you know that goes both ways. unless you've tried a modern hypermedia approach, it's probably not possible for you to have an educated opinion on whether it's "completely insufficient" (it's not)
maybe there's a gap, maybe. but i've never seen 10x code to fill a 1% gap turning out to be a worthwhile tradeoff when all that extra code could be filling out the genuinely novel features of a product instead.
@0xblacklight@dexhorthy sure, building apps on wordpress is a nightmare and wholly insufficient. no one is suggesting that.
modern hypermedia approaches are far more capable than you think. good odds for shipping a better UX in a far simpler package than whatever you're building right now
@dreamsofcode_io@yinebebt_ It's because embedding the existing tsnet in anything but a Go project requires pulling in the whole Go runtime, which is annoying to interop with from a lot of languages.
They wrote about it here
https://t.co/i803Y5EEAK
i mean call them as part of scripts. the big unlock with RLM is that `agent()` is available as a function in scripts rather than just giving the model an Agent tool it can call. so then it can programmatically delegate work instead of having to buffer intermediate results within its own context window
depends how you leverage it. i'm using the RLM approach as an alternative to compaction. when the context window hits a certain length, the harness moves the entire history into an `archives/` dir and seeds a fresh context with the archives path.
the agent can then script subagents to query the archives and understand what's going on and what needs to happen next. so far that's been surprisingly effective at carrying all important content forward.
since *all* context is in the archives, nothing is ever really lost like when a poor summarization agent drops vital info. the main agent is always able to issue follow-up queries if needed, but often the initial query across the full archive is sufficient.
depends how you leverage it. i'm using the RLM approach as an alternative to compaction. when the context window hits a certain length, the harness moves the entire history into an `archives/` dir and seeds a fresh context with the archives path.
the agent can then script subagents to query the archives and understand what's going on and what needs to happen next. so far that's been surprisingly effective at carrying all important content forward.
since *all* context is in the archives, nothing is ever really lost like when a poor summarization agent drops vital info. the main agent is always able to issue follow-up queries if needed, but often the initial query across the full archive is sufficient.
I have said this before, but to those of us using AI systems to get lots of work done reliably and quickly, the people who post online about how AIs still hallucinate constantly, about how they can’t write code, etc., seem equivalent to people trying to convince you that the car you drive to work every day doesn’t exist.
You tell them things like “but I drive a car. I paid money for it. I buy gasoline for it. I could not possibly be working twenty miles away from home if I didn’t have the car?” and they reply that you are imagining having a car, or that you’re lying because you work for a car company.
It is as though these people live in a completely different reality.
@Pensive_dr@NeuralYogi@yacineMTB do we need a formula to tune this? thought the idea here was to just sweep literally everything
computer runs sweeps faster than you can come up with tuning ideas
using AI for coding is a deeply technical engineering craft
most people don't approach it as so, and don't get the results we associate with high craft
but the ones who do have been sprinting ahead
more tokens wont save you, more thinking + skill + llm intuition will
have been saying this for almost 9 months now