Adoption dailySecurity & identity
Today's story: Passkeys
Passkeys outpace AI agents in daily adoption growth
Security credentials are climbing faster than coding tools, signaling a change in what developers prioritize for daily use.
The numbers: Passkeys is today's fastest-rising trend: installs at 1.33× the four-week pace. Passkeys: 1.33× momentum across 1 signal. 5 of 21 trends are climbing and 2 are cooling.
Backfilled on October 7, 2026 using only data published by September 28, 2026.
Passkeys, signal by signal
Each pedestal is one signal: its height is last week's typical day against the four weeks before, and the ring marks that normal. A lit pedestal is rising 1.15× or more.
Passkeys is today's fastest-rising trend: installs at 1.33× the four-week pace.
Passkeys: 887k npm installs a day on a typical day last week, against 667k the four weeks before
Bars are single days, the line is the 7-day average, the shaded band is the last 7 days and the dashed line is the four-week typical day (median).
Data through npm 09/26, Wikipedia 09/27, HN 09/27. Source: npm downloads API.
Passkeys now lead the adoption board with 887k/day installs, a 1.33× jump from the four-week pace. AI agents follow at 653k/day with 1.21× momentum. This gap shows users are prioritizing secure access over automated coding tasks. The difference in speed highlights a practical preference for immediate utility in login workflows.
Smart glasses also climb at 1.27× momentum, though from a lower base. While AI agents see 1,178/day Wikipedia searches, Passkeys draws fewer but more targeted interest. The contrast suggests that while AI generates broad curiosity, passkeys drive specific, high-volume implementation. Developers are choosing tools that solve immediate friction points in their daily routine.
Cooling trends like Robotaxis at 0.78× momentum fade as new hardware and security tools gain traction. The rise of passkeys alongside smart glasses indicates a focus on personal interface and identity. Builders are moving away from speculative concepts toward tangible improvements in how they interact with devices and secure their work.
Watch next: Whether passkey installs sustain 1.33× momentum or plateau as AI agent adoption accelerates.
Column drafted by qwen/qwen3.8-27b from the numbers below; every figure checked against them.
The numbers
On a typical day last week, npm installs of @simplewebauthn/browser reached 887k, 1.33× the 667k of a typical day in the four weeks before.
Next on the board: Smart glasses at 1.27×, led by Wikipedia readers at 1.27× (285 on a typical day against 224).
Quiet adoption: Bun runtime installs run at 1.18× their four-week pace while attention sits at 0.86×. Developers are adding it faster than people are reading about it.
Cooling fastest: Humanoid robots at 0.66×, with Hacker News stories down to 0.4 on a typical day from 1.0. Part of that is a spike on Sep 16 in the weeks before.
The long view: OpenAI Codex installs over the last four weeks are 117× the same four weeks a year earlier (3.4M a day against 29k).
Trend pages: PasskeysSmart glassesSmall modular reactorsHumanoid robots
Quiet adoptionBroad riseOne-day spike5 of 21 trends are climbing; Passkeys leads at 1.33×
Momentum is a typical day (median) of the last 7 divided by a typical day of the 28 before, averaged across every signal with enough data. 1× means no change. Rings mark each signal.
Data through npm 09/26, Wikipedia 09/27, HN 09/27. Source: npm downloads API, Wikimedia pageviews, Hacker News search.
3 of 7 developer tools are being installed faster than they are being read about
Across: attention momentum (Wikipedia readers and Hacker News stories). Up: install momentum (npm). Both on log scales; the 1× lines split the corners.
Data through npm 09/26, Wikipedia 09/27, HN 09/27. Source: npm downloads API, Wikimedia pageviews, Hacker News search.
AI agents draws the most Wikipedia readers against its own normal: 1.32× over the last week
Each cell is one day's readers divided by that trend's four-week typical day. Brighter runs hot, darker runs cold.
Data through npm 09/26, Wikipedia 09/27, HN 09/27. Source: Wikimedia pageviews API, user agents only.
Every number on the board
| Passkeys | Web platform | 1.33× | Climbing | 887k | 1.33× | 90 | 1.12×* | 0.6 | 1.33×* | 0 |
|---|---|---|---|---|---|---|---|---|---|---|
| Smart glasses | Devices | 1.27× | Climbing | – | – | 285 | 1.27× | 1.4 | 3.1×* | 0 |
| Small modular reactors | Energy | 1.23× | Climbing | – | – | 539 | 1.24× | 0.0 | 0×* | 0 |
| AI agents | AI agents | 1.21× | Climbing | 653k | 1.19× | 1,178 | 1.32× | 12 | 1.13× | 0 |
| WebGPU | Web platform | 1.15× | Climbing | 1.5M | 1.13× | 141 | 1.17× | 0.7 | 1.05×* | 0 |
| AI API SDKs | AI agents | 1.13× | Steady | 18M | 1.13× | – | – | – | – | 0 |
| Vibe coding | AI coding | 1.11× | Steady | – | – | 1,676 | 1.22× | 1.1 | 1× | 0 |
| SpaceX Starship | Space | 1.08× | Steady | – | – | 1,475 | 1.08× | 0.0 | 0×* | 0 |
| Gemini CLI | AI coding | 1.03× | Steady | 64k | 1.03× | – | – | 0.0 | n/a* | 0 |
| Model Context Protocol | AI agents | 1.01× | Steady | 10M | 1.14× | 1,620 | 1.14× | 5.6 | 0.8× | 0 |
| Bun runtime | Web platform | 1.01× | Steady | 715k | 1.18× | 225 | 0.86× | 0.7 | 0.74×* | 0 |
| Quantum computing | Devices | 1× | Steady | – | – | 1,919 | 1.1× | 3.0 | 0.9× | 1 |
| Heat pumps | Energy | 0.99× | Steady | – | – | 473 | 0.99× | 0.1 | 2×* | 0 |
| Starlink | Space | 0.98× | Steady | – | – | 1,989 | 0.98× | 0.6 | 1.46×* | 0 |
| Local LLMs | AI agents | 0.97× | Steady | 164k | 0.91× | 706 | 1.03× | 0.3 | 0.44×* | 0 |
| OpenAI Codex | AI coding | 0.96× | Steady | 3.5M | 1.06× | 39 | 1.11×* | 3.7 | 0.87× | 0 |
| Solid-state batteries | Energy | 0.94× | Steady | – | – | 370 | 0.94× | 0.0 | 0×* | 0 |
| eVTOL air taxis | Space | 0.91× | Steady | – | – | 190 | 0.91× | 0.0 | 0×* | 0 |
| Claude Code | AI coding | 0.89× | Steady | 2.0M | 0.8× | – | – | 6.7 | 0.98× | 0 |
| Robotaxis | Robots | 0.78× | Cooling | – | – | 195 | 0.78× | 0.0 | 0×* | 0 |
| Humanoid robots | Robots | 0.66× | Cooling | – | – | 372 | 1.01× | 0.4 | 0.43× | 0 |
| Vehicle-to-grid | Energy | n/a | Not enough data | – | – | 63 | 1.02×* | 0.0 | 0×* | 0 |
Method and sources
Every trend gets up to three free, daily signals. Installs: npm registry downloads for the packages named on each trend page (developer adoption; it counts CI and mirrors too, so it measures direction, not people). Attention: English Wikipedia readers per day, human agents only, and Hacker News stories with the term in the title. For each signal we divide a typical day (the median) of the last 7 complete days by a typical day of the 28 days before. Medians keep one launch-day spike from carrying a whole week; both windows are whole weeks, so weekend dips sit equally in both. Momentum is the geometric mean of the signals that clear a minimum base. npm reports some days as zero for every package at once; we treat those as missing.
The column is drafted by a free language model from the day's computed facts only, and is published only if every number in it matches those facts; otherwise the computed paragraphs stand alone. Theme labels come from Jev (or its Cloudflare Clef fallback, as noted) choosing from a fixed list of ten.
Stages: Breakout 1.5× and up, Climbing 1.15×, Steady 0.87× to 1.15×, Cooling below. Raw data: /api/trends/2026-09-28.json.