Reuse the reasoning as your SOP. Best prompts: → “Design a workflow with tools + exact steps + quality controls.” → “Map a content funnel and explain why each stage converts.” → “Build an ideal customer profile based on context and psychology.” Save this video, you’ll stop prompting and start building systems. Want the SOP? DM me. 💬

Julian Goldie SEO

10,722 просмотров • 6 месяцев назад

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 просмотров • 2 лет назад

🚨 "We are planning to dramatically slow down how quickly we grow." Sam Altman says OpenAI is shifting toward "majority AI co-workers." This is a massive vote of confidence in their product roadmap. It signals that their internal models are becoming so powerful that human capital is becoming hyper-leveraged. This is exactly what you want to see. It signals that we are finally entering the era where AI generates real, tangible ROI. OpenAI is "dogfooding" their way to a leaner, faster organization. This is also exactly what critics have been asking for years “why do you still hire developers” Well now we know officially it will slow down, — Dario held the same opinion at Davos as well

🚨 "We are planning to dramatically slow down how quickly we grow." Sam Altman says OpenAI is shifting toward "majority AI co-workers." This is a massive vote of confidence in their product roadmap. It signals that their internal models are becoming so powerful that human capital is becoming hyper-leveraged. This is exactly what you want to see. It signals that we are finally entering the era where AI generates real, tangible ROI. OpenAI is "dogfooding" their way to a leaner, faster organization. This is also exactly what critics have been asking for years “why do you still hire developers” Well now we know officially it will slow down, — Dario held the same opinion at Davos as well

Chris

109,141 просмотров • 5 месяцев назад

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

Andrew Ng

357,661 просмотров • 1 год назад