Distributed Systemsv0.1.0
eshanized-polaris
Primary facade crate for the Polaris distributed compute & cluster orchestration engine in Rust.
01 — The Introduction
eshanized
Eshan
Roy
Systems Engineer · Distributed Infrastructure Architect
Open-Source Founder
I build systems that help software do more. My work spans Rust distributed orchestration (Polaris), verifiable autonomous runtimes (M31A), and Arch Linux engineering (Snigdha OS) — engineered from the kernel outward.
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● Eshan Roy (eshanized) — Systems Engineer & Distributed Infrastructure Architect
Operating Environment: Linux x86_64 · Rust 2024 Edition · Arch Linux Kernel
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02 — The Engineering Mindset
The interesting part is always
the layer underneath.
My interest in computing starts not at the surface where buttons click and forms submit, but in the layers below: where memory is laid out, processes are scheduled, kernels decide what runs next, and one engineering decision cascades into a thousand downstream behaviors.
01
Root causes, not symptoms
Superficial patches defer failures; they do not prevent them. The interesting engineering happens at the boundary between what a system claims to do and what it actually does under pressure.
02
Architecture before implementation
The shape of a system — its modules, data flow, failure modes, and security boundaries — must be designed before code is written. Structure is cheaper to fix on a whiteboard than in production.
03
Open infrastructure
Systems that can be inspected, understood, and owned produce better long-term outcomes than black boxes. Open source is not a distribution model; it is an engineering discipline.
04
Verification over assertion
A model declaring success is not proof. Completion requires empirical evidence: clean compilers, passing tests, and verifiable digests. Claims are rejected until proven.
03 — From Ideas to Systems
How an idea
becomes a
functioning system.
Every project starts as a problem definition. Not a feature list — a clear statement of what is broken, who is affected, and what a correct solution would look like. That clarity drives every subsequent decision.
01
Concept
Identify a real problem worth solving. Define the boundary between what the system should and should not do.
02
Architecture
Design the structure: module hierarchy, data flow, security boundaries, and failure modes. The shape of the system before a single line of code.
03
Implementation
Build with the architecture as the guide. Strict module ordering, typed interfaces, and no shortcuts that bypass the security boundary.
04
Verification
Prove it works. Clean compilers, passing test suites, and cryptographic evidence. No claim of success without empirical proof.
05
Release
Ship with documentation, honest status, and open source. The work is not done until someone else can understand, operate, and depend on it.
Every project that follows — M31A, Snigdha OS, and the broader portfolio — is built this way.
04 — M31A & Verifiable Agents
ActiveThe model proposes.
The runtime decides.
A Rust-native autonomous software-engineering runtime with non-bypassable policy gates and verifiable execution. The model proposes actions; the runtime decides what is allowed to happen.
Most coding agents are chatbots with terminal access. M31A is an autonomous engineering runtime with an intelligence engine inside.
A deterministic 11-stage policy gate sits between the model and every file mutation, command execution, and network request. The model never possesses ambient permissions or direct shell access.
Completion is not declared — it is verified. A mission is admitted as complete only when compiler diagnostics are clean, tests pass in an isolated sandbox, and a cryptographic SHA-256 evidence digest is generated.
Failures are treated as recoverable execution states. Two-phase atomic SQLite WAL commits preserve verified checkpoints while isolating failures, and crash recovery verifies invariants before safely resuming.
RustTUILLMsGit InternalsLinux cgroups v2SQLite WAL
01
11-Stage Policy Pipeline
Monotonic evaluation across 10 authority layers. Layer 0 safety vetoes can never be weakened. Higher authority always overrules lower.
02
Process Confinement
Linux cgroups v2 memory bounds (default 512MB RSS), POSIX rlimits, and process group supervision prevent resource starvation.
03
Cryptographic Evidence
Completion requires SHA-256 evidence digests from compiler exit codes and passing unit/integration test suites. Claims are rejected until proven.
04
Two-Phase SQLite WAL
Verified task checkpoints commit atomically to local SQLite WAL storage. Crash recovery verifies invariants before safely resuming.
Exhibit 01 · Interactive Policy Gate Execution Simulator
M31A Monotonic Policy Pipeline Simulator
Scenario:
Stage 01 / 11Category: AuthorityStatus: passed
Layer 0 Safety Invariant
Monotonic evaluation across 10 authority layers. Safety vetoes cannot be weakened by any sub-agent or model prompt.
Deterministic Runtime Invariant:
INVARIANT: higher_authority > lower_authority && veto_state.is_permanent()
Runtime Telemetry Streamcgroups: 48MB / 512MB
[00.01s] scheduler: session initialized with isolation harness
[00.04s] monotonic_authority: layer 0 non-bypassable hooks engaged
05 — Snigdha OS
ActivePerformance × Elegance
An Arch-based Linux distribution focused on a cohesive desktop experience, system tooling, package management, and developer utilities. Performance meets elegance.
Snigdha OS is a lightweight, fast, and efficient operating system built for modern developers and security analysts. It combines the speed of Arch Linux with curated system tools and hardened configurations.
The ecosystem spans custom kernel tunings, desktop shell configurations, packaging via automated PKGBUILD pipelines, an interactive system assistant CLI/GUI, and developer utilities — all maintained in the open.
It is more than another Linux distribution. It is an operating environment designed to combine raw hardware efficiency, total user autonomy, and a cohesive developer workflow.
Desktop Experience
A cohesive desktop built around window management, theming, and system-level configuration that respects user preferences.
Package Management
PKGBUILD scripts for building and packaging software, with additional repositories that extend the core Arch ecosystem.
System Assistant
An interactive tool designed to help users with system setup, configuration, and troubleshooting.
Developer Utilities
Tools and utilities integrated into the system for developers who work directly on Linux.
Linux KernelArch LinuxRustShellPKGBUILDDesktop
06 — Distributed Systems & Open Source
Distributed runtimes &
open-source tooling.
Beyond single-node runtimes, engineering at scale demands distributed coordination, lock-free parallelism, and rigorous developer tooling. Here is the broader ecosystem of crates, libraries, and utilities built in the open.
Featured Distributed SystemReleased on crates.io
Polaris
Distributed compute & orchestration framework
A Rust-native distributed task orchestration engine featuring cluster state consensus, declarative procedural macros, and low-latency task scheduling across heterogeneous worker nodes.
Polaris eliminates the friction of writing distributed compute jobs in Rust. With declarative #[task] and cluster annotations, developers can turn async functions into distributed jobs with zero boilerplate.
Under the hood, eshanized-polaris-core manages cluster node discovery, fault-tolerant heartbeats, dynamic load balancing, and resilient message queues.
Ships with a high-performance terminal CLI (polaris) for submitting batch jobs, inspecting cluster health, and streaming distributed telemetry in real time.
RustDistributed SystemsTokioProc MacrosCLIRPC
Exhibit 02 · Crates.io Registry & Package Ecosystem
Filter Registry:
Distributed Systemsv0.1.0
eshanized-polaris-core
Underlying runtime, node heartbeat protocol, task queue scheduler, and networking primitives for Polaris.
Distributed Systemsv0.1.0
eshanized-polaris-cli
Terminal command-line utility for managing Polaris clusters, scheduling jobs, and live telemetry streaming.
Concurrencyv0.1.0
veda-rs
High-performance parallel runtime with adaptive work-stealing scheduling and lock-free thread coordination.
Developer Toolingv0.1.0
dep-insight
Cargo dependency analysis, duplicate crate detection, and security audit visualizer for Rust workspaces.
Engineering Systems & Tooling
Deterministic AST parsing, work-stealing scheduling, multi-remote Git synchronization
01Active
Veda
High-performance Rust parallel runtime
A parallel execution runtime in Rust featuring adaptive work-stealing scheduling, lock-free queues, and NUMA-aware thread affinity for compute-intensive pipelines.
Veda is engineered for workloads where standard thread pools bottleneck on task synchronization. Its adaptive work-stealing deque minimizes thread contention and delivers microsecond-level dispatch latency.
RustConcurrencyWork-StealingLock-FreeNUMA
02Released
dep-insight
Rust dependency analysis & audit visualizer
A developer tool for analyzing Cargo dependency trees, discovering transitive vulnerabilities, identifying duplicate crates, and visualizing crate dependency graphs directly in the terminal.
dep-insight inspects your Cargo lockfile and metadata to pinpoint bloated transitive dependencies, version duplicates, and dependency cycle risks before your build slows down.
RustCargo MetadataGraph AlgorithmsCLITUI
03Released
multigit
Multi-remote Git fleet synchronization CLI
Cross-platform Git synchronization utility for developers managing repositories mirrored across GitHub, GitLab, and self-hosted remotes simultaneously.
multigit orchestrates parallel push, fetch, and branch verification across multiple remote providers with conflict detection and unified status reporting.
RustGit InternalsConcurrencyCLI
04Building
Octate
Terminal-native AI code review CLI
Combines local Tree-sitter WASM AST parsing, cross-file reference graphs, and static linter discovery with multi-stage LLM review DAGs and a two-stage critic quality gate.
Octate brings deterministic repository intelligence together with LLM reasoning to produce code reviews that understand your codebase, not just your diff.
RustTree-sitterWASMLLMsAST Parsing
05Experimental
SLMGen
Small-language-model experimentation infrastructure
Infrastructure for experimenting with small language models — fine-tuning, evaluation, and training pipelines built around practical ML engineering and quantization.
SLMGen provides the scaffolding for experimenting with compact language models, making it practical to fine-tune, evaluate, and iterate without massive compute budgets.
PythonPyTorchSLMsLoRAFine-tuning
06Building
MCP SuperAssistant
Developer tooling for the Model Context Protocol
Developer tooling built around the Model Context Protocol ecosystem, making it easier to build, test, and compose MCP-compatible tools, prompts, and integrations.
MCP SuperAssistant is a collection of tools that make the Model Context Protocol more approachable — reducing the friction of building and composing MCP integrations.
TypeScriptMCPDeveloper ToolsAsync I/O
07 — Engineering Philosophy
The standards
behind the work.
These are not aspirational values posted to a mission page. They are the constraints that shape every design decision — from the structure of a module hierarchy to the behavior of a security boundary under failure.
Good engineering is not about the number of abstractions or the elegance of the code. It is about whether the system does what it claims, continues doing it under stress, and can be understood by someone who was not there when it was built.
01
01
Architecture supports maintainability
Systems should be designed so that the next person to touch the code — including your future self — can reason about them without spelunking through layers of indirection.
02
02
Reliability is verified, not asserted
A model declaring success is not proof. Completion requires empirical evidence: clean compilers, passing tests, and verifiable digests. Claims are rejected until proven.
03
03
Developer experience is system design
The ergonomics of a tool — its installation, its interface, its error messages — are not polish applied at the end. They are architectural decisions made from the first commit.
04
04
Open source should be useful and understandable
Shipping code in the open is not the same as making it accessible. Documentation, structure, and honest status labels matter as much as the implementation itself.
05
05
Automation needs boundaries and observability
Autonomous systems must fail closed. Every action should pass through non-bypassable gates, and every execution state should be observable and recoverable.
06
06
Performance improvements require measurement
Optimization without measurement is guessing. Improvements should be supported by data, not by intuition about what feels faster.
08 — The Road Ahead
What comes
next.
Research interests and areas of active exploration. These are directions, not promises — none are presented as completed products or guaranteed capabilities.
Each item is clearly labeled with its actual status.
01
More capable autonomous coding systems
Exploring how autonomous agents can move beyond single-mission execution toward long-running, multi-session engineering work with stronger guarantees.
02
Better developer infrastructure
Building the tools that sit underneath applications — code hosting, review systems, and the infrastructure that makes development reliable.
03
Reliable systems tooling
Low-level systems work: storage engines, process confinement, and the foundations that everything else depends on.
04
Open-source ecosystems
Contributing to and maintaining open-source ecosystems that can be understood, owned, and depended on by the people who use them.
The work continues.
The story is not finished.
09 — Closing & Contact
eshanized
Eshan Roy
Engineering, open source, and building useful systems.
The relationship between systems engineering and open source is simple: build things that can be understood, owned, and depended on. That is the work. That is the story.