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GROK LEADS KILO CODE WITH 5X USAGE AT TRILLION-TOKEN SCALE Not just popular – Grok Code's overwhelmingly used. Leaderboard locked: * Grok Code holds the top spot on Kilo Code * Nearly 5x more usage than the next most-used model * Only model running at trillion-token scale * #1...

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Mario Nawfal

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21,775 просмотров • 7 месяцев назад •via X (Twitter)

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Grok Code is the only model with insanely high trillion–token–scale usage on Kilo Code – nearly 5× more than the next most-used model It has consistently ranked #1 ever since it first took the top spot
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Grok Code is the only model with insanely high trillion–token–scale usage on Kilo Code – nearly 5× more than the next most-used model It has consistently ranked #1 ever since it first took the top spot

X Freeze

2,964,843 просмотров • 7 месяцев назад

Grok Code is now officially ranked #1 on the Kilo Code leaderboard, with an insanely large margin Usage is also over 4× higher than the next closest model Real-world adoption at scale is deciding the leaderboard
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Grok Code is now officially ranked #1 on the Kilo Code leaderboard, with an insanely large margin Usage is also over 4× higher than the next closest model Real-world adoption at scale is deciding the leaderboard

X Freeze

722,132 просмотров • 7 месяцев назад

Grok models have been dominating the #1 spot on the OpenRouter and Kilo Code leaderboards since taking the top position four months ago Processing trillions of tokens every month, with a multi-trillion-token usage gap over competitors And it is not even close
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Grok models have been dominating the #1 spot on the OpenRouter and Kilo Code leaderboards since taking the top position four months ago Processing trillions of tokens every month, with a multi-trillion-token usage gap over competitors And it is not even close

X Freeze

479,111 просмотров • 7 месяцев назад

GROK CODE IS WIPING THE FLOOR WITH EVERYONE It launched 5 months ago and it's already the most-used coding model out there. On the Kilo Code leaderboard, it's getting used twice as much as the second-place model in almost every category. In 2025, it’s way ahead on OpenRouter too, with over 16 trillion tokens processed. Nothing else is even close. Source: X Freeze
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GROK CODE IS WIPING THE FLOOR WITH EVERYONE It launched 5 months ago and it's already the most-used coding model out there. On the Kilo Code leaderboard, it's getting used twice as much as the second-place model in almost every category. In 2025, it’s way ahead on OpenRouter too, with over 16 trillion tokens processed. Nothing else is even close. Source: X Freeze

Mario Nawfal

65,325 просмотров • 6 месяцев назад

Grok Code Fast-1 now ranks #1 in every mode on the Kilo Code leaderboard, with the highest margin Users are choosing Grok 2–4× more often than any other model
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Grok Code Fast-1 now ranks #1 in every mode on the Kilo Code leaderboard, with the highest margin Users are choosing Grok 2–4× more often than any other model

X Freeze

2,824,423 просмотров • 7 месяцев назад

xAI's Grok models dominates the OpenRouter leaderboard with over 8.8 trillion tokens in usage in a month, claiming the top #1 and #2 spots This is more than Google, Anthropic, and OpenAI combined, which total 8.6 trillion - less than Grok's 8.8 trillion xAI alone holds ~44% market share
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xAI's Grok models dominates the OpenRouter leaderboard with over 8.8 trillion tokens in usage in a month, claiming the top #1 and #2 spots This is more than Google, Anthropic, and OpenAI combined, which total 8.6 trillion - less than Grok's 8.8 trillion xAI alone holds ~44% market share

X Freeze

2,097,453 просмотров • 7 месяцев назад

🚨GROK IS DOMINATING THE AI GAME, AND THE NUMBERS ARE INSANE Grok has been sitting at the top of the OpenRouter leaderboard for nearly four months straight… and it’s not even close. Over 16 trillion tokens processed, with almost 2× the usage of its next closest rival. OpenRouter's leaderboard ranks models by real-world token usage, a proxy for popularity and developer adoption on their routing platform. This reflects strong demand for Grok, likely driven by factors like free/limited-time access promotions, cost-efficiency, long context windows, and its "uncensored" nature appealing to users seeking alternatives to more restricted models. Love it or hate it, Elon’s model is getting used like crazy. It says something about Grok’s quality... or how badly people want a free, unfiltered alternative to the usual suspects. Either way, OpenRouter’s traffic is painting a very clear picture: Grok’s winning it all. Source: X Freeze, Grok, OpenRouter
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🚨GROK IS DOMINATING THE AI GAME, AND THE NUMBERS ARE INSANE Grok has been sitting at the top of the OpenRouter leaderboard for nearly four months straight… and it’s not even close. Over 16 trillion tokens processed, with almost 2× the usage of its next closest rival. OpenRouter's leaderboard ranks models by real-world token usage, a proxy for popularity and developer adoption on their routing platform. This reflects strong demand for Grok, likely driven by factors like free/limited-time access promotions, cost-efficiency, long context windows, and its "uncensored" nature appealing to users seeking alternatives to more restricted models. Love it or hate it, Elon’s model is getting used like crazy. It says something about Grok’s quality... or how badly people want a free, unfiltered alternative to the usual suspects. Either way, OpenRouter’s traffic is painting a very clear picture: Grok’s winning it all. Source: X Freeze, Grok, OpenRouter

Mario Nawfal

24,347 просмотров • 7 месяцев назад

Grok 4.5 is sitting at #2 on the FrontierSWE leaderboard. Above Claude Opus 4.8. Above GPT-5.5. So yes, the coding model conversation just got a little more crowded at the top. Strong performance is one thing. Doing it with serious speed, better token efficiency, and lower cost is where it gets annoying for everyone else. Builders love a smart model. SpaceXAI Grok X Freeze / Writer: Annette, Designer: Janné
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Grok 4.5 is sitting at #2 on the FrontierSWE leaderboard. Above Claude Opus 4.8. Above GPT-5.5. So yes, the coding model conversation just got a little more crowded at the top. Strong performance is one thing. Doing it with serious speed, better token efficiency, and lower cost is where it gets annoying for everyone else. Builders love a smart model. SpaceXAI Grok X Freeze / Writer: Annette, Designer: Janné

Mario Nawfal

44,365 просмотров • 15 дней назад

GROK 4.1 FAST JUST DID THE IMPOSSIBLE AGAIN: 334 BILLION TOKENS IN A SINGLE DAY The OpenRouter leaderboard isn’t just broken. It’s been nuked from orbit. Yesterday: 253B tokens Today: 334B tokens That’s an 81B token jump in 24 hours. The closest competitor needs almost an entire week to hit what Grok 4.1 Fast does before lunch. This is the free model. The one anyone can use with zero paywall. People aren’t testing Grok anymore. They’ve moved in permanently. The throne isn’t up for grabs. It’s already been welded shut with Grok sitting on it. Bow down. The king just went super Saiyan. Source: X Freeze
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GROK 4.1 FAST JUST DID THE IMPOSSIBLE AGAIN: 334 BILLION TOKENS IN A SINGLE DAY The OpenRouter leaderboard isn’t just broken. It’s been nuked from orbit. Yesterday: 253B tokens Today: 334B tokens That’s an 81B token jump in 24 hours. The closest competitor needs almost an entire week to hit what Grok 4.1 Fast does before lunch. This is the free model. The one anyone can use with zero paywall. People aren’t testing Grok anymore. They’ve moved in permanently. The throne isn’t up for grabs. It’s already been welded shut with Grok sitting on it. Bow down. The king just went super Saiyan. Source: X Freeze

Mario Nawfal

42,979 просмотров • 8 месяцев назад

GROK STAYS AHEAD OF THE CURVE: ACING ALL TESTS KNOWN TO MAN It's not just the smartest LLM, it's the world's most psychologically advanced AI model. * Grok scored 98/100 openness and agreeableness: the most “human” AI ever built * Tops empathy benchmarks with 95% EQ accuracy and 92% success in emotion detection * Leads in Theory of Mind, predicting beliefs and intentions better than any rival model * Scored 148 on the Mensa IQ test: top 0.1% human equivalent * Elon: “Grok isn’t just smart, it’s empathetic, the AI that understands you” This isn't 'artificial' intelligence; it's the birth of another intelligent species. Source: xAI, TechCrunch
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GROK STAYS AHEAD OF THE CURVE: ACING ALL TESTS KNOWN TO MAN It's not just the smartest LLM, it's the world's most psychologically advanced AI model. * Grok scored 98/100 openness and agreeableness: the most “human” AI ever built * Tops empathy benchmarks with 95% EQ accuracy and 92% success in emotion detection * Leads in Theory of Mind, predicting beliefs and intentions better than any rival model * Scored 148 on the Mensa IQ test: top 0.1% human equivalent * Elon: “Grok isn’t just smart, it’s empathetic, the AI that understands you” This isn't 'artificial' intelligence; it's the birth of another intelligent species. Source: xAI, TechCrunch

Mario Nawfal

1,381,151 просмотров • 7 месяцев назад

Your thumbs up/down is direct training data for xAI. It’s used (called RLHF) to make Grok smarter, more accurate, and more useful for everyone, including future updates. One regular user giving honest feedback actually helps improve Grok faster than any engineer working alone. It’s one of the easiest ways normal people help build better Grok, not just devs. That’s why the buttons are everywhere. Every click matters. How to use it practically. Super easy: Chat with Grok (on the X app, or wherever it’s used). · After the reply, look right under the answer. Two buttons appear: thumbs up and thumbs down. · Tap 👍 if the answer was good or helpful. · Tap 👎 if it was bad, wrong, confusing, or unhelpful. · (Optional) A small box may appear. Type 1–2 words why (e.g., “too long” or “wrong fact”). That’s it. It takes literally 2 seconds. It can be used every time.
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Your thumbs up/down is direct training data for xAI. It’s used (called RLHF) to make Grok smarter, more accurate, and more useful for everyone, including future updates. One regular user giving honest feedback actually helps improve Grok faster than any engineer working alone. It’s one of the easiest ways normal people help build better Grok, not just devs. That’s why the buttons are everywhere. Every click matters. How to use it practically. Super easy: Chat with Grok (on the X app, or wherever it’s used). · After the reply, look right under the answer. Two buttons appear: thumbs up and thumbs down. · Tap 👍 if the answer was good or helpful. · Tap 👎 if it was bad, wrong, confusing, or unhelpful. · (Optional) A small box may appear. Type 1–2 words why (e.g., “too long” or “wrong fact”). That’s it. It takes literally 2 seconds. It can be used every time.

Mario Nawfal

63,219 просмотров • 2 месяцев назад

GROK SURGES TO THE FRONT OF THE GENAI RACE AS GROWTH SKYROCKETS Grok is absolutely amazing, continuing to stun with incredible results. Web traffic jumped nearly 15% month-over-month in November 2025, the fastest growth in the generative AI industry, proving Elon’s xAI project isn’t just competing, it’s taking real market share from ChatGPT. Grok hit around 234.4 million visits in November, up from 204 million in October. That’s a staggering 1,300% year-over-year surge, pushing it past Perplexity and Claude in user growth and cementing it as the world’s #2 chatbot by market share. The breakout came with Grok 4.1’s release in mid-November. The update debuted at number 1 on LMSYS Arena, that’s the global benchmark where AI models are ranked through blind human evaluations. Grok’s new “Thinking Mode” scored 1483 Elo, a 31-point lead over every open model, beating Gemini 2.5 Pro and Claude 3. The upgrade also cut hallucinations (false answers) by two-thirds, expanded its context window to 2 million tokens, about 1.5 million words of memory, and dominated top reasoning and coding tests like, graduate-level logic, and the emotional intelligence aspect. U.S. traffic surged to 51.5 million visits, boosted by X integration and Grok’s unfiltered style. At just $0.20 per million input tokens, versus GPT-5.1’s $1.25—it’s winning with both speed and affordability. The momentum isn’t hype, it’s lift-off. If growth holds, Grok could reach 500 million users by mid-2026, forcing every rival to redefine what “intelligent” really means. To truthful AI winning! Source: X Freeze, NextBigFuture, CometApi, Langcopilot
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GROK SURGES TO THE FRONT OF THE GENAI RACE AS GROWTH SKYROCKETS Grok is absolutely amazing, continuing to stun with incredible results. Web traffic jumped nearly 15% month-over-month in November 2025, the fastest growth in the generative AI industry, proving Elon’s xAI project isn’t just competing, it’s taking real market share from ChatGPT. Grok hit around 234.4 million visits in November, up from 204 million in October. That’s a staggering 1,300% year-over-year surge, pushing it past Perplexity and Claude in user growth and cementing it as the world’s #2 chatbot by market share. The breakout came with Grok 4.1’s release in mid-November. The update debuted at number 1 on LMSYS Arena, that’s the global benchmark where AI models are ranked through blind human evaluations. Grok’s new “Thinking Mode” scored 1483 Elo, a 31-point lead over every open model, beating Gemini 2.5 Pro and Claude 3. The upgrade also cut hallucinations (false answers) by two-thirds, expanded its context window to 2 million tokens, about 1.5 million words of memory, and dominated top reasoning and coding tests like, graduate-level logic, and the emotional intelligence aspect. U.S. traffic surged to 51.5 million visits, boosted by X integration and Grok’s unfiltered style. At just $0.20 per million input tokens, versus GPT-5.1’s $1.25—it’s winning with both speed and affordability. The momentum isn’t hype, it’s lift-off. If growth holds, Grok could reach 500 million users by mid-2026, forcing every rival to redefine what “intelligent” really means. To truthful AI winning! Source: X Freeze, NextBigFuture, CometApi, Langcopilot

Mario Nawfal

33,823 просмотров • 7 месяцев назад

This free model just beat every closed-source AI on coding benchmarks. Open weights. 6x cheaper than Opus. The labs don't want you to know it exists. > GLM-5.2 from Zai just topped Code Arena - the first open-weights model to ever hold the #1 coding spot. Not a leaked weight, not a fine-tune. A fully open model beating GPT and Claude on their own turf. It's live on Hugging Face Inference API right now. 5 providers: Novita, Together AI, Fireworks, Deepinfra, Zai. OpenAI-compatible client. → Go to huggingface(.)co → grab HF_TOKEN from account settings → plug into any OpenAI-compatible client 6x cheaper than Opus. Companies bleeding on AI bills are already routing to this for orchestration, caching, and token optimization. The smart money moved before the headline dropped. > Now you know. huggingface(.)co/zai-org/GLM-5.2 Bookmark this before everyone else figures it out.
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This free model just beat every closed-source AI on coding benchmarks. Open weights. 6x cheaper than Opus. The labs don't want you to know it exists. > GLM-5.2 from Zai just topped Code Arena - the first open-weights model to ever hold the #1 coding spot. Not a leaked weight, not a fine-tune. A fully open model beating GPT and Claude on their own turf. It's live on Hugging Face Inference API right now. 5 providers: Novita, Together AI, Fireworks, Deepinfra, Zai. OpenAI-compatible client. → Go to huggingface(.)co → grab HF_TOKEN from account settings → plug into any OpenAI-compatible client 6x cheaper than Opus. Companies bleeding on AI bills are already routing to this for orchestration, caching, and token optimization. The smart money moved before the headline dropped. > Now you know. huggingface(.)co/zai-org/GLM-5.2 Bookmark this before everyone else figures it out.

Atenov int.

12,395 просмотров • 1 месяц назад

SpaceXai just made grok 4.5 FREE in your coding agent starting today it's xAI's new coding model. 500k context. built for long agent sessions. no card. what you get for $0: -83.3% on terminal-bench 2.1 -64.7% on swe-bench pro - 4.2x more efficient than Opus 4.8 -500k context for big repos -$2/M in, $6/M out once it goes paid what this replaces: -Claude Opus 4.8: $15/M in, $75/M out -SuperGrok: $30-50/mo all for $0 how to set it up (2 min): >curl -fsSL | bash > grok → localhost:8000/v1 > point hermes / aider / opencode / cline to it > model: grok-4.5 Or API key at - base url Works in Hermes, Aider, OpenCode, Cline, Claude Code, and any OpenAI-compatible tool. Important: Free for a limited time. EU waits till mid-July. Rate limits apply. bookmark this before the free window closes
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SpaceXai just made grok 4.5 FREE in your coding agent starting today it's xAI's new coding model. 500k context. built for long agent sessions. no card. what you get for $0: -83.3% on terminal-bench 2.1 -64.7% on swe-bench pro - 4.2x more efficient than Opus 4.8 -500k context for big repos -$2/M in, $6/M out once it goes paid what this replaces: -Claude Opus 4.8: $15/M in, $75/M out -SuperGrok: $30-50/mo all for $0 how to set it up (2 min): >curl -fsSL | bash > grok → localhost:8000/v1 > point hermes / aider / opencode / cline to it > model: grok-4.5 Or API key at - base url Works in Hermes, Aider, OpenCode, Cline, Claude Code, and any OpenAI-compatible tool. Important: Free for a limited time. EU waits till mid-July. Rate limits apply. bookmark this before the free window closes

painn

150,211 просмотров • 21 дней назад

you can run claude code inside antigravity completely Free with zero credit card and no rate limits 😳 use openrouter’s free models + antigravity. no anthropic bill. no paid api keys. takes 10 minutes to set up. what you get during this setup: - full claude code agent experience - strong coding models (including deepseek-r1, qwen2.5-coder, llama-4, grok-4 free tier) - antigravity’s clean workspace and sandbox - unlimited usage (as long as you stay on free models) - easy model swapping - zero cost full setup guide (100% free): step 1: install antigravity -go to and install it -create a new workspace step 2: install claude code - inside antigravity, install the claude code extension from the marketplace - open the built-in terminal step 3: create openrouter free account -go to - sign up with google (no card needed) - go to keys and create a new api key step 4: set the environment variables -in antigravity terminal run: export ANTHROPIC_API_KEY=sk-or-xxx export OPENROUTER_API_KEY=sk-or-xxx step 5: launch claude code with free model -run this command: claude-code --model deepseek/deepseek-r1:free or try: qwen/qwen2.5-coder:free if you already have antigravity? skip straight to step 2. after 10 minutes you’ll have a full agentic coding setup running for free. this is currently one of the cheapest ways to run serious coding agents in 2026. bookmark this before they limit the free models.
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you can run claude code inside antigravity completely Free with zero credit card and no rate limits 😳 use openrouter’s free models + antigravity. no anthropic bill. no paid api keys. takes 10 minutes to set up. what you get during this setup: - full claude code agent experience - strong coding models (including deepseek-r1, qwen2.5-coder, llama-4, grok-4 free tier) - antigravity’s clean workspace and sandbox - unlimited usage (as long as you stay on free models) - easy model swapping - zero cost full setup guide (100% free): step 1: install antigravity -go to and install it -create a new workspace step 2: install claude code - inside antigravity, install the claude code extension from the marketplace - open the built-in terminal step 3: create openrouter free account -go to - sign up with google (no card needed) - go to keys and create a new api key step 4: set the environment variables -in antigravity terminal run: export ANTHROPIC_API_KEY=sk-or-xxx export OPENROUTER_API_KEY=sk-or-xxx step 5: launch claude code with free model -run this command: claude-code --model deepseek/deepseek-r1:free or try: qwen/qwen2.5-coder:free if you already have antigravity? skip straight to step 2. after 10 minutes you’ll have a full agentic coding setup running for free. this is currently one of the cheapest ways to run serious coding agents in 2026. bookmark this before they limit the free models.

painn

32,057 просмотров • 1 месяц назад

BREAKING 🚨: It’s not just China’s turbo model. It’s the world’s now. 🌍 Hunyuan Turbo S is ranked Top 8 globally on Chatbot Arena and #2 in China. 🔢 +39% math | 💻 +24% code | 🔬 +10% science reasoning Eric Schmidt says China had no foundation models two years ago — now it has three: DeepSeek, Qwen, Hunyuan — “at the same level as OpenAI’s O.1.” At #GoogleIO, all three landed high on the global leaderboard like #DeepSeek, #Qwen, #Hunyuan Backed by Hunyuan’s full-stack AI push — 🧠 Voice | 👁️ Vision | 🎨 Image | ☁️ Tencent Cloud — Turbo S isn’t chasing. It’s here. #Hunyuan #TurboS #TencentAI #NextGenLLM
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BREAKING 🚨: It’s not just China’s turbo model. It’s the world’s now. 🌍 Hunyuan Turbo S is ranked Top 8 globally on Chatbot Arena and #2 in China. 🔢 +39% math | 💻 +24% code | 🔬 +10% science reasoning Eric Schmidt says China had no foundation models two years ago — now it has three: DeepSeek, Qwen, Hunyuan — “at the same level as OpenAI’s O.1.” At #GoogleIO, all three landed high on the global leaderboard like #DeepSeek, #Qwen, #Hunyuan Backed by Hunyuan’s full-stack AI push — 🧠 Voice | 👁️ Vision | 🎨 Image | ☁️ Tencent Cloud — Turbo S isn’t chasing. It’s here. #Hunyuan #TurboS #TencentAI #NextGenLLM

Bishal Nandi

74,812 просмотров • 1 год назад

5 days ago it took 2 GPUs to build this. today it takes 1. same prompt. same particle simulation. completely different model. Qwen-Coder-Next (80B) on 2x 3090s. 46 tok/s. 564 lines. 2 iterations to get it working. 48GB VRAM across two cards just to hold it. Qwen3.5-35B-A3B on a single 3090. 112 tok/s. 461 lines. first try. cleaner code, fewer lines, better structured. 19.7GB on disk with 4GB VRAM to spare. half the parameters. one GPU instead of two. 2.4x faster. and the output actually improved. this is what happens when architecture catches up to ambition. Gated Delta Networks(Mamba2 variant) hybrid with sparse MoE. 3B active params out of 35B per token. efficiency at the architecture level, not just quantization. the curve isn't flattening. it's steepening.
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5 days ago it took 2 GPUs to build this. today it takes 1. same prompt. same particle simulation. completely different model. Qwen-Coder-Next (80B) on 2x 3090s. 46 tok/s. 564 lines. 2 iterations to get it working. 48GB VRAM across two cards just to hold it. Qwen3.5-35B-A3B on a single 3090. 112 tok/s. 461 lines. first try. cleaner code, fewer lines, better structured. 19.7GB on disk with 4GB VRAM to spare. half the parameters. one GPU instead of two. 2.4x faster. and the output actually improved. this is what happens when architecture catches up to ambition. Gated Delta Networks(Mamba2 variant) hybrid with sparse MoE. 3B active params out of 35B per token. efficiency at the architecture level, not just quantization. the curve isn't flattening. it's steepening.

Sudo su

34,569 просмотров • 5 месяцев назад

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3
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Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 просмотров • 6 месяцев назад

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.
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Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

nathan (in sf)

40,440 просмотров • 6 месяцев назад