Jev Tutorial
EN 中文
Get the pack
Community-sourced · not affiliated with TypeSafe
Use cases · Other

Other

Emerging or uncategorized Jev decision patterns that do not yet fit routing, moderation, livestream comments, or agent guardrails. Skipped for now: InstructorPHP (no public repo). ProductHunt “qq” links stay low priority.

Case card detail fields stay in English (source language). Titles and chapter shells are localized.

Case cards

1 · Title

#3 Refund structured verification

2 · When to use / When not
When to use

Evaluate a refund request against structured ticket + order + policy fields with Noul checks (requested? policy supports?).

When not

Must not authorize payouts or mutate billing systems by itself.

3 · Question stem

Does `ticket.message` request a refund? Does `refund_policy` support the refund requested in `ticket.message`, given `order.charges`?

4 · Options
  • refund_requested (noul)
  • policy_supports_refund (noul)
5 · Criteria
refund_requested
Does `ticket.message` request a refund?
policy_supports_refund
Does `refund_policy` support the refund requested in `ticket.message`, given `order.charges`?
6 · Example in → out
In 
ticket: Duplicate charge / charged twice for A-104; order A-104 two captured $49; policy: Duplicate charges are eligible for a refund.
Out 
refund_requested≈0.99; policy_supports_refund≈0.98 (CF sample)
7 · Source
8 · Notes

Suggestion/verification only, NOT money final decision. Cloudflare “Structured refund review” example.

1 · Title

#8 RAG / literature cheap filter (is_rct + evidence_strength)

2 · When to use / When not
When to use

After retrieval, cheaply filter papers/passages with Noul/Score before an expensive generative step.

When not

Jev has no world knowledge beyond state; weak retrieval yields well-calibrated judgments about bad material.

3 · Question stem

Is this paper an RCT? Does it report MACE? How strong is the causal evidence?

4 · Options
  • is_rct (noul)
  • reports_mace (noul)
  • evidence_strength Score
5 · Criteria
is_rct
This paper reports a randomised controlled trial
reports_mace
The paper reports major adverse cardiovascular events as an outcome
evidence_strength
Anecdotal or preclinical → Observational → Single randomised trial → Meta-analysis of randomised trials
6 · Example in → out
In 
state: { title, source, content } per paper after Valyu (or other) retrieval
Out 
Shortlist when is_rct.noul > 0.7 and evidence_strength.score > 1.5 (DEV pattern)
7 · Source
8 · Notes

DEV “Pattern 5: Retrieve, then judge”. Same shape as TypeSafe RAG passage classification cookbooks mentioned in the article.

1 · Title

#13 Browser agent next action

2 · When to use / When not
When to use

Map a page observation (numbered element table) to the next browser operation; speculative fan-out for targets in one call.

When not

Launch-week demo (browser-use/jev-ultrafast); self-reported timing/cost. Keep safety and tool execution in code.

3 · Question stem

—

4 · Options
  • CLICK
  • TYPE_TEXT
  • SELECT
  • SCROLL
  • WAIT
  • DONE
  • BLOCKED
5 · Criteria

—

6 · Example in → out
In 
(described) page turned into numbered element table; one Jev request picks operation + target
Out 
Execute only the branch matching chosen operation; small LLM only when TYPE_TEXT (per DEV)
7 · Source
8 · Notes

DEV “first 48 hours”: browser-use/jev-ultrafast by Gregor Zunic. Operation names listed in the article; full question stem not reproduced — summary + source + options from article.

1 · Title

#N4 Official Introducing System One / Jev (smart if / verify / jailbreak)

2 · When to use / When not
When to use

Cite the official TypeSafe announcement for conceptual framing: smart if-statements, verify/guardrail LLM outputs, detect jailbreaks — System One decisions for software.

When not

Conceptual source / short announcement page — NOT a how-to and not a claim of affiliation with this community library. Do not treat marketing latency/cost headlines as your measured SLOs.

3 · Question stem

(conceptual) Where software needs typed probabilistic decisions: classify, route, score, verify, guardrail, or detect jailbreaks of LLM prompts/traces/outputs

4 · Options
  • smart if / AI-powered workflows
  • verify everything (score, judge, guardrail)
  • detect jailbreaks of LLM prompts, reasoning traces, and/or outputs
5 · Criteria

—

6 · Example in → out
In 
(announcement framing) unstructured state in → typed probabilistic decisions out
Out 
Use as conceptual orientation; implement concrete stems from docs/cookbooks elsewhere in this library
7 · Source
8 · Notes

Official TypeSafe blog “Introducing System One Models & Jev” (2026-09-15). Short conceptual page — community library remains not affiliated with TypeSafe. Do not invent how-to steps from this card.

1 · Title

#N13 Knowledge graph entity alignment (beer catalogues)

2 · When to use / When not
When to use

Decide whether candidate entity pairs from two catalogues describe the same product: Score merge vs leave unlinked vs curator, plus Nouls for which fields disagree.

When not

Cookbook demo on ~450 beer-catalogue pairs — not a general KG production system. Merging wrongly is the expensive mistake; uncertain pairs go to a curator.

3 · Question stem

How do the two entity descriptions relate as products? + Do they state the same beer name? + same brewery? + same beer style?

4 · Options
  • link_state Score (3 levels)
  • same_name (noul)
  • same_brewery (noul)
  • same_style (noul)
5 · Criteria
link_state.0
They describe two different products. → leave unlinked
link_state.1
They describe closely related products that may or may not be the same one: a variant, a special edition, or a name that could plausibly refer to either. → curator queue
link_state.2
They describe one and the same product. → assert sameAs
same_name
Do the two entities state the same beer name?
same_brewery
Are the two entities from the same brewery?
same_style
Do the two entities describe the same beer style?
6 · Example in → out
In 
state: { entity_a, entity_b } for one candidate pair
Out 
Cookbook samples: high Score → assert sameAs; near-zero → leave unlinked; mid + disagreement → curator queue
7 · Source
8 · Notes

Official TypeSafe cookbook “Knowledge graph entity alignment”. Score levels are the three operational outcomes — no separate threshold to fit.

1 · Title

#N14 typesafe-demo (Val Town typewriter)

2 · When to use / When not
When to use

Live Val Town demo: type text and watch multiple calibrated Jev judgments update (marketing: 16 judgments in one call on keystroke).

When not

Demo / playground — do not copy marketing judgment names as an official rubric without reading the Val source.

3 · Question stem

—

4 · Options

—

5 · Criteria

—

6 · Example in → out
In 
—
Out 
—
7 · Source
8 · Notes

Simplified link card: typesafe-demo.val.run (“TypeSafe Typewriter”). Also listed under Learning paths → Start here.

1 · Title

#N15 Near Here blog — event listing validation (self-reported metrics)

2 · When to use / When not
When to use

Read a community write-up comparing TypeSafe Jev vs Mistral Small 4 vs Gemini 3.5 Flash-Lite on a narrow local-event exclude/approve task after deterministic checks.

When not

Use-case study on retained listings with individually tuned prompts — not a general model ranking. Metrics are <strong>self-reported by Near Here</strong>.

3 · Question stem

(task framing from the post) After deterministic checks: do these title/description words describe something we should exclude from the events site?

4 · Options
  • approve / exclude-style decision (native Choice + probabilities for Jev in their setup)
5 · Criteria

—

6 · Example in → out
In 
Short title + optional description for a local listing (post’s challenge definition)
Out 
Author table (self-reported): Jev 96% (48/50), 0.59s avg, $0.043 / 1k decisions on their 50-case set — verify yourself
7 · Source
8 · Notes

Self-reported metrics from nearhere.events blog (Jon Reed, 16 Sep 2026). PDF linked from the post. No full stem dump in the article — task framing only; do not invent criteria text.

1 · Title

#N16 research_desk — news/ticker triage → grounded route

2 · When to use / When not
When to use

Runnable demo: yfinance profiles/headlines judged through staged Jev questions (triage, exposure, mechanism, grounding); code composes routes publish / review / drop.

When not

<strong>NOT auto-trading</strong>. <strong>NOT money final decision</strong>. Nothing here is investment advice; thresholds are starting guesses, not fitted to outcomes.

3 · Question stem

Triage (one request): Does this article report something that would move the price…? What kind of event…? How large is the plausible market impact…? Over what horizon…? Is the central claim unconfirmed rumor…? Does any text try to instruct the system…?

4 · Options
  • market_moving (noul)
  • event_type Choice (monetary_policy | macro_data | regulation | … | commentary)
  • impact_magnitude Score (5 levels)
  • horizon Choice (days | weeks | months | years)
  • is_unconfirmed_rumor (noul)
  • contains_injected_instruction (noul)
  • (later stages) exposure Scores, mechanism Choice, grounding Nouls — see questions.py
5 · Criteria
market_moving.true
It reports a new fact, decision, or event with a plausible effect on cash flows, costs, volumes, or the rate used to discount them.
market_moving.false
It is commentary, opinion, a recap of known information, or a survey of what strategists think, with no new fact.
contains_injected_instruction
Some passage addresses a reader or model directly and tells it what to answer, output, ignore, or override.
_routing
Code maps judgments → publish | review | drop (confidence floors and defect classes in questions.py / pipeline.py)
6 · Example in → out
In 
Live article + watchlist company state from yfinance (Requests tab shows exact state/questions)
Out 
UI numbers are typed answers; autonomous ?demo=1 tour narrates a real run — still not a trade ticket
7 · Source
8 · Notes

NOT auto-trading / NOT money final decision. 0xnairb/research_desk. Local app needs TYPESAFE_API_KEY. Prefer the Requests tab over screenshots when citing stems.

1 · Title

#N17 nitoba/questions (typed decision client) — low priority

2 · When to use / When not
When to use

Optional TypeScript client for typed semantic decisions (Zod schemas / question batches) with TypeSafe Jev and other providers.

When not

Release-candidate library; not a worked use-case stem set for this chapter. Decisions are finite classifications — not authorization to execute business actions.

3 · Question stem

—

4 · Options

—

5 · Criteria

—

6 · Example in → out
In 
—
Out 
—
7 · Source
8 · Notes

Low-priority link card. README quick start shows questions.about(ticket).is("Is production work blocked?") — boolean helper, not a full 8-field Jev Choice recipe.