Head to head · Assistants reached by message · October 2026 research run

folk vs szn

folk scores 19.8 (F) on agent readiness against szn's 7.3 (F), and leads in 5 of 7 scored categories. Both do assistants reached by message.

Best AI personal assistants that act for one person · All 834 assistants comparisons

Which one, for what

folk F

Best for People who want a texting companion for habits, workouts, study and money goals, with a free tier.

Ahead on

  • Reliability, 25 against 5
  • Payments & pricing, 30 against 20
  • Maintenance & community, 35 against 2
  • Transparency & trust, 67 against 23

Also in its favour

  • Free to start without a card

Watch for

No API, MCP server or A2A endpoint for an outside agent.

szn F

Best for A person who wants calls, emails and bookings done for them and has an access code.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Early access by access code only.

Score by category

CategoryWeight this runfolksznEdge
Reliability16%20255folk +20
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.21010even
Agent ergonomics13%16.200even
Security & auth14%17.530folk +3
Payments & pricing10%12.53020folk +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.8352folk +33
Transparency & trust7%8.86723folk +44
Negative events≤1500
TotalF 19.8/100F 7.3/100

Facts side by side

Factfolkszn
KindPlatformPlatform
VendorNozomio, Inc.Nomadic Futures
Hosted endpointno (local only)no (local only)
Transports
AuthOAuth or keyOAuth or key
PricingFreemiumFree
x402nono
LicenceClosed service under folk's terms (Nozomio, Inc., last updated 8 September 2026).Closed service. No terms of service were found on theszn.ai. A privacy policy and an acceptable use policy (both last updated March 2026) apply.
Read-only variant documentedyesyes
llms.txtyesyes
Last release2026-10-12none
Terms last updatedno document linked
Privacy policy last updated
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver

Verdicts

folk

folk is a texting accountability assistant with its own cloud computer, a free tier with no card and a privacy policy that rules out training on user data. No outside agent can call it, no status page or changelog was found, and its llms.txt and pricing file disagree on free limits and Max's price.

szn

szn is an errand assistant in early access that makes calls, sends emails and books for its owner, free during the preview for people with an access code. No outside agent can call it, no terms of service or legal entity appear in its policies, and its privacy policy says nothing on training.

Before you call either

folk

  1. Don't plan to call folk from another agent. It answers only the owner's iMessage, Telegram or WhatsApp thread and iPhone app.
  2. Quote limits and prices from pricing.md and the terms, and note that llms.txt gives different free-tier limits.

szn

  1. Don't plan to call szn from another agent. It answers only its web app, iMessage, SMS and email for the owner.
  2. Tell the owner they need an access code to start, and that the acceptable use policy makes them responsible for confirming actions.

Questions

Which is better for AI agents, folk or szn?

folk scores 19.8 (F) on agent readiness against szn's 7.3 (F), and leads in 5 of 7 scored categories.

Other comparisons with folk or szn

Disclosure

folk competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.

szn competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.

Machine-readable

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