New top story on Hacker News: Show HN: Our space game has a built-in RISC-V emulator that runs Linux

  October 03, 2026            No comments
Show HN: Our space game has a built-in RISC-V emulator that runs Linux
22 by nor-and-or-not | 9 comments on Hacker News.
(Edit: original URL was https://againstallodds.games/ , but we've switched it to https://againstallodds.games/blog/2026/10/03/our-risc-v-emul... in response to user requests for explanation.) We're a tiny indie studio, all with a background in the demoscene and for about 3 years now we're developing a space planet terraforming game called SEEDS - Echoes Beneath the Sands. Our protagonist Naxiah is stranded on a small, desolated planet. You're working for an intergalactic distributor of seeds and terraforming equipment, to kickstart new planets in far away galaxies. You must deliver seeds and utilities to an unchartered region, but on your way, you crash on a small planet. Luckily, you have some equipment with you in the ship, so you're able to spawn a base and survive. But for how long? And is the planet really deserted..? :) Well, of course not, there are aliens and other space creatures. And of course, there's ROBO RB-23, which accompanies your adventure. The game is written in Object Pascal and uses our own game engine PasVulkan, our own physics engine, as well as our own RISC-V 64-bit emulator PasRISCV. We love to build stuff, a lot of these things are FOSS. The emulator is quite complete (even with RVV, but that's way too slow emulated to be useful) and runs a stock kernel (6.18.3 as of today). We're using Alpine Linux for our base distribution. All our in-game programs to interact with the planet are native RISC-V Linux programs. We also have our own (free and open source) scripting language POCA (class and prototyped based, JS/Lua inspired, which makes it quite easy for us to script things like HUD, animal behaviour, our story flow graph, etc. We have most of the wiring ready for players to be able to program and reshape and populate the whole planet in automation, although it's not our primary concept for the game (building stuff by hand, making the planet beautiful is also fun). As it's a normal Linux system, you'll be able to use Ruby, Python, or even Rust, C, C++, and Go to change and automate your world. We're also thinking of making a bunch of small games which you can play inside the game, in the base on a computer, on an arcade machine, or in the spaceship. And of course, it also runs DOOM. We already prototyped a small 2D space shooter and a small arcade game, both playable within the game. We also feature a table with hologram games to be played, e.g. chess and a certain block game. We also plan to make this hologram game engine available from the computer, which means, you'll even be able to program own hologram style games. We generally want to make the in-game computer more hackable in future, so that people can create and share their own little games. Currently, it's only a sandbox game (with beginnings of automation), to build stuff with our pre-made items. You can also assemble these to other, ready-made and shareable bigger items. This is the foundation for us to further develop survival and complete our story mode. Our game is still under active development, and we finally got our Steam page ready. We have tons of other things planned, but right now, it's more about polishing and quality for the Early Access. You can watch some videos and screenshots on our website. And we'd love to read your thoughts about his. It has been quite a ride, so far..

New top story on Hacker News: Show HN: Open-source model routing for coding agents at Astra-level performance

  October 01, 2026            No comments
Show HN: Open-source model routing for coding agents at Astra-level performance
19 by adchurch | 1 comments on Hacker News.
A few months ago we started building a model router for coding agents because we thought we could outperform any single model with an ensemble approach. Recently we’ve achieved that milestone and I want to talk about how we did it. First of all, a quick explanation: the Weave Router ( https://ift.tt/gs8x79v ) plugs into any coding agent (e.g. Claude Code or Codex) and intelligently switches between LLMs. So, for example, Astra handles tricky debugging or complex system design tasks, and Deepseek v4 Flash handles simple frontend updates. What we’re announcing today is our new routing model, which we’re calling Weave Router 2.0. We benchmarked 2.0 against GPT-6 Astra on Terminal Bench 4.0 and SWE Atlas. On both benchmarks, the router had equivalent pass rates. On Terminal Bench, the router hit 52% of Astra’s cost, and completed tasks 2.2x faster. On SWE Atlas, the router cost 54% as much as Astra and ran 2.5x faster. (Full results on our website at https://ift.tt/89OmTBe !) It turns out training a model to route effectively - taking into consideration model capabilities, costs, cache awareness, and more - is a really hard problem! I want to talk about three ways we were able to improve so much over the last few months: 1) a new architecture, 2) larger training data set size, and 3) smarter cache-eviction impact calculation. 1) a new architecture. Our initial approach used an RL model without many priors. While RL is still an important part of the story, the cost of fully exploring the space of routing decisions is very high, so we’ve taken some shortcuts that have significantly improved performance. Consider how large the search space for the routing problem is. Take a typical coding agent session, with ~100 agent turns (i.e. 100 LLM API calls). Technically there are 100 chances to select a model. If we assume a roster of ~10 models (of course there are lots more but we can remove any that are Pareto dominated), then there are 10^100 possible paths through that session. We simply cannot explore all of them! So that's why clever tricks to shrink this space are so important. In particular: we trained a hidden Markov model to trace the session state, then a classifier maps the session to one of a few buckets of similar models. Using the HMM allows us to evaluate not just where a session is currently, but how it got there . We've gotten significantly better performance on bucket selection by incorporating that information - we believe this is because two sessions that might look quite similar to a naive classifier are much better distinguished by this HMM approach. Using this HMM + classifier to select a bucket first significantly shrinks the space to explore, by throwing out most models that could not reasonably serve the given session. This rearchitecture was the single biggest performance unlock! 2) larger training data set size (much less technically interesting but still an important part of the story). By using frontier LLMs to help us label a larger and more diverse set of coding agent sessions, we were able to bootstrap the two models discussed in 1) to a better state, while also providing even richer reward signals for RL. 3) smarter cache-eviction impact calculation. One of the hardest parts of routing well (if you care about saving money) is using the model caches intelligently. We built a subsystem that can calculate the expected value of switching models (and thus paying a high one-time cost to fill up a different cache) much more accurately, helping us avoid costly and unnecessary switches in more cases, while still switching when the benefit outweighs the cost. This is where most of our improvement on cost has come from. We still have a lot of room to continue to improve (we won’t rest until we’re consistently beating Astra/Fable, not just tying!) but matching frontier model performance was a huge milestone for our routing model, and in my opinion validates our initial hypothesis that an ensemble of models can do better than any single model ever could. Our router is open source ( https://ift.tt/gs8x79v ) so anyone can try it out. Or if you prefer you can use our hosted version ( https://ift.tt/89OmTBe ).

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