Editorial standards

HAKER.SI publishes documentation, architectural analyses, and technical perspectives centered on practical AI workflows, B2B digital operations, industrial information architecture, and structured content systems.

Our writing is intentionally differentiated from formal client advisory services. Publications examine operational models, software mechanics, systemic risks, and engineering patterns. They are not tailored professional recommendations for specific commercial situations, legal counsel, or financial advice.

All core publications are authored by or reviewed under the direct supervision of Marcin Białczyk. We enforce explicit, real attribution for every technical brief, operational breakdown, and benchmark.

We commit to clear, unembellished statements of practitioner background without inflated claims, unverifiable executive titles, or fabricated enterprise milestones. Contributions from external engineers are accepted only when the author has direct operational access to the systems described; anonymous or unvetted briefs are excluded. External subject-matter contributors, where retained, must adhere to this evidence policy and provide verifiable proof of domain authority in their stated operational discipline.

The individual author named on any piece is personally accountable for the factual veracity, logic consistency, and source accuracy of the material published.

To maintain structural clarity, every published item must explicitly belong to one of the following five standardised content classes:

Claims are only as valid as the empirical environment in which they are measured. All system behaviours and operational data must be classified under one of four non-overlapping evidence tiers (see the structured evidence states attached to this page).

These tiers serve strictly as factual environmental context. Under no circumstances are they to be construed as formal quality certifications, performance promises, or SLA commitments.

We reject the practice of publishing unsourced "marketing metrics" or invented benchmark numbers (e.g. claiming unverified precision gains or speed multipliers).

Every quantitative assertion, benchmark graph, or performance differential published on HAKER.SI must disclose:

Where synthetic workflow patterns, schema definitions, or architectural flowcharts are introduced, they must be plainly tagged as illustrative scenarios (on this site: "Illustrative example — fictional" or "hypothetical").

An illustrative example serves as an educational bridge to explain topological interactions. It cannot, under any editorial circumstances, be framed or implied as a completed real-world enterprise outcome or an achieved benchmark result for an active commercial client.

As an industrial intelligence practice, we use modern AI tooling across research and drafting cycles. We maintain transparency regarding how automated systems participate in content generation:

We treat factual integrity as an evolving engineering log. If an empirical observation is invalidated, code becomes deprecated due to upstream API changes, or numerical errors are identified, we apply transparent revisions.

To challenge a claim or report a factual inaccuracy, submit a structured issue via the workflow submission channel (Discuss a workflow).

Editorial neutrality requires complete disclosure of economic interest. HAKER.SI does not accept sponsored articles, paid software listicles, affiliate kickbacks, or stealth product endorsements.

To maintain transparency, we explicitly disclose the business portfolio and ownership stakes held by Marcin Białczyk (all OWNED projects):

When software products, services, or data flows from these ventures are examined in articles, their economic connection is reiterated explicitly in the context of the piece.

Our objective is to publish verifiable operational logic that advances industry infrastructure literacy while respecting contractual non-disclosure agreements (NDAs) and proprietary enterprise assets.

We achieve this through careful abstraction:

Modern artificial intelligence systems exhibit non-deterministic qualities. Models evolve, APIs introduce breaking schema alterations, and rate limits drift.

Engineering teams must deploy isolated test environments, continuous integration suites, and human-in-the-loop validation checkpoints before deploying any pipeline derived from public architectural patterns into live production environments.