A Stateful LLM API that works across providers

Hacker News

For the last year we've been building AI workspaces. With every new product, we found ourselves rebuilding the same context layer and infrastructure. We tried existing solutions, but we'd either have to lock into one provider or have to manage the storage infrastructure. What we wanted was an API that's stateful and isn't tied to a specific provider. So that we didn't have to think about compaction, differing schemas, or running the infrastructure ourselves. Since we'd already built most of the constituent parts, we’ve decided to release Twigg. The API is deliberately simple. You create a chat and get back an ID. When you have a new prompt or tool result, you send that plus the ID. There's no need to store or manage context yourself. It appends the new prompt and assembles the history for you. To switch models, you change one field in the next request. The dashboard covers most of what you'd want to configure: tool schemas, system prompts, context window limits and retention settings. We'd like to hear from anyone who has run into the same problems. How are you hosting your context right now, and what would make you not want to hand it to a third party?

Visit product View on Hacker NewsSep 16, 2026

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

78%78% 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: model, new, models · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, 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

53%53% 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: plus, host · Missing: platform, intuitive, reviews

33%33% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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