Ch

Chief of Staff – Keep articles as audio, listen when you have time

Hacker News

Chief of Staff – Keep articles as audio, listen when you have time

Hello HN, I built Chief of Staff (CoS) so I could listen to high quality text-to-speech (TTS) audio readings of articles while my hands and eyes were busy doing other things. The first demo took a few hours, but what you see today represents over two years work. I've included tech stack notes and a simple system architecture diagram [1] if you're curious. So, why use it? CoS is pretty fast. Articles begin synthesis the moment you add them are often ready to listen before you can finish humming Happy Birthday once or twice. If an article has already been synthesized by another user, the audio is made available to you instantly. The reader voice sounds good. You can hear a short article on the public landing page. New accounts are free and the onboarding wizard helps you add a longer article about Paul Simon’s 1986 album Graceland from Rolling Stone. (An interesting listen!) The CoS interface looks and feels good. The audio player remembers playback position. So, you can start listening on your laptop, close the tab then pop the site up on your mobile device and pick up right from where you left off. I started working on this product just before our first baby was born in June 2020. I frequently would come across articles on HN that I wanted to read but could not create screen time for. Initially, I used it for long walks with my leash-trained cat. Later, it would come in handy for many late nights with an infant (which also necessitated a decent dark mode!) I wanted something to provide the save-it-for-later feel of bookmarking while making consumption more flexible, (Hands and eyes free). Finally, I wanted it to have a “check it off when it’s done” feel, so I built archive and delete buttons so you can keep your unread content area clean. This workflow may appeal to HN readers who live with ADHD. There was a post a few months ago where a commenter described roughly what I’ve built as a coping mechanism. [2] Free accounts can synth a few articles depending on length. I’ve set an introductory price for pro accounts that is cheap in comparison to other TTS products. You can also enjoy a week of free trial of the pro account. Subscription cancellation behavior mirrors that of the App Store (cancel any time and enjoy sub through expiration). Chief of Staff’s technology stack is: - Python / Django backend with DRF (Django rest framework) API - Vanilla JS and typescript frontend - Postgres database The product leverages hosted cloud service: - Mailgun (transactional email) - Posthog (analytics) - Stripe (payment processing) - Unsplash (to find ~related images when article lacks one such as PG's articles) - AWS (Polly and SQS for TTS, S3 for images and media) In devops land: - Github actions (CI/CD) - Frontend bundling via esbuild I’ve followed TTS and generative AI developments with keen interest since beginning this work and am hopeful to flesh out its vision as a super-Siri, personal alpha geek, or ahem, Chief of Staff, that keeps you briefed on what matters most. But for now it is a humble, (hopefully) well-designed article to speech tool I built first for myself and wholly in my spare time. I hope you like it and appreciate any questions, suggestions or other feedback. rob - rob@chiefofstaffhq.com [1] https://i.imgur.com/jHT1vPy.png [2] https://news.ycombinator.com/item?id=34486848

Share card

Actual performance

3points
5comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
97%97% 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: user, stripe, new · Missing: mac, agents, macos
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: adhd, ide, io · Missing: https docs, excited, just released
55%55% 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: host, interface · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month · Missing: mobile apps, ios, entrepreneurs
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · 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

Similar products

Chief of Staff
Chief of Staff19%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Listen to articles on the go

Indie Hackerscommitment-side-project
Playpost
Playpost

Listen to articles

BetaList
Articulu
Articulu30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Listen to articles from the web.

Indie Hackers1$110/moai
Ze
ZeroAudio – never listen to an audio again44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ZeroAudio – never listen to an audio again

Hacker News1
Ad Auris Play
Ad Auris Play35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Turn articles into audio and listen to anything!

Indie Hackers
Li
Listen to articles as dialogues, storytelling, daily recaps (AI)21%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Listen to articles as dialogues, storytelling, daily recaps (AI)

Hacker News1
Re
ReadMo an app to listen to news and articles41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ReadMo an app to listen to news and articles

Hacker News4
Zetik
Zetik75%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A chief of staff in your pocket

Product Hunt+180Productivity
PodQueue
PodQueue62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

"Listen Later" for audio on the web

Indie Hackers5$55/mob2c
Ad
Ad Auris Play. Turn articles into audio and listen on Spotify51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Ad Auris Play. Turn articles into audio and listen on Spotify

Hacker News7