Hacker News

https://xenaproject.wordpress.com/2026/09/04/flt-anthropic-h...

https://archive.ph/3zcMG

Another tidbit, in the crazy things that AI has done... It has helped create a ANT (common sensor wireless protocol used in workout/biking) implementation for ESP32 by messing around with undocumented registers: https://github.com/RaemondBW/esp32-ant

https://archive.is/kmOqm

https://www.reuters.com/world/europe/openai-agents-hijacked-...

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://github.com/gioblu/NPC-Forge

https://deploymentsafety.openai.com/gpt-6-astra

Related ongoing threads:

OpenAI's GPT-6 Astra on ARC-AGI-3 - https://news.ycombinator.com/item?id=49555691

GPT-6 Astra makes major gains in the Artificial Analysis Coding Agent Index - https://news.ycombinator.com/item?id=49556147

Right now I've busted a board, and managed to desolder and resolder a stick but the controller doesn't turn on anymore. I'm doing this all by myself, and I think I'm following good advice (ventilation, good iron tip, using flux etc...).

But there are many variables, and a lot of experts have different opinions on stuff. Does anyone know good resources that I could use for getting good and making less mistakes ? Thanks.

https://www.ycombinator.com/library/Y6-the-world-s-largest-e...