2025-06-17
The Rise of Hybrid Blockchains: Nexchain and the Evolving Crypto Landscape
The ongoing surge in AI-powered blockchain adoption has placed Nexchain at the center of attention. Its hybrid model that merges artificial intelligence with blockchain scalability offers a refreshing alternative in...
Are Traditional Data Warehouses Being Devoured by Agentic AI?
The gates to the Agentic Data Stack are opening. Are you ready?
SpeakingAI 3.0
Now practice your speaking through real-life situations Discussion | Link
Borderlands 4 system requirements demand 8 CPU cores and 8 GB of VRAM but the reality is probably a bit more forgiving than that
An AMD Ryzen 5 9600X will surely run Borderlands 4 just fine.
Angry Pepe Fork: Multi-chain and Supercharged by GambleFi
Angry Pepe Fork ($APORK) is the first meme coin to launch alongside the GambleFi application. It is reinventing what meme currencies can achieve by establishing an environment in which users...
VLMs can Aggregate Scattered Training Patches
Published on June 16, 2025 6:25 PM GMTThis is the abstract and summary of our new paper. We show that vision-language models can learn to reconstruct harmful images from benign-looking...
XBO.com: Bridging the Gap Between Crypto and Everyone
Discover how Lior Aizik, Co-founder & COO of XBO.com, is building a user-friendly and regulated crypto exchange to make digital finance universally accessible, catering to both retail newcomers and seasoned...
How Effective Is LoRA Finetuning for Large Language Models?
LoRA underperforms full finetuning on code and math tasks, but preserves base model behavior, shows greater sensitivity to hyperparameters, and offers diverse outputs.
LoRA's Limitations in Code and Math Tasks
While LoRA offers a memory-efficient alternative to full finetuning, recent studies show mixed results—especially in complex domains like code and math.
How Module Type and Rank Impact LoRA’s Effectiveness in Model Training
Full finetuning on code and math reveals high-rank updates LoRA often misses. LoRA works best when tuned with high learning rates, all-module targeting, and r = 16.
Does LoRA Fine-Tuning Help AI Models Forget Less?
LoRA offers a better tradeoff than full fine-tuning—achieving strong target performance while reducing source domain forgetting and maintaining output diversity.
Over Time, LoRA Holds Up Better Than Full Finetuning
LoRA outperforms full finetuning by retaining more knowledge across benchmarks, especially in code-focused tasks, and shows less degradation with more data.
LoRA Falls Short of Full Finetuning in Programming and Math Tasks
Full finetuning consistently outperforms LoRA in code and math tasks, with higher accuracy and better sample efficiency across HumanEval and GSM8K benchmarks.
AI Scraping Bots Are Breaking Open Libraries, Archives, and Museums
"This is a moment where that community feels collectively under threat and isn't sure what the process is for solving the problem.”
LLMs Are Changing the Way We Animate
LogoMotion leverages LLMs to generate animation code based on visual layouts and prompts, enabling non-designers to break free from rigid templates using natural language.
How to destroy sticky notes in FBC: Firebreak
Complete the Paper Chase job and defeat Sticky Ricky.
FBC: Firebreak review
A big old mess. Mainly by design, partly by mistake.
FBC: Firebreak best weapons tier list
Find out which gun is best for blasting Hiss invaders.
What's the best Crisis Kit to pick in FBC: Firebreak?
Try out the Fix Kit, Jump Kit, and Splash Kit when tackling dangerous jobs.
How to neutralize corrupted items in FBC: Firebreak
Track down a Black Rock Neutralizer and destroy these troublesome relics.
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