Freelancers in NashvilleFreelancers in Nashville
Kajabi Expert | CURRENTLY ON MATERNITY LEAVE
$10k+
Earned
6x
Hired
4.9
Rating
98
Followers
Kajabi Expert | CURRENTLY ON MATERNITY LEAVE
I solve complex problems in ways that look and feel good.
$10k+
Earned
1x
Hired
5.0
Rating
5
Followers
I solve complex problems in ways that look and feel good.
Brand & UI/UX Designer for SaaS, Web3 & E-commerce
$1k+
Earned
3x
Hired
5.0
Rating
10
Followers
Brand & UI/UX Designer for SaaS, Web3 & E-commerce
Bringing your vision to life 🎨
1x
Hired
5.0
Rating
2
Followers
Bringing your vision to life 🎨
Balancing creativity & entrepreneurship πŸš€
$10k+
Earned
13x
Hired
4.7
Rating
41
Followers
Balancing creativity & entrepreneurship πŸš€
Local-first AI engineer β€’ ex-Amazon finance β€’ Real products
New to Contra
Local-first AI engineer β€’ ex-Amazon finance β€’ Real products
Cover image for Local-first AI isn't a niche
Local-first AI isn't a niche anymore. It's how I build everything. Here's the full system architecture behind Aurora, my glass-box quantitative intelligence engine. 82 commits, 599 tests passing, 24+ analytical methods. No cloud. No API keys. No telemetry. Every piece runs on your hardware. Let me walk through what's actually happening in this diagram. The Substrate Layer Aurora isn't a wrapper around an LLM. The core engine is a multi-stage pipeline: your data hits a preflight layer (schema validation, missingness detection, irregular-sampling checks) before any analysis runs. If the data has problems, you know before a single method fires. 24+ Research-Grade Methods, Not Vibes The analytical engine runs Isolation Forest, Hampel z-scores, HMM Baum-Welch, Granger causality, persistent homology, SINDy (sparse identification of nonlinear dynamics), Gaussian processes, mutual information, VAR, DTW, BOCPD, Robust PCA, EMD, Kalman filtering, spectral entropy, and more. Each method either produces a cited finding or explicitly reports why it skipped (no time axis, negative values, cross-sectional data). No silent failures. Ever. The Glass-Box Contract Every finding is a typed, structured object: method, severity, threshold, evidence, citation. When Aurora says +448.6Οƒ with p < 0E+0, that's a Hampel z-score on a specific row you can re-run yourself. The 0 fabricated chip isn't marketing. It's a contractual counter audited live on every run. Knowledge-Grounded Synthesis The "What This Means" narrative cites seed:* entries from a local knowledge bank. Newton (1701), Pierson & Moskowitz (1964), Torrence & Compo (1998), NIST, NOAA NDBC. Real papers. Real institutions. No invented citations. Two Surfaces, One Engine Aurora Copilot is the human-facing studio: six analytical lenses (Overview, Anomalies, Regimes, Motifs, Forecast, Physics), spacetime worldlines, phase-space projections, causal do-calculus. Aurora Cortex is the machine-facing API: an MCP server (7 tools, path-allowlisted, 2MB output cap), a Python SDK, Decision Contracts that fire webhooks to Slack/Discord/email when findings match programmable predicates, and streaming mode with Kafka + Postgres CDC connectors. Fully Local, Fully Packaged The desktop app (Tauri 2 shell + PyInstaller-bundled backend) is a double-click install. No Python, no venv, no terminal. Drop a CSV on the window and watch it analyze. The Docker path is one command: docker compose up. BYO-LLM with 5 pluggable backends (Ollama, Anthropic, OpenAI, Gemini, OpenAI-compatible), or run with no LLM at all. Aurora computes. It doesn't need to guess. I built this because I spent years in Amazon finance watching decisions get made on black-box outputs nobody could trace. Cloud LLMs guess. Aurora computes. That's the whole thesis. Open source. Apache 2.0. 100% free. What's stopping you from running your AI stack locally?
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