Archive position — measured, not model output
0 likes on Devpost
2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #3,707 place in the like-ranked listing is a tie-break inside that group, not a ranking.
Projects (log scale)
Likes on Devpost. ▲ marks this project's group.
Show the figures
| Likes | Projects | Share of archive |
|---|---|---|
| 0 | 5,592 | 71.2% |
| 1 | 1,758 | 22.4% |
| 2 | 285 | 3.6% |
| 3–4 | 132 | 1.7% |
| 5–9 | 75 | 1.0% |
| 10+ | 14 | 0.2% |
Executive Summary
What the company appears to be
depgaze is a self-reported, single-person project that claims to offer dependency execution intelligence for software supply chain security. It is described as a Linux and Windows CLI tool that uses an ephemeral micro VM (via Firecracker) to monitor OS-level activity during package installation, then analyzes this data with a GPT model to determine trustworthiness of dependencies.
What changed
The project was submitted to the OpenAI 2026 hackathon. It is described as a pre-existing idea that was developed using AI tools like Codex and Azure Functions during the hackathon timeframe.
Single most important open question (commercial due-diligence read)
Is there evidence of market demand or traction for this tool, or any indication that developers or enterprises are actively seeking solutions to software supply chain risks?
What The Product Actually Is
The description states that depgaze is a Linux and Windows CLI tool. It leverages:
- An ephemeral Linux micro VM built with Firecracker
- A Rust agent inside the VM that captures OS-level activity during package installation (npm or pip)
- A vsock connection between host and VM for communication
- Integration with GPT models to analyze captured activity and produce a verdict on whether to trust or distrust a package
- Output in HTML-based reports
It can be used as:
- A standalone CLI tool
- Embedded into CI pipelines
- Instrumented as an Agent Skill
Evidence
- The author describes how it works using Firecracker, Rust agents, and vsock communication.
- It uses GPT models for analysis and generates structured payloads.
- It supports both Linux and Windows environments (though with limitations on Linux only for now).
Inference The tool is built to monitor package installations in real time and assess risk post-installation using behavioral data.
Positioning & Claim Evolution
The author positions depgaze as a novel approach to software supply chain security, focusing on what actually happens during installation rather than traditional detection methods like CVEs or static code analysis.
Key claims:
- It detects malicious payloads or data exfiltration without relying on known vulnerabilities or heuristics.
- It aims to provide a stronger trust layer between written code and dependencies introduced in modern software.
- It addresses the rise of sophisticated supply chain attacks such as Shai-Hulud.
Evidence
- The description explicitly mentions inspiration from software supply chain attacks.
- It contrasts its approach with traditional detection mechanisms.
- It positions itself as an alternative to static analysis or LLM-based tools.
Inference The product is positioned as a security tool targeting developers and enterprises concerned about dependency integrity, but it does not yet show adoption or market traction.
Target Customer & ICP
The description implies depgaze targets:
- Developers who install packages via npm or pip
- Enterprises shipping software with dependencies
- Users looking for a way to assess trustworthiness of third-party packages
It is described as useful in CI pipelines and for developers working on Linux or Windows environments.
Evidence
- It’s designed for use in development workflows.
- Can be embedded into CI pipelines.
- Supports both Linux and Windows (though limited support for Windows).
Inference The ICP likely includes developers and DevOps teams concerned with software supply chain integrity, but there is no evidence of customer segments or personas defined.
Business Model & Pricing Evidence
There is no evidence in the description of any business model or pricing structure.
Evidence
- No mention of monetization.
- No indication of paid features or tiers.
- No reference to enterprise licensing or SaaS offerings.
Inference The project appears to be a prototype or MVP, not yet commercialized. It may evolve into a SaaS offering or open-source tool, but no such direction is stated.
Technical & Delivery Signals
Technical details provided:
- Built with .NET, AOT, C#, Rust
- Uses Firecracker for micro VMs
- Cosign for verifying VM image integrity
- GPT models for analysis (with prompt engineering)
- HTML report generation
- Azure Functions backend for remote access (Windows support)
Evidence
- The author describes the architecture using Firecracker, vsock, Rust agents.
- It uses Codex to build features and improve code quality.
- AOT self-contained executable is used.
Inference The tool shows technical sophistication in leveraging virtualization and AI for security analysis. However, it’s limited to Linux-based environments currently.
Traction & Maturity Signals
There is no evidence of traction or maturity beyond a hackathon submission.
Evidence
- The project was built during the OpenAI 2026 hackathon.
- It is described as a v1/MVP.
- No mention of users, customers, or adoption metrics.
- No revenue or funding data.
Inference This is an early-stage prototype. There is no indication that it has been adopted by developers or enterprises.
Competitive Context
The description does not provide any information about existing competitors or market positioning in relation to other tools addressing software supply chain security.
Evidence
- No mention of competing products.
- No reference to similar tools or platforms in the ecosystem.
Inference It is unclear whether depgaze competes with or complements existing solutions like Snyk, WhiteSource, or others. The author does not discuss competitive landscape.
Key Risks & Red Flags
Key risks and red flags:
- Limited platform support: Only works on Linux (Ubuntu 24+), with limited Windows support.
- High technical prerequisites: Requires Firecracker and Cosign to be installed, which may hinder adoption.
- Dependency on AI models: Relies heavily on GPT models for verdicts; not clear how it handles model failures or biases.
- Single-person team: No evidence of team size beyond one person (Giancarlo Lelli).
- No commercialization strategy: Not evident that the project is intended to become a product or service.
Evidence
- Mentioned platform limitations and prerequisites.
- The tool is described as a hackathon prototype, not yet a product.
Inference The tool may struggle to gain traction due to its technical complexity and limited compatibility. It also lacks clarity on long-term commercial viability.
Diligence Questions To Ask The Founders
- What specific use cases or workflows are you targeting for adoption?
- How do you plan to address the high technical prerequisites (Firecracker, Cosign)?
- Are there any plans to expand support beyond Linux and Windows?
- Have you tested depgaze with real-world supply chain attacks or malicious packages?
- What is your roadmap for reducing false positives in AI-based verdicts?
- How do you intend to monetize this tool if at all?
- Is there a plan to integrate with existing CI/CD platforms or package managers?
Investment/Partnership Verdict
Not evidenced
There is no evidence of revenue, customers, traction, or commercialization plans beyond the hackathon submission.
The project appears to be an early-stage prototype with strong technical execution but no demonstrated market demand or business model. It may have potential as a security tool, but lacks any indication of viability for investment or partnership at this stage.
Confidence Level Low This analysis is based entirely on self-reported information and does not reflect any independent verification or historical data.
Source
Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.
The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.