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Evaluating GPT and Open-Source Models on Code Mutation Tasks

Unraveling Io’s Atmospheric Mysteries: Gaps, Contradictions, and Open Questions

Are you looking for a free alternative to ChatGPT? If so, you’re in the right place! In this article, I will introduce you to HuggingChat, an open-source AI chatbot from Hugging Face. Unlike other AI models like ChatGPT or Gemini, HuggingChat runs on various open-source models that you can explore, modify, and even fine-tune according to your needs. Let’s dive in! What is HuggingChat? HuggingChat is an AI chatbot similar to ChatGPT but built on open-source models. It is designed to provide conversational AI without the limitations of proprietary models like OpenAI’s ChatGPT or Google’s Gemini. Unlike ChatGPT, which is based on OpenAI’s proprietary model, HuggingChat uses multiple AI models from different providers, such as: Llama


Mistral


DeepSeek


Mixtral


Etc Since these models are open-source, you can see their code, modify them, and even integrate them into your own projects. How to Use HuggingChat Using HuggingChat is simple and doesn’t require setup. Step 1. Create an Account First, go to HuggingChat and create a free account. Once logged in, you will be taken to the chat interface. Step 2. Select a Model Unlike ChatGPT, where you only have access to a fixed model, HuggingChat lets you choose different AI models for different tasks. Here’s how: Click the Settings icon or the Models link in the sidebar. Browse the available models.
Select the one that best suits your needs. Each model has different strengths, such as better reasoning, faster responses, or the ability to generate images. Step 3. Start Chatting The interface is very similar to ChatGPT, so if you’ve used ChatGPT before, you’ll feel right at home. Simply enter a prompt and the model will generate a response. Features of HuggingChat 1. Search-Enabled Responses One of the most useful features of HuggingChat is the search function. If an AI model doesn’t have up-to-date information, you can enable search, which allows it to look up the latest facts online. For example, when I asked about the latest iPhone model, the default AI model didn’t know the answer. However, by enabling the search function, I was able to get the correct information. 2. Image Generation HuggingChat supports image generation! However, not all models can generate images. To use this feature: Go to Settings.
Choose a model that supports image generation (e.g., Llama 3).
Click on the image generation icon.
Enter your prompt (e.g., “Generate an image of a futuristic city”). 3. Multiple AI Models HuggingChat allows you to experiment with different AI models to see which works best for your task. Some models are better at reasoning, some are faster, and some support image generation. 4. Custom Assistants Similar to OpenAI’s Custom GPTs, HuggingChat has Assistants, which allow users to create custom AI configurations for specific tasks. For example, you can create an Assistant specialized in: Coding (Python, JavaScript, etc.)
Writing and summarizing text
Translating languages Or you can select Assistants from the community. 5. File and Image Uploads Certain AI models in HuggingChat allow you to upload files or images for processing. This is useful if you want to analyze data, read documents, or perform image-recognition tasks. 6. Free and Open-Source As I mentioned before, unlike ChatGPT or Gemini, HuggingChat is completely free and based on open-source AI models. Video Review of HuggingChat Watch on YouTube: ChatGPT Alternative — HuggingChat Conclusion If you want a free ChatGPT alternative that is customizable and privacy-friendly, HuggingChat is worth trying. Give it a try and let me know your thoughts! Do you think it can replace ChatGPT? Let me know in a comment below! Cheers! ;) Are you looking for a free alternative to ChatGPT? If so, you’re in the right place! In this article, I will introduce you to HuggingChat, an open-source AI chatbot from Hugging Face. Unlike other AI models like ChatGPT or Gemini, HuggingChat runs on various open-source models that you can explore, modify, and even fine-tune according to your needs. Let’s dive in! What is HuggingChat? HuggingChat is an AI chatbot similar to ChatGPT but built on open-source models. It is designed to provide conversational AI without the limitations of proprietary models like OpenAI’s ChatGPT or Google’s Gemini. Unlike ChatGPT, which is based on OpenAI’s proprietary model, HuggingChat uses multiple AI models from different providers, such as: Llama Mistral DeepSeek Mixtral Etc Llama Llama Mistral Mistral DeepSeek DeepSeek Mixtral Mixtral Etc Etc Since these models are open-source, you can see their code, modify them, and even integrate them into your own projects. How to Use HuggingChat Using HuggingChat is simple and doesn’t require setup. Step 1. Create an Account First, go to HuggingChat and create a free account. Once logged in, you will be taken to the chat interface. HuggingChat Step 2. Select a Model Unlike ChatGPT, where you only have access to a fixed model, HuggingChat lets you choose different AI models for different tasks. Here’s how: Click the Settings icon or the Models link in the sidebar. Click the Settings icon or the Models link in the sidebar. Browse the available models. Select the one that best suits your needs. Browse the available models. Select the one that best suits your needs. Each model has different strengths, such as better reasoning, faster responses, or the ability to generate images. Step 3. Start Chatting The interface is very similar to ChatGPT , so if you’ve used ChatGPT before, you’ll feel right at home. Simply enter a prompt and the model will generate a response. very similar to ChatGPT Features of HuggingChat 1. Search-Enabled Responses One of the most useful features of HuggingChat is the search function . If an AI model doesn’t have up-to-date information, you can enable search, which allows it to look up the latest facts online. search function For example, when I asked about the latest iPhone model , the default AI model didn’t know the answer. However, by enabling the search function, I was able to get the correct information. latest iPhone model 2. Image Generation HuggingChat supports image generation! However, not all models can generate images. To use this feature: Go to Settings. Choose a model that supports image generation (e.g., Llama 3). Click on the image generation icon. Enter your prompt (e.g., “Generate an image of a futuristic city”). Go to Settings . Settings Choose a model that supports image generation (e.g., Llama 3). Click on the image generation icon . image generation icon Enter your prompt (e.g., “Generate an image of a futuristic city”). 3. Multiple AI Models HuggingChat allows you to experiment with different AI models to see which works best for your task. Some models are better at reasoning, some are faster, and some support image generation. different AI models 4. Custom Assistants Similar to OpenAI’s Custom GPTs , HuggingChat has Assistants , which allow users to create custom AI configurations for specific tasks. Custom GPTs Assistants For example, you can create an Assistant specialized in: Coding (Python, JavaScript, etc.) Writing and summarizing text Translating languages Coding (Python, JavaScript, etc.) Writing and summarizing text Translating languages Or you can select Assistants from the community. 5. File and Image Uploads Certain AI models in HuggingChat allow you to upload files or images for processing. This is useful if you want to analyze data, read documents, or perform image-recognition tasks. 6. Free and Open-Source As I mentioned before, unlike ChatGPT or Gemini, HuggingChat is completely free and based on open-source AI models. Video Review of HuggingChat Watch on YouTube: ChatGPT Alternative — HuggingChat Watch on YouTube: ChatGPT Alternative — HuggingChat ChatGPT Alternative — HuggingChat Conclusion If you want a free ChatGPT alternative that is customizable and privacy-friendly, HuggingChat is worth trying. Give it a try and let me know your thoughts! Do you think it can replace ChatGPT? Let me know in a comment below! Cheers! ;)

Meet HuggingChat: A FREE and Open-Source Alternative to ChatGPT!

Design, Manufacturing and Open-Loop Control of a Soft Pneumatic Arm: Conclusions

Authors:
(1) Jorge Francisco Garcia-Samartın, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain (jorge.gsamartin@upm.es);
(2) Adrian Rieker, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain;
(3) Antonio Barrientos, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain. Table of Links Abstract and 1 Introduction 2 Related Works 2.1 Pneumatic Actuation 2.2 Pneumatic Arms 2.3 Control of Soft Robots 3 PAUL: Design and Manufacturing 3.1 Robot Design 3.2 Material Selection 3.3 Manufacturing 3.4 Actuation Bank 4 Data Acquisition and Open-Loop Control 4.1 Hardware Setup 4.2 Vision Capture System 4.3 Dataset Generation: Table-Based Models 4.4 Open-Loop Control 5 Results 5.1 Final PAUL version 5.2 Workspace Analysis 5.3 Performance of the Table-Based Models 5.4 Bending Experiments 5.5 Weight Carrying Experiments 6 Conclusions Funding Information A. Conducted Experiments and References 5.4 Bending Experiments The first experiment consisted of analysing the deflection of a segment versus swelling time. For this purpose, one of the bladders was inflated continuously, in intervals of 100 ms. For each time, PAUL end coord where x0 and y0 denote initial position of PAUL end. Since the weight of the subsequent modules influences the behaviour of the first segment, the experiment was repeated by placing first one and then two additional segments. The results are shown in Figure 18. As can be seen, PAUL is capable of bending up to 40◦ to its vertical axis and the addition of new segments does not cause any noticeable decrease in its bending capacity. Although it remains far from the 80◦ of [28] or the 70◦ of [32], Pneunet segments and therefore more flexible, this is an acceptable bending capacity. Moreover, the fact that it does not substantially lose its bending capacity by adding segments makes it possible to concatenate bending movements and thus overcome obstacles that a rigid robot would not be able to overcome. In conjunction with this, a validation test was proposed whose purpose was to demonstrate PAUL’s ability to flex thanks to its deformable geometry. The aim was to point points in lateral planes. The results of this experiment are shown in Figure 19. The images, extracted from the video of Appendix A, show how the manipulator can adopt different shapes, is able to bend up to 40◦ and adapt, in case of obstacles, to a wide variety of geometries, which undoubtedly makes PAUL a fundamental ally in inspection and exploration operations in very cluttered environments. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Jorge Francisco Garcia-Samartın, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain (jorge.gsamartin@upm.es); (2) Adrian Rieker, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain; (3) Antonio Barrientos, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain. Authors: Authors: (1) Jorge Francisco Garcia-Samartın, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain (jorge.gsamartin@upm.es); (2) Adrian Rieker, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain; (3) Antonio Barrientos, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works 2.1 Pneumatic Actuation 2.1 Pneumatic Actuation 2.2 Pneumatic Arms 2.2 Pneumatic Arms 2.3 Control of Soft Robots 2.3 Control of Soft Robots 3 PAUL: Design and Manufacturing 3.1 Robot Design 3.1 Robot Design 3.2 Material Selection 3.2 Material Selection 3.3 Manufacturing 3.3 Manufacturing 3.4 Actuation Bank 3.4 Actuation Bank 4 Data Acquisition and Open-Loop Control 4.1 Hardware Setup 4.1 Hardware Setup 4.2 Vision Capture System 4.2 Vision Capture System 4.3 Dataset Generation: Table-Based Models 4.3 Dataset Generation: Table-Based Models 4.4 Open-Loop Control 4.4 Open-Loop Control 5 Results 5.1 Final PAUL version 5.1 Final PAUL version 5.2 Workspace Analysis 5.2 Workspace Analysis 5.3 Performance of the Table-Based Models 5.3 Performance of the Table-Based Models 5.4 Bending Experiments 5.4 Bending Experiments 5.5 Weight Carrying Experiments 5.5 Weight Carrying Experiments 6 Conclusions 6 Conclusions Funding Information Funding Information A. Conducted Experiments and References A. Conducted Experiments and References 5.4 Bending Experiments The first experiment consisted of analysing the deflection of a segment versus swelling time. For this purpose, one of the bladders was inflated continuously, in intervals of 100 ms. For each time, PAUL end coord where x0 and y0 denote initial position of PAUL end. Since the weight of the subsequent modules influences the behaviour of the first segment, the experiment was repeated by placing first one and then two additional segments. The results are shown in Figure 18. As can be seen, PAUL is capable of bending up to 40◦ to its vertical axis and the addition of new segments does not cause any noticeable decrease in its bending capacity. Although it remains far from the 80◦ of [28] or the 70◦ of [32], Pneunet segments and therefore more flexible, this is an acceptable bending capacity. Moreover, the fact that it does not substantially lose its bending capacity by adding segments makes it possible to concatenate bending movements and thus overcome obstacles that a rigid robot would not be able to overcome. In conjunction with this, a validation test was proposed whose purpose was to demonstrate PAUL’s ability to flex thanks to its deformable geometry. The aim was to point points in lateral planes. The results of this experiment are shown in Figure 19. The images, extracted from the video of Appendix A, show how the manipulator can adopt different shapes, is able to bend up to 40◦ and adapt, in case of obstacles, to a wide variety of geometries, which undoubtedly makes PAUL a fundamental ally in inspection and exploration operations in very cluttered environments. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Design, Manufacturing and Open-Loop Control of a Soft Pneumatic Arm: Bending Experiments

Ditch the Phone Line and Try These 5 Free and Open-Source Platforms for Voice Calls Instead

ስልኩን ያውጡ እና እነዚህን 5 ነፃ እና ክፍት ምንጭ መድረኮች ለድምጽ ጥሪዎች ይሞክሩ

Línea Telefónica ukar zanjaña ukat uka lantix aka 5 Plataformas Gratis ukat Abiertas de Llamadas de Voz ukanakat yant’añamawa

Olvídate de la línea telefónica y prueba estas 5 plataformas gratuitas y de código abierto para realizar llamadas de voz

Irrota puhelinlinja ja kokeile sen sijaan näitä viittä ilmaista avoimen lähdekoodin alustaa äänipuheluille

עזוב את קו הטלפון ונסה את 5 הפלטפורמות החינמיות והקוד הפתוח האלה לשיחות קוליות במקום זאת

電話回線を捨てて、代わりに音声通話用の 5 つの無料オープンソース プラットフォームを試してみましょう

ຖອດສາຍໂທລະສັບອອກແລ້ວລອງໃຊ້ 5 ແພລດຟອມທີ່ບໍ່ເສຍຄ່າ ແລະເປີດແຫຼ່ງສຳລັບການໂທຫາສຽງແທນ.

Porzuć linię telefoniczną i wypróbuj zamiast tego te 5 darmowych i otwartych platform do połączeń głosowych

Откажитесь от телефонной линии и попробуйте вместо этого эти 5 бесплатных и открытых платформ для голосовых вызовов

Zahoďte telefónnu linku a namiesto toho vyskúšajte týchto 5 bezplatných platforiem s otvoreným zdrojom pre hlasové hovory

Hiqni linjën e telefonit dhe provoni në vend të tyre këto 5 platforma falas dhe me burim të hapur për thirrje zanore

Telefon Hattını Bırakın ve Bunun Yerine Sesli Aramalar İçin Bu 5 Ücretsiz ve Açık Kaynaklı Platformu Deneyin

The TechBeat: Why Pay for the Cloud? Build Your Own with Raspberry Pi and Open Media Vault (2/5/2025)

Why Pay for the Cloud? Build Your Own with Raspberry Pi and Open Media Vault

¿Kunatsa Cloud ukar qullqi churañax wakisi? Raspberry Pi ukat Open Media Vault ukampiw juma pachpa lurañama

Hvorfor betale for skyen? Byg din egen med Raspberry Pi og Open Media Vault

Γιατί να πληρώσω για το Cloud; Δημιουργήστε το δικό σας με το Raspberry Pi και το Open Media Vault

¿Por qué pagar por la nube? Cree la suya propia con Raspberry Pi y Open Media Vault

क्लाउड के लिए भुगतान क्यों करें? रास्पबेरी पाई और ओपन मीडिया वॉल्ट के साथ अपना खुद का क्लाउड बनाएँ

Miért fizet a felhőért? Készítse el sajátját a Raspberry Pi és az Open Media Vault segítségével

なぜクラウドにお金を払う必要があるのか？Raspberry Pi と Open Media Vault を使って自分だけのクラウドを構築しましょう

¿Imaraykutaq Puyumanta qullqita quy? Raspberry Piwan chaymanta Open Media Vaultwan Kikiykipaq ruway

Зачем платить за облако? Создайте свое собственное с Raspberry Pi и Open Media Vault

Чаро барои абр пардохт кунед? Бо Raspberry Pi ва Open Media Vault шахсии худро созед

کلاؤڈ کے لئے ادائیگی کیوں کریں؟ Raspberry Pi اور Open Media Vault کے ساتھ اپنا خود بنائیں

Kutheni Uhlawulela Ifu? Yakha eyakho ngeRaspberry Pi kunye neVault yeMedia evulekileyo

AI Crushes the Competition in Math, Machine Learning, and Open-Ended Tasks—Here’s How It Did It

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation C. Visualisation We illustrate a wide range of visualisations of promptable segmentation and open-vocabulary segmentation across multiple datasets. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation C. Visualisation We illustrate a wide range of visualisations of promptable segmentation and open-vocabulary segmentation across multiple datasets. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Visualizing Promptable and Open-Vocabulary Segmentation Across Multiple Datasets

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