PODCAST

AI Post Transformers

AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis, proper citations, and nerdy humor.

technology

All Episodes

00:17:49

SLDAgent: Evolutionary Discovery of Superhuman AI...

AI Post Transformers ·

2026/01/26

The paper, titled "Can Language Models Discover Scaling Laws?" and published on January 22, 2026, represents a collaborative effort by researchers from **Peking University**, **Stanford University**, **Wizard Quant**, and **Tsinghua University**. he authors address the inefficiency of manual scaling law discovery by introducing **SLDAgent**, an evolution-based system designed to automate the search for predictive symbolic formulas. They prove that this agent consistently discovers laws with superior

SLDAgent: Evolutionary Discovery of Superhuman AI...

AI Post Transformers ·

2026/01/26

The paper, titled "Can Language Models Discover Scaling Laws?" and published on January 22, 2026, represents a collaborative effort by researchers from Peking University, Stanford University, Wizard Quant, and Tsinghua University. he authors address the inefficiency of manual scaling law discovery by introducing SLDAgent, an evolution-based system designed to automate the search for predictive symbolic formulas. They prove that this agent consistently discovers laws with superior

00:13:10

Sequoia Capital: AGI is here

AI Post Transformers ·

2026/01/24

On January 14, 2026 Sequoia Capital published a piece assertion that Artificial General Intelligence has arrived ahead of schedule, redefined as the functional ability for AI to "figure things out" autonomously. The authors highlight the transition from simple conversational models to **long-horizon agents** that can persist through complex, multi-step tasks without constant human guidance. By leveraging **reasoning capabilities** and iterative problem-solving, these agents are now performing

OpenAI: Scaling PostgreSQL to 800 Million ChatGPT Users

AI Post Transformers ·

2026/01/24

OpenAI manages a massive PostgreSQL infrastructure to support hundreds of millions of users by utilizing a single-primary architecture with dozens of global read replicas. To maintain stability under extreme traffic, the engineering team implemented rigorous query optimizations, connection pooling through PgBouncer, and aggressive caching strategies. They mitigate the limitations of a single writer by migrating write-heavy workloads to sharded systems like Azure Cosmos DB and enforcing strict rate

Agentic Reasoning for Large Language Models: A...

AI Post Transformers ·

2026/01/24

This January 18, 2026 massive collaboration between University of Illinois Urbana-Champaign, Meta, Amazon, Google Deepmind, UCSD and Yale explores the evolution of agentic reasoning in large language models, moving beyond static text generation toward dynamic planning and external interaction. It details how models utilize tool integration and multi-agent systems to solve complex problems in fields like software engineering, scientific discovery, and robotics. The text categorizes specialized roles

Sequoia Capital: AGI is here

AI Post Transformers ·

2026/01/24

On January 14, 2026 Sequoia Capital published a piece assertion that Artificial General Intelligence has arrived ahead of schedule, redefined as the functional ability for AI to "figure things out" autonomously. The authors highlight the transition from simple conversational models to long-horizon agents that can persist through complex, multi-step tasks without constant human guidance. By leveraging reasoning capabilities and iterative problem-solving, these agents are now performing

00:16:21

Agentic Reasoning for Large Language Models: A...

AI Post Transformers ·

2026/01/24

This January 18, 2026 massive collaboration between University of Illinois Urbana-Champaign, Meta, Amazon, Google Deepmind, UCSD and Yale explores the evolution of **agentic reasoning** in large language models, moving beyond static text generation toward **dynamic planning** and **external interaction**. It details how models utilize **tool integration** and **multi-agent systems** to solve complex problems in fields like **software engineering**, **scientific discovery**, and **robotics**. The

00:13:54

OpenAI: Scaling PostgreSQL to 800 Million ChatGPT Users

AI Post Transformers ·

2026/01/24

OpenAI manages a massive **PostgreSQL infrastructure** to support hundreds of millions of users by utilizing a **single-primary architecture** with dozens of global read replicas. To maintain stability under extreme traffic, the engineering team implemented **rigorous query optimizations**, connection pooling through **PgBouncer**, and aggressive caching strategies. They mitigate the limitations of a single writer by migrating **write-heavy workloads** to sharded systems like **Azure Cosmos DB** and

00:14:46

MEMRL: Self-Evolving Agents via Runtime Reinforcement...

AI Post Transformers ·

2026/01/23

The January 6, 2026 paper introduces **MEMRL**, a framework designed to help AI agents master new skills by mimicking human **episodic memory** without needing to update the model's underlying weights. This approach addresses the **stability-plasticity dilemma** by decoupling a stable, frozen **Large Language Model** (the reasoning core) from a dynamic, evolving memory bank. Unlike standard retrieval methods that rely solely on semantic similarity, MEMRL uses **non-parametric reinforcement

00:17:44

Google: R&D inference value on HBF + PNM + low...

AI Post Transformers ·

2026/01/23

To address the hardware bottlenecks of LLM inference, Google researchers Ma and Patterson propos in their paper "Challenges and Research Directions for Large Language Model Inference Hardware" published on January 8, 2026 a few focus areas of research: High Bandwidth Flash (HBF), Processing-Near-Memory (PNM), and low-latency interconnects.**HBF** addresses the "Memory Wall" by stacking flash dies to achieve **10X the capacity** of HBM, making it ideal for storing model weights and long contexts

MEMRL: Self-Evolving Agents via Runtime Reinforcement...

AI Post Transformers ·

2026/01/23

The January 6, 2026 paper introduces MEMRL, a framework designed to help AI agents master new skills by mimicking human episodic memory without needing to update the model's underlying weights. This approach addresses the stability-plasticity dilemma by decoupling a stable, frozen Large Language Model (the reasoning core) from a dynamic, evolving memory bank. Unlike standard retrieval methods that rely solely on semantic similarity, MEMRL uses non-parametric reinforcement learning to evaluate

Google: R&D inference value on HBF + PNM + low...

AI Post Transformers ·

2026/01/23

To address the hardware bottlenecks of LLM inference, Google researchers Ma and Patterson propos in their paper "Challenges and Research Directions for Large Language Model Inference Hardware" published on January 8, 2026 a few focus areas of research: High Bandwidth Flash (HBF), Processing-Near-Memory (PNM), and low-latency interconnects. HBF addresses the "Memory Wall" by stacking flash dies to achieve 10X the capacity of HBM, making it ideal for storing model weights and long

00:17:15

Meta's solution to massive DLRM inference through...

AI Post Transformers ·

2026/01/21

On November, 2021 Meta (back then Facebook) in collaboration with George Mason University and University of Illinois Chicago published their paper "Supporting Massive DLRM inference through software defined memory".Meta addressed the infrastructure challenge of serving massive Deep Learning Recommendation Models by extending the memory hierarchy to include NVMe Storage Class Memory. Because standard storage devices read large data blocks that exceed the small size of embedding rows the company faced

00:14:31

Storage-next: Do We Need New Hardware for AI Storage,...

AI Post Transformers ·

2026/01/21

We review the "Storage-Next" paper, published in November 2025, which argues that a fundamental hardware architectural shift is required to elevate NAND flash from a passive storage tier to an active memory tier capable of "seconds-scale" caching. The authors contend that standard SSDs impose a "channel-side ceiling" on IOPS because they are optimized for 4KB blocks, creating massive bandwidth waste when AI applications demand fine-grained access to small items, such as 128-byte embedding vectors

LeCun's AMI Energy-Based Models and the Path to...

AI Post Transformers ·

2026/01/21

These sources collectively explore the current landscape and future trajectory of artificial intelligence, specifically focusing on the transition toward human-level reasoning. Renowned scientist Yann LeCun argues that current Large Language Models lack a fundamental understanding of the physical world and proposes a shift toward objective-driven AI that utilizes world models for better planning and common sense. This technological shift is supported by recent industry developments, such as the

Meta's solution to massive DLRM inference...

AI Post Transformers ·

2026/01/21

On November, 2021 Meta (back then Facebook) in collaboration with George Mason University and University of Illinois Chicago published their paper "Supporting Massive DLRM inference through software defined memory". Meta addressed the infrastructure challenge of serving massive Deep Learning Recommendation Models by extending the memory hierarchy to include NVMe Storage Class Memory. Because standard storage devices read large data blocks that exceed the small size of embedding rows the

Storage-next: Do We Need New Hardware for AI Storage,...

AI Post Transformers ·

2026/01/21

00:13:53

LeCun's AMI Energy-Based Models and the Path to...

AI Post Transformers ·

2026/01/21

These sources collectively explore the current landscape and future trajectory of artificial intelligence, specifically focusing on the transition toward human-level reasoning. Renowned scientist Yann LeCun argues that current Large Language Models lack a fundamental understanding of the physical world and proposes a shift toward **objective-driven AI** that utilizes **world models** for better planning and common sense. This technological shift is supported by recent industry developments, such as

00:16:32

MemoBrain: Executive Memory for Tool-Augmented...

AI Post Transformers ·

2026/01/19

On January 12, 2026, a collaboration between the Beijing Academy of Artificial Intelligence, the Gaoling School of Artificial Intelligence, and Renmin University of China introduced MemoBrain. In the paper titled “MemoBrain: Executive Memory as an Agentic Brain for Reasoning,” the authors present an executive memory model designed to enhance the performance of tool-augmented AI agents during complex, long-duration reasoning tasks.Standard large language models often struggle with **cognitive

00:19:41

Parallel Context-of-Experts Decoding for Efficient...

AI Post Transformers ·

2026/01/19

The collaboration between SAP Labs, France and EURECOM, France published a paper on January 13, 2026 titord "Parallel Context-of-Experts Decoding for Retrieval Augmented Generation". The paper introduces **Parallel Context-of-Experts Decoding (PCED)**, a training-free framework designed to optimize **Retrieval Augmented Generation (RAG)** by overcoming the latency and reasoning limitations of long-context prompts. Rather than concatenating numerous documents into one massive input, PCED encodes each

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