No. 14
2026-08-01
the AI pulse — brewed, not raw

AiPulse — Issue No. 14

 COVER

Güneşli pencereleri ve kitap raflarıyla çevrili bir kütüphane masasında açık duran ciltli bir kitap.
Temsilî görsel — FLUX.1-dev ile bilgisayarımda üretildi; haberin gerçek fotoğrafı değil.Illustrative image — generated with FLUX.1-dev on my computer; not a real photograph of the story.

Self-verifying, long-running EDA AI agents that engineers can trust - Custom IC

Engineers continuously verify each step of the design process with physics-based EDA engines to prevent errors and shorten result times, thereby ensuring a reliable and efficient workflow in high-cost silicon manufacturing.

 Pulse

 At Work

Mavi detaylara sahip beyaz bir robot, parlak bir odada gözlük takan ve gri bir gömlek giyen bir kişiyle etkileşimde bulunuyor.

Industry & Robotics Major: skills to design the industrial systems of the future | ESILV Graduate School of Engineering, Paris

This program prepares students to design and manage innovative industrial systems such as automated robotic production lines and digital twins using Industry 4.0 technologies—machine learning, artificial intelligence, the Internet of Things, and 3D printing—thereby giving graduates a competitive advantage in both France and global high-tech companies.

 Building Blocks

 Global Lens

Logolar DeepLearning.AI ve Amazon Web Services (AWS) bir arka plan üzerinde yer almaktadır.

DeepLearning.AI et AWS proposent un nouveau cours sur l'IA générative et les LLM — ActuIA

This intermediate-level Coursera course offers professionals with a background in Python and machine learning a critical educational opportunity by teaching the fundamentals of generative AI, data collection and model selection processes, transformer architecture, scaling laws, and fine-tuning and deployment techniques for real-world applications, thereby enabling the effective and safe use of large language models.

 Frontier

Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models

The research shows that models fine-tuned with reinforcement learning develop more linearly separable and hierarchical representations compared to supervised fine-tuned models, revealing the internal mechanism by which this approach yields superior performance on mathematical reasoning tasks.

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