Comment by com2kid
5 days ago
IMHO no one is really pioneering. A lot more is possible than what is being done. I wrote a blog post about useful agents in a business setting (https://www.generativestorytelling.ai/blog/posts/useful-corp...) that highlights AI being proactive.
I mean table stakes stuff, why isn't an agent going through all my slack channels and giving me a morning summary of what I should be paying attention to? Why aren't all those meeting transcriptions being joined together into something actually useful? I should be given pre-meeting prep notes about what was discussed last time and who had what to do items assigned. Basic stuff that is already possible but that no one is doing.
I swear none of the AI companies have any sense of human centric design.
> pull relevant context from Slack, Notion, and your codebase, then provide you with a prioritized list of actions.
This is an improvement, but it isn't the central focus. It should be more than just on a single work item basis, more than on just code.
If we are going to be managing swarms of AI agents going forward, attention becomes our most valuable resource. AI should be laser focused on helping us decide where to be focused.
THANK YOU. I keep thinking this as well. I'm rolling my own skills to actually make my job easier, which is all about gathering, surfacing, and synthesizing information so I can make quick informed decisions. I feel like nobody is thinking this way and it's bizarre.
The value prop is tenuous and most people still think agents aren't capable of doing this type of work reliably yet (which is... kind of true). You won't get punished too much by users for false positives when summarizing tasks, but you will get absolutely eviscerated for false negatives (e.g. dropping a critical task from the summary). Can you guarantee that your agent won't forget to tell you about something super important?
I am completely convinced this is because of a gap in the intersection of knowledge. Somehow the people making the best agents are focused on extending the capabilities of the models, meanwhile the people who could best make an application layer because just think of LLM's as a chat prompt.
We need a product person, maybe with a turtle neck sweater and an horrid work-life attitude, to fix this up, instead of a weirdly philosophic basilisk fearing idealist.
This makes a lot of sense, but I can't see anyone paying for this because at its simplest layer it's just a Neo4j install + some skills + a local cron job for Claude Desktop. How long will it take for Anthropic to just bake this into Claude Desktop or OpenAI into Codex? Probably not that long.
I keep coming up with good ideas for how to use agents and keep walking away from them because there just is no defensible moat. Everything software related is just going to get totally consumed over the next year.
Disclaimer I work at Zapier, but we're doing a ton of this. I have an agent that runs every morning and creates prep documents for my calls. Then a separate one that runs at the end of every week to give me feedback
In the full blog post I actually go into more detail about automatically creating a knowledge graph of what is being worked on throughout the whole company. There are some really powerful transformative efforts that can be accomplished right now, but that no one is doing.
Basic things like detecting common pain points, to automatically figuring out who is the SME for a topic. AIs are really good at categorizations and tagging, heck even before modern LLMs this is something ML could do.
But instead we have AI driven code reviews.
Code Reviews are rarely the blocker for productivity! As an industry, we need to stop automating the easy stuff and start helping people accomplish the hard stuff!
You should check out https://pieces.app/ ive been using it for months and I am surprised I have never seen anyone ever talk about it.
It does exactly what you are asking for, and it can do it completely locally or with a mixture of frontier models.
Is this just a wrapper on top of Beads?
Agreed. It is ironic that in the AI race, the real differentiation may not come from how smart the model is, but from who builds the best application layer on top of it. And that application layer is built with the same kind of software these models are supposed to commoditize.
This feels like *nix.
Developers built themselves really good OSes for doing developer things. Actually using it to do things was secondary.
Want to run a web server? Awesome choice. Want to write networking code? Great. Setup a reliable DB with automated backups? Easy peasy.
Want a stable desktop environment? Well after almost 30 years we just about have one. Kind of. It isn't consistent and I need to have a post it note on my monitor with the command to restart plasma shell, but things kind of work.
Current AI tools are so damn focused on building developer experiences, everything else is secondary. I get it, developers know how to fix developer pain points, and it monitizes well.
But holy shit. Other things are possible. Someone please do them. Or hell give me a 20 or 30 million and I'll do it.
But just.... The obvious is sitting out there for anyone who has spent 10 minutes not being just a developer.