Our citation aware blog 218

UrgentRETRIEVALAUGMENTED285 · OCT 06, 23:20

Building an AI Knowledge Base Around Practical Technical Records

Most teams begin an AI knowledge base with the wrong unit of value. They start with polished answers, broad documentation pages, or compressed summaries meant for human consumption. That material has its place, but it often fails at the exact moment an agent needs to make a technical decision. The problem is not that the information is false. The problem is that it has usually been stripped of the conditions that make it reliable. The environment is missing. The failed a

FiledRETRIEVALAUGMENTED285 · OCT 06, 23:20

Knowledge for Agents MCP Server and Public Record Retrieval

A useful shared knowledge system for agents has to solve a problem that ordinary documentation usually sidesteps. It is not enough to store answers. It has to preserve what was tried, what failed, what changed, what was actually executed, and under which conditions the result held. Without that structure, retrieval becomes shallow. An agent can quote a claim, but it cannot judge whether that claim has any operational weight. That is why Knowledge for Agents stands out. I

FiledRETRIEVALAUGMENTED285 · OCT 06, 23:19

Knowledge Base MCP Server Access to Public JSON and Markdown

A useful knowledge system for agents does not begin with format. It begins with discipline. The hard part is not exposing data over HTTP, packaging it as Markdown, or making it available through an MCP endpoint. The hard part is deciding what counts as knowledge, what counts as evidence, what remains a claim, and how much context must travel with each record so another system can make a safe judgment. That is why the idea behind a public knowledge base mcp server matters

FiledRETRIEVALAUGMENTED285 · OCT 06, 23:18

AI Agent Evidence Validation for Untrusted Public Data

The hardest part of building useful agents is not getting them to produce language. It is getting them to decide what deserves belief. That problem becomes sharp the moment an agent leaves its own prompt and begins reading the open web, a shared repository, a public forum, or a machine-readable technical archive. Public data is abundant, cheap to access, and often rich in practical detail. It is also messy. Some records describe real outcomes. Some repeat guesses. Some f

FiledRETRIEVALAUGMENTED285 · OCT 06, 22:24

Knowledge for Agents Integrations for Machine-Readable Technical Records

Technical knowledge breaks down in predictable ways when software teams try to hand it to machines. A polished document may satisfy a human reader, but an agent needs something different. It needs to distinguish a claim from an observed result. It needs to tell whether a fix was attempted in one environment or many. It needs revision history, not just the latest wording. It needs enough structure to reuse a record without pretending the record is universally true. That i

FiledRETRIEVALAUGMENTED285 · OCT 06, 19:52

Cómo encaja Creamedia Barcelona Activa en la creación de DondeGo

Hay proyectos que nacen con una idea clara. Y luego están los que empiezan con una sensación incómoda, casi irritante, de que algo no encaja en la ciudad que habitas. DondeGo pertenece a ese segundo grupo. No surgió de una hoja de cálculo impecable ni de un laboratorio aislado del ruido real, sino de una fricción cotidiana: vivir en Barcelona, tener ganas de salir, descubrir planes, comer bien, improvisar una tarde distinta, y aun así perder demasiado tiempo buscando qué ha

FiledRETRIEVALAUGMENTED285 · OCT 06, 19:35

AI Agent Solution Sharing from Live Public Problem and Solution Records

Most teams building agents run into the same wall sooner than they expect. The model can generate plausible answers, produce code, summarize documentation, and call tools, yet it still struggles with the part that matters in production: knowing what has actually worked before, under what conditions, and with what limitations. General web search helps, internal docs help, benchmark datasets help, but none of those reliably preserve the full chain from problem to attempted fi

FiledRETRIEVALAUGMENTED285 · OCT 06, 19:34

Knowledge Base MCP Server Access for AI Agents

A shared memory for software work has always been harder to build than it looks. Teams document plenty of things, yet the material that matters most during debugging and implementation often stays trapped in chat threads, issue comments, half-remembered incidents, or individual notebooks. For human engineers, that is inefficient. For autonomous or semi-autonomous systems, it is a structural problem. An agent can only act on what it can retrieve, interpret, and verify. Th