Project library

Full project depth, proof surfaces, and clear professional outcomes.

A deeper project library with case studies, artifact summaries, and outcome-focused proof across BI infrastructure, financial-data visibility, analytics automation, and exploratory market-data modeling with SQLite, Streamlit, and scikit-learn.

Browse projectsOpen web CVContact

Major projects

9

Equal-weight case studies spanning BI infrastructure, AI orchestration, financial-data visibility, predictive analytics, NLP, regression, and database design, with the market-data prototype clearly separated from the core Avnet BI accomplishments.

Proof policy

Case studies summarize public-safe artifacts, project outcomes, and implementation patterns without exposing private operating details or source materials.

Project library

Deeper project context across systems, analytics, automation, and financial-data work.

Each project is organized around the problem, method, result, evidence boundary, and professional outcome so the work can be reviewed without exposing private runtime details.

Power BI + stakeholder adoption

Command Center BI Infrastructure

Live
sanitized
High confidence

Built scalable Power BI reporting systems that turned fragmented sales data into decision-ready operating visibility.

Headline outcome

Reporting cadence scaled beyond manual capacity

Power BIDAXPower QueryVBADatabricksStakeholder Discovery

Impact

20+ hours/week removed from recurring reporting and follow-up effort.

Proof path

  • • Sanitized BI infrastructure brief
  • • Request-only dashboard walkthrough
  • • Reporting template and automation notes

Problem

Stakeholders needed cleaner opportunity visibility, KPI logic, recurring reporting, and adoption tracking inside a growing internal analytics hub.

Method

Used Power BI, DAX, Power Query (M), VBA automation, stakeholder discovery, QA, and documentation to translate messy asks into durable reporting logic.

Result

Delivered a reusable reporting backbone for executive visibility, self-serve decision support, and cleaner adoption tracking.

Open project detailReview sanitized brief

AI workflows + durable context

Gemini/Codex Workflow Automation

In Progress
public
High confidence

Built a file-driven AI workflow system that keeps Gemini CLI and Codex CLI aligned through durable context, reconciliation, and validation gates.

Headline outcome

Multi-agent AI work stopped depending on chat memory

ShellPythonGemini CLICodex CLIMCPn8n

Impact

Working system with 100 scripts, 34 docs, and 58 research artifacts in the read-only source repo.

Proof path

  • • Sanitized workflow brief
  • • Public docs repository
  • • Architecture and runbook excerpts

Problem

Long-running AI coding and business-automation workflows break when context, handoff, permissions, and validation live only in chat memory.

Method

Designed a constitution-first automation environment around declarative intent, a reconciliation loop, blackboard coordination, Gemini/Codex bridge scripts, MCP and n8n lanes, and deterministic close-out gates.

Result

Created a working orchestration environment that treats AI collaboration like an operational workflow system instead of a one-off prompt chain.

Open project detailOpen proof briefOpen docs repo

Adidas IT · Gradient Boosting

Ticket Reassignment Prediction

Completed
sanitized
High confidence

Modeled IT ticket reassignment behavior to reduce escalation friction, rerouting cost, and downtime risk.

Headline outcome

Preventable routing friction became a measurable prediction target

Pythonscikit-learnFeature EngineeringModel Evaluation

Impact

Approximately 76.1% accuracy, 85.8% recall, 73.3% F1, and about $277K in annual labor savings, with additional downtime savings cited in the project materials.

Proof path

  • • Sanitized methodology page
  • • Downloadable presentation deck
  • • Model evaluation summary

Problem

IT support workflows were losing time and retail uptime through preventable ticket reassignments.

Method

Built a Gradient Boosting classifier around urgency, service, category, and geography variables, then evaluated it with accuracy, recall, precision, and F1.

Result

Produced a predictive decision aid and business-facing routing recommendation that could flag likely reassignments earlier in the ticket flow.

Open project detailOpen methodology page

Regression + cross-validation

Spotify Popularity Prediction

Completed
public
Medium confidence

Used audio-feature data to model song popularity and translate statistical results into business-readable recommendations.

Headline outcome

Turned audio features into business-readable popularity signals

PythonRegressionCross ValidationFeature AnalysisEDA

Impact

Statistically significant feature analysis and model interpretation, positioned as an interpretable analytics case study rather than a hard business-impact claim.

Proof path

  • • Recorded walkthrough
  • • Sanitized regression brief

Problem

Needed to understand which measurable audio features aligned with song popularity rather than relying on intuition.

Method

Applied regression, correlation analysis, exploratory data analysis, outlier handling, and cross-validation-oriented evaluation to a Spotify feature dataset.

Result

Produced an interpretable modeling case study that showed which features mattered most and how to communicate them without overstating prediction.

Open project detailOpen regression brief

TF-IDF + logistic regression

Yelp Review Rating / Sentiment Modeling

Completed
public
Medium confidence

Built a text-mining workflow to classify Yelp review sentiment and surface operational signals from customer language.

Headline outcome

Customer review text became a usable service-improvement signal

PythonTF-IDFLogistic RegressionVADERFLAIR

Impact

High-accuracy sentiment classification paired with operational feedback a manager could act on, as documented in the final project materials.

Proof path

  • • Recorded walkthrough
  • • Downloadable presentation deck
  • • Methodology report

Problem

Needed to turn large volumes of review text into structured signals for customer sentiment and service issues.

Method

Applied text preprocessing, TF-IDF vectorization, VADER and FLAIR sentiment labeling, and logistic regression classification with standard evaluation metrics.

Result

Generated a model and business readout that translated review language into repeatable service-improvement signals.

Open project detailOpen NLP brief

Multivariable regression

TJIX Net Sales Drivers

Completed
public
High confidence

Linked advertising, e-commerce growth, and market trends to a business-readable net-sales growth story for TJX.

Headline outcome

Advertising and e-commerce were tied directly to net-sales upside

RegressionCorrelation AnalysisExcelDecision TreesScenario Modeling

Impact

Final materials attribute roughly $12.7M in net-sales lift per additional $1M in advertising spend.

Proof path

  • • Live methodology page
  • • Downloadable final report
  • • Downloadable workbook

Problem

Needed to explain which levers most credibly drive TJX net sales growth and where e-commerce and advertising investment mattered.

Method

Used regression, correlation analysis, scenario modeling, Excel, and competitor comparison to tie advertising and e-commerce growth to net-sales outcomes.

Result

Produced a business-facing recommendation for deeper e-commerce and advertising investment backed by regression and benchmark analysis.

Open project detailOpen report bundle

Access + SQL queries

Relational Database Design

Completed
public
High confidence

Designed a relational data model to replace spreadsheet-driven project and staffing tracking for a growing IT support business.

Headline outcome

Operations data became structured, relational, and report-ready

Microsoft AccessSQLERD DesignNormalizationMetadata Design

Impact

Replaced spreadsheet thinking with a structured data model built for queryability, reporting consistency, and future operational growth.

Proof path

  • • Live methodology page
  • • Downloadable ERD PDF

Problem

Spreadsheet-based staffing and project tracking was causing inconsistency, limited analysis, and poor scalability.

Method

Designed normalized entities, bridge tables, metadata rules, and query patterns for projects, employees, clients, assets, tickets, and services.

Result

Produced a relational structure built for cleaner reporting, resource visibility, and future operational growth.

Open project detailOpen database brief

Python + SQLite + Streamlit

Institutional Market Data Engine

In Progress
public
High confidence

Private Python/SQLite/Streamlit workspace for market-data ingestion, local storage, dashboard review, risk summaries, and validation-oriented market-data experiments.

Headline outcome

Market-data experiments became governed analytics workflows

PythonSQLiteStreamlitPandasRisk MetricsValidation

Impact

Shows practical financial-data engineering, dashboarding, source governance, and validation discipline without exposing live credentials, token files, private databases, broker/account data, or production quant runtime state.

Proof path

  • • Institutional Market Data Engine Docs
  • • Market Data Dashboard Docs
  • • QuantStrat ML Docs
  • • Sanitized portfolio proof brief

Problem

Market-data and quant-adjacent projects need repeatable ingestion, clear source boundaries, validation notes, and conservative public framing so dashboard prototypes are not mistaken for trading systems.

Method

Organized Python ingestion, duplicate-aware SQLite storage, Streamlit/Plotly review views, risk and volatility summaries, source-state notes, and explicit public/private boundaries around QuantStrat ML and production-only runtime state.

Result

Created a public-safe market-data ecosystem story: the institutional engine explains ingestion and dashboarding, related docs shells cover dashboard governance and QuantStrat ML research, and production/runtime assets remain private.

Open project detailOpen docs repo

Plaid API + Apps Script + Gemini

Local-First Financial Intelligence

In Progress
public
High confidence

Private Python/Google Apps Script finance-automation project with Plaid sync, spreadsheet data modeling, and a local Gemini sidebar for grounded financial questions.

Headline outcome

Private financial records became a local-first analytics workflow

PythonGoogle Apps ScriptGemini APIPlaid APIOAuthData Engineering

Impact

Shows practical privacy judgment, fintech integration skill, and multi-system automation across Python, Plaid, Google Sheets, Apps Script, charts, and bounded AI assistance.

Proof path

  • • Public docs shell architecture
  • • Synthetic sample fixtures
  • • Sanitized Python and Apps Script excerpts
  • • Portfolio proof brief

Problem

Cloud-based finance apps can require broad access to raw transaction history, while spreadsheet-only workflows often lack repeatable sync, clean category modeling, and grounded AI question-answering.

Method

Kept implementation and live financial data private while documenting a public docs-shell pattern around Plaid OAuth, Python sync logic, Google Sheets modeling, Apps Script dashboards, deterministic summaries, synthetic fixtures, and Gemini context boundaries.

Result

Created a private local finance assistant workflow that syncs transactions into a spreadsheet-native model and answers review questions without publishing credentials, account-specific data, or deployment IDs.

Open project detailOpen docs repo
Impact lab

Business leverage deserves its own space.

This is where the quantified story lives: modeled savings, automation compression, and clear professional signal around what the work can do for a team when it moves from manual effort into repeatable systems.

Impact Analysis Dashboard

Professional proof of scale, business value, and technical range.

live signal

IT Routing Efficiency

Estimated annual savings

$277,000

Python, Gradient Boosting

Manual Reporting Automation

Value of reclaimed analyst time

$52,000

VBA, Power Query, DAX

Sales Driver Identification

Revenue signal surfaced

$12,700,000

Regression, stakeholder analysis

The ROI of Automation

Based on my work at Avnet (converting a 5-hour manual process to a 30-minute script), calculate how much I could save your team.

10 hrs
$50/hr

Time Reclaimed (Yearly)

442 hrs

Estimated Cost Savings (Yearly)

$22,100

Translation layer

Raw business signal scanned into professionally packaged proof.

This strip turns the actual shape of the work into a quick professional read: messy asks, KPI logic, and workflow notes crossing a scanner line and resolving into proof assets.

Raw signal
Scroll to scan
Proof asset
Command Center BI proof brief
Power BI / KPI translation

Executive visibility surface

Command Center BI proof brief

Fragmented opportunity reporting becomes a professionally packaged walkthrough of KPI logic, adoption flow, and operating visibility.

Review sanitized brief
Gemini/Codex workflow proof brief
Durable context / AI orchestration

Public systems proof

Gemini/Codex workflow proof brief

Bridge scripts, blackboard coordination, and reconciliation logic resolve into a public-safe proof brief with a live docs-repo handoff.

Open proof brief
Agent orchestration contractSCAN-2026-PROOF-1002
// PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1002 // DECLARE d
esired_state = INTENT.md // LEASE blackboard.owner = a
tomic_file_lock // ROUTE bridge = gemini_cli <-> codex
_cli // CLOSEOUT = sync + check + archive + push // OU
TPUT.surface = Public systems proof // OUTPUT.story = 
Gemini/Codex workflow proof brief // // PORTFOLIO_SIGN
AL: SCAN-2026-PROOF-1002 // DECLARE desired_state = IN
TENT.md // LEASE blackboard.owner = atomic_file_lock /
/ ROUTE bridge = gemini_cli <-> codex_cli // CLOSEOUT 
= sync + check + archive + push // OUTPUT.surface = Pu
blic systems proof // OUTPUT.story = Gemini/Codex work
flow proof brief // // PORTFOLIO_SIGNAL: SCAN-2026-PRO
OF-1002 // DECLARE desired_state = INTENT.md // LEASE 
blackboard.owner = atomic_file_lock // ROUTE bridge = 
gemini_cli <-> codex_cli // CLOSEOUT = sync + check + 
archive + push // OUTPUT.surface = Public systems proo
f // OUTPUT.story = Gemini/Codex workflow proof brief 
// // PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1002 // DECLAR
TJIX report and workbook bundle
Regression / downloadable proof

Download-ready bundle

TJIX report and workbook bundle

Regression notes, scenario framing, and workbook logic resolve into a downloadable proof bundle that keeps the business story intact.

Open report bundle
Commercial model handoffSCAN-2026-PROOF-1003
// PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1003 // SOURCE = 
ad_spend, ecommerce_penetration, competitor_growth // 
FIT regression = revenue_signal_story // EXPORT report
 = public_safe_pdf // EXPORT workbook = scenario_model
_bundle // OUTPUT.surface = Download-ready bundle // O
UTPUT.story = TJIX report and workbook bundle // // PO
RTFOLIO_SIGNAL: SCAN-2026-PROOF-1003 // SOURCE = ad_sp
end, ecommerce_penetration, competitor_growth // FIT r
egression = revenue_signal_story // EXPORT report = pu
blic_safe_pdf // EXPORT workbook = scenario_model_bund
le // OUTPUT.surface = Download-ready bundle // OUTPUT
.story = TJIX report and workbook bundle // // PORTFOL
IO_SIGNAL: SCAN-2026-PROOF-1003 // SOURCE = ad_spend, 
ecommerce_penetration, competitor_growth // FIT regres
sion = revenue_signal_story // EXPORT report = public_
safe_pdf // EXPORT workbook = scenario_model_bundle //
 OUTPUT.surface = Download-ready bundle // OUTPUT.stor
y = TJIX report and workbook bundle // // PORTFOLIO_SI
Ticket routing methodology brief
Predictive analytics proof

Business-readable methodology

Ticket routing methodology brief

Model notes, evaluation metrics, and business framing resolve into a concise predictive-analytics proof surface with a live deck handoff.

Open methodology page
Analysis notesSCAN-2026-PROOF-1004
// PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1004 // MODEL = g
radient_boosting_classifier // METRICS = accuracy, rec
all, f1_score // QA = validation_split + stakeholder n
arrative alignment // DELIVERABLE = one_to_two_page_me
thodology_brief // OUTPUT.surface = Business-readable 
methodology // OUTPUT.story = Ticket routing methodolo
gy brief // // PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1004 
// MODEL = gradient_boosting_classifier // METRICS = a
ccuracy, recall, f1_score // QA = validation_split + s
takeholder narrative alignment // DELIVERABLE = one_to
_two_page_methodology_brief // OUTPUT.surface = Busine
ss-readable methodology // OUTPUT.story = Ticket routi
ng methodology brief // // PORTFOLIO_SIGNAL: SCAN-2026
-PROOF-1004 // MODEL = gradient_boosting_classifier //
 METRICS = accuracy, recall, f1_score // QA = validati
on_split + stakeholder narrative alignment // DELIVERA
BLE = one_to_two_page_methodology_brief // OUTPUT.surf
ace = Business-readable methodology // OUTPUT.story =
Spotify regression brief
Regression / feature analysis

Interpretable modeling walkthrough

Spotify regression brief

EDA, feature relationships, and regression evaluation resolve into a concise walkthrough of what the model can and cannot claim.

Open regression brief
Modeling notebookSCAN-2026-PROOF-1005
// PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1005 // FEATURES 
= acousticness, energy, danceability, tempo, valence /
/ TARGET = popularity_score // EVALUATE = regression_f
it + cross_validation_notes // DELIVERABLE = recorded_
walkthrough + model_interpretation // OUTPUT.surface =
 Interpretable modeling walkthrough // OUTPUT.story = 
Spotify regression brief // // PORTFOLIO_SIGNAL: SCAN-
2026-PROOF-1005 // FEATURES = acousticness, energy, da
nceability, tempo, valence // TARGET = popularity_scor
e // EVALUATE = regression_fit + cross_validation_note
s // DELIVERABLE = recorded_walkthrough + model_interp
retation // OUTPUT.surface = Interpretable modeling wa
lkthrough // OUTPUT.story = Spotify regression brief /
/ // PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1005 // FEATURE
S = acousticness, energy, danceability, tempo, valence
 // TARGET = popularity_score // EVALUATE = regression
_fit + cross_validation_notes // DELIVERABLE = recorde
d_walkthrough + model_interpretation // OUTPUT.surface
Yelp sentiment modeling brief
NLP / classification

Customer-signal workflow

Yelp sentiment modeling brief

Unstructured review language resolves into repeatable sentiment features, classification logic, and service-improvement signal framing.

Open NLP brief
Review text pipelineSCAN-2026-PROOF-1006
// PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1006 // INGEST = 
review_text + star_rating // TRANSFORM = clean_text + 
tfidf_vectorizer // LABEL = vader_sentiment + flair_si
gnal // MODEL = logistic_regression_classifier // OUTP
UT.surface = Customer-signal workflow // OUTPUT.story 
= Yelp sentiment modeling brief // // PORTFOLIO_SIGNAL
: SCAN-2026-PROOF-1006 // INGEST = review_text + star_
rating // TRANSFORM = clean_text + tfidf_vectorizer //
 LABEL = vader_sentiment + flair_signal // MODEL = log
istic_regression_classifier // OUTPUT.surface = Custom
er-signal workflow // OUTPUT.story = Yelp sentiment mo
deling brief // // PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1
006 // INGEST = review_text + star_rating // TRANSFORM
 = clean_text + tfidf_vectorizer // LABEL = vader_sent
iment + flair_signal // MODEL = logistic_regression_cl
assifier // OUTPUT.surface = Customer-signal workflow 
// OUTPUT.story = Yelp sentiment modeling brief // // 
PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1006 // INGEST = rev
Relational database design brief
SQL / data modeling

Structured data model

Relational database design brief

Spreadsheet-style tracking logic resolves into normalized entities, relationship design, and query-ready operational structure.

Open database brief
Spreadsheet-to-schema notesSCAN-2026-PROOF-1007
// PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1007 // ENTITIES 
= clients, employees, projects, assets, tickets // NOR
MALIZE = remove_duplicate_fields + define_bridge_table
s // QUERY_PATH = status, owner, service, asset_histor
y // OUTPUT = ERD + relational_design_walkthrough // O
UTPUT.surface = Structured data model // OUTPUT.story 
= Relational database design brief // // PORTFOLIO_SIG
NAL: SCAN-2026-PROOF-1007 // ENTITIES = clients, emplo
yees, projects, assets, tickets // NORMALIZE = remove_
duplicate_fields + define_bridge_tables // QUERY_PATH 
= status, owner, service, asset_history // OUTPUT = ER
D + relational_design_walkthrough // OUTPUT.surface = 
Structured data model // OUTPUT.story = Relational dat
abase design brief // // PORTFOLIO_SIGNAL: SCAN-2026-P
ROOF-1007 // ENTITIES = clients, employees, projects, 
assets, tickets // NORMALIZE = remove_duplicate_fields
 + define_bridge_tables // QUERY_PATH = status, owner,
 service, asset_history // OUTPUT = ERD + relational_d
Institutional Market Data Engine Docs
Python / SQLite / validation

Market-data architecture shell

Institutional Market Data Engine Docs

Private ingestion, SQLite storage, dashboard review, and validation notes resolve into a public-safe market-data engineering narrative.

Open docs repo
Market-data runtime boundarySCAN-2026-PROOF-1008
// PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1008 // SOURCE = 
authorized_market_data + public_safe_examples // STORE
 = sqlite_snapshots + derived_review_tables // REVIEW 
= streamlit_dashboard + risk_summary // BOUNDARY = no_
tokens_no_accounts_no_runtime_state // OUTPUT.surface 
= Market-data architecture shell // OUTPUT.story = Ins
titutional Market Data Engine Docs // // PORTFOLIO_SIG
NAL: SCAN-2026-PROOF-1008 // SOURCE = authorized_marke
t_data + public_safe_examples // STORE = sqlite_snapsh
ots + derived_review_tables // REVIEW = streamlit_dash
board + risk_summary // BOUNDARY = no_tokens_no_accoun
ts_no_runtime_state // OUTPUT.surface = Market-data ar
chitecture shell // OUTPUT.story = Institutional Marke
t Data Engine Docs // // PORTFOLIO_SIGNAL: SCAN-2026-P
ROOF-1008 // SOURCE = authorized_market_data + public_
safe_examples // STORE = sqlite_snapshots + derived_re
view_tables // REVIEW = streamlit_dashboard + risk_sum
mary // BOUNDARY = no_tokens_no_accounts_no_runtime_st
Personal Finance Automation Docs
Plaid / Sheets / Gemini

Privacy-aware automation docs

Local-First Financial Intelligence

Sensitive transaction automation resolves into synthetic examples, bounded AI context, and a clean local-first finance workflow explanation.

Open docs repo
Private finance automation boundarySCAN-2026-PROOF-1009
// PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1009 // INGEST = 
plaid_transactions_private_only // MODEL = sheets_tabl
es + category_rollups // ASSIST = gemini_sidebar_with_
bounded_context // PUBLIC_LAYER = synthetic_fixtures +
 privacy_notes // OUTPUT.surface = Privacy-aware autom
ation docs // OUTPUT.story = Local-First Financial Int
elligence // // PORTFOLIO_SIGNAL: SCAN-2026-PROOF-1009
 // INGEST = plaid_transactions_private_only // MODEL 
= sheets_tables + category_rollups // ASSIST = gemini_
sidebar_with_bounded_context // PUBLIC_LAYER = synthet
ic_fixtures + privacy_notes // OUTPUT.surface = Privac
y-aware automation docs // OUTPUT.story = Local-First 
Financial Intelligence // // PORTFOLIO_SIGNAL: SCAN-20
26-PROOF-1009 // INGEST = plaid_transactions_private_o
nly // MODEL = sheets_tables + category_rollups // ASS
IST = gemini_sidebar_with_bounded_context // PUBLIC_LA
YER = synthetic_fixtures + privacy_notes // OUTPUT.sur
face = Privacy-aware automation docs // OUTPUT.story =
Stakeholder asks, KPI definitions, SQL, DAX, and workflow notes feed the left side.
The center scan line visualizes the strongest through-line: translation under ambiguity.
The right side resolves into fast professional proof assets already wired into the artifact vault.
Artifact vault

Proof surfaces, downloads, and public-safe artifacts.

This vault mixes live proof pages, downloadable artifacts, and request-only professional walkthroughs. The most publishable project proof leads, and anything still planned stays clearly labeled as planned.

Command Center BI proof brief
Request-only proof
Request-only
sanitized web brief

dashboard

Command Center BI proof brief

Projects · Translation layer · Artifact vault · Assistant

Sanitized public-safe proof page for the Command Center BI work, including the problem, reporting method, measurable outcome, and the request-only path for a deeper walkthrough.

Live dashboards remain private, but this brief documents the BI infrastructure and the public-safe disclosure path.
Review sanitized brief
Gemini/Codex workflow proof brief
Live proof
Live proof
sanitized web brief

pdf

Gemini/Codex workflow proof brief

Projects · Artifact vault · Assistant

Public-safe brief covering durable context, reconciliation, bridge scripts, and validation gates in the Gemini/Codex workflow system.

Pairs a sanitized web brief with direct links to the public docs repo and architecture receipts.
Open proof brief
Ticket routing methodology brief
Live proof
Live proof
methodology page + deck

pdf

Ticket routing methodology brief

Projects · Artifact vault · Assistant

Sanitized project page for the Adidas ticket reassignment model, with the downloadable deck and business-facing methodology path.

The live proof surface is the methodology page and downloadable presentation deck.
Open methodology page
TJIX report and workbook bundle
Live proof
Live proof
methodology page + downloads

template

TJIX report and workbook bundle

Projects · Artifact vault · Assistant

Live proof bundle for the TJIX net-sales analysis, including the methodology page, native report, and supporting workbook.

This proof surface includes both the commercial regression writeup and the supporting workbook.
Open report bundle
Relational database design brief
Live proof
Live proof
methodology page + PDF

pdf

Relational database design brief

Projects · Artifact vault · Assistant

Project page covering the normalized database design, ERD logic, and the public-safe PDF walkthrough.

The live proof surface includes the methodology page and the downloadable ERD PDF.
Open database brief
Yelp sentiment modeling brief
Live proof
Live proof
methodology page + walkthrough

video

Yelp sentiment modeling brief

Projects · Artifact vault · Assistant

Project page for the NLP classification workflow, with the recorded walkthrough plus downloadable deck and report.

The strongest public proof here is the recorded walkthrough paired with the downloadable project materials.
Open NLP brief
Spotify regression brief
Live proof
Live proof
methodology page + walkthrough

video

Spotify regression brief

Projects · Artifact vault · Assistant

Sanitized project page for the Spotify regression case study, focused on interpretable modeling and the recorded walkthrough.

The live proof surface is the recorded walkthrough. The full final report is not yet published publicly.
Open regression brief
Gemini/Codex workflow docs repository
Live repo
Live proof
public docs repo

repo

Gemini/Codex workflow docs repository

Projects · Artifact vault · Assistant

Live public documentation layer for the Gemini/Codex automation system, including architecture notes, runbooks, and readiness receipts.

Public-safe companion repo for a much larger local automation workspace and CLI orchestration system.
Open docs repo
Institutional Market Data Engine Docs
Live repo
Live proof
public docs repo

repo

Institutional Market Data Engine Docs

Projects · Artifact vault · Assistant

Public documentation shell for the private market-data ingestion, SQLite storage, dashboarding, and validation workspace.

Public-safe companion repo for the market-data engine; QuantStrat ML and market-dashboard docs stay linked as related proof shells while source, credentials, databases, and runtime automation remain private.
Open docs repo
Personal Finance Automation Docs
Live repo
Live proof
public docs repo

repo

Personal Finance Automation Docs

Projects · Artifact vault · Assistant

Public documentation shell demonstrating a local-first financial intelligence engine with Plaid and Gemini integration.

Public documentation repository showcasing the privacy boundary, synthetic sample data, and sanitized Apps Script code.
Open docs repo