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Cargo Shear is a command-line tool that helps developers optimize their Rust code by identifying and removing unused dependencies, reducing the size of compiled binaries. This tool integrates seamlessly with the Cargo build system, providing users with a simple and efficient way to streamline their Rust projects.

Hi HN,

I've built an MCP Server for Himalayas (https://himalayas.app) to make our entire recruiting workflow accessible via AI chat.

It's great seeing Claude able to search for talent for a role, evaluate them, and then message them all by asking something like "Find me talent for <X> role in Australia".

I've also integrated the ability to manage and post job listings (including with Stripe checkout for paid postings).

Open to any ideas for how to build this out further and simplify the employer's workflow.

react-redact is a zero-dependency React “demo-safe mode” you can toggle with a single keyboard shortcut.

Wrap your app in <RedactProvider> and hit Cmd+Shift+X (or Ctrl+Shift+X) to instantly blur, mask, or replace marked (or auto-detected) PII like emails, phone numbers, SSNs, credit cards, and IPs.

It’s a visual-only helper for demos and screenshares (it does not remove data from the DOM), and it’s Next.js and TypeScript friendly.

E OS is an open-source operating system designed to provide a secure and private digital experience. It focuses on user privacy, secure communications, and ethical technology, aiming to empower individuals in an increasingly digital world.

The article discusses the European Parliament's efforts to reduce the dominance of Visa and Mastercard in the European payments market by accelerating the development of a digital euro. It highlights the potential benefits of a European-controlled digital currency, including increased competition and reduced transaction fees.

The article discusses best practices for organizing files and folders in an Angular application, emphasizing principles such as modularization, separation of concerns, and maintainability. It provides guidelines for structuring code, organizing shared resources, and managing feature-specific components to improve the overall scalability and organization of the project.

Researchers have successfully trained brain organoids, miniature 3D models of the human brain, to perform goal-directed learning tasks, providing new insights into how the brain processes information and learns.

I got tired of coding agents that feel like IDEs in disguise. Slow to start, full of chrome, opinionated about where you work. So I built one that doesn't get in the way.

It's called *mini-coder*. The command is `mc`. Tagline: Small. Fast. Gets out of your way.

Oh — and it mostly wrote itself (with some help from me).

---

## What it is

mini-coder is a terminal-native AI coding agent built on *Bun.js* — fast startup, native TypeScript, built-in SQLite, no ceremony. It routes to *Anthropic, OpenAI, Google, Ollama, and Zen* and auto-detects your provider from env vars. No config file to write. No account to create. No telemetry.

Session memory lives in local SQLite. Resume your last session with `mc -c`, or pick one with `mc -r <id>`. Your history stays on your machine.

---

## How it works

The LLM gets a tight set of tools: `glob`, `grep`, `read`, `create`, `replace`, `insert`, `shell`, and `subagent`. That last one is the interesting bit — mini-coder can spawn parallel sub-agents for independent subtasks, up to 3 levels deep. It delegates, waits, synthesizes. It works the way you do.

Inside the REPL, `!command` runs shell commands inline and `@file` references files with tab completion. Slash commands cover the essentials: `/model` to switch providers, `/plan` for read-only mode, `/ralph` for a fully autonomous loop, `/review`, `/undo`, `/new`, and `/help`.

16 ANSI colors only. It inherits your terminal theme. Always.

---

## The `.agents` folder

This is the part is to help with community configurations. mini-coder follows the `.agents` convention that's emerging across the AI coding tool ecosystem — the same one used by Claude Code, Opencode, and others. Write your config once, and it works everywhere. (It supports the .claude conventions as well out of the box!)

Here's what drops in there:

- *`.agents/commands/.md`* — a Markdown file becomes a slash command - *`.agents/agents/.md`* — a Markdown file becomes a custom agent, callable with `@agent-name` - *`.agents/skills/<name>/SKILL.md`* — reusable skills you inject with `@skill-name` - *`.agents/hooks/post-<tool>`* — scripts that auto-run after any tool call (e.g., auto-format after every file write) - *`AGENTS.md`* — project context injected into every system prompt

MCP is supported too — connect tools like Exa web search or the MMO Spacemolt all work. Wire them in via `/mcp`.

The philosophy here is community over invention. Follow existing conventions. Don't make people learn a new system.

---

## Get it

```sh bun add -g mini-coder # or npm install -g mini-coder ```

GitHub: [https://github.com/sacenox/mini-coder](https://github.com/sacenox/mini-coder)

---

The best tools disappear into your workflow. That's what I was going for. Here's what the early adopter had to say:

> "sean this is fucking sick" — vpr99

That's good enough for me. Give it a spin. I would love some feedback, contributions, or just a star on. Github!

This article explores the inner workings of Twitter's algorithm and how it shapes the user experience. It discusses the role of personalization, engagement-driven curation, and the potential impact on the spread of information on the platform.

Hey HN!

Over the past few months, I've been working on building Omni - a workplace search and chat platform that connects to apps like Google Drive/Gmail, Slack, Confluence, etc. Essentially an open-source alternative to Glean, fully self-hosted.

I noticed that some orgs find Glean to be expensive and not very extensible. I wanted to build something that small to mid-size teams could run themselves, so I decided to build it all on Postgres (ParadeDB to be precise) and pgvector. No Elasticsearch, or dedicated vector databases. I figured Postgres is more than capable of handling the level of scale required.

To bring up Omni on your own infra, all it takes is a single `docker compose up`, and some basic configuration to connect your apps and LLMs.

What it does:

- Syncs data from all connected apps and builds a BM25 index (ParadeDB) and HNSW vector index (pgvector) - Hybrid search combines results from both - Chat UI where the LLM has tools to search the index - not just basic RAG - Traditional search UI - Users bring their own LLM provider (OpenAI/Anthropic/Gemini) - Connectors for Google Workspace, Slack, Confluence, Jira, HubSpot, and more - Connector SDK to build your own custom connectors

Omni is in beta right now, and I'd love your feedback, especially on the following:

- Has anyone tried self-hosting workplace search and/or AI tools, and what was your experience like? - Any concerns with the Postgres-only approach at larger scales?

Happy to answer any questions!

The code: https://github.com/getomnico/omni (Apache 2.0 licensed)

I built a tool for static websites — blogs, docs, marketing pages — that lets visitors ask questions and get answers from your actual content.

You paste your URL, it crawls the pages, indexes them into a vector store, and hands you a script tag. The bot answers only from what's on your site and cites the specific pages it used.

Works well for content-heavy sites where people have questions but don't want to dig through navigation.

Stack: Rust on Cloudflare Workers, Vectorize, Cloudflare Queues.

when the AI so ahh you genuinely js take over its job

GraphToy is an interactive tool that allows users to create and visualize mathematical functions and their graphs in real-time. It provides a intuitive interface for exploring a variety of functions and their properties.

The article explores a vulnerability in Go's x509 certificate verification process, where a malicious actor can bypass certificate verification and establish a secure connection with a server. The author demonstrates a proof-of-concept attack and discusses the implications of this vulnerability for secure communication in Go applications.

The article provides a brief introduction to the SNOBOL programming language, highlighting its unique pattern-matching capabilities and its potential applications in text processing tasks. It outlines the basic syntax and structure of SNOBOL programs to give readers a general understanding of the language.

The tweet suggests that using software to automate routine tasks can free up time and mental energy to focus on more important, high-impact work. It emphasizes the value of leveraging technology to become more productive and efficient.

This article provides an overview of the AI Bot Skills feature, which allows developers to create custom AI-powered skills for their chatbots. It discusses the different types of skills, the development process, and how to integrate the skills into a chatbot application.

A Waymo autonomous vehicle in Austin, Texas blocked an ambulance following gunshots in an entertainment district, raising concerns about the interaction between self-driving cars and emergency services. The incident highlights the need to address such issues as self-driving technology becomes more prevalent.

Most AI systems today rely on probabilistic recall: RAG, embeddings, and prompt-based memory.

This makes it hard to enforce invariants, audit facts, or maintain a clear separation between reasoning and ground truth.

I built a minimal proof-of-concept showing a different approach: a deterministic symbolic memory layer accessible via MCP.

Instead of storing “memory inside the model”, knowledge is resolved just-in-time from an explicit symbolic layer.

The goal is not to replace RAG or assistant memory, but to provide a missing infrastructure layer: a controllable knowledge backbone for AI systems.

This repo demonstrates the minimal viable form of that idea.

The article explores the current and future trends in software development, including the rise of low-code and no-code platforms, the increasing importance of data and AI, and the growing need for multidisciplinary teams and continuous learning.

The article examines the Commodore 1541 diskette drive, a popular storage device for the Commodore 64 home computer. It provides an overview of the drive's technical specifications, its role in the Commodore ecosystem, and the challenges users faced in utilizing it effectively.

The article explores the parallels between the world depicted in George Orwell's novel '1984' and the ongoing 'forever wars' of the modern era, highlighting how the dynamics of surveillance, propaganda, and power struggles remain relevant today.

The article discusses the use of 'distorting time' as a tool for identifying assumptions. By considering how a situation would unfold over a different time frame, individuals can uncover hidden assumptions and biases that shape their thinking.

AgentFlow is an open-source system that enables the creation of intelligent software agents, with a focus on modularity, flexibility, and scalability. It provides a framework for developing and deploying agent-based applications across various domains.

The article explores author Junot Díaz's perspective on George Orwell's essay 'Hate Week,' examining how the themes of nationalism, jingoism, and demonization of the 'other' resonate in contemporary politics and society.

This pull request introduces a new dynamic scheduler for TornadoVM, a framework for heterogeneous computing. The new scheduler aims to improve the performance and efficiency of task scheduling on various hardware accelerators, including GPUs and FPGAs.

The article discusses the author's experience of being banned from the Lobsters community, a curated online forum for programmers, due to perceived violations of the community's guidelines. It explores the implications of such bans and the challenges of moderating online communities.