The article argues that when you start a new chat with AI coding assistants (Cursor, Claude Code, Windsurf, or Cortex Code), the session begins with no memory of prior context. It notes that the assistant does not know your team's Streamlit usage, your preference for Material icons over emojis, or past port changes (from 8501 to 8505). As a result, you end up repeating preferences session after session. The piece emphasizes that these tools are powerful but forgetful, and that memory gaps force manual state management by humans. It describes the ‘stateless reality’ of large language models, which do not remember individual users and treat each conversation as a blank slate. The article argues for a memory layer that could automate state management and reduce human-in-the-loop effort.

• Identify memory gaps across coding sessions
• Propose a memory layer to automate state management
• Highlight the stateless nature of LLMs
• Encourage addressing memory gaps to boost productivity

Ensembles improve accuracy by combining multiple models but come with latency and operational complexity. Rather than discarding them, Knowledge Distillation uses the ensemble as a teacher to train a smaller student model on its soft probability outputs. This article demonstrates a pipeline built from scratch: training a 12-model teacher ensemble, generating soft targets with temperature scaling, and distilling into a student that recovers 53.8% of the ensemble’s accuracy edge. The approach aims to retain much of the ensemble’s performance while delivering a lightweight model suitable for deployment. The result illustrates a practical path to high performance with lower latency in production.

• Showcase distillation as a path to deploy high-performance models
• Use temperature scaling to generate soft targets
• Distill knowledge from a 12-model teacher ensemble into a lightweight student
• Quantify recovery: 53.8% of the ensemble’s accuracy edge

An article discusses a statistic that seven in ten asset managers now use AI. The piece notes this level of adoption is widespread across the asset management industry. It then questions whether AI usage actually translates into better investment performance. The discussion highlights the ongoing debate about AI’s value in investing. It calls for evidence to assess the real impact of AI on outcomes.

• Report adoption rate of AI in asset management
• Question whether AI improves performance
• Highlight need for evidence to assess impact
• Reflect on industry-wide trend toward AI adoption