BTW: (Going through the Github repository.)
This is solid work. I haven’t seen anything this good by anyone else yet.
Well done.
Source date (UTC): 2026-02-09 20:50:05 UTC
Original post: https://twitter.com/i/web/status/2020963437921394728
BTW: (Going through the Github repository.)
This is solid work. I haven’t seen anything this good by anyone else yet.
Well done.
Source date (UTC): 2026-02-09 20:50:05 UTC
Original post: https://twitter.com/i/web/status/2020963437921394728
Just to confirm your point…
At Runcible we produce a governance layer with the equivalent of your ‘stop’ conditions. It’s much more than that.
It’s epistemic rigor for machines, and only LLMs have the informational density in semantic form to produce it.
Source date (UTC): 2026-02-09 20:48:25 UTC
Original post: https://twitter.com/i/web/status/2020963015647166843
Added you to our list.
We’ll treat you as part of our extended team.
Thanks for your investment in this project.
CD
Source date (UTC): 2026-02-09 19:56:46 UTC
Original post: https://twitter.com/i/web/status/2020950018753167849
it might be that I have too much else going on in my head at the moment, but I recognize both premises but I’m not sure the relation you’re making. Usually you’re correct. So please try again or give me an example.
Source date (UTC): 2026-02-09 19:03:57 UTC
Original post: https://twitter.com/i/web/status/2020936727502913584
–“I wonder if the programming AI could be tricked into seeing NL as code and therefore applying it more strictly.”–
@NoahRevoy
NLI, Runcible
Well I mean, it does – that’s why it works. Operational prose is just ‘code’ for human action at human scale in the existential reality we must navigate. That’s why we’re so strict about ‘enumeration, serialization, operationalization, and disambiguation into an identity”, and why we produce a dictionary, and dictionary terms on a dimension producing natural indexing and measurment – so that langauge becomes code.
The AI’s (or at least be better ones) understand this and why we’re doing it. That’s why they can render the output that they do.
Our problem (really) is that while we have created the language and the compiler, the present LLMs (operating system) are having as much problem running our ‘program’ with current memory limitations as did my original work in the 1980s using semantic indexes (tokens), possible actions (actions), and episodic memories (contexts) for predicting optimum choices (outcomes).
I couldn’t do it (well) in assembler back then because of memory limits, and I’m having a heck of a time with 256K context windows doing it with LLMs today. I ran into the same problem building the first serious legal AI. Semantic depth is a memory burden because it’s a relational density burden because in turn, the information is stored in terms that are relationally dense.
Whereas the human brain does it all in a massively parallel hierarchy, we have to produce domain, customer, individual protocols, then put them through our epistemic protocols to determine if they’re true.
We could parallelize some of the epistemic protocols but again, that’s a cost.
If we were to continue to use OpenaI for a hard question we could burn $2 per analysis and more for a certification. Whereas for most people with most questions our ChatGPT Custom GPT will do a better job already than any other LLM.
Fundamentally any of these LLMs without compartmentalization produce drift just like people with ADD produce drift.
So yes, it’s code. And the LLMs are operating systems that can run semantic code. But they were trained to favor normativity instead of truth, so until we can audit an entire 1T+ parameter LLM (which costs $$$$$!) we won’t have an operating system to run our ‘program’ on that doesn’t basically insert error.
Cheers 😉
Source date (UTC): 2026-02-06 23:11:42 UTC
Original post: https://twitter.com/i/web/status/2019911909915721836
first, I’m playing the usual king of the hill game to attempt to collect information. Second, practicality still means ‘only what is possible and won’t fail’. Third, yes, but only on abrahamic means of deception. over time that will accomplish what declaration and force won’t.
Source date (UTC): 2026-02-06 22:56:05 UTC
Original post: https://twitter.com/i/web/status/2019907979399819319
AFAIK if you produce the context the AI will pursue the context. if you train an AI on normativity (correlation). you will get normativity. Human normativity is full of falsehoods, deceptions, propaganda, bias, and excuses for criminal activity.
Source date (UTC): 2026-02-06 06:22:33 UTC
Original post: https://twitter.com/i/web/status/2019657951175401716
@leecronin
The amount of work we’ve put into de-glossing the AI’s so that we get truthful answers out of them is absurd.
AI companies sought to produce assistants for the general public and have effectively produced them for programmers.
They did not design them for testimony.
(That’s what we did.)
Prof: Thanks for your efforts in public awareness of science.
Source date (UTC): 2026-02-06 06:19:49 UTC
Original post: https://twitter.com/i/web/status/2019657261480833444
Correct.
Source date (UTC): 2026-02-06 06:09:21 UTC
Original post: https://twitter.com/i/web/status/2019654630561706341
Ok that was good… lol
Source date (UTC): 2026-02-06 02:29:02 UTC
Original post: https://twitter.com/i/web/status/2019599185499259241