That’s right.
(Not a fan of the implication that a lack of social mobility when sortition is already nearly exhausted is somehow a criticism. It’s more that they’ve succeeded in eliminating resistance to it.)
Source date (UTC): 2026-02-12 19:57:21 UTC
Original post: https://twitter.com/i/web/status/2022037329498845512
@Scientific_Bird
cc:
@GregoryClarkUCD
I would think that a small homogeneous northern european population, with existing traditions and institutions would sort so thoroughly that social mobility was limited simply because there was so little resistance to it in the first place
Source date (UTC): 2026-02-12 19:55:27 UTC
Original post: https://twitter.com/i/web/status/2022036849662050566
Yep. I’m INTP. :
Source date (UTC): 2026-02-11 18:51:03 UTC
Original post: https://twitter.com/i/web/status/2021658254301179930
(Runcible) RE: LLM R&D
TL/DR;
Yeah, well, LLMs are really pretty dumb and I’ve sought and hit the limit of their abilities. And it’s been an exasperating journey of dashed hopes. 😉 (well, mostly).
Problem Space:
We produce:
a) the runcible governance layer which is my epistemology applied to computability. It is extraordinary. It overcomes the problems of LLMs but not the limitations. It’s a wrapper around the LLM that binds it to limited pathways through the latent space.
b) control over this layer has forced us to create and modify one of the open source models. We had hoped to ‘help’ openAI, but they are a bit ‘unfocused’ as a company and getting access to discuss at a high enough level amidst their many pressures is almost impossible on one hand and more costly than doing it ourselves on the others. So in effect we have a router llm, and a set of target LLMs, functioning as a mixture of experts. But realistically it’s just cost control.
c) We also produce Oversing, which is a mobile, tablet, desktop universal application platform for running organizations of any sale, but basically it’s a host for contexts that you use to collaborate with one another and an LLM.
Result:
This is a crazy project of enormous scope but it’s the end point of what people need going forward. Cooperation at scale.
No one has done this yet. It’s partly because they don’t know how to, and it’s partly because it’s hard. 😉
Context:
While I have built many tech companies, and consulted for the fortune 400, and made the Inc500 three times, I work in many disciplines, and my fundamental skills are epistemology, especially in high dimensional closure domains such as cognitive science, language, economics law, and evolution.
The simple version is reducible to the fact that I don’t make mistakes that are common in the STEM fields by trying to reduce to mathematics or algorithm, that which is not reducible further than operational prose.
So this is why I can solve this problem for LLMs.
It’s just … let’s say… a little exasperating – just like every previous tech …until we understood it. 😉
CD
Source date (UTC): 2026-02-10 21:46:29 UTC
Original post: https://twitter.com/i/web/status/2021340016082034787
IMO:
1) They are exceptional synthetic search engines. In other words – pattern matchers.
2) They imitate the human language facility. In that sense they are amazing. But they don’t imitate the neocortical spatial faculty, (this is LeCunn’s argument) or the hippocampal episodic memory formation, or the frontal cortex recursive wayfinding (testing an idea). They are bad reasoners because reasoning requires reduction to episodic steps, and wayfinding by recursion. And recursion is expensive.
3) Our company’s governance layer( a wrapper around the LLM) can however determine whether a claim or assertion is true/false, ethical/not, possible/no, warrantable/not, liability-producing/not. But it does so by breaking down the problem and recursively testing each step.
4) IMO leCunn is only half right in that we need a world model, but he is wrong, in that linguistic reduction isn’t a necessary property. It’s that you need an LLM (sematic store) for hypothesis generation (auto-association), the equivalent of a router (prefrontal cortex) to manage the ‘reasoning’ process (wayfinding) and to maintain states (episodes), a spatial model to test operational possibility, the llm linguistic model as the input output protocol.
This means we’re just early and very demanding of one revolutionary insight, which is the attention model that mimic the human language facility, and the N-Dimensional sematic manifold as memory.
CD
Runcible
NLI
Source date (UTC): 2026-02-10 19:35:56 UTC
Original post: https://twitter.com/i/web/status/2021307165441732671
IMO
A) There are a limited number of agent patterns.
B) very few agents are of any meaningful value even to power users. Everything I have seen could have been easily produced by existing automation scripting or basic coding.
C) Those few useful agents will rapidly be commoditized
D) it’s not the agents that matter it’s the framework they operate within.
E) Agents are ripe for every kind of hacking and abuse that has existed for the past forty years.
F) There is every chance that enterprises will prohibit them unless each is approved.
G) General insight that they increase noise and not signal is hard to argue with.
I’ve been through every tech wave since the early seventies and it’s always the same game.
Source date (UTC): 2026-02-10 19:21:10 UTC
Original post: https://twitter.com/i/web/status/2021303445916115330
No idea why but I’m glad. 😉
Source date (UTC): 2026-02-09 22:59:04 UTC
Original post: https://twitter.com/i/web/status/2020995896138461216
Thx 😉
Source date (UTC): 2026-02-09 22:58:22 UTC
Original post: https://twitter.com/i/web/status/2020995720984265148
yes. Shared. 😉
Source date (UTC): 2026-02-09 21:28:42 UTC
Original post: https://twitter.com/i/web/status/2020973152780906625
Smart man. Yes. Our premise was escalation to ‘voice’ might lead to escalation to ‘influence’ and that is better than trying to obtain voice and influence amid chaos.
Better to be first or second than drowning in the cacophony.
Source date (UTC): 2026-02-09 20:51:55 UTC
Original post: https://twitter.com/i/web/status/2020963899215053153