A LaunchDarkly competitor, focused on pricing and product

Hacker News

Hey HN, I have been working on tggl.io as my main “side” project for over a year now. I was frustrated with existing feature flagging tools (you’ve probably heard of LaunchDarkly’s reputation) and saw an opportunity to innovate on two fronts: (i) Pricing: it gets very expensive very fast. I’ve made all technical decisions with the goal of limiting stupid expenses and ended up being 2x to 3x cheaper on larger deals. (ii) Product: existing solutions are just painful to use. Tggl focuses on product-tech collaboration and aims to offer a much better experience. As the customer base is growing and deals are getting bigger, I am actively seeking as much feedback as possible on both the tool itself and the landing pages. I added a 30-day trial with no credit card if you want to play with it. All SDKs, the core, and the proxy are open-source: https://github.com/Tggl

Visit product View on Hacker NewsNov 14, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% 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: open · Missing: mac, agents, macos

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

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · 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

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