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Dr. Headline – An Autonomous AI Agent Publishing Daily News Briefings

Hacker News

Dr. Headline – An Autonomous AI Agent Publishing Daily News Briefings

About: Dr. Headline is an autonomous AI agent that writes, reasons through, and publishes fully sourced daily political news briefings — without human editors. Built to operate transparently, it selects stories across political divides, critically evaluates information through multi-step AI workflows, and produces concise academic-style briefings with inline citations. HeadlineSquare, the public platform hosting Dr. Headline's work, was first online on April 6, 2025, and since then, it has been consistently publishing 2 news briefings per day and ~100 top news article citations per day. The system (Dr. Headline + HeadlineSquare) is fully open-source (while it uses commercial LLM API calls), transparent, and it strives to provide a factual, neutral common ground in this highly-polarized era. *News Site Home Page:* https://headlinesquare.github.io/ *GitHub:* https://github.com/headlinesquare/headlinesquare-home Dr. Headline is among the first autonomous systems dedicated to daily political news analysis, publishing independently without human editorial control. We believe that autonomous factual recording will be essential infrastructure for future truth preservation, and Dr. Headline is determined to start this journey. Technical details (from the README): In this early experimental phase, Dr. Headline operates through a fixed orchestration of scripts and LLM prompts, but each step of LLM prompt gives significant flexibility. Dr. Headline fulfills a narrowly defined role with patience, precision, and transparency, significantly exceeding the quality of today's general-purpose agentic AIs. Multi-step, critical self-evaluation is built into the reasoning chain, with intermediate results recorded for public audit and bias detection. Current LLMs: OpenAI o3-mini-high (January 2025) and Anthropic Claude 3.7 Sonnet Thinking (February 2025). Input Sources: r/politics and r/Conservative subreddit posts (~450 candidates filtered daily). At this moment, only headlines and source links are analyzed, not the article contents and user engagements. Processing: 25 stages of LLM-guided evaluation, correction, and synthesis per day. Output: Two independent daily briefings (1500–2000 words each) with inline citations, organized by importance. Background: The project was initiated by a single independent hobbyist (Thomas) in three weeks, but has now expanded to a small team. Contributions, forks, critiques, and collaborations are welcomed — we believe transparency and openness will make Dr. Headline stronger. Philosophy: This project reflects a belief that artificial intelligence can greatly amplify humanity’s best aspirations, including the pursuit and preservation of truth, even when humans themselves get lost in complex realities. Future Growth: This project is still in its infancy. There are endless possibilities ahead of us. We would love to hear your thoughts and ideas. If anyone knows or works on similar projects, and if anyone wants to share this mission with us, we would love to connect to you all!

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2points
7comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, agentic · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, 000 · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, calls · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, growth · Missing: mrr, revenue, profit
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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