The new API supports high-quality image synthesis from natural language prompts, marking a significant integration point for generative AI workflows in production environments. Available starting today, developers can now directly interact with the same image generation model that powers ChatGPT’s image creation capabilities.

Expanding the Capabilities of ChatGPT to Developers

The gpt-image-1 model is now available through the OpenAI platform, allowing developers to generate photorealistic, artistic, or highly stylized images using plain text. This follows a phased rollout of image generation features in the ChatGPT product interface and marks a critical transition toward API-first deployment.

The image generation endpoint supports parameters such as:

• Prompt: Natural language description of the desired image.
• Size: Standard resolution settings (e.g., 1024×1024).
• n: Number of images to generate per prompt.
• Response format: Choose between base64-encoded images or URLs.
• Style: Optionally specify image aesthetics (e.g., “vivid” or “natural”).

The API follows a synchronous usage model, which means developers receive the generated image(s) in the same response—ideal for real-time interfaces like chatbots or design platforms.

Technical Overview of the API and gpt-image-1 Model

OpenAI has not yet released full architectural details about gpt-image-1, but based on public documentation, the model supports robust prompt adherence, detailed composition, and stylistic coherence across diverse image types. While it is distinct from DALL·E 3 in naming, the image quality and alignment suggest continuity in OpenAI’s image generation research lineage.

The API is designed to be stateless and easy to integrate:

from openai import OpenAI
import base64
client = OpenAI()

prompt = """
A children's book drawing of a veterinarian using a stethoscope to 
listen to the heartbeat of a baby otter.
"""

result = client.images.generate(
    model="gpt-image-1",
    prompt=prompt
)

image_base64 = result.data[0].b64_json
image_bytes = base64.b64decode(image_base64)

# Save the image to a file
with open("otter.png", "wb") as f:
    f.write(image_bytes)

Unlocking Developer Use Cases

By making this API available, OpenAI positions gpt-image-1 as a fundamental building block for multimodal AI development. Some key applications include:

• Generative Design Tools: Seamlessly integrate prompt-based image creation into design software for artists, marketers, and product teams.
• AI Assistants and Agents: Extend LLMs with visual generation capabilities to support richer user interaction and content composition.
• Prototyping for Games and XR: Rapidly generate environments, textures, or concept art for iterative development pipelines.
• Educational Visualizations: Generate scientific diagrams, historical reconstructions, or data illustrations on demand.

With image generation now programmable, these use cases can be scaled, personalized, and embedded directly into user-facing platforms.

Content Moderation and Responsible Use

Safety remains a core consideration. OpenAI has implemented content filtering layers and safety classifiers around the gpt-image-1 model to mitigate risks of generating harmful, misleading, or policy-violating images. The model is subject to the same usage policies as OpenAI’s text-based models, with automated moderation for prompts and generated content.

Developers are encouraged to follow best practices for end-user input validation and maintain transparency in applications that include generative visual content.

Conclusion

The release of gpt-image-1 to the API marks a pivotal step in making generative vision models accessible, controllable, and production-ready. It’s not just a model—it’s an interface to imagination, grounded in structured, repeatable, and scalable computation.

For developers building the next generation of creative software, autonomous agents, or visual storytelling tools, gpt-image-1 offers a robust foundation to bring language and imagery together in code.

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Ironwood is the seventh generation in Google’s TPU architecture and brings substantial improvements in compute performance, memory capacity, and energy efficiency. Each chip delivers a peak throughput of 4,614 teraflops (TFLOPs) and includes 192 GB of high-bandwidth memory (HBM), supporting bandwidths up to 7.4 terabits per second (Tbps). Ironwood can be deployed in configurations of 256 or 9,216 chips, with the larger cluster offering up to 42.5 exaflops of compute, making it one of the most powerful AI accelerators in the industry.

Unlike previous TPU generations that balanced training and inference workloads, Ironwood is engineered specifically for inference. This reflects a broader industry trend where inference, particularly for large language and generative models, is emerging as the dominant workload in production environments. Low-latency and high-throughput performance are critical in such scenarios, and Ironwood is designed to meet those demands efficiently.

A key architectural advancement in Ironwood is the enhanced SparseCore, which accelerates sparse operations commonly found in ranking and retrieval-based workloads. This targeted optimization reduces the need for excessive data movement across the chip and improves both latency and power consumption for specific inference-heavy use cases.

Ironwood also improves energy efficiency significantly, offering more than double the performance-per-watt compared to its predecessor. As AI model deployment scales, energy usage becomes an increasingly important constraint—both economically and environmentally. The improvements in Ironwood contribute toward addressing these challenges in large-scale cloud infrastructure.

The TPU is integrated into Google’s broader AI Hypercomputer framework, a modular compute platform combining high-speed networking, custom silicon, and distributed storage. This integration simplifies the deployment of resource-intensive models, enabling developers to serve real-time AI applications without extensive configuration or tuning.

This launch also signals Google’s intent to remain competitive in the AI infrastructure space, where companies such as Amazon and Microsoft are developing their own in-house AI accelerators. While industry leaders have traditionally relied on GPUs, particularly from Nvidia, the emergence of custom silicon solutions is reshaping the AI compute landscape.

Ironwood’s release reflects the growing maturity of AI infrastructure, where efficiency, reliability, and deployment readiness are now as important as raw compute power. By focusing on inference-first design, Google aims to meet the evolving needs of enterprises running foundation models in production—whether for search, content generation, recommendation systems, or interactive applications.

In summary, Ironwood represents a targeted evolution in TPU design. It prioritizes the needs of inference-heavy workloads with enhanced compute capabilities, improved efficiency, and tighter integration with Google Cloud’s infrastructure. As AI transitions into an operational phase across industries, hardware purpose-built for inference will become increasingly central to scalable, responsive, and cost-effective AI systems.

.

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Current Status of AI Agents

AI agents mostly talk or find information, responding in natural language or searching knowledge bases. According to Amazon, they envision AI agents being able to complete tasks in digital environments for users.

However, agentic AI technology is still developing, meaning most AI agents rely heavily on existing application programming interfaces (APIs). Most real-world tasks lack comprehensive APIs, limiting what current agents can achieve reliably.

Amazon hopes agents will eventually manage complex, multi-step jobs, such as planning large events or handling IT support tasks. Currently, AI agents still need constant human guidance and checking, making them less practical for truly independent work.

What is Amazon Nova Act? Key Features and Functions

Amazon Nova Act is an AI agent that can control and perform tasks within a web browser. This new AI model is trained to complete tasks in a web browser using simple commands. It is available as a research preview through the Nova Act SDK. The tool allows agents to handle tasks like scheduling and email management. It is designed to complete real-world tasks without human intervention at every step.

Here are some features and functions:

• Web Action Focus: Amazon Nova Act is trained specifically to operate and interact with web browser elements.
• Developer SDK: A research preview SDK allows developers to build and test AI agent prototypes.
• Task Automation: The goal is to automate simple browser tasks. This includes filling out forms or managing calendar entries. It can also handle tasks like ordering items online.
• Atomic Commands: The SDK helps break down complex processes. It uses reliable basic commands like ‘search’ or ‘checkout.’
• Detailed Instructions: Developers can add specific guidance to commands. For example, instructing the agent to decline optional add-ons.
• API and Code Integration: The system allows calling external APIs, meaning developers can also insert Python code for checks or custom logic.
• Reliability Emphasis: Amazon focused on high accuracy for tricky web elements. These include date pickers, dropdown menus, and pop-up windows. Internal tests show strong performance here.
• Background Operation: AI agents can run without direct observation once set up using Amazon Nova Act. They can operate headlessly or on a schedule.
• Cross-Environment Potential: Early tests suggest Nova Act can apply its interface understanding to new areas. Surprisingly, this includes environments like web-based games.

Amazon stresses that Nova Act prioritizes reliability for foundational actions. Amazon is focused on targeting over 90% success on internal tests for specific web interactions. This focus means that built agents should work consistently once configured.

Amazon Nova Act AI agent has claimed strong results on benchmarks measuring direct web control ability. The browser-based AI agent performs well against competitors in specific interaction tests. However, it hasn’t been compared using all common AI agent evaluations yet.

Challenges to Autonomous AI Agent Workflow

The main challenge for all AI agents is consistency. Early AI systems often prove slow or error-prone, and they struggle with tasks humans find simple. Amazon hopes its focus on reliable building blocks will offer an advantage. The true test will be how Nova Act performs in real-world developer applications.

Conclusion

Amazon Nova Act clearly shows Amazon’s step and move into the AI agent domain. Its emphasis on reliable task components addresses a key weakness in current agent technology. Amazon hopes to encourage practical applications by providing developers with tools to create AI agents to automate browser tasks. This release from Amazon intensified competition in agentic AI workflow automation and its potential impact on productivity. A truly autonomous AI agent needs to sustain consistent performance; only then will true workflow automation be achieved.

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Developers can use the Anthropic Console to:

• Interact with the Anthropic API.
• Manage API usage and costs.
• Build and improve prompts for Claude or other AI systems.
• Test prompts under different scenarios.
• Simplify the prompt generation and evaluation process.
• Generate a test suite.

As we all know, prompt quality plays a huge role in the success of AI responses. Yet, mastering prompt engineering can be time-consuming and varies across different AI models. Anthropic AI’s prompt improver helps everyone, especially developers, refine their existing prompts automatically. The prompt improver uses advanced techniques, adapting prompts originally written for other AI models or improving hand-written prompts.

Here’s how the prompt improver strengthens prompts:

• Chain-of-thought reasoning: It adds a section for Claude to think through problems systematically. This way, users can expect higher accuracy and reliability.
• Example standardization: The prompt improver feature can convert examples into a consistent Extensible Markup Language (XML) format for clarity. XML helps users to store, transmit, and reconstruct data to be shared amongst computer systems.
• Example enrichment: It improves examples with reasoning that aligns with the new prompt structure.
• Rewriting: The feature can clarify any structure and correct minor grammatical or spelling issues.
• Prefill addition: The assistant message is prefilled to guide Claude’s actions and execute the output formats.

After generating a new prompt, you can tell Claude what’s working and what’s not to refine it further. Anthropic AI testing has shown significant improvements.

“According to Anthropic, the prompt improver increased accuracy by 30% for a multi-label classification test. It also achieved 100% compliance to the word count for a summarization task.“

Adding examples to prompts is one of the best ways to improve AI responses. It could help Claude follow specific formats precisely.

Now, you can directly manage examples in a structured format in the Workbench. This makes adding new examples or editing existing ones easier to refine response quality.

Claude can automatically create synthetic inputs and draft outputs if your prompt lacks examples to make the process easier.

Adding examples leads to increased:

• Accuracy: Reduces misinterpretation of instructions.
• Consistency: Ensures desired output formatting.
• Performance: Boosts Claude’s ability to handle complex tasks.

The prompt evaluator lets you test your prompts under different scenarios. An optional “ideal output” column is added in the Evaluations tab to benchmark and improve performance. This helps users consistently grade model outputs on a 5-point scale.

After testing, you can give Claude more feedback on the prompt improvement and repeat the process until satisfied. Claude AI can also modify the prompt and examples based on requests.

For example, you can ask for JSON-formatted outputs instead of XML.

Conclusion:

Anthropic AI’s latest features put more power in developers’ hands. The prompt improver can simplify prompt refinement and example management. Developers can build reliable AI applications due to the accessibility of better and more refined prompts. Anthropic Console features and tools can save time and boost and improve AI models’ and developers’ performance, output, and accuracy.

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ElevenLabs is not a newcomer. It has actually brought real innovation and competition to the generative AI voices market. ElevenLabs already has over 3,000 high-quality voices; however, sometimes you can’t find the voice you have imagined. That is where you can use the new Voice Design feature to fill in the gaps and design the voice of your imagination. You can describe the age, accent, tone, or character itself to generate a new and accurate AI voice in seconds. The new Voice Design is fairly easy to use, and ElevenLabs has also stated that the API will be available in 1 week.

How to use ElevenLabs’ new Voice Design:

Step 1: Getting Started

• To get started with this new AI feature. Head over to the ElevenLabs website using our link and click on Design Voice Free.

• You will be taken to the sign-up page, where you can sign up using your Gmail. Then, fill out some basic details.

Step 2: Getting Started with Voice Design

• Now, most people might not be able to locate the Voice Design feature. The feature is not located on the sidebar.
• You must first head over to Voices, then click on Add a new voice. On the very top, you’ll find the new Voice Design feature.

Step 3: Generate Your Custom AI Voice

• Once you click on Voice Design, you can prompt the description of the custom AI voice you need. Below that, you can enter the text you want your character to say.
• After adding the prompt and the text, click on the generate voice button.

Step 4: Finalize

• ElevenLabs will generate the AI voice for you and give you three options to choose from. You can choose the one you think fits the best.

• Give that voice a name, label, value, and description of your choice. At the end, save the custom AI voice. You can find your custom AI-generated voice in the personal section in Voices.

Conclusion:

What was once a very saturated market has now returned back to its glory. ElevenLabs brought the necessary innovation once again to the generative AI voices. This new Voice Design feature is unlike any other AI voice generation feature. Yes, most companies offer regular AI text-to-speech, voice cloning, and dubbing, but this custom AI Voice Design feature is the best. This feature can help many film, animation, and video production houses. We don’t know where the limits are with this new feature. ElevenLabs’ Voice Design is a good one! You should try it.

Check out the Details here. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter.. Don’t Forget to join our 55k+ ML SubReddit.

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Over recent times, Runway has announced new AI video-generation models and features that were once considered impossible without expensive equipment. The AI video generator truly democratized Hollywood-level movie production to common people like you and me. And Runway’s new Act-One is proof of that. Runway Act-One is a new way to generate expressive character performances using simple video inputs. You can create compelling animations using video and voice performances as inputs.

How Runway’s Act-One is Different:

Traditionally, we needed motion capturing, multiple footage references, manual face rigging, and other techniques to create an animated movie.

• With Runway’s Act-One, you no longer need any extra equipment, and everything is driven directly and only by an actor’s performance.

• You can also apply this feature to different reference images. The new model can preserve realistic facial expressions and accurately translate performances into characters. That is possible even for characters with a different proposal than the source video.

• Act-One relies more on the actor’s performance than anything else. Hence, it can produce high-quality outputs even from different angles.
• Creators can create life-like characters that deliver genuine emotion and expression for better viewer connection.

• What was once considered impossible is now possible with Runway’s Act-One only using a consumer-grade camera. You can create multi-turn, expressive dialogue scenes where one actor can read and perform different characters from a script.

Conclusion:

This new Act-One feature by Runway is looking strong. There’s no other tool in the market that can do anything remotely similar to an AI video generator. Act One is not yet available for use by the general public but will hopefully launch soon for consumer use. The film industry will change as soon as this new feature is commercially available. I saw someone on X (Twitter) say, “In a couple of years, we are going to have 6-year-olds making movies mostly indistinguishable from Hollywood.” That is not far from the truth. Let’s hope we can use this new AI video generation feature soon.

Check out the Details here. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter.. Don’t Forget to join our 55k+ ML SubReddit.

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What is the Message Batches API?

The Anthropic’s Message Batches API is a service that allows developers to process large amounts of data asynchronously. This means tasks are queued and processed in bulk.

• Submit up to 10,000 queries per batch.
• Processed within 24 hours.
• Costs 50% less than standard API calls.

The API makes it suitable for large-scale operations where real-time responses aren’t necessary. Once a Message Batch is created, it begins processing immediately. Developers can use it to process multiple Messages API requests at once.

Main Features and Benefits

Here’s a breakdown of the key features that make the Anthropic Message Batches API stand out:

• High throughput: Send and process large numbers of requests without hitting rate limits.
• Cost-effective: Get 50% off API costs for bulk operations.
• Scalability: Handle large-scale data tasks, from content moderation to data analysis, without worrying about infrastructure limitations.
• Batch processing: Submit up to 10,000 requests per batch, with results typically ready within 24 hours.

Batch Limitations

While the Anthropic’s Message Batches API offers impressive scalability, it comes with some limitations:

• Maximum batch size: 10,000 requests or 32 MB.
• Processing time: Up to 24 hours.
• Batches expire after 29 days.
• Rate limits apply to API requests, not the number of requests in a batch.

Supported Models

The Message Batches API currently works with several Claude models:

• Claude 3.5 Sonnet
• Claude 3 Haiku
• Claude 3 Opus

According to Anthropic, Amazon Bedrock customers can already access batch inference, and Google Cloud’s Vertex AI support is coming. Developers can batch requests for vision, system messages, multi-turn conversations, and more. Each request within a batch is handled independently, allowing flexibility in combining different types of operations.

How Does the Message Batches API Work?

When using the Anthropic’s API, developers can send large batches of requests to be processed asynchronously. This is ideal for tasks like analyzing massive data sets or conducting content moderation.

• A batch has been created with the requests you provided.
• Each request is processed independently, but results are available only after completing all tasks.
• The process is suited for tasks that don’t need immediate results.

Here’s the Python code showing how to interact with Anthropic’s Message Batches API and send batch requests to one of their AI models, Claude 3.5.

import anthropic

client = anthropic.Anthropic()

client.beta.messages.batches.create(
    requests=[
        {
            "custom_id": "my-first-request",
            "params": {
                "model": "claude-3-5-sonnet-20240620",
                "max_tokens": 1024,
                "messages": [
                    {"role": "user", "content": "Hello, world"}
                ]
            }
        },
        {
            "custom_id": "my-second-request",
            "params": {
                "model": "claude-3-5-sonnet-20240620",
                "max_tokens": 1024,
                "messages": [
                    {"role": "user", "content": "Hi again, friend"}
                ]
            }
        },
    ]
)

For cURL and JavaScript, you can check out Anthropic’s API reference here.

Conclusion

Anthropic’s Message Batches API is a game-changer for developers handling large-scale data operations. It provides an efficient, cost-effective way to process bulk requests. It takes the stress out of managing big data tasks. You can analyze large datasets or moderate content. This Anthropic’s API simplifies bulk operations, giving you the flexibility and scale you need.

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GPT-4o mini is significantly more affordable than previous models. The GPT-4o mini is priced at 15 cents per million input tokens and 60 cents per million output tokens. This makes it an order of magnitude cheaper than its predecessors, including GPT-3.5 Turbo.

GPT-4o mini has already outperformed other small models on various benchmarks:

• Reasoning Tasks: Scores 82% on MMLU, surpassing Gemini Flash and Claude Haiku in reasoning tasks involving text and vision.
• Math and Coding Proficiency: Scores 87% on MGSM and 87.2% on HumanEval, leading its competitors in mathematical reasoning and coding tasks.
• Multimodal Reasoning: Achieved 59.4% on MMMU, outperforming Gemini Flash and Claude Haiku at multimodal reasoning evaluation.

The model has a context window of 128K tokens and supports text and vision inputs and outputs. GPT-4o mini is highly versatile. Future updates will include image, video, and audio inputs and output support.

GPT-4o mini excels in applications that require:

• Chaining or parallelizing multiple model calls.
• Handling large volumes of context.
• Providing fast, real-time text responses.

OpenAI has integrated strong safety measures into GPT-4o mini. The company has filtered out harmful content and applied reinforcement learning with human feedback (RLHF). The model also uses an instruction hierarchy method to resist jailbreaks and prompt injections, making it safer for large-scale applications.

GPT-4o mini is available to developers through various APIs. Free, Plus, and Team users in ChatGPT can access it immediately, with Enterprise users gaining access next week.

OpenAI seems committed to reducing costs while improving model capabilities. Fine-tuning for GPT-4o mini will be available soon, further expanding its usability.

GPT-4o mini has set a new standard in affordable and high-performing AI models. This mini-model allows for different applications and makes AI more accessible to developers and businesses. The future of AI looks more promising with this new model by OpenAI.

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Here’s how to use ChatGPT to make eye-catching PowerPoint presentations:

Getting Started with ChatGPT

To use ChatGPT for creating PowerPoint presentations, follow these steps:

1. Create an Account: Create a ChatGPT account, although you can access it without an account. Creating an account will provide you with more features.

2. Recognize Your Audience: Understand who your audience is, as that will help you customize your content to meet their needs and preferences.
3. Define Your Purpose: Clearly state the purpose and aim of your presentation. Knowing your exact goal can help you create material that will educate, convince, or entertain.

Brainstorming Ideas with ChatGPT

ChatGPT is excellent for generating ideas. Here’s how to get the best out of it:

1. Prompt Examples: Use prompts like “What are some innovative ideas for a presentation on Artificial intelligence (AI)?” or “How can I make a presentation on Machine Learning (ML) engaging?”

2. Topic Refinement: Ask ChatGPT to narrow down broad topics. For example, “What are the key points I should cover in a presentation about Machine Learning for beginners?”

Structuring Your Presentation

A well-structured presentation is crucial for maintaining audience engagement. Use ChatGPT to outline your presentation:

1. Beginning: Request ChatGPT to write an engaging opening paragraph. For instance: “How can I effectively introduce the topic of Machine Learning (ML)?”

2. Main Content: Divide your text into manageable chunks. Use prompts such as “List the main points for a section on the benefits of Machine Learning in education.”

3. Conclusion: Conclude with a powerful statement. For instance: “How can I summarize the key takeaways of my presentation on Machine Learning?”

Creating Slide Content

Once you have the structure, it’s time to fill in the details. ChatGPT can help you prepare the content for each slide:

1. Text Content: Use writing exercises like “Write a brief sentence discussing how Machine Learning has affected modern businesses.”

2. Bullet Points: Highlight important details in bullet points. For example: “Convert this paragraph into bullet points.”

3. Visual Suggestions: Ask ChatGPT for suggestions on images. For example: “Create some images or charts that I should include in the presentation about the benefits of Machine Learning in day-to-day life?”

Designing the Presentation

While ChatGPT can’t design slides directly, it can offer valuable advice:

1. Slide Layouts: Request advice on how to organize your slides. For instance: “What is the best layout for the main body of this presentation?”

2. Color Schemes: Find color schemes that work well for your subject. For instance: “Show me the color scheme I should use for a Machine Learning and Generative AI presentation?”

3. Font Choices: You should always select a readable font. For instance, “What fonts are best for a technical professional business presentation?”

Practicing Your Presentation

ChatGPT can even help you practice your delivery:

1. Q&A Preparation: Prepare for audience questions during the Q&A period. For example: “What questions might be asked after a presentation on Machine Learning?”

2. Feedback and Enhancement: To improve your content, simulate feedback from the audience with ChatGPT. For example: “How can I improve my slide on Machine Learning?”

In Conclusion:

Using ChatGPT to create PowerPoint presentations can greatly improve your content, design, and way of delivery. You can take advantage of its capabilities to create engaging and polished presentations that grab your audience’s attention. Whether you’re presenting in a business meeting, academic setting, or public event, ChatGPT can be your valuable assistant in creating outstanding presentations.

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Unlike traditional Apple App Store or Google Play Store applications, anyone can build a GPT as you do not require any coding skills. You can start creating a GPT today, and all you need to do is start a conversation, give instructions and extra knowledge, and give it a specialized duty, which could be anything from web searches to data analysis and generating AI images to coding, and much more. Previously, accessing GPTs was a premium feature, and only those subscribed to ChatGPT Plus could access custom GPTs.

However, since the launch of GPT-4o, anyone with an internet connection and a ChatGPT account can use and access GPTs. GPTs have been a game-changer for many, including software developers and programmers. Yes! Custom GPTs are available to help developers write codes, debug coding errors, test codes, and learn more. There are many GPTs dedicated to programming, so we curated the 10 best for you, from GPTs that will help you code more efficiently to GPTs that could help you generate a whole website, from GPT for Python to GPT for SQL and more.

Here are some of the trending GPTs for software developers:

Code Copilot:

Code Copilot is the number 1 GPT for programming. Code Copilot can help you code far more smartly and even build faster than previously could. Code Copilot alone has the expertise of 10 programmers and is an essential tool to have by your side. Code Copilot can help you in code interpretation and data analysis, retrieving or being able to take actions outside of ChatGPT, and browsing. 

Python:

Python coding languages is one of the world’s most popular coding languages, if not the most popular. The Python GPT is rightly one of the most popular GPTs. It is very refined and has been customized specifically for Python programmers and optimized for GPT-4o. The main capabilities of Python GPT are that it can be used as a code interpreter and data analysis tool. 

Grimoire:

Do you believe in wizards and witches? If you don’t, then you will with Grimoire. Grimoire is a coding wizard and programming copilot that can help you build faster with 20+ Hotkeys for coding flows. With Grimoire’s help, you can create anything or choose from a start project and publish it directly using Replit or Netlify. Grimoire can also help you retrieve or take actions outside ChatGPT, browsing, code interpreter & data analysis. However, it can also have Dall-E image generation capabilities.

Website Generator:

As we said in the introduction, you can even generate websites using GPTs, so the Website Generator GPT is the first choice in the GPT store. Website Generator GPT can help you design, code, and create websites. It also provides copywriting facilities and integrates DALL-E 3 technology. The Website Generator GPT is powered by B12, an AI website-building tool.

Code Guru:

In Hindi, a guru is a teacher. A Code Guru could also be a coding teacher who can review your code, write pull requests, create and optimize functions, and write tests and comments on existing code. Code Guru GPT can do what most other GPTs in the field can do, whether retrieving or taking actions outside ChatGPT, using a code interpreter, performing analysis, and more.

Website Instantly [Multipage]:

Website Instantly [Multipage] GPT is another GPT that can create multipage websites. The website creation process has been simplified while delivering professional-looking and well-optimized websites. These websites are good enough or perfect for 98% of start-ups and small businesses needing a website. 

SQL Expert:

SQL, or Structured Query Language, is another well-known programming language used to perform database operations. SQL Expert GPT is an SQL expert used for optimization and queries. SQL Expert can help you with browsing, code interpretation, and data analysis, helping you become an SQL expert yourself. 

Software Architect GPT:

Software Architect GPT is a code interpretation and data analysis tool for modern software engineers who understand user requirements and design constraints and want to build innovative software architecture documents. Companies often forget to involve or understand what their users want, so using GPT, which can help you understand user requirements and constraints, is a game-changer. 

DesignerGPT:

DesignerGPT can not only create but also host websites. You can create a website using DesignerGPT, refine and personalize it further on Replit, and get your own personal website domain. DesignerGPT also integrates Dall-E into website creation, giving your website best-in-class design and images. DesignerGPT is your all-in-one AI web development solution. 

AskTheCode – Git Companion:

You may know about GitHub, as it is one of the most important spaces for developers and software engineers and has over 100 million users. AskTheCode GPT is your GitHub companion that helps you by providing you with a GitHub repository URL and allowing you to ask any question about the aspect of the code. Even though ChatGPT may not be perfect at coding with custom GPTs, you can certainly get tremendous help.

In Conclusion:

These GPTs can change software development by providing powerful tools for coding, website generation, SQL operations, and more. With the start of the GPT-4o era, these custom GPTs are now accessible to anyone with an internet connection and a ChatGPT account, making them invaluable resources for developers of all skill levels. Whether you need assistance with code interpretation, data analysis, website creation, or SQL queries, the wide range of GPTs are available to support you in your software development journey.

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