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reacted to Banaxi-Tech's post with 👍 about 2 hours ago

We're excited to release BananaMind 2 MoE, a new addition to the BananaMind 2 series! BananaMind 2 MoE is a sparse mixture-of-experts model with 25M total parameters but only 2M active per token, Like the rest of the series, it uses our custom digit-aware BPE tokenizer that keeps every digit isolated, fixing the core arithmetic weakness of our earlier models. It's trained on 30B tokens from FineWeb-Edu, DCLM, Cosmopedia-v2 and FineMath-4+, and outperforms Pythia-31M on average despite activating just 2M parameters. Check it out at https://huggingface.co/BananaMind/BananaMind-2-MoE BananaMind 2 Nano is Coming Next. Already training. Apache 2.0.

reacted to Smooke's post with 👍 about 2 hours ago

Say it with me, AI is moving beyond the phone, AI is moving beyond the phone, AI is moving beyond the phone. The weight Apple is putting on this says it all. They're fighting for the next hardware market before it hits the market. Harder to say what it means for OpenAI's consumer trust. Re https://huggingface.co/apple vs. https://huggingface.co/openai: https://hackernoon.com/apple-v-openai-trade-secrets-lawsuit-the-42-most-explosive-allegations-and-the-internets-reactions

reacted to satgeze's post with 👍 about 2 hours ago

Two abliterated Ornith-1.0 builds, now public. Refusal removed with a single rank-1 weight edit on the MLP down-projections, direction extracted from matched harmful/harmless pairs. Measured, not claimed: 35B at **0% refusal, KL 0.036, no capability loss** (GSM8K 96.0 vs 94.0); 9B at **2% residual, KL 0.070**, an honest 2.5-point tradeoff. Verified on a real-world gate (no language drift, output integrity, real engagement), not just a refusal count. GGUF, Q8_0/Q4_K_M. https://huggingface.co/satgeze/Ornith-1.0-35B-abliterated-SatGeZe https://huggingface.co/satgeze/Ornith-1.0-9B-abliterated-SatGeZe

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Imagine saving over 40% of your tokens and time on debugging... Now, what if I told you there's a tool that captures the "hidden patterns" of your AI agent and automatically corrects them in the background without any intervention from you? 🤯🚀

The ai-rule-learning-mcp tool

Works automatically in the background of every session with the agent (such as Cloud Code, Cline, Aider, or others).

https://lnkd.in/dw9bAbqy

It detects recurring errors and lapses and instantly corrects the AI's behavior.

The result?

Perfect consistency and real improvement across sessions because the tool automatically injects the correct approach into its memory. 🧠

Why do you need it now?

Professionals use this tool to manage agent behavior in the background, while most developers still waste time and tokens rewriting instructions and correcting the same silly mistakes every time they open a new session because the AI forgets the context.
As long as you can't monitor what's learning in the background, you'll keep spending tokens and wasting hours digging through the same problems. 💸

It's fully automated; you don't need to see or modify the patterns yourself.

Install it in one step:
pip install ai-rule-learning-mcp

⚡ It's completely free, and installation takes only 30 seconds.

The tool detects past patterns and automatically injects strict instructions to prevent repeating mistakes and establish the correct working method, whether at the beginning or in the middle of a session. 🎯 Save your effort and tokens now and let the tool automatically manage the AI agent's behavior.

For discussion and support, join our Discord: https://lnkd.in/dvHMS6Hd

👇 What's the most common pattern your agent makes that you wish the tool would fix?