ratatui VS assets

We use a cassowary based constraint solver within Ratatui [1]. It's kind of neat once you understand the algorithm and add your own UI rules system on top of it. Unfortunately the library[2] that we use for the actual solving is unmaintained for many years now. I've been meaning to rewrite[3] a bunch of things in the lib to be more ergonomic, but it hasn't bubbled to the top of my todo list yet.

[1]: https://github.com/ratatui/ratatui/blob/main/ratatui-core/sr...

[2]: https://crates.io/crates/cassowary/

[3]: https://crates.io/crates/kasuari

I think this may lack a bit of context, so I'll try to fill in.

This is a demo that was just showcased live by the author as part of their talk at FOSDEM'25: https://fosdem.org/2025/schedule/event/fosdem-2025-5496-brin...

The talk was centered on Ratatui (a TUI library in Rust, https://ratatui.rs/) now targeting terminals and web browsers with a shared approach. Video recording of the talk should appear online soon.

Proudly built with Ratatui: https://github.com/ratatui/ratatui :)

The graphics themselves are basically handmade pixel-by-pixel, you can take a look in the assets folders to get an idea.

For example, pirate images are generated by combining the different parts and then applying a color mapping for the different skin tones.

Planet gifs are adapted starting from https://deep-fold.itch.io/pixel-planet-generator

Excellent article of what is going on in the terminal space, agree on the TUI section where we are seeing lots of terminal tools being built in Rust and Go and libraries such as Ratatui [1] and Bubble Tea [2] becoming a new modern alternative to ncurses for building TUIs.

Python has Textualize which is also very popular for building terminal user interfaces [3]

And we've noticed this renaissance as well of new CLI and TUI tools that we list on Terminal Trove [4].

[1] https://ratatui.rs/

[2] https://github.com/charmbracelet/bubbletea

[3] https://textual.textualize.io/

[4] https://terminaltrove.com/

I recently had to look at the implementation of the Sparkline [1] widget in Ratatui which uses a similar Unicode technique but scales nicely for sparklines with larger vertical size.

[1] https://github.com/ratatui/ratatui/blob/20c88aaa5b9eb011a522...

3. ChatGPT treating me with respect vs Claude: https://github.com/user-attachments/assets/2bcbaaca-9d1b-4cb...

*The Real Question:*

👉 Watch Demo

I've been working on semantic search for Mac. It's called SmartSearch - uses SentenceTransformers for embeddings and FAISS for fast similarity search. Best of all, it runs locally on your computer.

Github: https://github.com/neberej/smart-search/

Demo: https://github.com/user-attachments/assets/aed054e0-a91f-459...

After getting frustrated with macOS's Spotlight search, e.g., typing "driver license" doesn't give me anything unless the file name matches exactly, I thought, why not index my entire Documents folder? This way, I can find that one PDF or image buried deep in subfolders using natural language queries.

So I built SmartSearch; it uses SentenceTransformers for embeddings and FAISS for fast similarity search. Best of all, it runs locally on your computer.

Github: https://github.com/neberej/smart-search/

Demo: https://github.com/user-attachments/assets/aed054e0-a91f-459...

Open to feedback!

Hey HN, Gabe and Alexander here from Hatchet. Today we're releasing Pickaxe, a Typescript library to build AI agents which are scalable and fault-tolerant.

Here's a demo: https://github.com/user-attachments/assets/b28fc406-f501-442...

Pickaxe provides a simple set of primitives for building agents which can automatically checkpoint their state and suspend or resume processing (also known as durable execution) while waiting for external events (like a human in the loop). The library is based on common patterns we've seen when helping Hatchet users run millions of agent executions per day.

Unlike other tools, Pickaxe is not a framework. It does not have any opinions or abstractions for implementing agent memory, prompting, context, or calling LLMs directly. Its only focus is making AI agents more observable and reliable.

As agents start to scale, there are generally three big problems that emerge:

Demo Video: https://github.com/user-attachments/assets/ecb05256-8bb0-427...

After an email exchange with the lead author, I just rendered one of their demo datasets using my Datoviz GPU rendering library [1]. It looks fast and great!

https://github.com/user-attachments/assets/6008d5ee-c539-451...

I'll add it to the official gallery soon, with proper credits.

[1] https://datoviz.org/

- workflows/{WorkflowName}/cancel – Terminate workflow

We’ve also implemented Temporal for durable execution [6], so agent workflows can be paused, resumed and retried in production settings.

This demo [7] shows Claude invoking an MCP agent server, running workflows when appropriate, and polling for status. It basically shows agentic behavior on both the MCP client and MCP server side.

We're excited about the potential this unlocks—especially as more applications become MCP-compatible clients. We'd love your feedback and ideas!

[1] - https://news.ycombinator.com/item?id=42867050

[2] - https://github.com/lastmile-ai/mcp-agent

[3] - https://www.anthropic.com/research/building-effective-agents

[4] - https://github.com/github/github-mcp-server

[5] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/...

[6] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/...

[7] - https://github.com/user-attachments/assets/f651af86-222d-4df... [DEMO]

That's one reason https://weather-sense.leftium.com shows hourly forecast data:

- The chance of precipitation line tends to gradually go up and then back down over several hours.

- The expected mm of precipitation bars tend to be more actionable: the chance of precipitation may be high, but if the expected amount is low you are not likely to experience much, if any rain; much less need an umbrella.

- Sample screen shot with precipitation in forecast: https://github.com/user-attachments/assets/e003badb-4832-430...

Hey HN,

I'm excited to share BrowserBee , a privacy-first AI assistant in your browser that allows you to run and automate tasks using your LLM of choice (currently supports Anthropic, OpenAI, Gemini, and Ollama). Short demo here: https://github.com/user-attachments/assets/209c7042-6d54-4fc...

Inspired by projects like Browser Use and Playwright MCP, its main advantage is the browser extension form factor which makes it more convenient for day to day use, especially for less technical users. Its also a bit less cumbersome to use on websites that require you to be logged in, as it attaches to the same browser instance you use (on privacy: the only data that leaves your browser is the communication with the LLM - there is no tracking or data collection of any sort).

Some of its core features are as follows:

- a memory feature which allows users to memorize common and useful pathways, making the next repetition of those tasks faster and cheaper