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Cursor dropped a ₹649/month (~$6.5) plan that includes Grok 4.5 and Composer 2.5. ✅ No 5-hour/weekly limits. ✅ ~25M tokens/month ✅ Grok 4.5 medium (grok 4.5 is GOOD daily driver) ✅ Cursor cloud agent, CLI, or IDE - pick whatever you want. Full review + task:

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Mehul Mohan

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38,637 görüntüleme • 2 gün önce •via X (Twitter)

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Grok 4.5 is a "Wow!" moment. 🤯 > Opus 4.8 & GPT-5.5 level on benchmarks > Costs less than Sonnet 5 Is that real? Hard to believe. Added Grok 4.5 as an implementer agent in fable-advisor. It replaces Sonnet 5. Use Fable as the orchestrator and a combo of: 1. Grok 4.5 2. Opus 4.8 3. GPT-5.5 As implementer agents. Uses the Grok Build CLI so if you log in with an X premium account you don't have to pay API fees.
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Grok 4.5 is a "Wow!" moment. 🤯 > Opus 4.8 & GPT-5.5 level on benchmarks > Costs less than Sonnet 5 Is that real? Hard to believe. Added Grok 4.5 as an implementer agent in fable-advisor. It replaces Sonnet 5. Use Fable as the orchestrator and a combo of: 1. Grok 4.5 2. Opus 4.8 3. GPT-5.5 As implementer agents. Uses the Grok Build CLI so if you log in with an X premium account you don't have to pay API fees.

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Grok 4.5 is out!!! here is what you need to know : On cursor bench it matched fable 5 high at 83% cheaper, although the ui generation feels similar to gpt 5.5 in a bad way. Important to note, cursor bench was LEAKED to grok 4.5 and may have skewed the results! My initial thoughts are this could be a insanely cheap opus 4.8 level model that runs insanely fast. Will you be using grok build?
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Grok 4.5 is out!!! here is what you need to know : On cursor bench it matched fable 5 high at 83% cheaper, although the ui generation feels similar to gpt 5.5 in a bad way. Important to note, cursor bench was LEAKED to grok 4.5 and may have skewed the results! My initial thoughts are this could be a insanely cheap opus 4.8 level model that runs insanely fast. Will you be using grok build?

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I'm building a localhost AI app builder, inspired by tools like Lovable / Bolt / v0. Works with your own coding agents: ✅ Claude Code CLI ✅ Codex CLI ✅ GitHub Copilot (Chat extension + CLI) ✅ Cursor CLI So no new subscription is required. It also works with your app framework: ✅ Next.js ✅ React (Vite) ✅ Vue ✅ Svelte ✅ Angular ✅ TanStack Start ✅ Other frameworks (.NET, Ruby, etc.) You can create & open apps from a GUI, chat with your coding agent directly in the browser, view diffs, and more. It's live today as a VS Code extension called 'Wuunu'. Let me know what you think. :) Wuunu is completely independent, it's not affiliated with or endorsed by Lovable, Bolt, or v0.
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I'm building a localhost AI app builder, inspired by tools like Lovable / Bolt / v0. Works with your own coding agents: ✅ Claude Code CLI ✅ Codex CLI ✅ GitHub Copilot (Chat extension + CLI) ✅ Cursor CLI So no new subscription is required. It also works with your app framework: ✅ Next.js ✅ React (Vite) ✅ Vue ✅ Svelte ✅ Angular ✅ TanStack Start ✅ Other frameworks (.NET, Ruby, etc.) You can create & open apps from a GUI, chat with your coding agent directly in the browser, view diffs, and more. It's live today as a VS Code extension called 'Wuunu'. Let me know what you think. :) Wuunu is completely independent, it's not affiliated with or endorsed by Lovable, Bolt, or v0.

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So which model is the best at frontend design? You can now eval pretty much all of them in pen(dot)dev in realtime in parallel. It's the most versatile design agent, where you can Sign in with ChatGPT, use your Claude subscription, OpenCode Go, Github CoPilot, connect Cursor Composer 2.5/Grok 4.5, Minimax M3, Kimi K2.7/K3, GLM 5.2, Gemini 3.5 Flash, Qwen 3.7 Plus etc, mix and match these models on design tasks directly in your codebase locally on your desktop.

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HERMES AGENT IS NOW IN THE CLOUD. NO VPS. NO TERMINAL. NO SETUP. PICK A MODEL. PICK A SERVER SIZE. AGENT IS LIVE IN 60 SECONDS. Nous Portal just launched hosted Hermes Agent. two clicks. one minute. done. Nous Research WHAT THIS MEANS: before today: install Hermes on a VPS or your laptop. configure providers. set up gateway. manage updates. run hermes setup. edit config.yaml. great for power users. friction for everyone else. now: go to pick a model. pick a server size. your agent is live and reachable in 60 seconds. no terminal. no SSH. no Docker. same Hermes. same features. same tools. someone else handles the infrastructure. FOR TEAMS: this is where it gets interesting. spin up agents for everyone at your org. each team member gets their own Hermes instance. granular access controls per user. unified billing through Nous Portal. your team gets Hermes on day one. no DevOps needed. no VPS per person. one admin dashboard. one bill. WHAT'S INCLUDED: → 300+ models via Nous Portal (Claude, GPT, Gemini, DeepSeek, Grok, MiniMax, and more) → Tool Gateway (web search, image generation, TTS, browser automation) → all messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal) → full feature set (profiles, cron, kanban, skills, memory, sub-agents, MoA, /goal, /learn, /journey) → automatic updates ONE PORTAL. FOUR TIERS: Free: $0/month. pay-as-you-go credits from $10. Plus: $20/month. $22 in monthly usage credit. Super: $100/month. $110 in monthly credit. Ultra: $200/month. $220 in monthly credit. highest rate limits. every paid tier includes Tool Gateway. one OAuth. one subscription. no extra API keys. SELF-HOSTED IS NOT GOING ANYWHERE: Hermes is MIT licensed. open source. free forever. you can still run it on your laptop, VPS, or GPU cluster. nothing changes for self-hosted users. the cloud version is for people who want the agent running without managing the machine. pick your path: → self-hosted: full control. you manage everything. → cloud: zero ops. Nous manages infrastructure. → hybrid: self-host your main agent, cloud for team members. HOW TO START: cloud: self-hosted: hermes setup --portal both connect to the same Nous Portal. same models. same tools. same billing. learn how to replace your entire team with 8 hermes agents 👇

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BREAKING: NotebookLM Just Got 10x Smarter 🧠 And it’s about to change how you rank on Google. NotebookLM now does everything: It researches. It builds outlines. It writes full content. It even checks keyword density, metadata & SEO health. It’s like having an SEO strategist, researcher, and content team — all in one AI. Here’s how we use it to dominate Google: ✅ Upload 20+ top-ranking pages ✅ Get instant keyword + gap analysis ✅ Auto-generate SEO-optimized blog structure ✅ Write 1,200+ word posts that sound human ✅ Check topic coverage + structured data ✅ Export to Docs, Sheets, Slides… Done. But here’s the truth: 📉 Content is useless if no one finds it. That’s where our team steps in. 🔥 Rank #1 on Google in 90 Days Or You Don’t Pay: We’ll handle your backlinks. We’ll fix your SEO. We’ll get you more leads. Or you don’t pay a cent. This isn’t theory. It’s how we took sites from 0 → 145,000+ visits/month. Still trying to DIY your traffic? Let us build the system for you. ✅ High-authority backlinks ✅ Weekly link reports ✅ Expert strategy + AI integration ✅ Full refund if we don’t deliver 📍Only a few client slots left. Once the price goes up, it’s locked in for good. 🧠 Let our AI + SEO team get you results:

Julian Goldie SEO

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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
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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

213,267 görüntüleme • 1 ay önce

"Claude Code is not just for coding. It's an AI agent that can do whatever you want it to do." Here's my new tutorial with where he showed me exactly how to build a life OS with Claude Code in 25 minutes. He saves 2+ hours daily using: ✅ /researcher to analyze competitors ✅ /daily_brief to get AI news every morning ✅ /brain_dump to find patterns from notes His setup uses slash commands and sub-agents and honestly blew my mind 🔥 Watch the full tutorial to learn how to set up a life operating system for yourself. 📌 Watch now: Also available on: Spotify: Newsletter:
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"Claude Code is not just for coding. It's an AI agent that can do whatever you want it to do." Here's my new tutorial with where he showed me exactly how to build a life OS with Claude Code in 25 minutes. He saves 2+ hours daily using: ✅ /researcher to analyze competitors ✅ /daily_brief to get AI news every morning ✅ /brain_dump to find patterns from notes His setup uses slash commands and sub-agents and honestly blew my mind 🔥 Watch the full tutorial to learn how to set up a life operating system for yourself. 📌 Watch now: Also available on: Spotify: Newsletter:

Peter Yang

190,023 görüntüleme • 10 ay önce

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇
2:24

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

Taranjeet

471,972 görüntüleme • 1 yıl önce

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns
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meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

133,801 görüntüleme • 20 gün önce

🚨 Most traders are trying to PASS prop firm challenges. Meanwhile… some firms are hiring people FULL-TIME just to manage automated prop firm systems. Salary advertised: 💰 $2,000–$10,000/month The industry is changing fast. This Upwork listing is looking for a: • Full-Time Virtual Trading Assistant • Managing funded/prop firm accounts • Running automated trading software daily • Setting up bots on MT5 & Tradovate • Tracking payouts/results • Following strict trading SOPs What stood out to me most? They explicitly say: “You don’t need to be an expert trader.” Read that again. The edge today is no longer just: ❌ predicting candles ❌ drawing more lines ❌ buying another course It’s: ✅ systems ✅ execution ✅ probabilities ✅ automation ✅ process The trading industry is quietly evolving from discretionary gambling… into data-driven operational businesses. And most retail traders still haven’t noticed. The question is: Will future traders survive on opinions… or on systems? 👇
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🚨 Most traders are trying to PASS prop firm challenges. Meanwhile… some firms are hiring people FULL-TIME just to manage automated prop firm systems. Salary advertised: 💰 $2,000–$10,000/month The industry is changing fast. This Upwork listing is looking for a: • Full-Time Virtual Trading Assistant • Managing funded/prop firm accounts • Running automated trading software daily • Setting up bots on MT5 & Tradovate • Tracking payouts/results • Following strict trading SOPs What stood out to me most? They explicitly say: “You don’t need to be an expert trader.” Read that again. The edge today is no longer just: ❌ predicting candles ❌ drawing more lines ❌ buying another course It’s: ✅ systems ✅ execution ✅ probabilities ✅ automation ✅ process The trading industry is quietly evolving from discretionary gambling… into data-driven operational businesses. And most retail traders still haven’t noticed. The question is: Will future traders survive on opinions… or on systems? 👇

Herman Trading

37,923 görüntüleme • 2 ay önce

Deleted again because misinformation 🥲 Gemini 3.5 Flash *is* available on the API. Yet, both the API and the CLI versions are 3x slower than on the IDE! See the video below. → Antigravity IDE: 4 seconds (smooth) → Antigravity CLI: 15 seconds (buggy) So the point holds: they want you to use the visual IDE. Problem is: it is 2026. NOBODY should be using IDEs anymore. Get over it. Let it GO. I’m certainly not launching a VSCode fork to use a model, no matter how great it is. They invent a portal gun, only to lock it behind a taxi subscription, because they completely fail to realize their very product deprecates that other thing they think will make them money? Cursor is a great example of a company that (sadly) is very likely fail because of that mindset. Composer is actually surprisingly good model. They should put all efforts in serving it. Yet, they keep locking it under an old school product that nobody wants to use. And even these who DO use IDEs probably won’t necessarily pick YOUR IDE. And they shouldn’t. You do NOT need them to, to make money. Your model is the product. You keep chasing old business models. Completely out of touch. Meanwhile Anthropic is all charging at full speed to sooner or later surpass Google by just serving great models under an API /ctrlv
0:20

Deleted again because misinformation 🥲 Gemini 3.5 Flash *is* available on the API. Yet, both the API and the CLI versions are 3x slower than on the IDE! See the video below. → Antigravity IDE: 4 seconds (smooth) → Antigravity CLI: 15 seconds (buggy) So the point holds: they want you to use the visual IDE. Problem is: it is 2026. NOBODY should be using IDEs anymore. Get over it. Let it GO. I’m certainly not launching a VSCode fork to use a model, no matter how great it is. They invent a portal gun, only to lock it behind a taxi subscription, because they completely fail to realize their very product deprecates that other thing they think will make them money? Cursor is a great example of a company that (sadly) is very likely fail because of that mindset. Composer is actually surprisingly good model. They should put all efforts in serving it. Yet, they keep locking it under an old school product that nobody wants to use. And even these who DO use IDEs probably won’t necessarily pick YOUR IDE. And they shouldn’t. You do NOT need them to, to make money. Your model is the product. You keep chasing old business models. Completely out of touch. Meanwhile Anthropic is all charging at full speed to sooner or later surpass Google by just serving great models under an API /ctrlv

Taelin

68,347 görüntüleme • 2 ay önce

2025 is the year of the CLI, not 𝘥𝘦𝘴𝘱𝘪𝘵𝘦 its simplicity but 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 of it. I’m thrilled to share today’s announcement. It’s a milestone for Qodo’s mission, and for every team that wants to put AI to work across the SDLC. Let’s dive in! 𝐌𝐞𝐞𝐭 𝐐𝐨𝐝𝐨 𝐆𝐞𝐧 𝐂𝐋𝐈 Built for creating, running, and managing AI agents that automate critical development tasks across the SDLC, from analyzing CI tests and logs to triaging production errors and more. 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 ✅ Runs everywhere ✅ Designed for task execution, not just Q&A ✅ Trigger from CI, hooks, or cloud functions—you choose ✅ Works with all major models ✅ Fully customizable agents with their own tools, credentials, and goals And yes, it plays nicely with our other Qodo agents too (e.g., Qodo Merge, the AI code-review tool). This isn’t just about writing code—it’s about catching bugs earlier, automating deeper, and shipping higher-quality software. 𝐓𝐫𝐲 𝐢𝐭 𝐭𝐨𝐝𝐚𝐲 𝐢𝐧 𝐬𝐞𝐜𝐨𝐧𝐝𝐬 Make sure you have npm installed, then run: npm install -g @qodo/gen Oh, one more thing- 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐨𝐮𝐫 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐐𝐨𝐝𝐨 𝐀𝐠𝐞𝐧𝐭𝐬, and contribute your own so others can benefit!
3:13

2025 is the year of the CLI, not 𝘥𝘦𝘴𝘱𝘪𝘵𝘦 its simplicity but 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 of it. I’m thrilled to share today’s announcement. It’s a milestone for Qodo’s mission, and for every team that wants to put AI to work across the SDLC. Let’s dive in! 𝐌𝐞𝐞𝐭 𝐐𝐨𝐝𝐨 𝐆𝐞𝐧 𝐂𝐋𝐈 Built for creating, running, and managing AI agents that automate critical development tasks across the SDLC, from analyzing CI tests and logs to triaging production errors and more. 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 ✅ Runs everywhere ✅ Designed for task execution, not just Q&A ✅ Trigger from CI, hooks, or cloud functions—you choose ✅ Works with all major models ✅ Fully customizable agents with their own tools, credentials, and goals And yes, it plays nicely with our other Qodo agents too (e.g., Qodo Merge, the AI code-review tool). This isn’t just about writing code—it’s about catching bugs earlier, automating deeper, and shipping higher-quality software. 𝐓𝐫𝐲 𝐢𝐭 𝐭𝐨𝐝𝐚𝐲 𝐢𝐧 𝐬𝐞𝐜𝐨𝐧𝐝𝐬 Make sure you have npm installed, then run: npm install -g @qodo/gen Oh, one more thing- 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐨𝐮𝐫 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐐𝐨𝐝𝐨 𝐀𝐠𝐞𝐧𝐭𝐬, and contribute your own so others can benefit!

Itamar Friedman

11,802 görüntüleme • 1 yıl önce