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In Christian Anderson’s 62-case test of claims in product descriptions and posts, adding Jev as a second checker reduced the number of unsupported claims that passed under his chosen combined rule. The rule got 61 cases right, with one false positive and no false negatives in that sample. That is a useful result for his publishing workflow—not proof that two checkers will improve every dataset or domain.
What Anderson tested
Anderson used claims from actual Gumroad product files and his DEV posts, asking whether each claim was supported by its source before publication. The test set contained 62 cases: 22 claims judged true of their source and 40 that went beyond what the source supported. These labels followed from how the cases were constructed; the post does not establish an independently audited ground truth or an independent reproduction from public raw cases and code.
He ran two checkers separately on the cases:
- DeepSeek chat: deepseek-v4-flash, prompted to read the source and answer PASS or FAIL.
- Jev: typesafe/jev-1.13, which returned a probability that a claim was supported.
Jev’s probability needed a cutoff to become a decision: at a 0.5 threshold, a score of at least 0.5 passed; at 0.9, only scores of at least 0.9 passed. Anderson then compared those individual rules with a combined rule.
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How the checkers performed on the 62 cases
In the table, a true positive (TP) is a supported claim passed, a false positive (FP) is an unsupported claim passed, a true negative (TN) is an unsupported claim rejected, and a false negative (FN) is a supported claim rejected. “No answer” means DeepSeek did not return a decision. The figures are Anderson’s reported results for this test set.
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| Checker or rule | TP | FP | TN | FN | No answer | Accuracy (answered) | Mean time |
|---|---|---|---|---|---|---|---|
| DeepSeek chat | 22 | 2 | 35 | 0 | 3 | 96.6% | 21.5 s |
| Jev, pass at p ≥ 0.5 | 22 | 4 | 36 | 0 | 0 | 93.5% | 0.35 s |
| Jev, pass at p ≥ 0.9 | 21 | 0 | 40 | 1 | 0 | 98.4% | 0.35 s |
| Both combined | 22 | 1 | 39 | 0 | 0 | 98.4% | not stated (Anderson, 2026) |
The combined rule’s 98.4% answered accuracy corresponds to 61 of 62 cases right. Its one remaining error was an unsupported claim that DeepSeek passed while Jev scored 0.63. At the 0.9 Jev-only cutoff, there were no false positives, but one supported claim was rejected; the combined rule handled that trade-off differently.
Why Anderson combined the checkers
Their mistakes did not fully overlap. For example, DeepSeek passed a claim that a holiday pricing guide would help readers “save at least £25,” while Jev assigned it a support probability of 0.13. In the other direction, Jev passed four unsupported claims at the 0.5 cutoff. Three were product descriptions that overstated coverage, and DeepSeek rejected those three. That error pattern—not merely the number of checkers—made a fail-if-either-fails policy useful to Anderson.
Anderson’s live policy is to fail a claim if either checker says FAIL. If DeepSeek returns no answer, Jev must score at least 0.8 for the claim to pass. That no-answer fallback is stricter than simply treating Jev’s ordinary 0.5 threshold as sufficient.
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Anderson reports that he ran all 62 cases through Jev twice. Scores changed by no more than 0.04 and by 0.007 on average. In the same reported run, Jev’s median time was 0.31 seconds compared with 20.6 seconds for DeepSeek; the table’s mean times were 0.35 seconds for Jev and 21.5 seconds for DeepSeek. He reports a total cost of $0.0018 for all 62 Jev checks. These are measurements and costs from that particular run, not present-day pricing or guaranteed latency.
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What this result does—and does not—say about routing
This experiment is about checking whether claims are supported by source material. It is not a test of Jev choosing the best model for arbitrary prompts. Jev’s routing documentation describes typed outputs such as a finite model choice, complexity score and probability of needing tools. It also describes jev-router as an open-source, OpenAI-compatible LiteLLM proxy that summarizes incoming messages, filters candidate models by capability, then lets Jev choose; the documentation says a rules-based cheapest-eligible fallback is used when no key is set. Jev routing documentation
A separate paired and self-audited evaluation by Jiawei Li, dated October 1, 2026, examined Jev and Laya at 11 agent decision points. Its abstract reports Jev was significantly more accurate on nine points, but neither system beat chance on zero-shot model routing, and both tied on RAG relevance gating. It also says errors in an earlier analysis distorted deployment claims. This is a different benchmark from Anderson’s claim-support test, and it is a reason not to generalize his result to unrelated routing tasks. Li’s October 2026 evaluation
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When a second checker may be worth the extra step
Anderson’s result supports testing a second checker when the cost of publishing unsupported claims matters and different tools may catch different errors. It does not establish that adding a checker will improve accuracy on your material. Before relying on such a policy, evaluate it on examples representative of your own publishing workflow and pay attention to:
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Quick Recap
- False positives: unsupported claims that still pass.
- False negatives: supported claims that are rejected, which can create unnecessary review work.
- Non-answers: define whether another checker can decide the case and what threshold it must meet.
- Threshold choice: changing Jev’s cutoff altered the balance between missed overclaims and rejected supported claims in Anderson’s sample.
- Repeatability, latency and cost: measure them in the same conditions and for the tools and versions you plan to use.
- Representativeness: a small, specific set of product descriptions and posts may not reflect another subject, writing style or source type.
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