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α - Extracting Alpha
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α - Extracting Alpha
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Achievement: Starstruck

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Achievement: Starstruck

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@gracemann365

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davidgracemann/README.md

David Grace

> [ Vision ] " I can't guarantee you a win — but I can engineer systems that make failure mathematically expensive, bounded, and observable. "

> [ Chief Engineer ] @ Graceman

> [ Research in Computer & Systems Engineering ] @ Technische Universität Ilmenau

[ Research & Engineering Portfolio ]

priority domain_tag domain_name domain_fields domain_role
01 FAiR Foundational AI Research Multi-agent architectures, reasoning systems, cognitive frameworks [CORE]
02 AAi Applied AI End-to-end implementations, empirical evaluation, local inference [CORE]
03 TMC Theoretical Math & Computing Ring-fenced for MSc research at TU Ilmenau [ACADEMICS]
04 Q HPC & Numerical Methods CUDA, training infrastructure, low-latency compute [MULTIPLIER]
05 SE Systems Engineering Linux internals, edge-node networking, IaaC, distributed systems [MULTIPLIER]
dynamic CC Competitive Coding Codeforces · Leetcode [SHARPNESS]
dynamic FLOSS Open Source Contributions to AI tooling and inference projects [COMMUNITY]

[ Domain Architecture ]

TMC  ──────────────────────────────► Mathematical foundation
SE + Q ────────────────────────────► Infrastructure & compute depth
CC ────────────────────────────────► Algorithmic precision
AAi ───────────────────────────────► Empirical validation
                                              │
                                              ▼
FAiR ──────────────────────────────► Research output & public record

[ Domain Specification ]

/ Layer 01 — Core Research (FAiR | AAi)

Primary allocation. All other layers serve this.

DOMAIN WORK DESCRIPTION DELIVERABLES
FAiR Multi-agent coordination protocols, self-verifying architectures, theoretical reasoning frameworks. Published research and documented experimental systems.
AAi API orchestration, local inference pipelines, empirical proof-of-concept implementations. Validated prototypes and reproducible evaluation results.

/ Layer 02 — Research Infrastructure (TMC | Q | SE)

Depth layers. Built once, compound indefinitely.

DOMAIN WORK DESCRIPTION DELIVERABLES
TMC Mathematical frameworks, combinatorics, optimization theory, systems theory. Exclusive: MSc deliverables at TU Ilmenau.
Q CUDA kernel work, numerical stability in training, performance-first infrastructure. Low-overhead compute environments for research workloads.
SE Node orchestration, kernel configuration, Wayland environments, distributed edge compute. Research infrastructure that does not become the bottleneck.

/ Layer 03 — Maintenance (CC | FLOSS)

Low allocation. High long-term return.

DOMAIN WORK DESCRIPTION DELIVERABLES
CC Codeforces and Leetcode — implementation precision and algorithmic reasoning. Sustained problem-solving sharpness.
FLOSS Contributions to AI research tooling, inference engines, and agentic frameworks. External visibility and upstream project engagement.

[ Connect ]

Pinned Loading

  1. statma statma Public

    stat-my-agent ; benchmark consistency, tool-use, failure-recovery and goal-faithfulness — locally reproducible & shareable

    Python 1

  2. wukong wukong Public

    Memory Efficient Coding Agent ( MECA ) vram < 8GiB wukong is the first MECA-class implementation. Primary target: 4–8GB VRAM via ollama.

    1

  3. samaltmanisanerd samaltmanisanerd Public template

    a scale-free network of applied and foundational AI resources

    4

  4. FlossPay FlossPay Public template

    The Linux Kernel Of Payments Infrastructures ; Enterprise Payments Aggregator for immutable reliability and forensic auditability. Rooted in Linux principles Of Community . No vendor lock-in—govern…

    Java 33 6

  5. Flossx83 Flossx83 Public template

    Card Payments Simulation Tool For Indie Devs : Core Card Switch Engine, Fraud Engine, ATM/POS GUI Simulator , Admin Dash (Real-time MSG Tracer). PCI-Safe , HSM Tokenization

    Python 20 1

  6. reconengine-visa reconengine-visa Public

    Reconciliation Engine : Visa Vs Bank General Ledger ; SLA : 10k Txn / 30 min

    Java 2