Research Automation
Systems across UI, structure, and context.
🌬️ Wisp
Context-aware, zero dependency UI engine for the modern web.
🧵 Weave
Lightweight web weaving framework.
Reusable modular component framework.
🧠 ContextFusion
Context composition and fusion toolkit for LLMS and AI Agents.
Lightweight context orchestration system for modular architectures.
⚡ CFAdv
Context compiler for LLMs.
Scores, selects and reorders context under a token budget using attention-based fusion.
🧩 ContextFission
ContextFission make context small, useful, budget-safe for LLM. ContextFission is context compiler for LLMs. Built on ContextFusion & CFAdv.
🧠 Hypercontext
Hypercontext is a standalone Python + TypeScript SDK for building AI agents that are aware of their own context...
🛡️ envguard
macOS-first Python environment orchestration: preflight validation, dependency resolution, lock files, and safe managed execution. Catches broken envs before they break your code. macOS-first Python environment orchestration...
🧠 NoB
NoB (Noticeably Better) is a high-level, dynamically-typed programming language.
Note: NoB is proprietary software. Use of this software is governed exclusively by the EULA.
Coming soon…
🧠 notion-Health-AI
Local-first Notion health tracker with TRIBEv2 brain analysis, AI health insights, symptom logging, goals, medications, appointments, and a browser UI.
☕ pot.of
A ridiculous but lovingly built virtual coffee pot controller inspired by RFC 2324, powered by Next.js and Google Gemini.
📊 RepoPulseAI
RepoPulseAI is a GitHub App and Next.js web app for repository analysis. It reads the signals GitHub already exposes, scores project health, and turns that into something a maintainer can actually act on.
🧮 REX Framework
REX Framework
Rex enables running machine learning inference where model weights are stored remotely (Google Drive, OneDrive, S3, or any HTTP server with Range requests) and never fully downloaded to the local machine. The system fetches weight chunks on demand, caches a bounded fraction locally, and evicts aggressively to enforce the invariant that the full model never resides in local memory.
🧪 Models
PyTorch U-Net models for gray leaf spot (Magnaporthe and related fungal) colony segmentation on 90 mm petri-dish images.
DOI: 10.57967/hf/8416
📦 grayleafspotr
Self-contained gray leaf spot analysis and plotting tools for RStudio. The package runs a SmallUNet segmentation pipeline (models/best_area_w_0.7.pt) via an ARM64 Python 3.11 environment, writes raw JSON and CSV exports, and provides template ggplot2 visuals for downstream exploration.
🧫 metrics-petri
Petri dish colony segmentation and morphometric analysis.
Python package metrics-petri measures how a biological sample grows on a petri dish: area, diameter, edge roughness, crack burden, texture entropy, and time-series growth rates — all in physical units calibrated from the dish geometry.
❤️🔥 Consciousness-Indicator Architecture (CIA):
A Theory-Grounded Framework for Evaluating Consciousness-Relevant Indicators in AI Systems
CIA is a theory-grounded cognitive simulation framework for evaluating consciousness-relevant architectural indicators in AI systems. It implements computational modules derived from seven established theories of consciousness and produces structured scorecards mapping system architecture to a 0-22 indicator scale.
CIA is designed as a research tool for cognitive scientists, AI safety researchers, and philosophers investigating the structural prerequisites for consciousness in computational systems.
⚠️ Disclaimer: This system does not claim, assert, or prove that any evaluated system possesses subjective experience.
SMGP — Spectral Memory Graph Processor
SMGP is a full-stack AI research system that tackles the three hardest problems in modern LLMs: catastrophic forgetting, quadratic attention complexity, and hallucination. It replaces conventional neural memory with a persistent knowledge graph encoded in hyperdimensional vectors, performs O(N log N) attention via graph Fourier analysis, and verifies every factual claim against graph paths – making hallucination structurally impossible. The project ships a production-grade Python library (with HuggingFace and LangChain drop-in support) alongside a complete FPGA accelerator (SMGPU) in synthesisable SystemVerilog, achieving 10–100× speed-up on spectral and HD operations. 230+ tests, RTL simulations 6/6 passed, and cloud-deployable on Xilinx Alveo U280 via Chameleon testbed.
