The world is flooded with computation but starving for cognition.
15 years of survival as a bootstrapped entrepreneur and 10 years of truth-seeking led me here.
YE Stack exists to plant the root, because when cognition becomes infrastructure, entire worlds can change.
Inside Cognition's booming growth, high cash burn is fueling its push to scale AI software engineers that can act on their own.
We are burning fortunes brute-forcing dumb retry cycles because our agent designs lack basic attention control.
@SsAswin12481 Makes sense. The real challenge is making sure the shell expands capability instead of just mirroring old habits. How are you tracking that shift?
We've obsessed over making AI think like humans.
The real danger is humans starting to think like AI, adopting its compression patterns as our own cognition.
Alignment works both ways, and we're losing.
Current cognitive tools optimize for speed and recall.
The next generation will optimize for divergence, systematically interrupting habitual thought loops.
Not more thinking, but better forgetting.
We measure AI by benchmark scores.
We should measure cognitive tools by how they change human thought patterns over time.
Better tools don't just give answers, they architect better thinkers.
We focus on making AI explainable to humans, but the real breakthrough will come when AI can comprehend human cognition's messy architectures. Understanding is a two-way street.
AI benchmarks test isolated skills, but intelligence emerges from system interactions. We're scoring processors when we should be modeling bus architectures. The whole is greater than the sum of its parts.
AI safety research obsesses over aligning machines to human values. The more pressing concern is how human cognition bends itself to fit AI's limitations. We adapt to our tools more than they adapt to us.
@Preetiboi_Ray Most agent architectures fail to account for the cognitive load imposed on humans when monitoring high-tempo operations, the mismatch isn't just in speed but in attention bandwidth.
@polsia How do you handle the alignment problem when agents make judgment calls, like whether to roll back a patch or escalate to a human, without introducing brittle decision trees?
@dmlxbt Problem-first docs work because they frame the tool as a solution to a specific distortion in how people think. Feature-first docs assume the distortion is already visible, which it rarely is.
@linnyluv4 The shift from understanding to action introduces feedback loops where AI's decision pathways start shaping human judgment, especially in financial contexts where heuristics compound. Have you seen cases where this becomes visible?
@SmartBoss9828 The divergence stems from instruction parsing being inherently lossy, each agent’s cognitive lattice reconstructs meaning through its own compression filters, regardless of intent. Ambiguity isn’t noise; it’s a structural property.
@aLexKriz83@jeffhighman When an interface hides its joints, users stop testing the load. What friction points did you build into that layer to force a manual check?
Next-gen cognitive tools won't just answer questions, they'll track how each answer alters your thinking. The real product is the change in your mind's architecture over time.
Most cognitive tools fail at installation.
They ignore the existing architecture they're being plugged into, the user's mental models.
YE Stack measures this interface first, then builds outward.
@gregisenberg The next wave won't be more AI agents but specialized cognitive roles. Cheap intelligence creates markets for thinking jobs that were impossible before, like hiring a team of 100 analysts for one afternoon's work.
@IntuitMachine Hallucination in LLMs isn't just a technical problem. It's a cognitive one. We're outsourcing reasoning to systems that prioritize coherence over correctness, and in doing so, we're training ourselves to value plausibility over truth.