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For agent memory, neither wins on its own: vector search finds past statements that mean something close to the current question, and a knowledge graph connects facts through the same person or company and tells which of two conflicting facts is current.
A graph database stores data as nodes and relationships and answers questions by following those relationships instead of joining tables; use one when your main questions are about paths across connected data, and keep a relational database for records and reporting.
As of 26 September 2026, the best open-source embedding model for most teams is Qwen3-Embedding, which is Apache 2.0, reads 32K tokens and ranks 5th on MTEB English v2 and 4th on MTEB Multilingual v2.
RAG, short for retrieval-augmented generation, is a technique that lets a large language model answer from information it was never trained on: when a question arrives, the application searches a knowledge source for the most relevant passages, places them in the prompt next to the question, and…
Short-term memory in an LLM is whatever sits in the context window on the current call: the system prompt, the recent conversation, tool results and any retrieved text, all re-sent by the application on every request and gone when the request ends.
Jev is TypeSafe AI’s first System One Model. It is 100x faster and cheaper than frontier LLMs.
Cost per task is what AI work really costs. The formula, the hidden multipliers, a 20-task test to measure your own, and a dated real-world case study.
Agentic Context Learning or ACE is a learning paradigm that lets an AI agent improve across tasks by editing the context it reads, while leaving model weights unchanged.
In 2023 most people doubted that there could be more than 1 or 2 frontier model labs. Now there are dozens.... and Stripe just bought the best known one for $7B.
RAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems. The post RAG Isn't an Agent — I Built the Layer Between Retrieval and Action appeared first on Towards Data Science.
Last Updated on September 25, 2026 Most engineers approaching their first production LLM system bring a mental model that works well for software: test your inputs and outputs with assertions, check your code into git, roll back if something breaks, and rely on system prompts or configuration files…
Try Superhuman Go, from the makers of Grammarly, today: http://superhuman.com/aish Voice agents are quickly becoming the most in demand AI systems. But how exactly does an AI system listen, understand, and take actions in real time without lagging?
Nine Claude Skills we run in production, plus one bonus, with the install command for each and what it saves you. Updated September 2026.
Grok API pricing starts at $1.00 per million input tokens and runs to $6.00 output on the flagship. Live rates from xAI's docs, plus how they compare.
Projects are the bridge between learning and becoming a professional. While theory builds fundamentals, recruiters value candidates who can solve real problems. A strong, diverse portfolio showcases practical skills, technical range, and problem-solving ability.
AI database governance can get overlooked when teams rush to connect AI tools to production data. IBM’s 2025 research found that 97% of organizations that reported a breach involving an AI model or application lacked proper AI access controls. The risk is easy to see.
Build a multi-account architecture that keeps each team's data in its own AWS account while giving AI agents a unified way to query across them. A central platform account runs the agent using Amazon Bedrock AgentCore Gateway and MCP, while line-of-business accounts expose their data as MCP servers…
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