Open-source engineering lab building AI-native tools for backend systems, security, and LLM infrastructure.
NeuroForgeLabs is an open-source engineering lab focused on tooling for engineers who work in demanding environments—production backend systems, AI/LLM pipelines, and security-critical applications.
We believe the next generation of developer tools must be AI-native from the ground up, not AI-bolted-on. Every project we build follows a single principle:
Deterministic systems as the foundation. AI as the intelligence layer on top.
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Production-grade tooling for RAG pipelines, embedding systems, vector databases, and debugging LLM behaviour at scale. |
Deterministic analysis engines for auditing code and infrastructure—reproducible results that don't rely on AI guesswork. |
Developer productivity tools and infrastructure libraries built for real-world backend workflows—CI pipelines, APIs, and beyond. |
Deterministic security analysis engine for Solidity smart contracts.
Static analysis with a rule-based vulnerability engine that separates detection from explanation—so audit results are always reproducible, auditable, and free from AI black-box decisions. An optional AI explanation layer sits on top to help engineers understand and action findings.
Solidity Source → Static Parser → AST → Rule Engine → Findings → AI Explanation
Diagnostic tool for broken and underperforming RAG systems.
When your LLM pipeline starts hallucinating or returning irrelevant results, RAG Doctor helps you trace the failure back to its root cause—whether that's retrieval quality, embedding drift, chunking strategy, or vector search configuration.
- Surfaces retrieval failures and re-ranking issues
- Detects embedding drift and chunking mismatches
- Identifies hallucination sources in context windows
- Provides actionable guidance to fix production pipelines
AI-powered technical interview preparation platform.
A full interview prep environment with structured coding challenges, AI-guided hints, and RAG-backed problem explanations. Designed to help engineers develop AI-assisted problem-solving skills—the kind that matter in modern engineering roles.
We build tools for environments where correctness matters—production systems, security audits, and AI pipelines that engineers depend on. Our architecture follows a clear separation:
| Layer | Role |
|---|---|
| Deterministic core | Analysis, rules, findings — reproducible, testable, auditable |
| AI layer | Explanations, hints, guided workflows — useful, but never the source of truth |
This means our tools give you results you can trust, with AI making those results easier to understand and act on.
We're building in public and welcome engineers who care about backend systems, AI infrastructure, and security tooling.
- ⭐ Star a repo — it helps the projects get visibility
- 🛠 Contribute — fork, branch, and open a PR on any project
- 💬 Start a discussion — questions, ideas, and feedback all welcome
Built by Anvar Baltakhojayev — AI Systems & Backend Infrastructure Engineer
Building the future of AI-native developer tooling · Open source · MIT License