AI Experiment Readiness Checklist
Problem statement, dataset fit, baseline availability, metric alignment, ablation coverage, robustness checks, case study plan, figure/table evidence chain, limitation statement.
Research assets
Free tools for serious AI research learners.
Use these assets to prepare a better first diagnostic. They are designed to expose research readiness, not hide weak evidence behind polished language.
Topic Diagnostic Brief
My AI domain is __. The problem is __. Existing papers solve __ but leave __. I can access dataset __. My target route is __. My current risk is __.
Ablation Planning Prompt
List each proposed module, the hypothesis it supports, the removal or replacement test, expected metric change, compute cost, and how the result would change the paper claim.
Academic integrity
Clear boundaries are part of the product.
Trust depends on making the compliance line explicit. The service teaches, reviews, and plans while leaving authorship, data, implementation, and claims with the researcher.
- Topic feasibility review and research-route planning.
- Paper reading coaching and replication guidance.
- Experiment design, visualization, and structure feedback.
- Submission strategy and reviewer-response coaching.
- No ghostwriting. No fabricated data. No fabricated experiments.
- No guaranteed acceptance, package publication, or author-slot sales.
- Submitting on behalf of an author or impersonating contribution.
- Manipulating peer review or hiding third-party involvement.
FAQ
Common questions before a diagnostic.
Do you write the paper for me?
No. We can review structure, claims, figures, and evidence plans, but the author owns writing, data, implementation, and submission.
Can you guarantee acceptance?
No. The work is scoped around better research decisions and reviewer-ready evidence, not acceptance guarantees.
What should I prepare?
Bring a target paper, rough idea, dataset notes, current code status, result tables, or the reviewer concerns you already know.
Who is this for?
Students and engineers working on AI/ML projects who need research judgment, experiment design, or submission strategy.