Hacker News
24K subscribers
117K links
Top stories from https://news.ycombinator.com (with 100+ score)
Contribute to the development here: https://github.com/phil-r/hackernewsbot
Also check https://t.me/designer_news

Contacts: @philr
Download Telegram
Open Hardware Ethernet Switch project, part 1 (❄️ Score: 151+ in 4 days)

Link: https://readhacker.news/s/6ubRf
Comments: https://readhacker.news/c/6ubRf
Ask HN: How are you acquiring your first hundred users? (Score: 155+ in 4 hours)

Link: https://readhacker.news/c/6upjx

I am building a B2C AI SaaS with $50/month price. How would you go about getting with first 100 users and then the next 500 users.
What we are currently doing:
1) Cold outreach to power users - to convert them into affiliates.
2) Cold outreach to individuals who have target ICP communities.
3) SEO for more long term (not for the first 500)
Multiple Security Issues in GNU Screen (🔥 Score: 152+ in 3 hours)

Link: https://readhacker.news/s/6upBe
Comments: https://readhacker.news/c/6upBe
Show HN: Airweave – Let agents search any app (Score: 150+ in 1 day)

Link: https://readhacker.news/s/6umd3
Comments: https://readhacker.news/c/6umd3

Hey HN, we're Lennert and Rauf. We’re building Airweave (https://github.com/airweave-ai/airweave), an open-source tool that lets agents search and retrieve data from any app or database. Here’s a general intro: https://www.youtube.com/watch?v=EFI-7SYGQ48, and here’s a longer one that shows more real-world use cases, examples of how Airweave is used by Cursor (0:33) and Claude desktop (2:04), etc.: https://youtu.be/p2dl-39HwQo
A couple of months ago we were building agents that interacted with different apps and were frustrated when they struggled to handle vague natural language requests like "resolve that one Linear issue about missing auth configs", "if you get an email from an unsatisfied customer, reimburse their payment in Stripe", or "what were the returns for Q1 based on the financials sheet in gdrive?", only to have the agent inefficiently chain together loads of function calls to find the data or not find it at all and hallucinate.
We also noticed that despite the rise of MCP creating more desire for agents to interact with external resources, the majority of agent dev tooling focused on function calling and actions instead of search. We were annoyed by the lack of tooling that enabled agents to semantically search workspace or database contents, so we started building Airweave first as an internal solution. Then we decided to open-source it and pursue it full time after we got positive reactions from coworkers and other agent builders.
Airweave connects to productivity tools, databases, or document stores via their APIs and transforms their contents into searchable knowledge bases, accessible through a standardized interface for the agent. The search interface is exposed via REST or MCP. When using MCP, Airweave essentially builds a semantically searchable MCP server on top of the resource. The platform handles the entire data pipeline from connection and extraction to chunking, embedding, and serving. To ensure knowledge is current, it has automated sync capabilities, with configurable schedules and change detection through content hashing.
We built it with support for white-labeled multi-tenancy to provide OAuth2-based integration across multiple user accounts while maintaining privacy and security boundaries. We're also actively working on permission-awareness (i.e., RBAC on the data) for the platform.
So happy to share learnings and get insights from your experiences. looking forward to comments!
Branch privilege injection: Exploiting branch predictor race conditions (🔥 Score: 160+ in 1 hour)

Link: https://readhacker.news/s/6uqBV
Comments: https://readhacker.news/c/6uqBV
Dusk OS (🔥 Score: 150+ in 2 hours)

Link: https://readhacker.news/s/6urf8
Comments: https://readhacker.news/c/6urf8
Launch HN: Miyagi (YC W25) turns YouTube videos into online, interactive courses (Score: 152+ in 10 hours)

Link: https://readhacker.news/c/6upPT

Hey HN, we’re Tyrone and Guang, founders of Miyagi Labs (https://miyagilabs.ai), an AI-powered education platform that transforms educational YouTube videos into interactive courses. It helps you learn better through active practice and personalized feedback.
We use LLMs to automatically generate quizzes, practice questions, and real-time feedback from any educational video or resource—turning passive watching into active learning. Here’s a short demo: https://youtu.be/alO7FaorHOY.
Improving education has always been tricky. Bloom’s 2-sigma problem (showing that a high-quality personal tutor is far more effective than conventional methods) has persisted, even as technology has advanced.
We met at MIT as CS majors and have always been passionate about education. Over the years, we’ve become teachers and experts in subjects like chess, algorithms, math, languages, and ninja warrior. A common theme was that we both heavily relied on YouTube to learn.
YouTube has incredible content for learning pretty much anything, but it’s buried in a lot of distractions. Also, passively watching videos is far less effective than taking notes, asking questions, and doing practice problems, which is what we aim to do with Miyagi Labs.
Our solution is essentially a multi-step function that takes in a YouTube playlist (or list of any resources) and outputs an entire course with summaries, questions, answers, and more. The pipeline is roughly: video/resource —> transcript/text —> chunks —> summary and question —> answers to questions, with some other features along the way.
We mostly use prompting and different models at each step to make the course as useful as possible. Certain topics require more practice problems vs. comprehension, and we’d use reasoning models for highly technical subjects.
We launched about three months ago and currently have 400+ courses and partnerships with some businesses and awesome creators. Some of our popular courses include 3Blue1Brown’s linear algebra course, a botany course on plants and ecology, and YC’s How to Start a Startup series.
Our product resembles classical MOOC-style course platforms in terms of UI, but is more interactive. It’s really easy to ask a question or receive custom feedback compared to a static course on Coursera. It’s also comparable to AI tutor sites, but we try to build more of a community and require less activation energy as a learner. We’re basically betting that AI can hugely improve education, but that students still want to learn from their favorite creators and want baseline shared resources for standard topics that are then augmented with personalized features.
You can try it here: https://miyagilabs.ai (no login required for most courses—but if you sign up you can also create your own course).
We’d love your feedback on what kinds of videos/resources you’d like to learn from, what’s missing from current learning tools, and if you know any creators or educators who would like to collaborate. Happy to hear any feedback and answer any questions!