Launch HN:Simantic(YC F26)— 面向AI智能体的固件仿真
Launch HN: Simantic (YC F26) – Firmware Simulation for AI Agents
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Launch HN:Simantic(YC F26)— 面向AI智能体的固件仿真。我们正在打造一个仿真层,让工程师和AI智能体无需硬件即可测试和验证固件。
- 来源:Hacker News(发现于 2026-10-30)
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- 主题:AI Agent
- 初筛评分:17.7/100 · 收录 1 次
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中文辅助译文(全文)
大家好 HN,我们是 Ahnaf 和 Seungmin,来自 Simantic(https://simantic.com)。我们正在构建一个仿真层,让工程师和 AI 智能体无需硬件即可测试和验证固件。在创办 Simantic 之前,我们曾在 Neuralink 和 Tesla 等公司担任固件工程师。我们参与过一些非常酷的项目,例如 Cybertruck 和 Roadster——那时它们仍处于早期原型阶段。我们开始从事固件工作时,ChatGPT 还不存在;但多年来,随着前沿模型发展至今,我们亲眼看到工程和开发工作被自动化的程度越来越高。在软件领域,AI 可以在几秒钟内运行并测试你计算机中的代码。但作为固件工程师,我们却没有这种便利。AI 智能体可以在几秒钟内写出固件,但为了测试固件,我们必须将固件刷入实体电路板,然后使用十多年前开发的昂贵且笨重的工具手动运行测试。为了加快验证循环,我们构建了仿真层,让 AI 智能体能够测试固件并根据证据作出决策,而不必等待工程师代为运行测试。过去我们常常花费数小时在开发板上手动测试固件,而现在我们使用自己的仿真系统,只需几秒钟即可验证固件。
我们提供: - 虚拟开发板,只需一次 API 调用即可编程和运行,无需离开终端。 - 面向固件的 AI 代码审查器,以我们的仿真系统作为验证引擎,根据证据作出明智判断。 - 面向硬件项目的 CI/CD 流水线,在每次提交时针对微控制器的仿真版本验证固件。
以下是一段使用我们的仿真系统进行 AI 代码审查的演示:https://www.youtube.com/watch?v=kBK1ZjGWWbQ
我们的用户正在将 Simantic 用于飞行控制器、电动汽车、机器人和卫星等应用的裸机及 RTOS 开发。对于航空电子和汽车等安全关键系统,稳健的仿真系统极其重要,因为固件的每次变更都关乎人的生命安全,尤其是对于价值从数十万美元到数百万美元的机器。我们最近帮助一家早期太空初创公司测试其卫星固件,而他们当时尚无相应硬件。仅这些卫星的开发模块成本就超过 10 万美元,而我们帮助他们以仅百分之一的成本和时间完成了测试。在创业初期,我们确立了打造更好仿真系统的理念,但并不清楚具体该如何实现。我们曾考虑围绕 Renode(https://github.com/renode/renode)和 QEMU(https://www.qemu.org/)等开源仿真框架开发软件,因为这些工具已经用于固件行业。我们构建的 MVP 本质上是对 Renode 的封装,但很快意识到这种方式存在许多局限: 1. 我们的开发速度受制于 Renode 自身。我们尝试在 Github 上提交贡献来修复遇到的问题,但对于一家需要快速行动的初创公司来说,我们合并功能的速度不够快。 2. Renode 使用 Dotnet 构建,其表现速度未达到我们的期望。Renode 发布于 2017 年,当时所有代码仍需手工编写,所以现在回头看可以理解,但该语言本身也存在局限。 3. 这些开源工具臃肿庞大,包含许多我们永远不会使用的功能。它们确实功能广泛,但其中很多东西我们不得不绕开,因为我们无法移除或修改。
用户一直在抱怨仿真精度问题。Renode 的核心是模拟器,但我们的用户,尤其是那些构建对时序精度至关重要之系统的用户,认为其精度不足以验证固件。由于这些问题,我们从头开始构建了自己的仿真引擎,完全使用 Rust 编写。 - 我们的仿真专注于精度。开源框架通常侧重于可扩展性和开发者体验,这意味着为了支持广泛数量的模型,往往需要作出许多取舍,而无法深入做好少数选定模型。 - 我们拥有自己的硬件验证测试台,全天候 24/7 运行,以确保仿真模型尽可能贴近真实硬件,并尽快发现和修复任何偏差。目前看起来,连接至 Saleae Logic MSO 和 SWD 总线、由 Linux PC 控制的开发板,正全天候 24/7 运行 Claude,持续查找这些偏差。
我们的云托管仿真器现已可供使用!我们还随开发者计划许可证提供原生二进制文件,让你可以在自己的机器上本地运行固件,不受任何限制。访问 https://simantic.com/cloud-sim 即可了解详情,也可以在这里开始在自己的 Github 仓库中使用 AI 固件审查机器人:https://simantic.com/dashboard/simantic-bot
我们目前支持 STM32F0、F4、H7 系列 MCU 的部分型号,以及 ESP32-S3/C3 和 RP2040,但我们正持续努力,每周发布新模型并改进现有模型。感谢阅读!如有任何问题,欢迎给我们发送电子邮件至 founders@simantic.dev
译文由上游机器翻译生成,可能有误;判断请以英文原文为准。
英文原文(来源本站未改写)
Hi HN, we're Ahnaf and Seungmin from Simantic ( https://simantic.com ).We are building the simulation layer that lets engineers and AI agents test and validate firmware without hardware.Before Simantic, we were ex-firmware engineers at companies like Neuralink and Tesla.We’ve worked on some of the coolest projects, like the Cybertruck and Roadster, back when they were still early prototypes.We started working on firmware back when ChatGPT didn’t exist yet, but over the years, we saw how much of the engineering and development work has been automated as the frontier models improved to this point.In software, AI can run and test code inside your computer in a matter of seconds.
But as firmware engineers, we didn’t have that luxury.An AI agent can write firmware in seconds, but in order to test it, we had to flash a physical board, then manually run tests using expensive and clunky tools with software made over a decade ago.To speed up the validation loop, we built the simulation layer that lets AI agents test the firmware and make decisions based on evidence, instead of waiting for the engineer to run tests for them.We used to spend hours testing our firmware manually on devboards, but now we use our own simulation to validate firmware in seconds.
What we offer: - Virtual devboards you can program and run with a single API call, without leaving your terminal. - An AI code reviewer for firmware, that makes informed decisions based on evidence, using our simulation as a verification engine. - A CI/CD pipeline for your hardware projects that validates your firmware against a simulated version of the microcontroller on every commit.Here is a demo of our simulation used in an AI code review session: https://www.youtube.com/watch?v=kBK1ZjGWWbQ Our users are using Simantic for bare-metal and RTOS development for applications like flight controllers, electric vehicles, robotics, and satellites.
For safety critical systems like avionics and automotives, a robust simulation system is incredibly important, because every change in the firmware puts a human life at stake, especially for machines that cost hundreds of thousands to millions of dollars.We recently helped an early stage space startup building satellites, to test their firmware on hardware that they don't yet have.Even the development modules for those satellites costs over $100k, and we helped them do that at just a hundredth of the cost and time.
Early on in our journey, we established our idea to make better simulation but didn’t have a clear idea of how we would do it, we considered developing our software around open source emulation frameworks like Renode ( https://github.com/renode/renode ) and QEMU ( https://www.qemu.org/ ), because they were tools already used within the firmware industry.We built our MVP that was essentially a wrapper around Renode, but we quickly realized there were a lot of limitations to doing this. 1.We were bottlenecked by the development speed of Renode themselves.
We tried making contributions on Github to fix issues we came across, but the speed in which we could merge our features in was not fast enough for a startup like us trying to move fast. 2.Renode is built using Dotnet, and it was proving itself to be slower than we would have liked.Renode was released in 2017, which was when all code was still being written by hand, so I think it made sense looking back, but there were limitations because of the language itself. 3.The open source tools were bloated, with features that we would never use.Yes, there were a breadth of features, but we had a lot of things we had to work around just because we couldn’t remove or change them.
Users were complaining about simulation accuracy.Renode is an emulator at the core, but our users, especially those who are building systems where timing accuracy is crucial did not find the accuracy good enough to validate their firmware.Because of these issues, we made our own simulation engine from scratch, fully written in Rust. - Our simulation is focused on accuracy.
Open source frameworks often focus on expandability and developer experience, which means there were a lot of tradeoffs made to prioritize a breadth of models rather than going deep into a set of select models. - We have our own hardware validation rigs running 24/7 to ensure our simulation models are as close to real world hardware as possible, and any gaps are detected and fixed as soon as we can.It currently looks like devboards connected to a Saleae Logic MSO and a SWD bus, controlled by a Linux PC, running Claude 24/7 just finding these gaps.Our cloud-hosted simulator is available to use right now!
We also provide a native binary with our developer plan licenses that let you run your firmware on your machines locally without any limitations.Check it out at https://simantic.com/cloud-sim , and you can start using our AI firmware reviewer bot in your own Github repos here: https://simantic.com/dashboard/simantic-bot .We currently have support for some models of the STM32F0, F4, H7 series MCUs, ESP32-S3/C3, and RP2040, but we’re constantly working to release new models every week, and improve our existing ones.Thank you, and if you have any questions, feel free to send us an email at founders@simantic.dev
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