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The article explores the history and purpose of the equal sign (=), delving into its mathematical and linguistic origins, as well as its evolution in computer programming and various fields of study.

Rentahuman.ai is an AI-powered platform that connects individuals with virtual assistants, offering a range of services including administrative support, research, and content creation. The platform leverages artificial intelligence and machine learning to provide personalized, on-demand assistance to users.

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

A new study suggests that reducing screen time may help improve children's mental health, as excessive screen use is linked to negative impacts on well-being. The research highlights the need for balanced digital habits and encourages parents to monitor and limit their children's screen time.

The article explores how data brokers can enable violence against public servants by selling sensitive personal information, which can be used to harass or threaten them. It highlights the need for better regulation and oversight to protect the privacy and safety of government officials and other public figures.

The article discusses the PreK project, a GitHub repository focused on providing early childhood education resources, including lesson plans, activities, and teaching materials for preschool and kindergarten teachers.

The European Union is seeking to assert its 'digital sovereignty' by regulating tech giants and promoting homegrown digital services, in an effort to reduce its reliance on U.S. and Chinese firms and gain more control over its digital landscape.

The article discusses the DIFI project, an open-source initiative that aims to create a decentralized, interoperable finance infrastructure. It highlights the project's goals of enabling seamless cross-blockchain transactions and fostering a more inclusive and transparent financial ecosystem.

Hi everyone,

I built a runtime to isolate untrusted code using wasm sandboxes.

Basically, it protects your host system from problems that untrusted code can cause. We’ve had a great discussion about sandboxing in Python lately that elaborates a bit more on the problem [1]. In TypeScript, wasm integration is even more natural thanks to the close proximity between both ecosystems.

The core is built in Rust. On top of that, I use WASI 0.2 via wasmtime and the component model, along with custom SDKs that keep things as idiomatic as possible.

For example, in Python we have a simple decorator:

  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"}
  });

It's still quite early, but I'd love feedback. I’ll be around to answer questions.

GitHub: https://github.com/mavdol/capsule

[1] https://news.ycombinator.com/item?id=46500510

A security vulnerability in WhatsApp allows malicious media files to spread through group chats, posing a potential threat to users' devices and data. The bug enables the execution of malicious code on targeted devices, highlighting the importance of maintaining the security and privacy of messaging applications.

The article discusses a new feature in GitHub that allows users to see which AI model was used to contribute to a pull request, providing transparency and accountability around the use of AI in software development.

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.

Deno introduces Deno Sandbox, a secure and isolated environment for running untrusted code, allowing developers to safely execute scripts without compromising system security. Deno Sandbox leverages WebAssembly and other security features to provide a robust sandbox for running potentially unsafe code in a controlled manner.

The article 'Boring Go' explores the benefits of writing simple, straightforward Go code that is easy to understand and maintain, rather than overly complex or feature-rich implementations. It emphasizes the value of prioritizing clarity, readability, and maintainability over perceived technical sophistication.

The article examines the debate surrounding the accuracy of death toll reporting from the conflict between Israel and Gaza, with critics arguing that the figures may be inflated or distorted for political purposes, while defenders maintain that the data is reliable and transparent.

The article discusses Shakespeare's Sonnet 5, which explores themes of time, transience, and the preservation of beauty through poetry. It analyzes the poetic techniques and imagery used in the sonnet to convey these ideas.

The article explores the growing backlash against big tech companies and the emergence of alternative social media platforms, such as Upscrolled, that aim to provide a more ethical and user-centric approach to online interactions and content moderation.

Hi all,

I have built Cimba, a multithreaded discrete event simulation library in C.

Cimba uses POSIX pthread multithreading for parallel execution of multiple simulation trials, while coroutines provide concurrency inside each simulated trial universe. The simulated processes are based on asymmetric stackful coroutines with the context switching hand-coded in assembly.

The stackful coroutines make it natural to express agentic behavior by conceptually placing oneself "inside" that process and describing what it does. A process can run in an infinite loop or just act as a one-shot customer passing through the system, yielding and resuming execution from any level of its call stack, acting both as an active agent and a passive object as needed. This is inspired by my own experience programming in Simula67, many moons ago, where I found the coroutines more important than the deservedly famous object-orientation.

Cimba turned out to run really fast. In a simple benchmark, 100 trials of an M/M/1 queue run for one million time units each, it ran 45 times faster than an equivalent model built in SimPy + Python multiprocessing. The running time was reduced by 97.8 % vs the SimPy model. Cimba even processed more simulated events per second on a single CPU core than SimPy could do on all 64 cores.

The speed is not only due to the efficient coroutines. Other parts are also designed for speed, such as a hash-heap event queue (binary heap plus Fibonacci hash map), fast random number generators and distributions, memory pools for frequently used object types, and so on.

The initial implementation supports the AMD64/x86-64 architecture for Linux and Windows. I plan to target Apple Silicon next, then probably ARM.

I believe this may interest the HN community. I would appreciate your views on both the API and the code. Any thoughts on future target architectures to consider?

Docs: https://cimba.readthedocs.io/en/latest/

Repo: https://github.com/ambonvik/cimba

Hi HN,

We built LUML (https://github.com/luml-ai/luml), an open-source (Apache 2.0) MLOps/LLMOps platform that covers experiments, registry, LLM tracing, deployments and so on.

It separates the control plane from your data and compute. Artifacts are self-contained. Each model artifact includes all metadata (including the experiment snapshots, dependencies, etc.), and it stays in your storage (S3-compatible or Azure).

File transfers go directly between your machine and storage, and execution happens on compute nodes you host and connect to LUML.

We’d love you to try the platform and share your feedback!

Proton, a privacy-focused tech company, successfully raised over $2 million through a lifetime fundraiser, allowing them to continue building secure and private digital solutions for their users.