Entity trail

Bespoke Nimble

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-09-19 / thought-leaders-researcherBespoke Nimble closes most of the Jev gap with a LoRA fine-tune of Qwen3.5-9B at 100ms on an H100@madiator's clone uses contrastive data curation to fine-tune Qwen3.5-9B, lifting the base model from 66% to 90% against Jev's 93% on the same task, at 100ms latency on an H100. That is the most informative data point in the whole clone wave: it suggests most of Jev's accuracy is reachable with a public base model and a curated dataset, and that the remaining three points plus the pricing come from the architecture and the serving stack. For a builder, it means a self-hosted decision model is a weekend of data work, not a research program.
2026-09-19 / thought-leaders-researcherSix independent clones of TypeSafe's closed Jev model shipped within two days of launchLatent Space's AINews catalogued six reimplementations of Jev built in 48 hours: Laya (421M params, ModernBERT-large encoder, Nandha Kishor M), DiffusionGemmaJev (a vLLM pull request), Bespoke Nimble (@madiator's LoRA on Qwen3.5-9B), SemIf/OpenJev (Alex Wortega, Qwen3.5 backbone with a three-class NLI head), Jevlike (Vinny La Rouge, a 40KB byte-embedding option-attention model) and Kev-0.5B (Jared Palmer, a LoRA adapter on Qwen2.5-0.5B that runs on a MacBook Pro). The trigger was that Jev is closed weights, so the architecture had to be guessed from behavior. Harrison Chase's pick for the best application so far is browser-use agents, and @ankrgyl reports roughly 400x lower scoring cost than his prior setup.

Graph relationships

BENCHMARKED AGAINST
Jev
Bespoke Nimble achieves 90% accuracy versus Jev's 93% on the same decision task
Source finding
BUILT BY
madiator
madiator built Bespoke Nimble by fine-tuning Qwen3.5-9B with contrastive data curation
Source finding
COMPETES WITH
Jev
Bespoke Nimble is a clone of Jev
Source finding

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