fishaudio/openaudio-s1-mini
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Florent Daudens
fdaudens
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This is the story of how open source AI created a $3M business for a news company:
Clare Spencer tells on the GAIN blog how a Danish software engineer found OpenAI's Whisper model and turned it into Good Tape. It's now generating $3M ARR for news service Zetland.
Great playbook on how to build a good product:
- This idea came from a software engineer, Jakob Steinn, who was not only able to spot a new model, but also listen to feedback from his colleagues in the newsrooms (he thought they would use it for translation, but they were more interested in transcription in Danish)
- They built iteratively: they went from running the model in the terminal to a notebook to a full-fledged web interface
- They didn't just wrap the API. They rebuilt the transcription engine from scratch, moved it to TPUs for 45-second processing of hour-long audio, and added EU-based data sovereignty
Now Good Tape has 2.5M users worldwide, with only 30-35% being journalists.
Small languages (Danish, Finnish, Croatian, Hebrew) were underserved by existing tools - suddenly there's a "very very big market" when you put them together.
This shows how open source AI can solve real workflow problems and create sustainable businesses. Sometimes the best opportunities emerge from solving your own daily problems.
Worth a read: https://generative-ai-newsroom.com/how-a-danish-news-service-made-a-profit-with-its-transcription-tool-285bc05b7cf9
Clare Spencer tells on the GAIN blog how a Danish software engineer found OpenAI's Whisper model and turned it into Good Tape. It's now generating $3M ARR for news service Zetland.
Great playbook on how to build a good product:
- This idea came from a software engineer, Jakob Steinn, who was not only able to spot a new model, but also listen to feedback from his colleagues in the newsrooms (he thought they would use it for translation, but they were more interested in transcription in Danish)
- They built iteratively: they went from running the model in the terminal to a notebook to a full-fledged web interface
- They didn't just wrap the API. They rebuilt the transcription engine from scratch, moved it to TPUs for 45-second processing of hour-long audio, and added EU-based data sovereignty
Now Good Tape has 2.5M users worldwide, with only 30-35% being journalists.
Small languages (Danish, Finnish, Croatian, Hebrew) were underserved by existing tools - suddenly there's a "very very big market" when you put them together.
This shows how open source AI can solve real workflow problems and create sustainable businesses. Sometimes the best opportunities emerge from solving your own daily problems.
Worth a read: https://generative-ai-newsroom.com/how-a-danish-news-service-made-a-profit-with-its-transcription-tool-285bc05b7cf9
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Bigger isn't always better: how to choose the most efficient model for context-specific tasks 🌱🧑🏼💻
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🎵 Dream come true for content creators! TIGER AI can extract voice, effects & music from ANY audio file 🤯
This lightweight model uses frequency band-split technology to separate speech like magic. Kudos to @fffiloni for the amazing demo! fffiloni/TIGER-audio-extraction
This lightweight model uses frequency band-split technology to separate speech like magic. Kudos to @fffiloni for the amazing demo! fffiloni/TIGER-audio-extraction
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Just completed the AI Agents course and wow, that capstone project really makes you understand how to build agents that can handle real-world complexity!
The final project uses the GAIA dataset - your agent has to solve tasks like analyzing Excel files, processing audio recordings, answering questions about YouTube videos, and diving into research papers. This isn't toy examples, it's the messy, multimodal stuff agents need to handle in practice.
Whether you’re just getting started with agents or want to go deeper with tools like LangChain, LlamaIndex, and SmolAgents, this course has tons of useful stuff. A few key insights:
- Code agents are incredibly versatile once you get the architecture right
- The sweet spot is finding the right balance of guidance vs autonomy for each use case
- Once the logic clicks, the possibilities really are endless - it's like letting LLMs break free from the chatbox
The course is free and the certification deadline is July 1st, 2025.
The Hugging Face team built something special here. If you're tired of AI that impresses in demos but fails in practice, this is your path to building agents that actually deliver. https://huggingface.co/learn/agents-course/unit0/introduction
Best part? There's the MCP course next!
The final project uses the GAIA dataset - your agent has to solve tasks like analyzing Excel files, processing audio recordings, answering questions about YouTube videos, and diving into research papers. This isn't toy examples, it's the messy, multimodal stuff agents need to handle in practice.
Whether you’re just getting started with agents or want to go deeper with tools like LangChain, LlamaIndex, and SmolAgents, this course has tons of useful stuff. A few key insights:
- Code agents are incredibly versatile once you get the architecture right
- The sweet spot is finding the right balance of guidance vs autonomy for each use case
- Once the logic clicks, the possibilities really are endless - it's like letting LLMs break free from the chatbox
The course is free and the certification deadline is July 1st, 2025.
The Hugging Face team built something special here. If you're tired of AI that impresses in demos but fails in practice, this is your path to building agents that actually deliver. https://huggingface.co/learn/agents-course/unit0/introduction
Best part? There's the MCP course next!
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