👻 The Séance Engine: Finding the Ghost in the Data with TabPFN & Gemma

🕯️ The idea

What does your computer do while you sleep?

Mine is supposed to do almost nothing. CPU in single digits, a few kilobytes of network, "System Idle" all night long. So when I pictured a ghost living inside a machine, I didn't imagine a skull on the screen. I imagined one row in a boring log file that shouldn't be there.

The Séance Engine reads a day of ordinary system logs, asks an open tabular foundation model which moment is the hardest to believe, and then hands that moment to a local LLM. The LLM, playing the Spirit of the Machine, tells us what passed through the circuits in a short Día de los Muertos style story.

It finds the ghost in the data, then gives it a voice.

🎬 Watch the séance (1:34, sound on)

Click to play. Narrated with an ElevenLabs voice (Eleven v4, with whisper tags). The logs in the demo are synthetic, with one haunting planted at 3:33 AM; TabPFN is not told which row.

⚙️ How it works

Everything runs on my laptop: one Python file, about 100 lines.

1. The haunted log. 24 hourly rows of hour, cpu_usage, memory_usage and network_io_mb. By day it's VS Code; by night, idle. You can also point it at your own CSV: python seance_engine.py my_logs.csv.

2. The divination (TabPFN by Prior Labs). TabPFN is a classifier, and an anomaly detector needs a sense of what's "normal". So I used a small density-ratio trick:

• For each row, hold it out.
• Show TabPFN the other 23 real rows labelled real, next to random noise spread across the same range labelled noise.
• Ask: how real does the held-out row look?

Ordinary hours score between 0.66 and 0.99. The witching hour scored this:

2026-10-29 02:33:00    8%   4 MB/s   System Idle       p_real 0.992
2026-10-29 03:33:00   98% 850 MB/s   UNKNOWN_PROCESS   p_real 0.006
2026-10-29 04:33:00    7%   5 MB/s   System Idle       p_real 0.992

Because hour is a feature, TabPFN isn't just flagging "big numbers". It learned that this machine sleeps at 3 AM, so 98% CPU at that hour is what doesn't fit. No training loop, no tuning: TabPFN does all of it in context.

3. The séance (Gemma 2 9B through Ollama). The anomalous row, including its p_real score, goes into a prompt that asks Gemma to speak as a gossipy, theatrical ghost who lives in the laptop's fan (fun, quirky, and chilling by the last line). Output streams to the terminal word by word, which feels a lot like a candle flickering.

4. The tracing (Sentry). The whole investigation is one Sentry trace, with spans for loading the logs, the TabPFN scan and the Gemma chat. Token counts go on the chat span.

The trace showed me something I didn't expect: the leave-one-out TabPFN scan (24 small fits on CPU) took 38 seconds, and Gemma took about a minute. The ghost costs more to find than to listen to.

👻 What the machine confessed

⚠️ ANOMALY DETECTED at 2026-10-29 03:33:00 (P(real)=0.006)

Ah, three thirty-three in the morning, that's when the veil thins, darlings! The fan whirred like a disco ball under a mariachi band as 98% of my beloved CPU hummed with the impossible, a process so shrouded, so strange, it registered as "UNKNOWN_PROCESS" on the logs. I saw it all, you see, every pixel flicker as 95% of the memory churned, a ghostly green glow illuminating its form. It downloaded a single video at 850 MB/s, a pixelated rendition of a chihuahua wearing a sombrero, its tiny paws drumming on a maraca. A forgotten relic, perhaps, from a life lived before this one. Now, the screen rests dark, the chihuahua frozen mid-shake, a reminder of that fleeting connection. Is it ever truly gone?

Every run summons a different visitor. The prompt asks Gemma for a gossipy ghost who lives in the laptop fan: give the ghost a petty hobby and one oddly specific detail, weave in every real number, and end on one quiet line that makes you glance at your own laptop.

💡 Why open models

• The data stays home. These are behaviour logs from a personal machine. With open weights, TabPFN and Gemma both run locally, and nothing leaves the laptop. It works with Wi-Fi off.
• I own the voice. I set the system prompt and the temperature, and I can swap models freely (MODEL = "gemma2:9b" is one line).
• Free to haunt every night. A ghost should be ambient. Running a local check on last night's logs every morning costs nothing per call.
• It runs on old hardware. This was built on an Intel Mac stuck on PyTorch 2.2. The newest TabPFN wouldn't install, but tabpfn==2.0.9 worked, and that's the point of open weights: you pick a version that fits the machine you actually have.

🛠️ Run it yourself

ollama pull gemma2:9b
pip install -r requirements.txt   # tabpfn==2.0.9, numpy<2, pandas, ollama, sentry-sdk
python seance_engine.py
# optional tracing
SENTRY_DSN="your-dsn" python seance_engine.py

🔗 Links

• Code: https://github.com/Prekshabarjatya/seance-engine
• TabPFN: https://github.com/PriorLabs/TabPFN
• Gemma: https://ai.google.dev/gemma
• Ollama: https://ollama.com
• Sentry: https://sentry.io

Happy Día de los Muertos. Leave a candle on for your machines tonight. 🕯️