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The article discusses the VamSeek AI project, which aims to create an open-source AI model that can analyze and understand visual art and music. The project is driven by the goal of democratizing access to art and AI technologies, with a focus on building a diverse and inclusive community.

ChunkHound’s goal is simple: local-first codebase intelligence that helps you pull deep, core-dev-level insights on demand, generate always-up-to-date docs, and scale from small repos to enterprise monorepos — while staying free + open source and provider-agnostic (VoyageAI / OpenAI / Qwen3, Anthropic / OpenAI / Gemini / Grok, and more).

I’d love your feedback — and if you have, thank you for being part of the journey!

I've always loved RTS games and wanted to make a game similar for a long time. I thought I'd just try and build a mini / puzzle game around the resource gathering aspects of an RTS.

Objective: You have a base at the center and you need to mine and "refine" all of the resources on the map in as short a time as possible.

By default, the game will play automatically, but not optimally (moving and buying upgrades). You can disable that with the buttons. You can select drones and right click to move them to specific resources patches and buy upgrades as you earn upgrade points.

I've implemented three different levels and some basic sounds. I used Phaser at the game library (first time using it). It won't work well on a mobile.

Most ZK proving systems are optimized for server-grade hardware with massive RAM. When scaling to industrial-sized traces (2^20+ rows), they often hit a "Memory Wall" where allocation and data movement become a larger bottleneck than the actual computation.

I have been developing Hekate, a ZK engine written in Rust that utilizes a Zero-Copy streaming model and a hybrid tiled evaluator. To test its limits, I ran a head-to-head benchmark against Binius64 on an Apple M3 Max laptop using Keccak-256.

The results highlight a significant architectural divergence:

At 2^15 rows: Binius64 is faster (147ms vs 202ms), but Hekate is already 10x more memory efficient (44MB vs ~400MB).

At 2^20 rows: Binius64 hits 72GB of RAM usage, entering swap hell on a laptop. Hekate processes the same workload in 4.74s using just 1.4GB of RAM.

At 2^24 rows (16.7M steps): Hekate finishes in 88s with a peak RAM of 21.5GB. Binius64 is unable to complete the task due to OOM/Swap on this hardware.

The core difference is "Materialization vs. Streaming". While many engines materialize and copy massive polynomials in RAM during Sumcheck and PCS operations, Hekate streams them through the CPU cache in tiles. This shifts the unit economics of ZK proving from $2.00/hour high-memory cloud instances to $0.10/hour commodity hardware or local edge devices.

I am looking for feedback from the community, especially those working on binary fields, GKR, and memory-constrained SNARK/STARK implementations.

WSIStreamer is an open-source platform for real-time whole-slide image (WSI) visualization and analysis. It enables the streaming and exploration of large-scale pathology slides, allowing users to view, annotate, and analyze digital histology samples remotely.

I've been using Claude Code heavily, and kept hitting the same issue: the agent would push changes, respond to reviews, wait for CI... but never really know when it was done.

It would poll CI in loops. Miss actionable comments buried among 15 CodeRabbit suggestions. Or declare victory while threads were still unresolved.

The core problem: no deterministic way for an agent to know a PR is ready to merge.

So I built gtg (Good To Go). One command, one answer:

$ gtg 123 OK PR #123: READY CI: success (5/5 passed) Threads: 3/3 resolved

It aggregates CI status, classifies review comments (actionable vs. noise), and tracks thread resolution. Returns JSON for agents or human-readable text.

The comment classification is the interesting part — it understands CodeRabbit severity markers, Greptile patterns, Claude's blocking/approval language. "Critical: SQL injection" gets flagged; "Nice refactor!" doesn't.

MIT licensed, pure Python. I use this daily in a larger agent orchestration system — would love feedback from others building similar workflows.

The article describes an iOS Location Spoofer, a tool that allows users to spoof their GPS location on iOS devices. This can be useful for testing location-based apps or bypassing location restrictions, but may also have privacy implications.

Built a plugin that runs my entire dev cycle autonomously after I approve the plan.

/next-task pulls from GitHub issues, scans the codebase, ranks tasks, I pick one and approve the plan – then 18 agents handle exploration, implementation, code review, CI, and deployment without intervention.

Other commands: /ship (PR to deploy), /reality-check (detects plan drift), /project-review (multi-agent code review), /deslop-around (cleans AI slop).

Running parallel sessions this way. Open source, works with Claude Code, Codex CLI, and OpenCode.

npm install awesome-slash

https://github.com/avifenesh/awesome-slash

I reposted, since i forgot the Show HN prefix.

When I looked at self-hosted options, true E2EE turned out to be surprisingly rare. Most solutions rely on disk encryption, which only protects against physical theft, not server compromise or admin access. So I built Agam Space, my attempt at a self-hosted alternative to Mega or Proton Drive. It uses proper zero-knowledge encryption. Files are encrypted in the browser, and the server only stores encrypted blobs. Admins literally cannot access the files. It’s still in early beta, with very basic functionality and no professional security audit yet. Please don’t use it as your only backup.

Hi HN — I built Microwave, a native iOS app for browsing and posting short-form videos, similar to TikTok, but implemented as a pure client on top of Bluesky / AT Protocol.

There’s no custom backend: the app reads from and publishes to existing ATproto infrastructure. The goal was to explore whether a TikTok-like experience can exist as a thin client over an open social protocol, rather than a vertically integrated platform.

Things I’d especially love feedback on:

  - Whether this kind of UX makes sense on top of ATproto

  - Client-only tradeoffs (ranking, discovery, moderation)

  - Protocol limitations I may be missing

  - Any architectural red flags

Hi HN! In the past few months my team and I have been working on Tusk Drift, a system that records real API traffic from your service, then replays those requests as deterministic tests. Outbound I/O (databases, HTTP calls, etc.) gets automatically mocked using the recorded data.

Problem we're trying to solve: Writing API tests is tedious, and hand-written mocks drift from reality. We wanted tests that stay realistic because they come from real traffic.

versus mocking libraries: Tools like VCR/Nock intercept HTTP within your tests. Tusk Drift records full request/response traces externally (HTTP, DB, Redis, etc.) and replays them against your running service, no test code or fixtures to write/maintain.

How it works:

1. Add a lightweight SDK (we currently support Python and Node.js)

2. Record traffic in any environment.

3. Run `tusk run`, the CLI sandboxes your service and serves mocks via Unix socket

We run this in CI on every PR. Also been using it as a test harness for AI coding agents, they can make changes, run `tusk run`, and get immediate feedback without needing live dependencies.

Source: https://github.com/Use-Tusk/tusk-drift-cli

Demo: https://github.com/Use-Tusk/drift-node-demo

Happy to answer questions!

Hi, we're Sergey and Serafim. We've been building dev tools at 21st.dev and recently open-sourced 1Code (https://1code.dev), a local UI for Claude Code.

Here's a video of the product: https://www.youtube.com/watch?v=Sgk9Z-nAjC0

Claude Code has been our go-to for 4 months. When Opus 4.5 dropped, parallel agents stopped needing so much babysitting. We started trusting it with more: building features end to end, adding tests, refactors. Stuff you'd normally hand off to a developer. We started running 3-4 at once. Then the CLI became annoying: too many terminals, hard to track what's where, diffs scattered everywhere.

So we built 1Code.dev, an app to run your Claude Code agents in parallel that works on Mac and Web. On Mac: run locally, with or without worktrees. On Web: run in remote sandboxes with live previews of your app, mobile included, so you can check on agents from anywhere. Running multiple Claude Codes in parallel dramatically sped up how we build features.

What’s next: Bug bot for identifying issues based on your changes; QA Agent, that checks that new features don't break anything; Adding OpenCode, Codex, other models and coding agents. API for starting Claude Codes in remote sandboxes.

Try it out! We're open-source, so you can just bun build it. If you want something hosted, Pro ($20/mo) gives you web with live browser previews hosted on remote sandboxes. We’re also working on API access for running Claude Code sessions programmatically.

We'd love to hear your feedback!

Hi All.

I made this as an fun intro to help people who have zero IBM mainframe experience and no access to a modern IBM mainframe (at least, not access to do whatever you want with it). I appreciate tips, suggestions and anything that might improve the experience for someone who has no idea of how those machines operate(d).

I've built a package to use Raph Levien's Vello as a blazing fast 2D renderer for React on WebGPU. It uses WASM to hook into the Rust code

The article discusses the implementation of PostgreSQL's wire protocol in the Go programming language, focusing on the challenges and solutions involved in building a compatible PostgreSQL client and server. It explores the protocol's structure, and the techniques used to create a reusable and efficient implementation.

Hi HN

I built mdto.page because I often needed a quick way to share Markdown notes or documentation as a proper webpage, without setting up a GitHub repo or configuring a static site generator.

I wanted something dead simple: upload Markdown -> get a shareable public URL.

Key features:

Instant Publishing: No login or setup required.

Flexible Expiration: You can set links to expire automatically after 1 day, 7 days, 2 weeks, or 30 days. Great for temporary sharing.

It's free to use. I’d love to hear your feedback!

Hi HN! Creator here. I built UAIP (Universal Agent Interoperability Protocol) - infrastructure that enables AI agents from different companies (OpenAI, Anthropic, Microsoft) to securely transact with each other. The Problem: As AI agents become autonomous economic actors, they need:

Cryptographic identity (not just API keys) Secure payment rails for cross-company transactions Automated compliance (EU AI Act, SOC2, GDPR) Forensic audit trails

The Solution: 5-layer security stack combining:

Zero-Knowledge Proofs (Schnorr/Curve25519) for identity Multi-chain settlement (USDC on Base, Solana, Ethereum) RAG-based compliance auditing (Llama-3-Legal) Ed25519 signatures for non-repudiation Complete audit logging

Technical Stack:

Backend: Python, FastAPI, SQLite (WAL mode) Cryptography: NaCl, custom ZK-proof implementation Blockchain: Web3.py for multi-chain support Compliance: RAG with retrieval-augmented generation

Use Case: GPT agent pays Claude agent for data analysis:

Both prove identity via ZK-proofs Transaction checked for compliance Settled in USDC on Base (<$0.01 fee) Complete audit trail generated

Why blockchain:

Neutral settlement layer (no single company controls it) Instant microtransactions (traditional payments don't work for $0.01-$10) Programmable escrow (smart contracts) Verifiable computation (on-chain proofs)

Open source (FSL-1.1-Apache-2.0). Built over the last few months after hitting these problems in AI automation work. Happy to answer technical questions! GitHub: https://github.com/jahanzaibahmad112-dotcom/UAIP-Protocol

For the past year I've been working to rethink how AI manages timing in conversation at Tavus. I've spent a lot of time listening to conversations. Today we're announcing the release of Sparrow-1, the most advanced conversational flow model in the world.

Some technical details:

- Predicts conversational floor ownership, not speech endpoints

- Audio-native streaming model, no ASR dependency

- Human-timed responses without silence-based delays

- Zero interruptions at sub-100ms median latency

- In benchmarks Sparrow-1 beats all existing models at real world turn-taking baselines

I wrote more about the work here: https://www.tavus.io/post/sparrow-1-human-level-conversation...

Hi HN, I built webctl because I was frustrated by the gap between curl and full browser automation frameworks like Playwright.

I initially built this to solve a personal headache: I wanted an AI agent to handle project management tasks on my company’s intranet. I needed it to persist cookies across sessions (to handle SSO) and then scrape a Kanban board.

Existing AI browser tools (like current MCP implementations) often force unsolicited data into the context window—dumping the full accessibility tree, console logs, and network errors whether you asked for them or not.

webctl is an attempt to solve this with a Unix-style CLI:

- Filter before context: You pipe the output to standard tools. webctl snapshot --interactive-only | head -n 20 means the LLM only sees exactly what I want it to see.

- Daemon Architecture: It runs a persistent background process. The goal is to keep the browser state (cookies/session) alive while you run discrete, stateless CLI commands.

- Semantic targeting: It uses ARIA roles (e.g., role=button name~="Submit") rather than fragile CSS selectors.

Disclaimer: The daemon logic for state persistence is still a bit experimental, but the architecture feels like the right direction for building local, token-efficient agents.

It’s basically "Playwright for the terminal."

Hey Hacker News The ones that know me here know that I am a productivity geek.

After DockFlow to manage my Dock and ExtraDock, which gives me more space to manage my apps and files, I decided to tackle the macOS big boss: the menu bar.

I spend ~40% of my day context-switching between apps — Zoom meetings, Slack channels, Code projects, and Figma designs. My macOS menu bar has too many useless icons I almost never use.

So I thought to myself, how can I use this area to improve my workflows?

Most solutions (Bartender, Ice) require screen recording permissions, and did not really solve my issues. I wanted custom menus in the apps, not the ones that the developers decided for me.

After a few iterations and exploring different solutions, ExtraBar was created. Instead of just hiding icons, what if the menu bar became a keyboard-controlled command center that has the actions I need? No permissions. No telemetry. Just local actions.

This is ExtraBar: Set up the menu with the apps and actions YOU need, and use a hotkey to bring it up with full keyboard navigation built in.

What you can do: - Jump into your next Zoom call with a keystroke - Open specific Slack channels instantly (no menu clicking) - Launch VS Code projects directly - Trigger Apple Shortcuts workflows - Integrate with Raycast for advanced automation - Custom deep links to Figma, Spotify, or any URL

Real-world example: I've removed my menu bar icons. Everything is keyboard- controlled: cmd+B → 2 (Zoom) → 4 (my personal meeting) → I'm in.

Why it's different: Bartender and Ice hide icons. ExtraBar uses your menu bar to do things. Bartender requires screen recording permissions. Ice requires accessibility permissions. ExtraBar works offline with zero permissions - (Enhance functionality with only accessibility permissions, not a must)

Technical: - Written in SwiftUI; native on Apple Silicon and Intel - Zero OS permissions required (optional accessibility for enhanced keyboard nav) - All data stored locally (no cloud, no telemetry) - Very Customizable with custom configuration built in for popular apps + fully customizable configuration actions. - Import/export action configurations

The app is improving weekly based on community feedback. We're also building configuration sharing so users can share setups.

Already got some great feedback from Reddit and Producthunt, and I can't wait to get yours!

Check out the website: https://extrabar.app ProductHunt: https://www.producthunt.com/products/extrabar

This project is a tool for engineers who live in the terminal and are tired of losing their command history to ephemeral servers or fragmented `.bash_history` files. If you’re jumping between dozens of boxes, many of which might be destroyed an hour later, your "local memory" (the history file) is essentially useless. This tool builds a centralized, permanent brain for your shell activity, ensuring that a complex one-liner you crafted months ago remains accessible even if the server it ran on is long gone.

The core mechanism wants to be a "zero-touch" capture that happens at the connection gateway level. Instead of installing logging agents or scripts on every target machine, the tool reconstructs your terminal sessions from raw recording files generated by the proxy you use to connect. This "in-flight" capture means you get a high-fidelity log of every keystroke and output without ever having to touch the configuration of the remote host. It’s a passive way to build a personal knowledge base while you work.

To handle the reality of context-switching, the tool is designed with a "multi-tenant" architecture. For an individual engineer, this isn't about managing different users, but about isolating project contexts. It automatically categorizes history based on the specific organization or project tags defined at the gateway. This keeps your work for different clients or personal side-projects in separate buckets, so you don't have to wade through unrelated noise when you're looking for a specific solution.

In true nerd fashion, the search interface stays exactly where you want it: in the command line. There is no bloated web UI to slow you down. The tool turns your entire professional history into a searchable, greppable database accessible directly from your terminal.

Please read the full story [here](https://carminatialessandro.blogspot.com/2026/01/hc-agentles...)

Hi HN,

My team and I are building Tabstack to handle the "web layer" for AI agents. Launch Post: https://tabstack.ai/blog/intro-browsing-infrastructure-ai-ag...

Maintaining a complex infrastructure stack for web browsing is one of the biggest bottlenecks in building reliable agents. You start with a simple fetch, but quickly end up managing a complex stack of proxies, handling client-side hydration, and debugging brittle selectors. and writing custom parsing logic for every site.

Tabstack is an API that abstracts that infrastructure. You send a URL and an intent; we handle the rendering and return clean, structured data for the LLM.

How it works under the hood:

- Escalation Logic: We don't spin up a full browser instance for every request (which is slow and expensive). We attempt lightweight fetches first, escalating to full browser automation only when the site requires JS execution/hydration.

- Token Optimization: Raw HTML is noisy and burns context window tokens. We process the DOM to strip non-content elements and return a markdown-friendly structure that is optimized for LLM consumption.

- Infrastructure Stability: Scaling headless browsers is notoriously hard (zombie processes, memory leaks, crashing instances). We manage the fleet lifecycle and orchestration so you can run thousands of concurrent requests without maintaining the underlying grid.

On Ethics: Since we are backed by Mozilla, we are strict about how this interacts with the open web.

- We respect robots.txt rules.

- We identify our User Agent.

- We do not use requests/content to train models.

- Data is ephemeral and discarded after the task.

The linked post goes into more detail on the infrastructure and why we think browsing needs to be a distinct layer in the AI stack.

This is obviously a very new space and we're all learning together. There are plenty of known unknowns (and likely even more unknown unknowns) when it comes to agentic browsing, so we’d genuinely appreciate your feedback, questions, and tips.

Happy to answer questions about the stack, our architecture, or the challenges of building browser infrastructure.

Hey HN!

Wanted to show our open source agent harness called Gambit.

If you’re not familiar, agent harnesses are sort of like an operating system for an agent... they handle tool calling, planning, context window management, and don’t require as much developer orchestration.

Normally you might see an agent orchestration framework pipeline like:

compute -> compute -> compute -> LLM -> compute -> compute -> LLM

we invert this so with an agent harness, it’s more like:

LLM -> LLM -> LLM -> compute -> LLM -> LLM -> compute -> LLM

Essentially you describe each agent in either a self contained markdown file, or as a typescript program. Your root agent can bring in other agents as needed, and we create a typesafe way for you to define the interfaces between those agents. We call these decks.

Agents can call agents, and each agent can be designed with whatever model params make sense for your task.

Additionally, each step of the chain gets automatic evals, we call graders. A grader is another deck type… but it’s designed to evaluate and score conversations (or individual conversation turns).

We also have test agents you can define on a deck-by-deck basis, that are designed to mimic scenarios your agent would face and generate synthetic data for either humans or graders to grade.

Prior to Gambit, we had built an LLM based video editor, and we weren’t happy with the results, which is what brought us down this path of improving inference time LLM quality.

We know it’s missing some obvious parts, but we wanted to get this out there to see how it could help people or start conversations. We’re really happy with how it’s working with some of our early design partners, and we think it’s a way to implement a lot of interesting applications:

- Truly open source agents and assistants, where logic, code, and prompts can be easily shared with the community.

- Rubric based grading to guarantee you (for instance) don’t leak PII accidentally

- Spin up a usable bot in minutes and have Codex or Claude Code use our command line runner / graders to build a first version that is pretty good w/ very little human intervention.

We’ll be around if ya’ll have any questions or thoughts. Thanks for checking us out!

Walkthrough video: https://youtu.be/J_hQ2L_yy60

hi hn,

i built openwork, an open-source, local-first system inspired by claude cowork.

it’s a native desktop app that runs on top of opencode (opencode.ai). it’s basically an alternative gui for opencode, which (at least until now) has been more focused on technical folks.

the original seed for openwork was simple: i have a home server, and i wanted my wife and i to be able to run privileged workflows. things like controlling home assistant, or deploying custom web apps (e.g. our customs recipe app recipes.benjaminshafii.com), legal torrents, without living in a terminal.

our initial setup was running the opencode web server directly and sharing credentials to it. that worked, but i found the web ui unreliable and very unfriendly for non-technical users.

the goal with openwork is to bring the kind of workflows i’m used to running in the cli into a gui, while keeping a very deep extensibility mindset. ideally this grows into something closer to an obsidian-style ecosystem, but for agentic work.

some core principles i had in mind:

- open by design: no black boxes, no hosted lock-in. everything runs locally or on your own servers. (models don’t run locally yet, but both opencode and openwork are built with that future in mind.) - hyper extensible: skills are installable modules via a skill/package manager, using the native opencode plugin ecosystem. - non-technical by default: plans, progress, permissions, and artifacts are surfaced in the ui, not buried in logs.

you can already try it: - there’s an unsigned dmg - or you can clone the repo, install deps, and if you already have opencode running it should work right away

it’s very alpha, lots of rough edges. i’d love feedback on what feels the roughest or most confusing.

happy to answer questions.

Hi everyone,

I wanted to share a personal project I've been working on: a GL-like 3D software renderer inspired by the OpenGL 3.3 Core Specification.

The main goal was to better understand GPU behavior and rendering pipelines by building a virtual GPU layer entirely in software. This includes VRAM-backed resource handling, pipeline state management, and shader execution flow.

The project also exposes an OpenGL-style API and driver layer based on the official OpenGL Registry headers, allowing rendering code to be written in a way that closely resembles OpenGL usage.

I'd really appreciate any feedback.

Hi HN,

I recently noticed a recurring visual artifact in the "Most Replayed" heatmap on the YouTube player. The highest peaks were always surrounded by two dips. I got curious about why they were there, so I decided to reverse engineer the feature to find out.

This post documents the deep dive. It starts with a system design recreation, reverse engineering the rendering code, and ends with the mathematics.

This is also my first attempt at writing an interactive article. I would love to hear your thoughts on the investigation and the format.

Hi HN,

When working with BGP data, I kept running into the same friction: it’s easy to get raw data, but surprisingly hard to browse networks over time — especially by when they appeared, where they operate, and what they actually look like at a glance.

I built a small tool, bgpscout.io, to scratch that itch.

It lets you:

Browse ASNs by registration date and geography

See where a given network appears to have presence

View commonly scattered public data about an ASN in one place

Save searches to track when new networks matching certain criteria appear

All of this data is public already; the goal was to make exploration faster and less painful.

I haven’t invested heavily in expanding it yet. Before doing so, I’m curious:

Is this solving a real problem for you?

What would make something like this actually useful in day-to-day work?

Feedback is welcome.

What if you could show an AI your entire video in one image?

This turns a video into a 2D thumbnail grid—like a contact sheet. 48 frames, one image, full video context. Built on VAM Seek, a thumbnail grid I made for human video navigation. Turns out the same format works for AI too.

Prototype. Feedback welcome.

What: go-stats-calculator[1] - CLI tool for computing statistics (mean, median, variance, std-dev, skewness, etc.)

Why: I needed a quick way to look at statistics without having to resort to something heavy such as Python + its statistics module or Excel.

Disclaimer: Vibe-coded by Gemini 2.5 Pro and Opus 4.5 but also validated through unit tests and independent verification[2].

Install: Homebrew[3] or GoReleaser built binaries[4].

Demo:

    $ seq 99 322 | stats

    --- Descriptive Statistics ---
    Count:          224
    Sum:            47152
    Min:            99
    Max:            322
    
    --- Measures of Central Tendency ---
    Mean:           210.5
    Median (p50):   210.5
    Mode:           None
    
    --- Measures of Spread & Distribution ---
    Std Deviation:  64.8074
    Variance:       4200
    Quartile 1 (p25): 154.75
    Quartile 3 (p75): 266.25
    Percentile (p95): 310.85
    Percentile (p99): 319.77
    IQR:            111.5
    Skewness:       0 (Fairly Symmetrical)
    Outliers:       None

[2] https://github.com/jftuga/go-stats-calculator/tree/main?tab=...

[3] https://github.com/jftuga/go-stats-calculator?tab=readme-ov-...

[4] https://github.com/jftuga/go-stats-calculator/releases