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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
National Airspace System Status (Score: 150+ in 10 hours)
Link: https://readhacker.news/s/6tkVL
Comments: https://readhacker.news/c/6tkVL
Show HN: My from-scratch OS kernel that runs DOOM (Score: 150+ in 6 hours)
Link: https://readhacker.news/s/6thRt
Comments: https://readhacker.news/c/6thRt
Hi there! I've been on-and-off working on TacOS for a few months, which follows some UNIX-derived concepts (exec/fork, unix-style VFS, etc) and is now able to run a port of Doom, with a fairly small amount of modifications, using my from-scratch libc. The performance is actually decent compared to what I expected. Very interested to hear your thoughts. Thank you!
YAGRI: You are gonna read it (Score: 151+ in 8 hours)
Link: https://readhacker.news/s/6thvz
Comments: https://readhacker.news/c/6thvz
Shortest-possible walking tour to 81,998 bars in South Korea (Score: 150+ in 4 hours)
Link: https://readhacker.news/s/6thRT
Comments: https://readhacker.news/c/6thRT
Teaching LLMs how to solid model (Score: 150+ in 5 hours)
Link: https://readhacker.news/s/6tgSg
Comments: https://readhacker.news/c/6tgSg
You Wouldn't Steal a Font (🔥 Score: 182+ in 47 minutes)
Link: https://readhacker.news/s/6thaY
Comments: https://readhacker.news/c/6thaY
Show HN: Node.js video tutorials where you can edit and run the code (Score: 150+ in 7 hours)
Link: https://readhacker.news/c/6tfHx
Hey HN,
I'm Sindre, CTO of Scrimba (YC S20). We originally launched Scrimba to make video learning more interactive for aspiring frontend developers. So instead of passively watching videos, you can jump in an experiment with the code directly inside the video player. Since launch, almost two million people have used Scrimba to grow their skills.
However, one limitation is that we've only supported frontend code, as our interactive videos run in the browser, whereas most of our learners want to go fullstack—building APIs, handling auth, working with databases, and so forth.
To fix this, we spent the last 6 months integrating StackBlitz WebContainers into Scrimba. This enables a full Node.js environment—including a terminal, shell, npm access, and a virtual file system—directly inside our video player. Everything runs in the browser.
Here is a 2-minute recorded demo: https://scrimba.com/s08dpq3nom
If you want to see more, feel free to enroll into any of the seven fullstack courses we've launched so far, on subject like Node, Next, Express, SQL, Vite, and more. We've opened them up for Hacker News today so that you don't even need to create an account to watch the content:
https://scrimba.com/fullstack
Other notable highlights about our "IDE videos":
- Based on events (code edits, cursor moves, etc) instead of pixels
- Roughly 100x smaller than traditional videos
- Recording is simple: just talk while you code
- Can be embedded in blogs, docs, or courses, like MDN does here: https://developer.mozilla.org/en-US/curriculum/core/css-fund...
- Entirely built in Imba, a language I created myself: https://news.ycombinator.com/item?id=28207662
We think this format could be useful for open-source maintainers and API-focused teams looking to create interactive docs or walkthroughs. Our videos are already embedded by MDN, LangChain, and Coursera.
If you maintain a library or SDK and want an interactive video about it, let us know—happy to record one for free that you can use however you like.
Would love to answer any questions or hear people's feedback!
How I Blog with Obsidian, Hugo, GitHub, and Cloudflare – Zero Cost, Fully Owned (Score: 150+ in 4 hours)
Link: https://readhacker.news/s/6tfNx
Comments: https://readhacker.news/c/6tfNx
MinC Is Not Cygwin (Score: 150+ in 4 hours)
Link: https://readhacker.news/s/6tfr7
Comments: https://readhacker.news/c/6tfr7
The Gruen Transfer is consuming the internet (Score: 152+ in 5 hours)
Link: https://readhacker.news/s/6tfg2
Comments: https://readhacker.news/c/6tfg2
America's cyber defenses are being dismantled from the inside (🔥 Score: 154+ in 2 hours)
Link: https://readhacker.news/s/6tfpY
Comments: https://readhacker.news/c/6tfpY
The raccoons who made computer magazine ads great (Score: 151+ in 22 hours)
Link: https://readhacker.news/s/6tcBK
Comments: https://readhacker.news/c/6tcBK
Advanced Python Features (Score: 150+ in 4 hours)
Link: https://readhacker.news/s/6tf7Y
Comments: https://readhacker.news/c/6tf7Y
π0.5: A VLA with open-world generalization (Score: 153+ in 14 hours)
Link: https://readhacker.news/s/6tdvR
Comments: https://readhacker.news/c/6tdvR
Solidjs: Simple and performant reactivity for building user interfaces (❄️ Score: 150+ in 3 days)
Link: https://readhacker.news/s/6t46z
Comments: https://readhacker.news/c/6t46z
Graphics livecoding in Common Lisp (Score: 150+ in 13 hours)
Link: https://readhacker.news/s/6tgMy
Comments: https://readhacker.news/c/6tgMy
Google blocked Motorola use of Perplexity AI, witness says (Score: 150+ in 9 hours)
Link: https://readhacker.news/s/6thms
Comments: https://readhacker.news/c/6thms
Show HN: Rowboat – Open-source IDE for multi-agent systems (Score: 150+ in 1 day)
Link: https://readhacker.news/s/6tdmr
Comments: https://readhacker.news/c/6tdmr
Hi HN! We’re Arjun, Ramnique, and Akhilesh, and we are building Rowboat (https://www.rowboatlabs.com/), an AI-assisted IDE for building and managing multi-agent systems. You start with a single agent, then scale up to teams of agents that work together, use MCP tools, and improve over time - all through a chat-based copilot.
Our repo is https://github.com/rowboatlabs/rowboat, docs are at https://docs.rowboatlabs.com/, and there’s a demo video here: https://youtu.be/YRTCw9UHRbU
It’s becoming clear that real-world agentic systems work best when multiple agents collaborate, rather than having one agent attempt to do everything. This isn’t too surprising - it’s a bit like how good code consists of multiple functions that each do one thing, rather than cramming everything into one function.
For example, a travel assistant works best when different agents handle specialized tasks: one agent finds the best flights, another optimizes hotel selections, and a third organizes the itinerary. This modular approach makes the system easier to manage, debug, and improve over time.
OpenAI’s Agents SDK provides a neat Python library to support this, but building reliable agentic systems requires constant iterations and tweaking - e.g. updating agent instructions (which can quickly get as complex as actual code), connecting tools, and testing the system and incorporating feedback. Rowboat is an AI IDE to do all this. Rowboat is to AI agents what Cursor is to code.
We’ve taken a code-like approach to agent instructions (prompts). There are special keywords to directly reference other agents, tools or prompts - which are highlighted in the UI. The copilot is the best way to create and edit these instructions - each change comes with a code-style diff.
You can give agents access to tools by integrating any MCP server or connecting your own functions through a webhook. You can instruct the agents on when to use specific tools via ‘@mentions’ in the agent instruction. To enable quick testing, we added a way to mock tool responses using LLM calls.
Rowboat playground lets you test and debug the assistants as you build them. You can see agent transfers, tool invocations and tool responses in real-time. The copilot has the context of the chat, and can improve the agent instructions based on feedback. For example, you could say ‘The agent shouldn’t have done x here. Fix this’ and the copilot can go and make this fix.
You can integrate agentic systems built in Rowboat into your application via the HTTP API or the Python SDK (‘pip install rowboat’). For example, you can build user-facing chatbots, enterprise workflows and employee assistants using Rowboat.
We’ve been working with LLMs since GPT-1 launched in 2018. Most recently, we built Coinbase’s support chatbot after our last AI startup was acquired by them.
Rowboat is Apache 2.0 licensed, giving you full freedom to self-host, modify, or extend it however you like.
We’re excited to share Rowboat with everyone here. We’d love to hear your thoughts!
How to quickly charge your smartphone: fast charging technologies in detail (Score: 150+ in 1 day)
Link: https://readhacker.news/s/6teeJ
Comments: https://readhacker.news/c/6teeJ
Doge Worker's Code Supports NLRB Whistleblower (🔥 Score: 176+ in 51 minutes)
Link: https://readhacker.news/s/6thkN
Comments: https://readhacker.news/c/6thkN
They made computers behave like annoying salesmen (Score: 153+ in 4 hours)
Link: https://readhacker.news/s/6tgtq
Comments: https://readhacker.news/c/6tgtq
AI Horseless Carriages (🔥 Score: 161+ in 2 hours)
Link: https://readhacker.news/s/6tgvf
Comments: https://readhacker.news/c/6tgvf
How a 20 year old bug in GTA San Andreas surfaced in Windows 11 24H2 (🔥 Score: 174+ in 1 hour)
Link: https://readhacker.news/s/6tg2r
Comments: https://readhacker.news/c/6tg2r
Apple and Meta fined millions for breaching EU law (Score: 151+ in 4 hours)
Link: https://readhacker.news/s/6tfpb
Comments: https://readhacker.news/c/6tfpb
Show HN: I open-sourced my AI toy company that runs on ESP32 and OpenAI realtime (Score: 150+ in 23 hours)
Link: https://readhacker.news/s/6tcRB
Comments: https://readhacker.news/c/6tcRB
Hi HN! Last year the project I launched here got a lot of good feedback on creating speech to speech AI on the ESP32. Recently I revamped the whole stack, iterated on that feedback and made our project fully open-source—all of the client, hardware, firmware code.
This Github repo turns an ESP32-S3 into a realtime AI speech companion using the OpenAI Realtime API, Arduino WebSockets, Deno Edge Functions, and a full-stack web interface. You can talk to your own custom AI character, and it responds instantly.
I couldn't find a resource that helped set up a reliable, secure websocket (WSS) AI speech to speech service. While there are several useful Text-To-Speech (TTS) and Speech-To-Text (STT) repos out there, I believe none gets Speech-To-Speech right. OpenAI launched an embedded-repo late last year which sets up WebRTC with ESP-IDF. However, it's not beginner friendly and doesn't have a server side component for business logic.
This repo is an attempt at solving the above pains and creating a great speech to speech experience on Arduino with Secure Websockets using Edge Servers (with Deno/Supabase Edge Functions) for fast global connectivity and low latency.
Ping, You've Got Whale: AI detection system alerts ships of whales in their path (Score: 150+ in 18 hours)
Link: https://readhacker.news/s/6tdEm
Comments: https://readhacker.news/c/6tdEm
CSS Hell (Score: 150+ in 13 hours)
Link: https://readhacker.news/s/6teev
Comments: https://readhacker.news/c/6teev
Open Source Projects Receive Funding to Reclaim the Public Internet (🔥 Score: 156+ in 2 hours)
Link: https://readhacker.news/s/6tf7U
Comments: https://readhacker.news/c/6tf7U
Native visionOS platform support (🔥 Score: 153+ in 3 hours)
Link: https://readhacker.news/s/6teLX
Comments: https://readhacker.news/c/6teLX
Show HN: Morphik – Open-source RAG that understands PDF images, runs locally (Score: 151+ in 14 hours)
Link: https://readhacker.news/s/6tdiG
Comments: https://readhacker.news/c/6tdiG
Hey HN, we’re Adi and Arnav. A few months ago, we hit a wall trying to get LLMs to answer questions over research papers and instruction manuals. Everything worked fine, until the answer lived inside an image or diagram embedded in the PDF. Even GPT‑4o flubbed it (we recently tried O3 with the same, and surprisingly it flubbed it too). Naive RAG pipelines just pulled in some text chunks and ignored the rest.
We took an invention disclosure PDF (https://drive.google.com/file/d/1ySzQgbNZkC5dPLtE3pnnVL2rW_9...) containing an IRR‑vs‑frequency graph and asked GPT “From the graph, at what frequency is the IRR maximized?”. We originally tried this on gpt-4o, but while writing this used the new natively multimodal model o4‑mini‑high. After a 30‑second thinking pause, it asked for clarifications, then churned out buggy code, pulled data from the wrong page, and still couldn’t answer the question. We wrote up the full story with screenshots here: https://docs.morphik.ai/blogs/gpt-vs-morphik-multimodal.
We got frustrated enough to try fixing it ourselves.
We built Morphik to do multimodal retrieval over documents like PDFs, where images and diagrams matter as much as the text.
To do this, we use Colpali-style embeddings, which treat each document page as an image and generate multi-vector representations. These embeddings capture layout, typography, and visual context, allowing retrieval to get a whole table or schematic, not just nearby tokens. Along with vector search, this could now retrieve exact pages with relevant diagrams and pass them as images to the LLM to get relevant answers. It’s able to answer the question with an 8B llama 3.1 vision running locally!
Early pharma testers hit our system with queries like "Which EGFR inhibitors at 50 mg showed ≥ 30% tumor reduction?" We correctly returned the right tables and plots, but still hit a bottleneck, we weren’t able to join the dots across multiple reports. So we built a knowledge graph: we tag entities in both text and images, normalize synonyms (Erlotinib → EGFR inhibitor), infer relations (e.g. administered_at, yields_reduction), and stitch everything into a graph. Now a single query could traverse that graph across documents and surface a coherent, cross‑document answer along with the correct pages as images.
To illustrate that, and just for fun, we built a graph of 100 Paul Graham’s essays here: https://pggraph.streamlit.app/ You can search for various nodes, (eg. startup, sam altman, paul graham and see corresponding connections). In our system, we create graphs and store the relevant text chunks along with the entities, so on querying, we can extract the relevant entity, do a search on the graph and pull in the text chunks of all connected nodes, improving cross document queries.
For longer or multi-turn queries, we added persistent KV caching, which stores intermediate key-value states from transformer attention layers. Instead of recomputing attention from scratch every time, we reuse prior layers, speeding up repeated queries and letting us handle much longer context windows.
We’re open‑source under the MIT Expat license: https://github.com/morphik-org/morphik-core
Would love to hear your RAG horror stories, what worked, what didn’t and any feedback on Morphik. We’re here for it.