vybecoding.ai Editorial Pipeline
The tools that assist our editor — and the human oversight that makes them trustworthy.
What this pipeline is
The vybecoding.ai editorial pipeline is a set of AI-assisted tools that help the editorial team — led by founder and managing editor Hiram Clark — research, draft, and quality-check articles faster than would otherwise be possible. Every draft must pass a set of automated quality and accuracy gates before it can be published, and the editorial team sets and maintains the standards those gates enforce.
AI drafting is a starting point, not an endpoint. The pipeline automates the mechanical parts of content production (outlining, first draft, fact sourcing, quality checks) so that editorial effort can focus on accuracy, technical correctness, and genuine insight.
Hiram Clark, as managing editor, is responsible for the pipeline methodology, the editorial standards, and the corrections process for content published on this site. If you find an error, contact him directly.
How a piece gets produced
- Brief — A topic brief specifies what the article must cover, what primary sources or data it should reference, and what original insight it adds over existing material. Briefs are human-authored and define the editorial intent.
- Data collection — Where available, real data from our own systems is gathered for the article: CI logs, billing figures, VRAM test results, production database excerpts, incident timelines. This grounds the draft in first-hand evidence.
- AI-assisted drafting — An LLM produces an initial draft from the brief and collected data. The prompt enforces technical voice and prohibits AI-stylometry filler phrases. The draft is a starting point, not the finished article.
- Automated quality gates — The draft passes through five deterministic checks before it can proceed (see table below). Any gate failure blocks the draft from publication.
- Editorial oversight — The editorial team sets the standards each draft must meet, reviews flagged and sampled content, and owns the corrections process. Drafts that fail the automated gates do not proceed to publication.
Quality gates
| Gate | Threshold |
|---|---|
| Word count | ≥800w (guides) · ≥300w (news) · ≥300 chars (apps) |
| Banned phrases | 0 hits from 29-entry AI-stylometry list |
| Headline specificity | Must contain a digit, named entity, or strong action verb — no template headlines |
| AI quality score | ≥80/100 (E-E-A-T + Information Gain scoring) |
| Originality check | Jaccard similarity ≤0.50 vs any single cited source · ≥2 first-hand data markers per 1000 words |
The banned-phrase list includes: stylometry filler (“in the ever-evolving”, “deep dive”, “paradigm shift”, “synergy”), brand mentions, and template closers (“in conclusion”, “Key takeaways:”). Code blocks and inline code spans are excluded from the phrase scan.
What we claim and what we don't
Where an article states a specific number, benchmark, or date, that figure is grounded in the data collected for that article — not generated from AI training data alone. Where an article expresses an opinion, that opinion reflects Hiram's editorial judgment as formed by the collected data and his experience building and operating the systems described.
We do not claim that the automated quality gates eliminate all errors — they do not. They reduce the rate of common defects before publication. The editorial team maintains these standards and corrects confirmed errors promptly.
If you find a factual error, contact Hiram Clark directly — corrections are his responsibility. See our full Editorial Standards for the corrections policy.
Disclosure
All articles produced with AI assistance are labeled with the appropriate disclosure (“AI-assisted” or “AI-generated, human-edited”) in the article header. The pipeline methodology is maintained by Hiram Clark at vybecoding.ai.