The article explains how large language models (LLMs) are inherently stateless, forgetting all information after each interaction, which prevents them from improving over time. To solve this, the auth…
The article compares four AI governance tools—guardrails, AI gateways, model routers, and PolicyAware—explaining that while the first three handle response formatting, request routing, or model select…
The article describes how the author automated the manual process of aligning source and translated audio phrases for latency measurement in live speech-to-speech translation. By using an LLM to handl…
Based on the article, the author warns that autonomous AI trading agents built with Large Language Models (LLMs) are inherently probabilistic and prone to catastrophic failure, such as misinterpreting…
Based on the article, the authors replaced their RAG pipeline with a persistent KV cache system, which stores the full document's attention state after a single prefill and reuses it for every query. …
A production RAG system handling 12,000 queries per day suffered an estimated $40,000 in losses over three weeks due to silent errors caused by vector embedding drift. The root cause was an embedding …
The article describes a new AI-powered feature called Investigation Reports, which uses a large language model (LLM) to automatically investigate system alerts and generate detailed diagnostic reports…
The article argues that allowing AI to generate raw SQL queries against ERP databases is a poor architectural choice, despite being a common demo technique. It explains that ERP data is governed by co…
The article compares Vector RAG and Graph RAG, two retrieval-augmented generation patterns that help large language models use external knowledge. Vector RAG retrieves relevant context by finding sema…
In 2026, AI agents are performing real-world transactions like booking flights and mutating databases, but standard database transactions fail when dealing with asynchronous LLM API calls. To maintain…
To build a production-ready Retrieval-Augmented Generation (RAG) pipeline in Python using BM25 retrieval via Meilisearch instead of a vector database, arguing that BM25 achieves 85–95% of the recall o…
A web developer built a private AI assistant that queries git history and project management data using only local LLMs, with no API keys or cloud services. The system uses a Text-to-SQL approach wher…
The author reduced their LLM API costs by 90% by implementing a self-hosted, OpenAI-compatible proxy called freellmapi, which automatically routes non-critical requests across multiple free-tier provi…
Datasette Agent is a new, extensible AI assistant for Datasette that provides a conversational interface for querying data and generating charts via plugins. The live demo, running on Gemini 3.1 Flash…
Three key structural decisions made when building an enterprise RAG retrieval pipeline. First, the author chose deterministic lexical retrieval over semantic search for local validation to ensure reli…
Fragmented and largely off-topic forum discussion that debates desktop operating system market share, specifically comparing Windows, macOS, and Linux. The conversation then shifts to a heated argumen…
Researchers have developed CPPL, a compiler-mediated hardware design framework that converts large language model-assisted circuit generation into a statically checkable frontend problem. The system c…
Challenges of integrating an LLM into a 12-year-old fintech codebase, including issues like the LLM becoming a blocking dependency in a money flow, producing confident but incorrect outputs due to inc…
While standard RAG (Retrieval-Augmented Generation) is effective for simple lookup questions, it struggles with complex queries requiring an understanding of relationships between entities. GraphRAG a…
A project called Simple RAG Chatbot, which allows users to build a chatbot that answers questions based on their own documents (such as Markdown or text files) rather than relying on the model's gener…