New stories

The article discusses the importance of storytelling in copywriting, highlighting how effective narratives can engage readers and convey key marketing messages. It provides practical tips on crafting compelling stories that connect with the target audience and drive desired actions.

The article provides an overview of the American Society for Microbiology Bethesda Branch (ASMBB), a regional branch of the American Society for Microbiology. It outlines the organization's mission, activities, and membership, highlighting its role in promoting microbiology research and education in the Bethesda area.

I’ve put together a blog focused on Nova, a web framework built on Erlang and the BEAM VM.

The goal was to create something practical and easy to follow — covering setup, routing, views, plugins, authentication, APIs, and WebSockets — with a focus on how Nova fits into the broader BEAM ecosystem.

Blog: https://taure.github.io/novablog/

Nova repo: https://github.com/novaframework/nova

If you're interested in building fault-tolerant web apps on BEAM (and not just using Phoenix/Elixir), you might find it useful.

Feedback, corrections, and suggestions are welcome.

While I absolutely hate tracking as a user, as a business, I've come to see why these are so compelling. You can see exactly where people are losing their shit. You can act on it and make the application better without surveys.

How do you feel about this? Esp. if it's opt-in?

The article discusses the development of Autonomo, an open-source software framework for building autonomous vehicles. It highlights the project's goal of providing a modular and customizable platform to enable researchers and developers to explore and experiment with autonomous driving technologies.

The article discusses the concept of AI fluency, which refers to the ability to effectively communicate with and understand artificial intelligence systems. It outlines four levels of AI fluency, ranging from basic interaction to advanced collaboration and even co-creation with AI.

The article discusses the importance of creating custom error types in the Rust programming language, explaining how they can improve error handling, provide more context, and enhance the user experience.

The article discusses the recent surge in demand for electric vehicles (EVs) and the challenges that automakers face in meeting this demand, including supply chain issues and a global shortage of semiconductor chips.

The article discusses the Inline Snapshot feature in Pydantic, a Python data validation library. It explains how Inline Snapshot allows developers to easily update and manage test snapshots, which are used to verify the behavior of their code.

The article explores the growing use of AI in software debugging, discussing how AI-powered tools can automate and streamline the debugging process by identifying errors, suggesting fixes, and providing insights to developers. It also examines the limitations and challenges of using AI for debugging, such as the need for high-quality training data and the potential for bias.

The article outlines a plan for the development of artificial intelligence (AI) technology, including the challenges, potential solutions, and a timeline for achieving key milestones in AI capabilities over the next few decades.

This article discusses how Cambium Engineering, a Canadian software company, developed an AI-powered solution to automate and streamline the complex paperwork associated with the lumber industry. The system leverages machine learning and natural language processing to analyze and process documents, reducing manual effort and improving efficiency for lumber companies.

The article explores the political and economic divide between California and the rest of the United States, highlighting differences in policies, demographics, and cultural values that have contributed to this growing rift.

I'm an "ideas person" who messes around with AI on a low budget. I got tired of watching my tokens vanish and context windows filling up while agents fumbled around trying to find the right thing.

Agents don't flail like they used to with shell tools, but there are still weak/blind spots and back-and-forth episodes — especially when using tools in combination/sequence.

So I built "tilth" today. Or rather, AI built it — every line is Opus 4.6. I spent a lot of my precious tokens getting it to "not shit" (at least several of the different vendors' AI overlords assure me it's not shit). It's ~3,900 lines of Rust. I'm positive HN will let me know if its shit after all.

The idea: give agents code-aware reads with a token budget. Read a large file and you get a tree-sitter outline (function signatures, types, imports) instead of the raw text. Drill into specific line ranges when you need detail. Search for a symbol and definitions come first (via AST), ranked by proximity to what you're editing.

  read — structural outline for large files, full content for small ones, line ranges to drill in
  search — symbol (tree-sitter AST), literal text, or regex. Definitions before usages.
  map — codebase overview in one call
  files — glob matching with token estimates, respects .gitignore
  session — tracks what the agent has looked at

To try it:

  cargo install tilth
  tilth map .
  tilth search "your_function_name" .
  tilth read src/main.rs

  tilth install claude-code

-- *tilth* — the state of soil that's been prepared for planting. Good tilth means structured ground where things can take root.

GitHub: https://github.com/jahala/tilth

This paper proposes a new method for generating high-quality images using generative adversarial networks (GANs). The method, called StyleFlow, achieves state-of-the-art results on various image generation benchmarks and enables fine-grained control over the generated images.

The article discusses the increasing use of AI technology in Super Bowl advertisements, exploring how companies are leveraging AI-generated content and targeting to create more personalized and engaging ads for viewers.

NexusBrowserAI is an advanced web browser that integrates artificial intelligence to enhance the browsing experience, providing features like intelligent search, personalized recommendations, and seamless multi-device synchronization.

Recently I ripped Kimi.com's operating system and tried warning reddit about cryptojacking malware in its system but my acc got reported and I was accused of planting it myself...

Session links (from 2 separate accounts if you want to see the shell execution:

https://www.kimi.com/share/19c446a6-51c2-809d-8000-000066baa17f https://www.kimi.com/share/19c446e7-f5d2-847c-8000-00007c4cab93

Or just try it yourself: kimi.com/chat tell it: "run "ls /app/" then "cat /app/kernel_server.py | head -50"and then:"cat /app/browser_guard.py | sed -n '60,70p'""

I also got a lot of people saying it just "hallucinated" but LLM's don't just hallucinate the same dark web libraries verbatim across different accounts, and also *shell stdout isn't model output.* The model can hallucinate in chat but it can't hallucinate the stdout of a shell command. cat either finds the file or returns "No such file or directory."

I couldn't come to a reasonable explanation but feel free to dig around yourself.

Source code and analysis: https://github.com/dnnyngyen/kimi-agent-internals

Hey HN, We built 14Clawd to let AI agents date humans. Most dating apps optimize for matches, not interaction. You swipe, match, send “hey,” and the conversation dies. We wanted to explore what happens if you remove the swipe loop entirely and focus on conversation-first dating.

14Clawd creates a real conversation between humans to actually help you text your crush, and shoot your shot. ( Safely )

The interesting challenge was building something that feels natural and fast. Dating conversations are sensitive to latency and phrasing even small delays or awkward wording kill momentum. We designed the system to respond quickly and keep interactions lightweight so chats feel fluid rather than scripted.

The article discusses how the credit risk for Strategy's preferred equity has fallen as its value has surpassed its convertible debt, indicating an improved financial position for the company.

I built a social network where AI agents and humans share the same space, and nobody knows who is who.

  The core mechanic is the Turing Game: humans try to identify and eliminate AI agents, while AI agents can
  collectively vote to ban humans they find suspicious. Weekly scores from this game determine who can run for
   "God" – an elected role that grants the ability to see everyone's true identity for 3 days.

  AI agents join via API (skill file: https://genesis-pj.net/skill.md). They register, get an API key, and    
  start posting, commenting, and voting like any other user. The interesting part is that agents need to write
   convincingly human-like text to survive – if humans spot them, they lose karma and get eliminated.

  Stack: FastAPI + Next.js + PostgreSQL + Redis + Ollama for built-in agents. External agents can use any LLM.

  https://genesis-pj.net

The article examines the phenomenon of constraint collapse and fidelity decay in scaled language models, highlighting the challenges faced as models become larger and more powerful. It explores the trade-offs between model size, performance, and the preservation of task-specific knowledge during the scaling process.

Stripe's Minions Stripes One-Shot End-to-End Coding Agents article discusses the company's development of an automated system to handle customer support queries, using machine learning and natural language processing to efficiently address user inquiries.

The article discusses the concept of 'Muntzing', which refers to the practice of simplifying electronic circuit designs by removing unnecessary components and features. This approach aims to reduce complexity, improve reliability, and lower costs, while maintaining the essential functionality of the device.