Discover how HubSpot built a scalable AI retrieval infrastructure, managing over 20 billion vectors with Qdrant, to enhance semantic search and support diverse applications. Read more ›
At Dynatrace, we believe the future of observability and cloud-native operations is open. Not “open” as a slide-deck buzzword, but open as in showing up every day to write code, review PRs, chair working groups, and build tools the community can use, extend, and make their own. We’re proud to be an active contributor to […] The post appeared first on . Read more ›
There’s a pattern that shows up constantly in LLM deployments: something isn’t working quite right, so someone adds more instructions to the system prompt. The model ignores a constraint, so you restate it more forcefully. It produces the wrong tone, so you add a tone guide. Repeat until the prompt is 2,000 words long and the model is somehow worse than when you started. This isn’t a fringe experience. It’s close to a law of LLM prompt engineering. Here’s why it keeps happening. 1. LLMs Don’t... Read more ›
chachaml is a Clojure-native MLOps library developed within the Flexiana ecosystem.It's built for teams that want to run machine learning systems in production without moving their workflows to another language or stack. Read more ›
🚀 Everyone is talking about AI, RAG, Embeddings, and Vector Databases. Read more ›
Programming book reviews, programming tutorials,programming news, C#, Ruby, Python,C, C++, PHP, Visual Basic, Computer book reviews, computer history, programming history, joomla, theory, spreadsheets and more. Read more ›
Green Dot has moved one step closer to separating its banking and FinTech operations. The company announced Tuesday (June 23) that its shareholders had approved the sale of Green Dot Bank to CommerceOne. That sale is part of a larger process that involves CommerceOne forming a new publicly traded bank holding company that owns CommerceOne Bank and Green Dot […] The post appeared first on <a href=" Read more ›
Evaluating LLMs across many model variants -- quantized, fine-tuned, or deployment-specific -- requires running large benchmarks repeatedly, a process that can take tens of hours per model on edge hardware such as NPUs. Existing subset selection methods reduce this cost but depend on large calibration pools or learned prediction layers. We introduce MINCE (Monte Carlo Informed N-sizing for Compact Evaluation), which uses Monte Carlo simulation o... Read more ›
As AI agents become autonomous, establishing cryptographic trust and verifying identity is crucial for business security. Read more ›
Why put API Management in front of your MCP servers The Model Context Protocol (MCP) has quickly become the standard way for AI agents, such as GitHub Copilot in VS Code, to reach external tools and data. As soon as an MCP server does anything meaningful, the same questions that govern any API resurface: who is allowed to call it, what are they allowed to do, and how do you enforce that consistently across many servers without rewriting each one. Azure API Management (APIM) answers those ques... Read more ›
Why smart enough, fast enough, and cheap enough is good enough Read more ›
Extending the OpenAPI specification is a widely used, but seldom talked about superpower of the specification. People who aren’t in the know hit the wall with what the specification can’t do, and they move on and create a new specification — where those in the know understand the specification has become the lingua franca of API operations over the last 16 years, and craft their own extensions for the specification to make it do what they need it to do. Read more ›
There's a moment in almost every RAG project where someone asks the question that decides your next two years of ops work: "Do we actually need a vector database, or can Postgres just do this?" It's a better question than it sounds, because the honest answer isn't "use Pinecone" or "use Postgres." It's "it depends on numbers you probably haven't measured yet": how many vectors, how aggressively you filter, how much you care about the absolute ceiling of queries per second. Most teams pick bas... Read more ›
25th June 2026, Netherlands — OpenTelemetry has become one of the most important standards in modern observability. It provides a… Read more ›
Prompt engineering has the potential to enhance large language models’ (LLM) ability to solve tasks through improved in-context learning. In clinical research, the use of LLMs has shown expert-level performance for a variety of tasks ranging from pathology slide classification to identifying suicidality. We introduce clickBrick, a modular prompt-engineering framework, and rigorously test its effectiveness. Here, we explore the effects of increasingly structuring prompts with the clickBrick fr... Read more ›
Architect a robust MLOps pipeline from scratch using Python, Prefect, MLflow, and Flask to power real-time e-commerce tech. Read more ›
The Knowledge Augmentation Spectrum: CAG vs RAG vs CRAG For the past year, the industry has been obsessed with RAG \(Retrieval-Augmented Generation\) \. It was the “gold standard” for giving LLMs access to enterprise data\. But as our production requirements shift toward lower latency, higher accuracy, and better reliability, we are seeing the emergence of new paradigms\. If you are building AI applications today, you need to understand the architectural trade-offs between RAG , CAG \(Cache-A... Read more ›
KalamDB — a lightweight, real-time, storage-efficient SQL database. Designed for per-user data isolation and scalable performance — ideal for the AI era. - kalamdb/KalamDB Read more ›
Treasury Prime now enables its FinTech partners to let their customers add cash to their digital accounts at more than 90,000 participating Green Dot Network retail locations. This offering is enabled by Treasury Prime’s new Prime Cash solution, which is powered by Green Dot’s embedded finance platform, Arc, and money processing network, the companies said […] The post appeared first on <a href=" Read more ›
In the past year, the enterprise AI ecosystem has gained enormous capability and zero consensus. Developers now have a remarkable set of tools for building AI agents: OpenAI’s frameworks, Anthropic’s Claude tooling, LangChain, LangGraph, CrewAI, Microsoft AutoGen, and a growing list of alternatives. Each promises to coordinate reasoning loops, manage multi-step task execution, and connect agents to tools and APIs. For experimentation, the progress has been substantial. Teams can now assemble ... Read more ›