Inventions & Techniques

Project: cochranblock · Date: April 8, 2026 · Commit: 11c115f

GPU-zero-cost animated starfield using a static CSS mask with punched-out star holes over a screen-sized background-position loop. Standard approach: oversized element (200% viewport) with transform: translate3d — allocates 4x GPU memory. Fish Tank approach: element is 100% viewport, gradient is larger via background-size: 200%, animation is background-position only — compositor-only, no GPU texture reallocation.

Result: 72fps, 0.0000 CLS, 176ms first paint. Works on every Android phone. 1/4 the GPU memory.

Origin: Scrolling fish tank decoration from an Arizona garage sale, circa 2006-2010.

Benchmark: whobelooking perf https://cochranblock.org

Project: kova · Date: March 2026

Every function, type, and field in the codebase is tokenized to a compressed identifier: f0-fN (functions), t0-tN (types), s0-sN (fields). AI context consumption drops 75% — local models with 8K context windows can do work that previously required 100K+ cloud models. 368/368 symbols compressed in production.

Why it's novel: Codebases are compressed for humans (short names) or machines (minification). Nobody tokenizes for AI context budget. This is a new axis of compression.

Project: exopack · Date: March 2026

Run the full test suite 3 times. All 3 must produce identical results. Not "it passed" — "it passed identically three times." Catches flaky tests, race conditions, non-deterministic behavior, and timing-dependent bugs that pass once but fail under load.

Why it's novel: Every CI system runs tests once. Flaky test detection tools flag tests that sometimes fail. Nobody requires triple-deterministic as a hard gate before merge. If it can't pass 3 times in a row, it's not reliable — it's lucky.

Deployed: 6+ production projects via exopack test augmentation.

Project: pixel-forge · Date: April 2026

36-byte BLAKE3 signature embedded in every AI model file (.safetensors). Verified at load time before any inference. Unsigned or tampered models are rejected. The signature travels with the file — no certificate authority, no network call, no infrastructure.

Why it's novel: Model signing exists (Sigstore, Notary). But those require infrastructure — registries, certificate chains, network access. NanoSign is 36 bytes appended to the file. Verify with one hash. Works air-gapped.

Project: rogue-repo · Date: March 2026

Zero-downtime binary self-replacement. New binary starts, reads old PID from lockfile, sends SIGTERM to old process, binds the port via SO_REUSEPORT, writes its own PID. The old process dies gracefully. The new one takes over. No service manager, no systemctl, no container orchestration.

Why it's novel: Hot-reload exists (nginx, HAProxy). But those are daemons managed by init systems. This is the binary itself managing its own lifecycle — like the movie where young Will Smith replaces older Will Smith.

Named after: the 2019 film Gemini Man.

Project: kova / tmuxisfree · Date: April 2026

Rate-limit-aware broadcast across 28 concurrent AI agent sessions. Fast first pass sends to all panes. Collect failures (rate-limited panes). Retry with exponential backoff (10s, 20s, 30s, 40s, 50s). The mesh absorbs rate limits like a sponge — soaks up the failures and wrings them out on retry.

Why it's novel: Load balancers retry failed requests. But this retries failed AI agent dispatches across a tmux fleet with per-pane cooldowns and rate-limit detection via string matching on pane output. Nobody orchestrates AI agents through tmux panes with a retry mesh.

Project: entire portfolio · Date: April 2026

A development architecture where each cycle reduces external AI dependency. Stage 1: cloud APIs build tools. Stage 2: tools run locally. Stage 3: system trains on its own data. Stage 4: system tests itself. Stage 5: system deploys itself. Stage 6: system discovers its own work. The cloud APIs are scaffolding — dismantled as local capability matures.

Why it's novel: Self-improving AI exists as a concept. But a concrete 6-stage convergence from cloud-dependent to operationally autonomous, with named ratchets (techniques that permanently reduce dependency) and working code at each stage — that's an architecture, not a paper.

Current stage: 2 of 6. Stages 3-6 partially implemented.

Project: pixel-forge

Three diffusion models at increasing fidelity. Cinder (1.09M params) runs on anything. Quench (5.83M) needs a GPU. Anvil (16.9M) needs a good GPU. Mixture-of-Experts routing picks the right model for available hardware. Novel application of MoE to tiered pixel art diffusion.

Project: kova

LLM calls 13 tools (read/write/edit/bash/glob/grep/review) in an autonomous loop until the task is done. At 80% context budget, the agent summarizes its own prior work to free tokens. Novel application: the compaction is LLM-powered, not truncation — key decisions survive.

Project: approuter

One reverse proxy API that abstracts Cloudflare Tunnels, ngrok, Tailscale Funnel, Bore, and localtunnel. Swap tunnel providers without changing application code. Novel application of the adapter pattern to tunnel infrastructure.

Project: kova

Distributed build/test/deploy across 4 worker nodes via SSH with tokenized command protocol (c1-c9 for node ops, x0-x9 for cargo, g0-g9 for git). Nodes work in parallel, output streams with per-node prefix. Novel application of military C2 concepts to software development.