Transcript: I am also curious if I can move Whisper to my computer instead. That would reduce probably a fair bit of cost, honestly. Let me just check open.platform.openai. I'm just curious what my cost is right now. Yeah, it's like half of the cost is audio. Embedding is like nothing, like literally nothing. But audio is quite a lot and I think that's, I mean I had to rerun everything and it was like five bucks and like there's not even that much audio realistically in there. It's like I could cut five dollars out of this like that, you know.
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The individual is contemplating the potential local costs associated with using technology, such as Whisper, which may not be fully captured by the initial price. The economies of scale are considered, highlighting the complexity of determining the overall impact. There is uncertainty regarding the ultimate deployment of such technology, particularly in relation to large language models. The individual expresses a desire to further engage with the technological community and contribute to solving challenges faced as a developer.
The tension between ownership and renting, particularly in the context of technology and cloud computing, is a curious and complex topic that the speaker is exploring. They highlight the idea of effectively renting compute time and space through services like OpenAI and Microsoft Azure, and how reliance on the cloud has become a prevalent aspect of modern computing. The speaker also acknowledges the convenience of storing data in the cloud, even though they may own a computer. They mention OpenAI's work in this area, specifically their assistance API, and express a desire to further explore and challenge their own perspectives on these concepts.
The author is considering the dilemma between renting and buying AI hardware, particularly GPUs, for a company that requires significant compute resources to take off. Renting encourages minimal use of funds, which conflicts with the need for extensive GPU utilization to create something noteworthy. The author suggests that constantly running GPUs at full capacity for inference is a unique strategy that could provide a competitive edge by allowing real-time, high-performance applications. This approach implies a constant inference process on data, making it more accessible and valuable for sorting and classifying, a concept the author is pondering on.
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The speaker is focused on their API project and mentioned the stable diffusion model. They have also worked on running and testing various local language models, including Whisper and Orca 7 billion. They are curious to wire the models as a pipeline step and compare the output with GPT. The speaker is unsure of the success of their API project and the effectiveness of the language models, but they express eagerness to explore and experiment further.
The speaker is reflecting on the use of local large language models and the potential impact on the technology industry. They contemplate the reasons behind using local AI and express a desire to delve deeper into the topic. Additionally, they explore thoughts about their future aspirations of potentially becoming a venture capitalist and their excitement for shaping potential futures. The speaker also ponders about whether large language models will be implemented locally on devices and considers the potential influence of companies like Apple on the hardware market. They discuss the uncertainty around upcoming software development kits and the need to prepare for that transition. The speaker concludes with a remark about the thick fog outside and indicates a temporary pause to focus on driving.
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The author contemplates the process of converting an audio note into a transcript, then summarizing it on their "burrito" page. They express a desire to adjust the summarization voice to better represent themselves on the page. Recognizing that this feature may not have widespread appeal, the author nonetheless sees value in providing users with controls to personalize their "burrito." The concept of allowing users to fine-tune their experience is seen as an intriguing possibility.
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The speaker is reflecting on their experience with making audio burrito posts, noting that it often requires multiple attempts to get into the correct mindset—similar to drafting written posts. They're grappling with the challenge of monologuing without a clear understanding of the audience, as they are aware that at least John and CJ will hear it, but uncertainty about the wider audience affects their ability to communicate effectively. This creates a 'contextual membrane shakiness' as the speaker finds the lack of audience boundaries difficult to navigate, which they recognize may vary among different people. The speaker concludes by deciding to end the current note and start a new one.
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The speaker conveys their frustration with a difficult fundraising experience, describing a particularly unsatisfactory video call with a fund representative. The caller was in a bad mood, hadn't reviewed the provided materials, and hesitated to engage with the product's features. This led to a tense exchange where the speaker challenged the representative's commitment to valuing founders versus purely focusing on financial metrics. Feeling disillusioned, the speaker is left with a distaste for these disengaged "NPCs" and remains focused on their vision of fostering creative and engaging spaces.