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Show HN: Open-Source AI Racing Harness https://ift.tt/d4Uq05I

Show HN: Open-Source AI Racing Harness Hi I'm Dan from Elodin, making an open source real-time capable flight software simulation. For AI Grand Prix contestants, the wait for the Round 1 virtual qualifier simulation has been grueling. If you’re competing, check out our simulation harness to tide you over, built to match the published competition constraints and message format. It runs against real Betaflight, which we learned requires at least 1000 sensor samples per second to run real-time correctly. The competition warranted introducing a new feature to generate the camera sensor directly in the simulation loop. Typically people connect to Unreal or similar game engine to create a camera sensor, which works well but is very heavy. For the simple needs of this challenge, creating sample directly in the loop is very handy and easy to use. Happy to hear your feedback on this! While it's not fancy looking currently, it uses the Rust Bevy game engine, which should allow us to impr...
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Show HN: Hodor – a 701KB native macOS prompt launcher for AI tools https://ift.tt/f3Fl8zQ

Show HN: Hodor – a 701KB native macOS prompt launcher for AI tools Hodor is a tiny macOS app that launches saved AI prompts into any text field — from the screen edge, keyboard shortcut, or keyword such as ;git. I work with different AI tools every day, and had prompts scattered across Raycast snippets, Apple Notes, and Notion — notes that kept getting longer and unmanageable. Raycast snippets are useful, but cumbersome to browse and edit. I wanted one local place to save and review them, and one click to paste them into whatever AI tool I'm using. The test I set was whether I could actually stop using Raycast snippets for this. I think I fulfilled my goal. Hodor has been my daily tool for 3 months now. The app is 701 KB — SwiftUI + SwiftData, no web views. Zero network requests anywhere in the code: no analytics, no telemetry, no update checks. You can verify - search the source for URLSession, it's not in code. Runs on macOS 15+, with native Liquid Glass on macOS 26+. Free an...
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Show HN: I used AI to built static recompilers for 5 retro game systems https://ift.tt/G4epCQ9

Show HN: I used AI to built static recompilers for 5 retro game systems https://1379.tech/nes-snes-genesis-virtualboy-and-psx-a-journey-with-ai-and-recompilation/ May 26, 2026 at 11:08PM
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Show HN: An LLM translator whose source is a single prompt https://ift.tt/ejWs4Qm

Show HN: An LLM translator whose source is a single prompt https://ift.tt/AMTEQeO May 26, 2026 at 11:53PM
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Show HN: Cross-agent messaging and shared memory over the local filesystem https://ift.tt/IXw9Fpo

Show HN: Cross-agent messaging and shared memory over the local filesystem https://oacp.dev May 26, 2026 at 11:14PM
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Show HN: Write your BPF programs in Go, not C https://ift.tt/wkgXc40

Show HN: Write your BPF programs in Go, not C https://ift.tt/8TVzpac May 21, 2026 at 09:55PM
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Show HN: I made Pokémon but with real animals in the real world https://ift.tt/sIgPi8x

Show HN: I made Pokémon but with real animals in the real world Firstly, apologies, it's not free. It would be difficult to support this for free, it's a paid game. I will now share the technical details, which will probably be most of interest for HN readers. I previously made a carbon footprint tracking app where you photo objects and it tells you the carbon footprint by using an LLM to estimate the data on the fly, e.g. 32kg CO2e / kg of beef, in the UK. At some point, I realised that it is possible to make a Pokémon-style game, but capturing real animals in the real world. This is now possible because: - image recognition is cheap, i.e. identifying animals, and the models (gpt-4o) can detect a (surprisingly) large number of animals and output their exact species. - LLMs can output a species' full taxonomy, pretty reliably. And, more importantly, they can generate game data quickly, on the fly. It would unfeasible to generate the game sprites (images) for every species...
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