Kaleido – AI product manager for dev teams

Hacker News

Hey HN Let's talk about the ongoing debate: Will AI replace developers? Some say no, since setting clear tasks needs human touch, and well, product managers might not always get it. So, developers seem safe for now, as managers can't predict everything. But it seems developers might soon work without managers, figuring out tasks on their own. And we're up for a challenge. That's why we've built Kaleido, a tool to help devs understand users and nail important tasks without bugging the managers. Here's how Kaleido rolls: Grabbing Feedback: Kaleido rounds up feedback from all over—straight entries, custom forms, Slack, Telegram, even CSV files. It spices up the feedback with context about where it's coming from. Spotting Trends: Kaleido digs deep to find common threads. People often say similar things, just differently, and sometimes with a bunch of feelings. Kaleido's unbiased AI jumps in, making sense of feedback, spotting trends, and laying it all out clearly. Picking What Counts: Using numbers and words, Kaleido figures out what's most important. It's like sorting your priorities in a snap. Making Specs: Kaleido drafts specs that you can tweak. No more blank-page jitters—editing is easier than starting from scratch. The cool part? Once you plug in a review source, Kaleido does its thing every few weeks. It collects user thoughts, and you can pluck out the gold when making backlog. We've seen Kaleido shine for dev teams flying solo, no product manager in sight. It's a common scene in service companies dealing with heaps of clients. Give Kaleido a spin with your own product reviews https://app.kaleido.so/addfeedback/cca3da20-dc1d-4b02-8f88-3... , and shoot us your thoughts!

Visit product View on Hacker NewsAug 22, 2023

Share card

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, context · Missing: mac, agents, macos

89%89% 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 · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

41%41% 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: reviews, soon, users · Missing: plus, platform, intuitive

23%23% 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

20%20% 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.

Correct prediction on native model

Similar products

Your AI Product Manager38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Your AI Product Manager

Hacker News11

Prototyping with AI as a Product Manager38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Prototyping with AI as a Product Manager

Hacker News5

AethrionX30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI product manager with institutional memory

Indie Hackerscommitment-side-project

Sortlist50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI Product Organiser for Shoppers

Indie Hackerscommitment-side-project

Beval – Simple evaluations for your AI product50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Beval – Simple evaluations for your AI product

Hacker News2

ShopTag.ai72%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI product tagging and categorization

Product Hunt+8

Iteraite38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Your AI Product Team

Indie Hackerscommitment-full-time

Mazora29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI Product Context Wizard

Indie Hackers1ai

PulseBot23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI product feedback intelligence for SaaS teams

Indie Hackerscommitment-side-project

BrainGrid35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The AI Product Planner

Indie Hackerscommitment-full-time

Browse all products Analyze your launch