Editor at teknologiai.biz.id — I translate AI buzzwords into benchmarks you can trust. (Yes, we love transformers. No, not the robot kind… well, sometimes.)
Why I Write About “Decoding the Future with Smarter Machines”
AI is powerful—but power without clarity helps no one. I focus on separating signal from noise: practical evaluations, transparent methods, and clear guidance for builders, students, and curious readers.
Site in two lines:We explain AI with evidence, not hype. Grounded reviews, reproducible tests, real-world caveats.
Qualifications & Relevant Experience
- Hands-on evaluations: latency/throughput tests, quality scoring vs. baselines, cost & energy estimates, safety red-team prompts.
- Model literacy: LLMs, diffusion, vision transformers, retrieval-augmented generation, embeddings, fine-tuning vs. adapters.
- MLOps & deployment: inference configs, caching, tokenization quirks, monitoring for drift & abuse signals.
- Responsible AI: bias checks, privacy first (PII minimization), misuse scenarios, transparent disclosures.
- Writing for clarity: plain-English explainers with code snippets only when they add value.
What I Cover
AI Tool & Platform Reviews
Capabilities vs. claims, evals, pricing trade-offs, deployment notes.
Research, Decoded
Readable summaries + why it matters for real products.
Build & Ship
Design patterns for retrieval, guardrails, and evaluation loops.
AI & Society
Safety, bias, policy shifts, workforce impact—without the doom spiral.
Privacy & Security
PII handling, local vs. cloud trade-offs, data retention realities.
Beginners Welcome
Fundamentals, glossaries, and “start here” guides that don’t assume a PhD.
Editorial & Testing Methodology
- Primary-source first: official docs, papers, release notes. Secondary commentary only as support.
- Reproducible evals: publish prompts, seeds, model/version IDs, hardware/runtime, temperature/top-p.
- Baselines: compare against strong non-AI and classic ML baselines (because “AI better than nothing” is not a benchmark).
- Safety pass: light red-teaming for misuse; note filters, jailbreak resistance, and content boundaries.
- Privacy stance: no uploading sensitive data; clearly label data retention & training-on-user-data policies.
- Update cadence: scheduled audits; faster updates on version bumps, deprecations, or policy changes.
- Independence: no pay-to-rank. If a post is sponsored or includes affiliates, it’s labeled and doesn’t change conclusions.
Public Review Rubric
Weights: Claims vs. Evidence 25% • Performance & Reliability 25% • Privacy & Safety 20% • Cost & Efficiency 15% • Transparency & DX 10% • Accessibility 5%
- Evidence: demos are cute, numbers are better—eval scores, ablations, and known failure modes.
- Performance: quality vs. strong baselines, variance across seeds, long-context stability.
- Privacy/Safety: data handling, on-device options, guardrails, red-team results.
- Cost/Efficiency: $/1k tokens or per task, latency, memory/VRAM footprint, energy hints.
- Transparency/DX: docs quality, versioning, rate limits, support, export options.
- Accessibility: regional availability, pricing for students/edu, a11y in UI.
Important Notes & Safety
Educational content only
- No legal/financial/medical advice: consult qualified professionals for regulated decisions.
- Security first: do not paste secrets, PII, or client data into third-party tools without a DPA and clear retention terms.
- Fair use & licenses: verify content and model licenses before commercial use.
- Results vary: stochastic outputs can change across runs and versions—always validate.
Editorial Independence & Disclosures
We do not sell rankings or coverage. Sponsorships/affiliates, if present, are clearly disclosed and do not influence our testing or verdicts.
Optional Expert Review
Scope: methodology sanity check (no product endorsements).
Add a qualified reviewer when available to strengthen E-E-A-T. Remove this block if unused.
Contact the Author
Email: author@teknologiai.biz.id
Media & partnerships: editorial@teknologiai.biz.id
LinkedIn: linkedin.com/in/arifdsantosa
Site Snapshot (Shortened “About Us”)
- Mission: make AI understandable, testable, and responsibly deployable.
- Vision: smarter machines that serve people—safely, transparently, and efficiently.
- We publish: reviews, research explainers, build guides, and ethics notes you can actually use.
Sources & Citations
Articles link to papers, official docs, and release notes. This author page summarizes profile, methodology, and the public rubric used across reviews.
Corrections & Feedback
Notice something off? Email us. We aim to review and update within 5–10 business days.