IME LLMs are kind of like a projection of your current expertise - your prompting and guidance etc. biases LLM plans kind of 'in the direction' of your thinking. I think this is one reason why it seems like senior engineers get more lift vs. juniors.
What I am exploring is another step to the classic 'research / plan / implement' pattern: 'research / plan / LEARN / implement' where LEARN involves the human doing AI tutoring sessions to ensure a deep understanding the concepts etc. that the LLM is planning to implement so you can refine / iterate on plans and direct the LLM in ever more effective ways. My idea is that this then compounds your human capital and reduces the occurance of 'sounds smart, doesn't work' pattern.
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Commoditize your complement - I expect to see this most in consumer AI (after that starts actually working...)
It will be important for Apple to have good enough, cheap local LLM models that run on-device.
If the barrier to performance shifts from fundamental model capability to context collection and management I would expect to see folks focused on that problem continuing to drive open-weight LLM model development in some shape or form.
My take is that B2C AI applications are kind of structurally limited by how hard it is to build personalized context.
The idea of capable local models could be a huge unlock here if they are able to do the bottom-up context collection research / tagging / etc. at scale.
I think the research / plan / execute idea is good but feels like you would be outsourcing your thinking. Gotta review the plan and spend your own thinking tokens!
Yeah I vibe coded a simple app that takes an org-mode file, renders it as a kanban board, and lets me spin up agents for each task with the prompt in the body in a named tmux session. The frontend gets updated via Claude code hooks when an agent is idle.
I think the key is to combine human and agent task tracking in one pane of glass.
I have been doing the same but with happy. It works quite well for quick brainstorms etc. but for deeper work on a real research / plan / implement thing I think you need to actually engage with the output which is hard to do on mobile. Maybe if I had a better UI than terminus to read and check the remote files I would be able to get more done.
I am also hoping / trying to put Claude code on top of a personal zettlekasten to automate more of my “personal life” tasks and get more stuff done for me. Haven’t gotten it really singing yet but I think that could also be really cool.
Whoever writes the tool that can Actually Make a legitimate microsoft office powerpoint slide from text will make a lot of money.
From what I have seen most of these tools need to do more user research on how powerpoint slides actually look like in practice.
There's a lot of "you're doing it wrong, show don't tell, just keep the basics on the slide" but the people that use powerpoint to make $$$ make incredibly dense powerpoint materials that serve as reference documents, not presentation guides (i.e. they are intended as leave-behind documents that people can read in advance)
Presentations are also quite hard because:
1. It must "compile to" Powerpoint (it must compile to powerpoint because your end users will want to make direct edits and those end users will NOT be comfortable in markdown and in general will be very averse to change)
2. Powerpoint has no layout engine
3. Powerpoint presentations are in fact a beautiful medium in which VISUAL LAYOUT HAS SEMANTIC MEANING (powerpoint is like medieval art where larger is more important)
If anyone wants to help me build an engine that can get an LLM to ACTUALLY make powerpoints please let me know. I am sure this is a lot harder than you think it is.
yes i have been thinking about this for some time. One other thing with zettle I'm not sure was implemented here is you can have topic notes that just refer / summarize other notes; it would be very interesting if these could be autonomously created by some kind of clustering algorithm based on underlying links. Kind of like summary-of-summary.
What I am exploring is another step to the classic 'research / plan / implement' pattern: 'research / plan / LEARN / implement' where LEARN involves the human doing AI tutoring sessions to ensure a deep understanding the concepts etc. that the LLM is planning to implement so you can refine / iterate on plans and direct the LLM in ever more effective ways. My idea is that this then compounds your human capital and reduces the occurance of 'sounds smart, doesn't work' pattern.