3 ms·

I had a similar, admitted poorly thought out idea a few months back. I wanted to more or less build Jira for agents and track the context there. If I had to g

I had a similar, admitted poorly thought out idea a few months back. I wanted to more or less build Jira for agents and track the context there. If I had to guess 60 million is just enough to build the POC out. I don't see how this can compete though, Open AI or Anthro could easily spin up a competitor internally.

jasondigitized 9mo ago
Shouldn't this tool be agnostic to the models? Seems like a 3rd party is the way to go.
taude 9mo ago
Isn't that basically what things like this are for, open source, free.... https://github.com/steveyegge/beads https://github.com/steveyegge/beads
what 9mo ago
In what world do you need $60M to build a POC of whatever this is supposed to be?
handfuloflight 9mo ago
> If I had to guess 60 million is just enough to build the POC out. You're kidding.
gbnwl 9mo ago
You don't need to build anything. Just tell the agent to write tickets into .md files in a folder and move them to a closed foler as you go along. I've been using Claude Code with the Max plan nonstop essentially every day since last July and since then I've come to realize that the newer people are the more things they think they need to add to CC to get it work well. Eventually you'll find a way that works for you that needs none of it, and if any piece of all the extras IS ever helpful, Anthropic adds it themselves within a month or two.
- 999900000999 9mo ago
  I'm thinking a customized LLM would write notes in its own hyper compressed language which would allow it to be much much more efficient. For debugging you could translate it out to English, but if these agents can do stuff without humans in the loop, why do they need to take notes in English? I can't imagine creating this without hundreds of millions if not billions. I think the future is specialized models
  - gbnwl 9mo ago
    They're literally trained on natural language to output natural language. You would need to create the hyper compressed language first, convert all of your training data to that, and then train the models with that. But token efficiency per word already does vary between different languages, with Chinese being like 30%-40% more efficient than English last I heard
    - 999900000999 9mo ago
      Doesn't this mean the Chinese models have a significant advantage ? This isn't my domain, but say you had a massive budget, wouldn't a special LLM "thinking" language make sense ?

jasondigitized 9mo ago

Shouldn't this tool be agnostic to the models? Seems like a 3rd party is the way to go.

taude 9mo ago

Isn't that basically what things like this are for, open source, free.... https://github.com/steveyegge/beads https://github.com/steveyegge/beads

what 9mo ago

In what world do you need $60M to build a POC of whatever this is supposed to be?

handfuloflight 9mo ago

> If I had to guess 60 million is just enough to build the POC out. You're kidding.

gbnwl 9mo ago

You don't need to build anything. Just tell the agent to write tickets into .md files in a folder and move them to a closed foler as you go along. I've been using Claude Code with the Max plan nonstop essentially every day since last July and since then I've come to realize that the newer people are the more things they think they need to add to CC to get it work well. Eventually you'll find a way that works for you that needs none of it, and if any piece of all the extras IS ever helpful, Anthropic adds it themselves within a month or two.

999900000999 9mo ago
I'm thinking a customized LLM would write notes in its own hyper compressed language which would allow it to be much much more efficient. For debugging you could translate it out to English, but if these agents can do stuff without humans in the loop, why do they need to take notes in English? I can't imagine creating this without hundreds of millions if not billions. I think the future is specialized models
- gbnwl 9mo ago
  They're literally trained on natural language to output natural language. You would need to create the hyper compressed language first, convert all of your training data to that, and then train the models with that. But token efficiency per word already does vary between different languages, with Chinese being like 30%-40% more efficient than English last I heard
  - 999900000999 9mo ago
    Doesn't this mean the Chinese models have a significant advantage ? This isn't my domain, but say you had a massive budget, wouldn't a special LLM "thinking" language make sense ?