Sokit – a LangChain like harness for Jev (or other System 1 models)

Hacker News

Full disclosure, it was coded with AI, I don't claim otherwise. But I wanted to test out tool calls and iterative problem solving using Jev and needed a simple library/framework/harness to do that. SOKIT (System One Knowledge, Instructions and Tools) is the result and I figured it might be useful for more people than just me. I will likely be extending it a bit as I find more ways to use Jev, but it works as is for certain use-cases and it's easy to extend.

Visit product View on Hacker NewsSep 17, 2026

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

80%80% 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.

AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive

67%67% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 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.

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

41%41% 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

13%13% 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.

Incorrect prediction on native model

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