Show HN: Neurotrace – 我开发的浏览器扩展，但从未真正使用过
1 分•作者: CastleOneX•大约 7 小时前
我从事机器学习软件开发工作已有四年，很快就遇到了一个反复出现的问题：我总是问自己，“我为什么要把这个函数这样写？”或者“为什么这个代码块在这里？”
我尝试用Obsidian和其他笔记应用来整理我的思路，但说实话，为自己写文档感觉就像一项苦差事。文档总是感觉像是写给“别人”看的。
所以，我决定构建一个VS Code扩展，将我的推理和上下文记忆直接链接到代码片段、标签等。我甚至添加了一个优先任务列表，这样我就能确切地知道第二天需要处理什么。
结果呢？我从没用过它。
几个月后，我感到很失望。我觉得我把所有时间都浪费在了连我自己都觉得没用的东西上。
然后，智能体出现了。
与人工智能智能体一起工作是一次令人大开眼界的经历，但我遇到了一个瓶颈：“冷启动”问题。每次新会话都需要我从头开始解释所有内容。我尝试了MEMORIES.md、AGENTS.md和Claude的项目规则。具有讽刺意味的是，冷启动并没有像承诺的那样得到很大改善。一些基准测试甚至表明，当智能体被迫解析太多静态技能文件时，性能会变差，而另一些则仅显示了10%的微弱提升。
出于好奇，我决定实现一个本地MCP，这样我的智能体就可以自主使用Neurotrace了。
结果令人震惊。我没想到智能体真的会使用这个工具，但它们确实用了。我还没有正式的基准测试，但我可以自信地说，冷启动问题已经大大减少了。由于我使用了来自不同提供商的不同智能体，现在的“下一个”智能体确切地知道我们昨天进行到哪里了。它们决定保存哪些上下文记忆，而且它们做得出奇地好。我的工作流程得到了显著改善。
我很乐意听取您对智能体记忆的看法，或者您是否找到了更好的方法来处理上下文交接。
此致，
Irwing Castro (CastleOneX)
您可以在以下市场找到它：
```
VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=BlackIronTechnologies.neurotrace
Open VSX: https://open-vsx.org/extension/BlackIronTechnologies/neurotrace
```
查看原文
I’ve been working in ML software for 4 years, and I quickly ran into a recurring problem: I kept asking myself, "Why did I write this function this way?" or "Why is this block here?"<p>I tried to organize my thoughts with Obsidian and other note-taking apps, but let’s be honest, documenting for yourself feels like a chore. Documentation always feels like it's meant for "someone else."<p>So, I decided to build a VS Code extension to save my reasoning and contextual memory directly linked to snippets, tags, and more. I even added a prioritized task list so I’d know exactly what was pending the next day.<p>And what happened? I never used it.<p>Months later, I felt disappointed. I felt like I had wasted all that time on something even I didn't find useful.<p>Then, agents arrived.<p>Working with AI agents has been a mind-opening experience, but I hit a wall: the "Cold Start" problem. Every new session required me to explain everything from scratch. I tried MEMORIES.md, AGENTS.md, and Claude’s project rules. Ironically, the cold start didn't improve as much as promised. Some benchmarks even show that agents perform worse when forced to parse too many static skill files, while others only show a marginal 10% improvement.<p>Out of curiosity, I decided to implement a local MCP so my agents could use Neurotrace autonomously.<p>The result was startling. I didn't think the agents would actually use the tool, but they are. I don't have formal benchmarks yet, but I can say with confidence that the cold start has drastically decreased. Since I use different agents from different providers, the "next" agent now knows exactly where we left off yesterday. They decide what contextual memories to save, and they do it surprisingly well. My workflow has improved significantly.<p>I'd love to hear your thoughts on agentic memory or if you've found better ways to handle context hand-off.<p>Cheers,<p>Irwing Castro (CastleOneX)<p>You can find it on the marketplaces here:<p><pre><code> VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=BlackIronTechnologies.neurotrace
Open VSX: https://open-vsx.org/extension/BlackIronTechnologies/neurotrace</code></pre>