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Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:52

LLM Research Papers: The 2025 List (July to December)

Published:Dec 30, 2025 12:15
1 min read
Sebastian Raschka

Analysis

The article announces a list of research papers on Large Language Models (LLMs) to be published between July and December 2025. It mentions that the author previously shared a similar list with paid subscribers.
Reference

In June, I shared a bonus article with my curated and bookmarked research paper lists to the paid subscribers who make this Substack possible.

Analysis

The news article reports that Zepto, a quick grocery delivery startup based in Bengaluru, has confidentially filed for an Initial Public Offering (IPO) in India, aiming to raise approximately $1.3 billion. The company previously secured $450 million in funding in October 2025, which valued the company at $7 billion. The planned listing is scheduled for the July-September quarter of 2026. This indicates Zepto's ambition to expand its operations and potentially capitalize on the growing quick commerce market in India. The IPO filing suggests a positive outlook for the company and its ability to attract investor interest.
Reference

The listing is planned for the July-September quarter of 2026.

Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:09

Dissecting google/LangExtract - Deep Dive into Locating Extracted Items in Documents with LLMs

Published:Oct 9, 2025 01:46
1 min read
Zenn NLP

Analysis

This article analyzes google/LangExtract, a library released by Google in July 2025, focusing on its ability to identify the location of extracted items within a text using LLMs. It highlights the library's key feature: not just extracting items, but also pinpointing their original positions. The article acknowledges the common challenge in LLM-based extraction: potential inaccuracies in replicating the original text.
Reference

LangExtract is a library released by Google in July 2025 that uses LLMs for item extraction. A key feature is the ability to identify the location of extracted items within the original text.

Education#llm📝 BlogAnalyzed: Dec 25, 2025 15:25

Build Production-Ready LLMs From Scratch Starting on July 12th!

Published:Jun 16, 2025 15:02
1 min read
AI Edge

Analysis

This announcement highlights a course or program focused on building and deploying Large Language Models (LLMs) for production environments. The emphasis on scalability and a 6-week timeframe suggests a practical, hands-on approach. The title is concise and attention-grabbing, targeting individuals or teams looking to implement LLMs in real-world applications. The promise of moving from prototype to production is appealing, as it addresses a common challenge in AI development. However, the announcement lacks specific details about the course content, target audience prerequisites, and the technologies covered. More information would be beneficial for potential participants to assess its suitability.
Reference

From Prototype to Production: Ship Scalable LLM Systems in 6 Weeks

The Dinner Party (July 5, 2022)

Published:Jul 6, 2022 04:12
1 min read
NVIDIA AI Podcast

Analysis

This NVIDIA AI Podcast episode, titled "The Dinner Party," shifts focus from the political fallout of the Roe v. Wade reversal to media analysis. The episode critiques articles from The New York Times, suggesting they aim to manipulate public opinion. The podcast also includes commentary on a profile of individuals deemed "most annoying." The episode promotes the podcast's website for tickets, merchandise, and other content. The analysis suggests a critical perspective on mainstream media narratives and a focus on identifying those perceived as responsible for societal issues.
Reference

Will looks at a trio of pieces from the New York Times that appear to be buttering up the readership to place the blame squarely on those least responsible, plus time well-spent on a profile of the most annoying people on Earth!

Coming Soon: Hell of Presidents with Matt Christman and Chris Wade

Published:Jun 18, 2021 04:07
1 min read
NVIDIA AI Podcast

Analysis

This NVIDIA AI Podcast announcement highlights a new limited series podcast titled "Hell of Presidents" featuring Matt Christman and Chris Wade. The podcast aims to analyze all 46 U.S. Presidents, exploring their rise to power and their actions within the context of historical and material circumstances. The announcement emphasizes the hosts' unique styles: Christman's wit and analysis, and Wade's ability to influence Christman. The podcast will be available exclusively on Stitcher Premium starting July 2nd, with a promotional offer for a free month using the code "HELL".
Reference

All with the signature Christman flair of wit, recall and analysis, and the signature Wade flair of "getting Matt to do things".

Analysis

This article summarizes key developments in machine learning and artificial intelligence from the week of July 22, 2016. It highlights Google's application of machine learning to optimize data center power consumption, NVIDIA's release of a new, high-performance GPU, and a new technique for accelerating the training of Recurrent Neural Networks (RNNs) using Layer Normalization. The article serves as a concise overview of significant advancements in the field, providing links to further information for interested readers. The focus is on practical applications and technical innovations.
Reference

This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence.

Analysis

This article summarizes key developments in machine learning and artificial intelligence from the week of July 15, 2016. It highlights several interesting topics, including a discussion on public datasets, an AI-powered 'Wingman' for the popular game Pokémon Go, a new deep learning application for the iPhone, and Google's research into Wide & Deep learning models. The article also mentions that show notes for the episode can be found at a specific website, providing further details for interested readers. The focus is on presenting a concise overview of the week's most significant advancements in the field.
Reference

Show notes for this episode can be found at twimlai.com/9.

Analysis

This article summarizes key developments in machine learning and artificial intelligence from the week of July 8, 2016. It highlights several interesting topics, including the White House's AI Now workshop, the development of an 'AI BS meter,' research on predatory robots, and an AI capable of writing Python code. The article also mentions acquisitions, financing, and technology updates, indicating a broad overview of the AI landscape. The inclusion of a link to show notes suggests a focus on providing accessible information for a general audience interested in AI.
Reference

This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence.

Analysis

This article from Practical AI summarizes key events in the machine learning and artificial intelligence fields for the week of July 1, 2016. It highlights several significant developments, including the first fatal crash involving Tesla's autopilot system, raising concerns about autonomous vehicle safety. The article also mentions a potential EU law that could restrict machine learning applications, indicating growing regulatory scrutiny of AI. Furthermore, it covers the CVPR conference, a major event in computer vision research, and other noteworthy topics such as AI startups and chatbot projects, providing a broad overview of the AI landscape at the time.

Key Takeaways

Reference

This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence.

Business#Hiring👥 CommunityAnalyzed: Jan 10, 2026 17:37

Analyzing Hiring Trends from Hacker News (July 2015)

Published:Jul 1, 2015 15:02
1 min read
Hacker News

Analysis

This article's context, a Hacker News thread, provides a snapshot of tech hiring in July 2015. While informative about that period, its relevance to current AI trends is limited without further analysis connecting historical hiring with present-day AI.
Reference

The context is a Hacker News 'Who is hiring?' thread from July 2015.

Business#Hiring👥 CommunityAnalyzed: Jan 10, 2026 17:43

Analyzing the 2014 Hacker News Hiring Trends

Published:Jul 1, 2014 13:10
1 min read
Hacker News

Analysis

This article, though dated, provides a valuable glimpse into the tech hiring landscape of 2014. Analyzing such historical data can offer insights into the evolution of skills in demand and technology adoption trends.
Reference

The article is a 'Who is Hiring?' thread on Hacker News from July 2014.

Business#hiring👥 CommunityAnalyzed: Jan 10, 2026 17:45

Hacker News: July 2013 Hiring Trends & Insights

Published:Jul 1, 2013 12:43
1 min read
Hacker News

Analysis

This Hacker News thread provides a snapshot of the tech job market in July 2013, offering valuable context on hiring needs and company landscapes at that time. Analyzing such historical data can illuminate how the industry has evolved in terms of skills, technologies, and company focus.
Reference

The context is a 'Who is hiring?' thread, a recurring post on Hacker News.