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I run a card shop. The GPU cluster is load-bearing.

Self-hosted AI out of Oshkosh, WI — wAIve.online · Fox Valley AI Foundation

Python TypeScript vLLM Hardware Cloud


Everything here runs on hardware I can physically kick. Two threads tie it together: agents that ship finished, validated software — not snippets — and making big models cheaper to run through tokenizer surgery, expert pruning, and token-efficient context formats. Results get published either way, including the negative ones.


🔬 Model surgery & token economics

Smaller models, fewer tokens, same behavior — with the measurements to prove it.

Repo What it does
tokopt Post-hoc BPE tokenizer adaptation for deployed LLMs — script-aware pruning, continued BPE extension, embedding-only calibration. Validated end-to-end on Qwen3.5-4B; reproduces in ~3 hrs on a single RTX 3090. Ships a ~9,000-word PAPER.md and a step-by-step REPRODUCE.md. ★
research-test-Qwen3-Coder-Next-REAP-AWQ REAP expert pruning + AWQ quantization of Qwen3-Coder-Next (149 GB BF16 MoE). 512 → 410 experts per layer via 4-dataset saliency calibration with super-expert preservation, then W4A16 @ group_size 32 for consumer GPUs. ★
ctx CTX (Context Transfer Format) — an interchange format for LLM web consumption. Turns a 1.2 MB Wikipedia page into 150 KB of structure-preserving CTX: −87% bytes, ~90% fewer tokens. CLI, Python lib, and a FastAPI service with Redis caching + transparent proxy. ★

🤖 Agents that ship

Autonomy is easy. Finished is the hard part.

Repo What it does
cadillac Autonomous coding agent: a sentence in, a validated app out. 12-stage pipeline (SPEC → … → CRITIC → RUNTIME → PACKAGE) with operational gates, a completeness CRITIC, runtime flow verification, and surgical-mode stuck-loop recovery. 616 tests passing · 146 apps built · 403 lessons learned. ★
cadillac-builds The receipts: 26 runnable applications built unattended by Cadillac with zero human edits — published from a scan of ~99 build workspaces, failures acknowledged. Every project's README lists exactly which validation checks passed and which didn't. ★
workerAI Agentic workflow system on vLLM + LangGraph — a ReAct-style autonomous agent that plans, reasons, and executes multi-step tasks with a library of specialized tools. ★

Things the agents made (research artifacts, shipped as-is):

Artifact What it is
matrix-doom Matrix-themed ASCII FPS raycaster in pygame — built autonomously by a modular LLM code generator. ★
silence-of-lolth A 33,152-word, 14-chapter novel written end-to-end by an AI-authoring pipeline (AIRowling), voice-referenced on R.A. Salvatore's Dark Elf Trilogy. ★

🖥️ The cockpit

Tools for running a multi-node cluster from one chair.

Repo What it does
clusterspace Tiled desktop workspace (Electron + React + TS) for terminals, embedded Chromium, and SSH auto-wrapped in tmux — sessions survive everything. Optional AI co-pilot gets first-class tools to read, type into, and drive any pane toward a goal. ★
model-chat-cli Terminal command center for local AI servers. Auto-discovers Ollama, LM Studio, and vLLM on your network; chat with real TTFT/decode metrics, blind multi-model battles with auto-judged tournaments, load tests, and a 45-task agentic benchmark across 6 difficulty tiers. ★
cliide The AI-native terminal IDE — file tree, editor, and an agent with tool execution in one TUI. Built AI-first rather than AI-bolted-on. On PyPI. ★

🧰 Shop floor & one-offs

Repo What it does
cardboard Full-stack social platform for sports card collectors — share, buy, sell, trade. The day job and the code base, converging. ★
cblchat Real-time enterprise chat with LDAP / Active Directory auth. ★
koding CodeQuest — gamified, visual Python learning platform for kids 9+. ★
Cbl Native iOS WebView wrapper for a WordPress storefront (Swift). ★

📏 Measured, not vibed

Numbers pulled from the repos, on my own hardware:

Result Where
−4.9% bits/char and −4.2% tokens on held-out code, HumanEval within noise, greedy generation byte-identical tokopt
−18% INT4 model size (4.4 GB → 3.6 GB) after tokenizer surgery + calibration tokopt
20% of MoE experts removed (512 → 410/layer) from a 149 GB coder model, then W4A16 REAP-AWQ
−87% bytes / ~90% fewer tokens on real web pages ctx
146 applications built autonomously; 26 published unedited with per-check validation status cadillac · builds

🧾 House rules

  • Reproduce or it didn't happen. Research repos ship a REPRODUCE.md you can actually follow, not a citation to a vibe.
  • Negative results get published. tokopt's paper includes the method that didn't beat the baseline (hierarchical merge-tree init) right next to the ones that did.
  • Validation status is public. cadillac-builds lists every failed check per project. No project is dressed up to look better than it is.
  • Runs on hardware you can own. Everything above was built and validated on consumer GPUs — no cloud dependency, no API key required to reproduce.

wAIve.online — self-hosted AI platform · Fox Valley AI Foundation — open tooling for everyone else

Oshkosh, Wisconsin. Yes, really.

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