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jevx
A typed decision layer for Codex, built in Zig
Codex generates. Jev evaluates. Zig decides. jevx is an open-source terminal agent for governed Codex workflows, with typed preflight decisions, deterministic policy gates, encrypted audit records, crash recovery, and scriptable JSONL.
I built jevx after one too many close calls watching AI coding agents confidently hallucinate terminal commands and immediately try to run them. The panic of rushing to hit Ctrl+C made me realize that letting the exact same AI generate a script and then authorize its own execution is a disaster waiting to happen. When a model is generating, it’s optimizing to finish the task, not to secure your machine. If you rely on an LLM to grant itself permissions via free-form text, a single hallucination bypasses your entire system prompt because there are no hard edges.
To fix this, I separated the concerns entirely: Codex generates, Jev evaluates, and Zig decides. Codex handles the creative coding, but before anything touches your system, Jev steps in strictly as a typed evaluator. It doesn't return text, only bounded signals like `choice`, `noul`, and `score`. Finally, the Zig CLI takes those signals and runs them through hard-coded, deterministic policy. An AI cannot sweet-talk a compiled threshold.
I’ll be upfront that this is an early release, and the platform binaries are currently OS-unsigned. The installer verifies SHA-256 and you can check GitHub provenance, but I know that adds friction. For the developers and security teams messing with agents right now, I'd love to know which part of this boundary you most want to inspect—the deterministic policy thresholds, the encrypted audit evidence, or the recovery states? I’m not looking for upvotes today. I want your most brutal, specific criticism. Tear the architecture apart and tell me exactly how you’d bypass the split between Jev and Codex.
jevx was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #155 on the daily leaderboard. Codex generates. Jev evaluates. Zig decides. jevx is an open-source terminal agent for governed Codex workflows, with typed preflight decisions, deterministic policy gates, encrypted audit records, crash recovery, and scriptable JSONL.
On the analytics side, jevx competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how jevx performed against the three products that launched closest to it on the same day.
Who hunted jevx?
jevx was hunted by Supratim Sircar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of jevx including community comment highlights and product details, visit the product overview.
Supratim Sircar
I built jevx after one too many close calls watching AI coding agents confidently hallucinate terminal commands and immediately try to run them. The panic of rushing to hit Ctrl+C made me realize that letting the exact same AI generate a script and then authorize its own execution is a disaster waiting to happen. When a model is generating, it’s optimizing to finish the task, not to secure your machine. If you rely on an LLM to grant itself permissions via free-form text, a single hallucination bypasses your entire system prompt because there are no hard edges.
To fix this, I separated the concerns entirely: Codex generates, Jev evaluates, and Zig decides. Codex handles the creative coding, but before anything touches your system, Jev steps in strictly as a typed evaluator. It doesn't return text, only bounded signals like `choice`, `noul`, and `score`. Finally, the Zig CLI takes those signals and runs them through hard-coded, deterministic policy. An AI cannot sweet-talk a compiled threshold.
I’ll be upfront that this is an early release, and the platform binaries are currently OS-unsigned. The installer verifies SHA-256 and you can check GitHub provenance, but I know that adds friction. For the developers and security teams messing with agents right now, I'd love to know which part of this boundary you most want to inspect—the deterministic policy thresholds, the encrypted audit evidence, or the recovery states? I’m not looking for upvotes today. I want your most brutal, specific criticism. Tear the architecture apart and tell me exactly how you’d bypass the split between Jev and Codex.
Product: https://supratimsircar05.github.io/jev-zig-cli/