Creator of Spryzen (4.83M RPS Network Engine), open-source contributor (Bytecode Alliance Wasmtime, Tokio, Foundry, FoxIO JA4, smoltcp), and AI/RAG architect.
Designing ultra-low latency network inspection engines, production Python backends, and Agentic AI workflows. AWS & IBM certified with AICTE/IBM virtual internships.
A hyper-performance, zero-allocation network security & TLS telemetry engine achieving 4.83 Million RPS on a single bare-metal node with sub-microsecond latency.
Spryzen is designed for mission-critical enterprise edge infrastructure. It parses raw TCP/IP packets and extracts deterministic JA4 TLS fingerprints without performing expensive full SSL decryption or triggering heap allocations in the critical packet path.
Standard edge security appliances inspect traffic by decrypting full SSL/TLS handshakes. This is extremely CPU-expensive, introducing high latency and limiting edge capacity.
Spryzen solves this by doing single-pass byte matching on raw incoming packet headers. It parses the Client Hello directly to compute JA4 fingerprint hashes in real-time, allowing instant anomaly scoring without full decryption.
Click any pull request card below to directly inspect the merged code on GitHub.
What was achieved: Resolved POSIX symlink dereference bug in WASI filesystem engine (wasi-common). When stating paths with a trailing slash where the final component is a symlink to a directory under FollowSymlinks::No, POSIX requires resolving the symlink because the trailing slash forces directory resolution. Implemented stops_at_symlink helper on Context and added test coverage across symlinked files and directories, preventing erroneous ENOTDIR failures. Merged into main by Alex Crichton & Roman Volosatovs.
What was achieved: Discovered and resolved a critical permit leak bug in SemaphorePermit::merge where permit counts exceeding u32::MAX wrapped and silently leaked permits on drop. Converted internal storage to usize with safe checked arithmetic, passing Tokio's strict Loom concurrency model checker. Merged into master by Alice Ryhl (Darksonn).
What was achieved: Hardened session validation in Foundry's REPL (crates/chisel) by rejecting colons (:) along with path traversal characters. Prevents cache writes from escaping directory enumeration into Windows NTFS Alternate Data Streams (chisel-foo:bar.json), eliminating potential arbitrary disk escape vulnerabilities.
What was achieved: Implemented PostgreSQL wire protocol transaction abort state machine (SQLSTATE 25P02) and Extended Query sync error recovery in corro-pg, ensuring failed distributed queries correctly trigger transactional rollback instead of cascading session errors.
What was achieved: Accurately modeled language-level LLVM Undefined Behavior in Google's official unsafe Rust modules by using the intrinsic i32::unchecked_add rather than wrapping operations, passing Google Staff Engineer review.
What was achieved: Contributed zero-allocation memory optimizations to FoxIO's official industry-standard JA4 Network Fingerprinting suite in Rust. Replaced dynamic string splitting in HTTP/1 header filtering with zero-allocation name.eq_ignore_ascii_case() stack checks, and eliminated intermediate Vec<String> allocations during JA4T TCP options formatting.
What was achieved: Optimized the core hash12 helper function in both the ja4 and ja4x Rust crates. Directly hex-encodes 6-byte digest slices instead of encoding all 32 bytes into a 64-character heap string and slicing, eliminating 2 dynamic heap allocations and avoiding encoding 26 unused trailing bytes on every fingerprint calculation.
What was achieved: Engineered a high-speed, single-pass zero-allocation parser for TCP options summary. Eliminates dynamic memory allocations on the packet wire-path, drastically accelerating packet ingestion in bare-metal TCP/IP networking.
What was achieved: Optimized machine learning ensemble tree training performance by pre-allocating exact sample vector capacities, avoiding repeated heap buffer reallocations during Random Forest training cycles.
What was achieved: Accelerated GeoIP database binary lookups by utilizing native big-endian byte conversion (`u32::from_be_bytes`), enabling single-CPU-instruction decoding of 32-bit database nodes.
What was achieved: Resolved device fingerprint non-uniqueness in ESP32 embedded onboarding firmware (serial_onboarding.c) by extracting the hardware base MAC address via esp_base_mac_addr_get() as entropy for PSA SHA-256 digests. Engineered zero-dependency host C unit testing framework with isolated PSA crypto stubs and cross-platform compilation verification.
Engineered a Retrieval-Augmented Generation (RAG) backend utilizing vector embeddings, OpenAI APIs, and Python Flask microservices to deliver semantic search and contextual product Q&A.
Created an agentic assistant capable of dynamic tool calling, live web search, and automated code generation using LLM function calling and Python orchestration.
Nationally recognized internships supported by AICTE and IBM SkillsBuild.
Gained practical hands-on experience in Agentic AI and RAG systems to solve real-world problems. Developed intelligent data retrieval pipelines and responsible AI models aligned with UN Sustainable Development Goals.
6-week intensive training on core Machine Learning algorithms, Data Science pipelines, and Neural Network architectures in Python.
All verified credentials from IBM, Amazon Web Services (AWS), Microsoft, Coursera, and LinkedIn.
Skills: Generative AI, Artificial Intelligence (AI), Machine Learning.
Skills: Flask, IBM Watson, AI Application Development.
Skills: Python (Programming Language), Data Structures.
Skills: Search Engine Technology, AI for Business.
Skills: Generative AI Tools & LLM Architecture.
Skills: Python Data Science, APIs, JSON & Data Analysis.
Ready to contribute to your engineering team immediately. Let's connect on LinkedIn or explore my Spryzen engine and merged pull requests on GitHub.