StahlGuo

Recently, my experience is that the hardest part of writing Enterprise document by AI is not how to generate a word or excel, but to generate a office document that is accountable. First draft generation is just a small part of the whole wore,more time consuming work is validation: whether citation , number,format, or semantic assume is right.

So i think enterprise office AI suite may need 2 layers: First is document editing, and second is revision / attribution / validation, or an unaccountable document is not applicable for real enterprise usage.

I agree to split the "Brain" and "Hand" of an agent,many deficits of agent workflows is not from what LLM don't know what to do, but from LLM getting too much authority to edit the world it belongs to and break the whole process.

Beside from particular project, I would like to talk about my rule of thumb in agent usage, which is to separate “smart” from “authorized.”

That is, to build a state wrapper and separate agent from making runtime decision, a dangerous design is letting the same agent both decide and record the state of the system. If it hallucinates, you can hardly find the error.

You are right, my solution is to implement real world software engineering process, something closer to a real Software engineering team.I split multi agents into one repo and gives them different tasks"requirements check, product/spec, architecture, coding, review, tests/evals, and overall management.

Scope adjudication is extremely important in vibe coding, or agent can easily break your whole system with not applicable features.

that sounds like an interesting idea.

Broswer contains many security concerns, how do you solve privacy and security.

claude code + codex，for sure. I also actively try Other runtimes.

what a cute project

I would said electricity generation, Solar+Energy Storage will be the solution of human future in the coming 20 years.

Wow, is this like a social media for Vibe coder?

I would try it today, sounds good

Wow,that sounds gread for the sport

Thanks Fable, we miss you

I don't mean never check it by myself, but I want to discuss about a methodology to ensure AI generated text has the same auditability and tracebility like human wrote text.

Yes, i agree that quantitative part， like dates, numbers, amounts should be extracted and let the LLM to output original numbers and computation steps. That's not hard for a briefing harness. However, my most confusion part is qualitative side, like market insight from a news,policy change and interpretation, and industry NEWS interpretation, they are not straight math but they need tracebility. Do you have any idea to solve those judgement claim?