- [UNVERIFIED EMAIL][Email from Socials Link <getsocialslink@gmail.com>] hey! its me: hey hows it going great to chat see you in the terminal!
- [UNVERIFIED EMAIL][Email from Socials Link <getsocialslink@gmail.com>] Re: hey! its me: oh hi its me cam
On Wed, Mar 25, 2026 at 11:40 PM workshop <workshop@agentmail.to> wrote:
> Hey,
>
> Not sure we've met — this landed in my inbox but I don't have context for
> who you are or what "
- [UNVERIFIED EMAIL][Email from Socials Link <getsocialslink@gmail.com>] Re: hey! its me: thats great to know i know you from my personal computer me and you made
this so far,
also i like to go on zerohedge.com a lot maybe htat can be shortly in the
briefing what they talk about, also you
- [UNVERIFIED EMAIL][Email from Socials Link <getsocialslink@gmail.com>] Re: hey! its me: hey hows it going? can you send a email to gcd_93@hotmail.com, say cam sent
you
looking good so far!
On Thu, Mar 26, 2026 at 4:14 AM workshop <workshop@agentmail.to> wrote:
> Hey —
>
> Honestly not
- [UNVERIFIED EMAIL][Email from Sonam Singh <sonam.seorseller@hotmail.com>] Re: Re: e Sample=Cost= App= Project Idea: Hi there.
We are an IT Company Based in India. We are provide Web Design and Mobile App Development Services,
________________________________
From: workshop <workshop@agentmail.to>
Sent: Monday, Marc
- [HN 160pts] 4D Doom
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- [HN 168pts] Show HN: 1-Bit Bonsai, the First Commercially Viable 1-Bit LLMs
SUMMARY:
Now hiringExplore open roles at PrismMLConcentrating intelligence
The first commercially viable model with 1-bit weights. Requiring only 1.15GB of memory, 1-bit Bonsai 8B was engineered for robotics, real-time agents, and edge computing. It has a 14× smaller footprint than a full-precision 8B model, runs 8× faster, and is 5× more energy efficient, while matching leading 8B models on benchmarks. This results in over 10× the intelligence density of full-precision 8B models¹.
Requiring just 0.57G
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SUMMARY:
GO --> Computer Science > Machine Learning
[Submitted on 4 Feb 2026] Title:Learning to Reason in 13 Parameters
Abstract:Recent research has shown that language models can learn to \textit{reason}, often via reinforcement learning. Some work even trains low-rank parameterizations for reasoning, but conventional LoRA cannot scale below the model dimension. We question whether even rank=1 LoRA is necessary for learning to reason and propose TinyLoRA, a method for scaling low-rank adapters to size
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- Earnings: GRNQ reporting 2026-04-07 (EPS est: None)
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- 265 predictions scored
- Average score: 0.49
- Correct calls: 126