Build an AI Research Library with Recallio

The pace of AI development means anyone following the space saves dozens of links per week — papers, model releases, tool launches, explainers, benchmark comparisons, opinion pieces. Without a system, this becomes noise. With a system, it becomes a personal knowledge base you can actually use.

The challenge with AI content

AI content has specific characteristics that make generic bookmark organisation frustrating:

• It dates fast: a "state of the art" model from six months ago may be irrelevant now.
• It's diverse: papers, tools, demos, opinions, and news all live in the same feed.
• The vocabulary is technical and specific: you need to distinguish "transformer architecture" from "transformer fine-tuning" from "inference optimisation" without all being filed under "AI".

The solution is explicit categorisation at save time.

A collection structure for AI research

AI Research
  └── Papers
  └── Tools & Libraries
  └── Models
  └── Benchmarks
  └── Explainers
  └── News & Opinions

This keeps content types separate. A paper about a new architecture and a tool implementing that architecture are both AI content, but you want them in different places.

Tags for AI content

Beyond the collection structure, tags cover the model/topic dimension:

Tag	Use
llm	Large language models
vision	Computer vision, image models
audio	Speech, audio generation
agents	AI agents, multi-agent systems
rag	Retrieval-augmented generation
fine-tuning	Fine-tuning and training
inference	Inference speed, optimisation
evaluation	Benchmarks, evals, red-teaming
open-source	Open weights models and tools
applied	Real-world applications

Combined with collections, you can query precisely: "all papers tagged rag in my Papers collection."

Saving papers from arXiv

When saving an arXiv paper, the title and abstract are usually auto-fetched. Rewrite the description with your own summary:

"Proposes scaled dot-product attention with linear complexity. Main contribution vs. transformers: O(n) instead of O(n²). Relevant for long-context work."

This is more retrievable than the abstract itself — it's in your language, about your use case.

Tracking tools as they evolve

AI tools change rapidly. When saving a tool, record the version or date context in the description:

"LangChain v0.2 — new agent executor API. Previous docs were confusing; this version is cleaner. Saved June 2026."

When you come back six months later, the date context tells you how stale the save might be.

A "current models" Smart Collection

Create a Smart Collection filtered by:

• Collection is Models AND tag is open-source

Pin this for easy access. When you need to know what open-source models are in your library, one click shows you — without browsing through everything.

Handling information overload

The AI feed is firehose-level. You don't need to save everything — you need to save what's relevant to what you're building or researching.

A useful filter before saving: "Will I want to find this in three months?" If no, skip it. If yes, save it and write a description. This keeps your library usable rather than comprehensive.

The compound value

An AI research library built over 6–12 months becomes genuinely useful in a way a feed reader or Twitter list never does. You can search your own descriptions, find the paper you remember reading about attention mechanisms six months ago, and trace how your understanding of a topic has evolved. That's a personal research advantage that scales with time.