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When you think of AI videos, you think of something like Sora or Veo 3 (diffusion). What if the AI could write the code for a video like a website? This thought experiment led me to create Mike. It writes React code which can be rendered into a video. You can ask the AI to use any Node library to render graphs, animations, simulations.

I built this to provide transparency around enforcement activities. It uses [mention tech stack, e.g., Mapbox/Leaflet] and a verification system to prevent false positives. Feedback on the verification logic is welcome.

I&#x27;m working on (and hold IP around) an architecture pattern for P2P contest and oracle-resolved systems that focuses on deterministic settlement, dispute containment, and exactly-once execution between outcome resolution and payout.<p>The goal is to eliminate: - replay &#x2F; double-settlement conditions - ambiguous resolution states - arbitration loops caused by partial failures or conflicting outcomes<p>The pattern introduces a reconciliation layer that gates settlement, enforces finality, and holds contested states for resolution before funds move.<p>I&#x27;m curious if anyone here has implemented or seen similar patterns in: - prediction markets - fintech &#x2F; escrow platforms - marketplaces with disputes - gaming &#x2F; contest systems<p>Interested in architectural feedback, pitfalls, or pointers to teams working on this class of problem.

We prove that identifying decision-relevant coordinates in a decision problem is coNP-complete. Finding the minimum sufficient coordinate set is also coNP-complete.<p>Formally: given state space S = X_1 × ... × X_n and utility U : A × S → Q, a coordinate set I is sufficient if s_I = s&#x27;_I implies Opt(s) = Opt(s&#x27;). Checking whether I is sufficient reduces to TAUTOLOGY. Finding minimum I reduces to the same.<p>Main results:<p>SUFFICIENCY-CHECK is coNP-complete MINIMUM-SUFFICIENT-SET is coNP-complete (Sigma_2^P structure collapses) ANCHOR-SUFFICIENCY (fixed coordinates) is Sigma_2^P-complete Dichotomy: polynomial when |minimal set| = O(log |S|), exponential when Omega(n) Tractable cases: bounded |A|, separable U(a,s) = f(a) + g(s), tree-structured coordinates Engineering consequence: over-modeling is not laziness. Determining which configuration parameters matter requires solving coNP-complete problems. Including everything costs O(n). Minimizing costs Omega(2^n). For large n, over-specification is optimal.<p>This explains: config files that grow forever, heuristic feature selection (AIC&#x2F;BIC&#x2F;CV), absence of &quot;find minimal config&quot; tools. These are not tooling failures. They are optimal responses to intractability.<p>2760 lines of Lean 4 proofs. 106 theorems. Zero sorry.