What exactly does Jev decide on each page?

One thing: which of that page's link candidates to click, given the stated goal. The reply answers a question asking whether the model is handed a list of things to do and picks the next action, and the answer is yes in that narrow form, candidates in and one chosen link out. Nothing says whether it also sees link text, surrounding context, or the pages already visited.

How does this compare to asking an LLM for the next action?

The author says only that the speed is quite impressive compared to LLMs, with no figure attached, and calls the demo simple and not revolutionary. There is no side-by-side run, no identical-prompt comparison, no named baseline and no cost per page, so the comparison remains one builder's impression rather than a result.

How does it avoid clicking a dead end or looping forever?

The post does not address it. There is no described termination rule, no retry or backtrack behaviour, and nothing about what happens when a page exposes no usable candidates or when the chosen link moves away from the goal. Whether the visited path is even part of the model's input is also unstated, which is the detail that would decide whether the model can recover.

Is there code I can run?

Not in the capture. There is no repository, gist or link, only the author's description in a reply thread. To judge it you would need the code, because the parts that matter, candidate extraction, how the goal is represented, and how the loop ends, are all unstated, as is the wiki it operates on.

Which wiki does the demo operate on, and how many candidates?

Not documented. The reply does not name the wiki or corpus, does not say how the link candidates are extracted, and gives no count per page, so the scale of the decision on each page is unknown.

Is the goal restated on every page, and are visited pages passed in?

Not stated. Whether the goal is repeated on each page, whether the model sees the path already taken, and what happens when a page has no usable links are all absent from the captured reply.

Was the demo live or a recording, and where is it?

Unknown. The capture does not say whether the demo was running or captured as a video, and no link, repository or gist appears in the reply thread.

CommunityJev Guides & Articles

Wiki-link clicker demo

A page-by-page demo in which each wiki page yields a set of link candidates and Jev picks which one to click toward a stated goal, its author calling it simple and the speed the notable part.

See highlights

Open x.com

Format: Article
Published by: Community
Last verified: Oct 2026

Tagscommunity awesomejev catalog-import

Works forJev for Classification

Used inJev for Software & DevTools

Highlights

The loop runs per page: take the page's link candidates, then let Jev choose which link to click given the stated goal.
The author is explicit that it is a simple demo and nothing revolutionary, and that the speed is the impressive part.
It surfaced as a reply explaining an existing thread rather than a write-up, so the mechanics live in the demo, not the post.
No latency figure, candidate count, accuracy number or repository appears anywhere in the capture.

Watch out

The linked page is a single reply thread, and the entire technical content is two sentences: each page has some link candidates, Jev chooses which link to click based on the stated goal, and the speed is quite impressive compared to LLMs. The author himself downgrades it as a simple demo and nothing revolutionary. Not documented: which wiki or corpus, how link candidates are extracted and how many there are, whether the goal is restated on every page, whether visited pages are passed in, what happens when a page has no usable links, what happens when the chosen link leads away from the goal, how the loop terminates, whether the demo is running or was a video, and where the code is. No latency figure, cost, accuracy, win rate or comparison table is given, so the speed claim is unquantified and the LLMs it is compared against are unnamed. The natural read of the thread is that the model is given the candidate list and picks the next action, one page at a time, which is a bounded classification problem rather than multi-step planning, and nothing in the capture suggests anything more. Copy reviewed against the linked page on 2026-10-04.

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