Echoes & InnovationsJUSSTIN DEREMUS · PORTFOLIO
SYSTEMS · GOVERNANCE · VERIFICATION
I’m Jusstin DeRemus. I design and build evidence-oriented tools and protocols for AI operations, continuity, code analysis, and system integrity—with a bias toward inspectable behavior over impressive claims.
SELECTED WORK / 02
A focused selection of public work, described at the maturity the available source supports. Repository documentation is not a substitute for independent production validation.
Continuity artifacts with validation and evidence lineage.
A local-first Python pipeline for normalizing conversation threads, redacting sensitive content, compiling prompts, validating summaries, and emitting evidence and inspection reports. Static-provider end-to-end tests are defined in the public repository.
Static heuristics paired with structured AI review.
A Python MVP includes AST-oriented code analysis, a local SAST scanner, and an LLM review orchestration path. The inspected scanner has explicit heuristic rules; its limits mean it should not be presented as a comprehensive security analyzer.
Candidate project retained in the evidence inventory, not presented as a verified case study. The connected GitHub read returned 404 in this session, so current source, tests, and revision could not be inspected.
UNIVERSAL SEED SUMMARY ENGINE
Conversation handoffs can lose constraints, provenance, and important negative findings. USS formalizes a pipeline that turns a transcript into a normalized thread, applies redaction, compiles a runtime prompt, and produces a summary plus validation, evidence-map, inspection, and generation artifacts.
Local-first operation, explicit protocol input, redaction before generation, and inspectable output artifacts reduce dependence on opaque conversational state. Provider activation exists in source, but live-provider success is not established by the inspected documentation.
src/uss_engine/ contains CLI, transcript, redactor, validator, evidence, inspector, and run pipeline modules.
tests/test_e2e_static.py checks summary validity, six output artifacts, and the static CLI path.
The repository's live-provider validation document describes a procedure; it is not evidence that credentials or provider calls succeeded in this session.
Static-path tests do not establish semantic quality, resistance to every sensitive-data pattern, or reliable behavior with external providers. Those require additional environment-specific testing.
CRIT · CODE REVIEW & INTELLIGENCE TOOL
CRIT aims to combine local code-pattern scanning with structured AI-assisted evaluation. The inspected repository includes an AST-based scanner and a protocol orchestrator that defines structured findings and evidence references.
sast.py visits Python AST call nodes and flags selected patterns including eval/exec, subprocess.run(shell=True), weak hash calls, and a narrow SQL-string heuristic. The orchestrator includes Pydantic models and deterministic regex-based redaction.
The scanner is not a complete vulnerability detector; its SQL rule is based on limited syntax patterns.
The inspected scan() catches broad exceptions and returns accumulated findings, which can make parse failure hard to distinguish from a clean scan.
Source files and project metadata were inspected remotely; the application and its tests were not run in this session.
AI-assisted review depends on model availability, prompt design, and output validation. Security effectiveness needs adversarial test coverage and measured false-positive/false-negative analysis.
METHODS & ARCHITECTURE / 03
A practical method for making complex AI workflows legible: define boundaries, make state explicit, preserve provenance, and test failure paths.
Map responsibilities, interfaces, dependencies, data flow, and trust boundaries before adding automation.
ARCHITECTUREConnect material claims to source locations, test outputs, revisions, or explicit unknowns.
PROVENANCEDefine what happens when validation fails, context is missing, or a dependency cannot be trusted.
GOVERNANCESeparate intended design from implemented code, executed tests, reproducible demos, and deployment evidence.
VERIFICATIONArchitecture before code. Evidence before confidence. Limitations stay in the record.
WORKING PRINCIPLE / E&IEVIDENCE REGISTER / 04
Evidence labels describe the basis of each statement, not a grade of overall project quality.
Public default-branch tree and test source inspected; runtime tests were not executed here.
Documentation describes validation steps; no live-provider run was observed in this session.
Public source files inspected; implementation presence is not a security-effectiveness claim.
README language is stronger than the source review can substantiate; no adversarial evaluation was run.
GitHub API returned 404 for EAIN Stabilization Command Center, Void Sentinel Console, and Gatekeepers in this session.
No hosting or DNS access was confirmed; this deliverable is a local static website package.
This register is a point-in-time review. Source branches can change; linked repositories remain the authoritative place to inspect current code.
ABOUT / 05
I work at the intersection of AI operations, protocol design, continuity, and applied software. My focus is on the mechanisms that make systems easier to inspect: clear contracts, deliberate state transitions, validation, redaction, evidence maps, and honest failure reporting.
I value small stable implementations, local-first workflows where practical, and the distinction between “the design says” and “the system demonstrably does.”
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