New stories

How to digitalise the engineering diagram

Launchie is a web app that helps entrepreneurs launch and grow their businesses by providing tools, resources, and a community of like-minded individuals. The platform offers features such as marketing assistance, financial planning, and access to a network of mentors and investors.

The article describes the development of Doolang, a new programming language designed to be simple, efficient, and easy to learn. It highlights Doolang's key features, including its concise syntax, strong type system, and focus on developer productivity.

The article discusses Hopper, an open-source library for building distributed systems. Hopper provides a framework for creating and managing distributed applications, including features like task scheduling, data storage, and fault tolerance.

The article discusses Rsyslog, a popular open-source logging software, and its shift towards an AI-first approach. It highlights Rsyslog's vision to incorporate advanced machine learning and artificial intelligence capabilities to enhance log management and analysis.

The article discusses Oscar, a live coding environment that allows users to write, run, and share code directly in the browser. It highlights Oscar's features, such as real-time collaboration and the ability to embed code snippets on websites.

The article discusses a pure Go implementation of the Prism syntax highlighting library, which allows developers to add syntax highlighting to their Go applications without needing to rely on external dependencies or external processes.

I made an AI tool that applies to jobs via cold email because LinkedIn Easy Apply was getting me nowhere. Most applications just disappear into ATS filters, so I wanted something that sends a personalised application directly to the company or HR.

The tool extracts information from your resume, matches it with the job description, writes a personalised email in your style, and either finds the company email automatically or lets you set your own. It then sends the email with your CV attached through your own personal email account.

There are two modes: Rapid Mode uses LinkedIn search results, and CSV Mode lets you upload a list of jobs and apply to all of them in one go.

I originally built it for myself, but decided to share it in case it helps anyone else dealing with the same frustrations. Happy to answer questions or get feedback from the community.

The article discusses concerns that some sperm banks in Denmark are rejecting donors based on their IQ scores, potentially leading to genetic discrimination in assisted reproduction. This practice raises ethical questions about the use of genetic information in donor selection.

The article discusses the 'mundanity of excellence' - the idea that exceptional performance is often the result of consistent, focused effort over time, rather than natural talent. It examines the training and dedication of Olympic swimmers, highlighting the importance of routine, discipline, and a supportive environment in achieving elite-level success.

FigmaTrackJS is an open-source JavaScript library that allows developers to integrate Figma prototyping and design into their web applications. The library provides a simple API for interacting with Figma designs, enabling features like live previews, interactive components, and design data retrieval.

I built a RAG application that retrieves relevant Bhagavad Gita verses for ethical dilemmas and generates structured guidance.

The problem: The Gita has 701 verses. Finding applicable wisdom for a specific situation requires either deep familiarity or hours of reading.

How it works: 1. User describes their ethical dilemma 2. Query is embedded using sentence-transformers 3. ChromaDB retrieves top-k semantically similar verses 4. LLM generates structured output: 3 options with tradeoffs, implementation steps, verse citations

Tech stack: - Backend: FastAPI, PostgreSQL, Redis - Vector DB: ChromaDB with all-MiniLM-L6-v2 embeddings - LLM: Ollama (qwen2.5:3b) primary, Anthropic Claude fallback - Frontend: React + TypeScript + Tailwind

Key design decisions: - RAG to prevent hallucination — every recommendation cites actual verses - Confidence scoring flags low-quality outputs for review - Structured JSON output for consistent UX - Local LLM option for privacy and zero API costs

What I learned: - LLM JSON extraction is harder than expected. Built a three-layer fallback (direct parse → markdown block extraction → raw_decode scanning) - Semantic search on religious texts works surprisingly well for ethical queries - Smaller models (3B params) work fine when constrained by good prompts and retrieved context

GitHub: https://github.com/geetanjaliapp/geetanjali

Happy to discuss the RAG architecture or take feedback.

The article discusses the burden of tariffs and trade barriers on US businesses, highlighting the complexity and administrative costs associated with navigating the ever-changing trade policies, which can have a significant impact on their operations and profitability.

The article discusses the emerging 'Moon economy' and the legal ambiguity around who has the right to extract and utilize resources from the Moon. It examines the current international treaties and the challenges in establishing clear rules and regulations for lunar resource exploitation.

The article discusses the challenges faced by the British National Health Service (NHS) as it grapples with a growing backlog of patients waiting for routine operations and appointments due to the COVID-19 pandemic. It highlights the strain on the healthcare system and the efforts being made to address the issue.

The article discusses the importance of quality over quantity in wildlife photography, emphasizing the need to focus on capturing exceptional images rather than simply taking a high volume of photographs. It highlights the value of patience, attention to detail, and a thoughtful approach to composing and framing wildlife subjects.

Hi HN,

Over the last few months I’ve been working on IntentusNet, a small, language-agnostic runtime for routing “intents” between agents and tools, with optional encryption and multiple transports.

GitHub: https://github.com/Balchandar/intentusnet

What problem this tries to solve

Most multi-agent / tool-calling setups I’ve seen end up as a lot of ad-hoc glue:

- custom message formats between each agent - hand-rolled routing and fallback logic - HTTP in one place, WebSocket in another, maybe ZeroMQ somewhere else - no consistent tracing or error model - no clear place to add security (encryption, provenance, identity chain)

MCP is great for describing tools, but it doesn’t try to be a runtime or router. I wanted something that sits underneath or alongside MCP and other stacks, and just answers:

“Given an intent, which agent should handle it, through which transport, with what fallback, and how do we wrap it securely?”

What IntentusNet provides (today)

At the core it has a few small primitives:

- IntentEnvelope – structured message with context, metadata, routing options, and tags - AgentRegistry – in-memory registry of agents and capabilities (which intents they handle, optional fallback chains) - IntentRouter – picks an agent, executes it, and applies fallback if the primary fails - Transports – pluggable transports, currently: - in-process (direct router call) - HTTP (POST with a TransportEnvelope) - WebSocket (duplex, async) - ZeroMQ (REQ/REP client plus simple server) - Tracing – simple trace sink that records spans (agent, intent, latency, status, error) - EMCL (Encrypted Model Context Layer) – optional envelope: - a simple HMAC-based demo provider - an AES-GCM provider (AES-256-GCM, base64, identity chain) for real encryption

There’s also an MCP adapter that takes an MCP tool request ({ name, arguments }), wraps it into an IntentEnvelope, routes it through IntentusNet, and returns an MCP-style result. The idea is that MCP tools can be wired through the same router, with or without EMCL.

Example usage

A minimal flow looks like:

- define agents with capabilities like “summarize.document.v1”, “translate.text.v1”, “store.note.v1” - register them in the runtime - use the IntentusClient to send an intent with a payload; the router picks the right agent and handles fallback if configured

There’s also an example of a small “assistant”-style setup where an NLU-like agent parses a natural language request and emits downstream intents to specialized agents (calendar, maps, etc.), just to show multi-agent routing in practice.

Status / what’s missing

This is early-stage:

- Runtime is in Python; other SDKs (for example C#) are not ready yet - Orchestrator / workflow layer (sequences, parallel steps, branching) is sketched out in RFCs but only partially implemented - Docs and examples can definitely be improved - No claims of production readiness yet: it’s more of a structured reference implementation or starting point

I’d really appreciate feedback on:

- the architecture (does the separation between protocol, router, transports, EMCL make sense?) - whether this kind of intent router is actually useful under MCP or tool-based systems you’re building - missing primitives you’d expect in a runtime like this (timeouts, backpressure, richer tracing, etc.) - real-world workflows where a small, transport-agnostic, EMCL-capable router would help (or where it’s overkill)

If you’re working with MCP, multi-agent setups, or secure tool calling and have opinions on how this should work, I’d love to hear them—either here or via issues or PRs on GitHub.

The article discusses controversial comments made by venture capitalist Joe Lonsdale, who called for public hangings of government officials responsible for pandemic-related lockdowns. The article examines the reaction to Lonsdale's remarks and the broader debate surrounding government policies during the COVID-19 pandemic.

This article discusses the use of GitHub Actions as a powerful package manager, highlighting its ability to streamline the installation and management of dependencies for various programming languages and frameworks, making it a versatile and efficient tool for software development workflows.

The article discusses the watermelon mosaic virus, a common and destructive plant virus that affects watermelon crops. It explains the symptoms, transmission, and control methods for this virus, providing information for farmers and gardeners to identify and manage this agricultural pest.

I'm a founder (and ex-architect) building a logistics OS. Recently, I received feedback that my site looks "cheap and ugly" because I used Serif fonts and an engraving style aesthetic instead of the standard Sans-Serif "Clean Tech" look.

My intent was to evoke the "Age of Exploration" vibe, since the AI era feels like charting unknown territories. But users seem conditioned to trust only "Standard Blue SaaS UI."

My question to HN: Does a B2B tool have to follow the "Standard Modern UI" to be taken seriously? Or is there room for distinctive, maybe even polarizing, aesthetics in enterprise software?

I'm debating whether to cave in and redesign to "boring but safe" or double down on our soul. Would love to hear your thoughts on "Brand Distinctiveness vs. UI Familiarity."

The article discusses the tradeoffs between different package managers, highlighting their strengths and weaknesses in areas such as dependency management, security, and community support. It provides a balanced comparison to help developers choose the right package manager for their project needs.