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I created Wysspr to make it easy for the most wanted fugitives to be captured through crowd sourced information and intelligence while being able to get a cash reward for information. Just launched the platform and looking for users to test out the site and give feedback. You can find it here at stealth.wysspr.com. Tell me what you think!

The article provides a step-by-step tutorial on building a perceptron using Rust, a popular programming language for performance-critical and systems-level applications. It covers the mathematical foundations of perceptrons, implementation details, and examples of how to train and use a perceptron model for simple classification tasks.

Not only is 3.9 reached end-of-life, the last release was 3.9.25

% python3 Python 3.9.6 (default, Dec 2 2025, 07:27:58) [Clang 17.0.0 (clang-1700.6.3.2)] on darwin

The blog post discusses new features and improvements in WebGPU, a web API for interacting with a device's GPU. It highlights updates to the API, including improved performance, better cross-device compatibility, and support for new features like compute shaders and multi-threading.

I'm building a turn based multiplayer game where players can earn exp from playing matches and level up (levels will mainly be used for aesthetic purposes, although I might add restrictions such as "you must be level x to edit room names").

This is my first time designing an exp/leveling system though so I'm not 100% sure how to approach the database design. Is it best practice to store both exp and level? Or is it better to store only exp and derive level?

The latter sounds potentially nicer if I want to adjust the level curve later, although that implies that all users could see their level change hm...

The article provides an introduction to the Marten, a .NET document database and event store built on top of PostgreSQL. Marten offers features such as document storage, event sourcing, and querying capabilities, making it a versatile choice for building modern .NET applications.

I built a tag-driven changelog generator for an OSS project I maintain and used it to backfill our 2025 docs changelog.

It walks git tags, finds merged PRs between releases, buckets them into categories, and renders MDX (Docusaurus in our case) organized by year -> month -> category -> version. There is an optional LLM mode that produces structured JSON via a Pydantic schema for PR entry and monthly summaries.

Example:

python .scripts/changelog/generate.py --year 2025 --github --ai --ai-model gpt-5.2 (or --help)

Gotcha: if you use --github, set GITHUB_TOKEN or you will most likely hit GitHub rate limits.

Repo script: https://github.com/confident-ai/deepeval/blob/main/.scripts/...

Disclosure: I maintain DeepEval. Happy to answer questions and take feedback.

The article highlights several lesser-known scholarship opportunities that many students are unaware of, including scholarships for specific hobbies, unique talents, and even unusual physical characteristics. It encourages readers to explore these 'hidden' scholarships as a way to supplement traditional academic-based awards.

I was wondering what type of persons actually read HN, and instead of scrapping comments and anaLLMyze them, I figured it might be easier to just ask.

Hello HN,

I’m the developer behind Tooliz. I built this because I was frustrated with the current state of utility apps on the Play Store—most are either 100MB+ bloatware, require unnecessary permissions (like contacts/location for a flashlight), or force full-screen video ads just to use a simple ruler.

I wanted to build a single, lightweight app that respects the user and focuses on UI/UX efficiency.

The Tech Stack: It’s built with Flutter. I chose it to handle the custom painting required for the tools smoothly at 60fps+ on both Android and iOS.

Interesting Technical Challenges:

The Radial Menu: I didn't want a standard grid or drawer. I implemented a custom Radial Menu using CustomPainter and heavy trigonometry to handle the touch hit-testing and animations. It feels much faster for thumb navigation than reaching for a top-left hamburger menu.

Sensor Fusion & Hardware:

Metal Detector: Accesses the raw magnetometer stream (sensors_plus package). I had to implement a calibration step to filter out the background magnetic field and isolate local anomalies (metals).

Sound Meter: Uses the microphone stream to calculate decibels. Mapping the raw PCM data to accurate dB SPL values across different device microphones was... painful. I used a calibration offset system to mitigate hardware variance.

Offline-First Architecture: Except for a minimal ad banner (to keep the project alive), everything works offline. For the Unit Converter, I hardcoded the conversion logic instead of relying on an API, so it works instantly in airplane mode.

Monetization: It’s free. There is a small banner ad (AdMob) at the bottom.

I’d love to hear your feedback on the Radial UI implementation or the sensor accuracy on your specific device!

The article discusses the concentrated economics of artificial intelligence (AI), highlighting how a few large tech companies dominate the AI industry and how this concentration of power and resources could have significant implications for innovation, competition, and societal impact.

The article discusses the development of heat pumps that can store and distribute heat as needed, improving their efficiency and reducing energy costs. Researchers are working on innovative technologies to make heat pumps more versatile and better suited to meet the heating and cooling demands of modern buildings.

Most "Voice AI" demos today are just API wrappers around cloud LLMs. They are impressive, but they fail in production for three reasons: Latency, Compliance, and Lack of Control.

I built VAAK (Voice-Activated Autonomous-Knowledge-System) to solve exactly this.

It’s not just a "local chatbot." It is a domain-agnostic, production-grade Conversational Operating System built entirely in Rust.

Here is why Senior Architects and Product Leaders should pay attention:

1. The "Deterministic" Architecture Enterprise AI cannot hallucinate when discussing interest rates or medical advice. VAAK uses a hybrid architecture:

Generative Fluidity + Deterministic Guardrails: It flows naturally but adheres strictly to business logic for critical data.

Sophisticated Memory Systems: It doesn't just "remember" the last token. It separates Context Memory (conversation flow) from Agent Memory (long-term facts), allowing it to handle complex, multi-turn negotiations without losing the plot.

Real-Time RAG: Built-in Retrieval Augmented Generation that queries your internal knowledge base in milliseconds, ensuring answers are grounded in your documents, not the internet.

2. Built for Complex "Human" Behaviors VAAK isn't limited to Q&A. It is engineered for specific high-value modes:

Discovery & Intent: Accurately identifies why the user is calling before attempting to solve.

Objection Handling: A dedicated layer that manages pushback (e.g., "The price is too high") with empathetic, logic-backed retorts.

Sales & Conversions: Tracks sentiment and guides the conversation toward a closing event.

Finance & CRM: Securely handles transactional queries (Gold Loans, EMI calculations) with zero data leakage.

3. True Sovereignty & Security (GDPR Ready) In the era of "Data Sovereignty," VAAK offers what big cloud providers can’t: Total Isolation.

Air-Gapped Runtime: No data leaves your VPC.

Compliance First: Built-in PII redaction and audit logging.

Zero-Trust Design: You own the model, the weights, and the inference pipeline.

4. Performance Engineering (The Rust Advantage)

<500ms Latency: Achieves human-level "interruptibility" and response times.

No GPU Tax: Runs inference efficiently on standard CPUs.

Configurability: Change the entire domain (e.g., from Banking to Healthcare) just by swapping a YAML config file.

The Vision VAAK is proof that we don't need massive clusters to build intelligent systems. We need better engineering.

If you are building for Banking, Healthcare, or Government sectors where privacy and precision are non-negotiable—this is the blueprint.

Explore the architecture & code here: https://github.com/ayushmaanbhav/Voice-Activated-Autonomous-...

#VAAK #RustLang #SystemArchitecture #GenerativeAI #SovereignAI #EnterpriseTech #VoiceAI #RAG #OpenSource #SoftwareEngineering

I've been with a startup through two funding rounds, and growth has been very healthy.

I recently asked leadership how much I've been diluted, just for financial planning purposes. I assume I've been diluted "a normal amount" and am fine with that-- I just need to know the number. Instead I got a non-answer from leadership, which surprised me. So I'm curious:

- How common is this practice in mid-stage startups? - What is the actual rationale for withholding this information? I get why companies may want to keep the cap table confidential, but an employee's dilution factor seems like the kind of thing that doesn't matter for cap table confidentiality, but matters a lot to the employee.

Thanks in advance for any color or perspective on this.

Did you just wake up from a 20 year coma? Did you build a bunch of buzzword compliant web services back in the early 2000s and want all your SOAP and WSDL to be relevant again? Now you can put the smooth sheen of AI on your pile of angle brackets by exposing your SOAP-based web service as a Model Context Protocol (MCP) server.

The article explores the development of a new AI agent, discussing the challenges of training a system to navigate complex environments and interact with human users effectively. It highlights the importance of continued research and innovation in the field of artificial intelligence.

The article presents a novel deep learning-based approach for generating realistic synthetic images of human faces. The proposed method combines the strengths of generative adversarial networks and disentangled representation learning to produce high-quality face images with controllable attributes.

The article discusses the potential health benefits of coffee, including its ability to reduce the risk of certain diseases and conditions such as type 2 diabetes, liver disease, and Parkinson's disease. It also explores the potential risks associated with excessive coffee consumption and advises moderation.

The database of the hacking forum Breachforums has been leaked, exposing personal information of over 324,000 accounts. The leak includes email addresses, usernames, and encrypted passwords, raising concerns about the potential impact on users.

The article explores the use of a powerful AI language model called Claude, which was trained on 7 years' worth of the author's daily journal entries. The summary highlights how this experiment provided a glimpse into the potential future of AI and its capacity to engage in personalized, contextual interactions with humans.

KalpāDesktop is an open-source desktop environment that aims to provide a modern, customizable, and efficient user experience. It offers a range of features, including a minimalist interface, support for multiple workspaces, and integration with various applications and services.

This is my attempt in building a memory that evolves and persist for claude code.

My approach is inspired from Zettelkasten method, memories are atomic, connected and dynamic. Existing memories can evolve based on newer memories. In the background it uses LLM to handle linking and evolution.

I have only used it with claude code so far, it works well with me but still early stage, so rough edges likely. I'm planning to extend it to other coding agents as I use several different agents during development.

Looking for feedbacks!

This article discusses proposed enhancements to the Java programming language, including pattern matching for switch expressions and statements, and improvements to textual and numeric literals. The changes aim to enhance developer productivity and improve the readability and expressiveness of Java code.