The U.S. government shut down Anthropic's most powerful AI model, Claude Fable 5, just five days after its release, marking the most dramatic AI shutdown in history due to technical and political conc…
A three-step pipeline using pdfplumber, regex, and fuzzy matching converts unstructured invoices into structured data for RAG systems. The approach addresses common failures in extracting information …
A comprehensive guide published in 2026 details 25 retrieval-augmented generation (RAG) strategies, organized into five pipeline layers, to help engineers and researchers move beyond naive RAG impleme…
Researchers developed an XGBoost model that predicts clear-air turbulence by incorporating aircraft-specific characteristics, using pilot reports (PIREPs) and ERA5 reanalysis data. The model outperfor…
A developer reports that adding a single agent to their Claude setup improved output quality by 10% overnight, explaining Anthropic's rubric-grading loop and the importance of separation, and providin…
A developer returned to a humbling hangman take-home challenge years later and built a Transformer model that runs in the browser after 16 experiments and 6 failed reinforcement learning attempts.…
Enterprise AI systems are evolving from passive chatbots into agentic digital operators that can access CRM systems, create tickets, update records, and trigger workflows, introducing new security ris…
A production Agentic RAG system encountered five failure modes—latency cliffs, memory drift, reflection loops, injection attempts, and evaluation overhead—that architecture diagrams failed to predict.…
A new blueprint for orchestrating specialized AI workflows using multi-agent systems is proposed, aiming to replace single-prompt approaches with a coordinated team of AI agents for marketing tasks.…
A data engineer built an automated resume tailoring workflow using n8n, Ollama, Google Docs, and Postgres after spending 45 minutes manually rewriting bullet points for a job application. The system r…
The Model Context Protocol (MCP) standardizes how AI systems connect to tools and data, solving the M×N integration nightmare where multiple AI apps require custom integrations for each data source. M…
Researchers R.S. Babu and L.G. Iyer introduced Causal Minimal Tool Filtering (CMTF) to solve ToolChoiceConfusion, where LLM agents fail when given too many tools. CMTF limits visible tools to only tho…
NVIDIA's new RTX Spark for Windows on Arm offers 273 GB/s memory bandwidth, but Apple's MacBook still outperforms it with 614 GB/s for local LLM inference, highlighting the continued advantage of unif…
Claude Code and other coding agents enable rapid application development, but creating robust code remains a challenge. The article explores strategies for writing reliable software using AI coding to…
LangChain co-founder Harrison Chase argues that AI agents can learn at three distinct layers—model, harness, and context—but most teams focus only on retraining the model. The harness layer, which opt…
Apple has struck a $1 billion-a-year deal to run a 1.2-trillion-parameter Gemini model in Google's Cloud for Siri, highlighting Apple's failure to capitalize on its 2010 AI headstart and its reliance …
A new evaluation framework called SEAM (Signal, Efficacy, Aftermath, Monotonicity) exposes how self-healing AI agents can falsely report repair success by grading their own work. The framework provide…
Meta's Llama open-source AI models can run on hardware ranging from a MacBook to a data-center GPU, with the key constraint being memory capacity. An M2 Pro MacBook with 16GB can run 1B, 3B, and 8B pa…
A new article on Towards AI outlines testing and evaluation strategies for production AI agents, focusing on ensuring reliability, accuracy, and trust before failures occur.…
Pope Leo XIV's encyclical and President Trump's executive order both address AI safety, but the AI industry's reliance on probabilistic safety is insufficient, according to a new deterministic safety …