Friday, 4 September 2026

New top story on Hacker News: Adult Film Producer Unmasks Prolific 'John DOE' Torrent Pirate as Meta Executive

Adult Film Producer Unmasks Prolific 'John DOE' Torrent Pirate as Meta Executive
84 by speckx | 34 comments on Hacker News.


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

New top story on Hacker News: People that worked on the same idea for decades

People that worked on the same idea for decades
5 by sebg | 4 comments on Hacker News.


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.