Proton Spam and the AI Consent Problem
The article discusses the increasing problem of spam emails being sent from Proton Mail accounts, highlighting how the service's privacy features can be exploited by spammers. It suggests ways Proton Mail could address this issue, such as enhancing its security measures and collaborating with other email providers.
I built a light that reacts to radio waves [video]
https://rootkid.me/works/spectrum-slit
Capital One to acquire Brex for $5.15B
Archive link: https://archive.md/vk8ov
Capitol One statement: https://investor.capitalone.com/news-releases/news-release-d...
Brex statement: https://www.brex.com/journal/brex-and-capital-one-join-force...
GPTZero finds 100 new hallucinations in NeurIPS 2025 accepted papers
The article discusses the NeurIPS (Neural Information Processing Systems) conference, one of the premier annual events in the field of machine learning. It highlights the conference's focus on showcasing groundbreaking research and fostering discussions around the latest advancements in artificial intelligence and related technologies.
TI-99/4A: Leaning More on the Firmware
The article discusses how the TI-99/4A home computer began to rely more heavily on its firmware as a way to enhance its capabilities, allowing for features like improved graphics and sound without the need for additional hardware.
Show HN: isometric.nyc – giant isometric pixel art map of NYC
Hey HN! I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents.
I didn't write a single line of code.
Of course no-code doesn't mean no-engineering. This project took a lot more manual labor than I'd hoped!
I wrote a deep dive on the workflow and some thoughts about the future of AI coding and creativity:
http://cannoneyed.com/projects/isometric-nyc
Why does SSH send 100 packets per keystroke?
This article examines the high network traffic generated by SSH connections, with each keystroke triggering up to 100 packets being sent. It discusses the implications of this bandwidth consumption and suggests ways to optimize SSH usage for more efficient network performance.
I was banned from Claude for scaffolding a Claude.md file?
This article discusses the author's experience with being banned from using the Claude AI assistant due to violations of Anthropic's content policy. It explores the implications of such bans and the challenges faced by AI users in navigating platform guidelines.
Qwen3-TTS family is now open sourced: Voice design, clone, and generation
The article discusses the development of Qwen.AI's new text-to-speech (TTS) model, detailing the technical improvements and quality enhancements that have resulted in a more natural and expressive speech output.
Talking to LLMs has improved my thinking
The article discusses how the author's interactions with large language models (LLMs) have improved their thinking process. It explores how engaging with LLMs has helped the author develop new perspectives, challenge their assumptions, and refine their communication skills.
Bugs Apple Loves
Writing First, Tooling Second
The article discusses the importance of focusing on writing and content creation before worrying about the tools and technologies used to deliver it. It emphasizes the primacy of the message over the medium and encourages writers to prioritize the substance of their work over the latest software or platforms.
Douglas Adams on the English–American cultural divide over "heroes"
This article explores the cultural divide between the United States and the United Kingdom through the lens of Douglas Adams' writings, highlighting the differences in humor, language, and societal norms between the two countries.
Turso is an in-process SQL database, compatible with SQLite
Scaling PostgreSQL to power 800M ChatGPT users
The article discusses strategies for scaling PostgreSQL databases, including sharding, replication, and partitioning, to handle increasing data volumes and user loads. It explores the trade-offs between different scaling approaches and provides guidance on choosing the right solution for specific use cases.
Improving the usability of C libraries in Swift
The article discusses how the Swift team is working to improve the usability of C libraries in Swift, focusing on making the integration process more straightforward and providing better tooling and documentation to help developers work with C libraries effectively.
Why medieval city-builder video games are historically inaccurate (2020)
This article examines the historical inaccuracies found in medieval city-building video games, highlighting how they often fail to capture the complexities of urban life and development in the Middle Ages, such as the role of religion, feudal power structures, and technological limitations.
Show HN: Txt2plotter – True centerline vectors from Flux.2 for pen plotters
I’ve been working on a project to bridge the gap between AI generation and my AxiDraw, and I think I finally have a workflow that avoids the usual headaches.
If you’ve tried plotting AI-generated images, you probably know the struggle: generic tracing tools (like Potrace) trace the outline of a line, resulting in double-strokes that ruin the look and take twice as long to plot.
What I tried previously:
- Potrace / Inkscape Trace: Great for filled shapes, but results in "hollow" lines for line art.
- Canny Edge Detection: Often too messy; it picks up noise and creates jittery paths.
- Standard SDXL: Struggled with geometric coherence, often breaking lines or hallucinating perspective.
- A bunch of projects that claimed to be txt2svg but which produced extremely poor results, at least for pen plotting. (Chat2SVG, StarVector, OmniSVG, DeepSVG, SVG-VAE, VectorFusion, DiffSketcher, SVGDreamer, SVGDreamer++, NeuralSVG, SVGFusion, VectorWeaver, SwiftSketch, CLIPasso, CLIPDraw, InternSVG)
My Approach:
I ended up writing a Python tool that combines a few specific technologies to get a true "centerline" vector:
1. Prompt Engineering: An LLM rewrites the prompt to enforce a "Technical Drawing" style optimized for the generator.
2. Generation: I'm using Flux.2-dev (4-bit). It seems significantly better than SDXL at maintaining straight lines and coherent geometry.
3. Skeletonization: This is the key part. Instead of tracing contours, I use Lee’s Method (via scikit-image) to erode the image down to a 1-pixel wide skeleton. This recovers the actual stroke path.
4. Graph Conversion: The pixel skeleton is converted into a graph to identify nodes and edges, pruning out small artifacts/noise.
5. Optimization: Finally, I feed it into vpype to merge segments and sort the paths (TSP) so the plotter isn't jumping around constantly.
You can see the results in the examples inside the Github repo.
The project is currently quite barebones, but it produces better results than other options I've tested so I'm publishing it. I'm interested in implementing better pre/post processing, API-based generation, and identifying shapes for cross-hatching.
Your app subscription is now my weekend project
The article discusses a software engineer's experience taking on a weekend project to improve a subscription service they use, emphasizing the value of personal projects and the importance of understanding the tools and services we rely on.
Launch HN: Constellation Space (YC W26) – AI for satellite mission assurance
Hi HN! We're Kamran, Raaid, Laith, and Omeed from Constellation Space (https://constellation-io.com/). We built an AI system that predicts satellite link failures before they happen. Here's a video walkthrough: https://www.youtube.com/watch?v=069V9fADAtM.
Between us, we've spent years working on satellite operations at SpaceX, Blue Origin, and NASA. At SpaceX, we managed constellation health for Starlink. At Blue, we worked on next-gen test infra for New Glenn. At NASA, we dealt with deep space communications. The same problem kept coming up: by the time you notice a link is degrading, you've often already lost data.
The core issue is that satellite RF links are affected by dozens of interacting variables. A satellite passes overhead, and you need to predict whether the link will hold for the next few minutes. That depends on: the orbital geometry (elevation angle changes constantly), tropospheric attenuation (humidity affects signal loss via ITU-R P.676), rain fade (calculated via ITU-R P.618 - rain rates in mm/hr translate directly to dB of loss at Ka-band and above), ionospheric scintillation (we track the KP index from magnetometer networks), and network congestion on top of all that.
The traditional approach is reactive. Operators watch dashboards, and when SNR drops below a threshold, they manually reroute traffic or switch to a backup link. With 10,000 satellites in orbit today and 70,000+ projected by 2030, this doesn't scale. Our system ingests telemetry at around 100,000 messages per second from satellites, ground stations, weather radar, IoT humidity sensors, and space weather monitors. We run physics-based models in real-time - the full link budget equations, ITU atmospheric standards, orbital propagation - to compute what should be happening. Then we layer ML models on top, trained on billions of data points from actual multi-orbit operations.
The ML piece is where it gets interesting. We use federated learning because constellation operators (understandably) don't want to share raw telemetry. Each constellation trains local models on their own data, and we aggregate only the high-level patterns. This gives us transfer learning across different orbit types and frequency bands - learnings from LEO Ka-band links help optimize MEO or GEO operations. We can predict most link failures 3-5 minutes out with >90% accuracy, which gives enough time to reroute traffic before data loss. The system is fully containerized (Docker/Kubernetes) and deploys on-premise for air-gapped environments, on GovCloud (AWS GovCloud, Azure Government), or standard commercial clouds.
Right now we're testing with defense and commercial partners. The dashboard shows real-time link health, forecasts at 60/180/300 seconds out, and root cause analysis (is this rain fade? satellite setting below horizon? congestion?). We expose everything via API - telemetry ingestion, predictions, topology snapshots, even an LLM chat endpoint for natural language troubleshooting.
The hard parts we're still working on: prediction accuracy degrades for longer time horizons (beyond 5 minutes gets dicey), we need more labeled failure data for rare edge cases, and the federated learning setup requires careful orchestration across different operators' security boundaries. We'd love feedback from anyone who's worked on satellite ops, RF link modeling, or time-series prediction at scale. What are we missing? What would make this actually useful in a production NOC environment?
Happy to answer any technical questions!
CSS Optical Illusions
This article explores fascinating optical illusions that can be created using CSS, such as making objects appear to be rotating or changing shape. It provides detailed examples and code snippets to help readers understand and recreate these visual effects.
Stunnel
stunnel is an open-source software tool that provides a secure layer over a standard network connection, enabling the encryption of network traffic to and from any application or service.
Show HN: Text-to-video model from scratch (2 brothers, 2 years, 2B params)
Writeup (includes good/bad sample generations): https://www.linum.ai/field-notes/launch-linum-v2
We're Sahil and Manu, two brothers who spent the last 2 years training text-to-video models from scratch. Today we're releasing them under Apache 2.0.
These are 2B param models capable of generating 2-5 seconds of footage at either 360p or 720p. In terms of model size, the closest comparison is Alibaba's Wan 2.1 1.3B. From our testing, we get significantly better motion capture and aesthetics.
We're not claiming to have reached the frontier. For us, this is a stepping stone towards SOTA - proof we can train these models end-to-end ourselves.
Why train a model from scratch?
We shipped our first model in January 2024 (pre-Sora) as a 180p, 1-second GIF bot, bootstrapped off Stable Diffusion XL. Image VAEs don't understand temporal coherence, and without the original training data, you can't smoothly transition between image and video distributions. At some point you're better off starting over.
For v2, we use T5 for text encoding, Wan 2.1 VAE for compression, and a DiT-variant backbone trained with flow matching. We built our own temporal VAE but Wan's was smaller with equivalent performance, so we used it to save on embedding costs. (We'll open-source our VAE shortly.)
The bulk of development time went into building curation pipelines that actually work (e.g., hand-labeling aesthetic properties and fine-tuning VLMs to filter at scale).
What works: Cartoon/animated styles, food and nature scenes, simple character motion. What doesn't: Complex physics, fast motion (e.g., gymnastics, dancing), consistent text.
Why build this when Veo/Sora exist? Products are extensions of the underlying model's capabilities. If users want a feature the model doesn't support (character consistency, camera controls, editing, style mapping, etc.), you're stuck. To build the product we want, we need to update the model itself. That means owning the development process. It's a bet that will take time (and a lot of GPU compute) to pay off, but we think it's the right one.
What’s next? - Post-training for physics/deformations - Distillation for speed - Audio capabilities - Model scaling
We kept a “lab notebook” of all our experiments in Notion. Happy to answer questions about building a model from 0 → 1. Comments and feedback welcome!
'Askers' vs. 'Guessers' (2010)
https://web.archive.org/web/20250831074424/https://www.theat...
https://archive.ph/GBZBf
Recent discoveries on the acquisition of the highest levels of human performance
Composing APIs and CLIs in the LLM era
The article discusses the benefits of composing APIs and CLIs to create flexible and extensible software systems. It highlights how this approach allows developers to build modular components that can be easily combined and reused, leading to more efficient and maintainable software development.
'Active' sitting is better for brain health: review of studies
A study has found that not all types of sitting are equal in their effects on brain health. Specifically, sedentary activities involving mental engagement, such as reading, writing, or using a computer, were associated with better cognitive function and brain structure compared to passive sitting activities.
In Europe, wind and solar overtake fossil fuels
The article discusses the significant progress made by European countries in transitioning away from fossil fuels towards renewable energy sources, particularly wind and solar power, which now account for a larger share of the continent's electricity generation than fossil fuels.
Show HN: BrowserOS – "Claude Cowork" in the browser
Hey HN! We're Nithin and Nikhil, twin brothers building BrowserOS (YC S24). We're an open-source, privacy-first alternative to the AI browsers from big labs.
The big differentiator: on BrowserOS you can use local LLMs or BYOK and run the agent entirely on the client side, so your company/sensitive data stays on your machine!
Today we're launching filesystem access... just like Claude Cowork, our browser agent can read files, write files, run shell commands! But honestly, we didn't plan for this. It turns out the privacy decision we made 9 months ago accidentally positioned us for this moment.
The architectural bet we made 9 months ago: Unlike other AI browsers (ChatGPT Atlas, Perplexity Comet) where the agent loop runs server-side, we decided early on to run our agent entirely on your machine (client side).
But building everything on the client side wasn't smooth. We initially built our agent loop inside a Chrome extension. But we kept hitting walls -- service worker being single thread JS; not having access to NodeJS libraries. So we made the hard decision 2 months ago to throw away everything and start from scratch.
In the new architecture, our agent loop sits in a standalone binary that we ship alongside our Chromium. And we use gemini-cli for the agent loop with some tweaks! We wrote a neat adapter to translate between Gemini format and Vercel AI SDK format. You can look at our entire codebase here: https://git.new/browseros-agent
How we give browser access to filesystem: When Claude Cowork launched, we realized something: because Atlas and Comet run their agent loop server-side, there's no good way for their agent to access your files without uploading them to the server first. But our agent was already local. Adding filesystem access meant just... opening the door (with your permissions ofc). Our agent can now read and write files just like Claude Code.
What you can actually do today:
a) Organize files in my desktop folder https://youtu.be/NOZ7xjto6Uc
b) Open top 5 HN links, extract the details and write summary into a HTML file https://youtu.be/uXvqs_TCmMQ
--- Where we are now If you haven't tried us since the last Show HN (https://news.ycombinator.com/item?id=44523409), give us another shot. The new architecture unlocked a ton of new features, and we've grown to 8.5K GitHub stars and 100K+ downloads:
c) You can now build more reliable workflows using n8n-like graph https://youtu.be/H_bFfWIevSY
d) You can also use BrowserOS as an MCP server in Cursor or Claude Code https://youtu.be/5nevh00lckM
We are very bullish on browser being the right platform for a Claude Cowork like agent. Browser is the most commonly used app by knowledge workers (emails, docs, spreadsheets, research, etc). And even Anthropic recognizes this -- for Claude Cowork, they have janky integration with browser via a chrome extension. But owning the entire stack allows us to build differentiated features that wouldn't be possible otherwise. Ex: Browser ACLs.
Agents can do dumb or destructive things, so we're adding browser-level guardrails (think IAM for agents): "role(agent): can never click buy" or "role(agent): read-only access on my bank's homepage."
Curious to hear your take on this and the overall thesis.
We’ll be in the comments. Thanks for reading!
GitHub: https://github.com/browseros-ai/BrowserOS
Download: https://browseros.com (available for Mac, Windows, Linux!)
Design Thinking Books (2024)
This article provides an overview of seven essential books on design thinking, covering topics such as the design process, problem-solving, and innovation. It offers recommendations for both beginners and experienced designers looking to deepen their understanding of design thinking principles and practices.