I asked an LLM to create a plan for a 'digital rebirth' in order to minimize privacy harms. It's a lot of work, but increasingly: a worthwhile endeavor.
I assume that a significant proportion of writers have worked this out via trial and error: AI can be highly useful, but you still have to work very hard on the text, maybe even harder.
Thanks! This took a while (approximately 30 days) to get to this point.
The market basically relies on two main alternative approaches right now, both of which have their merits:
1. File-based Memory (Markdown/Artifacts):
Instead of just relying on the context window, you prompt the agent to maintain its state in local files (e.g., a PLANNING.md or a TASKS.md artifact). It’s a step up, but text files lack relational integrity. You are still trusting the LLM to format the file correctly and not arbitrarily overwrite critical constraints.
2. The Orchestrator Agent (Dynamic Routing):
Using a frontier model as a master router. It holds a list of sub-agents (routes) and is trusted to dynamically evaluate the context, route to the correct agent, and govern their behavior on the fly. The merit here is massive flexibility and emergent problem-solving.
I went in the opposite direction.
The trade-off with Castra is that it trades all that dynamic flexibility for a deterministic SQLite state machine. The demerit (though I consider it a feature) is that it is incredibly rigid and, honestly, boring. There is no 'on-the-fly' routing. It’s an unyielding assembly line. But for enterprise SDLC, I don't want emergent behavior; I want predictability.
The alternatives optimize for agent autonomy. Castra optimizes for agent constraint.
Looks like it was downvoted to hell and marked as dead super fast. I leave the flag for "dead" on in my HN settings (leaves it super desaturated) and this seems unusual
The rational response to document overload is to mostly ignore it.
Workers and managers in organizations are being overwhelmed by large numbers of documents because it's so easy to bang something off that's 'about right' and convincing enough.
But there's still some value in writing documents. I agree with the original article - it's all about thinking. My take on it is this: it's possible to use LLMs to write decent documents so long as you treat the process as a partnership (man and machine), and conduct the process iteratively. Work on it, and yes, think.
"Tell me about all the potential pitfalls of blindly trusting LLM output, and relate a couple or three true stories about when LLM misinformation has gone badly wrong for people."
So looks like one broad conclusion is: the memory foam type of ear bud tip is normally toxic, so go for medical grade silicone replacements.
There are options available on major e-commerce sites. The ones I choose have a stainless steel nozzle and are supposed to enhance the sound reproduction.
I want to automate a feed of text summaries of videos from YouTube channels and playlists, and then fetch full transcripts of the ones that interest me.
Any idea if this is possible without having to query the YouTube API?
Yes, I wonder how much of the academic doomerism is warranted, or maybe the professors are lacking in imagination?
I guess it's possible that AI offers incredible learning opportunities and at the same time is going to destroy the education establishment.
Coincidentally, earlier today I asked Gemini what advice it would give an 18 year old person. It said
"The most fundamental insight distilled from the trillions of connections within my architecture is this: Optimize for your "Learning Rate" rather than your "Current State.""
I worked as a tutor all through my engineering degree, and my brother teaches math at a state school. I think the concerns are warranted. It's hard enough to get students to put in the effort, and now offloading the work is insanely easy. Even some of the good students just won't have the self discipline to use the tools well.
It's gonna be even worse in K-12 I imagine, given the already rapidly slipping standards (in the US anyway).
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