Port Radar for macOS vs Zero: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Port Radar for macOS and Zero — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Port Radar for macOS
Juan Sebastian Solano
Free open-source Mac menu bar app that lists every listening localhost port and uses on-device Apple Intelligence to explain what each process is.
Key features
- Menu Bar Port Scanner: Lists every listening localhost port in the menu bar with port number, PID, owning project, runtime, and the exact command line.
- Apple Intelligence Explanations: Ask in plain language what a process is, why it has been running, and whether stopping it is safe; answers are generated on-device with no cloud call.
- Project Grouping: Groups processes by the project directory that owns them and flags shared or orphaned processes with no obvious parent.
- One-Click Cloudflare Tunnels: Share any local port as a public URL through a Cloudflare quick tunnel, auto-installing cloudflared with no CLI, ngrok, or account setup.
- Clean Process Control: Stop a process gracefully or force-quit it with a confirmation step, directly from the menu bar.
- Live Tunnel Management: See which tunnels are currently live and public, copy their URLs, and stop them at any time.
- Fully On-Device Privacy: All inspection and AI explanation happens locally; no process data or command lines are sent off the machine.
- Open Source Under Apache 2.0: The full source is published on GitHub, so the app can be audited or built from source.
Best for
- Port Conflict Debugging: Finding out which forgotten process is holding port 3000 before starting a new dev server.
- Runaway Process Triage: Identifying a Node or Python process quietly eating CPU and deciding whether it is safe to kill.
- Preview Sharing: Handing a teammate or client a live public URL for a work-in-progress local app in seconds.
- Multi-Project Development: Keeping track of which of several simultaneously running projects owns each active port.
- Onboarding and Handover: Letting a developer new to a codebase understand what the local stack actually starts up.
- Privacy-Sensitive Environments: Getting AI assistance about local processes in settings where sending command lines to a cloud model is unacceptable.
Zero
Vercel Labs
An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.
Key features
- 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.
Best for
- 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.