Ryan D. Allen

One URL goes in. Six models read it in parallel. Out comes a synthesis: where they agree, where they split, and what only one of them saw.

FIG 1Six model strands descend from one source, cross the ring, and converge to one synthesis.

It buys into the broad market when fear runs high, and protects profits gradually as prices rise. One engine produces every backtest and the daily public signal. Ten machine gates stand between signal and order, and two human steps follow: a person arms the run, then types transmit.

FIG 1Ten pre-trade gates in one-point perspective; all ten must close before anything transmits. Four are named in the list above (the full list lives in the repo). Two human steps follow: arm, then transmit.

FIG 2The three live strategies drawn as stacked layers: each one's main engine buys only when its fear reading rises past the floor.

FIG 360+ dated entries in a 219 KB experiment ledger.

Built for call-center reps mid-call: every answer carries a clickable citation, or the system refuses out loud instead of guessing.

FIG 1Up to 80 candidate filaments, 40 vector and 40 BM25, flow to the rerank portal in dimetric projection. Eight pass the reranker; one leaves as the cited answer. Below the 0.30 gate the answer path goes dark and REFUSES lights, because on weak retrieval the LLM is never called at all.

SEO gets you listed. GEO gets you quoted. It scores whether AI answer engines can access, extract, and quote a page.

FIG 1Dimensions I to IX: AI crawler access, renderability, answer-readiness, topical focus & metadata, evidence density, structured data, freshness, entity / E-E-A-T, SEO foundation.

FIG 2One day, drawn to scale of itself: spec in the morning, live the same day. Each tick is one of 15 PRs merged on day one, all through the human merge gate.

FIG 3The hosted fetcher inside its three guards. Plus politeness: robots.txt respected, a ten-fetch cap, "could not verify" over guessing.

I ship with AI agents as the workforce. An independent agent checks the work before I approve it.

FIG 1The 16-agent design workflow that chose kinefractal.com's production architecture: five code readers and four web researchers feed three independent proposals; a three-lens judge panel scores them; one agent synthesizes. Then the claims were re-verified before production.

audit.corewise.academy ↗ · a deterministic audit of this method across the whole portfolio: one scored report per repo, computed from GitHub records only, built and reviewed by the pipeline it measures.

LOCAL-CPU-ONLY-PTT

FIG 1Offline push-to-talk dictation for Windows: microphone, voice activity detection, Whisper on CPU only, clipboard. The boundary box is the feature: no cloud, no network path, nothing leaves the machine. Public repo ↗

GITHELP

FIG 2A workspace that scouts GitHub Discussions, ranks what's answerable, reads the full thread, and drafts a verified answer.

AI can write code. Reliable software still needs the right context, clear limits, real evidence, fresh review, and a way to recover when a plan fails.

Harness Firmware is the open-source repository system I built around Claude Code and Codex. It loads relevant project knowledge, keeps work small enough to inspect, checks the result in realistic conditions, calls fresh reviewers, and preserves recovery paths.

FIG 1Five ways the repository improves the model's working conditions.

The model provides capability. I design the conditions around it and set the goals, permissions, proof bar, and release decision. Every new repo starts with this engineering system.

One canonical skill source supports Claude Code and Codex through runtime-specific adapters. Confirmed project lessons stay with the repository. Reviewed improvements can move into future projects.

github.com/ryanportfolio/Harness-Firmware ↗

FAQ · about Ryan D. Allen

Who am I?

Builder who ships end to end Fable, Opus, and Sol as the workforce & audit team Editor of CoreWise Academy

How I build

I work to get the most out of models: the highest quality and accuracy at the lowest cost, tuning thinking budgets and model choice. Impact = (Solutions Identified × Solutions Delivered) ÷ Time

How I think about design

· Simplicity first: no one should be confused or have to guess Complexity does not need to be complicated · Context and specificity: vague goals produce bad results Define audience, purpose, and brand · Preferences change with audience and situation · Never stop after one pass from one model