neural_tile: a self-assembling tile computer in the abstract tile assembly model. A tile binds at a site when the summed strength of its matching glues reaches tau, which is the Heaviside gate H(strength.match - tau), so growth is governed by threshold neurons. Verified: the binding decision equals the gate; a general 2-input rule-tile set grows value(x,y)=f(W,S) for f in XOR/AND/OR (529 tiles each, checked against the recurrence, XOR = Sierpinski/Rule 90); a binary counter grows one integer per row (8-bit, 255 rows, row y encodes y) with carry by cooperative binding; both directed (deterministic). Turing-universal at tau=2 (Winfree 1998). Ships variants/neural_tile.safetensors (glue tables + binding-gate weights); eval_all skips it; README section and counts updated (9 standalone machines, 28-file family).

neural_ca: a reversible cellular-automaton computer with no processor. One fixed Margolus block rule (rotate 180, diagonal pairs swap) applied identically to every 2x2 block of a lattice, alternating partition each step. The rule is a self-inverse permutation of the 16 block states, so the whole lattice update is a bijection; verified reversible over random lattices, particle-conserving, with ballistic single-particle motion and deflecting reversible collisions (the billiard-ball model, Turing-universal by Margolus 1984). The rule is Heaviside threshold gates and compiles to a 6-layer ternary matrix tile that is a permutation with a 0.5 margin; that one tile applied to every block is one whole-lattice step, shipped as variants/neural_ca.safetensors. eval_all skips it; README adds the section and updates counts (8 standalone machines, 27-file family round-trip).

Give neural_attractor and neural_reversible a machine metadata field and eval_all skip entries, so python src/eval_all.py variants/ skips them cleanly instead of erroring (it scores fitness variants and skips standalone machines by that field). README sync: list both new machines in the variant table, intro, and repository layout; correct the standalone-machine count (5->7), the eval_all skip count (four->seven), and the universal-constructor family round-trip (23->26 files, 551->971 MB, both new files codec-verified byte-identical).

8-bit computer decode and PC sequencing as threshold gates: a 4-to-16 opcode one-hot decoder and a next-PC network (PC+2/PC+4 increment chains plus a priority mux over PC+2, PC+4, the conditional-jump mux, and the direct target). The runtime dispatches on the gate one-hots and takes the next PC from the mux instead of Python slicing and PC+2. Full 10/10 CPU suite passes; all variants rebuilt. Completes decode/PC-as-gates across every runtime (rv32, subleq, 8-bit computer).

Add neural_reflect: a threshold interpreter whose state holds its own weights

Move source library into src/; repoint module, tool, and README paths