AI & ML Research 3 Days

Last updated: July 13, 2026, 5:30 AM ET

LLM Techniques and Limitations

Frontier AI models continue to hallucinate, a phenomenon that can range from amusing to damaging. Addressing this, one author explored prompt-pruning techniques to mitigate the cost and latency associated with ever-growing long context windows, arguing LLMs fail from remembering too much rather than forgetting. The debate between Retrieval-Augmented Generation (RAG) and fine-tuning persists, with one perspective suggesting RAG is a temporary workaround and that future AI infrastructure will rely on persistent neural states rather than vector databases. Another article clarified that RAG and fine-tuning address distinct problems, and the question isn't which one "wins" but rather when to employ each.

Agentic AI and Orchestration

The concept of agentic AI is being critically examined, with one piece posing the question of whether over-reliance on external consultants mirrors our delegation of cognitive tasks to machines. On a more practical note, a guide demonstrated how to orchestrate over 100 agents in parallel using Claude code, offering a method for scaling.

Data Engineering Fundamentals

For aspiring data engineers, a walkthrough detailed the construction of an RSS pipeline using Python, Docker, Postgre SQL, and Kestra, emphasizing a. Building on foundational knowledge, a guide provided intermediate-level skills for PySpark, covering essential concepts like partitions, shuffles, joins, caching, and execution plans. Meanwhile, insights into Claude's internal workings and OpenAI's "super app" strategy were shared in a tech newsletter.