01 / THE SIGNAL

我们发现了什么

我构建了 Screenpipe(https://screenpipe.com),这是一款在本地(仅本地!)录制你的屏幕和音频的应用,并为 AI 智能体提供关于你所见、所说和所听内容的可搜索记忆。我在 https://www.tella.tv/video/build-your-ai-second-brain-with-s... 制作了一个 HN 风格的演示视频。

  • 来源:Hacker News发现于 2026-07-24
  • 证据等级:D · 发现产品或需求信号,暂未获得可核验的商业证据。
  • 商业模式:API / Usage-based
  • 主题:AI Agent
  • 初筛评分:30.4/100 · 收录 1
#工作流自动化#待验证#产品发现
02 / SOURCE & EVIDENCE

证据,比故事更重要。

发现产品或需求信号,暂未获得可核验的商业证据。

规则清洗与初筛,未经人工商业核验。原文语境、实际客户和付费情况仍需自行验证。

引用与数字披露

来源类型(原作者自述/第三方测算/媒体转引)需采集端标注,本版尚未落字段。

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中文辅助译文(全文)

你好 Hacker News,我是 Louis。我开发了 Screenpipe(https://screenpipe.com),一款仅在本地录制你的屏幕和音频的应用,并为 AI 智能体提供关于你所见、所说、所听的、可检索的记忆。这让你能更轻松地自动化重复任务,把它们变成 SOP(标准操作流程)等等。我做了一个 HN 风格的演示视频:https://www.tella.tv/video/build-your-ai-second-brain-with-s... ,还有一个营销视频:https://www.youtube.com/watch?v=c1jV6E9pyug 。我长期以来一直对这件事非常着迷。从 2020 年起我就一直在维护一个“第二大脑”,在里面存放日记、手写笔记、我听的音乐、我正在做的项目、我与人进行的对话、个人 CRM 等。我早期尝试过大量 RAG,使用 ParlAI、几百个微调过的 GPT2 模型以及 GPT3(https://forum.obsidian.md/t/fine-tuning-openai-api-gpt3-on-y... )。后来我做了 Ava,这是第一款 Obsidian AI 插件,并很快积累到几千名用户。它后来演变成了 Embedbase,一个让构建由 RAG 驱动的 AI 应用更容易的 API。我从这一切中学到的,是模型要拥有你正在电脑上做什么的上下文有多么重要,才能让它们做你希望的事。早期是微调,但太痛苦了;后来有了工具调用,让 AI 能够访问你使用的软件,但仍不够自主,需要微观管理。

再后来出现了 MCP,但它感觉过于静态,而且非技术用户难以构建和使用 MCP。然后我们有了 skills。最近我们看到 Karpathy 由 LLM 维护的 wiki、Garry 的 GBrain 等等,由智能体增量维护一组持续的 Markdown 页面。新来源会更新实体页,强化或反驳已有主张,并改进一种随时间复利的综合归纳。我喜欢这种模式,但它仍然以有人选择和导入来源为起点。仍然没有办法让 AI 知道你和你的公司每天在跨应用地做什么,而不仅仅是在某个应用内部。当然,并非每个人都想要这样。但我想要!我希望 AI 知道我在做什么,再也不丢失记忆,并且我希望它能使用人类所使用的同一套软件,而不需要痛苦的上下文切换。我在 2024 年为自己开始构建 Screenpipe —— 一个用于录制屏幕并将这些上下文接入 AI 的 CLI。一位 HN 用户在 2024 年发布了它(https://news.ycombinator.com/item?id=41695840 ),那次讨论影响了产品。最有用的批评集中在录制同意、本地安全性、CPU 使用率、信噪比,以及智能体能否基于这些数据采取行动。早期的天真实现是连续录制视频,并对每一帧运行 OCR。但这会产生重复数据,消耗大量资源(基本上把你的电脑变成了一个暖气片!),并且丢弃了操作系统原本已知的结构。因此 Screenpipe 现在改为监听诸如应用切…

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 Hacker News, I'm Louis.I built Screenpipe ( https://screenpipe.com ), an app that records your screen and audio locally (only!), and gives AI agents a searchable memory of what you've seen, said, and heard.This makes it easier to automate your repetitive tasks, turn them into SOPs (Standard Operating Procedure) and so on.I made a HN-style demo video at https://www.tella.tv/video/build-your-ai-second-brain-with-s... and there’s a marketing video at https://www.youtube.com/watch?v=c1jV6E9pyug .I’ve been obsessed with this for a long time.

I’ve been maintaining a “second brain” since 2020, in which I would store journals, handwritten notes, music I listen to, projects I'm working on, conversations I have with people, personal CRM etc.I experimented a lot of RAG in the early days with ParlAI, hundreds of fine-tuned GPT2 models, and GPT3 ( https://forum.obsidian.md/t/fine-tuning-openai-api-gpt3-on-y... ).Later I built Ava, the first Obsidian AI plugin, which grew to a few thousands of users quickly.It then became Embedbase, an API to make it easier to build AI apps powered by RAG.

What I learned from all this is how important it is for the models to have context about what you’re doing on your computer, in order to get them to do what you want.In the early days there was fine tuning but it was too much pain, then there was tool calling so that AI can access software you use but still kinda not autonomous enough. needing micro management.Then MCP came, but it felt too static, and non technical users struggled to build and use MCP.Then we got skills.Most recently we’ve seen Karpathy’s LLM-maintained wiki, Garry's GBrain, etc., where an agent incrementally maintains a persistent collection of Markdown pages.

New sources update entity pages, strengthen or contradict existing claims, and improve a synthesis that compounds over time.I like this pattern, but it still begins with someone selecting and importing the sources.There is still no way AI can know what you and your company are doing every day, across apps, not just inside of apps.Of course, not everyone wants this.But I do!I want AI to know what I'm doing and never lose memory ever again, and I want it to use the same software that humans do, without painful context switches.I started building Screenpipe for myself in 2024 - a CLI to record your screen and plug this context into AI.

An HN user posted it in 2024 ( https://news.ycombinator.com/item?id=41695840 ) and that discussion influenced the product.The most useful criticism concerned recording consent, local security, CPU usage, signal-to-noise, and whether agents could act on top of the data.The naive implementation started from continuously recording video and running OCR over every frame.But that creates duplicate data, consumes substantial resources (it basically turns your computer into a space heater!), and discards structure the operating system already knows.Screenpipe now instead listens for events such as app swi

出处https://news.ycombinator.com/item?id=49024620抓取日期 · 采集源 Hacker News

03 / EVIDENCE GAPS

这条还缺什么证据?

下面每条都由本条已有字段推出(等级、理由、商业模式、来源次数、是否演示), 本站不生成推测性结论;通用验证方法放在方法论页。

  • 可核验的收入或付费证据查官网定价页与付费口径;第三方数据源(如 GetLatka)只作旁证,需标注来源与时点。
  • 只有单一来源找一手站点或其他渠道是否重复出现同一产品;社区热帖数量不等于商业进展。

通用验证清单(谁有这个问题/谁愿意付费/一个人能交付哪一小步)见我们的筛选方法

04 / SIGNAL HISTORY

发现时间线