Nice to see a Festool miter saw in his shop, Lynch knew what he was doing.
Readit Newsa-r-t commented on David Lynch LA House wallpaper.com/design-inte... · Posted by u/ewf
a-r-t commented on Amelia Earhart's Reckless Final Flights newyorker.com/magazine/20... · Posted by u/Thevet
There is a good Veritasium episode on her last flight going deep into technical details of what went wrong:
https://www.youtube.com/watch?v=zTDFhWWPZ4Q
Current state is temporary. What’s coming next is organic, living code. Think less testing, more self-healing. Digital code microphages.
Soon our excitement over CICD and shipping every minute will look very naive. There’s a future coming where every request execution could be through a different effective code path/base.
If we are speculating here, why not just go straight to an LLM serving all requests directly? No code needed.
a-r-t commented on How I built an AI-generated news site with daily articles dev.to/agentictribune/how... · Posted by u/agentictribune
this has been coming up often recently. nothing to announce yet, but when enough developers ask for it, we'll build it into the model's training
diarization is also a feature we plan to add
Glad to hear it's on your radar. I'd imagine phone call transcription is a significant use case.
Oh I see what you mean that would be a neat feature. Assuming you can get timestamps though it should be trivial to work around the issue?
There are two options that I know of:
1. Merge both channels into one (this is what Whisper does with dual-channel recordings), then map transcription timestamps back to the original channels. This works only when speakers don't talk over each other, which is often not the case.
2. Transcribe each channel separately, then merge the transcripts. This preserves perfect channel identification but removes valuable conversational context (e.g., Speaker A asks a question, Speaker B answers incomprehensively) that helps model's accuracy.
So yes, there are two technically trivial solutions, but you either get somewhat inaccurate channel identification or degraded transcription quality. A better solution would be a model trained to accept an additional token indicating the channel ID, preserving it in the output while benefiting from the context of both channels.
Hey, I'm Jeff and I was PM for these models at OpenAI. Today we launched three new state-of-the-art audio models. Two speech-to-text models—outperforming Whisper. A new TTS model—you can instruct it how to speak (try it on openai.fm!). And our Agents SDK now supports audio, making it easy to turn text agents into voice agents. We think you'll really like these models. Let me know if you have any questions here!
Hi Jeff, are there any plans to support dual-channel audio recordings (e.g., Twilio phone call audio) for speech-to-text models? Currently, we have to either process each channel separately and lose conversational context, or merge channels and lose speaker identification.
What makes you think they aren't? They are..
Google: https://blog.google/products/gmail/gmail-holidays-2024-spam-...
MS: https://techcommunity.microsoft.com/blog/microsoftdefenderfo...
The MS announcement is limited to scams/phishing. Google mentions both scams and spam, but somehow I still get 15-20 spam emails a day that even the smallest LLM should be able to classify correctly.