Adult Film Producer Unmasks Prolific 'John DOE' Torrent Pirate as Meta Executive
84 by speckx | 34 comments on Hacker News.
Friday, 4 September 2026
New top story on Hacker News: Show HN: TERMy – A fast terminal assistant that does not use LLMs
Show HN: TERMy – A fast terminal assistant that does not use LLMs
14 by gioscarab | 5 comments on Hacker News.
I love research and development, you may have heard of me because of PJON (Padded Jittering Operative Network). It is a network protocol I started developing in 2010, which was recently implemented in silicon by the ETH Zurich university thanks to the research of Pius Sieber. I am excited to share with you TERMy, a terminal assistant built on top of the NPC-Forge framework. Unlike everything else being built today, TERMy does not use embeddings, machine-learning or LLMs. It runs on the CPU (even on a Raspberry Pi Zero) both in the terminal or client-side in a browser tab and responds in milliseconds. It is a cynical but very knowledgeable Linux terminal assistant that translates your natural language into shell commands without relying on a single artificial neuron. I had a chance to focus for 2 months on my personal projects since early July, during the strange times of AI price hikes and the end of subsidized tokenmaxing. I was curious to see if I could develop from scratch a terminal assistant capable of handling simple natural language requests. I have a bad memory and got used to ask to copilot "activate the virtual environment" or similar trivial operations spending a non negligible sum every month. I started thinking, maybe I can do something to make my workflow more efficient? Do I really need trillions of parameters to accomplish those tasks? How it Works When you type a prompt, it goes through a lightweight NLU pipeline written in ~1000 lines of Python that implement the following steps: 1. Strip expletives, interjections, encouraging, discouraging and thanking words (remove noise) 2. Sentiment analysis 3. Exact Match (very fast) 4. Template Match (slower) 5. Probabilistic Match (even slower) Step 5 relies on: 1. IDF (Inverse Document Frequency) to identify rare words. 2. BOW (Bag Of Words) to accommodate word inversions. 3. IDF weighted Levenshtein to safely handle typos. Permission gating is hardcoded into the dataset and enforced for all potentially destructive commands, so it's inherently safer than letting an unpredictable LLM run wild on your machine. - TERMy in operation: https://www.youtube.com/watch?v=qeIp0xePLBg - Variance and typo tolerance: https://www.youtube.com/watch?v=tQvGDk6fkk0 - Copilot integration: https://www.youtube.com/watch?v=Wzzouhq2a8A - Advanced features: https://www.youtube.com/watch?v=qeIp0xePLBg - Source Code: https://ift.tt/ISybX4o
14 by gioscarab | 5 comments on Hacker News.
I love research and development, you may have heard of me because of PJON (Padded Jittering Operative Network). It is a network protocol I started developing in 2010, which was recently implemented in silicon by the ETH Zurich university thanks to the research of Pius Sieber. I am excited to share with you TERMy, a terminal assistant built on top of the NPC-Forge framework. Unlike everything else being built today, TERMy does not use embeddings, machine-learning or LLMs. It runs on the CPU (even on a Raspberry Pi Zero) both in the terminal or client-side in a browser tab and responds in milliseconds. It is a cynical but very knowledgeable Linux terminal assistant that translates your natural language into shell commands without relying on a single artificial neuron. I had a chance to focus for 2 months on my personal projects since early July, during the strange times of AI price hikes and the end of subsidized tokenmaxing. I was curious to see if I could develop from scratch a terminal assistant capable of handling simple natural language requests. I have a bad memory and got used to ask to copilot "activate the virtual environment" or similar trivial operations spending a non negligible sum every month. I started thinking, maybe I can do something to make my workflow more efficient? Do I really need trillions of parameters to accomplish those tasks? How it Works When you type a prompt, it goes through a lightweight NLU pipeline written in ~1000 lines of Python that implement the following steps: 1. Strip expletives, interjections, encouraging, discouraging and thanking words (remove noise) 2. Sentiment analysis 3. Exact Match (very fast) 4. Template Match (slower) 5. Probabilistic Match (even slower) Step 5 relies on: 1. IDF (Inverse Document Frequency) to identify rare words. 2. BOW (Bag Of Words) to accommodate word inversions. 3. IDF weighted Levenshtein to safely handle typos. Permission gating is hardcoded into the dataset and enforced for all potentially destructive commands, so it's inherently safer than letting an unpredictable LLM run wild on your machine. - TERMy in operation: https://www.youtube.com/watch?v=qeIp0xePLBg - Variance and typo tolerance: https://www.youtube.com/watch?v=tQvGDk6fkk0 - Copilot integration: https://www.youtube.com/watch?v=Wzzouhq2a8A - Advanced features: https://www.youtube.com/watch?v=qeIp0xePLBg - Source Code: https://ift.tt/ISybX4o
Thursday, 3 September 2026
New top story on Hacker News: Porting my 1993 Amiga game to Godot, with an LLM reading the 68000 assembly
Porting my 1993 Amiga game to Godot, with an LLM reading the 68000 assembly
38 by rabahs | 8 comments on Hacker News.
These are my notes from porting my Amiga game, which I originally built in Baghdad in 1993 in MC68000 assembly, to Godot, using Claude Fable 5 during last July holiday. It took an evening! Getting the feel right and shipping it took a few more weekends and evenings. I spent the last few weeks analyzing what Claude did, feeding it my 33 years of memory of how I built the game, my notes and the git repos. It wrote the first draft of the article, and I edited line by line over a week. The screenshots of my 1993 map editor is the first I have run it since then. The one thing I never checked myself is the 108-byte explanation. Please post any questions. I am also releasing the original game for free.
38 by rabahs | 8 comments on Hacker News.
These are my notes from porting my Amiga game, which I originally built in Baghdad in 1993 in MC68000 assembly, to Godot, using Claude Fable 5 during last July holiday. It took an evening! Getting the feel right and shipping it took a few more weekends and evenings. I spent the last few weeks analyzing what Claude did, feeding it my 33 years of memory of how I built the game, my notes and the git repos. It wrote the first draft of the article, and I edited line by line over a week. The screenshots of my 1993 map editor is the first I have run it since then. The one thing I never checked myself is the 108-byte explanation. Please post any questions. I am also releasing the original game for free.
Wednesday, 2 September 2026
Tuesday, 1 September 2026
New top story on Hacker News: Launch HN: Nori Robotics (YC S26) – A low-cost humanoid robot for development
Launch HN: Nori Robotics (YC S26) – A low-cost humanoid robot for development
35 by AntonioLi | 5 comments on Hacker News.
Hey HN, I’m Antonio from Nori Robotics ( https://ift.tt/udnvzQG ). We build a $1,688 bimanual mobile robot in San Francisco for robotics developers and researchers. I started working on Nori while doing robotics research at Columbia. I was teaching robots through human demonstrations, but getting my hands on affordable hardware was difficult. Most labs have one or two expensive robots, which makes it hard to collect large datasets, run long experiments, or test across several robots. So I built my own. After seven iterations the latest Nori has: * 19 degrees of freedom * Two 7+1 DOF arms with a 1.5 kg payload per arm * A 55 kg telescoping lift * A differential wheeled base * Four 720p, 30 fps RGB cameras * 2D lidar * A dual microphone array with full-duplex voice communication * A 432 Wh battery * A Raspberry Pi 5 with 4 GB RAM (SLAM and safeties are run on board, heavier ACT and VLAs must be run from a computer via LAN or a server via WAN) Getting this under $2,000 was the main engineering challenge. Nori has more than 100 moving and structural parts, so costs add up quickly across actuators, bearings, wiring, power delivery, and assembly. Some main choices we made to get the cost low was using high-ratio servos instead of QDD motors, and using a wheel base instead of legs. We assemble each robot in San Francisco and have designed it to be easy to manufacture and repair (we offer 3D files to print repairs). Our open SDK includes teleoperation and demonstration tools: https://ift.tt/YZN1Gya We also built a browser-based simulator so you can try it out: https://ift.tt/mSgWlCz We’ve shipped our first robot and are building the next batch. Eventually, we want people without robotics experience to teach Nori tasks and share them with other owners. Currently the hardware is already capable of basic cleaning tasks, opening drawers, restocking shelves and pouring beers. Here is a video of Nori doing things: https://youtube.com/shorts/VRfVXHfQvD8 We make money by selling the hardware for $1,688, with optional paid software on top. Parts of hardware are open source. More details are in our hardware paper: https://ift.tt/85x4Jfw If you work in robotics, what would you build with a robot at this price? What would you change about the hardware?
35 by AntonioLi | 5 comments on Hacker News.
Hey HN, I’m Antonio from Nori Robotics ( https://ift.tt/udnvzQG ). We build a $1,688 bimanual mobile robot in San Francisco for robotics developers and researchers. I started working on Nori while doing robotics research at Columbia. I was teaching robots through human demonstrations, but getting my hands on affordable hardware was difficult. Most labs have one or two expensive robots, which makes it hard to collect large datasets, run long experiments, or test across several robots. So I built my own. After seven iterations the latest Nori has: * 19 degrees of freedom * Two 7+1 DOF arms with a 1.5 kg payload per arm * A 55 kg telescoping lift * A differential wheeled base * Four 720p, 30 fps RGB cameras * 2D lidar * A dual microphone array with full-duplex voice communication * A 432 Wh battery * A Raspberry Pi 5 with 4 GB RAM (SLAM and safeties are run on board, heavier ACT and VLAs must be run from a computer via LAN or a server via WAN) Getting this under $2,000 was the main engineering challenge. Nori has more than 100 moving and structural parts, so costs add up quickly across actuators, bearings, wiring, power delivery, and assembly. Some main choices we made to get the cost low was using high-ratio servos instead of QDD motors, and using a wheel base instead of legs. We assemble each robot in San Francisco and have designed it to be easy to manufacture and repair (we offer 3D files to print repairs). Our open SDK includes teleoperation and demonstration tools: https://ift.tt/YZN1Gya We also built a browser-based simulator so you can try it out: https://ift.tt/mSgWlCz We’ve shipped our first robot and are building the next batch. Eventually, we want people without robotics experience to teach Nori tasks and share them with other owners. Currently the hardware is already capable of basic cleaning tasks, opening drawers, restocking shelves and pouring beers. Here is a video of Nori doing things: https://youtube.com/shorts/VRfVXHfQvD8 We make money by selling the hardware for $1,688, with optional paid software on top. Parts of hardware are open source. More details are in our hardware paper: https://ift.tt/85x4Jfw If you work in robotics, what would you build with a robot at this price? What would you change about the hardware?
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