Blog
How we build with LLMs, agents, RAG and the models behind them: architecture, evaluation, cost and what holds up in production.
AI & Machine Learning
Cisco, Microsoft and Gartner score how ready an organization is for AI, Google and NIST offer frameworks, and Jumbi checks whether a codebase is ready to ship.
AI & Machine Learning
Fireflies.ai for Jira, Granola for no bot, Fathom for free recording, tl;dv for EU hosting, Otter.ai for live transcripts: six AI notetakers compared.
AI & Machine Learning
The best AI PRD generator depends on where specs live: ChatPRD for PMs, Notion AI or Confluence for wiki teams, Productboard for feedback, Shivo for lint.
AI & Machine Learning
Serving a 7B model is roughly 10-30x cheaper than a frontier model for tasks where accuracy is equivalent. The engineering question is which tasks those are, and how to find out without guessing.
AI & Machine Learning
Chat history is not memory. A memory layer is durable, retrievable state about decisions, preferences and facts that survives past the context window. What belongs in one, and what should stay in a log.
AI & Machine Learning
Long context windows and better tool use took work away from retrieval-augmented generation. RAG did not become obsolete — its job got narrower. A decision framework for when to retrieve, when to load, and when to call a tool.
AI & Machine Learning
An embedding is a list of numbers representing meaning, so that similar things sit close together. What they are, how similarity search uses them, and the practical decisions — dimensions, chunking, distance metric — that determine whether retrieval works.
AI & Machine Learning
Transcription became accurate and cheap, which made it a commodity. The value moved to what happens after the transcript - and to knowing which problems speech is genuinely the right interface for.
AI & Machine Learning
Prompt wording stopped being the bottleneck around 2025. The harder problem — deciding what lands in the context window at every turn — got a name. What context engineering is, and the four things it covers.
AI & Machine Learning
Asking a model politely for JSON produces JSON most of the time, and most of the time is not a contract. How constrained decoding, schemas and validation combine into something you can build on.
AI & Machine Learning
Benchmarks say your agent works; production says otherwise. A practical evaluation approach covering end-to-end, trajectory and component scoring, and how to build an eval suite from your own failures.
AI & Machine Learning
Multi-agent architectures are the default recommendation of 2026 and the wrong answer for most teams. A decision framework for choosing between one agent, a planner-executor split, and a full orchestration graph.
AI & Machine Learning
A working MCP server in five steps — choosing what to expose, defining tools the model can actually use, handling authorisation, testing against a host, and the mistakes that make a server unusable in practice.
AI & Machine Learning
Three ways to make a model behave the way you need, routinely treated as competitors when they solve different problems. What each actually changes, and the order to try them in.
AI & Machine Learning
MCP is an open standard that lets an AI model call your tools and read your data through one interface instead of a bespoke integration per vendor. Here is what it is, what it is not, and when it earns its place.
AI & Machine Learning
Master Cursor AI and the vibe coding revolution. Learn Composer mode, multi-file edits, agent planning, and the modern AI-powered dev stack that lets you go from idea to production app in hours.
AI & Machine Learning
The low-code market hits $44.5B in 2026. Learn when to use AI app builders like Lovable, Bolt, and v0 vs custom development, the hybrid approach, and enterprise considerations for rapid AI app delivery.
AI & Machine Learning
Learn how to build production AI agents for enterprise automation. Covers agent architectures, LLM selection, tool integration, security governance, and ROI measurement for agentic AI systems.
AI & Machine Learning
Discover the 10 AI-powered features that are becoming table stakes for SaaS products in 2026. From intelligent search and AI copilots to predictive analytics and workflow automation with practical implementation tips.
AI & Machine Learning
Master Claude API integration for enterprise applications. Learn authentication, streaming responses, tool use, vision capabilities, and best practices for building reliable AI-powered features with Anthropic's most advanced models.
AI & Machine Learning
Learn to build AI agents that can reason, plan, and execute complex tasks autonomously. Covers agent architectures, tool use, memory systems, and production deployment patterns.
AI & Machine Learning
Master LangChain for building production-ready AI applications. Learn chains, agents, memory systems, RAG pipelines, and deployment strategies with practical code examples for real-world use cases.
AI & Machine Learning
Choose the right vector database for your AI applications. Compare Pinecone, Weaviate, Qdrant, and Milvus on performance, scalability, features, and cost for production RAG and semantic search systems.
AI & Machine Learning
Master Retrieval Augmented Generation (RAG) for building accurate, grounded AI applications. Learn architecture patterns, vector databases, chunking strategies, and production deployment best practices.
AI & Machine Learning
Compare the leading AI coding assistants for 2026. In-depth analysis of GitHub Copilot, Cursor, and Claude Code features, performance, pricing, and which is best for different development workflows.
AI & Machine Learning
Learn advanced prompt engineering techniques for GPT-4, Claude, and other LLMs. Discover patterns for reliable outputs, chain-of-thought reasoning, structured data extraction, and production prompt management.
AI & Machine Learning
Learn to fine-tune large language models for domain-specific tasks. Covers data preparation, training strategies, evaluation metrics, and deployment of custom LLMs.
AI & Machine Learning
Build applications with multimodal AI that understands images, documents, and diagrams. Learn vision model capabilities, use cases, and implementation patterns.
AI & Machine Learning
Implement AI-powered code review in your development workflow. Learn about automated review tools, integration patterns, and how to maximize code quality with AI assistance.
AI & Machine Learning
Learn to build AI tool integrations with Model Context Protocol. Create MCP servers for databases, APIs, and custom tools that LLMs can use.
AI & Machine Learning
Explore how artificial intelligence is revolutionizing the way we build, test, and deploy software. From code generation to automated testing, discover the tools and techniques shaping the future of development.
AI & Machine Learning
Learn how to automate your machine learning workflows using MLOps best practices and tools like Kubeflow and MLflow.
