This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
Revenant is a ghost that moves into your terminal. COBOL, Fortran, Lisp and Perl, four languages people keep declaring dead, each get a voice and comment on the commands you run. Real lines from my laptop, generated locally by Qwen2.5:
Fortran, on chmod -R 777 .: Permissions set like this? Insecure, like an open door in Los Alamos.
Perl, on a failed docker build: Dockerfile issues? I'd have just s/Dockerfile/sourcecode/ and hoped for the best.
It comes in three parts:
ghost ofrenda builds one for the projects you stopped working on: it scans your git repos, writes each abandoned one an epitaph and an offering, and produces a single HTML page where you light a candle for each.
I built this as a pair with Claude (Sonnet 5.5) in Claude Code.
The Ofrenda: one candle per abandoned project
The Séance Room: chatting with a spirit, running locally
In the terminal: a spirit whispers after a failed command
Dead programming languages haunt your terminal. Everything runs locally on open-weight models via Ollama. No API keys, no network, no command history leaving your machine.
git clone https://github.com/vee-1745/vee-1745-revenant && cd vee-1745-revenant
ollama pull qwen2.5:3b # COBOL, Lisp
ollama pull qwen2.5:7b # Fortran, Perl (they need the bigger model)
python3 ghost.py doctor
python3 ghost.py summon cobol
python3 ghost.py demo
echo "eval \"\$(python3 $PWD/ghost.py hook bash)\"" >> ~/.bashrc # run from this folder
After the hook is loaded, ghost is available as a command:
ghost spirits # list the dead
ghost summon lisp # swap the spirit
ghost summon perl --model llama3.2:3b # swap spirit and model
ghost
…
MIT licensed. Python standard library only, with no pip dependencies and no build step. Setup is git clone, ollama pull qwen2.5:3b qwen2.5:7b, python3 ghost.py doctor.
Stack. Ollama running Qwen2.5 3B and 7B, on a laptop with an RTX 3050 (6 GB VRAM) inside WSL2. The backend is plain Python, and the UI is vanilla JS and hand-drawn SVG with the fonts bundled.
A persona is not a system prompt. My first test was "say boo in the voice of a bitter COBOL ghost". The 3B model replied with a description of what such a ghost would sound like. The fix was a system prompt plus few-shot examples written as real chat turns, with a random 4 of 8 shown on each call so the model can't memorize one set. My first demo still parroted the examples back at me, because I had reused the example commands as demo commands.
Measure, don't trust vibes. My first quality metric said "100% usable" everywhere. It only checked that a reply existed, so it was useless. I wrote validators per spirit: COBOL must be at least 90% caps, Lisp needs an s-expression, Perl needs line noise, Fortran needs era vocabulary, and a global filter rejects advice-shaped phrasing, because a ghost that says "be careful with force pushes" has broken the spell. Then I benchmarked both models on commands the spirits had never seen:
| spirit | 3B first try | 3B ≤3 tries | 3B median | 7B first try | 7B ≤3 tries | 7B median |
|---|---|---|---|---|---|---|
| COBOL | 67% | 100% | 0.71 s | 83% | 100% | 1.14 s |
| Lisp | 92% | 100% | 0.57 s | 100% | 100% | 0.78 s |
| Perl | 42% | 75% | 1.15 s | 67% | 100% | 0.93 s |
| Fortran | 25% | 42% | 1.21 s | 50% | 83% | 1.17 s |
Medians are per generation attempt, so a whisper that needed retries takes longer. Two things I took from this. First, the retry loop is the feature: 7B Fortran passes only half the time on the first try but 83% within three, and 7B Perl goes from 67% to 100%. Second, the model should be chosen per persona. COBOL and Lisp pass 100% on the 3B model and it is 25-40% faster for them, while Fortran (42% on 3B) and Perl (75% on 3B) need the 7B one. Each spirit names its own model in its config file.
Caveats: that is 12 whispers per cell, so treat it as indicative, not statistics. I wrote the validators after reading samples from an earlier run, and "pass" measures form (caps, s-expressions, no advice), not whether a line is funny.
The Ofrenda is grounded in git. The scanner runs read-only git commands and collects the facts: dates, commit count, languages, the last commit message, and unsaved changes. The cause of death ("Died mid-refactor", "Died with 1 unsaved change") comes from plain heuristics on those facts, so the model never has to guess one. The model writes only the epitaph and the offering. Every epitaph is fact-checked: a digit that isn't in the repo's facts rejects the draft, and after three rejected drafts the page falls back to a plain template and says so in its footer. On my real repos the model passed on the first try.
Built as if the page would be exposed. The Séance Room binds to loopback only and rejects foreign Host and Origin headers. The Ofrenda is one self-contained HTML file with a Content-Security-Policy that allows a single hashed script and nothing from the network. Commit messages are untrusted input, so by design they only ever go into the page as plain text, never as HTML.
Your shell history is private. The commands you type contain paths, project names, and sometimes secrets. A shell hook that sent them to a cloud API would be a bad idea. Revenant works with the Wi-Fi off, and the Séance Room page says so on screen.
Free inference changes the design. The hook fires on every failed command, and the retry-until-valid loop can generate three times per whisper. On a metered API that would be a bill and a rate limit. Here a generation costs about 0.6 to 1.2 seconds of my own GPU. The validate-and-retry approach only makes sense because retries are free.
I could pick a model per character. Because the weights are open, I benchmarked two sizes and routed each spirit to the one that worked, instead of taking whatever one model a service gives me.
The Ofrenda reads my project history. Repo names and commit messages stay on the machine that holds the repos. The epitaph is written by the same laptop.
It can't be switched off. This week's theme is honoring the past. A tool built on a hosted model dies when the service does. Open weights and a local runtime keep working after the vendor is gone, which seemed right for a project about things that outlived their eulogies.
Where a closed model would have done better: persona quality. The 3B model drifts into advice, and even 7B needs the retry loop. A frontier model would probably nail the voice on the first try. For this project, I took a slightly worse ghost that is private, free and offline.
One of the first whispers I got after installing the hook was about the command I used to summon it. The Fortran spirit looked at my python3 ghost.py and said:
Python dares to summon such spirits. Fortran would have used punched cards for this.
Another time, a mistyped tar command got this from Perl: "Path is wrong, or permissions fail. tar -f help?"
Every project in that altar started with someone excited to type git init. If one of yours is on the altar, light a candle for it.
Best Use of Render: The Ofrenda demo page is hosted as a Render static site at https://revenant-ofrenda.onrender.com. To be upfront about the scope: Render only serves the finished page. The models run locally through Ollama, and the Séance Room can't be hosted because it needs a local model.