Hazumi News

Hi HN,

I'm Emilie, I have a literature background (which explains the well-written documentation!) and I've been learning Rust and distributed systems by building minikv over the past few months. It recently got featured in Programmez! magazine: https://www.programmez.com/actualites/minikv-un-key-value-st...

minikv is an open-source, distributed storage engine built for learning, experimentation, and self-hosted setups. It combines a strongly-consistent key-value database (Raft), S3-compatible object storage, and basic multi-tenancy.

Features/highlights:

- Raft consensus with automatic failover and sharding - S3-compatible HTTP API (plus REST/gRPC APIs) - Pluggable storage backends: in-memory, RocksDB, Sled - Multi-tenant: per-tenant namespaces, role-based access, quotas, and audit - Metrics (Prometheus), TLS, JWT-based API keys - Easy to deploy (single binary, works with Docker/Kubernetes)

Quick demo (single node):

```bash git clone https://github.com/whispem/minikv.git cd minikv cargo run --release -- --config config.example.toml curl localhost:8080/health/ready

# S3 upload + read curl -X PUT localhost:8080/s3/mybucket/hello -d "hi HN" curl localhost:8080/s3/mybucket/hello

Docs, cluster setup, and architecture details are in the repo. I’d love to hear feedback, questions, ideas, or your stories running distributed infra in Rust!

Repo: https://github.com/whispem/minikv Crate: https://crates.io/crates/minikv

from capsule import task @task( name="analyze_data", compute="MEDIUM", ram="512mb", allowed_files=["./authorized-folder/"], timeout="30s", max_retries=1 ) def analyze_data(dataset: list) -> dict: """Process data in an isolated, resource-controlled environment.""" # Your code runs safely in a Wasm sandbox return {"processed": len(dataset), "status": "complete"}

import { task } from "@capsule-run/sdk" export const analyze = task({ name: "analyzeData", compute: "MEDIUM", ram: "512mb", allowedFiles: ["./authorized-folder/"], timeout: 30000, maxRetries: 1 }, (dataset: number[]) => { return {processed: dataset.length, status: "complete"} });

Hello HN. I’d like to start by saying that I am a developer who started this research project to challenge myself. I know standard protocols like MCP exist, but I wanted to explore a different path and have some fun creating a communication layer tailored specifically for desktop applications.

The project is designed to handle communication between desktop apps in an agentic manner, so the focus is strictly on this IPC layer (forget about HTTP API calls).

At the heart of RAIL (Remote Agent Invocation Layer) are two fundamental concepts. The names might sound scary, but remember this is a research project:

Memory Logic Injection + Reflection Paradigm shift: The Chat is the Server, and the Apps are the Clients.

Why this approach? The idea was to avoid creating huge wrappers or API endpoints just to call internal methods. Instead, the agent application passes its own instance to the SDK (e.g., RailEngine.Ignite(this)).

Here is the flow that I find fascinating:

-The App passes its instance to the RailEngine library running inside its own process.

-The Chat (Orchestrator) receives the manifest of available methods.The Model decides what to do and sends the command back via Named Pipe.

-The Trigger: The RailEngine inside the App receives the command and uses Reflection on the held instance to directly perform the .Invoke().

Essentially, I am injecting the "Agent Logic" directly into the application memory space via the SDK, allowing the Chat to pull the trigger on local methods remotely.

A note on the Repo: The GitHub repository has become large. The core focus is RailEngine and RailOrchestrator. You will find other connectors (C++, Python) that are frankly "trash code" or incomplete experiments. I forced RTTR in C++ to achieve reflection, but I'm not convinced by it. Please skip those; they aren't relevant to the architectural discussion.

I’d love to focus the discussion on memory-managed languages (like C#/.NET) and ask you:

-Architecture: Does this inverted architecture (Apps "dialing home" via IPC) make sense for local agents compared to the standard Server/API model?

-Performance: Regarding the use of Reflection for every call—would it be worth implementing a mechanism to cache methods as Delegates at startup? Or is the optimization irrelevant considering the latency of the LLM itself?

-Security: Since we are effectively bypassing the API layer, what would be a hypothetical security layer to prevent malicious use? (e.g., a capability manifest signed by the user?)

I would love to hear architectural comparisons and critiques.