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BonSplit is an open-source app that allows friends to easily split bills and track expenses. It simplifies the process of dividing costs and managing shared payments, making it a useful tool for groups and households.

Author here. I made this little TUI program for managing default applications on the Linux desktop.

Maybe some of you will find it useful.

Happy to answer any questions.

- Added support for document metadata preservation - Bring your own LLM - No OS limitations

Hi HN!

Fence wraps any command in a sandbox that blocks network by default and restricts filesystem writes. Useful for running semi-trusted code (package installs, build scripts, unfamiliar repos) with controlled side effects, or even just blocking tools that phone home.

> fence curl https://example.com # -> blocked

> fence -t code -- npm install # -> template with registries allowed

> fence -m -- npm install # -> monitor mode: see what gets blocked

One use-case is to use it with AI coding agents to reduce the risk of running agents with fewer interactive permission prompts:

> fence -t code -- claude --dangerously-skip-permissions

You can import existing Claude Code permissions with `fence import --claude`.

Fence uses OS-native sandboxing (macOS sandbox-exec, Linux bubblewrap) + local HTTP/SOCKS proxies for domain filtering.

Why I built this: I work on Tusk Drift, a system to record and replay real traffic as API tests (https://github.com/Use-Tusk/tusk-drift-cli). I needed a way to sandbox the service under test during replays to block localhost outbound connections (Postgres, Redis) and force the app to use mocks instead of real services. I quickly realized that this could be a general purpose tool that would also be useful as a permission manager across CLI agents.

Limitations: Not strong containment against malware. Proxy-based filtering requires programs to respect `HTTP_PROXY`.

Curious if others have run into similar needs, and happy to answer any questions!

The Project: I am building R3-Engine, a from-scratch, local AI inference engine for Microsoft's bitnet-b1.58-2B-4T. It is written in 100% Safe Rust, natively cross-compiles to Wasm SIMD128, and uses Zero heap allocations in the execution loop.

The Physics: By mapping a 64-byte aligned .r3 file directly from NVMe to CPU L3 Cache (Zero-Copy) and using AVX-512 VPOPCNTDQ for branchless math, the Ryzen 9950X3D achieves 117 Tokens/Second latency.

The Problem: The AI is mute (Outputting <unk>*)* The matrix multiplication pipeline is mathematically complete, but the output is stuck at Token ID 0 (<unk>). The issue lies in the transition between the quantized weights and the float-based non-linear activations.

Where I need expert input:

    Weight Tying in BitNet: Microsoft's 2B model ties Embeddings with the LM Head. I am cloning the embedding matrix for the output projection, but I suspect a scaling factor is missing.

    RMSNorm & SiLU in 1.58-bit: How should the raw integer accumulators (from the VPOPCNTDQ loop) be scaled before entering the SiLU activation and the subsequent layer?

If you know the physics of LLM Logit Sampling or ternary activation math, I would love your eyes on the codebase.

This repository explores the use of large language models (LLMs) for password generation and analysis, demonstrating the potential of LLMs to create and evaluate secure passwords while highlighting the associated risks and considerations.

To power the firewalling for our agents so that they couldn't contact arbitrary services, I build netfence. It's like Envoy but for eBPF filters.

It allows you to define different DNS-based rules that are resolved in a local daemon to IPs, then pushed to the eBPF filter to allow traffic. By doing it this way, we can still allow DNS-defined rules, but prevent contacting random IPs.

There's also no network performance penalty, since it's just DNS lookups and eBPF filters referencing memory.

It also means you don't have to tamper with the base image, which the agent could potentially manipulate to remove rules (unless you prevent root maybe).

It automatically manages the lifecycle of eBPF filters on cgroups and interfaces, so it works well for both containers and micro VMs (like Firecracker).

You implement a control plane, just like Envoy xDS, which you can manage the rules of each cgroup/interface. You can even manage DNS through the control plane to dynamically resolve records (which is helpful as a normal DNS server doesn't know which interface/cgroup a request might be coming from).

We specifically use this to allow our agents to only contact S3, pip, apt, and npm.

LLMNet is an open-source library for building and deploying large language models (LLMs) using PyTorch. It provides a modular and extensible framework for training, fine-tuning, and evaluating LLMs, allowing developers to quickly experiment with different model architectures and techniques.

CertRadar is a platform that provides comprehensive information and analysis on global certification bodies and standards, helping businesses navigate the complex world of certifications.

The article discusses how to effectively repurpose YouTube videos into blog content, highlighting strategies for extracting key points, structuring the content, and optimizing it for search engines and social media platforms.

I kept running into the same problem: modern Python tooling, but deployments to air-gapped systems are a pain. Even with uv, moving a fully locked environment into a network-isolated machine was no fun.

uv-pack should make this task less frustrating. It bundles a locked uv environment into a single directory that installs fully offline—dependencies, local packages, and optionally a portable Python interpreter. Copy it over, run one script, and you get the exact same environment every time.

Just released, would love some feedback!

The article explores the history and evolution of the classic Creamsicle ice cream treat, tracing its origins and describing the unique combination of orange-flavored ice cream and vanilla-flavored cream that makes it a beloved summertime treat.

This article discusses the development of a tool called AutoShorts, which automatically generates short video clips from long-form video content. The tool uses machine learning techniques to identify key moments and summarize the content, enabling efficient content repurposing and distribution.

Hi HN,

I built *Sightline*, a Shodan-style search engine for *physical-world infrastructure*.

Shodan makes it easy to explore exposed internet services. Sightline applies the same idea to the real world, using OpenStreetMap as the data source.

You can search things like:

* “telecom towers in karnataka” * “power plants near mumbai” * “data centers in paris france”

or use structured queries:

* `type:telecom operator:airtel region:karnataka` * `type:data_center operator:google`

Sightline:

* uses Overpass API for querying OSM features * uses Nominatim for resolving countries, regions, and cities * avoids hardcoded geography * uses deterministic, rule-based parsing (no AI inference)

Repo: https://github.com/ni5arga/sightline Try it out: https://sightline-maps.vercel.app

Seven modules teaching C the way safety-critical systems are actually built: MATH → STRUCT → CODE → TEST.

Each module answers one question: Does it exist? (Pulse), Is it normal? (Baseline), Is it regular? (Timing), Is it trending? (Drift), Which sensor to trust? (Consensus), How to handle overflow? (Pressure), What do we do about it? (Mode).

Every module is closed (no dependencies), total (handles all inputs), deterministic, and O(1). 83 tests passing.

Built this after 30 years in UNIX systems. Wanted something that teaches the rigour behind certified systems without requiring a decade of on-the-job learning first.

MIT licensed. Feedback welcome.

I built this because I am perpetually drowning in file transfer hell.

As a systems engineer, I am constantly moving massive files. Whether it's getting new releases of custom ISOs to clients, downloading entire filesystems to spin up clones, or uploading massive support packages to L3.

I always have to get something to somebody. Usually, it's something large that lives on a headless server. I can't tell you how many times I've modified my 'clone' script to tar most of a filesystem over ssh ("hello --exclude, my old friend...") just to get 10GB that someone will need for two days.

Tedious is an understatement.

And the truth is, whatever I am sending, it is ephemeral. I download it, I upload it, I delete it. They download it only to delete soon after.

I kept wondering - why isn't there a simpler tool that works where I spend 90% of my time -- the terminal? I wanted something that I could install quickly on whatever server I was currently connected to, and run a simple command to push to the cloud.

Enter Bucket – a secure, encrypted file sharing platform that works the way I do. I upload from the CLI, share with a link and unique secret, done. No browser required (unless you want it). The people you're sharing with can simply click the link you share to download, no need for them to make an account (unless they want it).

What makes it different:

- CLI-first: Built for terminal workflows first, web UI second

- Encryption: End-to-end AES-256 encryption. We never see your files or your secrets. The secret generated during a push is required for the pull, keeping your data private even from us. If you lose the secret, you can't access the file.

- Simple scalability: Free tier for trying it out, plans up to 250GB for serious use, enterprise coming soon.

- File retention: Bucket is designed for ephemeral storage. Upload things that people need now. Free tier keeps files for 3 days (email notifications are sent to you before they are deleted, in case you need to upload again)

Colleagues I have introduced this to tell me they use it every day. I know that I do.

Free tier gives you 3 GB to try it out - enough for most one-off transfers.

Here's the workflow:

    $ bucket push api.bin
    Verifying upload...
     Upload complete!

    bID: bkdf1e848ea23e
    bURL: api.bucketlabs.org/d/bkdf1e848ea23e
    Secret: 9b1e15167403a88cadb7d0f4d2890856
    Expires: 2026-01-28T03:15:18.277988Z
 

... but if the person you're sharing with has bucket installed they simply run:

    $ bucket pull bkdf1e848ea23e
    Enter secret: 
    ⠇ Downloading...
    Downloaded: api.bin

    $ bucket list 
    ID               Filename             Size         Expires
    ----------------------------------------------------------------------
    bkdf1e848ea23e api.bin              204.2 MB      2026-01-28T03:15:18.277988Z

    $ bucket account 
    Account Info
    ------------
    Subscription: free
    Used: 204.2 MB
    Quota: 3.00 GB

    To increase storage limits, visit:
    https://bucketlabs.org/auth

    $ bucket del bkdf1e848ea23e
    Deleted: bkdf1e848ea23e

Hi HN, founders of VibeFlow (YC S25) here.

We mostly work on backend and workflow tooling, but we needed a way to turn Figma designs into frontend code as a kickstart for prototyping. It takes a Figma frame and converts it into React + Tailwind components (plus assets).

If you want to try it: You can run it locally or use it via the VibeFlow UI to poke at it without setup (https://app.vibeflow.ai/)

VibePrompting.dev is a website dedicated to the art of crafting effective and engaging prompts for AI language models. The site covers various prompt engineering techniques, best practices, and resources to help users optimize their interactions with AI systems.

We just released a fully computer-animated multivariable calculus course in six languages: English, Spanish, German, French, Italian, and Portuguese. Free and open to everyone. Half the course is live now; the rest is coming very soon.

We're an early-stage edtech startup. Our goal: use AI to build high-quality, computer-animated versions of every STEM course in the world, in every language, and bring them together on a platform like Coursera, but built for the AI age, with deep LLM integration. We want that literally. Every course. Every language.

This project simply couldn't exist without generative AI. We're two co-founders, and we don't speak five of the languages we just published in. There may be mistakes, but these courses wouldn't exist in those languages otherwise.

We produce lectures fast with AI. But once a lecture is done, adding another language is even faster, it's O(1). So we just made this in six languages. We could keep going.

These videos aren't static MP4s, they're open for improvement. If you have ideas, suggestions, or find mistakes, please open an issue on GitHub: https://github.com/academa-dev/multivariable-calculus

Would love your feedback. Happy to answer questions.

Join the waitlist for upcoming courses at https://academa.ai.

Tech stack: We forked 3Blue1Brown's Manim, synced TTS with animations using OpenAI Whisper, and used Inworld AI for text-to-speech.

Hi HN — I’m Maaz. I built Free PDF Editor by TechRex, a privacy-first PDF toolkit that runs entirely in the browser (client-side). No signup, no watermark.

Why: I was frustrated that many “free” PDF tools require uploads, add watermarks, or force accounts. I wanted a simple tool where files stay on-device by default.

What it includes: - Edit & annotate: type on PDF, highlight, draw/markup, add notes - Add images/branding: insert images/photos, add a logo to a PDF - Organize: merge, split, extract pages, delete pages - Compression: compress for email/WhatsApp/portal uploads + target sizes (100KB, 200KB, 500KB, 1MB, 2MB, 5MB, 10MB) - OCR: detect scanned PDFs, make PDFs searchable (Ctrl+F), improve copy/paste + conversion accuracy - Converters: PDF ↔ Word/Excel/PPTX, image ↔ PDF, HTML ↔ PDF, PDF ↔ text, image-to-text

I’d love feedback on: 1) UX: should the homepage focus on Edit vs Compress vs OCR? 2) Quality: which formats/conversions/OCR cases break most for you? 3) Trust: what privacy assurances would you want to see (copy, UI, technical notes)?

Thanks — I’ll respond to every comment and prioritize fixes/features based on feedback.

I usually build web games in C++, but using Emscripten always felt like overkill for what I was doing. I don't need full POSIX emulation or a massive standard library just to render some stuff to a canvas and handle basic UI.

The main thing I wanted to solve was the JS/WASM interop bottleneck. Instead of using the standard glue code for every call, I moved everything to a Shared Memory architecture using Command and Event buffers.

The way it works is that I batch all the instructions in WASM and then just send a single "flush" signal to JS. The JS side then reads everything directly out of Shared Memory in one go. It’s way more efficient, I ran a benchmark rendering 10k rectangles on a canvas and the difference was huge: Emscripten hit around 40 FPS, while my setup hit 100 FPS.

But writing DOM logic in C++ is painful, so I built Coi. It’s a component-based language that statically analyzes changes at compile-time to enable O(1) reactivity. Unlike traditional frameworks, there is no Virtual DOM overhead; the compiler maps state changes directly to specific handles in the command buffer.

I recently benchmarked this against React and Vue on a 1,000-row table: Coi came out on top for row creation, row updating and element swapping because it avoids the "diffing" step entirely and minimizes bridge crossings. Its bundle size was also the smallest of the three.

One of the coolest things about the architecture is how the standard library works. If I want to support a new browser API (like Web Audio or a new Canvas feature), I just add the definition to my WebCC schema file. When I recompile the Coi compiler, the language automatically gains a new standard library function to access that API. There is zero manual wrapping involved.

I'm really proud of how it's coming along. It combines the performance of a custom WASM stack with a syntax that actually feels good to write (for me atleast :P). Plus, since the intermediate step is C++, I’m looking into making it work on the server side too, which would allow for sharing components across the whole stack.

Example (Coi Code):

component Counter(string label, mut int& value) {

    def add(int i) : void {
        value += i;
    }

    style {
        .counter {
            display: flex;
            gap: 12px;
            align-items: center;
        }
        button {
            padding: 8px 16px;
            cursor: pointer;
        }
    }

    view {
        <div class="counter">
            <span>{label}: {value}</span>
            <button onclick={add(1)}>+</button>
            <button onclick={add(-1)}>-</button>
        </div>
    }

component App { mut int score = 0;

    style {
        .app {
            padding: 24px;
            font-family: system-ui;
        }
        h1 {
            color: #1a73e8;
        }
        .win {
            color: #34a853;
            font-weight: bold;
        }
    }

    view {
        <div class="app">
            <h1>Score: {score}</h1>
            <Counter label="Player" &value={score} />
            <if score >= 10>
                <p class="win">You win!</p>
            </if>
        </div>
    }

app { root = App; title = "My Counter App"; description = "A simple counter built with Coi"; lang = "en"; }

Live Demo: https://io-eric.github.io/coi

Coi (The Language): https://github.com/io-eric/coi

WebCC: https://github.com/io-eric/webcc

I'd love to hear what you think. It's still far from finished, but as a side project I'm really excited about :)

Authoritarian regimes create crises to saturate our attention

AI can help us fight back by tracking and prioritizing threats to our freedom

250+ types of events tracked daily from national coverage

Completely automated and community funded

https://www.worstdaysofar.com/

Example Daily Situation Report:

SITREP - Authoritarian Consolidation (Last Update Jan 24, 7:11 PM)

New Today

Tactics: Scene access denial -- federal teams block state investigators after lethal force incidents (Minneapolis)

Violence/Detention: Border Patrol lethal force -- urban shooting escalates risk during routine immigration operations (Minneapolis) • Stun grenades and tear gas -- crowd control spikes after shootings, raising bystander injury risk (Minneapolis)

New Yesterday

Tactics: Generic POLICE vests -- misidentification enables federal teams to evade accountability during grabs (Minnesota)

Lawfare: Grant drawdown toggles -- federal health funding access paused to enforce priority compliance (US) • Sealing special-counsel report -- privilege claims used to suppress scrutiny of executive misconduct (US)

Disinformation: National parks passes with leader portrait -- voiding altered passes coerces visible loyalty (US) • Geofenced ICE recruitment memes -- wartime branding accelerates staffing for enforcement surges (US)

New This Week ... ...

Basically was getting annoyed jumping between 5 different sites during this winter storm season, so I built "StormWatch". It's a no-fuss, mobile-friendly webpage (dashboard) that shows all the stuff I was looking for, but in one simple UI.

Features:

- Real-time NWS alerts with safety tips - Snow/ice/precip accumulation forecasts (+wind) - Dynamic preparation checklists based on your alerts - Supply calculator for your household size - Regional weather news

It's free, no login required, works on any device. Just enter your ZIP.

https://jeisey.github.io/stormwatch/

Uses NWS and GDELT APIs and open source. Feel free to fork and modify however you'd like.

For builders: - Used an API-testing agent to verify all endpoints, response patterns, types, and rate limits - Used a scope & validation agent to keep the slices simple, focused, and tested - VS-code Copilot (Sonnet 4 for dev agents + Opus 4.5 for scope and validation)

The article discusses the Tick Cache, an open-source cache system designed to handle high-volume time-series data. It provides an overview of the Tick Cache's architecture, performance characteristics, and use cases, making it a valuable resource for developers working with time-series data.

I've long wanted to harness QEMU/KVM for my desktop virtual machines, but I'm befuddled by virt-manager's lack of support for working NVIDIA 3D acceleration, dogmatic embrace of ugly XML, and the puzzling UI decision of having to click what seems like 15 buttons to attach an ISO to a VM image. When I further learned that NVIDIA's broken 3D acceleration is the fault of libvirt as opposed to QEMU's virtio driver, I had an idea...

Behold, vm-curator! A fast and friendly VM management TUI written in Rust. You can create, configure, organize, and manage VMs directly with QEMU. No libvert. No XML. No wonky UI's. Just the right level of friendliness, customization, and speed to be really really useful.

The best part? 3D para-virtualization works with NVIDIA cards (via virtio-vga-gl!) No jumping through hoops to get GPU passthrough working!

(Disclaimer: This works great with other guest Linux VMs, but is not suitable for Windows gaming. If you want to game on Windows within a VM, passthrough is a must. vm-curator will have fast and friendly support soon.)

Looking for contributors (especially to help with the ascii art,) and donations are welcome. (Claude was a big help, but this was not a vibe-coded affair. We pair-programmed approx. 10,000 lines of code here. It was a great way to learn Rust, actually!)

This repository contains a modified version of the XV6 operating system, an educational UNIX-like operating system. The modifications aim to enhance the system's functionality, including support for scheduling, memory management, and file systems.

Hi HN, I'm an independent developer and programmer。

I built HomeGenGuide because I found it surprisingly difficult to get a clear estimate for home backup power installation costs without jumping through hoops with sales reps。

It’s a tool that calculates estimated costs based on generator type, fuel source, and potential labor rates。 On the tech side, it's built with Next.js and serves as a real-world case study for my interest in programmatic SEO (pSEO) and niche market analysis。

I'd love your feedback on:

The accuracy of the cost estimation logic.

The overall UX for a niche utility tool.

Any other variables I should consider (e.g., specific regional regulations).

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

Hi HN! I built Ghibli Search, a semantic search engine for Studio Ghibli movie scenes (e.g. Spirited Away, My Neighbor Totoro, Howl's Moving Castle, etc.).

Describe a dreamscape like "flying through clouds at sunset" or upload an image, and it finds visually similar scenes from the films.

Live demo: https://ghibli-search.anini.workers.dev/

Full Cloudflare stack: Workers, AI Search, R2, Workers AI

Open source: https://github.com/aninibread/ghibli-search

Would love feedback on the search quality and any ideas for improvements!