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FLUX Text Tree Prompt - Mindmap Generator
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Generate hand-drawn diagrams using text prompts
Klee Young
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๐ MoneyRadar - AI-Powered Global News Analysis System
๐ป Live Demo: openfree/MoneyRadar
๐ฏ Core Features
1. ๐ค 24/7 Automated News Scanning
Auto-collection of Top 100 trending news
Real-time monitoring across 60 countries
Smart filtering of investment-critical news
2. ๐ Advanced Custom Search
Unlimited keyword search capability
Country/language-specific search options
Real-time trend-based related keywords
3. ๐จ Smart Analysis & Visualization
AI-powered sentiment analysis
Automated content summarization
Investment decision-supporting insights
โก Automated Information Collection
Key Companies (NVIDIA, APPLE, TESLA, etc.)
Earnings/Forecasts
Product/Technology announcements
Market share changes
M&A and major news
Financial Markets & Digital Assets
Macroeconomic indicators
Regulatory changes
Market sentiment analysis
Major exchange updates
๐ Business Applications
Real-time market trend tracking
Competitor movement monitoring
Early investment opportunity detection
Risk early warning system
๐ Key Differentiators
Full Automation
Zero manual intervention
Real-time data updates
Automated result storage/management
User-Centric Design
Intuitive interface
Customizable alerts
Mobile optimization
Advanced Analytics
News cross-checking
Historical tracking
Trend prediction support
Join Community ๐ฌ
"With MoneyRadar, never miss a beat in the global market movements!"
๐ป Live Demo: openfree/MoneyRadar
๐ฏ Core Features
1. ๐ค 24/7 Automated News Scanning
Auto-collection of Top 100 trending news
Real-time monitoring across 60 countries
Smart filtering of investment-critical news
2. ๐ Advanced Custom Search
Unlimited keyword search capability
Country/language-specific search options
Real-time trend-based related keywords
3. ๐จ Smart Analysis & Visualization
AI-powered sentiment analysis
Automated content summarization
Investment decision-supporting insights
โก Automated Information Collection
Key Companies (NVIDIA, APPLE, TESLA, etc.)
Earnings/Forecasts
Product/Technology announcements
Market share changes
M&A and major news
Financial Markets & Digital Assets
Macroeconomic indicators
Regulatory changes
Market sentiment analysis
Major exchange updates
๐ Business Applications
Real-time market trend tracking
Competitor movement monitoring
Early investment opportunity detection
Risk early warning system
๐ Key Differentiators
Full Automation
Zero manual intervention
Real-time data updates
Automated result storage/management
User-Centric Design
Intuitive interface
Customizable alerts
Mobile optimization
Advanced Analytics
News cross-checking
Historical tracking
Trend prediction support
Join Community ๐ฌ
"With MoneyRadar, never miss a beat in the global market movements!"
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Pony Realism / Cyber Realistic Pony / Stallion Dreams
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Generate detailed images using prompts and models
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Omni-Zero
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Restylize & repose person ID
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Omni-Zero-Couples
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Create stylized portraits from images of couples
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HuggingFace Trending Board
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Execute dynamic Python code
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CineDiffusion
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Create cinematic images in widescreen aspect ratios.
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Video Transcription Smart Summary
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Generate summaries from YouTube videos or uploaded videos
Can you please tell exactly what parameters were used?
2
#2 opened 11 months ago
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YauhenLogos
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Made a new app to visualize the LLM race โ ๐ก๐ผ ๐๐๐ฟ๐ผ๐ฝ๐ฒ๐ฎ๐ป ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ป๐ ๐ถ๐ป ๐๐ต๐ฒ ๐๐ผ๐ฝ ๐ญ๐ฌ ๐ช๐บโ
See the app here ๐ m-ric/llm-race-to-the-top
I've adapted an app by @andrewrreed that tracks progress of LLMs ( andrewrreed/closed-vs-open-arena-elo), on the Chatbot Arena leaderboard, to compare companies from different countries.
The outcome is quite sad, as a Frenchman and European.
The top 10 is exclusively US ๐บ๐ธ and Chinese ๐จ๐ณ companies (after great Chinese LLM releases recently, like the Qwen2.5 series), with the notable exception of Mistral AI ๐ซ๐ท.
American companies are making fast progress, Chinese ones even faster. Europe is at risk of being left behind. And the EU AI Act hasn't even come into force yet to slow down the EU market. We need to wake up ๐ฌ
โ ๏ธ Caution: This Chatbot Arena ELO ranking is not the most accurate, especially at high scores like this, because LLM makers can game it to some extent.
See the app here ๐ m-ric/llm-race-to-the-top
I've adapted an app by @andrewrreed that tracks progress of LLMs ( andrewrreed/closed-vs-open-arena-elo), on the Chatbot Arena leaderboard, to compare companies from different countries.
The outcome is quite sad, as a Frenchman and European.
The top 10 is exclusively US ๐บ๐ธ and Chinese ๐จ๐ณ companies (after great Chinese LLM releases recently, like the Qwen2.5 series), with the notable exception of Mistral AI ๐ซ๐ท.
American companies are making fast progress, Chinese ones even faster. Europe is at risk of being left behind. And the EU AI Act hasn't even come into force yet to slow down the EU market. We need to wake up ๐ฌ
โ ๏ธ Caution: This Chatbot Arena ELO ranking is not the most accurate, especially at high scores like this, because LLM makers can game it to some extent.
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The 5 Most Under-Rated Tools on Hugging Face
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๐ง Stanford paper might be the key to OpenAI o1โs performance: Whatโs so effective about Chain of Thought? โ it unlocks radically different sequential tasks!
๐ญย Reminder: A Chain of Thought (CoT) means that you instruct the model to โthink step by stepโ. Often itโs literally just putting in the prompt โletโs think step by step.โ
๐คย This method has been shown to be unreasonably effective to increase perf on benchmarks. However why it works so well remains unclear.
Here's the scoop: Transformers are amazing at parallel processing, but they've always struggled with tasks that require sequential reasoning.
โ๏ธ For instance if you ask them the result of 3^2^2^2^โฆ, with 20 iterations, theyโll nearly always fail.
๐กย Indeed, researchers prove mathematically, by assimilating transformers networks to logical circuits, that effectively they cannot solve sequential tasks that require more than a certain threshold of sequences.
But CoT enables sequential reasoning:
- ๐งฑ Each step in the CoT corresponds to simulating one operation in a complex circuit.
- ๐ This allows the transformer to "reset" the depth of intermediate outputs, overcoming previous limitations.
- ๐ Thus, with CoT, constant-depth transformers can now solve ANY problem computable by polynomial-size circuits! (That's a huge class of problems in computer science.)
- ๐ Transformers can now handle tricky tasks like iterated squares (computing 3^2^2^2^2) composed permutations and evaluating circuits - stuff that requires serial computation.
- ๐ย The improvement is especially dramatic for transformers with a limited depth. Empirical tests on four arithmetic problems showed massive accuracy gains with CoT on inherently serial tasks.
Main takeaway: Chain-of-thought isn't just a neat trick - it fundamentally expands what transformer models can do!
Read the paper ๐ย Chain of Thought Empowers Transformers to Solve Inherently Serial Problems (2402.12875)
๐ญย Reminder: A Chain of Thought (CoT) means that you instruct the model to โthink step by stepโ. Often itโs literally just putting in the prompt โletโs think step by step.โ
๐คย This method has been shown to be unreasonably effective to increase perf on benchmarks. However why it works so well remains unclear.
Here's the scoop: Transformers are amazing at parallel processing, but they've always struggled with tasks that require sequential reasoning.
โ๏ธ For instance if you ask them the result of 3^2^2^2^โฆ, with 20 iterations, theyโll nearly always fail.
๐กย Indeed, researchers prove mathematically, by assimilating transformers networks to logical circuits, that effectively they cannot solve sequential tasks that require more than a certain threshold of sequences.
But CoT enables sequential reasoning:
- ๐งฑ Each step in the CoT corresponds to simulating one operation in a complex circuit.
- ๐ This allows the transformer to "reset" the depth of intermediate outputs, overcoming previous limitations.
- ๐ Thus, with CoT, constant-depth transformers can now solve ANY problem computable by polynomial-size circuits! (That's a huge class of problems in computer science.)
- ๐ Transformers can now handle tricky tasks like iterated squares (computing 3^2^2^2^2) composed permutations and evaluating circuits - stuff that requires serial computation.
- ๐ย The improvement is especially dramatic for transformers with a limited depth. Empirical tests on four arithmetic problems showed massive accuracy gains with CoT on inherently serial tasks.
Main takeaway: Chain-of-thought isn't just a neat trick - it fundamentally expands what transformer models can do!
Read the paper ๐ย Chain of Thought Empowers Transformers to Solve Inherently Serial Problems (2402.12875)