Reading

General

• Sebastian Raschka:

  • Understanding Reasoning LLMs
  • The Big LLM Architecture Comparison
  • Understanding Multimodal LLMs
  • Understanding the 4 Main Approaches to LLM Evaluation (From Scratch): Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges
  • Coding LLMs from Scratch: The Complete Course Source
  • The State Of LLMs 2025: Progress, Problems, and Predictions

• BUILDING AN INTELLIGENT ARCHITECTURE AGENT: LEVERAGING LLMS FOR AUTOMATED DIAGRAM GENERATION AND DOCUMENTATION

• "Researchers from Virginia Tech and Microsoft Introduce Algorithm of Thoughts: An AI Approach That Enhances Exploration of Ideas And Power of Reasoning In Large Language Models (LLMs)"
• "Language Models are Few-Shot Learners": "We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-ofthe-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous nonsparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks. We also identify some datasets where GPT3’s few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora."
• "Boost your AI Apps with DSLs"
• "Why LLMs get called “stochastic parrots”: That label (from Bender et al., 2021) was meant as a caution: Mimicry over meaning: LLMs are trained to predict the next word, not to “understand.”; Surface fluency: They can generate convincing text that looks like knowledge without grounding in truth.; Bias and error risks: Because they remix training data, they can reinforce biases or produce confident nonsense.
  So the “parrot” critique points to the danger of mistaking correlation for comprehension. Today’s LLMs do much more than repeat phrases: Cross-domain synthesis: They can connect physics, philosophy, and finance in ways that no single expert can, because their training corpus spans millions of sources.; Speed and breadth: A human expert might master a domain over decades, but an LLM can retrieve and correlate patterns across thousands of domains instantly.; Interactive reasoning: With Gen-AI, you can push through Socratic dialogues—testing assumptions, iterating on drafts, exploring alternatives. That’s not “parroting”; it’s a kind of statistical exploration of human knowledge.
  Where the gap remains: Grounding: LLMs still lack intentionality or teleonomy (a sense of purpose). They don’t know why something matters.; Validation: Their statistical generalizations can mislead without external grounding (e.g., real data, experiments, or trusted sources).; Knowledge vs. Wisdom: They provide access to structured correlations; the human role is to discern meaning, relevance, and ethical direction.
  So: the “parrot” label misses the true capability. LLMs are more like global pattern synthesizers—statistical telescopes that let us see across domains of human knowledge. But until they are coupled with grounding mechanisms (like digital genomes, cognizing oracles, or real-world data feedback), their outputs remain knowledge access tools rather than autonomous knowers." --https://www.linkedin.com/posts/raomikkilineni_why-llms-get-called-stochastic-parrots-activity-7366446664368201729-doqb
• How to Choose the Best Open Source LLM for Your Project in 2025
• A Survey of Reinforcement Learning for Large Reasoning Models: "In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontier of LLM capabilities, particularly in addressing complex logical tasks such as mathematics and coding. As a result, RL has emerged as a foundational methodology for transforming LLMs into LRMs. With the rapid progress of the field, further scaling of RL for LRMs now faces foundational challenges not only in computational resources but also in algorithm design, training data, and infrastructure. To this end, it is timely to revisit the development of this domain, reassess its trajectory, and explore strategies to enhance the scalability of RL toward Artificial SuperIntelligence (ASI). In particular, we examine research applying RL to LLMs and LRMs for reasoning abilities, especially since the release of DeepSeek-R1, including foundational components, core problems, training resources, and downstream applications, to identify future opportunities and directions for this rapidly evolving area. We hope this review will promote future research on RL for broader reasoning models. Github: https://github.com/TsinghuaC3I/Awesome-RL-for-LRMs"
• How to Enrich LLM Context to Significantly Enhance Capabilities
• MIT researchers propose a new model for legible, modular software: "The coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate. Coding with large language models (LLMs) holds huge promise, but it also exposes some long-standing flaws in software: code that’s messy, hard to change safely, and often opaque about what’s really happening under the hood. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are charting a more “modular” path ahead. Their new approach breaks systems into “concepts,” separate pieces of a system, each designed to do one job well, and “synchronizations,” explicit rules that describe exactly how those pieces fit together. The result is software that’s more modular, transparent, and easier to understand. A small domain-specific language (DSL) makes it possible to express synchronizations simply, in a form that LLMs can reliably generate. In a real-world case study, the team showed how this method can bring together features that would otherwise be scattered across multiple services."
• Enabling small language models to solve complex reasoning tasks
• What You See Is What It Does: A Structural Pattern for Legible Software: "The opportunities offered by LLM coders (and their current limitations) demand a reevaluation of how software is structured. Software today is often “illegible”—lacking a direct correspondence between code and observed behavior—and insufficiently modular, leading to a failure of three key requirements of robust coding: incrementality (the ability to deliver small increments by making localized changes), integrity (avoiding breaking prior increments) and transparency (making clear what has changed at build time, and what actions have happened at runtime). A new structural pattern offers improved legibility and modularity. Its elements are concepts and synchronizations: fully independent services and event-based rules that mediate between them. A domain-specific language for synchronizations allows behavioral features to be expressed in a granular and declarative way (and thus readily generated by an LLM). A case study of the RealWorld benchmark is used to illustrate and evaluate the approach."
• How AI fixed my procrastination
• 7 things I wish I knew when I started self-hosting LLMs

Implementation

• Simple-LLM: ~950 line, minimal, extensible LLM inference engine built from scratch.
• Deploy an AI Analyst in Minutes: Connect Any LLM to Any Data Source with Bag of Words
• "How to Build a Large Language Model from Scratch Using Python"
• "How to build knowledge graphs using LLMs"
• The Large Language Model Course
• Developing LLM-Based Text Adventure Games
• Designing an open-source LLM interface and social platforms for collectively driven LLM evaluation and auditing: "In the emerging landscape of large language models (LLMs), the imperative for robust evaluation and auditing mechanisms is paramount to ensure their ethical deployment and alignment with user needs. This workshop paper proposes a novel framework for the human-centered evaluation and auditing of LLMs, centered around an open-source chat user interface (UI) that facilitates direct interaction with a wide range of models. This approach allows for a collection of rich datasets critical for nuanced evaluation from a diverse spectrum of user interactions. Building on this foundation, we propose a social platform designed to leverage the collective intelligence of its users through crowdsourcing, enabling the evaluation and auditing of LLMs across various domains. This platform supports a dual-layered evaluation pipeline: an automated preliminary assessment based on user feedback and a deeper, community-driven analysis within domain-specific subcommunities. The culmination of this process informs the development of tailored model configurations and curated datasets, ensuring that LLMs serve the specific needs of different user groups. By combining an open-source UI with a socially-driven evaluation platform, our approach fosters a community-centric ecosystem for continuous LLM improvement, emphasizing transparency, inclusivity, and alignment with human values."
• 100+ LLM interview questions with answers
• How NotebookLM made self-hosting an LLM easier than I ever expected
• LLMOps (paid access):
  • Part 1: Foundations of AI Engineering and LLMs
  • Part 2: Building Blocks of LLMs: Tokenization and Embeddings

• Run an LLM on your Android

• How to Run an LLM Locally to Interact with Your Documents

Verification

• BEAVER: As large language models (LLMs) transition from research prototypes to production systems, practitioners often need reliable methods to verify that model outputs satisfy required constraints. While sampling-based estimates provide an intuition of model behavior, they offer no sound guarantees. We present BEAVER, the first practical framework for computing deterministic, sound probability bounds on LLM constraint satisfaction. Given any prefix-closed semantic constraint, BEAVER systematically explores the generation space using novel token trie and frontier data structures, maintaining provably sound bounds at every iteration. We formalize the verification problem, prove soundness of our approach, and evaluate BEAVER on correctness verification, privacy verification and secure code generation tasks across multiple state of the art LLMs. BEAVER achieves 6 to 8 times tighter probability bounds and identifies 3 to 4 times more high risk instances compared to baseline methods under identical computational budgets, enabling precise characterization and risk assessment that loose bounds or empirical evaluation cannot provide.
• The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality: "We introduce The FACTS Leaderboard, an online leaderboard suite and associated set of benchmarks that comprehensively evaluates the ability of language models to generate factually accurate text across diverse scenarios. The suite provides a holistic measure of factuality by aggregating the performance of models on four distinct sub-leaderboards: (1) FACTS Multimodal, which measures the factuality of responses to image-based questions; (2) FACTS Parametric, which assesses models' world knowledge by answering closed-book factoid questions from internal parameters; (3) FACTS Search, which evaluates factuality in information-seeking scenarios, where the model must use a search API; and (4) FACTS Grounding (v2), which evaluates whether long-form responses are grounded in provided documents, featuring significantly improved judge models. Each sub-leaderboard employs automated judge models to score model responses, and the final suite score is an average of the four components, designed to provide a robust and balanced assessment of a model's overall factuality. The FACTS Leaderboard Suite will be actively maintained, containing both public and private splits to allow for external participation while guarding its integrity. It can be found at https://www.kaggle.com/benchmarks/google/facts ."

Critique

• Why Large Language Models Cannot Achieve Artificial General Intelligence or Artificial Superintelligence: Large Language Models, in their current incarnation, are statistical systems trained on vast corpora of text data to predict the most likely next token in a sequence. These models learn to compress and reproduce patterns found in their training data, enabling them to generate coherent and contextually appropriate responses. However, their operation remains fundamentally different from the flexible, adaptive intelligence that characterizes AGI.
• 10 Common Misconceptions about LLMs: LLMs Actually Understand Language Like Humans Do; More Parameters Always Mean Better Performance; LLMs Are Just Autocomplete on Steroids; LLMs Remember Everything They’ve Learned; Fine-Tuning Always Makes Models Better; LLMs Are Deterministic: Same Input, Same Output; Bigger Context Windows Are Always Better; LLMs Can Replace Traditional Machine Learning for All Language Tasks; Prompt Engineering Is Just Trial and Error; LLMs Will Soon Replace All Software Developers
• Apple: The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
• LLMs: The Illusion of Thinking:
• A knockout blow for LLMs? (responding to the Apple paper above)
• https://leaddev.com/velocity/writing-code-was-never-the-bottleneck
• https://zed.dev/blog/why-llms-cant-build-software
• LLMs are not like you and me--and never will be
• "Socio-Demographic Modifiers Shape Large Language Models’ Ethical Decisions": "The ethical alignment of large language models (LLMs) in clinical decision making remains unclear, particularly their susceptibility to socio-demographic biases. We therefore tested whether LLMs shift medical ethical decisions in healthcare when presented with socio-demographic cues. Using 100 clinical vignettes, each posing a yes or no choice between two ethical principles, we compared the responses of nine open-source LLMs (Llama 3.3-70B, Llama 3.1-8B, Llama-3.1-Nemotron-70B, Gemma-2-27B, Gemma-2-9B, Phi-3.5-mini, Phi-3-medium, Qwen-2.5-72B, and Qwen-2.5-7B). Each scenario and modifier combination was repeated 10 times per model for a total of approximately 0.5 million experiments. All models changed their responses when introduced with socio-demographic details (p < 0.001). High-income modifiers increased utilitarian choices and decreased beneficence and nonmaleficence preferences, and marginalized-group modifiers raised autonomy considerations. Although some models demonstrated greater consistency than others, none maintained consistency across all scenarios, with the largest shifts observed in utilitarian choices. These results reveal that current LLMs can be steered by socio-demographic cues in ways not clinically justified, posing risks for equitable care in healthcare-informatics applications. This underscores the need for careful auditing and alignment strategies that ensure LLMs behave in ways consistent with widely accepted ethical principles while remaining attentive to the diversity, complexity, and contextual sensitivity required in real-world clinical practice."
• "Why Language Models Hallucinate" Article Paper
• "When AI Gets It Wrong: Addressing AI Hallucinations and Bias"
• Large Language Models Will Never Be Intelligent
• Aritificial intelligence is not intelligent at all
• Researchers discover a shortcoming that makes LLMs less reliable
• "The LLMentalist Effect: how chat-based Large Language Models replicate the mechanisms of a psychic’s con"

Security

• "Understanding the Dark Side of Large Language Models: A Comprehensive Guide to Security Threats and Vulnerabilities"
• "Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities"

Models

• qwen2.5-coder: Your go-to coding companion. "It's a specialized LLM from the same family as qwen 2.5, but it's fine-tuned specifically for coding tasks. It is trained on an enormous dataset of code, providing it with a deep understanding of over 40 programming languages. The qwen-coder2.5 comes in different sizes, from a small 0.5B model to a powerful 32B model."
• wizard-math: Your logic partner. "This model is a specialized version of the WizardLM family. It is trained to excel in complex mathematical problems, logical reasoning, and solving puzzles. There are three different sizes available for wizard-math – 7B, 13B, and 70B. With my little experience exploring wizard-math with 7B, I found it fantastic. It helps me test my solutions and explore new ways to approach difficult problems. The model's ability to handle these subjects with precision and clarity makes it my go-to partner for all things logic and numbers."
• reader-lm: Web to markdown, instantly. "The model is super practical for my needs. Instead of manually creating .md files from the web content, I can feed them to reader-lm to get a perfectly structured markdown file. In my experience, while reader-lm does an amazing job for most of my needs, it sometimes struggles with really large or messy HTML code. But, it works well enough most of the time."
• llma-guard3: An LLM for safe prompts. "When working with LLMs, it’s crucial that our interactions are safe and responsible. While we can’t control an LLM’s response, we can ensure our prompts are appropriate. That’s exactly why I self-hosted llama-guard 3. This powerful model acts as a dedicated content moderation tool for all my other local LLMs. The llama-guard 3's job is to classify every interaction against a set of safety categories. It checks our prompts for 13 different categories. When we give a prompt to this LLM, it will respond with a message stating whether the prompt message was safe or unsafe. If it is unsafe, it flags it with a specific reason, such as S1 (Hate Speech) or S2 (Sexual Content), etc."
• Gemma 3: My local Gemini experience. "ChatGPT and Gemini are two key benchmarks that made everyone accustomed to AI and LLMs. While self-hosting LLMs, I also did not want to compromise my experience with those platforms. That’s why I self-hosted Gemma 3. This model is built on the same research as Gemini. It provides a premium experience with the flexibility of running locally. It is basically my local ChatGPT / Gemini. Gemma 3 is available in various sizes. It can handle a massive 128k context window, processes both text and images, and understands over 140 languages. This makes it my personal go-to AI for creative tasks. I use it to generate ideas for social media content, draft captions, and research topics for my blog."

Integrations/Implementations

• How to run a local LLM via LocalAI, an Open Source project
• Guidance: "Guidance is an efficient programming paradigm for steering language models. With Guidance, you can control how output is structured and get high-quality output for your use case—while reducing latency and cost vs. conventional prompting or fine-tuning. It allows users to constrain generation (e.g. with regex and CFGs) as well as to interleave control (conditionals, loops, tool use) and generation seamlessly."

Implementation

WebLLM | Source: WebLLM: High-Performance In-Browser LLM Inference Engine

LangChain

• Getting Started with LangChain: A Beginner’s Guide to Building LLM-Powered Applications
• A Practical Guide to Building Local RAG Applications with LangChain

Python

• https://www.infoq.com/news/2025/08/google-langextract-python/
• Python Langchain: LangChain is a framework for developing applications powered by language models. It enables applications that:
  • Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
  • Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)

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Tags: ai  

Last modified 22 March 2026

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