Chapter 267: Offline Sync and the Eve of the Demo

The waveform chart was still scrolling across the screen. Lin Chen adjusted the threshold parameter in realtime_alert_v0.1.py from 0.9 to 0.88, saved it, and recompiled. The terminal printed [OK] Monitoring process mounted. He closed the laptop and got up to fetch water from the hot-water room at the end of the corridor. His left foot landed on the tile as if it were stepping on a numb block of wood; he could only rely on the inertia in his ankle to drag his lower leg forward. The window at the far end of the corridor had not been shut tight. Early-autumn night wind poured in, making the fire evacuation diagram on the wall curl slightly at the edges.

Back in the ward, Xiaoman's breathing was steady. The monitor's beeps held at sixty-eight per minute. Lin Chen removed the SD card that held the data, inserted it into the card reader, and connected it to the local encrypted terminal. The script ran automatically, performing AES-256 encryption and hash verification. The progress bar reached 100%, taking eleven minutes. He pulled the card out, replaced it with a blank one, and inserted it into the portable acquisition box. The hardware indicator shifted from blue to green. The local cache was cleared and ready to receive the next stream of data.

His phone screen lit up. Su Man had sent a PDF: MVP_Demo_Outline_v3.pdf. A voice message followed: "Zhao Qiming's side changed the agenda. The first fifteen minutes are for the data stream and latency metrics, and the last fifteen are for the commercialization path and compliance boundaries. He's bringing legal counsel and a medical advisor. I rearranged your offline-architecture slides around the logic of 'edge nodes plus local inference.' I left an interface for cloud sync, but didn't hard-code it. Take a look."

Lin Chen opened the file. Slide three read: Architectural advantages: data never leaves the hospital, zero privacy leakage, latency <50 ms. Slide four read: Commercialization bottleneck: high single-machine deployment cost; cross-campus replication requires a hardware procurement cycle. He stared at the fourth line for a long time. Su Man's layout was restrained, but the logic of capital was already embedded between the lines. What Zhao Qiming wanted was a replicable SaaS model, a curve of declining marginal costs. The system in Lin Chen's hands was, in essence, a medical-grade edge device with heavy assets and heavy operations. Those two lines were bound to collide on the demo stage.

He replied: "Keep slide four. Don't dodge the cost. Bold 'compliance first' and 'false-positive rate below 0.5%.' We'll present it this way tomorrow."

Su Man sent back a simple "Okay." Nothing more.

Lin Chen uploaded the encrypted data package to the test server through his phone hotspot. The signal weakened inside the hospital's concrete walls, and the upload speed stuck at 1.2 MB/s. He leaned against the folding chair and closed his eyes. The muscles in his left calf began twitching faintly beyond his control. He pressed his palm hard against the gastrocnemius until the spasm subsided. The coffee had gone completely cold, a dark brown film forming across its surface. He took a sip. The bitterness slid down his throat, and a hollow burn spread through his stomach.

At 1:40 a.m., the upload completed. The terminal displayed: [INFO] Feature package synced to staging environment. [INFO] Front-end Alpha integration successful. He opened the demo test console and imported the data package. The front end refreshed, unfolding the EEG waveform as a three-dimensional heat map. Abnormal clusters were highlighted, the timeline aligned, and the confidence curve smoothed out. He dragged the slider to simulate inference across different seizure stages. Latency held steady at 41 milliseconds. The false-positive rate was 0.3%. The data was clean; the logic formed a closed loop.

But clean data could not block out the roughness of reality. He thought of Doctor Shen's tone during rounds, of the ethics committee's long approval process, of the three medical AI projects Zhao Qiming's fund had invested in before—two dead because they could not obtain Class III medical device certification, one crushed by a major company's open-source model. The technology could run, but business needed a pass. He created a new text file and typed: "Demo strategy: 1. Do not promise to replace clinical diagnosis. 2. Position as an 'auxiliary warning terminal for family members/caregivers.' 3. Hardware cost can be amortized to within 3,000 yuan per unit; pursue the out-of-hospital self-pay channel. 4. Reserve a cloud-based federated-learning interface for smooth upgrades once compliance permits."

He read it three times. Every line was a concession, and every line was a way to stay alive. Startups were not about writing code; they were about finding cracks to squeeze through. He saved the file and turned off the screen.

At three in the morning, the ward held only the low hum of the server fan. Lin Chen placed his phone beside the pillow and turned the volume to maximum. He leaned back in the chair and tried to close his eyes, but his nerves were still taut. His left foot had completely lost the sense of pain, replaced by a heavy, sagging pressure, as if it had been filled with lead. He simply stretched the leg out straight and let the blood flow back.

Beep—

The phone vibrated. It was not an alarm. It was a Bluetooth push notification.

Lin Chen's eyes snapped open. The screen lit up: [WARN] Local model triggered. Confidence: 0.86. Waveform feature: abnormal clustering in theta-band frequency. Recommendation: maintain observation.

He immediately sat upright and pulled up the real-time waveform. Xiaoman's sleep cycle was entering a light-sleep phase, and the EEG background showed a brief frequency shift. It was not a precursor to a generalized seizure; it looked more like a normal fluctuation caused by drug metabolism. The model was hesitating at the 0.86 threshold. He quickly opened the logs and compared them with the baseline data. The phase differences across three consecutive frames were within tolerance. It was a false positive.

He modified the script, added sliding-window smoothing, and raised the number of consecutive confirmation frames from two to three. Recompile. Eight seconds.

The waveform stabilized. The alert cleared.

Lin Chen leaned back against the chair and exhaled slowly. A 0.86 false positive would be magnified into a fatal defect at the demo. Before three in the afternoon, he had to solidify the filtering logic into the front-end presentation layer. Only eleven hours remained.

Outside the window, the sky shifted from ink black to gray-blue. The sound of the early-shift nurse's cart came from the corridor, rubber wheels rubbing against the terrazzo floor in a steady rustle. Lin Chen got up and splashed cold water on his face. In the mirror, the man looking back had sunken eyes and a blue shadow of stubble, but his gaze was clear. He changed into a pressed light-gray shirt and buttoned the cuffs. He put a shoe on his left foot, tied the laces tight, and placed all his weight on his right leg.

At 2:40 p.m., he entered the online meeting room early. The test environment self-check passed. Su Man's avatar lit up, the background a white wall in a shared office space. She said nothing, only made a "ready" gesture.

At 2:55, the meeting-room member list began to jump. Zhao Qiming entered. Lawyer Zhang (Legal) entered. Consultant Li (Medical) entered.

The video loaded. Zhao Qiming's background was a dark bookshelf; he wore a well-tailored suit. He glanced at the camera and spoke in an even voice. "Lin Chen, Su Man. Time is tight, so let's go straight to the data. I've read the report on your offline architecture—latency and accuracy are indeed good. But the first question is this: if the hospital doesn't allow devices into the ward, or if family members are unwilling to bear the hardware cost, how do you scale DAU? Second, a 0.5% false-positive rate is a red line in clinical settings. One false alarm causes panic among family members, and the brand is gone. How do you cover that risk?"

Lin Chen's fingers rested on the keyboard. Under the desk, his left foot tingled faintly. He looked at the waveform chart pulsing on the screen and did not answer immediately. He brought up the demo interface he had prepared in advance and hovered the mouse over the four words "auxiliary warning."

"Mr. Zhao," he began, his voice not loud, but every word deliberate, "we don't sell devices, and we don't promise scale. We solve only one problem: giving family members a chance to press the call button ninety seconds before an episode. The fallback plan for false positives is written in the first clause of the user agreement: this product does not replace clinical diagnosis; it is only an observational aid. All data is encrypted locally. It is not uploaded, not used for training, and not commercialized. If hospitals won't allow it in, we do out-of-hospital leasing. If families think it's too expensive, we push the cost below three thousand and offer installments."

He paused for one second. On the screen, Zhao Qiming's gaze sank slightly.

"But the premise," Lin Chen continued, "is that this system first has to run through one hundred real cases. The data won't be pretty, but it can save lives. What you're investing in is medicine, not traffic."

The meeting room went quiet for two seconds. Only the electrical noise remained.

Zhao Qiming did not answer. He turned his head and asked the medical consultant beside him something in a low voice. The consultant nodded and drew a line on the paper.

Lin Chen watched the time in the lower right corner of the screen. 2:58. The demo had only just begun. And Xiaoman's monitor, in the next room, was giving off one extremely faint, frequency-abnormal beep.