Page 131 of Glitched Fates & Stolen Mates

My wolf howled in despair, the sound trapped behind my ribs where no one could hear it.

I was going to lose everything.

And there wasn’t a damn thing I could do to stop it.

29

Felix

The facial recognition algorithm had been running for over twelve hours now, and my eyes burned from staring at endless streams of pixelated faces. I rubbed them hard, then focused back on the monitor displaying London Underground’s CCTV network.

I glanced at the clock in the corner of my screen. 10:46 p.m. The others left an hour ago. I told them I was just finishing up.

But I had seventeen possible matches. Seventeen moments where the system thought it might have glimpsed Kit’s face in the maze of tunnels and platforms beneath the city. Each one had been a false alarm—wrong jaw structure, different height, shadows playing tricks with the resolution.

I pulled up the map again, tracing the Piccadilly line with my cursor. Kit had entered at Clapham North on Wednesday evening, captured on camera striding confidently towards the barriers. Unaware he’d never come back out. From there, I could follow him: Northern line to King’s Cross, transfer to Piccadilly. Then… nothing. No more cameras picked him up until whatever happened at Arnos Grove. The trail went cold.

Of course it bloody did. End of the line, quiet residential area, older infrastructure that the Met barely bothered maintaining. Half the cameras on the network were broken anyway—they refused to publish official numbers because it would be embarrassing. I’d hacked their maintenance logs multiple times. Sixty-seven percent operational on a good day.

Kit knew this. He’d been overconfident in his plan. Foolish. Thought a station selected at random, with the eyes of the public streaming past them, was a sensible precaution. Thought he could handle whatever vampire he was meeting without backup.

But he’d been wrong.

My hands clenched into fists. If he’d told me he was going to Arnos Grove, I’d have explained exactly why it was a terrible idea. I’d have made him choose somewhere else, somewhere with proper coverage, somewhere safe.

But Kit had only vaguely mentioned he had a meeting set up, and little else.

The anger shifted, turning inward like it always did. Sharp and familiar, cutting deeper than rage at Kit ever could.

I should have known something was wrong sooner, when Kit didn’t text me goodnight on Wednesday. He always texted goodnight. Even when he was working late, even when he was exhausted. Sometimes words, sometimes simply emojis. Small things that anchored my day.

Wednesday night, nothing.

Thursday morning, I’d waited under the lime tree for a stupid amount of time before accepting he wasn’t coming.

My head dropped onto the desk with a hollow thud. The cool surface pressed against my forehead, and I closed my eyes, breathing in the smell of electronics and stale coffee.

Three entire days since Kit had vanished. Seventy-two hours of feeling like half of myself had been torn away.

I forced my head up. Forced my hands back to the keyboard.

I’d exhausted Clapham North and Arnos Grove. Time to expand the search. Three more stations beyond Arnos Grove—Southgate, Oakwood, Cockfosters. Maybe Kit had stayed on the Tube for some reason. Maybe the meeting location had changed. Maybe—

My analysis software chimed. Anomaly detected.

I sat up straighter, clicking through to the flagged file. Oakwood station, Wednesday 21:47 to 22:27. Forty minutes of standard platform footage.

The metadata made me frown. File size: 2.3GB. For forty minutes of static footage, that should compress to maybe 800MB. The timestamp microseconds showed gaps that shouldn’t exist in standard H.264 encoding. The hash values didn’t match Transport for London’s compression algorithms.

I ran a hex dump on the raw file data, my pulse quickening. Repeating byte sequences appeared in what should have been random compression artifacts. The FFmpeg analysis revealed embedded data streams that weren’t supposed to be there.

Someone had tampered with this footage.

Greywatch. Had to be. They’d tried to scrub their tracks but hadn’t counted on someone with my skillset digging this deep.

Ha!

I launched my steganographic analysis tools, targeting frames 1247 through 1251 where the anomalies clustered thickest. The software peeled back layers of hidden data, revealing information embedded in the least significant bits of the video frames—invisible to the naked eye but detectable through pixel value analysis.