Plus, OpenAI says ChatGPT's growth is surging despite the DeepSeek buzz

Apple's new iPhone, Nvidia's new tool, and Palantir stock tanks: AI news roundup

About 30 researchers and engineers at Thinking Machines Lab come from rivals such as OpenAI, Meta, and Mistral, with ex-OpenAI staff making up two-thirds of the team.

Ex-OpenAI CTO Mira Murati launches AI startup, recruits top talent from rivals – Computerworld

Other key executives on Murati’s team include John Schulman, who co-founded OpenAI in 2015 and will be chief scientist at Thinking Machines Lab, and Barret Zoph, who served as OpenAI’s vice president of research and will be the new startup’s CTO.

Former OpenAI CTO Murati to launch new AI startup | Financial Post

Artificial intelligence (AI) is increasingly being used by companies not just for cost-cutting, but also as a way to drive new revenue streams. Premium AI services, such as OpenAI’s subscription models, reflect the growing costs of delivering such technologies.

Which companies will win the AI race? One VC shares his take.

Former OpenAI CTO Mira Murati has announced her new startup. Unsurprisingly, it's focused on AI.

Thinking Machines Lab is ex-OpenAI CTO Mira Murati's new startup | TechCrunch

Former OpenAI chief technology officer Mira Murati, who departed the company in September 2024, has formally announced her new AI startup, Thinking Machines Lab.

Former OpenAI CTO Mira Murati finally unveils her Thinking Machines Lab startup—and a leadership team stacked with former OpenAI colleagues

Safe Superintelligence, an AI startup founded by former OpenAI chief scientist Ilya Sutskever, could be close to raising more than $1 billion at a $30 billion valuation.

Safe Superintelligence, Ilya Sutskever's AI startup, is reportedly close to raising roughly $1B | TechCrunch

AI models are being cranked out at a dizzying pace, by everyone from Big Tech companies like Google to startups like OpenAI and Anthropic. Adding to the confusion is that AI models are often promoted based on industry benchmarks. To cut through the noise, TechCrunch has compiled an overview ...

The hottest AI models, what they do, and how to use them

The model is also highly skilled in coding, reportedly outperforming OpenAI's most advanced accessible reasoning model (o3-mini on high reasoning mode) in some programming tests. While OpenAI is focused on consumer AI, Anthropic is going after the enterprise market, offering more flexibility ...

Anthropic's New AI Model Challenges OpenAI with Smarter Reasoning and Coding

Generative artificial intelligence (AI) has taken Wall Street by storm since the launch of OpenAI's ChatGPT in 2022. Let's discuss why Nvidia (NASDAQ: NVDA), Tesla (NASDAQ: TSLA), and Palantir Technologies (NASDAQ: PLTR) could face downside risk as AI excitement potentially fades in 2025 and beyond.

3 Top AI Stocks That Could Crash in 2025

OpenAI

openai

30 World-Changing Prompts: OpenAI's AI Singularity, Deep Research Has Arrived

DeepSeek's Janus Pro, OpenAI o3-mini, Mistral's 24B Parameter Model, and More

Is OpenAI’s o3 Finally Thinking Like a Human?

Dink OpenAI se o3 uiteindelik soos 'n mens?

Най-накрая o3 на OpenAI мисли ли като човек?

¿El o3 de OpenAI finalmente piensa como un humano?

האם ה-o3 של OpenAI סוף סוף חושב כמו אדם?

क्या OpenAI का o3 अंततः मानव की तरह सोचने लगा है?

Apakah o3 OpenAI Akhirnya Berpikir Seperti Manusia?

OpenAI の o3 はついに人間のように考えるようになったのか?

តើ o3 របស់ OpenAI ទីបំផុតគិតដូចមនុស្សទេ?

Adakah o3 OpenAI Akhirnya Berfikir Seperti Manusia?

के OpenAI को o3 अन्ततः मानव जस्तै सोच्दै छ?

Неужели o3 от OpenAI наконец-то думает как человек?

Да ли ОпенАИ-ов о3 коначно размишља као човек?

Googled Tricked OpenAI Into Thinking It Was Ahead (It Wasn't)

Google engañó a OpenAI haciéndole creer que estaba por delante (no lo estaba)

Googled je prevario OpenAI da misli da je ispred (nije)

Googled te twonpe OpenAI pou l te panse li te devan (li pa t)

Googled menipu OpenAI dengan berpikir bahwa ia unggul (padahal tidak)

Googled は OpenAI を騙して自分たちが先行していると信じ込ませた (実際はそうではなかった)

Google OpenAI-ді алда деп ойлап, алдады (ол емес)

Google piemānīja OpenAI, liekot domāt, ka tas ir priekšā (tā nebija)

गुगलले ओपनएआईलाई ठग्यो यो अगाडि थियो भनेर सोच्दै (यो थिएन)

Google обманул OpenAI, заставив его думать, что он впереди (но это не так)

Google podviedol OpenAI, aby si myslel, že to bolo dopredu (nebolo)

Googled Yakanyengedza OpenAI Kufunga Kuti Yaive Mberi (Yanga isiri)

Googled Ilidanganya OpenAI Kufikiria Ilikuwa Mbele (Haikuwa)

OpenAI has recently announced a new subscription plan called ChatGPT Pro, priced at $200 per month. This plan includes access to the advanced o1 model, which now supports images and has improved skills in mathematics and coding. As someone who has been following these developments closely, here are my thoughts on this new offering. Exciting Updates with the o1 Model I think it's really cool that OpenAI is making progress again. Recently, there were concerns that AI development had hit a plateau because it required much more power for even small breakthroughs. With the new o1 model, these fears are eased. The o1 model's ability to handle images and perform better in math and coding is impressive. It shows that AI is still advancing and becoming more capable. Is ChatGPT Pro Worth the $200 Monthly Fee? The price of $200 per month for ChatGPT Pro seems reasonable to me. There will definitely be people who are willing to pay this without hesitation. However, the big question is whether the service is good enough to keep subscribers. Some online reviews mention that ChatGPT Pro is about 11% better in coding compared to the standard version. But considering it's usually 10 times more expensive than the regular subscription, it might not be the best deal for everyone. For some, a cheaper alternative like Claude 3.5 might be more appealing. I'm waiting to see what the first users have to say before making a final judgment. Benefits of the o1 Pro Mode Access to the o1 pro mode is a major plus for users who work with more complex queries. I believe this will have a positive impact, especially for those in scientific fields like math, physics, chemistry, and medicine. With more powerful reasoning capabilities, the o1 pro mode can help achieve breakthroughs and solve difficult problems more efficiently. o1 Model Now Available to All Paid Users Finally, OpenAI decided to make the o1 model available to all paid users and move it out of the preview stage. I'm really excited about this change and can't wait to try it out. It's great to see OpenAI making advanced models more accessible, allowing more people to benefit from their capabilities. New Features: Voice Module and Better Responses ChatGPT Pro also offers unlimited access to the voice module and improved answers to complex questions. However, I'm not sure how many users will regularly use the unlimited voice feature. It might be useful for some, but it may not be a deciding factor for everyone. Rumors of a $2,000 Monthly Subscription There are also rumors about a $2,000 per month subscription. I can see this being a reality for professional, governmental, and scientific users. It would be something between the regular web version and API access, possibly providing access to AGI (Artificial General Intelligence) that can work on tasks for hours. This higher-tier subscription could be very valuable for those who need advanced AI capabilities for their work. Conclusion OpenAI's ChatGPT Pro offers exciting new features and improved models like o1, but the high price tag may limit its appeal to a broader audience. It will be interesting to see how users respond and whether the benefits justify the cost. For those who need advanced AI for complex tasks, ChatGPT Pro could be a great investment. However, others might prefer sticking with more affordable options until they see more feedback from early adopters. OpenAI has recently announced a new subscription plan called ChatGPT Pro , priced at $200 per month . This plan includes access to the advanced o1 model , which now supports images and has improved skills in mathematics and coding. As someone who has been following these developments closely, here are my thoughts on this new offering. ChatGPT Pro ChatGPT Pro $200 per month o1 model Exciting Updates with the o1 Model Exciting Updates with the o1 Model I think it's really cool that OpenAI is making progress again. Recently, there were concerns that AI development had hit a plateau because it required much more power for even small breakthroughs. With the new o1 model, these fears are eased. The o1 model's ability to handle images and perform better in math and coding is impressive. It shows that AI is still advancing and becoming more capable. Is ChatGPT Pro Worth the $200 Monthly Fee? Is ChatGPT Pro Worth the $200 Monthly Fee? The price of $200 per month for ChatGPT Pro seems reasonable to me. There will definitely be people who are willing to pay this without hesitation. However, the big question is whether the service is good enough to keep subscribers. Some online reviews mention that ChatGPT Pro is about 11% better in coding compared to the standard version. But considering it's usually 10 times more expensive than the regular subscription, it might not be the best deal for everyone. For some, a cheaper alternative like Claude 3.5 might be more appealing. I'm waiting to see what the first users have to say before making a final judgment. 11% better in coding 10 times more expensive Claude 3.5 Benefits of the o1 Pro Mode Benefits of the o1 Pro Mode Access to the o1 pro mode is a major plus for users who work with more complex queries. I believe this will have a positive impact, especially for those in scientific fields like math, physics, chemistry, and medicine. With more powerful reasoning capabilities, the o1 pro mode can help achieve breakthroughs and solve difficult problems more efficiently. o1 pro mode o1 Model Now Available to All Paid Users o1 Model Now Available to All Paid Users Finally, OpenAI decided to make the o1 model available to all paid users and move it out of the preview stage. I'm really excited about this change and can't wait to try it out. It's great to see OpenAI making advanced models more accessible, allowing more people to benefit from their capabilities. New Features: Voice Module and Better Responses New Features: Voice Module and Better Responses ChatGPT Pro also offers unlimited access to the voice module and improved answers to complex questions. However, I'm not sure how many users will regularly use the unlimited voice feature. It might be useful for some, but it may not be a deciding factor for everyone. Rumors of a $2,000 Monthly Subscription Rumors of a $2,000 Monthly Subscription There are also rumors about a $2,000 per month subscription. I can see this being a reality for professional, governmental, and scientific users. It would be something between the regular web version and API access, possibly providing access to AGI (Artificial General Intelligence) that can work on tasks for hours. This higher-tier subscription could be very valuable for those who need advanced AI capabilities for their work. $2,000 per month AGI (Artificial General Intelligence) Conclusion Conclusion OpenAI's ChatGPT Pro offers exciting new features and improved models like o1, but the high price tag may limit its appeal to a broader audience. It will be interesting to see how users respond and whether the benefits justify the cost. For those who need advanced AI for complex tasks, ChatGPT Pro could be a great investment. However, others might prefer sticking with more affordable options until they see more feedback from early adopters.

OpenAI Launches ChatGPT Pro: Is the $200 Monthly Subscription Worth It?

OpenAI ChatGPT Proን ይጀምራል፡ የ$200 ወርሃዊ የደንበኝነት ምዝገባ ዋጋ አለው?

OpenAI ChatGPT Pro-nu işə salır: 200 dollarlıq aylıq abunə buna dəyərmi?

OpenAI пуска ChatGPT Pro: Заслужава ли си месечният абонамент от $200?

OpenAI lancerer ChatGPT Pro: Er det $200 månedlige abonnement værd?

OpenAI lanza ChatGPT Pro: ¿Vale la pena la suscripción mensual de $200?

OpenAI-k ChatGPT Pro abiarazten du: merezi al du 200 $ hileko harpidetzak?

OpenAI lansira ChatGPT Pro: Isplati li se mjesečna pretplata od 200 USD?

OpenAI משיקה את ChatGPT Pro: האם המנוי החודשי של $200 שווה את זה?

OpenAI が ChatGPT Pro をリリース: 月額 200 ドルのサブスクリプションは価値があるか?

OpenAI го лансираше ChatGPT Pro: Дали вреди месечната претплата од 200 долари?

OpenAI د ChatGPT پرو پیل کوي: ایا د $ 200 میاشتنۍ ګډون ارزښت لري؟

OpenAI Inotangisa ChatGPT Pro: Iyo $200 yeMwedzi Kunyoreswa Kwakakodzera Iko?

In a bold move highlighting growing tensions between artists and AI developers, a protest group known as "Sora PR Puppets" has leaked access to OpenAI's unreleased text-to-video model, Sora, on the Hugging Face platform. This act of defiance sheds light on alleged exploitative practices within OpenAI's early access program and offers a rare glimpse into the capabilities of the Sora model. Understanding Sora: OpenAI's Text-to-Video Model Sora is OpenAI's advanced text-to-video model designed to generate high-quality videos from textual descriptions. Building upon the technologies behind DALL·E and GPT models, Sora utilizes a recaptioning technique to produce videos that closely adhere to user prompts. The model is capable of creating complex scenes featuring multiple characters, specific motions, and detailed backgrounds, demonstrating an understanding of physical world dynamics. The Leak and Its Implications The leaked version of Sora allowed users to generate 1080p, 10-second video clips, reportedly at faster render times than previously disclosed. Notably, these clips bore OpenAI's watermark, indicating their origin. The temporary availability of Sora on Hugging Face lasted several hours before OpenAI intervened to shut down access. https://x.com/btibor91/status/1861433835528839632?embedable=true Artists' Grievances: Unpaid Labor and Strict Controls The protest group claims that OpenAI enlisted hundreds of artists for unpaid testing, maintaining strict control over all generated content and its dissemination. In an open letter accompanying the leak, the artists stated: "We received access to Sora with the promise to be early testers, red teamers, and creative partners. However, we believe instead we are being lured into 'art washing' to tell the world that Sora is a useful tool for artists." The Verge They further criticized the program as being more about public relations than genuine creative collaboration. OpenAI's Response and Ongoing Developments In response to the leak, OpenAI emphasized that participation in its research preview was voluntary, with no obligation to provide feedback or use the tool. The company stated: "Sora is still in research preview, and we're working to balance creativity with robust safety measures for broader use." The Verge Reports suggest that OpenAI has been training a new version of Sora to address long render times, with rumored features like in-painting and image generation. Industry Context: A Glimpse into AI Video Generation While competitors race to ship AI video tools, Sora has remained behind closed doors, making this leak a dramatic first look at OpenAI's progress. The capabilities seem impressive but do not leap ahead of the competition, and this leak may expose some underlying tension with the creatives they depend on for testing. https://x.com/slow_developer/status/1861440783867388298?embedable=true Conclusion: A Call for Ethical Collaboration The "Sora PR Puppets" leak underscores the need for ethical collaboration between AI developers and the creative community. As AI-generated content becomes more prevalent, establishing fair and transparent practices is crucial to ensure that technological advancements benefit all stakeholders involved. In a bold move highlighting growing tensions between artists and AI developers, a protest group known as "Sora PR Puppets" has leaked access to OpenAI's unreleased text-to-video model, Sora, on the Hugging Face platform. This act of defiance sheds light on alleged exploitative practices within OpenAI's early access program and offers a rare glimpse into the capabilities of the Sora model. Understanding Sora: OpenAI's Text-to-Video Model Understanding Sora: OpenAI's Text-to-Video Model Sora is OpenAI 's advanced text-to-video model designed to generate high-quality videos from textual descriptions. Building upon the technologies behind DALL·E and GPT models, Sora utilizes a recaptioning technique to produce videos that closely adhere to user prompts. The model is capable of creating complex scenes featuring multiple characters, specific motions, and detailed backgrounds, demonstrating an understanding of physical world dynamics. OpenAI The Leak and Its Implications The Leak and Its Implications The leaked version of Sora allowed users to generate 1080p, 10-second video clips, reportedly at faster render times than previously disclosed. Notably, these clips bore OpenAI's watermark, indicating their origin. The temporary availability of Sora on Hugging Face lasted several hours before OpenAI intervened to shut down access. https://x.com/btibor91/status/1861433835528839632?embedable=true https://x.com/btibor91/status/1861433835528839632?embedable=true Artists' Grievances: Unpaid Labor and Strict Controls Artists' Grievances: Unpaid Labor and Strict Controls The protest group claims that OpenAI enlisted hundreds of artists for unpaid testing, maintaining strict control over all generated content and its dissemination. In an open letter accompanying the leak, the artists stated: "We received access to Sora with the promise to be early testers, red teamers, and creative partners. However, we believe instead we are being lured into 'art washing' to tell the world that Sora is a useful tool for artists." "We received access to Sora with the promise to be early testers, red teamers, and creative partners. However, we believe instead we are being lured into 'art washing' to tell the world that Sora is a useful tool for artists." The Verge The Verge The Verge They further criticized the program as being more about public relations than genuine creative collaboration. OpenAI's Response and Ongoing Developments OpenAI's Response and Ongoing Developments In response to the leak, OpenAI emphasized that participation in its research preview was voluntary, with no obligation to provide feedback or use the tool. The company stated: "Sora is still in research preview, and we're working to balance creativity with robust safety measures for broader use." "Sora is still in research preview, and we're working to balance creativity with robust safety measures for broader use." The Verge The Verge The Verge Reports suggest that OpenAI has been training a new version of Sora to address long render times, with rumored features like in-painting and image generation. Industry Context: A Glimpse into AI Video Generation Industry Context: A Glimpse into AI Video Generation While competitors race to ship AI video tools, Sora has remained behind closed doors, making this leak a dramatic first look at OpenAI's progress. The capabilities seem impressive but do not leap ahead of the competition, and this leak may expose some underlying tension with the creatives they depend on for testing. https://x.com/slow_developer/status/1861440783867388298?embedable=true https://x.com/slow_developer/status/1861440783867388298?embedable=true Conclusion: A Call for Ethical Collaboration Conclusion: A Call for Ethical Collaboration The "Sora PR Puppets" leak underscores the need for ethical collaboration between AI developers and the creative community. As AI-generated content becomes more prevalent, establishing fair and transparent practices is crucial to ensure that technological advancements benefit all stakeholders involved.

Artists Leak OpenAI's Sora Video Model, Exposing Controversial Testing Practices

This guide will teach you how to use the Streamlit st.audio_input widget to record your voice on your device microphone paired with the OpenAI Whisper model to transcribe and/or translate your speech to text in English. You can later download the transcribed content as a text file in the .txt format. Prerequisites Basic Python knowledge
Streamlit
OpenAI API key. Sign up for an account What is Whisper Whisper is a trained open-source neural network that approaches human-level robustness and accuracy in English speech recognition. The OpenAI API provides two endpoints: Transcriptions
Translations What is Streamlit From the official website, Streamlit is a faster way to build and share data apps. It is an open-source Python library that helps you build web applications for sharing analytical results, building complex interactive experiences, and iterating on top of new machine-learning models. Streamlit is a top choice for Python developers because it has built-in and convenient methods, from taking in user inputs like text, numbers, and dates to showing interactive graphs using the most popular and powerful Python graphing libraries. Installing Streamlit To run any Streamlit apps, you must first install Streamlit using the command: pip install streamlit Installing Other Libraries Since we are working with transforming audio to text, we need to store our environment variables securely. pip install openai python-dotenv Creating the Environment Variable Create a new file in the root project directory and name it .env. Paste in your OpenAI API key: .env OPENAI_API_KEY="sk-..." Creating the App In your directory, create this file, streamlit_app.py which will contain all the Python code to transcribe and translate our audio and output the resulting text. To initialize an instance of the OpenAI client, copy-paste this code: streamlit_app.py import os
from dotenv import load_dotenv
from openai import OpenAI

load_dotenv()

api_key = os.getenv('OPENAI_API_KEY')

client = OpenAI() The code block connects and reads our secret key in the .env file, making sure we are authenticated as users. P.S.: Using the OpenAI API is not free as you have to buy some credits to use the service. Transcription with Whisper Let's update the streamlit_app.py with the following: streamlit_app.py ...
import streamlit as st

st.logo(
  "logo.png",
  size="medium",
  link="https://platform.openai.com/docs",
)

st.title("Transcription with Whisper")

audio_value = st.audio_input("record a voice message to transcribe")

if audio_value:
  transcript = client.audio.transcriptions.create(
    model="whisper-1",
    file = audio_value
  )

  transcript_text = transcript.text
  st.write(transcript_text) The transcriptions API will convert our audio using the st.audio_input widget to record our voice. If the recording exists, the model, Whisper is used to create the desired file format for the transcription of the audio and output the text using the st.write() function which takes a string and writes it directly into our web app. To use the exact logo at the top left of the app, download this and save it in your project directory. Now, let's run this app with this command in the terminal: streamlit run streamlit_app.py Downloading the Transcribed Text The ability to download the transcribed message for later would serve for record-keeping when you need it. Streamlit offers an input widget that allows for the display of a download button. Back to the streamlit_app.py file, update the codebase with the following: streamlit_app.py ...

  txt_file = "transcription.txt"

  # Initialize session state for download confirmation
  if "downloaded" not in st.session_state:
        st.session_state.downloaded = False

    # Download button
  if st.download_button(
       label="Download Transcription",
       file_name="transcription.txt",data=transcript_text,
  ):
       st.session_state.downloaded = True

    # Show success message after download
  if st.session_state.downloaded:
        st.success("Transcription file downloaded successfully!") The following occurs in the lines of code above: st.session_state in Streamlit allows you to share variables between reruns, for each user session
The transcript_text variable will contain the content of the transcribed text
The txt_file variable with the assigned value, transcription.txt is the file name of the transcribed text when the file is downloaded.
Within the function of the st.download_button(), the label describes to the user what the button is for. The st.success status element displays a success message when the file is saved as shown: Translation with Whisper The process of creating the translation is similar to creating the transcription. The translation endpoint will translate a foreign language into written text in English from the input of the audio file. Copy and paste this code. streamlit_app.py ...

st.header("Translation with Whisper", divider="gray")

audio_translate = st.audio_input("record a voice message to translate")

if audio_translate:
  translate = client.audio.translations.create(
    model="whisper-1",
    file=audio_translate
  )

  st.write(translate.text) If you want to create a file to save your translated audio file as text, you can do the same with the Download transcription button. The complete source code is in this repository and give this app a try to transcribe and translate your voice to text. Good luck! Conclusion Instead of using pre-recorded audio from the internet as seen in the OpenAI docs, Streamlit offers you the opportunity to use your voice and pair it with the transcription and translation endpoints provided by OpenAI to create this outstanding project. The microphone in your device can do so much as technology has made it possible to go beyond using it for communication during meetings and calls. This guide will teach you how to use the Streamlit st.audio_input widget to record your voice on your device microphone paired with the OpenAI Whisper model to transcribe and/or translate your speech to text in English. You can later download the transcribed content as a text file in the .txt format. st.audio_input .txt Prerequisites Basic Python knowledge Streamlit OpenAI API key. Sign up for an account Basic Python knowledge Python Streamlit Streamlit OpenAI API key. Sign up for an account Sign up What is Whisper Whisper is a trained open-source neural network that approaches human-level robustness and accuracy in English speech recognition. The OpenAI API provides two endpoints: Transcriptions Translations Transcriptions Translations What is Streamlit From the official website , Streamlit is a faster way to build and share data apps. It is an open-source Python library that helps you build web applications for sharing analytical results, building complex interactive experiences, and iterating on top of new machine-learning models. official website Streamlit is a top choice for Python developers because it has built-in and convenient methods, from taking in user inputs like text, numbers, and dates to showing interactive graphs using the most popular and powerful Python graphing libraries. Installing Streamlit To run any Streamlit apps, you must first install Streamlit using the command: pip install streamlit pip install streamlit Installing Other Libraries Since we are working with transforming audio to text, we need to store our environment variables securely. pip install openai python-dotenv pip install openai python-dotenv Creating the Environment Variable Create a new file in the root project directory and name it .env . .env Paste in your OpenAI API key : API key .env .env OPENAI_API_KEY="sk-..." OPENAI_API_KEY="sk-..." Creating the App In your directory, create this file, streamlit_app.py which will contain all the Python code to transcribe and translate our audio and output the resulting text. streamlit_app.py To initialize an instance of the OpenAI client, copy-paste this code: streamlit_app.py streamlit_app.py import os
from dotenv import load_dotenv
from openai import OpenAI

load_dotenv()

api_key = os.getenv('OPENAI_API_KEY')

client = OpenAI() import os
from dotenv import load_dotenv
from openai import OpenAI

load_dotenv()

api_key = os.getenv('OPENAI_API_KEY')

client = OpenAI() The code block connects and reads our secret key in the .env file, making sure we are authenticated as users. .env P.S.: Using the OpenAI API is not free as you have to buy some credits to use the service. Transcription with Whisper Let's update the streamlit_app.py with the following: streamlit_app.py streamlit_app.py streamlit_app.py ...
import streamlit as st

st.logo(
  "logo.png",
  size="medium",
  link="https://platform.openai.com/docs",
)

st.title("Transcription with Whisper")

audio_value = st.audio_input("record a voice message to transcribe")

if audio_value:
  transcript = client.audio.transcriptions.create(
    model="whisper-1",
    file = audio_value
  )

  transcript_text = transcript.text
  st.write(transcript_text) ...
import streamlit as st

st.logo(
  "logo.png",
  size="medium",
  link="https://platform.openai.com/docs",
)

st.title("Transcription with Whisper")

audio_value = st.audio_input("record a voice message to transcribe")

if audio_value:
  transcript = client.audio.transcriptions.create(
    model="whisper-1",
    file = audio_value
  )

  transcript_text = transcript.text
  st.write(transcript_text) The transcriptions API will convert our audio using the st.audio_input widget to record our voice. If the recording exists, the model, Whisper is used to create the desired file format for the transcription of the audio and output the text using the st.write() function which takes a string and writes it directly into our web app. transcriptions API st.audio_input transcription of the audio st.write() To use the exact logo at the top left of the app, download this and save it in your project directory. download this Now, let's run this app with this command in the terminal: streamlit run streamlit_app.py streamlit run streamlit_app.py Downloading the Transcribed Text The ability to download the transcribed message for later would serve for record-keeping when you need it. Streamlit offers an input widget that allows for the display of a download button. Back to the streamlit_app.py file, update the codebase with the following: streamlit_app.py streamlit_app.py streamlit_app.py ...

  txt_file = "transcription.txt"

  # Initialize session state for download confirmation
  if "downloaded" not in st.session_state:
        st.session_state.downloaded = False

    # Download button
  if st.download_button(
       label="Download Transcription",
       file_name="transcription.txt",data=transcript_text,
  ):
       st.session_state.downloaded = True

    # Show success message after download
  if st.session_state.downloaded:
        st.success("Transcription file downloaded successfully!") ...

  txt_file = "transcription.txt"

  # Initialize session state for download confirmation
  if "downloaded" not in st.session_state:
        st.session_state.downloaded = False

    # Download button
  if st.download_button(
       label="Download Transcription",
       file_name="transcription.txt",data=transcript_text,
  ):
       st.session_state.downloaded = True

    # Show success message after download
  if st.session_state.downloaded:
        st.success("Transcription file downloaded successfully!") The following occurs in the lines of code above: st.session_state in Streamlit allows you to share variables between reruns, for each user session The transcript_text variable will contain the content of the transcribed text The txt_file variable with the assigned value, transcription.txt is the file name of the transcribed text when the file is downloaded. Within the function of the st.download_button(), the label describes to the user what the button is for. st.session_state in Streamlit allows you to share variables between reruns, for each user session st.session_state The transcript_text variable will contain the content of the transcribed text transcript_text The txt_file variable with the assigned value, transcription.txt is the file name of the transcribed text when the file is downloaded. txt_file transcription.txt Within the function of the st.download_button() , the label describes to the user what the button is for. st.download_button() The st.success status element displays a success message when the file is saved as shown: st.success Translation with Whisper The process of creating the translation is similar to creating the transcription. The translation endpoint will translate a foreign language into written text in English from the input of the audio file. translation endpoint Copy and paste this code. streamlit_app.py streamlit_app.py ...

st.header("Translation with Whisper", divider="gray")

audio_translate = st.audio_input("record a voice message to translate")

if audio_translate:
  translate = client.audio.translations.create(
    model="whisper-1",
    file=audio_translate
  )

  st.write(translate.text) ...

st.header("Translation with Whisper", divider="gray")

audio_translate = st.audio_input("record a voice message to translate")

if audio_translate:
  translate = client.audio.translations.create(
    model="whisper-1",
    file=audio_translate
  )

  st.write(translate.text) If you want to create a file to save your translated audio file as text, you can do the same with the Download transcription button. translated audio Download transcription The complete source code is in this repository and give this app a try to transcribe and translate your voice to text. in this repository this app a try Good luck! Conclusion Instead of using pre-recorded audio from the internet as seen in the OpenAI docs , Streamlit offers you the opportunity to use your voice and pair it with the transcription and translation endpoints provided by OpenAI to create this outstanding project. OpenAI docs The microphone in your device can do so much as technology has made it possible to go beyond using it for communication during meetings and calls.

How to Build a Voice Transcription and Translation App with OpenAI Whisper and Streamlit

The HackerNoon Newsletter: Predicted Outputs: The OpenAI Feature You Probably Missed (11/20/2024)

OpenAI recently introduced a powerful feature called Predicted Outputs, which quietly entered the scene without much attention from the technical media—an oversight that deserves correction. I noticed that they mentioned this on X in their developer account, but it didn't get much publicity. I decided to draw attention to it because it's a really cool and useful feature. Predicted Outputs significantly reduce latency for model responses, especially when much of the output is known ahead of time. This feature is particularly beneficial for applications that involve regenerating text documents or code files with minor modifications. What Are Predicted Outputs? Predicted Outputs allow developers to speed up API responses from Chat Completions when the expected output is largely predictable. By providing a prediction of the expected response using the prediction parameter in Chat Completions, the model can generate the required output more efficiently. This functionality is currently available with the latest gpt-4o and gpt-4o-mini models. How Do Predicted Outputs Work? When you have a response where most of the content is already known, you can supply that expected content as a prediction to the model. The model then uses this prediction to expedite the generation of the response, reducing latency and improving performance. Custom Example: Updating Configuration Files Imagine you have a JSON configuration file that needs a minor update. Here's an example of such a file: {
  "appName": "MyApp",
  "version": "1.0.0",
  "settings": {
    "enableFeatureX": false,
    "maxUsers": 100
  }
} Suppose you want to update "enableFeatureX" to true. Instead of generating the entire file from scratch, you can provide the original file as a prediction and instruct the model to make the necessary change. import OpenAI from "openai";

const config = `
{
  "appName": "MyApp",
  "version": "1.0.0",
  "settings": {
    "enableFeatureX": false,
    "maxUsers": 100
  }
}
`.trim();

const openai = new OpenAI();

const updatePrompt = `
Change "enableFeatureX" to true in the following JSON configuration. Respond only with the updated JSON, without any additional text.
`;

const completion = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "user", content: updatePrompt },
    { role: "user", content: config }
  ],
  prediction: {
    type: "content",
    content: config
  }
});

// Output the updated configuration
console.log(completion.choices[0].message.content); In this example, the model quickly generates the updated configuration file, leveraging the prediction to minimize response time. Streaming with Predicted Outputs For applications that require streaming responses, Predicted Outputs offer even greater latency reductions. Here's how you can implement the previous example using streaming: import OpenAI from "openai";

const config = `...`; // Original JSON configuration

const openai = new OpenAI();

const updatePrompt = `...`; // Prompt as before

const completion = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [ /* ... */ ],
  prediction: {
    type: "content",
    content: config
  },
  stream: true
});

for await (const chunk of completion) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
} At the time of writing, there are no alternatives to this solution from competitors. OpenAI's Predicted Outputs feature appears to be a unique offering that addresses the specific need for latency reduction when regenerating known content with minor modifications. Tips for Developers The cool thing is that to start using it, you need almost nothing. Just take it and use it by adding just one new parameter to the API request. This makes it very easy for developers to implement this feature in their existing applications. Limitations While Predicted Outputs offer significant advantages, there are important considerations: Model Compatibility: Only the gpt-4o and gpt-4o-mini models support Predicted Outputs. I think that the fact it's only available on these models is not a problem at all, because they are currently the best models from OpenAI, and the original GPT-4 is too slow for this anyway.
Billing Implications: Rejected prediction tokens are billed at the same rate as generated tokens. Monitor the rejected_prediction_tokens in the usage data to manage costs.
Unsupported Parameters:

n (values higher than 1)
logprobs
presence_penalty (values greater than 0)
frequency_penalty (values greater than 0)
max_completion_tokens
tools (function calling is not supported)


Modality Restrictions: Only text modalities are supported; audio inputs and outputs are incompatible with Predicted Outputs. Conclusion OpenAI's Predicted Outputs is a groundbreaking feature that addresses a common challenge in AI applications: reducing latency when the response is largely predictable. By allowing developers to supply expected outputs, it accelerates response times and enhances user experience. In my personal opinion, OpenAI models are not as strong as Anthropic's models. However, OpenAI produces many cool and really necessary solutions in other areas. Features like Predicted Outputs set OpenAI apart from other AI providers, offering unique solutions that meet specific needs in the developer community. OpenAI recently introduced a powerful feature called Predicted Outputs , which quietly entered the scene without much attention from the technical media—an oversight that deserves correction. I noticed that they mentioned this on X in their developer account , but it didn't get much publicity. I decided to draw attention to it because it's a really cool and useful feature. Predicted Outputs Predicted Outputs in their developer account Predicted Outputs significantly reduce latency for model responses, especially when much of the output is known ahead of time. This feature is particularly beneficial for applications that involve regenerating text documents or code files with minor modifications. What Are Predicted Outputs? Predicted Outputs allow developers to speed up API responses from Chat Completions when the expected output is largely predictable. By providing a prediction of the expected response using the prediction parameter in Chat Completions, the model can generate the required output more efficiently. This functionality is currently available with the latest gpt-4o and gpt-4o-mini models. prediction gpt-4o gpt-4o-mini How Do Predicted Outputs Work? When you have a response where most of the content is already known, you can supply that expected content as a prediction to the model. The model then uses this prediction to expedite the generation of the response, reducing latency and improving performance. Custom Example: Updating Configuration Files Imagine you have a JSON configuration file that needs a minor update. Here's an example of such a file: {
  "appName": "MyApp",
  "version": "1.0.0",
  "settings": {
    "enableFeatureX": false,
    "maxUsers": 100
  }
} {
  "appName": "MyApp",
  "version": "1.0.0",
  "settings": {
    "enableFeatureX": false,
    "maxUsers": 100
  }
} Suppose you want to update "enableFeatureX" to true . Instead of generating the entire file from scratch, you can provide the original file as a prediction and instruct the model to make the necessary change. "enableFeatureX" true import OpenAI from "openai";

const config = `
{
  "appName": "MyApp",
  "version": "1.0.0",
  "settings": {
    "enableFeatureX": false,
    "maxUsers": 100
  }
}
`.trim();

const openai = new OpenAI();

const updatePrompt = `
Change "enableFeatureX" to true in the following JSON configuration. Respond only with the updated JSON, without any additional text.
`;

const completion = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "user", content: updatePrompt },
    { role: "user", content: config }
  ],
  prediction: {
    type: "content",
    content: config
  }
});

// Output the updated configuration
console.log(completion.choices[0].message.content); import OpenAI from "openai";

const config = `
{
  "appName": "MyApp",
  "version": "1.0.0",
  "settings": {
    "enableFeatureX": false,
    "maxUsers": 100
  }
}
`.trim();

const openai = new OpenAI();

const updatePrompt = `
Change "enableFeatureX" to true in the following JSON configuration. Respond only with the updated JSON, without any additional text.
`;

const completion = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "user", content: updatePrompt },
    { role: "user", content: config }
  ],
  prediction: {
    type: "content",
    content: config
  }
});

// Output the updated configuration
console.log(completion.choices[0].message.content); In this example, the model quickly generates the updated configuration file, leveraging the prediction to minimize response time. Streaming with Predicted Outputs For applications that require streaming responses, Predicted Outputs offer even greater latency reductions. Here's how you can implement the previous example using streaming: import OpenAI from "openai";

const config = `...`; // Original JSON configuration

const openai = new OpenAI();

const updatePrompt = `...`; // Prompt as before

const completion = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [ /* ... */ ],
  prediction: {
    type: "content",
    content: config
  },
  stream: true
});

for await (const chunk of completion) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
} import OpenAI from "openai";

const config = `...`; // Original JSON configuration

const openai = new OpenAI();

const updatePrompt = `...`; // Prompt as before

const completion = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [ /* ... */ ],
  prediction: {
    type: "content",
    content: config
  },
  stream: true
});

for await (const chunk of completion) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
} At the time of writing, there are no alternatives to this solution from competitors. OpenAI's Predicted Outputs feature appears to be a unique offering that addresses the specific need for latency reduction when regenerating known content with minor modifications. Tips for Developers The cool thing is that to start using it, you need almost nothing. Just take it and use it by adding just one new parameter to the API request. This makes it very easy for developers to implement this feature in their existing applications. just one new parameter Limitations While Predicted Outputs offer significant advantages, there are important considerations: Model Compatibility: Only the gpt-4o and gpt-4o-mini models support Predicted Outputs. I think that the fact it's only available on these models is not a problem at all, because they are currently the best models from OpenAI, and the original GPT-4 is too slow for this anyway. Billing Implications: Rejected prediction tokens are billed at the same rate as generated tokens. Monitor the rejected_prediction_tokens in the usage data to manage costs. Unsupported Parameters:

n (values higher than 1)
logprobs
presence_penalty (values greater than 0)
frequency_penalty (values greater than 0)
max_completion_tokens
tools (function calling is not supported) Modality Restrictions: Only text modalities are supported; audio inputs and outputs are incompatible with Predicted Outputs. Model Compatibility : Only the gpt-4o and gpt-4o-mini models support Predicted Outputs. I think that the fact it's only available on these models is not a problem at all, because they are currently the best models from OpenAI, and the original GPT-4 is too slow for this anyway. Model Compatibility gpt-4o gpt-4o-mini Billing Implications : Rejected prediction tokens are billed at the same rate as generated tokens. Monitor the rejected_prediction_tokens in the usage data to manage costs. Billing Implications rejected_prediction_tokens Unsupported Parameters : n (values higher than 1)
logprobs
presence_penalty (values greater than 0)
frequency_penalty (values greater than 0)
max_completion_tokens
tools (function calling is not supported) Unsupported Parameters n (values higher than 1) logprobs presence_penalty (values greater than 0) frequency_penalty (values greater than 0) max_completion_tokens tools (function calling is not supported) n (values higher than 1) n logprobs logprobs presence_penalty (values greater than 0) presence_penalty frequency_penalty (values greater than 0) frequency_penalty max_completion_tokens max_completion_tokens tools (function calling is not supported) tools Modality Restrictions : Only text modalities are supported; audio inputs and outputs are incompatible with Predicted Outputs. Modality Restrictions Conclusion OpenAI's Predicted Outputs is a groundbreaking feature that addresses a common challenge in AI applications: reducing latency when the response is largely predictable. By allowing developers to supply expected outputs, it accelerates response times and enhances user experience. In my personal opinion, OpenAI models are not as strong as Anthropic's models. However, OpenAI produces many cool and really necessary solutions in other areas. Features like Predicted Outputs set OpenAI apart from other AI providers, offering unique solutions that meet specific needs in the developer community.

Predicted Outputs: The OpenAI Feature You Probably Missed

Yatiyat mistuwinaka: OpenAI ukan lurawipax Inas jan uñjkchiti

Прагназаваныя вынікі: функцыя OpenAI, якую вы, верагодна, прапусцілі

Resultados previstos: la función de OpenAI que probablemente no conocía

Predviđeni rezultati: OpenAI značajka koju ste vjerojatno propustili

Sòti Prevwa: Karakteristik OpenAI ou Pwobableman rate

予測出力: おそらく見逃している OpenAI の機能

Болжолдонгон натыйжалар: Сиз өткөрүп жиберген OpenAI функциясы

Willasqa Lluqsiykuna: OpenAI Ruway Yaqapaschá faltarqanki

Zvakafanotaurwa Zvinobuda: Iyo OpenAI Feature Iwe Pamwe Wakapotsa

Rezultatet e parashikuara: Tipari OpenAI që ndoshta ju ka humbur

ዝተገመቱ ውጽኢታት፡ ምናልባት ዝሰኣንካዮ ናይ OpenAI ባህሪ

Bashorat qilingan natijalar: Siz o'tkazib yuborgan OpenAI xususiyati

From OpenAI to Closed AI: Custom Chips Are Closing The Doors—What’s Next? 

Ад OpenAI да закрытага AI: Карыстальніцкія мікрасхемы зачыняюць дзверы - што далей? 

De l'OpenAI a la IA tancada: els xips personalitzats estan tancant les portes: què passa? 

Von OpenAI zu Closed AI: Kundenspezifische Chips schließen die Türen – was kommt als Nächstes?

Από το OpenAI στο Closed AI: Τα προσαρμοσμένα τσιπ κλείνουν τις πόρτες—Τι ακολουθεί; 

De la IA abierta a la IA cerrada: los chips personalizados están cerrando las puertas. ¿Qué sigue?

Az OpenAI-tól a zárt mesterséges intelligenciaig: Az egyedi chipek bezárják az ajtókat – mi a következő lépés? 

Dari OpenAI ke AI Tertutup: Chip Kustom Menutup Pintu—Apa Selanjutnya?

OpenAI からクローズド AI へ: カスタム チップが扉を閉ざす — 次は何が起こるのか?

Kobanda na OpenAI tii na AI oyo ekangami: Ba puces personnalisées ezali kokanga ba portes —What’s Next? 

له OpenAI څخه تړل شوي AI ته: دودیز چپس دروازې بندوي — بیا څه دي؟ 

От открытого ИИ к закрытому ИИ: кастомные чипы закрывают двери — что дальше?

Від відкритого штучного інтелекту до закритого штучного інтелекту: спеціальні мікросхеми закривають двері — що далі? 

Stories, News & Mentions about OPENAI

Openai’s Stories on HackerNoon

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