Papers vs A/B Smartly
Compare research AI Tools
Papers
Community platform that links ML papers with open source implementations benchmarks and leaderboards to make research more reproducible and accessible.
A/B Smartly
Enterprise experimentation platform with a sequential testing engine event based pricing and flexible deployment so product teams run faster trustworthy A B tests share insights broadly and keep governance strong across web mobile and backend.
Feature Tags Comparison
Only in Papers
Shared
Only in A/B Smartly
Key Features
Papers
- • Task pages: Browse leaderboards datasets methods and metrics for a clear view of the SOTA landscape
- • Paper pages: See official code repos versions and licenses linked directly from publications
- • Filters and compare: Slice by dataset metric task or framework to evaluate methods quickly
- • Community edits: Propose changes and add repos with moderation to keep entries accurate
- • APIs and dumps: Pull structured task and result data for meta analysis and education at scale
- • Trends and guides: Explore curated topics tutorials and learning paths for emerging areas
A/B Smartly
- • Sequential testing engine: stop earlier without inflating error rates so winners ship faster and inconclusive tests end decisively saving time and traffic
- • Warehouse native workflows: route events to your lake or house so analysts reuse metrics segments and joins with lineage and reproducibility across teams
- • SDKs across stacks: integrate once into web mobile and backend so feature flags exposures and metrics remain consistent across platforms and services
- • Source control friendly: treat experiments as code with reviewable configs CI checks and templates that prevent errors before traffic hits production
- • Collaboration and notes: attach hypotheses screenshots and decisions to each test so outcomes are searchable and shareable in postmortems and planning
- • Event based pricing: avoid per seat or per test limits grow programs with predictable unit economics and fewer internal license battles
Use Cases
Papers
- → Find baseline code for a new task and run it quickly
- → Compare methods across datasets and metrics before experiments
- → Build teaching labs with real repos and tasks for students
- → Extract benchmark data for reviews and meta analysis
- → Track trending tasks and papers in a research area
- → Check licenses and versions before reuse in products
A/B Smartly
- → Feature rollout gates: validate impact behind flags then graduate safely once primary metrics clear with acceptable side effects across segments
- → Checkout funnel fixes: trial copy layout and sequencing while monitoring revenue and refunds to avoid profitable but risky changes
- → Search relevance tuning: compare ranking tweaks with guardrails for speed stability and engagement beyond a single click proxy
- → Performance tradeoffs: measure latency shifts alongside conversion so teams understand when speed investments or regressions are acceptable
- → Paywall and pricing tests: explore presentation and eligibility while keeping fairness guardrails and refund tracking visible to finance
- → Notification systems: iterate cadence and targeting while measuring retention spam complaints and app store optics over weeks
Perfect For
Papers
ml researchers, engineers, students, educators, reviewers and data scientists who need fast paths from papers to code and reproducible benchmarks
A/B Smartly
growth leaders, data scientists, product managers, experimentation engineers, analysts and SRE partners at companies with strong telemetry security and compliance expectations
Capabilities
Papers
A/B Smartly
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