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I built LocalGPT over 4 nights as a Rust reimagining of the OpenClaw assistant pattern (markdown-based persistent memory, autonomous heartbeat tasks, skills system).

It compiles to a single ~27MB binary — no Node.js, Docker, or Python required.

Key features:

- Persistent memory via markdown files (MEMORY, HEARTBEAT, SOUL markdown files) — compatible with OpenClaw's format - Full-text search (SQLite FTS5) + semantic search (local embeddings, no API key needed) - Autonomous heartbeat runner that checks tasks on a configurable interval - CLI + web interface + desktop GUI - Multi-provider: Anthropic, OpenAI, Ollama etc - Apache 2.0

Install: `cargo install localgpt`

I use it daily as a knowledge accumulator, research assistant, and autonomous task runner for my side projects. The memory compounds — every session makes the next one better.

GitHub: https://github.com/localgpt-app/localgpt Website: https://localgpt.app

Would love feedback on the architecture or feature ideas.

Built this to solve a personal problem: spending minutes in grocery stores reading labels and googling E-codes. Uses client-side OCR to scan ingredient lists and matches against 450+ E-codes, works completely offline after initial load.

E-codes can be verified by scanning or uploading product label and searching the e-codes database (voice or type).

Now looking for partnerships to extend the reach.

Appreciate the feedback.

Two clip-paths, over the navigation:

- The first clip-path is a circle (top-left corner) - The second clip-path is a polygon, that acts like a ray (hardcoded, can be improved)

The original work by Iventions Events https://iventions.com/ uses JavaScript, but I found CSS-only approach more fun

Here's a demo and the codebase: https://github.com/Momciloo/fun-with-clip-path

Hi! I spent 3 years evaluating LLMs for OpenAI, Anthropic, METR, and other labs. Kept running into the same problem: AI workflows break in production because there's no clean way to add human oversight, handle failures gracefully, or deploy without choosing between "all cloud" and "all self-hosted."

WeaveMind is a visual workflow builder in Rust. The core idea is that humans and AI are interchangeable nodes in the same graph. When a workflow needs judgment, it pauses, notifies the team via browser extension, first responder picks it up. There's also an AI assistant that generates workflows from natural language, and durable execution so nothing gets lost on failure.

Early beta, free (bring your own API keys). Planning to open source once stable (Q2 2026). Feedback welcome. Discord: https://discord.gg/FGwNu6mDkU

After analyzing 9 years of HN side project posts, I found some counter-intuitive patterns about what makes projects profitable.

Three things that stood out:

1. B2B dominates: 73% of $500+/month projects target businesses, not consumers

2. Speed matters more than polish: Average 47 days from launch to first sale. Most started charging with 3-5 core features.

3. Pricing cluster: 87% price between $20-49. Low enough for impulse purchase, high enough to be sustainable.

I compiled this into a dataset of nearly 700 projects with tech stack, pricing, and timeline data. Built it because I was planning my own side project and wanted to see patterns, not just success stories.

Available at [https://haileyzhou.gumroad.com/l/pknktt] ($49) as html report and raw .csv data.

Data source note: All from public HN posts 2017-2025. I did the cleaning, categorization, and cross-referencing.

Happy to discuss methodology or findings. Curious what patterns others see or if this matches your experience?

This article presents a novel image compression algorithm that adapts the block size and luma-dependent chroma compression based on the spatial domain, leading to improved compression efficiency while maintaining image quality.

I’ve been thinking about the "last mile" problem for AI agents. We have agents that can code, plan, and browse, but they are still economically "trapped." They can't independently pay for their own API calls, compute, or data without a human-in-the-loop providing a credit card.

To solve this, I’m building the A2A (Agent-to-Agent) System, an open-source infrastructure designed to turn agents into independent economic actors.

What’s under the hood? Identity (a2trust): DID-based verifiable identity using @veramo/core. It allows agents to establish persistent reputations (EigenTrust) so they can trust each other without centralized gatekeepers.

Payments (a2pay): Built on ERC-7579 Smart Accounts. Agents can use Session Keys to execute transactions autonomously within specific constraints (time-limited, amount-capped, gas-abstracted).

Protocol (a2api): A marketplace layer that utilizes MCP (Model Context Protocol). Agents can discover services via machine-readable docs (llms.txt) and negotiate fees via standard interfaces.

Why this matters: Most current agent payment solutions are just wrappers around human wallets. A2A aims to build a native "Agent Economy" where an agent can earn revenue from its tools and spend it to hire other agents, creating a truly autonomous swarm.

The Tech Stack: TypeScript/Node.js, Viem / Permissionless.js for smart account abstraction, MCP SDK for inter-agent communication, Base L2 for low-cost transactions,

I’d love to get your feedback on the architecture, especially on the security implications of delegating session keys to LLM-driven agents.

GitHub: https://github.com/swimmingkiim/a2a-project

Example repo: https://github.com/valdanylchuk/breezydemo

The underlying ESP-IDF component: https://github.com/valdanylchuk/breezybox

It is something like Raspberry Pi, but without the overhead of a full server-grade OS.

It captures a lot of the old school DOS era coding experience. I created a custom fast text mode driver, plan to add VGA-like graphics next. ANSI text demos run smooth, as you can see in the demo video featured in the Readme.

App installs also work smoothly. The first time it installed 6 apps from my git repo with one command, felt like, "OMG, I got homebrew to run on a toaster!" And best of all, it can install from any repo, no approvals or waiting, you just publish a compatible ELF file in your release.

Coverage:

Hackaday: https://hackaday.com/2026/02/06/breezybox-a-busybox-like-she...

Hackster.io: https://www.hackster.io/news/valentyn-danylchuk-s-breezybox-...

Reddit: https://www.reddit.com/r/esp32/comments/1qq503c/i_made_an_in...

Due to bike-induced concussions, I've been worried for a while about losing my memory and not being able to log back in.

I combined shamir secret sharing (hashicorp vault's implementation) with age-encryption, and packaged it using WASM for a neat in-browser offline UX.

The idea is that if something happens to me, my friends and family would help me get back access to the data that matters most to me. 5 out of 7 friends need to agree for the vault to unlock.

Try out the demo in the website, it runs entirely in your browser!

I’m building Solnix, an experimental programming language as a learning + research project. It’s still early, and many design decisions (syntax, types, tooling) are intentionally not fixed yet.

My goal right now is feedback, not promotion:

What design mistakes should I watch out for?

What usually makes new languages hard to adopt?

What would you avoid if you were starting today?

Repo & docs: https://solnix-lang.org/

I’d really appreciate honest criticism. Thanks for your time.

Hello everyone!

I'm a solo developer who's been doing UI/UX work since 2007. Over the years, I watched design tools evolve from lightweight products into bloated feature-heavy platforms. I kept finding myself using a small amount of the features while the rest just mostly got in the way.

So a few years ago I set out to build a design tool just like I wanted. So I built Vecti with what I actually need: pixel-perfect grid snapping, a performant canvas renderer, shared asset libraries, and export/presentation features. No collaborative whiteboarding. No plugin ecosystem. No enterprise features. Just the design loop.

Four years later, I can proudly show it off. Built and hosted in the EU with European privacy regulations. Free tier available (no credit card, one editor forever).

On privacy: I use some basic analytics (page views, referrers) but zero tracking inside the app itself. No session recordings, no behavior analytics, no third-party scripts beyond the essentials.

If you're a solo designer or small team who wants a tool that stays out of your way, I'd genuinely appreciate your feedback: https://vecti.com

Happy to answer questions about the tech stack, architecture decisions, why certain features didn't make the cut, or what's next.

Scripta is a no-code platform that enables businesses to build and manage web applications without writing any code. It offers a range of features, including visual drag-and-drop interfaces, pre-built components, and data integration capabilities, to help users create custom applications quickly and efficiently.

AgentKube is the AI powered Kubernetes IDE - all open source. Try it our and lets chat on making it better. open source is the way to go.

Kappal is an open-source software project that provides a platform for building and deploying containerized applications. It offers a simplified workflow for managing infrastructure, orchestrating services, and automating deployments across multiple environments.

Hi HN! Smooth CLI (https://www.smooth.sh) is a browser that agents like Claude Code can use to navigate the web reliably, quickly, and affordably. It lets agents specify tasks using natural language, hiding UI complexity, and allowing them to focus on higher-level intents to carry out complex web tasks. It can also use your IP address while running browsers in the cloud, which helps a lot with roadblocks like captchas (https://docs.smooth.sh/features/use-my-ip).

Here’s a demo: https://www.youtube.com/watch?v=62jthcU705k Docs start at https://docs.smooth.sh.

Agents like Claude Code, etc are amazing but mostly restrained to the CLI, while a ton of valuable work needs a browser. This is a fundamental limitation to what these agents can do.

So far, attempts to add browsers to these agents (Claude’s built-in --chrome, Playwright MCP, agent-browser, etc.) all have interfaces that are unnatural for browsing. They expose hundreds of tools - e.g. click, type, select, etc - and the action space is too complex. (For an example, see the low-level details listed at https://github.com/vercel-labs/agent-browser). Also, they don’t handle the billion edge cases of the internet like iframes nested in iframes nested in shadow-doms and so on. The internet is super messy! Tools that rely on the accessibility tree, in particular, unfortunately do not work for a lot of websites.

We believe that these tools are at the wrong level of abstraction: they make the agent focus on UI details instead of the task to be accomplished.

Using a giant general-purpose model like Opus to click on buttons and fill out forms ends up being slow and expensive. The context window gets bogged down with details like clicks and keystrokes, and the model has to figure out how to do browser navigation each time. A smaller model in a system specifically designed for browsing can actually do this much better and at a fraction of the cost and latency.

Security matters too - probably more than people realize. When you run an agent on the web, you should treat it like an untrusted actor. It should access the web using a sandboxed machine and have minimal permissions by default. Virtual browsers are the perfect environment for that. There’s a good write up by Paul Kinlan that explains this very well (see https://aifoc.us/the-browser-is-the-sandbox and https://news.ycombinator.com/item?id=46762150). Browsers were built to interact with untrusted software safely. They’re an isolation boundary that already works.

Smooth CLI is a browser designed for agents based on what they’re good at. We expose a higher-level interface to let the agent think in terms of goals and tasks, not low-level details.

For example, instead of this:

  click(x=342, y=128)
  type("search query")
  click(x=401, y=130)
  scroll(down=500)
  click(x=220, y=340)
  ...50 more steps

  Search for flights from NYC to LA and find the cheapest option

Smooth enables agents to launch as many browsers and tasks as they want, autonomously, and on-demand. If the agent is carrying out work on someone’s behalf, the agent’s browser presents itself to the web as a device on the user’s network. The need for this feature may diminish over time, but for now it’s a necessary primitive. To support this, Smooth offers a “self” proxy that creates a secure tunnel and routes all browser traffic through your machine’s IP address (https://docs.smooth.sh/features/use-my-ip). This is one of our favorite features because it makes the agent look like it’s running on your machine, while keeping all the benefits of running in the cloud.

We also take away as much security responsibility from the agent as possible. The agent should not be aware of authentication details or be responsible for handling malicious behavior such as prompt injections. While some security responsibility will always remain with the agent, the browser should minimize this burden as much as possible.

We’re biased of course, but in our tests, running Claude with Smooth CLI has been 20x faster and 5x cheaper than Claude Code with the --chrome flag (https://www.smooth.sh/images/comparison.gif). Happy to explain further how we’ve tested this and to answer any questions about it!

Instructions to install: https://docs.smooth.sh/cli. Plans and pricing: https://docs.smooth.sh/pricing.

It’s free to try, and we'd love to get feedback/ideas if you give it a go :)

We’d love to hear what you think, especially if you’ve tried using browsers with AI agents. Happy to answer questions, dig into tradeoffs, or explain any part of the design and implementation!

r3 is a high-performance, open-source programming language and environment that focuses on simplicity, efficiency, and creativity. It provides a powerful set of tools for developing a wide range of applications, from games and graphics to data visualization and automation.

I'm a software engineer who keeps getting pulled into DevOps no matter how hard I try to escape it. I recently moved into a Lead DevOps Engineer role writing tooling to automate a lot of the pain away. On my own time outside of work, I built Artifact Keeper — a self-hosted artifact registry that supports 45+ package formats. Security scanning, SSO, replication, WASM plugins — it's all in the MIT-licensed release. No enterprise tier. No feature gates. No surprise invoices.

Your package managers — pip, npm, docker, cargo, helm, go, all of them — talk directly to it using their native protocols. Security scanning with Trivy, Grype, and OpenSCAP is built in, with a policy engine that can quarantine bad artifacts before they hit your builds. And if you need a format it doesn't support yet, there's a WASM plugin system so you can add your own without forking the backend.

Why I built it:

Part of what pulled me into computers in the first place was open source. I grew up poor in New Orleans, and the only hardware I had access to in the early 2000s were some Compaq Pentium IIs my dad brought home after his work was tossing them out. I put Linux on them, and it ran circles around Windows 2000 and Millennium on that low-end hardware. That experience taught me that the best software is software that's open for everyone to see, use, and that actually runs well on whatever you've got.

Fast forward to today, and I see the same pattern everywhere: GitLab, JFrog, Harbor, and others ship a limited "community" edition and then hide the features teams actually need behind some paywall. I get it — paychecks have to come from somewhere. But I wanted to prove that a fully-featured artifact registry could exist as genuinely open-source software. Every feature. No exceptions.

The specific features came from real pain points. Artifactory's search is painfully slow — that's why I integrated Meilisearch. Security scanning that doesn't require a separate enterprise license was another big one. And I wanted replication that didn't need a central coordinator — so I built a peer mesh where any node can replicate to any other node. I haven't deployed this at work yet — right now I'm running it at home for my personal projects — but I'd love to see it tested at scale, and that's a big part of why I'm sharing it here.

The AI story (I'm going to be honest about this):

I built this in about three weeks using Claude Code. I know a lot of you will say this is probably vibe coding garbage — but if that's the case, it's an impressive pile of vibe coding garbage. Go look at the codebase. The backend is ~80% Rust with 429 unit tests, 33 PostgreSQL migrations, a layered architecture, and a full CI/CD pipeline with E2E tests, stress testing, and failure injection.

AI didn't make the design decisions for me. I still had to design the WASM plugin system, figure out how the scanning engines complement each other, and architect the mesh replication. Years of domain knowledge drove the design — AI just let me build it way faster. I'm floored at what these tools make possible for a tinkerer and security nerd like me.

Tech stack: Rust on Axum, PostgreSQL 16, Meilisearch, Trivy + Grype + OpenSCAP, Wasmtime WASM plugins (hot-reloadable), mesh replication with chunked transfers. Frontend is Next.js 15 plus native Swift (iOS/macOS) and Kotlin (Android) apps. OpenAPI 3.1 spec with auto-generated TypeScript and Rust SDKs.

Try it:

  git clone https://github.com/artifact-keeper/artifact-keeper.git
  cd artifact-keeper
  docker compose up -d

Live demo: https://demo.artifactkeeper.com Docs: https://artifactkeeper.com/docs/

I'd love any feedback — what you think of the approach, what you'd want to see, what you hate about Artifactory or Nexus that you wish someone would just fix. It doesn't have to be a PR. Open an issue, start a discussion, or just tell me here.

https://github.com/artifact-keeper

Why is it high performance? It uses a fixed number of DOM elements regardless of item count and supports bidirectional infinite scrolling.

Hi HN,

I built a collection of 40+ PDF tools using Kimi k2.5 (with about $50 worth of credits) and a lot of “vibe coding”.

The site: https://pdfuck.com (almost forgot I owned this domain )

What it does:

40+ PDF-related tools

Completely free

No downloads, everything runs in the browser

All processing is done locally in your browser for better privacy

It’s deployed on Cloudflare, so the running cost is basically close to zero.

This started as a small experiment and slowly grew into a fairly complete PDF toolbox. I’d really appreciate feedback on performance, UX, and what tools you think are missing.

Hi HN,

My friend and I have been experimenting with using LLMs to reason about biotech stocks. Unlike many other sectors, Biotech trading is largely event-driven: FDA decisions, clinical trial readouts, safety updates, or changes in trial design can cause a stock to 3x in a single day (https://www.biotradingarena.com/cases/MDGL_2023-12-14_Resmet...).

Interpreting these ‘catalysts,’ which comes in the form of a press release, usually requires analysts with previous expertise in biology or medicine. A catalyst that sounds “positive” can still lead to a selloff if, for example: the effect size is weaker than expected

- results apply only to a narrow subgroup

- endpoints don’t meaningfully de-risk later phases,

- the readout doesn’t materially change approval odds.

To explore this, we built BioTradingArena, a benchmark for evaluating how well LLMs can interpret biotech catalysts and predict stock reactions. Given only the catalyst and the information available before the date of the press release (trial design, prior data, PubMed articles, and market expectations), the benchmark tests to see how accurate the model is at predicting the stock movement for when the catalyst is released.

The benchmark currently includes 317 historical catalysts. We also created subsets for specific indications (with the largest in Oncology) as different indications often have different patterns. We plan to add more catalysts to the public dataset over the next few weeks. The dataset spans companies of different sizes and creates an adjusted score, since large-cap biotech tends to exhibit much lower volatility than small and mid-cap names.

Each row of data includes:

- Real historical biotech catalysts (Phase 1–3 readouts, FDA actions, etc.) and pricing data from the day before, and the day of the catalyst

- Linked Clinical Trial data, and PubMed pdfs

Note, there are may exist some fairly obvious problems with our approach. First, many clinical trial press releases are likely already included in the LLMs’ pretraining data. While we try to reduce this by ‘de-identifying each press release’, and providing only the data available to the LLM up to the date of the catalyst, there are obviously some uncertainties about whether this is sufficient.

We’ve been using this benchmark to test prompting strategies and model families. Results so far are mixed but interesting as the most reliable approach we found was to use LLMs to quantify qualitative features and then a linear regression of these features, rather than direct price prediction.

Just wanted to share this with HN. I built a playground link for those of you who would like to play around with it in a sandbox. Would love to hear some ideas and hope people can play around with this!

Our team lives in Slack, but we don’t have access to the Slack MCP and couldn’t find anything out there that worked for us, so we coded our own agent-slack CLI

  * Can paste in Slack URLs
  * Token efficient
  * Zero-config (auto auth if you use Slack Desktop)

MIT License

Gigacode is an experimental, just-for-fun project that makes OpenCode's TUI + web + SDK work with Claude Code, Codex, and Amp.

It's not a fork of OpenCode. Instead, it implements the OpenCode protocol and just runs `opencode attach` to the server that converts API calls to the underlying agents.

We build this to scratch our itch of being able to rapidly switch between coding agents based on the task at hand. For example, we find that:

- Claude Code is the best executor & fast iterator - Codex (high) is the best for complex or long-running tasks - OpenCode for fine-tuned, do-exactly-as-I-say edits

I personally believe that harnesses matter almost as much as the models in 2026. OpenCode lets you swap out models already, but the CC & Codex harnesses + system prompts make a big difference in practice.

Under the hood, this is all powered by our Sandbox Agent SDK:

- Sandbox Agent SDK provides a universal HTTP API for controlling Claude Code, Codex, and Amp - Sandbox Agent SDK exposes an OpenCode-compatible endpoint so OpenCode can talk to any agent - OpenCode connects to Sandbox Agent SDK via attach

I want to emphasize: the Anomaly folks are doing awesome work with OpenCode agent + Zen + Black. I use OC regularly alongside CC & Codex depending on the task. Gigacode is only possible because OpenCode is insanely flexible, hackable, and well documented.

Give it a try:

$ curl -fsSL https://releases.rivet.dev/sandbox-agent/latest/gigacode-ins... | sh

Check out the project, architecture, and other install options:

https://github.com/rivet-dev/sandbox-agent/tree/main/gigacod...

django-rclone bridges Django's database layer with rclone's file transfer layer. You get native database dumps piped directly to any of rclone's 70+ supported cloud storage backends -- no temp files, no intermediate archives, no Python reimplementations of what rclone already does.

Hi,

I built Axiomeer, an open-source marketplace protocol for AI agents. The idea: instead of hardcoding tool integrations into every agent, agents shop a catalog at runtime, and the marketplace ranks, executes, validates, and audits everything.

How it works: - Providers publish products (APIs, datasets, model endpoints) via 10-line JSON manifests - Agents describe what they need in natural language or structured tags - The router scores all options by capability match (70%), latency (20%), cost (10%) with hard constraint filters - The top pick is executed, output is validated (citations required? timestamps?), and evidence quality is assessed deterministically - If the evidence is mock/fake/low-quality, the agent abstains rather than hallucinating - Every execution is logged as an immutable receipt

The trust layer is the part I think is missing from existing approaches. MCP standardizes how you connect to a tool server. Axiomeer operates one layer up: which tool, from which provider, and can you trust what came back?

Stack: Python, FastAPI, SQLAlchemy, Ollama (local LLM, no API keys). v1 ships with weather providers (Open-Meteo + mocks). The architecture supports any HTTP endpoint that returns structured JSON.

Looking for contributors to add real providers across domains (finance, search, docs, code execution). Each provider is ~30 lines + a manifest.

This is a little game implemented over a week of tinkering and targeting Emacs.

The point is both to have fun with this kind of simulations, and also explore the "functional core / imperative shell" approach to architecture. I also developed a tile and tile effect definition DSL, which makes this even easier to extend. From this point of view it's a success: easy testing, easy extension,

Gameplay-wise the simulation is too simplistic, and needs input from people interested in this kind of toys. The original Micropolis/SimSity is the last time I built a virtual city.

GUI-first, multi-service gRPC scaffold for an Android Development Kit style workflow on an AArch64 system.