Gu

GuideLab – in-app user guides for their entire journey

Hacker News

GuideLab – in-app user guides for their entire journey

Hi HN, I’m James and I’m excited to share GuideLab, on-demand in-app guides to reduce support load and make your users happier. After a decade of working on products with small (20k) and large (millions) user bases, one thing remained clear - level 1 support takes up a lot of time. From universal usability (’how do I reset my password?’, ’how do I invite team members?’) to product specific problems, the answer is sometimes in a chat bot or knowledge base, but it’s cumbersome for the user and often gets lost in translation. Using GuideLab, you create in-app guides through a UI ("no code") which can then be shared with your users via a link special link over any platform: email, Zendesk, Intercom, in a KB, social media etc. As soon as a user clicks the link, they’re taken to your app and instantly see the guide. There’s also our search widget that you can embed directly in your web app. A 'Help Guides' tab appears for the user, where they can search and view any guide you've created. In that way GuideLab is fundamentally different to other guiding software like AppCues or userpilot. They focus on opting users into guides based on cohorts/attributes whereas GuideLab lets users view guides when they need them most. There’s a quick (<1m) video on the homepage ( https://guidelab.io ) walking you through how GuideLab works. If you have any other questions, or if there’s anything you’d love to see in this space, please share.

Share card

Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, using · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, users, way · Missing: mobile apps, ios, personal
68%68% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
62%62% 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, soon, users · Missing: plus, intuitive, reviews
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

GuideLab
GuideLab11%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tailored in-app user guides

Indie Hackerscommitment-side-project
Re
Redesigned hack.guides()44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Redesigned hack.guides()

Hacker News2
Op
OpenBallot, Aggregated SF/California Voter Guides55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

OpenBallot, Aggregated SF/California Voter Guides

Hacker News38
Ti
TipTour, A Tooltip that guides you41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

TipTour, A Tooltip that guides you

Hacker News2
cookingdom Walkthrough Guides
cookingdom Walkthrough Guides53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

cookingdom Walkthrough,cookingdom Guides,cookingdom прохожде

Indie Hackers1$5/moadvertising
Get Around Italy
Get Around Italy17%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Itineraries, guides, and curiosities about Italy

Indie Hackers2community
Gu
GuideLab – Guide your users through their entire journey62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GuideLab – Guide your users through their entire journey

Hacker News1
FlowNavi
FlowNavi45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

In-app guides to take your users from signup to power user

Product Hunt+3
Apuphi
Apuphi20%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI that guides your entire career, not just your next job

Indie Hackerscommitment-full-time
Lo
London Underground journey depths35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

London Underground journey depths

Hacker News8