The real innovation lies in designing systems that can act on data with autonomy and context—my analysis on project44 shows how intelligence at scale is no longer an ambition, but a working architecture.
MACHINE LEARNING
-
Chai-2 AI Discovers Antibodies for 26 Diseases in Breakthrough
By
–
OpenAI-backed Chai Discovery dropped Chai-2, an AI that can design working antibodies
— Rowan Cheung (@rowancheung) 1 juillet 2025
It designed antibodies against 52 diseases and found successful treatments for half of them by testing just 20 candidates each
A big breakthrough in drug discovery!pic.twitter.com/2sov4rVHfVOpenAI-backed Chai Discovery dropped Chai-2, an AI that can design working antibodies It designed antibodies against 52 diseases and found successful treatments for half of them by testing just 20 candidates each A big breakthrough in drug discovery!
-
Baidu Open-Sources 10 ERNIE 4.5 Model Versions, 424B Beats DeepSeek V3
By
–
Baidu open-sourced 10 versions of its ERNIE 4.5 model family. Key highlights: —Models from 300M to 424B params
—424B version beats DeepSeek V3 on 22/28 benchmarks
—“Heterogeneous” training architecture for increased efficiency
—Apache 2.0 license -
Multiple LLMs Solve Previously Unsolvable ARC-AGI-2 Examples Together
By
–
Many ARC-AGI-2 examples that were unsolvable by any single LLM were solved by combining multiple LLMs. In some cases, an initially incorrect attempt by o4-mini is used by R1-0528 and Gemini-2.5-Pro as a hint to get to the correct solution. ARC-AGI-2 code: https://
github.com/SakanaAI/ab-mc
ts-arc2
… -
Graph Data Modeling with Python: Practical Guide for Data Scientists
By
–
Graph Data Modeling with #Python — A practical guide to curating, analyzing, and modeling data with graphs: http://
amzn.to/3PYxCPF v/ @PacktPublishing ——
#DataScience #GraphDB #LinkedData #AI #MachineLearning #DataScientist
——
𝒦𝑒𝓎 𝐹𝑒𝒶𝓉𝓊𝓇𝑒𝓈: Transform relational -
Building Neo4j-Powered Applications with LLMs and AI Frameworks
By
–
New from @PacktDataML >> "Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI" Available at http://
amzn.to/4l9lLKO -
AB-MCTS: Inference-Time Scaling for Frontier AI Model Cooperation
By
–
Inference-Time Scaling and Collective Intelligence for Frontier AIhttps://t.co/3qSUaEixQU
— hardmaru (@hardmaru) 1 juillet 2025
We developed AB-MCTS, a new inference-time scaling algorithm that enables multiple frontier AI models to cooperate, achieving promising initial results on the ARC-AGI-2 benchmark.… pic.twitter.com/9guSv7Dtv0Inference-Time Scaling and Collective Intelligence for Frontier AI https://
sakana.ai/ab-mcts/ We developed AB-MCTS, a new inference-time scaling algorithm that enables multiple frontier AI models to cooperate, achieving promising initial results on the ARC-AGI-2 benchmark. -
JEPA: Latent-Space Reasoning and Planning Architecture
By
–
JEPA is designed for latent-space reasoning and planning.
-
GR00T N1.5: NVIDIA’s VLA Model Teaching Robots Like Toddlers
By
–
📢GR00T N1.5 Explained: NVIDIA’s VLA Model for Humanoids
— Satya Mallick (@LearnOpenCV) 30 juin 2025
🧠 Imagine teaching a robot like you’d teach a toddler, show, guide, repeat. Only this time, it’s not just blocks, it’s fruit, microwaves, tools… the whole world.
Now, with NVIDIA’s GR00T N1.5, robots can start learning… pic.twitter.com/FiQRuPapGoGR00T N1.5 Explained: NVIDIA’s VLA Model for Humanoids Imagine teaching a robot like you’d teach a toddler, show, guide, repeat. Only this time, it’s not just blocks, it’s fruit, microwaves, tools… the whole world.
Now, with NVIDIA’s GR00T N1.5, robots can start learning -
Draft Model Pruning Achieves 43% Fewer MACs with Strong Performance
By
–
The results: – 1.59× higher Mean Accepted Length (MAL) than layer-pruned draft models
– 43.87% fewer MACs (Multiply-Accumulate operations) than dense draft models
– Only 8.36% reduction in MAL vs. dense models — a strong tradeoff for efficiency