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Voyager 1, the iconic NASA spacecraft, is nearing a significant milestone as it approaches a distance of one light-day from Earth. This remarkable achievement highlights the enduring legacy of the Voyager program and its ongoing exploration of the outer reaches of our solar system.

Related: Someone at YouTube needs glasses - https://news.ycombinator.com/item?id=43846487 - April 2025 (694 comments)

The article discusses a new Google project called 'Antigravity' that can reportedly exfiltrate data from computers without physically accessing them. It raises concerns about the potential security and privacy implications of such a technology.

The article discusses Zig's decision to migrate its project from GitHub to Codeberg, citing Codeberg's focus on user privacy and ethical technology as key factors in the move. The article outlines the steps taken during the migration process and the benefits Zig expects to see from the new platform.

The article discusses the common causes of software failures in IT management, including poor requirements gathering, inadequate testing, and lack of project management. It highlights the importance of addressing these issues to ensure successful software implementation and adoption.

The article discusses the ongoing challenges facing the European Union as it navigates the recovery from the COVID-19 pandemic. It examines the political and economic tensions within the EU, particularly around issues of fiscal policy and the distribution of financial aid.

The European Union has mandated the adoption of new Wi-Fi standards, leading Apple to implement these standards in its devices. As a result, Android devices can now support AirDrop, a popular file-sharing feature previously exclusive to Apple's ecosystem.

This article criticizes the over-reliance on AI and automation, arguing that human empathy, creativity, and judgment are still essential in many situations. It cautions against blindly trusting AI systems and highlights the importance of maintaining a balance between human and machine capabilities.

Kagi, a startup focused on building a new search engine, has announced the launch of its 'Orion' project, which aims to challenge the dominance of major tech companies in the search market by offering a user-centric, privacy-focused alternative that leverages blockchain technology.

Penpot is an open-source, multi-page design and prototyping platform that allows teams to collaborate on digital design projects. It provides features like version control, real-time collaboration, and export options for various file formats.

The article explores an interview with Ilya Sutskever, co-founder and chief scientist at OpenAI, who discusses the potential of artificial general intelligence (AGI), the challenges in developing safe and ethical AGI, and his views on the long-term implications of advanced AI systems.

Hi HN! For the past bit we’ve been building Flowglad (https://flowglad.com) and can now feel it’s just gotten good enough to share with you all:

Repo: https://github.com/flowglad/flowglad

Demo video: https://www.youtube.com/watch?v=G6H0c1Cd2kU

Flowglad is a payment processor that you integrate without writing any glue code. Along with processing your payments, it tells you in real time the features and usage credit balances that your customers have available to you based on their billing state. The DX feels like React, because we wanted to bring the reactive programming paradigm to payments.

We make it easy to spin up full-fledged pricing models (including usage meters, feature gates and usage credit grants) in a few clicks. We schematize these pricing models into a pricing.yaml file that’s kinda like Terraform but for your pricing.

The result is a payments layer that AI coding agents have a substantially easier time one-shotting (for now the happiest path is a fullstack Typescript + React app).

Why we built this:

- After a decade of building on Stripe, we found it powerful but underopinionated. It left us doing a lot of rote work to set up fairly standard use cases - That meant more code to maintain, much of which is brittle because it crosses so many server-client boundaries - Not to mention choreographing the lifecycle of our business domain with the Stripe checkout flow and webhook event types, of which there are 250+ - Payments online has gotten complex - not just new pricing models for AI products, but also cross border sales tax, etc. You either need to handle significant chunks of it yourself, or sign up for and compose multiple services

This all feels unduly clunky, esp when compared to how easy other layers like hosting and databases have gotten in recent years.

These patterns haven’t changed much in a decade. And while coding agents can nail every other rote part of an app (auth, db, analytics), payments is the scariest to tab-tab-tab your way through. Because the the existing integration patterns are difficult to reason about, difficult to verify correctness, and absolutely mission critical.

Our beta version lets you:

- Spin up common pricing models in just a few clicks, and customize them as needed - Clone pricing models between testmode and live mode, and import / export via pricing.yaml - Check customer usage credits and feature access in real time on your backend and React frontend - Integrate without any DB schema changes - you reference your customers via your ids, and reference prices, products, features and usage meters via slugs that you define

We’re still early in our journey so would love your feedback and opinions. Billing has a lot of use cases, so if you see anything that you wish we supported, please let us know!

The article argues that people should be cautious about downloading apps, as they can pose risks to privacy and security. It suggests alternative solutions, such as using web-based services and being mindful of the permissions granted to apps.

The article discusses Flux, a popular open-source front-end framework used for building complex user interfaces. It provides an overview of Flux's architecture, its key features, and how it differs from other state management solutions like Redux.

I got DOOM running in KiCad by rendering it with PCB traces and footprints instead of pixels.

Walls are rendered as PCB_TRACK traces, and entities (enemies, items, player) are actual component footprints - SOT-23 for small items, SOIC-8 for decorations, QFP-64 for enemies and the player.

How I did it:

Started by patching DOOM's source code to extract vector data directly from the engine. Instead of trying to render 64,000 pixels (which would be impossibly slow), I grab the geometry DOOM already calculates internally - the drawsegs[] array for walls and vissprites[] for entities.

Added a field to the vissprite_t structure to capture entity types (MT_SHOTGUY, MT_PLAYER, etc.) during R_ProjectSprite(). This lets me map 150+ entity types to appropriate footprint categories.

The DOOM engine sends this vector data over a Unix socket to a Python plugin running in KiCad. The plugin pre-allocates pools of traces and footprints at startup, then just updates their positions each frame instead of creating/destroying objects. Calls pcbnew.Refresh() to update the display.

Runs at 10-25 FPS depending on hardware. The bottleneck is KiCad's refresh, not DOOM or the data transfer.

Also renders to an SDL window (for actual gameplay) and a Python wireframe window (for debugging), so you get three views running simultaneously.

Follow-up: ScopeDoom

After getting the wireframe renderer working, I wanted to push it somewhere more physical. Oscilloscopes in X-Y mode are vector displays - feed X coordinates to one channel, Y to the other. I didn't have a function generator, so I used my MacBook's headphone jack instead.

The sound card is just a dual-channel DAC at 44.1kHz. Wired 3.5mm jack → 1kΩ resistors → scope CH1 (X) and CH2 (Y). Reused the same vector extraction from KiDoom, but the Python script converts coordinates to ±1V range and streams them as audio samples.

Each wall becomes a wireframe box, the scope traces along each line. With ~7,000 points per frame at 44.1kHz, refresh rate is about 6 Hz - slow enough to be a slideshow, but level geometry is clearly recognizable. A 96kHz audio interface or analog scope would improve it significantly (digital scopes do sample-and-hold instead of continuous beam tracing).

Links:

KiDoom GitHub: https://github.com/MichaelAyles/KiDoom, writeup: https://www.mikeayles.com/#kidoom

ScopeDoom GitHub: https://github.com/MichaelAyles/ScopeDoom, writeup: https://www.mikeayles.com/#scopedoom

The article argues that Python, while popular, has limitations in areas such as performance, scalability, and lack of static type checking. It suggests that other languages like Rust, Go, or C++ may be better choices for certain types of projects or workloads.

A new study from the University of Cambridge reveals that cognitive development in humans continues throughout life, challenging the traditional view of cognitive decline in later stages. The research suggests that the brain's flexibility and ability to adapt and learn persist into old age, with important implications for education, healthcare, and our understanding of human cognitive potential.

The article provides a series of tips and tricks for using the Gemini command-line interface, a utility for capturing and diffing screenshots. It covers various features and shortcuts to streamline the workflow when working with Gemini.

The article discusses the CSS Subgrid feature, which allows grid items to inherit the grid structure of their parent container, making it easier to create complex layouts within grid systems. It explains the benefits of Subgrid and how it can simplify the implementation of nested grids.

The article explores the discovery of a new type of cellular organism, Candidatus Plasticity, which challenges traditional definitions of life. This organism has a highly minimalistic genome and metabolic capabilities, raising questions about the essential characteristics of living systems.

Unison is a functional programming language that aims to simplify software development by introducing a novel approach to code distribution and evolution. It features a decentralized code database, automatic versioning, and a flexible type system that allows for seamless code updates and collaboration.

The article discusses the ongoing challenges faced by the Linux kernel community in managing the increasing complexity of the kernel, including issues with code quality, technical debt, and the need for better tooling and processes to address these problems.

Recent and related: Roblox CEO interview about child safety didn't go well - https://news.ycombinator.com/item?id=46013477 - Nov 2025 (96 comments)

The article explores the mathematical concept of infinity and its connection to computer science, highlighting how researchers have found a new way to link these seemingly disparate fields using a new bridge that helps understand the strange properties of infinity.

DRAM prices have been rising significantly, with industry experts citing various factors such as increased demand, supply chain disruptions, and a global semiconductor shortage. However, the article cautions that some of the industry's stated reasons for the price hikes may not be entirely accurate or complete.

The Linux Kernel Explorer is a web-based tool that allows users to explore the Linux kernel source code interactively. It provides a user-friendly interface to navigate the kernel's file structure, view code, and search for specific functions or variables.

Hey HN, Chris and Yuhong here from Onyx (https://github.com/onyx-dot-app/onyx). We’re building an open-source chat that works with any LLM (proprietary + open weight) and gives these LLMs the tools they need to be useful (RAG, web search, MCP, deep research, memory, etc.).

Demo: https://youtu.be/2g4BxTZ9ztg

Two years ago, Yuhong and I had the same recurring problem. We were on growing teams and it was ridiculously difficult to find the right information across our docs, Slack, meeting notes, etc. Existing solutions required sending out our company's data, lacked customization, and frankly didn't work well. So, we started Danswer, an open-source enterprise search project built to be self-hosted and easily customized.

As the project grew, we started seeing an interesting trend—even though we were explicitly a search app, people wanted to use Danswer just to chat with LLMs. We’d hear, “the connectors, indexing, and search are great, but I’m going to start by connecting GPT-4o, Claude Sonnet 4, and Qwen to provide my team with a secure way to use them”.

Many users would add RAG, agents, and custom tools later, but much of the usage stayed ‘basic chat’. We thought: “why would people co-opt an enterprise search when other AI chat solutions exist?”

As we continued talking to users, we realized two key points:

(1) just giving a company secure access to an LLM with a great UI and simple tools is a huge part of the value add of AI

(2) providing this well is much harder than you might think and the bar is incredibly high

Consumer products like ChatGPT and Claude already provide a great experience—and chat with AI for work is something (ideally) everyone at the company uses 10+ times per day. People expect the same snappy, simple, and intuitive UX with a full feature set. Getting hundreds of small details right to take the experience from “this works” to “this feels magical” is not easy, and nothing else in the space has managed to do it.

So ~3 months ago we pivoted to Onyx, the open-source chat UI with:

- (truly) world class chat UX. Usable both by a fresh college grad who grew up with AI and an industry veteran who’s using AI tools for the first time.

- Support for all the common add-ons: RAG, connectors, web search, custom tools, MCP, assistants, deep research.

- RBAC, SSO, permission syncing, easy on-prem hosting to make it work for larger enterprises.

Through building features like deep research and code interpreter that work across model providers, we've learned a ton of non-obvious things about engineering LLMs that have been key to making Onyx work. I'd like to share two that were particularly interesting (happy to discuss more in the comments).

First, context management is one of the most difficult and important things to get right. We’ve found that LLMs really struggle to remember both system prompts and previous user messages in long conversations. Even simple instructions like “ignore sources of type X” in the system prompt are very often ignored. This is exacerbated by multiple tool calls, which can often feed in huge amounts of context. We solved this problem with a “Reminder” prompt—a short 1-3 sentence blurb injected at the end of the user message that describes the non-negotiables that the LLM must abide by. Empirically, LLMs attend most to the very end of the context window, so this placement gives the highest likelihood of adherence.

Second, we’ve needed to build an understanding of the “natural tendencies” of certain models when using tools, and build around them. For example, the GPT family of models are fine-tuned to use a python code interpreter that operates in a Jupyter notebook. Even if told explicitly, it refuses to add `print()` around the last line, since, in Jupyter, this last line is automatically written to stdout. Other models don’t have this strong preference, so we’ve had to design our model-agnostic code interpreter to also automatically `print()` the last bare line.

So far, we’ve had a Fortune 100 team fork Onyx and provide 10k+ employees access to every model within a single interface, and create thousands of use-case specific Assistants for every department, each using the best model for the job. We’ve seen teams operating in sensitive industries completely airgap Onyx w/ locally hosted LLMs to provide a copilot that wouldn’t have been possible otherwise.

If you’d like to try Onyx out, follow https://docs.onyx.app/deployment/getting_started/quickstart to get set up locally w/ Docker in <15 minutes. For our Cloud: https://www.onyx.app/. If there’s anything you'd like to see to make it a no-brainer to replace your ChatGPT Enterprise/Claude Enterprise subscription, we’d love to hear it!

The article chronicles the creation of the Crash Bandicoot video game franchise, detailing the challenges faced by the development team at Naughty Dog and their innovative approaches to overcoming technical limitations to create a successful and iconic series.