At a glance

What is AI News Curator?

The AI News Curator is an internal research workflow that turns saved links and approved public sources into a reviewable daily briefing. It normalizes each item, groups the archive by date and topic, separates public-ready notes from private research, and keeps human review in the loop before anything becomes content or client context.

The Problem

What was broken.

Useful AI and automation signals arrive from everywhere: newsletters, GitHub releases, social posts, saved links, documentation updates, community notes, and client-adjacent research. Without a system, they sit in browser tabs, chat threads, and private notes until they are forgotten.

The real problem is not collection. It is conversion. A saved link only becomes useful when it is normalized, tagged, reviewed, and connected to a decision: keep it for research, turn it into a public note, use it for client context, or archive it for later.

The goal was to build a private research command center that turns scattered reading into a daily briefing without exposing raw link dumps, private notes, account data, source credentials, or prompt text.

The Approach

What was built.

The system treats each link as a record, not a loose bookmark. Every item gets normalized into a consistent shape: title, source, topic, local date, visibility, status, and review notes. The archive can then be grouped by day, month, source, or theme instead of depending on memory.

Daily briefing generation sits on top of that archive. The system pulls the latest relevant items, drafts a short review brief, and keeps the result behind a human approval step. That matters because the output is not meant to publish automatically. It is a thinking aid for research, content planning, client context, and product decisions.

The public-safe version of the system is the workflow pattern: import, normalize, classify, brief, review, decide, archive. The private implementation details, saved links, raw imports, credentials, logs, prompts, and source configuration stay out of the public case study.

How It Works

Architecture in plain English.

01

Link intake

Saved links and approved public sources enter the workflow through a controlled import step. Raw dumps stay private and are not published.

02

Record normalization

Each item becomes a consistent record with source, topic, date, visibility, status, and review fields so the archive can be searched and grouped.

03

Topic classification

Items are classified into practical themes such as AI tooling, coding workflows, design systems, automation patterns, public GitHub work, and client research.

04

Daily briefing

The latest useful items are summarized into a short daily brief that highlights why each signal matters and what decision it supports.

05

Human review gate

A person decides whether each signal becomes public content, private research, client context, or an archived note. Nothing is published automatically.

06

Archive memory

Reviewed items stay grouped by local day, month, year, source, and topic so future research starts from an organized memory instead of old tabs.

Try It

See it in action.

Briefing workflow

Public-safe workflow view only. This sample shows the structure, not the private source list, prompts, account data, or saved links.

Watch Guided Demo

Saved Links

Normalize

Classify

Daily Brief

Review

Stack

Built with.

Python Markdown JSON records LLM API Saved-link archive Daily briefing Human review Public/private visibility Research workflow

Outcomes

What changed.

Daily reviewable briefing routine

1 archive organized research memory

Human gate before public reuse

Public-safe case study without private links

The outcome is a calmer research loop. Links stop disappearing into tabs, and useful signals become reviewable material for content, client context, or internal product thinking. The system is useful because it keeps the decision point human: AI helps organize and brief the signal, but publishing stays intentional.

Service