Transcript: One of the things that I think I need to do tomorrow is implement my own agent. Something probably very basic and referring to I believe it's Simon Willison's blog. Just something stupidly simple in JavaScript and seeing what happens because whatever currently is there I can use Microsoft's thing. I am hesitant to use LangChain but there's got to be something there and I think maybe I do need to enumerate more or less what I expect of the agent. What steps that it might want to do to get there and also what kinds of things generally need to happen like recursive summarization and that kind of thing. That is a very easy GPT-3 task and possibly even a local model which would save on compute significantly. And the other thing that I'm wondering is do I just fucking prompt it in an array to get my answer of I know exactly what it needs to do so I'm just going to tell it and it can figure it out. That guy is going really slow on the freeway. Unnecessarily slow. That was going 35. That's not okay.
The speaker aspires to be part of communities that empower individuals to explore their data and bring value back to themselves. They are willing to take a job in such a space and believe it's worth doing. The goal is to build tools that make it easy for the individual to work with their data directly on a web page. They plan to move to a more reactive front end using Next.js and React, designing a feed and query system possibly using natural language. The speaker also mentions working on embedding audio and ensuring embeddings are accessible. The text discusses the process of obtaining and manipulating data and emphasizes the importance of experimentation and innovation. It uses the metaphor of building a playground to illustrate the iterative nature of the process, acknowledging that initial attempts may be imperfect but can be improved upon through learning from mistakes. The writer anticipates challenges but expresses a hope to avoid negative consequences and eventually achieve success. Finally, the text concludes with a lighthearted remark and a reference to going to sleep.
You are considering various options for your demo, including showing how to quickly create a website from random content and exploring integration with reminders, calendar, and to-do lists. You are also interested in demonstrating the potential of extracting and managing to-do items from a webpage in a useful and intelligent manner. Overall, you are seeking a prompt that could efficiently fulfill these tasks and ultimately enhance productivity.
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The speaker is excited about tomorrow but acknowledges that as a developer facing new challenges, the work is not trivial, especially given the lack of extensive documentation and the solitary nature of their current work process. They express a desire to share their learnings, possibly by writing them down, and emphasize the importance of collaboration, suggesting that "if we do this together, it will be a better world." The speaker is tired of creating misleadingly impressive demos and aims to write code and interact with large language models in a more genuine and transparent way. Lastly, they recognize the complexity of building an effective agential system, admitting their current limitations while believing in its importance, and they present open questions about processing and connecting large amounts of data to better understand who we are.
The user is looking to implement a caching mechanism to quickly summarize new content added to a pipeline. They are considering a simple approach, such as selecting the most recent items and creating a summary, as well as exploring the possibility of summarizing content on a weekly basis. The user also expresses a desire for the summarization process to involve natural language queries rather than programming, and seeks to explore methods to refine natural language programming capabilities.
In the first bucket, the focus is on achieving AI-level parallelism, creating a better pipeline, enabling the execution of different LLM tasks in parallel, and allowing future agents to add information to an execution graph. This parallelization is crucial for distributed systems processing and likely to advance the distribution and parallel running of models. The second bucket involves implementing transformations, such as converting unstructured transcripts into organized bullet point lists, and making this adaptable and viable through JSON. The goal is to seamlessly convert text into a GitHub issue, providing instructions for transformation and capturing context to refine models.
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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 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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I've always been drawn to the peculiar and unexplored, which makes me wonder if I can pepper my writing with a bit of the offbeat—things that don't quite fit the mold. Question is, can I make it work? Ditching the third-person narrative and opting for a chat with you in the first person could make my stories feel more intimate, more like we're in this together. And hey, isn't that what storytelling's all about? Let's find out.
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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.