OpenAI once mulled buying AI chip startup Cerebras, but since the deal fell through, has turned to alternative hardware options.

Sam Altman and Ilya Sutskever Invested in Cerebras But OpenAI Snubbed Its AI Chips

Cleverly forging a path to the future, OpenAI’s Startup Fund emerges as a catalyst for breakthrough technologies. Instead of hoarding technology, it spreads its roots across the tech industry, enabling new ventures to blossom and grow.

How OpenAI's Startup Fund is Quietly Shaping the Future of Technology

Since its founding in 2021, OpenAI Startup Fund has invested in over a dozen startups, including many of the hottest startups in AI.

OpenAI’s startup empire: The companies backed by its venture fund | TechCrunch

A recent study from a team of cybersecurity researchers has revealed severe security flaws in commercial-grade Large Reasoning Models (LRMs), including OpenAI’s o1/o3 series,...

Sliver C2 Server Vulnerability Let Attackers Open a TCP connection to Read Traffic

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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

OpenAI

openai

Hello AI Enthusiasts! Welcome to the ninth edition of "This Week in AI Engineering"! "This Week in AI Engineering" OpenAI launched a $50M funding connecting 15 research institutions, Inception Labs released Mercury with speeds 10x faster than current LLMs, Cohere For AI unveiled Aya Vision for multilingual capabilities, and Alibaba's QwQ-32B matches DeepSeek-R1 with far fewer parameters. With this, we’ll also be talking about some must-know tools to make developing AI agents and apps easier. NextGenAI: OpenAI's $50M Consortium Connecting 15 Research Institutions NextGenAI: OpenAI's $50M Consortium Connecting 15 Research Institutions OpenAI has launched NextGenAI, an alliance uniting 15 leading research institutions with $50M in funding to accelerate scientific breakthroughs and transform education through AI. The initiative provides research grants, compute resources, and API access to support academic innovation across disciplines. NextGenAI NextGenAI Technical Architecture: Technical Architecture: API Integration: Direct access for model training, fine-tuning, and application development
Resource Allocation: Dedicated compute resources for university-led AI model development API Integration: Direct access for model training, fine-tuning, and application development API Integration: Resource Allocation: Dedicated compute resources for university-led AI model development Resource Allocation: Key Capabilities: Key Capabilities: Cross-Institutional Collaboration: Shared resources and findings across consortium members
Educational Enhancement: Student access to hands-on AI model training and application development
Research Acceleration: AI-powered manufacturing, energy, and healthcare advancement at Ohio State
Knowledge Accessibility: Boston Public Library digitizing public domain materials for broader access Cross-Institutional Collaboration: Shared resources and findings across consortium members Cross-Institutional Collaboration: Educational Enhancement: Student access to hands-on AI model training and application development Educational Enhancement: Research Acceleration: AI-powered manufacturing, energy, and healthcare advancement at Ohio State Research Acceleration: Knowledge Accessibility: Boston Public Library digitizing public domain materials for broader access Knowledge Accessibility: Implementation Focus: Implementation Focus: Medical Research: Harvard and Boston Children's Hospital accelerating rare disease diagnostics
Scientific Discovery: Duke University conducting metascience research to identify high-impact AI fields
Educational Development: Texas A&M implementing Generative AI Literacy Initiative
Historical Preservation: Oxford digitizing rare texts at Bodleian Library with OpenAI's API Medical Research: Harvard and Boston Children's Hospital accelerating rare disease diagnostics Medical Research: Scientific Discovery: Duke University conducting metascience research to identify high-impact AI fields Scientific Discovery: Educational Development: Texas A&M implementing Generative AI Literacy Initiative Educational Development: Historical Preservation: Oxford digitizing rare texts at Bodleian Library with OpenAI's API Historical Preservation: The initiative expands on OpenAI's educational commitment following ChatGPT Edu's launch in May 2024. NextGenAI focuses specifically on providing API-level access and research funding to drive innovations in scientific research, university operations, and educational methodologies. Mercury: Inception Labs Launches 10x Faster Diffusion LLMs Mercury: Inception Labs Launches 10x Faster Diffusion LLMs Inception Labs has released Mercury, the first commercial-scale diffusion large language model (dLLM) family that achieves output speeds faster than DeepSeek Coder V2 Lite, GPT-4o Mini, and Claude 3.5 Haiku. The technology demonstrates breakthrough performance with Mercury Coder running at over 1000 tokens per second on standard NVIDIA H100s. Mercury Mercury Technical Architecture: Technical Architecture: Generation Method: Coarse-to-fine diffusion process instead of traditional autoregressive generation
Processing Pipeline: Transformer-based neural network that modifies multiple tokens in parallel
Hardware Support: Compatible with existing NVIDIA GPUs without requiring specialized chips
Deployment Options: Available via API and on-premise installations with fine-tuning support Generation Method: Coarse-to-fine diffusion process instead of traditional autoregressive generation Generation Method: Processing Pipeline: Transformer-based neural network that modifies multiple tokens in parallel Processing Pipeline: Hardware Support: Compatible with existing NVIDIA GPUs without requiring specialized chips Hardware Support: Deployment Options: Available via API and on-premise installations with fine-tuning support Deployment Options: Performance Metrics: Performance Metrics: Throughput: 1109 tokens/second for Mercury Coder Mini vs 59 tokens/second for GPT-4o Mini
HumanEval: 88.0% for Mercury Coder Mini, matching GPT-4o Mini's 88.0%
EvalPlus: 78.6% for Mercury Coder Mini vs 78.5% for GPT-4o Mini
Fill-in-the-Middle: 82.2% for Mercury Coder Mini, significantly outperforming GPT-4o Mini's 60.9% Throughput: 1109 tokens/second for Mercury Coder Mini vs 59 tokens/second for GPT-4o Mini Throughput: HumanEval: 88.0% for Mercury Coder Mini, matching GPT-4o Mini's 88.0% HumanEval: EvalPlus: 78.6% for Mercury Coder Mini vs 78.5% for GPT-4o Mini EvalPlus: Fill-in-the-Middle: 82.2% for Mercury Coder Mini, significantly outperforming GPT-4o Mini's 60.9% Fill-in-the-Middle: Comparative Analysis: Comparative Analysis: Speed Advantage: 20x faster than some frontier models running below 50 tokens/second
Quality Benchmarks: Mercury Coder Small scores 90.0% on HumanEval, tied with Gemini 2.0 Flash-Lite
User Preference: Second place in Copilot Arena, outperforming GPT-4o Mini and Gemini 1.5-Flash
Efficiency Gain: 5-10x reduction in inference costs while maintaining competitive code quality Speed Advantage: 20x faster than some frontier models running below 50 tokens/second Speed Advantage: Quality Benchmarks: Mercury Coder Small scores 90.0% on HumanEval, tied with Gemini 2.0 Flash-Lite Quality Benchmarks: User Preference: Second place in Copilot Arena, outperforming GPT-4o Mini and Gemini 1.5-Flash User Preference: Efficiency Gain: 5-10x reduction in inference costs while maintaining competitive code quality Efficiency Gain: The models enable new capabilities for latency-sensitive applications that previously required compromising on model quality to meet speed requirements. Mercury's architecture allows continuous refinement of outputs to correct mistakes and hallucinations, similar to approaches used in leading image and video generation systems. Aya Vision: Cohere For AI Launches State-of-the-Art Multilingual Vision Model Aya Vision: Cohere For AI Launches State-of-the-Art Multilingual Vision Model Cohere For AI has released Aya Vision, an advanced open-weight vision model that significantly expands multilingual and multimodal capabilities across 23 languages spoken by over half the world's population. The model excels in image captioning, visual question answering, and cross-modal translation tasks. Aya Vision Aya Vision Technical Architecture: Technical Architecture: Parameter Variants: Available in 8B and 32B parameter configurations
Language Support: Processes 23 languages with consistent performance across linguistic domains
Training Methodology: Combines synthetic annotations, translation rephrasing, and multimodal merging
Processing Pipeline: Unified image and text understanding with cross-modal translation capabilities Parameter Variants: Available in 8B and 32B parameter configurations Parameter Variants: Language Support: Processes 23 languages with consistent performance across linguistic domains Language Support: Training Methodology: Combines synthetic annotations, translation rephrasing, and multimodal merging Training Methodology: Processing Pipeline: Unified image and text understanding with cross-modal translation capabilities Processing Pipeline: Performance Metrics: Performance Metrics: AyaVisionBench: Aya Vision 8B achieves up to 70% win rates against comparable models
m-WildVision: 79% win rate in multilingual vision tasks for the 8B variant
Cross-Model Comparison: Aya Vision 8B outperforms Llama-3.2 90B Vision with 63% win rates
Efficiency Ratio: 32B model outperforms models 2x its size (Llama-3.2 90B, Molmo 72B) AyaVisionBench: Aya Vision 8B achieves up to 70% win rates against comparable models AyaVisionBench: m-WildVision: 79% win rate in multilingual vision tasks for the 8B variant m-WildVision: Cross-Model Comparison: Aya Vision 8B outperforms Llama-3.2 90B Vision with 63% win rates Cross-Model Comparison: Efficiency Ratio: 32B model outperforms models 2x its size (Llama-3.2 90B, Molmo 72B) Efficiency Ratio: Development Features: Development Features: Iterative Improvement: Performance scaling from 40.9% to 79.1% win rates through technical refinements
Evaluation Framework: Open-sourced Aya Vision Benchmark for multilingual multimodal assessment
Resource Efficiency: Optimized for researchers with limited computation resources
Accessibility Focus: Free access via WhatsApp integration for global usability Iterative Improvement: Performance scaling from 40.9% to 79.1% win rates through technical refinements Iterative Improvement: Evaluation Framework: Open-sourced Aya Vision Benchmark for multilingual multimodal assessment Evaluation Framework: Resource Efficiency: Optimized for researchers with limited computation resources Resource Efficiency: Accessibility Focus: Free access via WhatsApp integration for global usability Accessibility Focus: The release includes open-weights for both model sizes on Kaggle and Hugging Face, continuing Cohere's expansion of multilingual AI research that began with the Aya initiative two years ago. The model builds upon Aya Expanse, supporting research collaboration across 3,000 researchers from 119 countries. QwQ-32B: Alibaba's New Reasoning Model Achieves DeepSeek-R1 Level Performance QwQ-32B: Alibaba's New Reasoning Model Achieves DeepSeek-R1 Level Performance Alibaba has released QwQ-32B, a new open-source reinforcement learning (RL) enhanced language model that achieves performance comparable to DeepSeek-R1 despite using significantly fewer parameters. The model demonstrates that strategic RL applications can dramatically close the performance gap with much larger models. QwQ-32B QwQ-32B Technical Architecture: Technical Architecture: Parameter Size: 32B parameters versus DeepSeek-R1's 671B (37B activated)
Training Pipeline: Two-stage reinforcement learning with outcome-based rewards
First Stage: Math and coding task optimization using accuracy verifiers
Second Stage: General capability enhancement with reward models and rule-based verifiers
License: Apache 2.0 open-source availability Parameter Size: 32B parameters versus DeepSeek-R1's 671B (37B activated) Parameter Size: Training Pipeline: Two-stage reinforcement learning with outcome-based rewards Training Pipeline: First Stage: Math and coding task optimization using accuracy verifiers First Stage: Second Stage: General capability enhancement with reward models and rule-based verifiers Second Stage: License: Apache 2.0 open-source availability License: Performance Metrics: Performance Metrics: AIME24: 79.5% accuracy, matching DeepSeek-R1's 79.8%
LiveCodeBench: 63.4% score compared to DeepSeek-R1's 65.9%
LiveBench: 73.1% performance versus 71.6% for DeepSeek-R1
IFEval: 83.9% accuracy, comparable to R1's 83.3%
BFCL: 65.4% score, outperforming R1's 60.3% AIME24: 79.5% accuracy, matching DeepSeek-R1's 79.8% AIME24: LiveCodeBench: 63.4% score compared to DeepSeek-R1's 65.9% LiveCodeBench: LiveBench: 73.1% performance versus 71.6% for DeepSeek-R1 LiveBench: IFEval: 83.9% accuracy, comparable to R1's 83.3% IFEval: BFCL: 65.4% score, outperforming R1's 60.3% BFCL: Integration Capabilities: Integration Capabilities: Tool Use: Built-in agent capabilities for environmental interaction
Adaptive Reasoning: Dynamic thought process adjustment based on feedback
API Access: Available through Hugging Face, ModelScope, and Alibaba Cloud DashScope
Deployment: Accessible via Qwen Chat with Python integration examples Tool Use: Built-in agent capabilities for environmental interaction Tool Use: Adaptive Reasoning: Dynamic thought process adjustment based on feedback Adaptive Reasoning: API Access: Available through Hugging Face, ModelScope, and Alibaba Cloud DashScope API Access: Deployment: Accessible via Qwen Chat with Python integration examples Deployment: Alibaba's team identifies this as an initial step toward developing more capable AGI systems by combining stronger foundation models with scaled RL and computational resources. Tools & Releases YOU Should Know About Tools & Releases YOU Should Know About BoringUI is a technical platform that automates the creation of user interfaces (UIs) from JSON data. It generates UIs in HTML and Tailwind CSS, allowing users to copy and share the code via links. This tool streamlines UI development by providing a straightforward method to transform data into functional interfaces, enhancing productivity and collaboration among developers.
ChatWithGit is a specialized search engine designed to efficiently scan and index public GitHub repositories. It enables users to quickly find specific code snippets, files, or functionalities within the vast ecosystem of open-source projects. By offering targeted search capabilities, GitSearch helps developers discover and leverage existing code, contributing to increased efficiency and collaboration in software development.
DiffBlue is an enterprise-grade AI solution designed to automate unit test generation and management for complex Java code. Unlike LLM-driven assistants, it uses reinforcement learning to produce reliable, executable, and correct test code, ensuring IP security through on-premises operation. It integrates into IntelliJ and CI pipelines, generating tests 250x faster than manual methods and increasing code coverage. Diffblue Cover aims to
Swimm is an AI-powered platform designed to help developers understand and modernize complex mainframe codebases. It leverages AI to uncover code insights, generate documentation for languages like COBOL and Assembly, and extract business logic from legacy systems. Swimm provides features such as visualizing program flows, identifying dependencies, and assessing the impact of changes. It aims to reduce mainframe complexity, create missing specs, and ensure secure, compliant, and scalable operations, with options for both cloud and on-premises deployment. BoringUI is a technical platform that automates the creation of user interfaces (UIs) from JSON data. It generates UIs in HTML and Tailwind CSS, allowing users to copy and share the code via links. This tool streamlines UI development by providing a straightforward method to transform data into functional interfaces, enhancing productivity and collaboration among developers. BoringUI ChatWithGit is a specialized search engine designed to efficiently scan and index public GitHub repositories. It enables users to quickly find specific code snippets, files, or functionalities within the vast ecosystem of open-source projects. By offering targeted search capabilities, GitSearch helps developers discover and leverage existing code, contributing to increased efficiency and collaboration in software development. ChatWithGit DiffBlue is an enterprise-grade AI solution designed to automate unit test generation and management for complex Java code. Unlike LLM-driven assistants, it uses reinforcement learning to produce reliable, executable, and correct test code, ensuring IP security through on-premises operation. It integrates into IntelliJ and CI pipelines, generating tests 250x faster than manual methods and increasing code coverage. Diffblue Cover aims to DiffBlue Swimm is an AI-powered platform designed to help developers understand and modernize complex mainframe codebases. It leverages AI to uncover code insights, generate documentation for languages like COBOL and Assembly, and extract business logic from legacy systems. Swimm provides features such as visualizing program flows, identifying dependencies, and assessing the impact of changes. It aims to reduce mainframe complexity, create missing specs, and ensure secure, compliant, and scalable operations, with options for both cloud and on-premises deployment. Swimm And that wraps up this issue of "This Week in AI Engineering." This Week in AI Engineering. Thank you for tuning in! Be sure to share this with your fellow AI enthusiasts and follow for the latest weekly updates. Until next time, happy building!

OpenAI Launches $50 million AI fund

Hello JavaScript Enthusiasts! Welcome to a new edition of "This Week in JavaScript"! A lot happened this week, let’s take a look at OpenAI's groundbreaking GPT-4.5 model, Doom running in TypeScript, major Bun updates, Firefox's privacy controversy, and the hottest tool updates in the dev community! OpenAI Releases GPT-4.5 Orion OpenAI has launched its most advanced model yet, GPT-4.5, code-named "Orion." And devs, this one’s going to change your workflow! Why it matters: AI-Powered Development: GPT-4.5 outshines its predecessor in coding tasks, with higher scores on benchmarks like SWE-Lancer.
Fewer Mistakes: This model reduces hallucinations in code generation, leading to fewer bugs!
Improved Persuasion: It excels in creative and human-centric tasks, performing better in persuasive scenarios. Though it comes at a higher cost, GPT-4.5's accuracy and enhanced problem-solving make it a valuable tool for developers working on creative and complex projects. Doom Runs in TypeScript’s Type System In what might be the wildest engineering feat of the year, Dimitri Mitropoulos has successfully run Doom in TypeScript’s Type system. The Setup: Using TypeScript’s Turing-complete type system, Mitropoulos ran the 1993 classic game entirely within types.
Technical Feat: It took 177 terabytes of type definitions processed over 12 days just to get the first frame running! This project is a technical marvel, showcasing the creative potential of TypeScript, even though it's unlikely you’ll be running games in your type definitions anytime soon. It pushes the boundaries of what’s possible with TypeScript. Bun 1.2.3 and 1.2.4 Double Release Bun versions 1.2.3 and 1.2.4 are here, packed with updates designed to improve your development experience! Key highlights: Frontend Bundling: Just run bun ./index.html and let Bun handle the rest. It simplifies starting new projects with tools like React using bun init.
Built-in Routing: For backend devs, dynamic path parameters in Bun.serve() enable quick and efficient routing.
SQL Enhancements: Bun.SQL now has better array support and multi-statement queries.
MacOS Speed Boost: A 60% increase in build speed for macOS apps, with new code-signing options. These updates make Bun an even more attractive option for JavaScript developers building full-stack apps. Firefox’s Privacy Controversy Mozilla sparked controversy this week with its updated Terms of Use and Privacy Notice. It suggested that Mozilla could use data from Firefox input, which worried many developers. What you need to know: Clarifications: Mozilla has reassured users that no personal data is used for AI training, and ad data is de-identified.
Opt-Out: If you’re concerned, Firefox allows users to opt out of technical data collection in the settings, especially important for sensitive dev projects. Tools & Releases You Should Know About Let’s take a quick look at the latest tools making waves in the JavaScript world this week! Next.js 15.2: Redesigned debugging, experimental React View Transitions, and improved Node.js runtime in middleware. Faster stack traces and smoother rendering with Turbopack improvements.
Astro 5.4: New remote image optimization in Markdown, experimental responsive image support, and enhanced security features. Markdown images now get automatic optimization!
ESLint v9.21.0: Introduces the --ext CLI option, helping you lint specific file extensions more easily, along with improved metadata for deprecated rules.
Ember 6.2: A key fix resolves a TypeScript issue that had been affecting developers, while Ember CLI now allows app creation in any directory. And EmberData continues rebranding to WarpDrive!
Angular 19.2: New experimental httpResource, better TypeScript 5.8 support, and core enhancements like streaming resources and improved form validator sets. The update addresses race conditions, change detection timing issues, and animation handling on the server, plus a new migration tool that automatically converts templates to self-closing tags to save you time and improve code consistency. And that's it for the twenty-fourth issue of "This Week in JavaScript." If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers, and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious! Hello JavaScript Enthusiasts! Welcome to a new edition of " This Week in JavaScript "! This Week in JavaScript A lot happened this week, let’s take a look at OpenAI's groundbreaking GPT-4.5 model, Doom running in TypeScript, major Bun updates, Firefox's privacy controversy, and the hottest tool updates in the dev community! OpenAI Releases GPT-4.5 Orion OpenAI Releases GPT-4.5 Orion OpenAI has launched its most advanced model yet , GPT-4.5, code-named "Orion." And devs, this one’s going to change your workflow! launched its most advanced model yet launched its most advanced model yet Why it matters: Why it matters: AI-Powered Development: GPT-4.5 outshines its predecessor in coding tasks, with higher scores on benchmarks like SWE-Lancer. Fewer Mistakes: This model reduces hallucinations in code generation, leading to fewer bugs! Improved Persuasion: It excels in creative and human-centric tasks, performing better in persuasive scenarios. AI-Powered Development: GPT-4.5 outshines its predecessor in coding tasks, with higher scores on benchmarks like SWE-Lancer. AI-Powered Development: Fewer Mistakes: This model reduces hallucinations in code generation, leading to fewer bugs! Fewer Mistakes: Improved Persuasion: It excels in creative and human-centric tasks, performing better in persuasive scenarios. Improved Persuasion: Though it comes at a higher cost, GPT-4.5's accuracy and enhanced problem-solving make it a valuable tool for developers working on creative and complex projects. Doom Runs in TypeScript’s Type System Doom Runs in TypeScript’s Type System In what might be the wildest engineering feat of the year, Dimitri Mitropoulos has successfully run Doom in TypeScript’s Type system. Dimitri Mitropoulos has successfully run Doom Dimitri Mitropoulos has successfully run Doom The Setup: Using TypeScript’s Turing-complete type system, Mitropoulos ran the 1993 classic game entirely within types. Technical Feat: It took 177 terabytes of type definitions processed over 12 days just to get the first frame running! The Setup : Using TypeScript’s Turing-complete type system, Mitropoulos ran the 1993 classic game entirely within types. The Setup Technical Feat : It took 177 terabytes of type definitions processed over 12 days just to get the first frame running! Technical Feat This project is a technical marvel, showcasing the creative potential of TypeScript, even though it's unlikely you’ll be running games in your type definitions anytime soon. It pushes the boundaries of what’s possible with TypeScript. Bun 1.2.3 and 1.2.4 Double Release Bun 1.2.3 and 1.2.4 Double Release Bun versions 1.2.3 and 1.2.4 are here, packed with updates designed to improve your development experience! Bun versions 1.2.3 1.2.3 1.2.4 1.2.4 Key highlights: Key highlights: Frontend Bundling: Just run bun ./index.html and let Bun handle the rest. It simplifies starting new projects with tools like React using bun init. Built-in Routing: For backend devs, dynamic path parameters in Bun.serve() enable quick and efficient routing. SQL Enhancements: Bun.SQL now has better array support and multi-statement queries. MacOS Speed Boost: A 60% increase in build speed for macOS apps, with new code-signing options. Frontend Bundling : Just run bun ./index.html and let Bun handle the rest. It simplifies starting new projects with tools like React using bun init. Frontend Bundling Built-in Routing : For backend devs, dynamic path parameters in Bun.serve() enable quick and efficient routing. Built-in Routing SQL Enhancements : Bun.SQL now has better array support and multi-statement queries. SQL Enhancements MacOS Speed Boost : A 60% increase in build speed for macOS apps, with new code-signing options. MacOS Speed Boost These updates make Bun an even more attractive option for JavaScript developers building full-stack apps. Firefox’s Privacy Controversy Firefox’s Privacy Controversy Mozilla sparked controversy this week with its updated Terms of Use and Privacy Notice. It suggested that Mozilla could use data from Firefox input, which worried many developers. Mozilla sparked controversy Mozilla sparked controversy What you need to know: What you need to know: Clarifications: Mozilla has reassured users that no personal data is used for AI training, and ad data is de-identified. Opt-Out: If you’re concerned, Firefox allows users to opt out of technical data collection in the settings, especially important for sensitive dev projects. Clarifications : Mozilla has reassured users that no personal data is used for AI training, and ad data is de-identified. Clarifications Opt-Out : If you’re concerned, Firefox allows users to opt out of technical data collection in the settings, especially important for sensitive dev projects. Opt-Out Tools & Releases You Should Know About Tools & Releases You Should Know About Let’s take a quick look at the latest tools making waves in the JavaScript world this week! Next.js 15.2: Redesigned debugging, experimental React View Transitions, and improved Node.js runtime in middleware. Faster stack traces and smoother rendering with Turbopack improvements. Astro 5.4: New remote image optimization in Markdown, experimental responsive image support, and enhanced security features. Markdown images now get automatic optimization! ESLint v9.21.0: Introduces the --ext CLI option, helping you lint specific file extensions more easily, along with improved metadata for deprecated rules. Ember 6.2: A key fix resolves a TypeScript issue that had been affecting developers, while Ember CLI now allows app creation in any directory. And EmberData continues rebranding to WarpDrive! Angular 19.2: New experimental httpResource, better TypeScript 5.8 support, and core enhancements like streaming resources and improved form validator sets. The update addresses race conditions, change detection timing issues, and animation handling on the server, plus a new migration tool that automatically converts templates to self-closing tags to save you time and improve code consistency. Next.js 15.2 : Redesigned debugging, experimental React View Transitions, and improved Node.js runtime in middleware. Faster stack traces and smoother rendering with Turbopack improvements. Next.js 15.2 Next.js 15.2 Astro 5.4 : New remote image optimization in Markdown, experimental responsive image support, and enhanced security features. Markdown images now get automatic optimization! Astro 5.4 Astro 5.4 ESLint v9.21.0 : Introduces the --ext CLI option, helping you lint specific file extensions more easily, along with improved metadata for deprecated rules. ESLint v9.21.0 ESLint v9.21.0 Ember 6.2 : A key fix resolves a TypeScript issue that had been affecting developers, while Ember CLI now allows app creation in any directory. And EmberData continues rebranding to WarpDrive! Ember 6.2 Ember 6.2 Angular 19.2 : New experimental httpResource, better TypeScript 5.8 support, and core enhancements like streaming resources and improved form validator sets. The update addresses race conditions, change detection timing issues, and animation handling on the server, plus a new migration tool that automatically converts templates to self-closing tags to save you time and improve code consistency. Angular 19.2 Angular 19.2 And that's it for the twenty-fourth issue of " This Week in JavaScript. " This Week in JavaScript. If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers, and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious!

Bun just got 60% faster, Doom now runs on TypeScript, and OpenAI launched GPT 4.5 - This Week in JS

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

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