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Blend AIv0.9.1

Access ChatGPT, Claude, Gemini, Llama, Grok, Midjourney, Ideogram, Flux Perplexity, Deepseek, Runway and Luma all in one place.
Enjoy advanced AI models, image/video creation, and web search.
Save up to 50% in 2025.

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GlobalGPTv3.0.0

🚀 Smarter, Faster, and More Controllable: Announcing Release 1.3.0!

We're thrilled to announce a major platform update packed with features designed to accelerate your results, give you more control, and improve your overall experience. This release introduces parallel task execution, a completely revamped UI, and powerful new ways to manage your tasks.

✨ Key Feature Highlights

Run Subtasks in Parallel for Faster Results

Full Control Over Your Tasks

A Revamped User Interface

✅ Other Improvements

We can't wait for you to try out these new features!

✨ New Features Just Dropped!

🔁 Recurring Tasks – Automate your workflow by setting any task to run on a custom schedule. No more daily copy-paste.

🌐 Community Sharing – Share your favorite tasks with the world and get a chance to be featured on our homepage!

✨ Added

🔁 Changed

🐛 Fixed

🔒 Security & Ops

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ComputerXv1.3.0

We're thrilled to announce the launch of ConsoleX AI 2.0, featuring the new Tool Vault! This exciting update makes it incredibly easy and cost efficient to invoke any tools, Workflows, or MCP servers, transforming large language models into autonomous AI agents with advanced tool-calling capabilities.

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ConsoleX AIv2.0

AppDeploy is an AI-assisted tool designed to allow users to deploy applications directly from AI platforms such as ChatGPT and Claude. By simply describing the desired application in a chat interface, the AI platform writes the code, which is then passed to AppDeploy for deployment.

It negates the need for setting up screens or making technical decisions. Once your application is ready to go live, AppDeploy provides a live URL for accessibility.

Notably, AppDeploy handles a variety of tasks such as cloud hosting, database and storage setup, user login and authentication, and real-time user synchronization, thereby streamlining the process and removing much of the usual hassle and complexity associated with app deployment.

Furthermore, it integrates several features aimed at ensuring error-free and efficient deployment, including visual bug reports, version rollback capability, and live deployment status tracking.

Though AppDeploy is designed to be user-friendly and accessible to non-coders, coding knowledge can enhance the speed and efficiency of application deployment.

The tool is hosted on scalable cloud infrastructure, meaning users don't have to worry about choosing a hosting provider. Besides ChatGPT and Claude, AppDeploy also supports other MCP-compatible platforms like Codex and Cursor.

A new major version is available for FREE in the ChatGPT Store!

In this release, we added an automatic testing AI agent that tests your app on every change, making sure it never breaks.

Chat, create, auto-test, and deploy in minutes. 100% free with no credit card required. Find us now in the ChatGPT Store!

AppDeploy is now live in the ChatGPT app store.

Deploy real web apps directly from ChatGPT today, 100% free. No subscription or credit card required.

Added support for mobile notifications and built-in AI.

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AppDeployv2.0.0

GPTConsole is a tool designed to assist developers in generating web/mobile applications and enabling web automation through prompts. It provides an NPM package that developers can install on their local machines.

The tool offers an Intelligent CLI and Autonomous AI Agents, which are lightweight and easy to use. Developers can conveniently install the GPTConsole package using the global add command in Yarn or by using the npm i command.

By logging in or signing up, they can access the platform's features. GPTConsole also provides a blog and documentation for users who want to explore its capabilities further.

Additionally, a Pixie Demo and a Bird Demo are available on YouTube, showcasing the tool's functionality and potential use cases. For those interested in creating their own agents with GPTConsole, there is an option to join the waitlist.

Users can reach out to the GPTConsole team via email or connect on Twitter, Discord, and YouTube. Overall, GPTConsole serves as a valuable resource for developers seeking to streamline the development process of web/mobile applications and execute web automation tasks efficiently.

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GPTConsole

Llama vs Vicena

LLaMA is an open source Artificial Intelligence (AI) model designed with flexibility and versatility in mind. Developed to provide users with the capability to fine-tune its underlying algorithms to better align with their requirements, this tool stands out due to its customizability.

Additionally, it is equipped with distillation functionality that enables users to simplify complex AI models into more manageably sized forms, thereby improving efficiency and performance.Available in different variants, it offers users a range of functionalities in terms of capacity and complexity depending on their specific needs and system capabilities.

Regardless of the version chosen, it's devised to be portable and easily deployable across various environments, ensuring seamless integration with existing systems.As an open-source tool, LLaMA promotes transparency and extends the opportunity for AI enthusiasts, professionals, and organizations to explore, modify, and improve its algorithms.

This openness fosters a collaborative approach towards the development of AI tools, contributing to the overall advancement and resourcefulness in the AI field.

Overall, LLaMA proves to be an adaptable and scalable solution for those seeking to incorporate customized AI models into their systems, with the added benefits of distillation and portability presented in an open-source framework.

Model Sizes:

Multimodal Capabilities: Processes text, images, and videos.

Architecture: Introduced Mixture-of-Experts (MoE) for computational efficiency.

Performance Enhancements:

Improved coding, reasoning, and multilingual tasks.

Optimized for long-context processing.

Applications: General assistant tasks, creative writing, document summarization, and advanced visual comprehension.

Licensing: Stricter terms, especially in the EU; special approval required for enterprises with >700M MAUs.

Llama 3.2 builds upon Llama 3.1 by introducing new model sizes—1B, 3B, 11B, and 90B parameters—allowing deployment on devices ranging from mobile to servers.

The 11B and 90B models add multimodal capabilities, processing both text and images for tasks like visual reasoning, which Llama 3.1 lacks.

Developer experience is enhanced with Llama Stack, offering streamlined development, better tool integration, and flexible deployment options across various programming languages.

Overall, Llama 3.2 offers more versatility, multimodal support, and improved developer tools compared to Llama 3.1.

LLaMA 3.1 is an updated version of LLaMA 3, bringing several key enhancements in performance, efficiency, and functionality. It offers improved accuracy and contextual relevance in responses, with better handling of complex queries and specialized tasks compared to LLaMA 3.

Safety features in LLaMA 3.1 have been further advanced, incorporating enhanced mechanisms to minimize the risk of generating harmful or biased content. The model aligns more closely with ethical standards, ensuring that its outputs are even more reliable and responsible.

LLaMA 3.1 benefits from an upgraded training dataset, providing access to a broader and more current range of information. This results in more precise and relevant answers, improving the quality of interactions and content creation beyond what was achieved with LLaMA 3.

The user experience is smoother with LLaMA 3.1, featuring more natural conversation patterns and improved engagement. Enhanced support for diverse applications, including more sophisticated multimodal tasks, makes it a more versatile tool compared to LLaMA 3.

Overall, LLaMA 3.1 builds on the foundation of LLaMA 3 with significant improvements in accuracy, safety, and usability, making it a more advanced and effective AI model.

LLaMA 3 is a significant upgrade over LLaMA 2, offering notable improvements in performance, efficiency, and versatility. It provides more accurate and contextually nuanced responses, with enhanced capabilities in handling complex tasks and specialized domains compared to LLaMA 2.

Safety features in LLaMA 3 have been further refined, reducing the risk of generating harmful or biased content. The model is better aligned with ethical standards and incorporates advanced mechanisms to ensure that its outputs are both reliable and responsible.

LLaMA 3 benefits from an even more updated training dataset, which allows it to draw from a wider and more current range of information. This results in more precise, relevant, and up-to-date answers, significantly enhancing the overall quality of interactions and content generation.

The user experience with LLaMA 3 is markedly improved, featuring smoother and more natural conversation patterns along with greater engagement. The model offers enhanced support for diverse applications, including more advanced multimodal tasks and integration with other data types.

Overall, LLaMA 3 builds on the advancements of LLaMA 2 with substantial enhancements in accuracy, safety, and usability, making it a more advanced and capable AI tool for a wide range of applications.

LLaMA 2 is an enhanced version of LLaMA 1, bringing notable improvements in performance, versatility, and safety. It delivers more accurate and contextually relevant responses, with enhanced capabilities in handling complex tasks and specialized domains compared to LLaMA 1.

Safety features in LLaMA 2 are further refined, reducing the risk of generating harmful or biased content. The model is better aligned with ethical standards, ensuring that its outputs are more reliable and responsible.

LLaMA 2 benefits from an updated training dataset, which allows it to draw from a broader and more current range of information. This results in more precise and relevant answers, improving the overall quality of interactions and content generation.

The user experience is notably enhanced with LLaMA 2, featuring more natural conversation patterns and improved engagement. It also offers better support for diverse applications, making it more adaptable for various use cases compared to LLaMA 1.

Overall, LLaMA 2 builds on the foundation of LLaMA 1 with significant advancements in accuracy, safety, and usability, making it a more powerful and refined AI tool.

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LLaMAv4

Vicena is an AI research agent that assists with scientific workflows across literature search, experimental design, lab simulation, and computational chemistry.

It reasons through research questions, selects the appropriate tools from a catalog of 24, executes them, and iterates until it produces cited results.For research and discovery, Vicena searches PubMed, arXiv, Google Scholar, and Google Patents in parallel, refining queries adaptively until coverage is comprehensive.

Every output links back to original sources with DOIs, authors, and publication dates. It also extracts step-by-step experimental protocols from papers, cross-referencing variables across sources and assigning confidence scores based on agreement.For lab simulation and validation, Vicena audits synthesis protocols for safety, yield feasibility, and reagent compatibility before the bench.

It checks protocols against physical constraints including thermodynamics, kinetics, vapor pressure, and GHS hazards, using curated rules and open libraries like RDKit, PubChem, Thermo, and ChemPy.

Outputs cite their sources and explicitly report coverage gaps rather than implying safety where data is unavailable.For computational chemistry, Vicena provides a full toolkit for reaction prediction (including Suzuki, Buchwald, Heck couplings via the Molecular Transformer neural network), retrosynthesis, molecular descriptors with Lipinski analysis, structural similarity via Morgan fingerprints, spectroscopy prediction (IR, NMR, MS), and quantum chemistry calculations (DFT, Hartree-Fock, MP2) built on PySCF and ASE.Vicena also includes a persistent Science Computer with a JupyterLab environment and over 30 scientific Python packages pre-installed.

The AI writes and executes code in real notebooks, debugging and iterating as it works. Files persist across sessions.The platform is built on 26 open scientific libraries, databases, and models including RDKit, PubChem, PySCF, the Molecular Transformer, SMARTS, and the Arrhenius equation.

Every numerical result traces back to the specific library that produced it.

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Compare	LLaMAv4 5.0 27 reviewsVisit Website	Top pick Vicena 5.0 20 reviewsVisit Website
Overview
Tagline	Open-source AI models for customization and deployment.	AI for real scientific research (Vibe Research)
Category	IT Management	IT Management
Ratings

Overall Rating	5.0	5.0
Ease of Use	5.0	5.0
Features	5.0	5.0
Value for Money	5.0	5.0
Customer Service	5.0	5.0
Total Reviews	27	20
Pricing

Pricing Model	—	Freemium
Starting Price	Contact vendor for pricing	Free plan available
Features

AI Agents
AI Customization
AI For Science
AI Research Agent
API
Advanced AI
Agent
Chemistry Tools
Free + From $5/Mo
Model Distillation
Open Source AI
Portable AI
Scalable AI
Deployment

Deployment Options	Cloud, SaaS, Web-Based	Cloud, SaaS, Web-Based
Pros & Cons

Pros	High customizability In-built model distillation Improved efficiency and performance	Autonomous multi-step research agent Combines literature, patents, and web search Provides fully cited outputs
Cons	Complex customization process Difficult distillation functionality Various versions add confusion	Requires domain knowledge to use effectively May be overkill for simple research tasks Results still require human verification

Llama vs Vicena

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