I read Replika's privacy policy and then built a competitor

Hacker News

I'm genuinely surprised at what people are willing to share with AI companions. Read Replika's privacy policy. Then Character.AI's. These apps store your most personal conversations on their servers, linked to your email address. A breach or subpoena and your identity is attached to everything you ever told your "AI friend." Eek. The only thing I think actually solves this is local inference. I remember browing r/LocalLLaMA and years ago and thinking this is the future. Local models are finally good enough. I was playing with the bonsai 8B 1-bit quant model a few weeks back and I think we're almost there. I built friendAI to see if there's market demand for local inference. Everything runs on your phone. What's actually on-device: - Bonsai-8B (1-bit quantized Qwen3-8B, ~1.3GB) via MLX for speed - Gemma 4 E2B (~4.5GB, GGUF) via llama.cpp for vision - A unified client that routes between them A few things I'm reasonably proud of solving in about a week: - Turns out the hardest part was actually managing the background model downloads that survive crashes, network drops and reboots. You can start chatting before the download finishes. - Runtime thread auto-tuning that benchmarks your actual device at startup rather than guessing with a static heuristic - Local memory without a vector DB. TF-IDF style ranking with recency decay. No embedding model needed. Happy to go deep on any of it. www.friendai.pro

Visit product View on Hacker NewsApr 26, 2026

Share card

Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, models · Missing: mac, agents, macos

87%87% 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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released

57%57% 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 · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, apps · Missing: mobile apps, ios, entrepreneurs

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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

A privacy policy you might actually read43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A privacy policy you might actually read

Hacker News6

NoHoops – opt-in consent capitalism (how to ditch the privacy policy)51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

NoHoops – opt-in consent capitalism (how to ditch the privacy policy)

Hacker News3

UltraFastUtilities52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The simple privacy policy generator

Indie Hackers1b2b

Privacy policy as a feature36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Hacker News1

A generic policy and constraint evaluator (Go)35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A generic policy and constraint evaluator (Go)

Hacker News1

Same-origin policy? What same-origin policy?47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Same-origin policy? What same-origin policy?

Hacker News3

TOS and Privacy Policy management - TOS.io58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

TOS and Privacy Policy management - TOS.io

Hacker News6

limepolicy28%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Professional Privacy Policy Builder

Indie Hackerscommitment-side-project

Another Tailscale Competitor46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Another Tailscale Competitor

Hacker News1

They chose your competitor. Find out why46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

They chose your competitor. Find out why

Hacker News6

Browse all products Analyze your launch