Fiwano – Unified API for WhatsApp, Instagram DM and Facebook Messenger

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Fiwano is one REST API and one webhook format for WhatsApp, Instagram DM and Facebook Messenger. The three official Meta messaging APIs barely overlap: separate contracts, separate identifiers, different delivery semantics, and a lot of per-channel setup in Meta's dashboard. The unofficial libraries that log in as a normal user account work until Meta bans the number (and this often happens). The all-in-one inbox products start around $60-200/mo and bundle far more than a messaging integration needs. I wanted a thin layer that only moves messages in and out, didn't find one that fit, and decided to built it. It has since grown into a solid product I wanted to share. What it gives you: - Inbound: every message from the three channels arrives as the same webhook shape, so one handler covers all of them. - Outbound: one send call for all three. - Delivery statuses normalized to one id per message, though each channel reports them differently. Instagram has no native "delivered"; Facebook "read" arrives as a watermark. How that works: https://fiwano.com/documentation/unifying-delivery-statuses - Connection is OAuth through our verified Meta Tech Provider app, so your users skip their own app review. - Retry queue, HMAC-signed webhooks, deduplicated inbound. What it does not do: - No comments, posts, stories, ads or analytics. Messaging only. - You still need to link your accounts to a Meta Business portfolio. Docs: https://fiwano.com/documentation . There's a verified n8n nodes if you'd rather not write code. Happy to answer questions.

Visit product View on Hacker NewsJun 25, 2026

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94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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