Fine-tuning massive LLMs used to be painfully slow, but not anymore! 4 open source libraries that accelerate fine-tuning of Large Language Models 1. Unsloth AI • Fine-tune models like Qwen3, Llama 4, and Gemma 3 up to 2× faster with 70% less VRAM
• Uses optimized Triton
MACHINE LEARNING
-
Accelerating LLM Fine-tuning with Open Source Libraries
By
–
-
AI-RAN Enables Genuine Edge Intelligence in Networks
By
–
AI-RAN unlocks true edge intelligence in networks
→ View original post on X — @haroldsinnott, 2026-04-11 12:16 UTC
-
AI Enhances Supply Chain Traceability and Transparency
By
–
Leverage #AI for Enhanced Traceability and Transparency in the #SupplyChain by @antgrasso #Logistics #MachineLearning #ArtificialIntelligence #ML #DL
→ View original post on X — @ronald_vanloon, 2026-04-11 10:56 UTC
-
Healthcare Data Hackathon: AI Extraction Anonymization Competition
By
–
Can your AI solution efficiently extract, verify and anonymise critical information from unstructured healthcare data? Join the Hackathon to refine your solution with support and guidance from the Central Drugs Standard Control Organisation (CDSCO). Winners receive a chance to
-
AI-Powered Healthcare Claims Auto-Adjudication Hackathon
By
–
The processing of claims in healthcare can be slow, complex, and inconsistent. But what if AI could change that? Join the AB PM-JAY #AutoAdjudicationHackathon and build solutions that bring faster, smarter and fairer systems. From NLP to computer vision, this hackathon
-
DVD Achieves Video Depth Estimation Without Geometric Errors
By
–
Can we finally achieve highly accurate video depth estimation without geometric errors or massive datasets? Researchers from HKUST(GZ), Princeton University, and a global team introduce DVD. DVD ingeniously transforms pre-trained video diffusion models into precise, single-pass
-
Machine Learning Models Explained by Python Developer
By
–
#MachineLearning Models Explained by @Python_Dv #AI #ArtificialIntelligence #ML #MI
→ View original post on X — @ronald_vanloon, 2026-04-11 08:45 UTC
-
Reducing search space for laws matters significantly
By
–
No, the reason it’s important is that it greatly reduces the search space for laws, which is not the same.
-
Scaling Drug Discovery Through Unified Biomedical Workflows
By
–
Drug discovery is where this becomes strategic. Instead of manually stitching databases: → ADMET evaluation → Drug-likeness scoring → Repurposing signals via disease pathway and drug-target mapping → Multi-omics integration → Regulatory network inference and regulon activity scoring 100+ biomedical tools and thousands of recent papers unified into reproducible workflows. This is how you scale insight across labs and teams.
→ View original post on X — @ronald_vanloon, 2026-04-11 08:30 UTC
-
AI-Powered Drug Discovery Pipeline: From Sentence to Full Analysis
By
–
What if your next drug discovery pipeline started with one sentence, not 6 months of scripting?
— Ronald van Loon (@Ronald_vanLoon) 11 avril 2026
In this latest deep dive with @scispace, I saw a shift that feels bigger than incremental AI gains.
Upload single-cell data and get clustered cell types, UMAPs, marker annotations,… pic.twitter.com/91MIdlMKUwWhat if your next drug discovery pipeline started with one sentence, not 6 months of scripting? In this latest deep dive with @scispace, I saw a shift that feels bigger than incremental AI gains. Upload single-cell data and get clustered cell types, UMAPs, marker annotations, causal gene rankings, and ADMET analysis in one structured flow. This changes the operating model. Here’s the breakdown…
→ View original post on X — @ronald_vanloon, 2026-04-11 08:30 UTC