shipwithjev

Blog

What the builds in the catalog add up to. Every number is as reported by its author.

  1. 01

    LLM as a Judge: How It Works and What It Costs in 2026

    LLM as a judge, explained with real numbers. How the pattern works, where it breaks, and why fast decision models changed the cost math in 2026.

  2. 03

    Jev Use Cases: What 600+ Real Builds Say People Actually Do With It

    Every Jev use case worth knowing, drawn from 600+ cataloged builds: email triage, browser agents, real-time games, judging, trading, and the weird stuff.

  3. 04

    Email Classification With AI: 500 Emails for 3.5 Cents

    Email classification with AI, priced from real builds: 500 emails for 3.5 cents, fraud detection at 96/100 for $0.07. How AI email triage works with Jev.

  4. 05

    Jev vs GPT: Decision Model vs Chat Model, Honestly Compared

    Jev vs GPT is the wrong fight and the right question. When a decision model beats a chat LLM, when it loses badly, and when to run both. Real numbers.

  5. 06

    How to Get Started With Jev (Without Cargo-Culting the Hype)

    Getting started with Jev, TypeSafe AI's decision model: the mental model, how to design judge questions, your first project, and where the docs live.

  6. 07

    Support Ticket Triage With AI: The Most Underrated Jev Use Case

    Support ticket triage is the highest-ROI, least-hyped decision-model use case. How LLM routing works with Jev, what it costs, and the lead-scoring twin.

  7. 08

    AI Browser Agents on Jev: 7-Second Tasks for Tenths of a Cent

    AI browser agents when the decision layer costs $0.001 and answers instantly: flight searches in 7 seconds, Stagehand runs, screenshot-free computer use.

  8. 09

    Jev Plays Doom: What Real-Time Games Prove About Instant AI

    Builders have Jev playing Doom at 10 decisions a second, Mario, and Slay the Spire 2 at 0.7s a move. Why real-time games are the proof that matters.

  9. 10

    Jev Pricing in Practice: What 600+ Real Builds Actually Cost

    No marketing math: the reported real-world costs of Jev builds, from $0.0004 scoring runs to $7/hour Doom. Plus where it lands in the cheapest-LLM hunt.

  10. 11

    AI Lead Scoring in 2026: 700 Leads in 40 Seconds for 9 Cents

    AI lead scoring got cheap enough to run on every lead, live. How decision-model scoring works, what it costs, and how to replace the points spreadsheet.

  11. 12

    LLM Evals: How to Build an Eval Suite That Actually Catches Regressions

    A practical guide to LLM evals in 2026: what to test, how judge models grade at scale, and why cheap verdicts mean you can eval every commit.

  12. 13

    How to Write LLM Judge Questions (The Skill Behind Every Good Build)

    The craft behind every working judge pipeline: turning fuzzy criteria into rulings. Question patterns, worked examples, and the failure modes to avoid.

  13. 14

    Jev Review: One Week and 600 Builds In, Is It Actually Good?

    An honest Jev review from the site that catalogs every build: what the decision model is genuinely great at, where it disappoints, and who should skip it.

  14. 15

    Jev Limitations: What It Can't Do (Read Before You Build)

    The unhyped list: everything Jev can't do, where verdicts wobble, and the design mistakes that turn a great decision model into a bad experience.

  15. 16

    Jev Alternatives: The Honest Landscape for Fast, Cheap AI Decisions

    Every real alternative to Jev for decision workloads: small chat LLMs, trained classifiers, embeddings, rules, and when each one beats the decision model.

  16. 17

    AI Content Moderation: Judging Every Post Instead of Sampling

    AI content moderation used to mean sampling. At decision-model prices you judge every post, comment, and review live. Architecture, costs, and limits.

  17. 18

    LLM Routing: The Cascade Architecture Eating Every AI Stack

    LLM routing explained: how cheap-model-first cascades hit near-frontier accuracy at 1% of frontier cost, with confidence-gate design and real numbers.

  18. 19

    Jev vs Claude Haiku vs Gemini Flash: The Small-Model Bracket

    The comparison that actually matters: Jev against the fast cheap chat tiers. Interface, economics, and workload-by-workload calls, hype-free.

  19. 20

    Structured Outputs From LLMs: Stop Parsing Prose for a Living

    Structured outputs from LLMs: schema modes, their failure points, and why decision models sidestep the parsing problem for classification work.

  20. 21

    LLM vs Traditional ML for Classification: An Honest Decision Guide

    LLM or trained classifier? A 2026 guide: where each wins on cost, accuracy, and maintenance, and why decision models moved the crossover point.

  21. 22

    AI Data Labeling: When the Judge Becomes the Annotator

    AI data labeling flipped: models now label datasets for humans to audit. The workflow, the reported costs, and the quality controls that matter.

  22. 23

    AI Agent Verification: Trust, but Judge

    Agents claim they finished the task. Verification is how you know. The judge-layer pattern for checking agent work, with real builds and failure modes.

  23. 24

    Natural Language Database Queries: WHERE jev(people, 'could work from home')

    A Postgres extension puts an AI judge inside the WHERE clause: natural language filters over real rows, 129 rows a second. How it works, where it belongs.

  24. 25

    Jev for Developers: Verdicts in Your CI, PRs, and Agent Loops

    Where a decision model earns a place in a dev workflow: PR verdicts, test triage, CI quality gates, log classification, and context compaction.

  25. 26

    Jev for Marketers: When Growth Decisions Stop Being Vibes

    What the content-and-growth builds prove: ad teardowns at scale, pre-publish scoring, reply filtering, and marketing judgment as cheap measurable verdicts.

  26. 27

    Jev for Ecommerce: Catalog, Reviews, and Support at Verdict Prices

    The ecommerce jobs that are secretly classification: product categorization, review screening, support triage, and listing moderation, priced in cents.

  27. 28

    The First Week of Jev: A Launch Told Through What Got Built

    Jev launched in mid-September 2026 and the builds arrived faster than the takes. The first week's story, told through what shipped, category by category.

  28. 29

    Jev Trading Bots: What People Built, and Why We Catalog It With an Eyebrow

    Within 48 hours people wired Jev to real money: a $10K live experiment, a 300ms on-chain bot. What the trading builds show, and why speed is not edge.

  29. 30

    Jev, Robotics, and the Edge: The Smallest Category With the Longest Fuse

    Six builds, one big implication: a model fast enough for game loops is flirting with control loops. The honest state of Jev in robotics and edge devices.

  30. 31

    How to Reduce LLM Costs: The Playbook, Ranked by Payback

    The LLM cost-cutting playbook in payback order: cascades, right-sizing, caching, batching, and prompt diet, with receipts on what each lever saves.

  31. 32

    RAG Evaluation: Measuring Whether Your Retrieval Actually Grounds Anything

    RAG evaluation decomposed: retrieval relevance, answer faithfulness, and completeness as judge verdicts you can run on every query, not every quarter.

  32. 33

    LLM Guardrails: The Judge Between Your Model and the Send Button

    Guardrails are verdicts on your own AI's outputs before they reach users: policy, grounding, tone, and safety checks cheap enough to check everything.

  33. 34

    Prompt Injection Detection: Judging Input Before Your AI Reads It

    Prompt injection is the SQL injection of the LLM era. How a cheap judge layer screens untrusted input before your main model reads it, and its limits.

  34. 35

    Sentiment Analysis With LLMs: From Vibes Dashboard to Verdicts

    Sentiment analysis grew up: decision models judge emotion, intent, and churn signals per message for fractions of a cent. How to avoid the old traps.

  35. 36

    Intent Classification: The NLU Job That Just Got a New Engine

    Intent detection powered chatbots for a decade, badly. How decision models replace trained NLU intent classifiers: no training data, editable taxonomies.

  36. 37

    The Decision Model Glossary: Every Term This Site Uses, Defined

    The working vocabulary of the decision-model era, defined in plain language: verdicts, cascades, confidence gates, judge questions, blocking, grounding.

  37. 38

    Entity Resolution: "Are These the Same Thing?" at a Cent a Thousand

    Entity resolution is the oldest data problem wearing new prices: record matching, dedup, and identity linking as pairwise judge verdicts, with blocking.

  38. 39

    Document Classification: Sorting the PDF Mountain for Cents

    Document classification with decision models: route contracts, invoices, forms, and reports by type, urgency, and risk, chunking realities included.

  39. 40

    Churn Prediction With AI: Reading the Leaving Before the Left

    Churn prediction without the data-science project: judging cancellation language, sentiment trajectory, and risk signals in the text customers already send

  40. 41

    AI Call QA Scoring: Grading Every Conversation, Not Two Percent

    Contact-center QA reviews 1-2% of calls and calls it quality. How judge verdicts on transcripts score every call for compliance, empathy, and outcomes.

  41. 42

    Fine-Tuning vs Prompting vs Decision Models: Picking Your Adaptation

    The three ways to make a model yours: prompting, fine-tuning, and decision-model question design. When each wins, what each costs, and the sequencing.

  42. 43

    AI Spam Detection: Filtering an Adversary, Not a Category

    Spam is the one classification problem that fights back. How decision-model filters handle adversarial text, what breaks them, and the layered defense.

  43. 44

    AI Invoice Processing: The Back Office Meets the Verdict Machine

    Invoice processing as judgments: categorization, PO matching, anomaly flags, and approval routing at verdict prices, with the controls finance requires.

  44. 45

    Jev in Production: The Ops Guide the Launch Threads Skipped

    The unglamorous guide to running a decision model in production: caching, retries, versioning, monitoring, batching, and the failure modes that page you.

  45. 46

    Jev Security and Privacy: What Leaves Your Systems, and What Shouldn't

    The security questions to ask before wiring a decision model into real data: what leaves, PII minimization, local-perception pattern, and audit posture.

  46. 47

    Jev for Founders: The Solo Operator's Unfair Advantage Stack

    What one person can automate with a verdict machine: inbox, leads, support, content QA, and ops checks, assembled from real builds into a solo stack.

  47. 48

    Why Decisions Became Free (And What Gets Built Because of It)

    The essay version of what 600+ builds are saying: when structured judgment costs nothing, software grows a judging layer everywhere, and the map redraws.

  48. 49

    AI Resume Screening: The Use Case That Demands Adult Supervision

    Resume screening with judge verdicts: what it does well, where bias law applies, why NYC-style audit rules exist, and the design that keeps hiring human.

  49. 50

    AI Grading: Feedback at the Speed of Homework

    AI grading done honestly: rubric verdicts on short answers, instant formative feedback, and the line between grading support and grade automation.