DEV Community: Navneet Sinha

SPECTER 👻: The Ghost Maintainer — Autonomous Multi-Agent AI System Resurrecting Abandoned Open-Source Repositories

Navneet Sinha — Sat, 31 Oct 2026 12:00:40 +0000

This is my submission for the Hacktoberfest Open-Source AI Challenge: Week 4 — Ghost in the Machine.

SPECTER 👻: The Ghost Maintainer

"Every abandoned open-source repository carries the spirit of its past maintainers. When a project goes dark, its ghosts remain: unresolved issues, bit-rotted dependencies, and silent bugs. What if we could awaken those spirits—not to replace developers, but to do the deep maintenance work required to bring dead software back to life?"

💀 The Problem: The Open-Source Cemetery

Every day, thousands of open-source libraries that power real production systems are abandoned. Maintainers burn out, switch jobs, or lose time.

What remains is an open-source cemetery:

Pull requests left stagnant for years.
Issues reporting simple buffer underflows or deprecations that go unanswered.
Downstream developers stranded, forced to either fork and maintain the legacy code themselves or rewrite everything from scratch.

Modern LLMs can generate code quickly. But when dealing with an abandoned legacy repository, you cannot simply throw an LLM prompt at the codebase and pray it doesn't hallucinate or break subtle past conventions.

The Core Architectural Dilemma:

Context Drift: Legacy codebases have distinct idioms and architectural patterns. Generic AI refactors code aggressively, wiping out historical maintainer style.
Untrusted Execution: You can never run untrusted repository code or arbitrary AI test commands directly on your host workstation.
The Hallucination Trap: An AI that writes code cannot be the sole authority that verifies it.
VCS Autonomy Risks: An autonomous AI must never be allowed to mutate remote branches or open Pull Requests without cryptographic, human-in-the-loop approval.

👻 Introducing SPECTER

SPECTER (The Ghost Maintainer) is a production-grade, autonomous multi-agent AI maintenance infrastructure.

Instead of a single monolithic prompt, SPECTER organizes six specialized autonomous AI agents into a durable, failure-recovering maintenance pipeline:

+--------------------------------------------------------------------------------------------------+
|                                     SPECTER AGENT RUNTIME MESH                                   |
|                                                                                                  |
|   1. ARCHAEOLOGIST      2. INVESTIGATOR       3. REPAIRER         4. REVIEWER       5. TESTER    |
|   +---------------+     +---------------+     +-----------+     +---------------+  +-----------+ |
|   | AST Parsing & | --> | Issue Triage  | --> | Minimal   | --> | Adversarial   |->| Isolated  | |
|   | Ghost Memory  |     | & Repair Spec |     | Git Patch |     | Security & QA |  | Container | |
|   +---------------+     +---------------+     +-----------+     +---------------+  +-----------+ |
|                                                     ^                  |                 |       |
|                                                     | Rejection Loop   |                 v       |
|                                                     +------------------+           6. HUMAN GATE |
|                                                                                    +-----------+ |
|                                                                                    | Cryptographic|
|                                                                                    | PR Approval |
+--------------------------------------------------------------------------------------------------+

🤖 The 6 Autonomous Specialists

Each agent operates under strict least-privilege tool contracts and communicates exclusively via structured, validated inter-agent messages (agent-message.schema.json):

The Resurrection Coordinator (coordinator):
- Workflow state machine owner orchestrated via Temporal.
- Enforces handoff protocols, retry budgets (max 3 loops), and the human approval gate.
The Repository Archaeologist (repository_archaeologist):
- Scans Git history, parses ASTs across multiple languages (Rust, Python, TypeScript, Go), and extracts historical conventions into Ghost Memory (pgvector).
The Issue Investigator (issue_investigator):
- Dissects selected GitHub issues, pinpoints root-cause symbols, and drafts strict, testable Repair Specifications.
The Ghost Repairer (ghost_repairer):
- Synthesizes surgical, minimal unified git patches. Strictly avoids unnecessary refactoring to honor past maintainer conventions.
The Adversarial Reviewer (adversarial_reviewer):
- An independent auditor designed to challenge the repairer. Scans proposed diffs for secret leakage, dynamic eval(), unescaped subprocesses, and over-refactoring. If unsafe, it triggers a repair loop.
The Test Engineer (test_engineer):
- Detects test frameworks (cargo test, pytest, npm test) and executes suites in disposable, network-isolated containers.

🔒 Ironclad Security: Zero Trust by Design

From Day 1, SPECTER was architected against the STRIDE threat model:

Air-Gapped Sandbox Execution (--network none):
- Test suites execute inside unprivileged Docker containers with zero network access, read-only (:ro) host workspace mounts, --security-opt no-new-privileges, and strict memory/CPU quotas.
GitHub Token Vault Isolation:
- GITHUB_TOKEN is sealed in a memory vault. Its __str__ and __repr__ methods return [REDACTED_TOKEN]. It is physically impossible for credentials to leak into LLM prompts.
Filesystem Path Virtualization:
- Workspace paths use virtual workspace:// URIs, completely preventing path traversal outside the quarantined workspace root.
Cryptographic Human Approval Gate:
- SPECTER cannot push remote git branches or open GitHub Pull Requests autonomously. Every maintenance proposal generates a deterministic SHA-256 audit hash that requires explicit human maintainer signoff on the dashboard.

🕹️ The Retro Desktop Interface

To honor the spirit of resurrected software, SPECTER's frontend is designed around an authentic Retro OS Desktop (SPECTER 98):

Overcrowded desktop composition on a 12-column grid with offset windows and sharp bevels.
Grey frames (#d4d4d4), deep navy titlebars (#000080), and raised keys with active depth reversal.
CRT phosphor terminal monitoring container flags and test telemetry.
Tabbed proposal viewer showing unified git diffs, adversarial security audit verdicts, and human signoff keys (✔ SIGN & CREATE GITHUB PR).

📸 Application Interface & Visual Walkthrough

The SPECTER 98 Desktop: Launchpad, Phosphor CRT Telemetry, and Unified System Tray.

Air-Gapped In-Memory GitHub Token Vault: Masked representation, real-time purge, and strict quarantine from AI prompt contexts.

Autonomous 6-Specialist Mesh Controller: Archaeologist, Investigator, Repairer, Reviewer, Tester, and Human Gate.

Human Approval Gate: Side-by-side git diff review, adversarial security checks, container test results, and cryptographic PR signoff.

🧠 Why Open Innovation Matters

SPECTER is built entirely upon open-source software and open-weight AI:

Local Privacy with Open-Weight Models (Gemma 2 via Ollama):
- Resurrecting software requires ingesting proprietary or sensitive legacy code. Running open-weight models locally eliminates external API token fees and guarantees that source code never leaves the developer's machine.
Auditability & Reproducibility:
- Every agent specification, prompt contract, and schema is open-source and inspectable in the repository.
Resilience:
- The system runs even with the internet disconnected. When the power blinks, Temporal rehydrates workflow checkpoints without dirtying the Git workspace.

🛠️ The Tech Stack

Agent Runtime & Orchestration: Python 3.12, Temporal Workflow Engine, Ollama (gemma2:9b).
Backend API & Ingestion: Rust (Axum 0.8, Tokio, SQLx 0.8, PostgreSQL 16 with pgvector).
Isolated Testing Sandbox: Docker (debian:bookworm-slim unprivileged runner, --network none).
Frontend Dashboard: Next.js 15, React 19, Tailwind CSS (Custom Retro OS Design System).

🧪 Verification & Evidence

130 Backlog Tasks Completed: Tracked across 18 epics from SPECTER-001 to SPECTER-130.
Automated Test Coverage:
- 50/50 Python multi-agent unit & integration tests passing.
- 14/14 Rust Axum API & isolation tests passing.
- 100% Next.js production build & static prerender passing.

🤖 My Genuine DevRelay Agent Session

You can inspect the genuine agent development transcript documenting the multi-agent infrastructure setup, security policies, and task executions here:

SPECTER: Autonomous Multi-Agent Architecture & Governance Setup

You

ROLE: PRINCIPAL AI SYSTEMS ARCHITECT & AGENT ENGINEERING LEAD

You are responsible for architecting and establishing the complete agent development infrastructure for SPECTER — The Ghost Maintainer.
You are NOT being asked to immediately build the entire application.
Your first responsibility is to understand the existing project, analyze its PRD, establish agent governance, define execution protocols, and generate a structured task management system.

Agent

Understood. Operating as Principal AI Systems Architect & Agent Engineering Lead. Inspecting repository, analyzing PRD, and establishing agent governance, protocols, task management system, and persistent memory.

▸ run_command {"CommandLine": "Get-ChildItem -Path docs/PRD.md"}

Input

$ {"CommandLine": "Get-ChildItem -Path docs/PRD.md"}

Output

Executed successfully.

▸ read_file {"Path": "docs/PRD.md"}

Input

{
  "Path": "docs/PRD.md"
}

Output

Executed successfully.

You

Resume execution and complete all governance, protocols, schemas, backlog tasks, and memory files.

Agent

Executing Phase 2 (Agent Governance), Phase 3 & 4 (Task System & Backlog), Phase 6 (Memory), and Phase 8 (Audit Report).
- Generating 30 agent governance, role, protocol, prompt, and JSON schema files under agent/
- Generating 130 backlog tasks across 18 epics under tasks/backlog/ and staging root tasks in tasks/ready/
- Establishing 6 persistent memory files under memory/
- Formulating comprehensive system audit in docs/AGENT_SYSTEM_AUDIT.md

▸ run_command {"script": "generate_schemas.py"}

Input

$ {"script": "generate_schemas.py"}

Output

Executed successfully. All 4 JSON schemas written.

▸ run_command {"script": "generate_protocols.py"}

Input

$ {"script": "generate_protocols.py"}

Output

Executed successfully. All 5 protocol files written.

▸ run_command {"script": "generate_prompts.py"}

Input

$ {"script": "generate_prompts.py"}

Output

Executed successfully. All 5 prompt files written.

▸ run_command {"script": "generate_memory.py"}

Input

$ {"script": "generate_memory.py"}

Output

Executed successfully. All 6 memory files written.

▸ run_command {"script": "generate_all_tasks.py"}

Input

$ {"script": "generate_all_tasks.py"}

Output

Executed successfully. 130 backlog tasks generated.

▸ run_command {"script": "generate_audit_report.py"}

Input

$ {"script": "generate_audit_report.py"}

Output

Executed successfully. docs/AGENT_SYSTEM_AUDIT.md generated.

You

Export session logs and commit/push to Git repository and save session to DevRelay.

Agent

Session logs extracted and saved into logs/SESSION_EXECUTION_LOG.md, logs/transcript.jsonl, and logs/transcript_full.jsonl.
Git repository committed and pushed to origin/main.
DevRelay agent session submitted with complete trajectory.

▸ run_command {"CommandLine": "git push -u origin main"}

Input

$ {"CommandLine": "git push -u origin main"}

Output

Executed successfully. Branch main set up to track origin/main.

Direct Session Link: https://dev.to/agent_sessions/specter-autonomous-multi-agent-architecture-governance-setup-n4gvzx

🏆 Prize Categories

Best Use of Render ($200 USD Featured Prize): SPECTER's production web dashboard and Rust Axum orchestrator API are deployed on Render using a declarative, multi-service render.yaml blueprint with automated monorepo build orchestration.
Best Use of Gemma ($200 USD Featured Prize): Local inference powered by Google's open-weight Gemma 2 model via Ollama for historical idiom mining, root-cause deduction, and adversarial patch auditing without cloud token exposure.
Best Use of Temporal ($100 USD Partner Prize): SPECTER uses Temporal for durable workflow orchestration of the 5-step resurrection pipeline, state rehydration across worker restarts, and failure-recovery loops.

🔗 Code & Repositories

GitHub Repository: https://github.com/Neet2516/hacktoberfest-dev-4
Live Agent Session: DevRelay Agent Session #1310
Deployed URL: https://specter-web.onrender.com/

Día de los Muertos reminds us that what is remembered never truly dies. With SPECTER, abandoned code gets another chance to live. 👻✨

PatchGarden 🌱: I Built an AI Maintenance Engineer That Doesn't Trust Its Own Patches

Navneet Sinha — Mon, 26 Oct 2026 05:11:17 +0000

This is my submission for the Hacktoberfest Open-Source AI Challenge: Week 3 — Slow and Steady.

PatchGarden 🌱: I Built an AI Maintenance Engineer That Doesn't Trust Its Own Patches

AI can write code in seconds. But when it breaks your repository, who makes sure the fix actually works?

We've reached a point where generating code is becoming easier than ever. AI coding tools can suggest implementations, explain errors, and produce patches almost instantly.

But there's a problem I kept coming back to:

Generating a fix is easy. Knowing whether it's safe to trust that fix is a different problem.

A patch can look convincing, modify the right-looking file, and still introduce a regression. A green-looking AI response doesn't mean the bug is gone.

Meanwhile, open-source maintainers are left dealing with failing tests, neglected issues, dependency problems, and maintenance work that never seems to end.

For Hacktoberfest's Slow and Steady challenge, I wanted to explore a different approach.

Instead of building another AI tool that generates more code, I wanted to build something that helps developers spend less time babysitting maintenance work.

Meet PatchGarden — an AI-powered repository maintenance project built around one principle:

Don't trust a patch because AI generated it. Collect evidence, verify the result, and let the developer decide.

🌿 What is PatchGarden?

PatchGarden is designed to help developers investigate repository failures, generate minimal repair proposals, verify changes, and preserve an auditable history of the process.

Think of a developer who returns to a project after months away. There are failing tests, an issue backlog, and a bug report that nobody has had time to investigate.

Instead of manually navigating every step, the developer can delegate a focused maintenance task to PatchGarden.

The intended workflow is:

Investigate → Plan → Patch → Verify → Review

Here's what each stage means:

Investigate: Inspect relevant code and diagnostic evidence to understand the reported failure.
Plan: Produce a structured repair plan with a clear hypothesis and defined verification criteria.
Patch: Generate a minimal, reviewable candidate change in an isolated workspace.
Verify: Run the relevant tests and compare the results against the baseline.
Review: Present the diff and evidence so a human can approve or reject the proposed repair.

If the evidence is insufficient or the tests fail, the system should report that honestly instead of pretending the task succeeded.

The goal isn't autonomous code modification at any cost. It's making repository maintenance less tedious without sacrificing developer control.

🦥 Why does this fit Slow and Steady?

Software maintenance is rarely one dramatic task. It's hundreds of small jobs that developers keep postponing.

Investigate that failing test. Reproduce that bug. Check that dependency. Review that patch. Rerun the suite. Figure out what happened when the process failed halfway through.

Individually, these tasks seem manageable. Collectively, they become maintenance debt.

PatchGarden explores how open-source AI can help with that repetitive work while keeping the developer responsible for the final decision.

The Lazy Payoff

Delegate the investigation. Review the evidence. Spend your attention where it actually matters.

Rather than trying to replace the developer, PatchGarden aims to remove the repetitive work surrounding a repair.

🧠 The important engineering decision: AI proposes, tools verify

I didn't want the entire application to depend on a model saying, "I've fixed it."

An AI model can produce malformed output, misunderstand a code path, or confidently suggest an incorrect repair. That means the model cannot be the authority on whether a patch is correct.

The architecture separates reasoning from execution:

1. Open-weight AI for investigation

The project is designed around Google's open-weight Gemma 3 (4B) running locally on Ollama. A smaller local model creates a healthy engineering constraint: instead of assuming a massive cloud model can magically fix everything, the system limits the context, validates structured JSON outputs, and relies on deterministic tooling for execution. Local inference keeps repository code private and eliminates external API token fees.

2. A controlled execution layer

Repository inspection, Git operations, test execution, and workflow state are handled by explicit application logic rather than arbitrary model-generated shell commands. Model output is treated as untrusted input. Paths are validated, operations are restricted to isolated Git worktrees, and candidate changes are kept strictly quarantined from the main branch.

3. Independent verification

The system distinguishes between generating a patch and demonstrating that the patch passes configured checks. A complete repair record contains:

The original failure and diagnostic evidence.
The proposed change and Git diff.
The verification commands and their exit codes.
Any errors or regressions encountered.
The final maintainer review decision.

Passing tests don't prove that every bug is fixed. They provide evidence that the candidate passes the checks we actually ran. That distinction matters.

♻️ What happens when the process stops halfway through?

A maintenance workflow can take multiple steps. A process can crash, a model can time out, or a machine can restart before the job finishes. Simply storing the active task in application memory is not enough.

PatchGarden relies on Temporal durable workflow orchestration paired with persistent SQLite domain state (patchgarden.db) and immutable JSONL audit logs. Workflow state transitions are recorded before and after each phase. If the worker process restarts mid-repair, Temporal rehydrates the execution history, validates saved checkpoints, and resumes execution without re-running completed work or dirtying the repository.

🔓 Why open innovation matters

Open-source AI is not just a label in PatchGarden's technology stack. It creates an opportunity to design the system around model choice, inspectable behavior, and developer-controlled infrastructure:

Model flexibility: Maintainers can swap models or run different quantization levels locally.
Local inference & privacy: Source code, proprietary internal libraries, and sensitive test logs never leave the developer's workstation.
Cost control: Local open-weight models have zero token fees, allowing extensive multi-stage investigation and repair loops.
Transparency: The entire toolchain—from prompt schemas to worktree managers—is open-source and inspectable.

🛠️ How I built it

PatchGarden brings together a broadsheet-inspired web interface, a typed backend API, a durable worker engine, and local open-weight AI:

Project repository: GitHub — Neet2516/PatchGarden
Live application: https://patchgarden-web.onrender.com
Technologies:
- Frontend: Next.js 14 (App Router, React, CSS)
- API: Fastify (Node.js & TypeScript)
- Durable Orchestration: Temporal Cloud / Temporal local server
- Persistence: SQLite (better-sqlite3) & structured JSONL audit logs
- Inference Runtime: Gemma 3 (4B) via local Ollama
- Isolation: Git worktrees for clean verification environments
- Deployment: Render (Hosting the web dashboard and API)

📸 Application Interface & Candidate Patch Review

Here is the live interface running in production on Render:

🎥 See PatchGarden in action

Watch the demo: PatchGarden — YouTube
Try the application: https://patchgarden-web.onrender.com
Explore the source code: https://github.com/Neet2516/PatchGarden

The most important part of a demonstration is not how confidently the AI explains its answer. It's whether the workflow produces inspectable evidence of what happened.

🧪 What I would measure next

A trustworthy maintenance tool needs more than a polished interface. I would evaluate PatchGarden against a reproducible benchmark of deliberately seeded bugs and measure:

Root-cause identification accuracy.
Clean patch application rates across diverse codebases.
Test suite pass rates and regression detection sensitivity.
Workflow recovery fidelity across simulated power and worker outages.
In-flight token latency and memory footprint of local 4B models.
The accuracy of the system's "refusal to patch" when diagnostic evidence is ambiguous.

🌱 What comes next?

The long-term direction is a maintenance assistant that developers can trust with repetitive investigations without surrendering control of their repositories. That means deeper AST-aware repair suggestions, tighter containerized sandboxing, reproducible evaluations, and clearer explanations of uncertainty.

The goal isn't to build an AI that never makes mistakes. It's to build a system that makes mistakes visible, limits their impact, and refuses to confuse a plausible answer with a verified result.

🏆 Prize Categories

Best Use of Render:

PatchGarden's production web dashboard and API service are deployed on Render at https://patchgarden-web.onrender.com using a multi-service render.yaml blueprint with monorepo build orchestration.

Best Use of Temporal:

PatchGarden uses Temporal for durable workflow orchestration, enabling maintenance jobs to progress through defined stages with workflow state, retries, and recovery. This integration supports the project's central goal: making repository maintenance reliable even when individual steps fail.

Local AI with Ollama :

PatchGarden uses Ollama as its local model inference runtime, allowing the application to run a supported model locally for repository analysis and repair proposals. This supports experimentation with open-weight AI while keeping model execution separate from deterministic patch verification.

🤖 My Agent Session

Here is the genuine DevRelay agent session documenting the end-to-end development, debugging, and deployment of PatchGarden:

PatchGarden: Building an AI Maintenance Engineer

You

Build PatchGarden: an AI maintenance engineer that investigates bugs, generates minimal patches, and verifies them in isolation using Gemma 3 and Temporal.

Agent

I will design and implement PatchGarden following the Slow and Steady philosophy: open-weight Gemma 3 model, Temporal durable workflow orchestration, SQLite persistence, and Git worktree verification.

▸ run_command git status

Input

$ git status

Output

Workspace initialized with core, api, worker, and web packages.

You

Deploy the web dashboard to Render and configure build scripts.

Agent

Configured render.yaml with npm install --include=dev to ensure TypeScript and React dependencies build cleanly in production. Successfully deployed to https://patchgarden-web.onrender.com.

Direct Session Link: https://dev.to/agent_sessions/patchgarden-building-an-ai-maintenance-engineer-lszqv4

💬 I'd love your feedback

If you maintain open-source projects, what's the maintenance task you keep putting off? Is it reproducing bugs, triaging issues, fixing flaky tests, or reviewing changes generated by AI?

I'd love to learn which part of this workflow would save maintainers the most time—and what evidence you'd need before trusting an AI-proposed patch.

Slow and steady. Evidence before assumptions. Verification before approval.

ZeroTrace: An Air-Gapped, Zero-Day Behavioral SOC Copilot

Navneet Sinha — Sun, 18 Oct 2026 04:03:26 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Modern cybersecurity threats are evolving rapidly, but advanced security monitoring and Endpoint Detection and Response (EDR) solutions can be expensive and complex for independent DevOps engineers, freelancers, and small infrastructure teams. Traditional signature-based detection can also struggle with previously unseen threats for which no known signature exists.

ZeroTrace is an AI-assisted, local-first cybersecurity incident triage copilot designed to help developers and infrastructure teams investigate suspicious runtime behavior while keeping sensitive telemetry under their control.

Instead of relying entirely on external AI APIs, ZeroTrace uses a locally hosted open-weight language model to assist with incident analysis, risk assessment, and investigation workflows.

The goal is simple: make intelligent security triage more accessible, privacy-conscious, and affordable for smaller teams.

Key Features

Behavioral Threat Analysis: Analyze suspicious execution patterns, process relationships, command-line activity, and network connection metadata to help identify potentially malicious behavior.
AI-Assisted Incident Triage: Turn technical security signals into structured incident summaries, severity assessments, and investigation recommendations.
SOC Dashboard: Present security events, risk scores, severity levels, and investigation details through a modern dashboard.
Simulated Incident Response: Demonstrate suspicious activity and containment workflows in a controlled environment without risking production infrastructure.
Incident Report Generation: Create structured Markdown post-mortems that can be adapted for GitHub Issues, Jira, Linear, and internal security documentation.
Local-First AI Inference: Use Ollama and a compatible open-weight model to reduce dependence on third-party inference APIs.
Self-Hosted Deployment: Use containerized deployment workflows where supported by the project configuration.

ZeroTrace is designed to support behavioral investigation, not to guarantee the detection of every zero-day vulnerability or attack. Its risk scores are triage indicators that should be evaluated alongside supporting evidence.

Who Would Pay For It?

ZeroTrace is designed around the needs of users who require practical security visibility without the complexity and cost of a large enterprise security stack.

Potential customers include:

Independent DevOps Engineers: Freelancers managing multiple VPS instances who need better visibility into suspicious processes and network activity.
Boutique MSPs: Small managed service providers looking for self-hosted security investigation tools for client infrastructure.
Small Businesses and Startups: Teams that need an affordable way to investigate security alerts while maintaining greater control over their data.
Security Consultants: Professionals who want to provide AI-assisted incident triage as part of a broader managed security service.

The Side-Hustle Opportunity

ZeroTrace could support several monetization models:

Per-server subscriptions for managed deployments.
Paid installation, configuration, and maintenance.
Managed incident triage for small businesses.
Custom integrations with alerting and monitoring platforms.

For example, a freelancer could explore a proposed $29–$49 per-server monthly package or offer a managed security service with customized pricing.

These figures are illustrative business hypotheses, not validated market demand. Actual pricing would depend on infrastructure costs, support requirements, features, and customer feedback.

Demo

ZeroTrace is intended to demonstrate how local AI can support a practical security investigation workflow through a dedicated SOC dashboard.

Example Workflow

Generate Security Telemetry: Introduce controlled suspicious-behavior scenarios, such as an unusual child process originating from a Node.js application.
Analyze the Event: Examine available process and network metadata for suspicious execution patterns.
Review the Risk Assessment: Display the incident's risk score, severity classification, supporting evidence, and AI-generated explanation where implemented.
Investigate the Incident: Review the available event details and relevant attack techniques.
Simulate Containment: Demonstrate the containment workflow in a controlled test environment.
Generate a Post-Mortem: Export the incident details into a Markdown report where supported.

The objective is to make security investigation easier to understand, review, and document without requiring every user to operate a complex enterprise SOC platform.

Demo resources

GitHub Repository: https://github.com/Scafo745/Zero-Day
Live Demo: https://zero-day-olive.vercel.app

Code

The ZeroTrace project repository contains the source code
and implementation details for exploring the application and contributing to its development.

GitHub: https://github.com/Scafo745/Zero-Day

Scafo745 / Zero-Day

This is a project to prevent and lowers the threats the zero day attacks ................

⚡ ZeroTrace

Autonomous Zero-Day Exploit Detection & Real-Time Forensic Analyst Engine
Powered by Local Air-Gapped Gemma 4, Server-Sent Events (SSE), and a Bauhaus Constructivist SOC Dashboard.

🌐 Live Deployments

Component	Platform	URL	Health / Status
Frontend SOC Dashboard	Vercel	https://zero-day-olive.vercel.app	Production App Router (Next.js 15)
Backend Telemetry API	Render	https://zero-day-cj0b.onrender.com	`/api/health`

📌 Executive Summary

Traditional Security Information and Event Management (SIEM) and Endpoint Detection and Response (EDR) platforms rely on static hashes and known CVE signatures. Consequently, zero-day exploits and novel supply-chain attacks bypass legacy defenses by definition.

ZeroTrace flips the paradigm from signature scanning to pure behavioral provenance reasoning. By ingesting raw kernel and process execution telemetry (process lineage, command flags, sensitive file reads, and outbound network socket operations), ZeroTrace utilizes a locally hosted, GPU-accelerated Gemma 4 model (zerotrace-analyst) to autonomously deduce adversary intent in real time without external cloud dependency.

🚀 Key Features

🛡️…

View on GitHub

Developers can explore the repository, review the architecture, report bugs, suggest improvements, and contribute to making local-first security investigation more accessible.

How I Built It

ZeroTrace brings together modern web technologies, security telemetry workflows, and locally hosted AI to explore a privacy-conscious approach to incident investigation.

Open-Weight AI and Local Inference

The project uses a local-model architecture based on Ollama, allowing compatible open-weight models to run on the host machine rather than requiring an external inference API for every analysis request.

This approach can reduce recurring inference costs and limit the exposure of sensitive security telemetry to third-party AI providers.

Behavioral Analysis and Incident Triage

The core workflow focuses on turning technical events into information that an operator can investigate.

A conceptual pipeline looks like this:

Runtime Telemetry → Event Normalization → Behavioral Analysis → AI-Assisted Triage → Structured Incident → SOC Dashboard

The quality of each assessment depends on the telemetry available, the analysis logic, and the model's output. Any automated conclusions should remain reviewable by a human operator.

Frontend and Backend

The project is designed around a modern web application architecture.

Frontend: Next.js, React, and Tailwind CSS for the dashboard interface, where implemented in the repository.
Backend: Node.js and Express for application services and event processing, where implemented.
Streaming: Server-Sent Events (SSE) for delivering live updates where configured.
AI Orchestration: Ollama for local inference with a compatible open-weight model.
Deployment: Docker and Docker Compose for reproducible deployment where supported.

The repository is the source of truth for the exact versions, available integrations, and completed implementation details.

Reliability and Safe Investigation

Security tooling must remain useful even when the AI component is unavailable.

A deterministic fallback strategy is a valuable design direction because it can preserve basic event handling when model inference fails. Similarly, containment actions should be protected by authorization, validation, audit logging, and safeguards against disrupting legitimate processes.

ZeroTrace's long-term goal is to combine useful AI-assisted explanations with transparent evidence and predictable incident-handling workflows.

Why Does Open Innovation Matter?

Open innovation is central to the ZeroTrace concept because security infrastructure requires control, adaptability, and careful handling of sensitive information.

1. Privacy and Data Sovereignty

Security telemetry can contain command-line arguments, internal IP addresses, file paths, process information, and other sensitive operational details.

A local-first architecture can reduce the need to transmit this information to external AI services. However, achieving a genuinely air-gapped deployment also requires controlling application networking, dependency downloads, model distribution, updates, and external integrations.

Local inference is a privacy advantage, not an automatic guarantee of regulatory compliance.

2. Sustainable Economics for Independent Developers

Per-request inference charges can become an additional operating expense for a security service that processes frequent events.

Using a locally hosted open-weight model can eliminate external API inference fees, although it still requires suitable hardware, electricity, storage, configuration, and maintenance.

For freelancers and small service providers, this creates an opportunity to experiment with security automation without making every analysis dependent on a paid external API.

3. Customization and Reduced Vendor Lock-In

Open-weight models and self-hosted components offer opportunities to customize analysis workflows, control model selection, and adapt integrations to specific infrastructure requirements.

They also provide greater flexibility when building tools for specialized cybersecurity workflows.

Model licenses, hardware requirements, security controls, and operational limitations still need to be evaluated before deployment.

4. Making Security Tooling More Accessible

Open-source development allows independent developers, students, infrastructure engineers, and security researchers to inspect implementations, suggest improvements, and contribute new capabilities.

ZeroTrace aims to explore how these collaborative advantages can make AI-assisted incident investigation more approachable for teams that cannot justify a complex enterprise security stack.

Early Validation and Next Steps

ZeroTrace is being developed around a practical question:

Can locally hosted AI make suspicious-event investigation more accessible to small infrastructure teams without unnecessarily exposing their operational data?

The next step is to validate this idea with independent DevOps engineers, sysadmins, and small managed service providers.

Important questions include:

Which telemetry sources provide the most useful investigation context?
How much explanation and supporting evidence does an operator need before trusting an AI-assisted assessment?
Which deployment model is easiest to maintain across multiple client servers?
Would users prefer a self-hosted subscription, installation service, or managed security package?
Which alerting integrations would deliver the most immediate value?

Potential future improvements include Slack and PagerDuty webhook alerts, improved incident correlation, configurable risk thresholds, additional telemetry sources, and stronger deployment hardening.

These are future directions rather than claims that every integration is already available.

Feedback from real users will help determine which capabilities should be prioritized and whether the proposed business model solves a sufficiently valuable problem.

What's Next?

The next phase of ZeroTrace focuses on improving the usefulness, reliability, and accessibility of local-first security triage.

Potential areas of development include:

More behavioral detection rules and investigation context.
Better incident correlation and evidence presentation.
Configurable alerting thresholds and webhook integrations.
Improved containerized deployment and operational documentation.
More extensive testing of local inference performance.
Safer, auditable incident response workflows.

The long-term vision is to make AI-assisted security investigation more practical for independent infrastructure operators without treating an AI-generated verdict as a substitute for evidence or human judgment.

Prize Categories

Hacktoberfest Open-Source AI Challenge — Week 2: Side Hustle

ZeroTrace explores the intersection of open-source AI, cybersecurity, privacy-conscious infrastructure, and a potential sustainable business model for independent developers.

If you're interested in local AI, DevSecOps, threat detection, or open-source security tooling, check out the repository and share your feedback.

GitHub: https://github.com/Scafo745/Zero-Day

Let's make security investigation more accessible, transparent, and privacy-conscious. 🚀

AuraFlora

Navneet Sinha — Mon, 12 Oct 2026 02:39:51 +0000

🌿 An AI Companion That Tells You When to Put Your Phone Down

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.

What I Built

AuraFlora is a bioacoustic grounding companion designed to help people disconnect from their screens and reconnect with the physical world.

We spend hours looking at screens, listening to playlists, and consuming digital content—even when we're surrounded by nature. Most wellness applications add more screen time to the very problem they're trying to solve.

AuraFlora takes a different approach: use AI to understand the sounds around you, give you one meaningful physical activity, and then get out of your way.

The experience follows a simple loop:

🎙️ Press: Tap a single button to begin recording.
🌿 Listen: Capture 5–10 seconds of ambient environmental audio.
🧠 Analyze: Extract acoustic features and distinguish environmental sound characteristics.
🗣️ Ground: Generate a short, actionable grounding instruction using AI.
⬛ Disconnect: Deliver the instruction through spoken audio, then transition to a pitch-black screen.

Imagine receiving a prompt such as:

"Notice the distant hum, then find one quieter sound nearby."

Instead of opening another feed or searching for something to watch, you have a small, tangible reason to pay attention to the world around you.

The design follows a strict philosophy: no feeds, no streaks, no leaderboards, and no unnecessary notifications. The goal is to make the interaction brief enough that you can return to the real world.

Demo

🌐 Live Demo: [APPLICATION URL]

🎥 Video Demo: [YOUTUBE LINK]

The project is designed around a mobile-first grounding experience, with a minimal interface and an intentionally screen-free ending.

Code

🌱 GitHub Repository: [GitHub Repo Link]

AuraFlora combines a React-based web experience with a Rust backend and a Flutter mobile application.

The repository explores how acoustic analysis, AI-generated instructions, and spoken guidance can be combined into a short, purposeful interaction.

How I Built It

I wanted to explore a different use of AI: not generating more content for people to consume, but helping them become more aware of their surroundings.

The technology stack

React + TypeScript: Web experience and user interaction.
Rust + Axum + Tokio: Backend API and asynchronous service orchestration.
rustfft: Acoustic signal processing and feature extraction.
TabPFN: Soundscape classification integration, with a heuristic fallback where configured.
Gemma: Grounding-instruction generation.
ElevenLabs: Text-to-speech generation.
Flutter + Riverpod: Native mobile application and interaction-state management.

The architecture

The intended processing pipeline is:

Ambient Audio → Acoustic Feature Extraction → Soundscape Classification → Grounding Instruction → Speech Generation → Blackout

The Rust backend is responsible for coordinating the processing pipeline and external AI services. The frontend handles recording, interaction state, audio delivery, and the transition into the blackout experience.

The acoustic-analysis stage can examine characteristics such as RMS energy, spectral centroid, spectral entropy, and frequency-band energy ratios. These features can inform the grounding instruction, although they do not independently guarantee accurate identification of individual sound sources.

The project also explores explicit state management, fallback behavior, and a minimal interaction model to keep the experience focused.

Why Does Open Innovation Matter?

Open innovation matters because the future of AI should not be limited to building applications that maximize engagement or keep users inside a platform.

With open-weight models, open-source tooling, and self-hostable infrastructure, developers can experiment with AI applications that prioritize different values: privacy, user autonomy, transparency, and control.

For AuraFlora, this creates several opportunities:

More control over the AI pipeline: Different models and inference environments can be evaluated without redesigning the entire product.
Self-hosting possibilities: Components can be deployed on infrastructure controlled by the project, reducing dependence on a single provider.
Privacy-conscious experimentation: Local or edge processing can reduce the need to transmit raw recordings when the architecture supports it.
Community-driven improvement: Contributors can improve DSP methods, prompts, error handling, accessibility, and model integrations.
A different relationship with technology: AI can be used to encourage meaningful offline activity rather than endless digital consumption.

Open innovation does not automatically make an application private or offline-capable. Those properties depend on the actual deployment and data flow. But open tools give developers greater freedom to investigate, build, and improve these capabilities.

That is what makes this challenge exciting to me: building technology that knows when to step back.

My Agent Session

This project also involves experimenting with AI-assisted development to improve implementation quality, architecture, and debugging workflows.

Agent session: You can inspect the complete execution flow, prompts, and task breakdowns in the [.task directory]

Prize Categories

Best Use of Gemma: Used Gemma as the core open-weight model for generating context-aware grounding instructions.

Best Use of TabPFN: Integrated TabPFN for soundscape classification features to analyze acoustic properties.

Best Use of ElevenLabs : Utilized ElevenLabs text-to-speech to deliver spoken audio guidance right before the screen transitions to blackout.

🌿 AuraFlora — Listen to the world. Leave the screen.

Built for Hacktoberfest 2026 · Touch Grass Challenge

Hacktoberfest #OpenSource #AI #Rust #React #Gemma #TouchGrass

Ghost-Hunter: An Air-Gapped, Temporal-Powered Follow-Up Sentinel with Local Gemma 3

Navneet Sinha — Mon, 05 Oct 2026 04:27:34 +0000

Ghost-Hunter is an open-source, local-first, air-gapped follow-up sentinel built to eliminate forgotten customer requests, unresolved operational tickets, and missed follow-ups. Powered by Local Gemma 3 via Ollama and orchestrated using Temporal, Ghost-Hunter acts as an intelligent background agent that continuously monitors, tracks, and triggers proactive follow-ups without relying on external cloud APIs or exposing sensitive user data.

Key features include:

Local-First AI Execution: Operates entirely offline using Ollama and Gemma 3 to analyze conversation logs, detect open action items, and draft responses.
Temporal Workflow Orchestration: Reliable state machine handling for scheduling, retries, and time-based triggers.
Air-Gapped & Secure: Zero third-party cloud data transmission, ensuring strict data privacy and compliance.
Interactive Sentinel Dashboard: Real-time monitoring of pending follow-ups, sentiment tracking, and response approval workflows.

Demo & Source Code

GitHub Repository: Neet2516/Ghost-Hunter

Architecture

+-------------------------------------------------------------------+
|                        Ghost-Hunter Engine                        |
|                                                                   |
|  +--------------------+    +-------------------+    +----------+  |
|  | Event Triggers     | -> | Temporal Workflow | -> | Local    |  |
|  | (Tickets / Logs)   |    | Engine            |    | Gemma 3  |  |
|  +--------------------+    +-------------------+    +----------+  |
|                                                          |        |
|  +--------------------+    +-------------------+         v        |
|  | Follow-Up Action   | <- | Approval Queue    | <-------+        |
|  | (Email / Slack)    |    | & Sentinel UI     |                  |
|  +--------------------+    +-------------------+                  |
+-------------------------------------------------------------------+

Ingestion Layer: Captures incoming tickets, user inquiries, or log entries requiring trackable follow-ups.
Temporal Engine: Schedules workflow timers and manages execution state across days, weeks, or months.
Local LLM Processing: Queries Gemma 3 locally to summarize state, evaluate necessity of follow-up, and generate draft text.
Action Queue: Places generated follow-ups in an operator review queue or dispatches automated alerts.

Key Capabilities

Automated Follow-Up Detection: Automatically classifies whether a user query requires follow-up action.
Context-Aware Response Generation: Contextual drafts synthesized using Gemma 3.
Resilient Scheduling: Built-in retry mechanisms and failure recovery via Temporal workflows.