https://concludia.org/step/6f3cbaa2-65d4-3c44-8c1f-23f6ddf2b...

Reading the thread below, I'm always curious where in the argument the various counterpoints would attach. Like if a counterpoint is fatal or just an offshoot. I didn't have the system try and semantically analyze it for flaws/counterpoints yet, I just tried to get it to depict the article's reasoning. Not sure yet how good a job it did.

1. Is it clear?

2. Does it actually exist? Is it true?

3. Does the cause actually cause the effect?

4. For the proposed cause, do its other implied effects exist?

5. Is the cause sufficient for the effect?

6. Are cause and effect reversed?

I do think there's quite a lot that could be done with LLM assistance here, like finding "duplicate" candidates; statements with the same semantic meaning, for potential merge. It's really complicated to think through side effects though so I'm going slow. :)

I've actually had a lot of fun hooking it up to LLM. I have a private MCP server for it. The tools tell it how to read a concludia argument and validate it. It's what generated all the counterpoints for the "carbon offset" argument (https://concludia.org/step/9b8d443e-9a52-3006-8c2d-472406db7...) .

And yeah... when I've tried to fully justify my own conclusions that I was sure were correct... it's pretty humbling to realize how many assumptions we build into our own beliefs!

It's possible some of it is due to codebase size or tech stack, but I really think there might be more of a human learning curve going on here than a lot of people want to admit.

I think I am firmly in the average of people who are getting decent use out of these tools. I'm not writing specialized tools to create agents of agents with incredibly detailed instructions on how each should act. I haven't even gotten around to installing a Playwright mcp (probably my next step).

But I've:

- created project directories with soft links to several of my employer's repos, and been able to answer several cross-project and cross-team questions within minutes, that normally would have required "Spike/Disco" Jira tickets for teams to investigate

- interviewed codebases along with product requirements to come up with very detailed Jira AC, and then,.. just for the heck of it, had the agent then use that AC to implement the actual PR. My team still code-reviewed it but agreed it saved time

- in side projects, have shipped several really valuable (to me) features that would have been too hard to consider otherwise, like... generating pdf book manuscripts for my branching-fiction creating writing club, and launching a whole new website that has been mired in a half-done state for years

Really my only tricks are the basics: AGENTS.md, brainstorm with the agent, continually ask it to write markdown specs for any cohesive idea, and then pick one at a time to implement in commit-sized or PR-sized chunks. GPT-5.2 xhigh is a marvel at this stuff.

My codebases are scala, pekko, typescript/react, and lilypond - yeah, the best models even understand lilypond now so I can give it a leadsheet and have it arrange for me two-hand jazz piano exercises.

I generally think that if people can't reach the above level of success at this point in time, they need to think more about how to communicate better with the models. There's a real "you get out of it what you put into it" aspect to using these tools.