Original streaming-algorithms research in high school
Louis implemented six streaming algorithms, designed two comparative experiments, and published the paper on Zenodo with a permanent DOI.
Read Louis's case studyCase Studies
Evidence in context, not isolated success claims.
A useful case study explains the starting condition, diagnosed constraint, intervention, observed evidence, limitations, and what happened next. Pristone publishes only what can be supported and safely shared.
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The Pristone Method
One capability loop across every track.
- Diagnose
- Prioritize
- Build
- Test transfer
- Measure
- Re-prioritize
Pristone case studies document how the method was applied. They do not imply that another learner will receive the same result, and composites or illustrative examples must never be presented as real clients.
Written and operationally reviewed by Edward Mabonga, founder of Pristone Academy.
Published case studies
Student and professional work
Returning to senior frontend engineering
A detailed account of rebuilding current-stack fluency, system-design readiness, interview performance, portfolio evidence, and professional confidence after a career break.
Read the case studyFrom a nontraditional path to software engineering
Marquise's transition into engineering through foundational development, structured technical coaching, and increasing independence—with the material Pristone relationship disclosed.
Read Marquise's case studyEvidence standard
What every Pristone case study should disclose
Starting condition
The relevant baseline and context, stated without unnecessary identifying details.
Diagnosed constraint
The evidence supporting the highest-priority bottleneck rather than a retrospective success narrative.
Intervention
What was taught, practiced, built, reviewed, or changed—and over what general kind of period.
Observed change
The work or behavior that changed, with the source and level of verification made clear.
Limitations
What cannot be attributed to Pristone and what remains uncertain or incomplete.
Consent and privacy
The permission basis and the steps taken to prevent identification, especially for minors.
What this page will not become
No manufactured library of transformations
No invented outcomes
A plausible story is not evidence and will not be published as a case study.
No unlabeled composites
Combined or illustrative scenarios must be labeled clearly and kept separate from real-client evidence.
No unnecessary identifiers
Names, schools, employers, exact dates, distinctive artifacts, and personal histories are removed unless essential and explicitly authorized.
No universal promises
A documented outcome demonstrates what happened in one context, not what every buyer should expect.
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