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Show HN: F32 – An Extremely Small ESP32 Board

As part of a little research and also some fun I decided to try my hand at seeing how small of an ESP32 board I can make with functioning WiFi.

Show HN: F32 – An Extremely Small ESP32 Board

As part of a little research and also some fun I decided to try my hand at seeing how small of an ESP32 board I can make with functioning WiFi.

Show HN: F32 – An Extremely Small ESP32 Board

As part of a little research and also some fun I decided to try my hand at seeing how small of an ESP32 board I can make with functioning WiFi.

Show HN: Browser-based interactive 3D Three-Body problem simulator

Features include:<p><pre><code> - Several preset periodic orbits: the classic Figure-8, plus newly discovered 3D solutions from Li and Liao's recent database of 10,000+ orbits (https://arxiv.org/html/2508.08568v1) - Full 3D camera controls (rotate/pan/zoom) with body-following mode - Force and velocity vector visualization - Timeline scrubbing to explore the full orbital period </code></pre> The 3D presets are particularly interesting. Try "O₂(1.2)" or "Piano O₆(0.6)" from the Load Presets menu to see configurations where bodies weave in and out of the orbital plane. Most browser simulators I've seen have been 2D.<p>Built with Three.js. Open to suggestions for additional presets or features!

Show HN: Browser-based interactive 3D Three-Body problem simulator

Features include:<p><pre><code> - Several preset periodic orbits: the classic Figure-8, plus newly discovered 3D solutions from Li and Liao's recent database of 10,000+ orbits (https://arxiv.org/html/2508.08568v1) - Full 3D camera controls (rotate/pan/zoom) with body-following mode - Force and velocity vector visualization - Timeline scrubbing to explore the full orbital period </code></pre> The 3D presets are particularly interesting. Try "O₂(1.2)" or "Piano O₆(0.6)" from the Load Presets menu to see configurations where bodies weave in and out of the orbital plane. Most browser simulators I've seen have been 2D.<p>Built with Three.js. Open to suggestions for additional presets or features!

Show HN: I made a down detector for down detector

After down detector went down with the rest of the internet during the Cloudflare outage today I decided to build a robust, independent tool which checks if down detector is down. Enjoy!!

Show HN: I made a down detector for down detector

After down detector went down with the rest of the internet during the Cloudflare outage today I decided to build a robust, independent tool which checks if down detector is down. Enjoy!!

Show HN: I made a down detector for down detector

After down detector went down with the rest of the internet during the Cloudflare outage today I decided to build a robust, independent tool which checks if down detector is down. Enjoy!!

Show HN: I made a down detector for down detector

After down detector went down with the rest of the internet during the Cloudflare outage today I decided to build a robust, independent tool which checks if down detector is down. Enjoy!!

Show HN: Optimizing LiteLLM with Rust – When Expectations Meet Reality

I've been working on Fast LiteLLM - a Rust acceleration layer for the popular LiteLLM library - and I had some interesting learnings that might resonate with other developers trying to squeeze performance out of existing systems.<p>My assumption was that LiteLLM, being a Python library, would have plenty of low-hanging fruit for optimization. I set out to create a Rust layer using PyO3 to accelerate the performance-critical parts: token counting, routing, rate limiting, and connection pooling.<p>The Approach<p>- Built Rust implementations for token counting using tiktoken-rs<p>- Added lock-free data structures with DashMap for concurrent operations<p>- Implemented async-friendly rate limiting<p>- Created monkeypatch shims to replace Python functions transparently<p>- Added comprehensive feature flags for safe, gradual rollouts<p>- Developed performance monitoring to track improvements in real-time<p>After building out all the Rust acceleration, I ran my comprehensive benchmark comparing baseline LiteLLM vs. the shimmed version:<p>Function Baseline Time Shimmed Time Speedup Improvement Status<p>token_counter 0.000035s 0.000036s 0.99x -0.6%<p>count_tokens_batch 0.000001s 0.000001s 1.10x +9.1%<p>router 0.001309s 0.001299s 1.01x +0.7%<p>rate_limiter 0.000000s 0.000000s 1.85x +45.9%<p>connection_pool 0.000000s 0.000000s 1.63x +38.7%<p>Turns out LiteLLM is already quite well-optimized! The core token counting was essentially unchanged (0.6% slower, likely within measurement noise), and the most significant gains came from the more complex operations like rate limiting and connection pooling where Rust's concurrent primitives made a real difference.<p>Key Takeaways<p>1. Don't assume existing libraries are under-optimized - The maintainers likely know their domain well 2. Focus on algorithmic improvements over reimplementation - Sometimes a better approach beats a faster language 3. Micro-benchmarks can be misleading - Real-world performance impact varies significantly 4. The most gains often come from the complex parts, not the simple operations 5. Even "modest" improvements can matter at scale - 45% improvements in rate limiting are meaningful for high-throughput applications<p>While the core token counting saw minimal improvement, the rate limiting and connection pooling gains still provide value for high-volume use cases. The infrastructure I built (feature flags, performance monitoring, safe fallbacks) creates a solid foundation for future optimizations.<p>The project continues as Fast LiteLLM on GitHub for anyone interested in the Rust-Python integration patterns, even if the performance gains were humbling.<p>Edit: To clarify - the negative performance for token_counter is likely in the noise range of measurement, suggesting that LiteLLM's token counting is already well-optimized. The 45%+ gains in rate limiting and connection pooling still provide value for high-throughput applications.

Show HN: Reversing a Cinema Camera's Peripherals Port

Show HN: A subtly obvious e-paper room air monitor

In the cold season we tend to keep the windows closed. The air gets "stale": humidity often rises above 60 %, which can harm our wellbeing and promote mould. At the same time the CO₂ level in the air increases, which impacts our ability to concentrate.<p>So I built a room air monitor that stays unobtrusive as long as everything is in the green zone, but becomes deliberately noticeable once thresholds are exceeded. For my personal love of statistics I also visualise the measurements in a clear dashboard.

Show HN: A subtly obvious e-paper room air monitor

In the cold season we tend to keep the windows closed. The air gets "stale": humidity often rises above 60 %, which can harm our wellbeing and promote mould. At the same time the CO₂ level in the air increases, which impacts our ability to concentrate.<p>So I built a room air monitor that stays unobtrusive as long as everything is in the green zone, but becomes deliberately noticeable once thresholds are exceeded. For my personal love of statistics I also visualise the measurements in a clear dashboard.

Show HN: Guts – convert Golang types to TypeScript

Show HN: Guts – convert Golang types to TypeScript

Show HN: Guts – convert Golang types to TypeScript

Show HN: RowboatX – open-source Claude Code for everyday automations

Claude Code is great, but it’s focused on coding. The missing piece is a native way to build and run custom background agents for non-code tasks. We built RowboatX as a CLI tool modeled after Claude Code that lets you do that. It uses the file system and unix tools to create and monitor background agents for everyday tasks, connect them to any MCP server for tools, and reason over their outputs.<p>Because RowboatX runs locally with shell access, the agents can install tools, execute code, and automate anything you could do in a terminal with your explicit permission. It works with any compatible LLM, including open-source ones.<p>Our repo is <a href="https://github.com/rowboatlabs/rowboat" rel="nofollow">https://github.com/rowboatlabs/rowboat</a>, and there’s a demo video here: <a href="https://youtu.be/cyPBinQzicY" rel="nofollow">https://youtu.be/cyPBinQzicY</a><p>For example, you can connect RowboatX to the ElevenLabs MCP server and create a background workflow that produces a NotebookLM-style podcast every day from recent AI-agent papers on arXiv. Or you can connect it to Google Calendar and Exa Search to research meeting attendees and generate briefs before each event.<p>You can try these with: `npx @rowboatlabs/rowboatx`<p>We combined three simple ideas:<p>1. File system as state: Each agent’s instruction, memory, logs, and data are just files on disk, grepable, diffable, and local. For instance, you can just run: grep -rl '"agent":"<agent-name>"' ~/.rowboat/runs to list every run for a particular workflow.<p>2. The supervisor agent: A Claude Code style agent that can create and run background agents. It predominantly uses Unix commands to monitor, update, and schedule agents. LLMs handle Unix tools better than backend APIs [1][2], so we leaned into that. It can also probe any MCP server and attach the tools to the agents.<p>3. Human-in-the-loop: Each background agent can emit a human_request message when needed (e.g. drafting a tricky email or installing a tool) that pauses execution and waits for input before continuing. The supervisor coordinates this.<p>I started my career over a decade ago building spam detection models at Twitter, spending a lot of my time in the terminal with Unix commands for data analysis [0] and Vowpal Wabbit for modeling. When Claude Code came along, it felt familiar and amazing to work with. But trying to use it beyond code always felt a bit forced. We built RowboatX to bring that same workflow to everyday tasks. It is Apache-2.0 licensed and easily extendable.<p>While there are many agent builders, running on the user's terminal enables unique use cases like computer and browser automation that cloud-based tools can't match. This power requires careful safety design. We implemented command-level allow/deny lists, with containerization coming next. We’ve tried to design for safety from day one, but we’d love to hear the community’s perspective on what additional safeguards or approaches you’d consider important here.<p>We’re excited to share RowboatX with everyone here. We’d love to hear your thoughts and welcome contributions!<p>—<p>[0] <a href="https://web.stanford.edu/class/cs124/kwc-unix-for-poets.pdf" rel="nofollow">https://web.stanford.edu/class/cs124/kwc-unix-for-poets.pdf</a> [1] <a href="https://arxiv.org/pdf/2405.06807" rel="nofollow">https://arxiv.org/pdf/2405.06807</a> [2] <a href="https://arxiv.org/pdf/2501.10132" rel="nofollow">https://arxiv.org/pdf/2501.10132</a>

Show HN: RowboatX – open-source Claude Code for everyday automations

Claude Code is great, but it’s focused on coding. The missing piece is a native way to build and run custom background agents for non-code tasks. We built RowboatX as a CLI tool modeled after Claude Code that lets you do that. It uses the file system and unix tools to create and monitor background agents for everyday tasks, connect them to any MCP server for tools, and reason over their outputs.<p>Because RowboatX runs locally with shell access, the agents can install tools, execute code, and automate anything you could do in a terminal with your explicit permission. It works with any compatible LLM, including open-source ones.<p>Our repo is <a href="https://github.com/rowboatlabs/rowboat" rel="nofollow">https://github.com/rowboatlabs/rowboat</a>, and there’s a demo video here: <a href="https://youtu.be/cyPBinQzicY" rel="nofollow">https://youtu.be/cyPBinQzicY</a><p>For example, you can connect RowboatX to the ElevenLabs MCP server and create a background workflow that produces a NotebookLM-style podcast every day from recent AI-agent papers on arXiv. Or you can connect it to Google Calendar and Exa Search to research meeting attendees and generate briefs before each event.<p>You can try these with: `npx @rowboatlabs/rowboatx`<p>We combined three simple ideas:<p>1. File system as state: Each agent’s instruction, memory, logs, and data are just files on disk, grepable, diffable, and local. For instance, you can just run: grep -rl '"agent":"<agent-name>"' ~/.rowboat/runs to list every run for a particular workflow.<p>2. The supervisor agent: A Claude Code style agent that can create and run background agents. It predominantly uses Unix commands to monitor, update, and schedule agents. LLMs handle Unix tools better than backend APIs [1][2], so we leaned into that. It can also probe any MCP server and attach the tools to the agents.<p>3. Human-in-the-loop: Each background agent can emit a human_request message when needed (e.g. drafting a tricky email or installing a tool) that pauses execution and waits for input before continuing. The supervisor coordinates this.<p>I started my career over a decade ago building spam detection models at Twitter, spending a lot of my time in the terminal with Unix commands for data analysis [0] and Vowpal Wabbit for modeling. When Claude Code came along, it felt familiar and amazing to work with. But trying to use it beyond code always felt a bit forced. We built RowboatX to bring that same workflow to everyday tasks. It is Apache-2.0 licensed and easily extendable.<p>While there are many agent builders, running on the user's terminal enables unique use cases like computer and browser automation that cloud-based tools can't match. This power requires careful safety design. We implemented command-level allow/deny lists, with containerization coming next. We’ve tried to design for safety from day one, but we’d love to hear the community’s perspective on what additional safeguards or approaches you’d consider important here.<p>We’re excited to share RowboatX with everyone here. We’d love to hear your thoughts and welcome contributions!<p>—<p>[0] <a href="https://web.stanford.edu/class/cs124/kwc-unix-for-poets.pdf" rel="nofollow">https://web.stanford.edu/class/cs124/kwc-unix-for-poets.pdf</a> [1] <a href="https://arxiv.org/pdf/2405.06807" rel="nofollow">https://arxiv.org/pdf/2405.06807</a> [2] <a href="https://arxiv.org/pdf/2501.10132" rel="nofollow">https://arxiv.org/pdf/2501.10132</a>

Show HN: RowboatX – open-source Claude Code for everyday automations

Claude Code is great, but it’s focused on coding. The missing piece is a native way to build and run custom background agents for non-code tasks. We built RowboatX as a CLI tool modeled after Claude Code that lets you do that. It uses the file system and unix tools to create and monitor background agents for everyday tasks, connect them to any MCP server for tools, and reason over their outputs.<p>Because RowboatX runs locally with shell access, the agents can install tools, execute code, and automate anything you could do in a terminal with your explicit permission. It works with any compatible LLM, including open-source ones.<p>Our repo is <a href="https://github.com/rowboatlabs/rowboat" rel="nofollow">https://github.com/rowboatlabs/rowboat</a>, and there’s a demo video here: <a href="https://youtu.be/cyPBinQzicY" rel="nofollow">https://youtu.be/cyPBinQzicY</a><p>For example, you can connect RowboatX to the ElevenLabs MCP server and create a background workflow that produces a NotebookLM-style podcast every day from recent AI-agent papers on arXiv. Or you can connect it to Google Calendar and Exa Search to research meeting attendees and generate briefs before each event.<p>You can try these with: `npx @rowboatlabs/rowboatx`<p>We combined three simple ideas:<p>1. File system as state: Each agent’s instruction, memory, logs, and data are just files on disk, grepable, diffable, and local. For instance, you can just run: grep -rl '"agent":"<agent-name>"' ~/.rowboat/runs to list every run for a particular workflow.<p>2. The supervisor agent: A Claude Code style agent that can create and run background agents. It predominantly uses Unix commands to monitor, update, and schedule agents. LLMs handle Unix tools better than backend APIs [1][2], so we leaned into that. It can also probe any MCP server and attach the tools to the agents.<p>3. Human-in-the-loop: Each background agent can emit a human_request message when needed (e.g. drafting a tricky email or installing a tool) that pauses execution and waits for input before continuing. The supervisor coordinates this.<p>I started my career over a decade ago building spam detection models at Twitter, spending a lot of my time in the terminal with Unix commands for data analysis [0] and Vowpal Wabbit for modeling. When Claude Code came along, it felt familiar and amazing to work with. But trying to use it beyond code always felt a bit forced. We built RowboatX to bring that same workflow to everyday tasks. It is Apache-2.0 licensed and easily extendable.<p>While there are many agent builders, running on the user's terminal enables unique use cases like computer and browser automation that cloud-based tools can't match. This power requires careful safety design. We implemented command-level allow/deny lists, with containerization coming next. We’ve tried to design for safety from day one, but we’d love to hear the community’s perspective on what additional safeguards or approaches you’d consider important here.<p>We’re excited to share RowboatX with everyone here. We’d love to hear your thoughts and welcome contributions!<p>—<p>[0] <a href="https://web.stanford.edu/class/cs124/kwc-unix-for-poets.pdf" rel="nofollow">https://web.stanford.edu/class/cs124/kwc-unix-for-poets.pdf</a> [1] <a href="https://arxiv.org/pdf/2405.06807" rel="nofollow">https://arxiv.org/pdf/2405.06807</a> [2] <a href="https://arxiv.org/pdf/2501.10132" rel="nofollow">https://arxiv.org/pdf/2501.10132</a>

Show HN: Parqeye – A CLI tool to visualize and inspect Parquet files

I built a Rust-based CLI/terminal UI for inspecting Parquet files—data, metadata, and row-group-level structure—right from the terminal. If someone sent me a Parquet file, I used to open DuckDB or Polars just to see what was inside. Now I can do it with one command.<p>Repo: <a href="https://github.com/kaushiksrini/parqeye" rel="nofollow">https://github.com/kaushiksrini/parqeye</a>