Christmas Ornament Has Hidden Compartment, Clever Design via @hackaday hackaday.com/2025/12/22/chri…
Chris Mayfield retweeted
What a finish! Gemini 2.5 Pro just completed Pokémon Blue!  Special thanks to @TheCodeOfJoel for creating and running the livestream, and to everyone who cheered Gem on along the way.
A Programming Language For Building NES Games via @hackaday hackaday.com/2025/02/08/a-pr…
Chris Mayfield retweeted
Replying to @jiayi_pirate
Chris Mayfield retweeted
We reproduced DeepSeek R1-Zero in the CountDown game, and it just works
Through RL, the 3B base LM develops self-verification and search abilities all on its own
You can experience the Ahah moment yourself for < $30
Code: github.com/Jiayi-Pan/TinyZer…
Here's what we learned 🧵
This QR Code Leads To Two Websites, But How? via @hackaday hackaday.com/2025/01/23/this…
3D-Printed RC Car Focuses On Performance Fundamentals via @hackaday hackaday.com/2025/01/20/3d-p…
USB-C For Hackers: Reusing Cables via @hackaday hackaday.com/2024/11/21/usb-…
Chris Mayfield retweeted
Our last CEO f'd things up. I'm fixing them.
Introducing white label hot sauce.
Go fucking buy it now.
stickermule.com/custom-hot-s…
Ricky Berwick (@rickyberwick)
CEO, Sticker Mule
P.S. 20 people who repost this win $500. Follow us so we can DM you.
New Part Day: A Hackable Smart Ring via @hackaday hackaday.com/2024/06/16/new-…
Chris Mayfield retweeted
Nemotron-4 340B is a huge release by NVIDIA!
Why?
The Nemotron-4 340B instruct model lets you generate high-quality data and then the reward model (also released) can filter out data on several attributes.
It's often not enough just to have a good instruct model for generating high-quality synthetic data. You also need to filter that generated data on a set of requirements. It's common to use the same model to grade the outputs (LLM-as-Judge) but the reward model (Reward-Model-as-Judge), which is often not released, is ideal for this.
Also, check out in the paper how Nemotron-4-340B compares with GPT-4-1106-preview when evaluated by humans on several tasks.
Seems that GPT-4 is a lot better at rewrite and extraction but Nemotron-4-340B compared nicely on other tasks. Multi-turn chat has a high score!
Something else that caught my attention is that the majority of preference data used for alignment are synthetic.
The results show that Nemotron-4 340B is a strong model. Check out those MMLU, GSM8K, and Arena Hard numbers.
GPT-4 is still ahead but the gap keeps closing.
What's Meta's move now? Are we going to see a similar type of release? Could be a huge moment for the field and open LLMs.
Nice release from NVIDIA. They even released a preference dataset (link in the replies). Well done to the team!
After I recently finished building my new RatRig V-Core 3, I thought it would be a great test for ... cnckitchen.com/blog/calibrat…
A Scope Test Tool You Can Build With Just A Pico via @hackaday hackaday.com/2024/05/31/a-sc…
Optimizing database queries in Django for improved performance by @s_emanuilov link.medium.com/KZmWh0t2ZJb
Chris Mayfield retweeted
I extensively tested GPT-4o and found it to be as good as Claude 3 Opus in programming for most cases.
But I was particularly impressed by its performance in mathematics.
To test this, I gave it the latest math exam from X-ENS MP for 2024, which is probably one of the hardest exams for Classes Préparatoires schools.
I chose this exam because it took place exactly one month ago, so it's unlikely to be in its training data unless the questions were copied from somewhere else, which I doubt given its difficulty.
To my surprise, GPT-4o managed to answer all but one question correctly.
The single missed question wasn't even an error, the AI simply didn't detail the answer as requested and skipped some steps.
This is remarkable to me. Despite the limitations of large language models like GPT-4o, which sometimes struggle with simple questions, answering the most difficult exam for prépas is on another level.
















