AirProof AI vs Zero: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of AirProof AI and Zero — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

AirProof AI

Freemium

AI-powered airflow simulator that finds the best spot for your air purifier in seconds using a physics-informed model.

Physics-Informed AI Engine: Runs a real-time airflow prediction model trained on physics rules and room scenarios instead of pure heuristics.
Instant Airflow Prediction: Move the purifier around a 3D room preview and see updated airflow coverage in seconds without any simulation setup.
Purifier Brand Presets: Pick your model from Dyson, Xiaomi, Philips, Levoit, Blueair, Coway, Honeywell, Winix, Shark, Rabbit Air, Oransi, and more.
AI-Trained Room Scenarios: A library of ready-made rooms — standard bedroom, living room, office, plus premium double bedroom, studio, open plan, meeting room, clinic, classroom, and cabin layouts.
Free Starter Setups: Three standard room setups (bedroom, living room, office) are free after signup so you can validate placement before upgrading.
Multi-Zone Room Support: Premium adds L-shaped and open-plan rooms with kitchens, lounges, and beds so multi-zone homes get accurate placement guidance.

Home Purifier Placement: Homeowners test where to put a new Dyson or Levoit purifier before drilling holes or committing to an outlet.
Landlord & Airbnb Prep: Property managers optimize purifier placement across bedrooms and living spaces before guest stays.
Small Office Air Quality: Offices with open workstations visualize coverage to make sure air is filtered near desks and meeting areas.
Clinic & Classroom Compliance: Vet clinics, meeting rooms, and classrooms use scenario templates to place purifiers where airflow matters most.
Purifier Purchase Decisions: Buyers compare how different brand models cover the same room before deciding which unit to buy.

Vercel Labs

Free

An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.

Graph as the Program: A compiler-owned semantic graph of symbols, calls, types, effects and node IDs is the source of truth, so agents reason over program structure rather than parsing and regenerating text.
Hash-Guarded Patches: Every edit carries an expected graph hash and expected field values, so a stale or conflicting patch is rejected before it reaches the store instead of silently corrupting the program.
Compiler in the Loop: Shape, type, stale-state and repository metadata checks run as part of applying a patch, collapsing the write-build-test-inspect cycle into a single checked operation.
Readable Text Projections: The graph renders to reviewable .0 source projections so humans can read diffs, audit what an agent changed and make rare manual edits.
Structured JSON Diagnostics: The compiler emits machine-readable diagnostics rather than prose error text, so agents can act on failures without parsing terminal output.
Explicit Effects via World: Side effects are passed through an explicit World capability parameter, making what a function can touch visible in its signature.
Runtime Constraints by Design: Targets token efficiency, low memory, fast startup, fast builds, low latency and zero dependencies rather than relaxing systems goals for agent ergonomics.
Query and Patch CLI: zero init, zero query, zero patch and zero run give agents a direct command surface over the graph, with agent skills carrying the graph discipline instead of rigid human prompts.

Reliable Agent Code Edits: Let a coding agent make semantic changes that are rejected outright if its view of the program is stale, instead of producing plausible-looking but broken text diffs.
Reducing Agent Token Spend: Query the specific symbols, types and nodes relevant to a task rather than feeding whole files into context on every turn.
Outcome-Driven Development: Describe a desired result in conversation — add auth, fix a failing route, build a CRM API — and review the resulting projection rather than writing the code.
Auditable AI-Written Code: Review what changed through readable .0 projections and graph hashes, keeping a human checkpoint over agent-authored programs.
Language and Tooling Research: Explore what a compiler and program representation look like when machine editors, not human typists, are the primary writers.
Sandboxed Experimentation: Prototype agent-driven codebases in an isolated environment where breaking changes and pre-1.0 churn are acceptable.