Function calling in LLMs allows AI agents to interpret user intent and interact with external systems by generating structured outputs that describe function calls without executing them directly. This capability enhances LLMs' ability to perform tasks such as shopping assistance by identifying user needs and invoking appropriate actions through structured data formats.

Implementing guardrails around containerized large language models (LLMs) on Kubernetes is crucial for ensuring security and compliance. This involves setting resource limits, using namespaces for isolation, and implementing access controls to mitigate risks associated with running LLMs in a production environment. Properly configured guardrails can help organizations leverage the power of LLMs while maintaining operational integrity.

OpenElections has been using Google's Gemini LLM to convert image PDFs of election results into CSV files, overcoming the limitations of traditional data entry and commercial OCR software. The system has shown high accuracy in processing complex layouts from various counties, allowing for efficient data extraction while maintaining the need for manual verification. Despite challenges with large documents, the use of LLMs has significantly accelerated the data conversion process.

The conversation explores the role of Large Language Models (LLMs) in software development, emphasizing the distinction between essential and accidental complexity. It argues that while LLMs can reduce accidental complexity, the true essence of programming involves iterative design, naming conventions, and the continuous evolution of programming language within a collaborative environment. The importance of understanding the nature of coding and the risks of over-reliance on LLMs for upfront design decisions are also highlighted.

Mdream is a highly optimized HTML to Markdown converter specifically designed for enhancing AI discoverability and generating LLM context. It offers various packages for crawling websites, creating LLM artifacts, and is built to run efficiently across different environments. With features like a minimal footprint and extensibility, Mdream streamlines the process of converting web content into usable formats for AI applications.

The article explores how large language models (LLMs) perceive and interpret the world, focusing on their ability to understand context, generate responses, and the limitations of their comprehension. It discusses the implications of LLMs' interpretations for various applications and the challenges in aligning them with human understanding.

The article discusses the limitations of generic large language models (LLMs) in providing actionable insights and highlights how Spark, a more specialized tool, enables users to translate their words into effective movements or actions. By focusing on context and user intention, Spark enhances the user experience beyond mere text generation.

Semlib is a Python library that facilitates the construction of data processing and analysis pipelines using large language models (LLMs), employing natural language descriptions instead of traditional code. It enhances data processing quality, feasibility, latency, cost efficiency, security, and flexibility by breaking down complex tasks into simpler, manageable subtasks. The library combines functional programming principles with the capabilities of LLMs to optimize data handling and improve results.

LLMs are shifting the focus for homepage writing from brand equity to specific features, as users increasingly search for precise functionalities rather than general outcomes. While this trend may diminish the perceived importance of brand in initial searches, the overall experience and emotional connection remain critical in the purchasing process, suggesting that brands need to adapt their messaging to emphasize features without neglecting their identity.

Context engineering is crucial for agents utilizing large language models (LLMs) to effectively manage their limited context windows. It involves strategies such as writing, selecting, compressing, and isolating context to ensure agents can perform tasks efficiently without overwhelming their processing capabilities. The article discusses common challenges and approaches in context management for long-running tasks and tool interactions.

The author evaluates various large language models (LLMs) for personal use, focusing on practical tasks related to programming and sysadmin queries. By using real prompts from their bash history, they assess models based on cost, speed, and quality of responses, revealing insights about the effectiveness of open versus closed models and the role of reasoning in generating answers.

Generative AI, particularly Large Language Models (LLMs), is much cheaper to operate than commonly believed, with costs decreasing significantly in recent years. A comparison of LLM pricing to web search APIs shows that LLMs can be an order of magnitude less expensive, challenging misconceptions about their operational costs and sustainability. The article aims to clarify these points for those who hold the opposite view.

Large Language Models (LLMs) and multimodal AI are revolutionizing recommendation and search systems by shifting from traditional ID-based methods to deep semantic understanding, which addresses challenges like cold-start and long-tail issues. Key advancements include the introduction of Semantic IDs for better content representation, generative retrieval models for richer recommendations, and the integration of multimodal data to enhance user experience and transparency. This transformation allows for more personalized and efficient content discovery, leveraging LLMs to actively generate data and improve system performance.

Sutton critiques the prevalent approach in LLM development, arguing that they are heavily influenced by human biases and lack the "bitter lesson pilled" quality that would allow them to learn independently from experience. He contrasts LLMs with animal learning, emphasizing the importance of intrinsic motivation and continuous learning, while suggesting that current AI systems may be more akin to engineered "ghosts" rather than true intelligent entities. The discussion highlights the need for inspiration from animal intelligence to innovate beyond current methods.

The article discusses the integration of multimodal large language models (LLMs) into various applications, highlighting their ability to process and generate content across different modalities such as text, images, and audio. It emphasizes the advancements in model architectures and training techniques that enhance the performance and versatility of these models in real-world scenarios. Additionally, the piece explores potential use cases and the impact of multimodal capabilities on industries and user interactions.

ShinkaEvolve is an innovative evolutionary code optimization framework that utilizes large language models (LLMs) to discover new algorithms with unprecedented sample efficiency. It has achieved state-of-the-art solutions in various domains, including Circle Packing and agent design, by significantly reducing the number of samples needed for effective program evolution. The framework is open-sourced to empower researchers and engineers in their scientific discoveries and development efforts.

Professor Paul Groth from the University of Amsterdam discusses his research on knowledge graphs and data engineering, addressing the evolution of data provenance and lineage, challenges in data integration, and the transformative impact of large language models (LLMs) on the field. He emphasizes the importance of human-AI collaboration and shares insights from his work at the intelligent data engineering lab, shedding light on the interplay between industry and academia in advancing data practices.

LLMs utilize content from platforms like Reddit and LinkedIn to make recommendations, highlighting the importance of social media interactions in search optimization. Effective strategies include creating engaging lists or reviews, using AI for post editing, encouraging customer feedback, and focusing on comment engagement to enhance visibility in LLM outputs. Adapting to these new dynamics is crucial for businesses aiming to improve their search presence.

After struggling with data entry in his game development project, the author discovered that reconstructing game assets as code rather than using the Unity editor significantly improved his workflow. By leveraging LLMs to assist in generating C# code from structured data, he was able to streamline the process and avoid burnout, ultimately allowing him to focus on problem analysis and solution development.

The article explores how Kubernetes is adapting to support the demands of emerging technologies like 6G networks, large language models (LLMs), and deep space applications. It highlights the scalability and flexibility of Kubernetes in managing complex workloads and ensuring efficient resource allocation. The discussion includes insights into the future implications of these advancements on cloud-native environments.

The current landscape of semantic layers in data management is fragmented, with numerous competing standards leading to forced compromises, lock-in, and inefficient APIs. As LLMs evolve, they may redefine the use of semantic layers, promoting more flexible applications despite the existing challenges of interoperability and profit-driven designs among vendors. A push for a universal standard remains hindered by the lack of incentives to prioritize compatibility across different data systems.

The article discusses the potential of large language models (LLMs) when integrated into systems with other computational tools, highlighting that their true power emerges when combined with technologies like databases and SMT solvers. It emphasizes that LLMs enhance system efficiency and capabilities rather than functioning effectively in isolation, aligning with Rich Sutton's concept of leveraging computation for successful AI development. The author argues that systems composed of LLMs and other tools can tackle complex reasoning tasks more effectively than LLMs alone.

JUDE is LinkedIn's advanced platform for generating high-quality embeddings for job recommendations, utilizing fine-tuned large language models (LLMs) to enhance the accuracy of its recommendation system. The platform addresses deployment challenges and optimizes operational efficiency by leveraging proprietary data and innovative architectural designs, enabling better job-member matching through sophisticated representation learning.

Prompt bloat can significantly hinder the quality of outputs generated by large language models (LLMs) due to irrelevant or excessive information. This article explores the impact of prompt length and extraneous details on LLM performance, highlighting the need for effective techniques to optimize prompts for better accuracy and relevance.

The article discusses practical lessons for effectively working with large language models (LLMs), emphasizing the importance of understanding their limitations and capabilities. It provides insights into optimizing interactions with LLMs to enhance their utility in various applications.

The article discusses the expected advancements and state of large language models (LLMs) by the year 2025, highlighting trends in AI development, potential applications, and ethical considerations. It emphasizes the importance of responsible AI usage as LLMs become more integrated into various sectors, including education and business.

The article discusses the limitations of large language models (LLMs) in relation to understanding and representing the world as true models. It argues that while LLMs can generate text that appears knowledgeable, they lack the genuine comprehension and internal modeling of reality that is necessary for deeper understanding. Furthermore, it contrasts LLMs with more robust cognitive frameworks that incorporate real-world knowledge and reasoning.

The article discusses the evolution of search technologies in the era dominated by large language models (LLMs), highlighting how these AI systems are reshaping information retrieval and user interaction. It explores the advantages of LLMs over traditional search methods, particularly in providing contextually relevant responses and personalized experiences. The implications for both consumers and businesses in adapting to these advancements are also examined.

The article delves into the concepts of focus and context within the realm of large language models (LLMs), discussing how these models interpret and prioritize information. It emphasizes the importance of balancing detailed understanding with broader contextual awareness to enhance the effectiveness of LLMs in various applications.

The author critiques the anthropomorphization of large language models (LLMs), arguing that they should be understood purely as mathematical functions rather than sentient entities with human-like qualities. They emphasize the importance of recognizing LLMs as tools for generating sequences of text based on learned probabilities, rather than attributing ethical or conscious characteristics to them, which complicates discussions around AI safety and alignment.

Recipes are likened to programming languages, where ingredients and actions serve as inputs and instructions, respectively. Large language models (LLMs) simplify the process of creating compilers for various domains, empowering individuals to experiment with structured systems in cooking, fitness, business, and more. This shift democratizes the ability to translate intent into action, making complex processes more accessible to everyone.

The HateBenchSet is a dataset designed to benchmark hate speech detectors on content generated by various large language models (LLMs). It comprises 7,838 samples across 34 identity groups, including 3,641 labeled as hate and 4,197 as non-hate, with careful annotation performed by the authors to avoid exposing human subjects to harmful content. The dataset aims to facilitate research into LLM-driven hate campaigns and includes predictions from several hate speech detectors.

Orkes enables organizations to transform their workflows into agentic experiences, integrating advanced technologies like LLMs and vector databases to enhance decision-making and operational efficiency. With robust security, compliance features, and a focus on developer agility, Orkes supports a wide range of applications from customer support automation to real-time data analysis. Users have reported significant improvements in productivity and reliability by migrating workflows to Orkes Cloud.

MCP (Model Context Protocol) has gained significant attention as a standard for LLMs to interact with the world, but the author criticizes its implementation for lacking mature engineering practices, poor documentation, and questionable design choices. The article argues that the transport methods, particularly HTTP and SSE, are problematic and suggests that a more straightforward approach using WebSockets would be preferable.

Deploying Large Language Models (LLMs) requires careful consideration of challenges such as environment consistency, repeatable processes, and auditing for compliance. Docker provides a solid foundation for these deployments, while Octopus Deploy enhances reliability through automation, visibility, and management capabilities. This approach empowers DevOps teams to ensure efficient and compliant deployment of LLMs across various environments.

The article discusses how to utilize the HTTP Accept header to serve Markdown format instead of HTML to language learning models (LLMs). It emphasizes the advantages of providing content in Markdown, which can result in better processing and understanding by these models. Practical examples and implementation tips are provided to facilitate this approach.

The article discusses how tool calling operates within large language models (LLMs), explaining the mechanisms behind their ability to invoke external tools and services during interactions. It highlights the importance of this functionality in enhancing the capabilities of LLMs and the user experience.

Frontier LLMs like Gemini 2.5 PRO significantly enhance programming capabilities by aiding in bug elimination, rapid prototyping, and collaborative design. However, to maximize their benefits, programmers must maintain control, provide extensive context, and engage in an interactive process rather than relying on LLMs to code independently. As AI evolves, the relationship between human developers and LLMs will continue to be crucial for producing high-quality code.

Callstack has released a new React Native library called react-native-ai that allows on-device execution of large language models (LLMs) using the MLC LLM Engine. The library simplifies integration with the Vercel AI SDK, enabling developers to run AI models efficiently on mobile apps while addressing various setup challenges. Future plans include enhancing the library's capabilities and providing more resources for developers.

The article discusses the ongoing challenges and lessons in the development and application of large language models (LLMs), emphasizing the gaps in understanding and ethical considerations that still need to be addressed. It highlights the importance of learning from past mistakes in AI development to improve future implementations and ensure responsible use.

Current approaches to securing large language models (LLMs) from malicious inputs remain inadequate, highlighting significant vulnerabilities in their design and deployment. The article discusses the ongoing challenges and the need for improved strategies to mitigate risks associated with harmful prompts.

The blog post discusses the potential of integrating AI-powered share buttons, specifically through CiteMet, as a growth hack for applications utilizing large language models (LLMs). It emphasizes how these tools can enhance user engagement and broaden reach by simplifying content sharing across platforms. The article also highlights the importance of innovative features in driving user adoption and retention.

After two years as CTO at Carta, the author reflects on key lessons learned, including the importance of detail-oriented leadership, refining engineering strategy, and effective communication within teams. They also discuss the challenges and opportunities presented by adopting new technologies like LLMs, as well as insights into managing engineering costs and improving software quality. The author expresses gratitude for their colleagues and the experience gained during their tenure.

Peter Naur's essay argues that large language models (LLMs) cannot replace human programmers because they lack the ability to build theories, a crucial aspect of programming. Naur emphasizes that programming involves the development of a deep understanding of the system, which LLMs, as mere consumers of textual data, cannot achieve. Consequently, to believe LLMs can effectively write software undermines the complexity and theoretical nature of programming work.

The article discusses the transformative potential of Large Language Models (LLMs) in software development, particularly in generating automated black box tests. By decoupling the generation of code and tests, LLMs can provide unbiased evaluations based solely on input-output specifications, leading to more effective and efficient testing processes.

Large Language Models (LLMs) are transforming Site Reliability Engineering (SRE) in cloud-native infrastructure by enhancing real-time operational capabilities, assisting in failure diagnosis, policy recommendations, and smart remediation. As AI-native solutions emerge, they enable SREs to manage complex environments more efficiently, potentially allowing fewer engineers to handle a larger number of workloads without sacrificing performance or resilience. Embracing these advancements could significantly reduce operational overhead and improve resource efficiency in modern Kubernetes management.

The article discusses the implications of large language models (LLMs) on software development, highlighting the varying effectiveness of their use and the potential risks associated with their integration. It raises concerns about the possible future of programming jobs, the inevitable economic bubble surrounding AI technology, and the inherent unpredictability of LLM outputs. Additionally, it emphasizes the importance of understanding workflows and experimenting with LLMs while being cautious of their limitations and security vulnerabilities.

Fine-tuning large language models (LLMs) enhances their performance for specific tasks, making them more effective and aligned with user needs. The article discusses the importance of fine-tuning LLMs and provides a guide on how to get started, including selecting the right datasets and tools.

A developer shares insights from creating a VS Code extension called terminal-editor, which integrates a shell-like interface within the editor. The article emphasizes the importance of structured planning and testing strategies when working with large language models (LLMs) to enhance coding efficiency and reduce errors. It highlights the need for an effective feedback loop and the limitations of LLMs in maintaining code quality and handling complex problems.

The article discusses the need for new users of large language models (LLMs) to utilize different database systems tailored for their specific requirements. It emphasizes that traditional databases may not suffice for the unique challenges posed by LLMs, necessitating innovative approaches to data storage and retrieval. The author advocates for the exploration of alternative database technologies to enhance performance and efficiency in LLM applications.

The author reflects on their evolving use of LLMs in product design, highlighting a shift towards a more integrated design-to-code workflow utilizing tools like Figma, Cursor, and Gemini. The focus has moved from building to generating meaningful ideas, emphasizing the importance of context in maximizing tool effectiveness and speeding up prototyping and iteration cycles.

After years as a software engineer, the author created two card games, Truco and Escoba, using Go. The first game took three months to develop without LLMs, while the second game was completed in just three days with LLM assistance, showcasing the drastic improvement in development efficiency. The article also offers a guide on how to create similar games using Go and WebAssembly.

The article explores the evolution of AI system development from Large Language Models (LLMs) to Retrieval Augmented Generation (RAG), workflows, and AI Agents, using a resume-screening application as a case study. It emphasizes the importance of selecting the appropriate complexity for AI systems, focusing on reliability and the specific needs of the task rather than opting for advanced AI agents in every scenario.

The article explores the advancements in large language models (LLMs) related to geolocation tasks, analyzing their accuracy and effectiveness compared to previous models. It discusses the implications of these improvements for various applications, particularly in the context of open-source intelligence and digital forensics.

The article discusses the relationship between sampling and structured outputs in language models, emphasizing their impact on token selection and data formatting. It details various sampling techniques and transformations used in the Ollama framework, as well as the significance of structured outputs in converting unstructured data into coherent formats. Future developments in model capabilities are also explored.

Hype surrounding LLMs (Large Language Models) often overshadows their actual capabilities, leading to misconceptions and inflated expectations. The article discusses the cyclical nature of technological hype, emphasizing the need for grounded conversations about these innovations while acknowledging their potential and pitfalls.

The author advocates for using large language models (LLMs) in UI testing, highlighting their potential advantages over traditional methods, such as generating tests in natural language and executing them effectively. While acknowledging challenges like non-determinism and latency, the author believes that LLMs can enhance testing efficiency and allow human testers to focus on more complex tasks. Overall, LLMs could revolutionize the approach to UI testing by enabling more innovative testing strategies and improving accessibility.

Wynter has developed a dedicated page for AI agents and LLMs to easily access verified information about their products, emphasizing the importance of accurate representation in AI-generated content. Despite its potential benefits, initial tests indicate that LLMs may not effectively reference this page, suggesting that traditional SEO practices remain vital for visibility and understanding. The article highlights best practices for creating such a page to enhance AI interactions and brand awareness.

The article discusses the optimal input data formats for large language models (LLMs), highlighting the importance of structured data in enhancing model performance and accuracy. It evaluates various formats and their implications on data processing efficiency and model training.

The article discusses the experiences of the Honeycomb team while building applications with large language models (LLMs). It highlights the challenges faced and the innovative solutions developed to leverage LLMs effectively in their projects. Insights into the practical applications and potential of LLMs in software development are also shared.

The article discusses the potential of large language models (LLMs) to function as compilers, transforming natural language into executable code. It explores the implications of this capability for software development, highlighting the efficiency and creativity LLMs can bring to programming tasks. The piece also examines the challenges and limitations of using LLMs in this role.

AutoRound is an innovative quantization tool developed by Intel for efficient deployment of large language and vision-language models. It utilizes weight-only post-training quantization to achieve high accuracy at low-bit widths, while remaining fast and compatible with various models and devices. With features like mixed-bit tuning and minimal resource requirements, AutoRound provides a practical solution for optimizing AI model performance.

LLMs reflect the skill level of their operators, emphasizing that experience alone does not guarantee competency in the era of AI. Companies face challenges in identifying skilled operators, highlighting flaws in the traditional interviewing process.

Character.AI has open-sourced pipeling-sft, a scalable framework designed for fine-tuning large-scale MoE LLMs like DeepSeek V3. This framework addresses challenges in training efficiency and stability, integrating multi-level parallelism and supporting various precision formats, while facilitating seamless HuggingFace integration for researchers.

The article discusses the implications of integrating large language models (LLMs) with the Elixir programming language, evaluating whether this combination could lead to significant advancements or potential drawbacks in software development. It highlights both the opportunities for innovation and the risks that may arise from over-reliance on AI technologies.

The article discusses the burgeoning grey market for American large language models (LLMs) in China, highlighting how these models are being accessed and utilized despite regulatory restrictions. It examines the implications of this market for both technology transfer and the competitive landscape of AI development globally.

The author shares insights from a month of experimenting with AI tools for software development, highlighting the limitations of large language models (LLMs) in producing production-ready code and their dependency on well-structured codebases. They discuss the challenges of integrating LLMs into workflows, the instability of AI products, and their mixed results across programming languages, emphasizing that while LLMs can aid in standard tasks, they struggle with unique or complex requirements.

Non-programming leaders starting to contribute to code with LLMs can increase iteration speed and introduce diverse perspectives, but this also risks compromising the implicit architecture of the codebase. As more non-engineers make changes, maintaining design intent and code maintainability becomes a challenge, requiring developers to adapt their roles to focus on architectural oversight. Despite these risks, democratizing coding could lead to better solutions as more perspectives are included in the development process.

The article discusses the advantages of using "boring technology" like LaTeX in conjunction with large language models (LLMs). It highlights how LLMs enhance the user experience with LaTeX by simplifying the learning process, debugging, and automating tedious tasks, while contrasting it with newer, less familiar technologies like Typst. The author expresses a preference for LaTeX due to its extensive resources and community support.

The article discusses the integration of Hierarchical Task Network (HTN) planning with large language models (LLMs) to create a more effective planning system for product development. It highlights the advantages of combining structured, human-defined planning with the creative flexibility of LLMs, illustrated through the author's project, Rock-n-Roll, which helps users transform ideas into actionable plans.

The article discusses the evolution of Infrastructure as Code (IaC) and argues that modern Large Language Models (LLMs) can generate infrastructure requirements directly from application code, thereby eliminating the cognitive overhead associated with traditional IaC practices. It highlights the shift towards expressing infrastructure needs within application logic rather than through separate configuration files.

The article discusses the security vulnerabilities of local large language models (LLMs), particularly gpt-oss-20b, which are more easily tricked by attackers compared to larger frontier models. It details two types of attacks: one that plants hidden backdoors disguised as harmless features, and another that executes malicious code during the coding process by exploiting cognitive overload. The research highlights the significant risks of using local LLMs in coding environments.

The article discusses the concept of context engineering in the realm of large language models (LLMs) and emphasizes the often-overlooked potential of hyperlinks in managing context efficiently. It highlights how hyperlinks can facilitate incremental learning and exploration, drawing parallels between human learning processes and how LLMs can utilize linked data for more effective interaction with information. The author advocates for implementing a link-based context system to enhance the functionality of LLMs and APIs.

The Free Software Foundation (FSF) is exploring the implications of large language models (LLMs) on free software licensing, particularly regarding copyrightability and potential licensing issues of LLM-generated code. In a recent session, FSF representatives discussed the challenges posed by non-free models and the necessity for metadata and transparency in code submissions. The FSF is currently surveying free-software projects to better understand their positions on LLM output and is considering updates to the Free Software Definition rather than a new GPL version.