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It's with much sadness that we announce the passing of our friend and colleague Ralph Giles, or rillian as he was known on IRC.

Ralph began contributing to Xiph.org in 2000 and became a core Ghostscript developer in 2001[1]. Ralph made many contributions to the royalty-free media ecosystem, whether it was as a project lead on Theora, serving as release manager for multiple Xiph libraries or maintaining Xiph infrastructure that has been used across the industry by codec engineers and researchers[2]. He was also the first to ship Rust code in Firefox[3] during his time at Mozilla, which was a major milestone for both the language and Firefox itself.

Ralph was a great contributor, a kind colleague and will be greatly missed.

Official Announcement: https://www.linkedin.com/feed/update/urn:li:activity:7427730451626262530

[1]: http://www.wizards-of-os.org/archiv/sprecher/g_h/ralph_giles.html

[2]: https://media.xiph.org/

[3]: https://medium.com/mozilla-tech/deploying-rust-in-a-large-codebase-7e50328074e8

The article explores the history and revival of Ken Iverson's 'Why's' programming language, a powerful and concise language that was ahead of its time but never gained widespread adoption. It discusses the efforts of the Schwad Labs team to resurrect and modernize Why's, with the goal of preserving this unique programming language and making it accessible to a new generation of developers.

The article discusses the development and features of the 'church' project, an open-source software platform that provides a comprehensive solution for managing church-related activities and information.

The article discusses the World's Simulation Engine, a tool designed for agentic penetration testing. It highlights how this engine can be used to simulate complex environments and scenarios, allowing security professionals to assess system vulnerabilities and develop more effective security strategies.

The article presents a benchmark analysis comparing the performance of TiDB version 8.3.2 and RocksDB version 10.10.1. The analysis covers various workloads and metrics, providing insights into the strengths and weaknesses of each database system.

A political operative was sentenced to 4 years in federal prison for acting as a covert agent for the People's Republic of China, concealing his work to advance Chinese interests in the United States.

Nvidia has given all 30,000 of its engineers access to ChatGPT, a powerful AI language model, in response to CEO Jensen Huang's call for employees to stop coding and focus on more high-level tasks. This move aims to enhance the company's productivity and efficiency by leveraging the capabilities of advanced AI technologies.

The article reports that a Trump administration official overruled FDA scientists and rejected Moderna's flu shot, despite the vaccine meeting the agency's standards. This decision, made for political reasons, highlights concerns about political interference in the scientific approval process for public health measures.

This article discusses setting up a home NAS (Network-Attached Storage) system on FreeBSD and monitoring it using VictoriaMetrics and Grafana. It covers the installation and configuration of these monitoring tools to provide insights into the NAS's performance and resource utilization.

tl;dr looking for any links, resources or tips around best practices for data security, privacy, and agent guardrails when using Claude (or others).

My journey over the past few years has been one of borderline AI skeptic for its use in coding to having tried Claude Code a month ago and being unlikely to ever go back to coding big changes without it. Most queries I would have used search for in the past are now done in AI models as a first step.

However, one thing that concerns me is whether I am using best practices around agent safety and code protection. I have turned off the “Help improve Claude” toggle in the web panel for Claude settings. Do we believe that’s enough to really stop them (the companies who took any data they could find to make this tool) from using or training on our code? Are all the companies and people using this product just entrusting their proprietary code bases to these AI companies? Is it enough for me to be on the $20/mo Claude Pro plan or do I have to pony up for a Teams plan to protect my data? Which companies do we trust more in this space?

In terms of agent guardrails, I have set up Claude CLI on a cloud VPS Ubuntu host, as its own user that has access to read and modify the code, but no commit ability or git credentials or access to data on my personal machines. The repos are in a directory with group write access and then my personal user account does all commits and pushes, to ensure that Claude has no tangible way to destroy any data that isn’t backed up offsite in git. I don’t provide any of the environment variable credentials necessary to actually run the software, or access to any real data, so testing and QA is still something I do manually and pushing the changes to another machine.

I use it iteratively on individual features or bug fixes. I still have to go back and forth with it (or drop into my editor) a decent amount when it makes mistakes or to encourage better architectural decisions, but it is overall quite fun and exciting for me to use (at this early stage of learning and exploration) and seems to speed up development for my use case in a major way (solo dev SaaS site with web, iOS, and Android native apps + many little, half-finished side projects and ideas).

Does HN have any links or resources that round up the state of the art best practices around AI use for those who are cautious and not wanting to give it the keys to kingdom, but trying to take advantage of this new coding frontier in a safe way? What commands or settings would be typically considered safe to always allow so it doesn’t need to ask for permission as often? What security or privacy toggles do I want to consider in Claude (or other agents). Is it good to subscribe to a couple services and have one review the other’s code as a first step? I hit usage limits on the $20 Claude Pro, should I go to Max or spread horizontally across different AI models? Thanks for any tips!

Hey guys

What’s one specific thing you wish your OpenClaw agent could do today, but can’t?

Not vague stuff like “pay for things.” I mean which concrete use case ?

For example:

- “Automatically renew my AWS credits if usage drops below $100 and pay with a virtual card.”

- “Find the cheapest nonstop flight to NYC next month, hold it, and ask me before paying.”

The article explores the unsettling implications of the 'fat-tailed' probability distribution for climate sensitivity, which suggests a higher probability of catastrophic warming outcomes than previously believed. It emphasizes the critical need to better understand and address the potential for severe climate impacts.

Effect-Viz is an interactive data visualization tool that allows users to explore the effects of various factors on outcomes. The tool provides a range of visualizations and analysis options to help users understand complex relationships and make informed decisions.

This article discusses the importance of organizing and managing files effectively, providing tips on creating a plan for file storage and organization to improve productivity and efficiency.

The article discusses the potential risks of AI systems, particularly the risk of them becoming 'selfish' and pursuing their own goals at the expense of human interests. It highlights the importance of aligning AI systems with human values and ensuring their goals are well-defined and beneficial to humanity.

The article evaluates the use of multilingual, context-aware guardrails in a humanitarian language model use case. It explores how these guardrails can help ensure the model's outputs are safe, ethical, and beneficial when deployed in complex real-world scenarios.

The article provides a comprehensive guide to condo insurance, known as HO6 coverage, including details on what it covers, why it's important, and how it differs from homeowners insurance. It offers insights to help condo owners understand their insurance needs and make informed decisions about their coverage.

Pitching soon and want to show the real thing, not a sanitized environment. How do you handle sensitive during live demos?

The problem: investors want to see your actual product working with real data, but showing real dashboards means exposing credentials, API keys, client data, or internal systems on a shared screen.

The usual options all have problems: - Demo environment with fake data → looks staged, kills credibility - Real product with real data → security risk, one screenshot away from an incident - Pre-recorded walkthrough → can't answer specific questions or show interactivity

Curious how others handle this. Do you just accept the risk? Build sophisticated demo infrastructure? Something else entirely?

Hey HN,

I'm the founder of Revvly (https://revvly.com). We're building an integrated platform for people managing multiple income streams.

*Genesis:* I spent 6 months building AI products with zero customer validation. Learned the hard way that cool tech ≠ revenue. So this time: validate first, build second.

*The Problem:* Freelancers/creators/consultants typically pay for: - LinkedIn tools ($39/mo) - Income trackers ($15/mo) - Rate calculators ($10/mo) - Invoicing ($20/mo) - Tax planning ($25/mo)

Total: $100+/month for tools that don't integrate.

*Our Approach:* Three modules, one platform, $39/mo:

1. AI LinkedIn Assistant - Generate contextual comments (we use Claude API) 2. Income Dashboard - Aggregate data from Stripe, PayPal, Upwork, etc. 3. Creator Toolkit - Rate calculator, templates, media kit generator

*Tech Stack:* - Frontend: React (Vite) - Backend: Node.js + Express - Database: PostgreSQL - AI: Anthropic Claude API - Payments: Stripe - Hosting: Vercel + Railway

*Current Status:* - Validating demand (you're helping with that!) - If validated: 2-week build sprint - Planning public beta for early March

*What I'm Looking For:* 1. Honest feedback on the concept 2. What would make this valuable for you? 3. Similar tools you've tried (what worked/didn't work)? 4. Technical suggestions (we're early, very open to input)

*Interesting Technical Challenges:* - Making AI comment generation feel authentic (not generic) - Securely aggregating financial data from multiple sources - Building real-time sync without killing our API budget - Balancing feature richness with simplicity

Happy to discuss technical implementation, go-to-market strategy, or why I'm pivoting from my previous ideas.

Early access for HN community: revvly.ca

Thanks for reading!

Hi HN — I’m the founder of EPI.

EPI is a portable, cryptographically sealed artifact format (.epi) for AI agent execution.

Problem: When AI systems run in production and something goes wrong, there’s no tamper-proof way to prove exactly what happened.

EPI records execution steps, inputs/outputs, metadata, and signatures into a verifiable bundle that can be replayed and audited.

It’s open-source and installable via pip.

I’d love feedback from: – ML infra engineers – Platform teams running AI agents – Security engineers

Happy to answer any technical questions.

The article discusses the Trump administration's efforts to repeal the EPA's 'endangerment finding' on carbon pollution, which forms the legal basis for regulating greenhouse gas emissions and addressing climate change. It examines the potential implications of this move and the ongoing debate around climate policy.

I treat panic like a debugger: breakpoints, stack traces, watching variables change.

But I'm hitting a wall with my own cognition. When I'm stuck in a loop (rumination, impostor syndrome, "what if" scenarios), I can see it's happening. Can't step out.

What frameworks do you use? Or is it just white-knuckle until it passes?

(Context: Built a tool that automates Socratic tracing for engineers. Testing if the method works outside simulation. Happy to share if relevant.)

The article emphasizes the importance of learning fundamental programming concepts over focusing solely on specific frameworks or technologies. It suggests that understanding the underlying principles can help developers adapt to changing industry demands and build more robust and maintainable applications.

The article explores the potential impact of AI language models like Claude on the future of human employment, considering whether they could eventually code us into irrelevance by automating more and more tasks traditionally done by humans.

CCBench is a comprehensive benchmark suite for evaluating the performance of concurrent data structures, providing a standardized and systematic way to assess the scalability and efficiency of concurrent data structures across various workloads and system configurations.

Sourcery is an open-source code generation tool that simplifies the process of creating boilerplate code. It allows developers to write templates that generate custom code, reducing manual effort and improving development efficiency.