gormen

7 ms·

1.
by gormen 6mo ago
I have this thought. In many stochastic environments, over a long interval, patterns emerge that occupy an optimal position. This is how structure arises, for example cognitive structure and possibly consciousness.
2.
by gormen 6mo ago
Of course, we need to give the robot a cognitive architecture so that it understands the task, the context, and corrects its actions, and then it will autonomously make such sandwiches every morning for breakfast.
3.
by gormen 6mo ago
It is possible to force AI to understand intent before responding.
4.
by gormen 6mo ago
Different approach: copy the programmer's logic, not the agent's behavior.
5.
by gormen 6mo ago
Excellent article. But to be fair, many of these effects disappear when the model is given strict invariants, constraints, and built-in checks that are applied not only at the beginning but at every stage of generation.
6.
by gormen 7mo ago
As far as I understand, this is about banning the use of Anthropic for autonomous weapons and domestic surveillance. And while the idea of building one fully controlled, nationwide AI system may sound tempting, in reality it’s still just a
7.
by gormen 7mo ago
I approached the puzzle using an A11‑style reasoning architecture, which focuses on compressing the hypothesis space rather than decoding every neuron. Instead of “understanding the network”, the task reduces through successive narrowing: m
8.
by gormen 7mo ago
Assessing "AI capabilities" only makes sense if we stop viewing it as a technical skill. The real measure is a person's ability to structure their intentions, constrain their model, and evaluate results without relying on oth
9.
by gormen 7mo ago
Indeed, fears about deanonymization are a reaction to three structural shifts: the cost of analysis has plummeted, the volume of stored data has increased dramatically, and models have become better at identifying patterns that humans miss,
10.
by gormen 7mo ago
Most of what helps LLMs here is exactly what helps humans: less noise, clearer signals, predictable output.
11.
by gormen 7mo ago
The cost of code never lived in the typing — it lived in the intent, the constraints, and the reasoning that shaped it. LLMs make the typing cheap, but they don’t make the reasoning cheap. So the economics shift, but the bottleneck doesn’t
12.
by gormen 7mo ago
Most interpretability methods fail for LLMs because they try to explain outputs without modeling the intent, constraints, or internal structure that produced them. Token‑level attribution is useful, but without a framework for how the model
13.
by gormen 7mo ago
The appeal of libraries like this is not minimalism for its own sake, but the reduction of long‑term operational risk.
14.
by gormen 7mo ago
t’s interesting how different CSS approaches end up shaping completely different mental models: utility classes, semantic blocks, scoped styles — each tool pushes the architecture in its own direction.