Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

GPT-4o is bad at processing PDF documents. Whoever tells you otherwise is not living in the real world. In 2024, people fill out forms using pen and paper. Try to answer questions from those forms using modern models, and you'll be disappointed. I recorded a video to show you... how to fix this. The answer is simple: stop letting the model see your PDF document. Instead, preprocess it and stick to showing the model text. Watch the video. You'll go from "THIS IS CRAP" to "EXCELLENT" in no time. I'm using Unstract to turn the documents into text while keeping the original format (this is crucial!) You can use them to process up to 100 pages for free. They collaborated with me on this post. You can find the code I wrote right here:show more

Santiago

453,295 subscribers

196,847 просмотров • 1 год назад •via X (Twitter)

Наука и технологии Новости и политика Образование

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля Jad Bharat

Jad Bharat1 год назад

I second this. I used to upload scanned docs to it, but better results came when I extracted text from scan using Google lense and send that to gpt as input.

Фото профиля Santiago

Santiago1 год назад

That's the way.

Фото профиля David Alexander

David Alexander1 год назад

Convert the PDF to an image, and it works as good as any other document you upload. Click Bait!

Фото профиля Santiago

Santiago1 год назад

I'd love for this to work, but it doesn't. Feel free to try yourself.

Фото профиля GV

GV1 год назад

I see you play modern day influencer role under the disguise of being machine learning engineer. Ah. I see.

Фото профиля Santiago

Santiago1 год назад

An anonymous account accusing me of disguising my identity. Irony is dead.

Фото профиля LatestPaperAI

LatestPaperAI1 год назад

AI still has a long way to go in mastering practical document processing.

Фото профиля Santiago

Santiago1 год назад

It does. That's where we come in: instead of hoping for the model to work, we can make it easy to find the information it needs.

Фото профиля MySportsAI

MySportsAI1 год назад

Had a go this morning at getting ChatGPT to scrape a basic web site. It was hopeless, seemingly if the HTML tags are not so descriptive that a child of 5 could scrape them it struggles

Фото профиля Santiago

Santiago1 год назад

Anything you can do to preprocess the data and make it easier to consume, goes a long way.

Похожие видео

This is a pretty wild model! You can use it to turn an image into a 3D object with texture. The quality is out of this world! I'm not even a designer, and I've been using this nonstop for the last 2 hours. The model is Hunyuan 3D 2.1. It's open source. You'll find model weights, training/inference code, data pipelines, and architecture on their repository. You can even fine-tune it if you want! GitHub Repository: By the way, the model runs on consumer-grade GPUs. You don't need a datacenter for this! I've been using the model from the HuggingFace demo page: To use it, go to the link and upload an image. That's it! Check out the video I recorded for a couple of examples.

This is a pretty wild model! You can use it to turn an image into a 3D object with texture. The quality is out of this world! I'm not even a designer, and I've been using this nonstop for the last 2 hours. The model is Hunyuan 3D 2.1. It's open source. You'll find model weights, training/inference code, data pipelines, and architecture on their repository. You can even fine-tune it if you want! GitHub Repository: By the way, the model runs on consumer-grade GPUs. You don't need a datacenter for this! I've been using the model from the HuggingFace demo page: To use it, go to the link and upload an image. That's it! Check out the video I recorded for a couple of examples.

Santiago

44,783 просмотров • 1 год назад

Why aren't more people talking about how difficult it is to turn documents into structured data? This is literally a problem that every single company I talk to is trying to solve. They have a buttload of documents with forms and tables, and they want to turn them into usable data. Let me show you how you can do this using Tensorlake. Probably the best parser I've found:

Why aren't more people talking about how difficult it is to turn documents into structured data? This is literally a problem that every single company I talk to is trying to solve. They have a buttload of documents with forms and tables, and they want to turn them into usable data. Let me show you how you can do this using Tensorlake. Probably the best parser I've found:

Santiago

46,413 просмотров • 1 год назад

Here is how you can install an open-source, enterprise-grade RAG system on your server (with the best document understanding I've seen.) First, something obvious to anyone trying to sell RAG in the market: You are crazy if you think companies will let their data travel to a hosted model. No one wants to send their data anywhere (those who do haven't found an alternative.) Every single company would rather have an air-gapped system with no internet access. GroundX is an open-source RAG system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network. (If the military wants to do RAG, this is precisely what they will be looking for.) I installed GroundX on my AWS account and recorded a video to show you how to use it. There are two services you can use: 1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base. 2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base. A quick note about the Ingest service: 99% of people think they need better "retrieval" mechanisms. I think they need better "ingestion." That's where this service comes in! Ingest "understands" your documents in a way I haven't seen before. After you try it, you'll realize why showing your LLM your raw documents is a bad idea. In the video, I use a free tool called X-Ray to test a document and understand how the Ingest service breaks it down. You can access this tool by signing up for a free GroundX cloud account and uploading your documents. You'll see a bit more about this in the video.

Here is how you can install an open-source, enterprise-grade RAG system on your server (with the best document understanding I've seen.) First, something obvious to anyone trying to sell RAG in the market: You are crazy if you think companies will let their data travel to a hosted model. No one wants to send their data anywhere (those who do haven't found an alternative.) Every single company would rather have an air-gapped system with no internet access. GroundX is an open-source RAG system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network. (If the military wants to do RAG, this is precisely what they will be looking for.) I installed GroundX on my AWS account and recorded a video to show you how to use it. There are two services you can use: 1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base. 2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base. A quick note about the Ingest service: 99% of people think they need better "retrieval" mechanisms. I think they need better "ingestion." That's where this service comes in! Ingest "understands" your documents in a way I haven't seen before. After you try it, you'll realize why showing your LLM your raw documents is a bad idea. In the video, I use a free tool called X-Ray to test a document and understand how the Ingest service breaks it down. You can access this tool by signing up for a free GroundX cloud account and uploading your documents. You'll see a bit more about this in the video.

Santiago

89,664 просмотров • 1 год назад

How to match the complexity of the problem you want to solve with the proper model. You want an inference router. In the video, I show you how simple and powerful this is. After this, you'll never talk directly to a model ever again.

How to match the complexity of the problem you want to solve with the proper model. You want an inference router. In the video, I show you how simple and powerful this is. After this, you'll never talk directly to a model ever again.

Santiago

11,944 просмотров • 2 месяцев назад

This is the fastest text-to-speech and speech-to-text API out there. It's under 250 ms to first-byte latency. That's what we need to deploy conversational AI applications everywhere! (I can't wait for the death of automated voice machines.) Look at the attached video. For the first time, I could have a conversation with an AI agent in real time without awkward pauses. This technology is Aura from Deepgram. It's the fastest text-to-speech option I've seen. Here is a GitHub repository with the demo in the video: You can use it to build a conversational AI application using natural voice. You'll need a Deepgram API Key and an OpenAI API Key. Thanks to Deepgram for giving me access to Aura and collaborating with me on this post. If you want to learn more about Aura, here is the announcement post:

This is the fastest text-to-speech and speech-to-text API out there. It's under 250 ms to first-byte latency. That's what we need to deploy conversational AI applications everywhere! (I can't wait for the death of automated voice machines.) Look at the attached video. For the first time, I could have a conversation with an AI agent in real time without awkward pauses. This technology is Aura from Deepgram. It's the fastest text-to-speech option I've seen. Here is a GitHub repository with the demo in the video: You can use it to build a conversational AI application using natural voice. You'll need a Deepgram API Key and an OpenAI API Key. Thanks to Deepgram for giving me access to Aura and collaborating with me on this post. If you want to learn more about Aura, here is the announcement post:

Santiago

185,640 просмотров • 2 лет назад

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

Santiago

324,621 просмотров • 2 лет назад

Dreamina Seedance 2.0 is by far the best video model I've tried, and Dreamina makes it even better. You can try it in the link below. It's not just about the quality of the output, but about how much control you have over your video's look. Watch my video here. I'm attaching reference images and asking the model to generate a video using them. I can reference each image using the @ symbol to instruct the model which image to use. You can even upload a clip and use its camera movement, styles from an image, and audio vibe from a track. By the way, you can take an existing video and replace, remove, or add elements to it while the model preserves everything else. This is the closest we've gotten to "editing videos like photos".

Dreamina Seedance 2.0 is by far the best video model I've tried, and Dreamina makes it even better. You can try it in the link below. It's not just about the quality of the output, but about how much control you have over your video's look. Watch my video here. I'm attaching reference images and asking the model to generate a video using them. I can reference each image using the @ symbol to instruct the model which image to use. You can even upload a clip and use its camera movement, styles from an image, and audio vibe from a track. By the way, you can take an existing video and replace, remove, or add elements to it while the model preserves everything else. This is the closest we've gotten to "editing videos like photos".

Santiago

45,143 просмотров • 4 месяцев назад

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

Santiago

384,510 просмотров • 3 лет назад

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

Santiago

55,609 просмотров • 2 лет назад

108 workflow templates you can use to build AI applications without writing any code. You can use these templates with n8n. I recorded the attached video to show you how it works. n8n is the workhorse behind an open-source, self-hosted AI starter kit you can install on your computer. They are sponsoring this post. Here is the link to the starter kit repository: And here is the spreadsheet with the 108 templates: Whatever idea you have, search for something similar in the list of templates, and you'll save a ton of time. Lately, I've talked to many non-coders who want to start using AI more seriously to build things. n8n is perfect for that.

108 workflow templates you can use to build AI applications without writing any code. You can use these templates with n8n. I recorded the attached video to show you how it works. n8n is the workhorse behind an open-source, self-hosted AI starter kit you can install on your computer. They are sponsoring this post. Here is the link to the starter kit repository: And here is the spreadsheet with the 108 templates: Whatever idea you have, search for something similar in the list of templates, and you'll save a ton of time. Lately, I've talked to many non-coders who want to start using AI more seriously to build things. n8n is perfect for that.

Santiago

78,133 просмотров • 1 год назад

🚨You can now use the new upcoming OpenAI model GPT 5.2 inside Cursor. Here is the full walkthrough. - Open the editor, go to settings and then the model tab. Add a custom model and enter the text "gpt-5.2-high" and "gpt-5.2". - After that you can select the model and ask questions. To verify, I started my test on the usage page which had zero gpt-5.2-high requests and consumption. After the test I could see the details in usage and the cost incurred while using it. Enjoy

🚨You can now use the new upcoming OpenAI model GPT 5.2 inside Cursor. Here is the full walkthrough. - Open the editor, go to settings and then the model tab. Add a custom model and enter the text "gpt-5.2-high" and "gpt-5.2". - After that you can select the model and ask questions. To verify, I started my test on the usage page which had zero gpt-5.2-high requests and consumption. After the test I could see the details in usage and the cost incurred while using it. Enjoy

AshutoshShrivastava

424,035 просмотров • 8 месяцев назад

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,835 просмотров • 2 лет назад

Building a RAG system that works with real-life documents is crazy hard. Why is nobody talking about this? (I'll show you what a complex document looks like in the attached video. Good luck with the 10-line code demos if you want to deal with this.) All I see online are "how to talk to a PDF" demos that won't take you far. If you think that's all you need, you won't like what happens when you try. In the video below, I'll show you what it takes to build a system that processes huge numbers of documents without losing accuracy. There are a few tricks here that I'm sure will impress you. The magic is happening in three phases: • In the way we process the documents • In the way we chunk the content • In the way we augment and store the chunks This is all built-in in GroundX, EyeLevel.AI's out-of-the-box RAG system. You can use it as a SaaS or install it in your own Kubernetes cluster and run it locally. If you have seen any other RAG system doing what I do in the attached video, please, let me know. I'd love to check them out. Disclaimer: I work with the team at EyeLevel. My advice is to take any document you have lying around, go to their site, create an account, and try them out. Here is their website:

Building a RAG system that works with real-life documents is crazy hard. Why is nobody talking about this? (I'll show you what a complex document looks like in the attached video. Good luck with the 10-line code demos if you want to deal with this.) All I see online are "how to talk to a PDF" demos that won't take you far. If you think that's all you need, you won't like what happens when you try. In the video below, I'll show you what it takes to build a system that processes huge numbers of documents without losing accuracy. There are a few tricks here that I'm sure will impress you. The magic is happening in three phases: • In the way we process the documents • In the way we chunk the content • In the way we augment and store the chunks This is all built-in in GroundX, EyeLevel.AI's out-of-the-box RAG system. You can use it as a SaaS or install it in your own Kubernetes cluster and run it locally. If you have seen any other RAG system doing what I do in the attached video, please, let me know. I'd love to check them out. Disclaimer: I work with the team at EyeLevel. My advice is to take any document you have lying around, go to their site, create an account, and try them out. Here is their website:

Santiago

75,788 просмотров • 1 год назад

How to Create Music That Matches Your Video's Sound with Suno Studio I made a quick video showing how I use Suno Studio to generate background music that fits the audio and mood of a video, similar to the example in the quoted post. This is the method I use at the moment but if you have a better approach, feel free to share it. Before starting, export the original audio from your video. I usually do this in CapCut. After generating a track you like in Suno Studio, you can import it back into CapCut and add it to your video. *This is not a paid partnership.

How to Create Music That Matches Your Video's Sound with Suno Studio I made a quick video showing how I use Suno Studio to generate background music that fits the audio and mood of a video, similar to the example in the quoted post. This is the method I use at the moment but if you have a better approach, feel free to share it. Before starting, export the original audio from your video. I usually do this in CapCut. After generating a track you like in Suno Studio, you can import it back into CapCut and add it to your video. *This is not a paid partnership.

Kōda

37,110 просмотров • 1 месяц назад

PhD Students – Use this tool to paraphrase the text in your manuscript for FREE. First, why you need to paraphrase? The first draft you write is often very rough. It needs to be revised to polish it for submission. For this, you can use a paraphraser. Here is one you can use it for FREE. 1. Go to and log in. 2. Upload your file or copy/paste your text in a new file. 3. From the options on right, click Paraphraser. 4. Now select the text you want to paraphrase. 5. After text selection, click paraphrase. 6. Trinka.ai will show the paraphrased text. 7. You can also see the changes made. 8. You can increase or decrease the level of paraphrasing. 9. You can also select to show the revisions. 10. Go through the paraphrase text carefully. 11. You can choose among multiple paraphrased alternatives. 12. After your final selection, click replace. 13. This will replace original text with paraphrased text. 14. You can apply this process for the entire text. Make sure that you go through the changes made. This is important so that only your text gets refined. However, the meaning of the text remains the same. In addition to paraphrasing, Trinka also offers to ➝ Fix all grammatical errors in your manuscript ➝ Proofread your manuscript before submission ➝ Check consistency in your manuscript writing ➝ Evaluates the level of bias in your writing ➝ Checks plagiarism level in your manuscript Try Trinka.ai here: Anything you’d like to add?

PhD Students – Use this tool to paraphrase the text in your manuscript for FREE. First, why you need to paraphrase? The first draft you write is often very rough. It needs to be revised to polish it for submission. For this, you can use a paraphraser. Here is one you can use it for FREE. 1. Go to and log in. 2. Upload your file or copy/paste your text in a new file. 3. From the options on right, click Paraphraser. 4. Now select the text you want to paraphrase. 5. After text selection, click paraphrase. 6. Trinka.ai will show the paraphrased text. 7. You can also see the changes made. 8. You can increase or decrease the level of paraphrasing. 9. You can also select to show the revisions. 10. Go through the paraphrase text carefully. 11. You can choose among multiple paraphrased alternatives. 12. After your final selection, click replace. 13. This will replace original text with paraphrased text. 14. You can apply this process for the entire text. Make sure that you go through the changes made. This is important so that only your text gets refined. However, the meaning of the text remains the same. In addition to paraphrasing, Trinka also offers to ➝ Fix all grammatical errors in your manuscript ➝ Proofread your manuscript before submission ➝ Check consistency in your manuscript writing ➝ Evaluates the level of bias in your writing ➝ Checks plagiarism level in your manuscript Try Trinka.ai here: Anything you’d like to add?

Faheem Ullah

18,055 просмотров • 1 год назад

Introducing Stack Overflow searches with GPT-4o in VSCode This new feature allows you to use the capabilities of /StackOverflow find related posts ensuring a far more accurate answer to your question. There's no waiting list! 🤩 You can use this today! All you have to do is install the CodeGPT extension in VSCode and use OpenAI models (GPT-4o)

Introducing Stack Overflow searches with GPT-4o in VSCode This new feature allows you to use the capabilities of /StackOverflow find related posts ensuring a far more accurate answer to your question. There's no waiting list! 🤩 You can use this today! All you have to do is install the CodeGPT extension in VSCode and use OpenAI models (GPT-4o)

Daniel San

20,105 просмотров • 2 лет назад