Every source GILD analyzed, newest first. Each says who the source is, how it knows, whether it still applies and which findings it counts toward. Open a count to see only its sources.
Source
52 sources in Teams and technical leaders, with product documentation Showing all 139 sources
No opinion stated
Neurotype experiments scored by JevX post
Kyle Wild runs a few million steering experiments on Qwen3.5-4B, 67 prompts per configuration, and has Jev and Fireworks Rune evaluate every output.
Positive
Jev inside the Lightpanda browserX post
Lightpanda put Jev inside its browser binary and built a judge() call on it that asks predefined questions and says whether a page has real content, and what kind, in about 0.2 seconds per call. In its video, 30 sites took about 14 seconds in total.
Mixed
Decision Models: Jev vs. a Fine-Tuned ClassifierEngineering blog post, Anaconda
On 2,400 held-out consumer complaints, Anaconda found zero-shot Jev below a most-common-answer baseline on every question. It also trailed XGBoost, even with the labeling rules in the prompt. Its adjusted probabilities matched its accuracy, at about US$0.00003 per complaint.
No opinion stated
We tested Jev as a reranker against Voyage, Cohere and Qwen on 1,000 eval casesReddit post, r/LLMDevs
kapa.ai ran rerankers over 1,000 cases from its Company Knowledge Bench. Jev 1.14 scored 0.532, 0.003 behind Voyage rerank-3 at 0.535 and ahead of Cohere rerank-v4.0-pro at 0.506, but Voyage was also the cheapest and fastest, so kapa.ai is staying with Voyage.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
How to Build a Decision Model Like JevEngineering blog post, Oumi
Oumi's tests found Jev 1.13 the most accurate model they tried at every document length. Its confidence tracks accuracy on rare-fact questions but grows overconfident on long inputs and on tasks such as prompt injection.
Not counted
Its authors work at Oumi, which sells a platform for building models that compete with Jev, and the post compares them. Kept in the record; it takes part in no figure.
Positive
Beyond the leaderboard: decision models in a real workflowEngineering blog post, Isotopes AI blog
Isotopes AI ran its 88-scenario workflow suite three times per model. Across the three runs Jev 1.13 passed 100% at a 0.807-second median against 68.2% and 1.546 seconds for OpenAI Decisions. The Jev 1.14 pre-release build passed 93.6%, and Jev 1.13 stays the production default.
Counts toward
Mixed
Jev decisions on photos with the box typeX post
A Clouted AI engineer finds most foundation-lab models beat Jev on photo accuracy, while Jev's speed and the small engineering effort stand out against production computer vision.
Negative
Jev Talking: Security Concerns for Models That Return Decisions Instead of TextEngineering blog post, HiddenLayer
HiddenLayer Research found a planted ad steered a Jev browser agent to the attacker's button with 91% probability, and spelling edits slipped toxic prompts past Jev used as a filter.
Positive
Jev handed our LLM the wrong page a tenth as often as our embeddingsEngineering blog post, Sayora
Sayora's write-up reports Jev selecting a wrong page on 3% of 168 questions versus 33% for chunk embeddings, while noting the test setup and remaining retrieval caveats.
Counts toward
Mixed
We started using JEV to clarify queries before retrieval, and the results have been pretty interestingReddit post, r/Rag
The team puts Jev before retrieval to check whether intent is clear and whether a user asked one question or several. They report five Jev checks per query at about US$0.000282, while still working on missing conditions in generated answers.
Mixed
I got Jev to zero mistakes. I'm still using Flash-Lite.Developer blog post, DEV Community
Major League Hacking compared Jev with Gemini Flash-Lite across thousands of routing runs. Swift reports zero dangerous calls from Flash-Lite and says the team still uses it in production, so the post compares the models without endorsing Jev.
Mixed
Jev: Decision Models on TrialEngineering blog post, Cisco Blogs
Cisco's AI Defense team found a 1B encoder trained on its policy beat zero-shot Jev at a 0.5% false-positive budget on all six safety datasets. Recall was 61% against 34% on Cisco multi-turn. Jev's AUC beat a prompted Gemma 4 31B judge on seven of eight benchmarks.
Mixed
Hidden Risks of Jev: An Empirical Study of Security, Privacy, and Dual UseIndependent benchmark, arXiv
The paper finds Jev open to prompt injection and to inference attacks that recover private information despite its typed outputs, while its decisions also serve detection work such as spotting jailbreak inputs.
Counts toward
No opinion stated
Daily usage endpoint that works with an API key?GitHub issue, typesafe-ai/typesafe-sdk-python
iFixit runs Jev in FixBot and has to script the logged-in console to pull daily usage, because Jev's API offers no usage endpoint for an API key.
Counts toward
No opinion stated
Comment on: choosing a decision architectureReddit comment, r/LLMDevs
A magicdoor.ai contributor, whose product routes with Jev, advises comparing it against a cheap rules or encoder baseline on a held-out, time-split set of real decisions, tracking error cost, calibration and p95 latency per route.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
Positive
Building a harness with JevEngineering blog post, LangChain
LangChain describes building a harness around Jev and the typed decision interface. It is an engineering account from a model-platform vendor and is kept as context for integration, separate from independent measurements.
No opinion stated
Comment on: where decision models fitReddit comment, r/LLMDevs
Encatch runs Jev live in production as an optional feature of its feedback platform, for decisions where speed and cost beat a traditional LLM call.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
Positive
Jev guardrails in production: a security platform's front door for five cents a day.Engineering blog post, Metaheuristic
Metaheuristic replaced a domain blocklist and an LLM judge with Jev at two gates for a cybersecurity client. Both gates together cost about five cents a day. The team keeps a person behind every stop because text on an untrusted site can sway Jev's answer.
Negative
Jev vs. Claude vs. GPT: Your "Critical" Findings Are Stealing Your SprintEngineering blog post, Casco
Casco scored 2,449 findings with eight models against its published CVSS 3.1 scores. Jev assigned 550 critical scores where the reference held 55 critical findings, and 500 of those were below critical; every model over-scored, GPT-6 Astra least overall.
No opinion stated
TypeSafe Jev guide and practical use casesBlog post, Composio
Composio added a Jev toolkit to its Tool Router with two tools, Evaluate State and List Models, so an agent can call Jev beside its other connected tools on the customer's own API key.
"Composio now supports the Jev toolkit. It has two API tools: Evaluate State for running Choice, Score, or Noul questions and List Models for retrieving available Jev models."
Not counted
Its publisher, Composio, offers a Jev toolkit in its own Tool Router and promotes that integration here. Kept in the record; it takes part in no figure.
No opinion stated
Jev for beginners: how to use it and what to buildPodcast episode, How I AI
Claire Vo runs Jev on five real projects in the episode, including analyzing 1,700 pull requests for 9 cents and a ChatPRD product-insights project of 1,100 signals and 200,000 classifications.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
Positive
Racing Spinny's production ticket routing against JevX post
A Spinny engineer raced the team's production OpenAI ticket categorization and urgency detection against Jev and found Jev about 9 times faster and 10 times cheaper.
Positive
Deel put Jev head-to-head with frontier LLMsX post
Deel reports Jev up to 59 times cheaper and 4 times faster than frontier LLMs. Quality was at parity or better on 6 of 8 checks, and repeat-question accuracy rose from 70% to 97%.
No opinion stated
Jev vs. Kev: open-source Jev alternative tested side by sideReddit post, r/LocalLLaMA
On 362 items published after both models shipped, Jev and the open Kev 4B landed within 2 points on every task, with Jev ahead on paraphrase detection at 87.0% against 74.5%. Jev counts a fixed 257 extra input tokens per request, so short requests cost up to 12 times more.
"Jev vs. Kev: open-source Jev alternative tested side by side We hosted Kev 4B (Jared Palmer's Apache-2.0 fine-tune of Qwen3.5-4B) and ran it side by side with Jev on the same endpoint to see how it compares. We built a fresh set of 362 items published after both models shipped (new arXiv papers, Stack Exchange questions, GitHub issues), with answers taken from the source. A few findings: \- Accuracy lands within 2 points on every task, inside the noise at this sample size \- Jev is better calibrated and pulls ahead on paraphrase detection (PAWS 87.0% vs 74.5%) \- Same list price, but Jev counts a fixed \~257 extra input tokens per request (same count calling TypeSafe directly), so short requests cost up to 12x more Benchmark code, test items and results are on GitHub if you want to run your own. Both models routed via my startup Opper. Happy to dig into specifics."
Not counted
Its author routes Jev through Opper, his own startup, which resells model access. Kept in the record; it takes part in no figure.
Positive
Decision Models vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same PlacesIndependent benchmark, arXiv
Rao and Callison-Burch find flash-tier LLM judges cost 16 to 357 times as much as Jev and take 5 to 350 times as long. Jev trails them significantly in at most 6 of 27 comparisons.
No opinion stated
JevOut: Natural Context Can Flip Decision ModelsIndependent benchmark, arXiv
The paper shows short, natural-looking context additions redirect 61.4% to 73.2% of initially correct decisions in four decision systems, Jev included, often into confident wrong choices.
Counts toward
Positive
Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment FailuresIndependent benchmark, arXiv
Across 44 alignment benchmarks, one generic Jev question reaches a median AUROC of 0.886 zero-shot, beats supervised baselines on most benchmarks, and costs 63 times less than LLM-judge scorers.
Mixed
We Added Jev as a Reranker. Here's What We Learned.Engineering blog post, Hindsight
Hindsight shipped Jev as a reranker in 0.10.1 after measuring recall@1 of 0.950 against 0.800 for its default local reranker. The team left Jev's pruning off by default because it cut 19% of the gold evidence, and warns that memory text goes to a hosted third-party API.
"**It is a hosted third-party API.** Jev runs at TypeSafe's endpoint and needs an API key."
No opinion stated
Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document UnderstandingIndependent benchmark, arXiv
On ContractNLI, the paper finds Jev the cheapest and fastest option for the joint baseline while hosted language models reach higher accuracy.
No opinion stated
Jev isn't an LLM killer, and it isn't just a classifier. We put it in production with real users.Reddit post, r/AI_Agents
Vestra runs Jev in its agent harness for tool-call gating, guardrails and computer use: the harness lists what is on screen, Jev picks the next action, and an LLM steps in only for planning or writing. The founder gates actions on confidence, because a wrong answer in the right format is harder to catch than a broken one.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
Positive
How Jev cut the answer time in half for our smart search agentEngineering blog post, Serverpod
Serverpod put Jev into Starguide to pick the right documentation pages before the answer model runs, falling back to full retrieval when nothing fits. Time to the first streamed token roughly halved, and Jev now also checks whether each question was answered.
Mixed
JEV-as-a-Judge: Accept When Confident, Escalate When UnsureIndependent benchmark, arXiv
The paper finds Jev within three points of GPT-6 as a judge wherever the verdict can be read off the text. It costs 0.36% of the fee at a 0.15-second median and trails in math, code, and logic.
No opinion stated
Replacing 10,000 Jev calls with one Opus 5.5 callX post
dessn replaced more than 10,000 Jev calls with a single Opus 5.5 call, which cost a bit more and ran slightly slower but gave significant accuracy gains.
"We replaced over 10,000 jev calls and a few fly brains with 1 opus 5.5 call."
Counts toward
No opinion stated
Bookkeeping with JevX post
Says a weekend build fed Jev 34 months of work a bookkeeping firm had charged over US$20,000 for, and Jev did it better in 20 seconds for US$0.32. The post gives no method or accuracy check.
No opinion stated
299 real user intents tested Jev against production base lineReddit post, r/AI_Agents
On 299 real intents from a Feishu life-assistant router, Jev scored 87.6% against 90.6% for the production glm-4-flash, at a 0.55-second median against 3.6 seconds and with zero format failures against 16. The author would put Jev first on the critical path, with a generative step still needed to extract parameters.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
Positive
Introducing prompt_jev(): bringing Jev to MotherDuck SQLEngineering blog post, MotherDuck
MotherDuck shipped prompt_jev() for text classification in SQL. On 100,000 AG News rows, MotherDuck measured Jev matching frontier LLM accuracy in 40 seconds for fifty cents, against more than half an hour and US$37 for the comparable LLM.
"Today we're shipping an integration with Jev, a new kind of AI model from TypeSafe AI. On a 100,000-row benchmark it matched frontier LLM accuracy in 40 seconds for fifty cents. The comparable LLM took more than half an hour and cost $37."
Not counted
Its publisher, MotherDuck, ships prompt_jev(), a Jev integration on its paid plans, and the post announces it. Kept in the record; it takes part in no figure.
Mixed
Models watching modelsEngineering blog post, Southbridge AI
Southbridge used Jev as an observer on its agent runs. Adding Jev's readings cut held-out forecast error for remaining time by 17.84% for $2.30 of usage. Adaptive attacks found 15 bypasses of its harmful-command gate.
Mixed
Jev lost to Gemini on our email classification benchmarkX post
Gemini beat Jev on Bryo's benchmark of 1,565 business emails from industrial suppliers, and Bryo's CTO still wants to put Jev into production because of where its mistakes fell.
Counts toward
Mixed
A day of benchmarking Jev against Brief's production judgesX post
Brief found Jev matched or beat its production judges on fixed-label tasks at about 100 times lower cost and a 100 to 200 ms median. It failed on judgment tasks, finding none of 13 human-identified code-review problems.
Positive
jev-ultrafastGitHub repository
In Browser Use's jev-ultrafast agent, Jev picks each operation and target element and a small LLM types only when text is needed. Its own test on one Google Flights task cut the median from 9.450 s to 7.092 s over three runs per version.
"Median task time went from **9.450 s → 7.092 s**, a **25% reduction**; median browser protocol calls went from **1,092 → 101**."
Not counted
The repository is branded Browser Use × TypeSafe and promotes Browser Use's Cloud waitlist. Kept in the record; it takes part in no figure.
Positive
We're seeing extraordinary results from @typesafeaiX post
Vercel's CEO reports Jev up to 18 times faster at p95 than GPT Luna as the safety reviewer for every command in fx auto mode. He finds it more accurate too and expects it to become the default.
Positive
We benchmarked fx auto mode (safety) classifier with @typesafeai's JevX post
Vercel's fx team benchmarked its auto-mode safety classifier on Jev and found it about 5 to 18 times faster than GPT-5.6 Luna, its top choice at the time, and more accurate.
No opinion stated
Tested decision models (Jev, Kev, Clef, OpenAI Decisions): the confidence score really worksReddit post, r/LLMDevs
Testing Jev, Kev, Clef and OpenAI's Decisions API on comment categorization, the author found the confidence score held up: accept the confident answers and review only the doubtful ones.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: open decision modelsHacker News comment
On a first use case, informal tests of decision models on OpenRouter found only respan/span-02, an open and cheaper model, came close to Jev.
"I had my first use case recently, and did some very non-systematic benchmarking of models on OpenRouter. The only one that came close to Jev itself was the one from respan (respan/span-02), which is also open source and cheaper too."
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
decide(): no way to enable Jev long-context mode (TypeSafe-Danger-Zone-Long-Context header)GitHub issue, mattapperson/noetic
A probe placing one decisive fact at the start, middle or end of states from 16k to 243k tokens got correct Jev 1.14 answers at every tested position. The workflow's 51k-token decisions already exceed the documented 32k default.
No opinion stated
Comment on: classifiers and decision modelsHacker News comment
The commenter's team is trying Jev to match near-random customer product descriptions to category lists a government revises several times a year, and asks which dedicated classifier would do that better.
"What we've been trying Jev for is interpreting things like short product descriptions provided by customers, and determine which of the natural language categories fits best. The list of categories are provided by the government and can change several times a year, and the product descriptions are almost random (think the various titles of AliExpress listings of essentially the same product). What kind of dedicated classifier would be better? Genuinely curious."
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: classifiers and decision modelsHacker News comment
Points to a benchmark the commenter ran in which model-based classifiers, Jev among them, fall behind from about four classes.
"> and not using in anger Is this a typo? are you saying that this is so new people haven't struggled with Jev enough yet? very interesting > it starts to break down over 20 classifications Yes, I recently ran a benchmark on exactly that: https://jeffyclassify.com/blog/when-llms-fail-at-classificat... At around 4 classes already LLM-based classifiers (Jev/Laya/GLiNER et al) start falling behind…"
Not counted
Its author created Jeffy, fast local classifiers that run and train on CPU and compete with Jev, and the benchmark sits on Jeffy's site. Kept in the record; it takes part in no figure.
No opinion stated
Jev for image classification on an art datasetX post
On an art image set, Jev scored 90.7% at $0.00015 and 0.5 seconds per image, against 95.6% at $0.00012 and 2.6 seconds for GLM 5.3 Flash.
Positive
Weekend tests of Jev against ClefX post
Jeremy Morrell's weekend tests put Jev ahead of Clef on cost, quality, and latency by wide margins.
Mixed
Comment on: The rise and fall of Jev hypeHacker News comment
Says TypeSafe overhyped a useful product and got it in front of AI developers, and calls that, snarkily, founders doing their job well.
No opinion stated
Remove the Jev (TypeSafe) injection classifier; keep the regex scanGitHub issue
The relevo maintainer is removing the optional Jev injection classifier and keeping the regex scan. Nobody had configured it, its 0.7 threshold was never calibrated, and it added a paid dependency and a secret to protect.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: Using LLM for confidence scoring is a bad ideaHacker News comment
Points to section 5 of a Sebastian Raschka article on training LLMs for confidence scoring. RLCR adds a Brier penalty to the reward; in the commenter's hypothetical model that is right 60% of the time, answering 0.9 on easy items and 0.3 on hard ones cuts the expected penalty from 0.24 to 0.15. The commenter presumes TypeSafe's RLCD method is similar and notes TypeSafe has published nothing on it.
No opinion stated
[Harness]: JevGitHub issue
The author runs Jev as a kagent judge agent for yes/no and multiple-choice decisions inside agent workflows, where a decision model is cheaper and more consistent than a general LLM. It keeps no conversation history and has no streaming or tool calls, because System One offers none.
"[Harness]: Jev ### Integration A harness that runs TypeSafe's [Jev decision model](https://docs.typesafe.ai) as a kagent agent on the BYO runtime. Each A2A message is a System One request, and the reply is System One's response as-is. The agent's system prompt becomes the default question, so callers only need to send the state. I use it for "judge" agents that make yes/no and multiple-choice decisions inside agent workflows, where a decision model is cheaper and more consistent than a general LLM. It currently lives at github.com/airohq/agent-harnesses, and I'd like to move it here, as Eitan suggested in Discord. ### Maintainers @ernoaapa ### Integration and build approach A small Go module built on kagent's own packages: - adk/pkg/app for the A2A server - harness/runtime/a2a for turn handling. It reads the model and base URL from the agent's ModelConfig via KAGENT_CONFIG_JSON. Tested with kagent 1.0.0-alpha16 and Agent Substrate 0.6.0-alpha3 in my own cluster. Builds with a plain multi-stage Dockerfile into a distroless image `docker build --tag byo-ext/jev:local harnesses/jev` ### Known limitations and dependencies - Needs a TypeSafe API key. Use an OpenAI-type ModelConfig pointing at TypeSafe's API, and kagent's egress gateway adds the key, so it never enters the sandbox. - Stateless: each message is evaluated on its own, with no conversation history. - No streaming and no tools/MCP. System One doesn't have them."
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: content moderationReddit comment, r/LLMDevs
The author chose Jev for moderation after testing it against OpenAI's published moderation test set, where it did better than OpenAI's moderation endpoint for the product's needs in limited tests.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
TypeSafe reranker can exceed Jev context within the local token budgetGitHub issue, vectorize-io/hindsight
Jev's default context limit rejected a reranking request that fit Hindsight's 26,000-token budget, which surfaced as HTTP 500 in production until the long-context header was sent.
Counts toward
Positive
Replacing a FastText and Gemini pipeline with one Jev callX post
Arpit Tripathi replaced MemX's FastText and multi-stage Gemini pipeline with a single Jev call at about 98% accuracy.
No opinion stated
Jev Router Experiment with OSS models for Coding TasksIndependent benchmark, GitHub repository
Routing a coding agent with Jev passed 40 of 50 hidden-test tasks against 42 of 50 on fixed Kimi K3 at 24% lower total cost. The 101 Jev routing calls cost $0.0024 at a 342 ms median.
No opinion stated
Comment on: email-reply classificationReddit comment, r/LLMDevs
On an email-reply classifier with about 14 categories, ianreboot measured Jev at 98.69% against 96.08% for DeepSeek Flash, at a 334 ms median against 1,117 ms and lower cost for the whole run.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
Mixed
Comment on: We're releasing Jev 1.14.0 along with experimental features for dreamersHacker News comment
Grants that the team is trying to build something useful, then raises three objections. Adding bounding boxes compounds Jev's main issue, a massive model doing what purpose-built models have done for years, a little worse and far more expensive. The commenter also calls the launch charts borderline dishonest and the job postings insulting, and sees no production use for Jev 1.x.
Positive
Comment on: We're releasing Jev 1.14.0 along with experimental features for dreamersHacker News comment
Recommends a linked TypeSafe blog post on AI benchmarks, titled antibenchmaxxing.
No opinion stated
Comment on: Jev meets the Trolley ProblemHacker News comment
The trolley-problem post's author accepts the criticism but argues nobody, TypeSafe AI included, knows yet what Jev is for. The post explores what one can ask a model like this and how to ask it.
No opinion stated
Comment on: Jev meets the Trolley ProblemHacker News comment
Recalls testing Jev on the "Food Cube Rule" and asking whether ravioli is a dumpling, which Jev rated 82% true. The commenter reads the trolley example as showing that context drives the answer, and says users must steer any model's bias toward the outcome they want.
No opinion stated
0.7.0 fails every request on Bun with "TypeError: Body already used" (bufferResponse)GitHub issue, typesafe-ai/typesafe-sdk-js
Since SDK 0.7.0, every Jev call under Bun fails with a bare TypeError while the same code works on Node 26 and on SDK 0.6.0.
Counts toward
No opinion stated
Evaluate and upgrade the Jev judge to jev-1.14.0GitHub issue, discobox-ai/discobox
The discobox team pins jev-1.13.0 because its thresholds were tuned to that release's probabilities, and gates the 1.14 upgrade on its judge evaluations.
No opinion stated
Comment on: We're releasing Jev 1.14.0 along with experimental features for dreamersHacker News comment
Says many process and workflow systems can become deep decision trees, and that the team's customer presentations went out once TypeSafe took its call and sorted out the paperwork. The team runs evals across every "System One" API through one decision API and trusts only benchmarks it has run itself.
No opinion stated
Building a Fact-Extraction Pipeline w/ JevReddit post, r/LLMDevs
The author uses Jev to classify and rank the facts in long posts for a newsletter's infographic cards, and chose it because the step had to be reproducible and auditable.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
Positive
Our RAG kept retrieving the wrong page, so we tested letting Jev pick pages insteadReddit post, r/Rag
On 168 questions across four sites, Jev handed the answer model a wrong page 3% of the time versus 33% for chunk embeddings. The author says GPT-6 Luna tied the ranking result but was more than twice as slow, and is testing open Jev-style models next.
Positive
Using Jev to make your agents write the code and design the app the way you doPersonal engineering blog post, a7ul.com
Atul uses Jev as a yes/no rule checker around an agent's planning, coding, and review loop, with screenshot checks alongside code rules. The post stresses that rules need tests and describes a Jev result as a flag for review, without a verdict.
Counts toward
No opinion stated
Comment on: Build your own decision modelHacker News comment
Asks whether the author is sharing all of their email content with Jev and TypeSafe AI.
No opinion stated
Comment on: Build your own decision modelHacker News comment
Says the commenter calls Jev through Vercel, which claims Zero Data Retention with TypeSafe, and trusts Vercel about as much as an email provider.
No opinion stated
Comment on: Build your own decision modelHacker News comment
Asks whether this mysticism is what earned TypeSafe AI its US$7.5B valuation, and says the AI-bubble warnings may have a point. The commenter believes AI labs have a solid business model that could soon turn very profitable, and this round raises doubts.
No opinion stated
Comment on: Build your own decision modelHacker News comment
Says the Jev moment is about articles like this one, which let a million Jevs bloom, and has little to do with Jev or TypeSafe. The commenter expects building a Jev or fine-tuning an LLM to become something ordinary developers do, and a bubble once people see that.
Positive
Task classifier with Jev - pretty good experience so far!Reddit post, r/JevAI
The Keen Code creator uses Jev with a TypeSafe-compatible API to label each prompt "simple", "standard" or "complex", then route each class to one of three user-selected models.
No opinion stated
Comment on: routing questions to the right keyReddit comment, r/LLMDevs
On 650 Polish messages with a known correct key, Jev ranked the right key first about 98% of the time, level with the Perplexity router the author uses day to day, while local Von barely told the options apart.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Credits TypeSafe's launch buzz to doomers.ai, a "launch virality agency", and says TypeSafe paid it at least US$100K for that "organic" buzz. The comment links the agency's TypeSafe case study and its marketing guide.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Links the doomers.ai case study on TypeSafe AI, found through an earlier comment, and remarks that obscene marketing seems to be all you need.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Calls TypeSafe's "Safety Javeroni" job opening a good laugh and links the Ashby posting.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Calls Jev a neat API after trying it, and says TypeSafe's job application takes only a few minutes to fill out, unlike Oxide's. The commenter applied and wants to run Jev locally.
Negative
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Suspects the claim covering 1/3 of the Fortune 500 counts companies where one employee put in a credit card against procurement and security policy. The commenter says TypeSafe lacks SOC 2 certification and other enterprise security checks, and expects few procurement teams to approve it soon.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Calls it petty but notes the title is wrong. It reads "Typesafe A Raises Series AI" where it should read "Typesafe AI Raises Series A".
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
A non-native English speaker asks whether the title "TypeSafe A raises Series AI" is wordplay or a misplaced "I" that should read "TypeSafe AI raises Series A".
No opinion stated
Comment on: OpenAI's decision modelHacker News comment
Says the team ran evals on production use cases where it uses Jev and found OpenAI's decision model not close. At Jev's price the commenter sees no need to look for an alternative.
"I have ran a bunch of evals on production use cases where we are using Jev and OpenAI's model isn't close, but I'm hopeful about some of the OSS ones. IDK it's very cheap so I don't really feel like I need to look for an alternative. Why root for OpenAI over Typesafe? Isn't more diversity in the market good?"
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
I tested dynamic model routing for an agentic browser-testing system. Guardrails via Jev worked pretty good.Reddit post, r/LLMDevs
With Jev guarding a fixed GPT-5.5 execution model, Testronaut completed 15 of 15 missions on 13.7% fewer model tokens than the unguarded control. Routing each step to a different provider through Jev produced no overall token advantage over 120 runs.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Points out that the funding headline read "TypeSafe A raises series AI" and asks whether that is AI slop.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Doubts the VCs did their due diligence and says that for a hot company like TypeSafe AI, little diligence was most likely done.
Mixed
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Reads the round as a bet that one more startup AI lab will break out beside OpenAI and Anthropic, with TypeSafe near the top on its people and shipping record. The US$870M check itself raises the odds, because the money draws in more talent and customers. Only a few investors, Martin Casado's team among them, can play at this level.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Links a TypeSafe AI job posting on Ashby and remarks that the company's postings are full of jokes.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Suggests a major VC could fund TypeSafe AI, then require a larger AI company raising a Series E or later round to acquire TypeSafe as part of the deal. The commenter says that arrangement can benefit the VC firm.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Says the commenter only just realized TypeSafe AI is the company behind Jev. The commenter had taken "System One" for the company name, and it names a larger project.
No opinion stated
Comment on: Microsoft-Decision-1, our model for fast decision-makingHacker News comment
Notes that TypeSafe's blog says it benchmarks only public models, and points to section 2.3 of TypeSafe's legal terms.
Mixed
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Says the parent claim is false in theory, because TypeSafe uses a different reinforcement training method that targets calibration. The commenter grants that TypeSafe has shared few details and that the evidence is spotty.
No opinion stated
Comment on: Typesafe AI raises $870M at $7.5BHacker News comment
Counts 30-40 TypeSafe staff on LinkedIn, many of them engineers, and reads a developer-market focus with several Fortune 500 clients already claimed. The commenter credits a real product and strategy, and questions the VC valuations as token prices fall and unit revenue risks falling faster than unit cost.
No opinion stated
Comment on: Microsoft-Decision-1, our model for fast decision-makingHacker News comment
Notes in passing that TypeSafe AI had just raised an enormous amount of money.
No opinion stated
I built a Claude Code plugin that uses Jev for compaction, model routing and skill selectionReddit post, r/LLMDevs
In the optim-jev plugin, Jev decides which older tool output Claude Code can drop during compaction and picks Haiku, Sonnet or Opus for each subagent task, with Claude's own summary as the fallback.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Telling my agent to delegate its browser clicks didn't work; hiding its own click and type tools didReddit post, r/AI_Agents
The author's tool lets Jev pick the element for each browser step an agent plans, and benchmarked it against Claude Sonnet 5.5 clicking on its own. Asking the agent to delegate did not work; removing its own click and type tools did.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
Positive
Comment on: Decisions API is in public betaHacker News comment
Argues an LLM is orders of magnitude slower and costlier for these decisions, pointing to TypeSafe's Doom example, which runs complex queries tens of times per second for about US$7 an hour. For some problems that makes software cheaper than a human.
Counts toward
No opinion stated
Comment on: action-set size in agent gatesReddit comment, r/LLMDevs
Running Jev gates all day, the author finds the mispick rate climbs as the menu of options grows and keeps each step under about 10 options. A forced pick when no option fits was the failure the team had to design around.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: [dead]Hacker News comment
Warns that within days of Jev's September launch, third parties began impersonating Jev with their own "Jev AI" websites and intercepting data meant for TypeSafe. The commenter says the rush to adopt Jev exposes companies to data breaches.
Negative
Comment on: Decisions API is in public betaHacker News comment
Says the commenter's use case would cost about US$11 a month, but the team already has OpenAI keys and billing in place. The commenter wants no model infrastructure to run and no approval process for a new typesafe.ai account.
Positive
Comment on: Decision models like Jev don't beat LLM-as-a-judge or traditional classifiersHacker News comment
Says it is too early to call the race and finds the abstraction useful. Jev is a general-purpose classifier that needs no task-specific training and turns unstructured input into a structured judgment, with a focus on speed and cost. Benchmarks are still scarce, and the commenter adds that TypeSafe has no real moat.
No opinion stated
Comment on: Decision models like Jev don't beat LLM-as-a-judge or traditional classifiersHacker News comment
Agrees Jev differs from LLM-as-a-judge, citing Almeida's claim that RLHF has biased LLM probabilities for good. The commenter reads TypeSafe's docs as computing confidence straight from the probability distribution, allows that this may misread them, and doubts the parent comment's account of it.
No opinion stated
Comment on: prompt-injection checksReddit comment, r/LLMDevs
Asked whether text holds instructions aimed at an AI agent, Jev scored a harmless documentation note addressed to AI assistants at 0.92 to 0.93 and a real attack at 0.92 to 0.94, so no threshold separated them. The author found 100 checks cost US$0.0017.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Jev Isn't an LLM: I Tried TypeSafe AI's Decision Model in GoVideo, YouTube (What The Func?)
Ed Zynda calls Jev from Go for yes/no decisions, routing choices and scoring customer frustration, and says where he would still reach for a regular LLM. He describes the video as hands-on use, without a benchmark.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: jev-pagesHacker News comment
The jev-pages example ran once deployed with a TypeSafe key, but a real landing page built with it disappointed the commenter, who says the idea works and the implementation needs more work.
"…cool idea. I had chatGPT deploy to Control Plane (https://controlplane.com). here is my prompt: "can you create a gvc called jev-pages in aws-us-west-2 and use this for the code https://github.com/sagapranav/jev-pages" and it ran great. I supplied it a TypeSafe API key and it worked. However, I tried using it to create a real landing page and the result was somewhat disappointing. It proves the IDEA, but implementation needs a bunch more work."
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: Gemini 4 ArgonHacker News comment
A formal-methods expert argues LLMs need more safeguards, because with so much generated code, assured code quality is what holds value. The commenter expects verified languages such as Rocq and Lean to matter more than plain type safety.
Mixed
Comment on: Is sandboxing sufficient to contain rogue agents?Hacker News comment
Warns that TypeSafe's Jev jaggedness documentation lists adversarial content as something Jev is very susceptible to, so using Jev as part of a prompt-injection guard is risky. Anthropic, OpenAI and Muse use regular LLM calls for that job.
Mixed
Comment on: Clef: Open-weight decision models, and new RL fine-tuning platformHacker News comment
Finds Jev's confidence score useless as evidence, since TypeSafe's docs say "Confidence is derived from the probabilities." The commenter wants something closer to Bayesian evidence. AI-slop sites quoting that line were easier to find than the docs themselves.
No opinion stated
Feature request: Jev / TypeSafe AI support in @posthog/aiGitHub issue
The author uses Jev through TypeSafe's SDK alongside Gemini and asks PostHog to trace both in one place; until then every Jev call has to be instrumented by hand.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: Clef FlashHacker News comment
ralusek measured Jev at a 230 ms median through TypeSafe and 237 ms through OpenRouter, against 661 ms for Clef Flash.
"Just tested clef-flash vs jev: - Jev/TypeSafe: 230 ms median, 254 ms mean - Jev/OpenRouter: 237 ms median, 267 ms mean - Clef Flash: 661 ms median, 806 ms mean What gives?"
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: ChimpBench - Chimpanzee Population SimulatorHacker News comment
The ChimpBench author runs a basic FastinoAI GLiNNER 2.5-Decider in the hosted browser version. Locally the author runs TypeSafe's Jev and Decider fine-tunes that lean toward aggression or toward collaboration, and plans to open-source the code.
No opinion stated
Updated my memory system to use jev as a reranker 97% on LongMemEvalReddit post, r/Rag
turlockmike added Jev as the reranker in deep-recall, a memory system built on hybrid vector search and graph search, reports 97% on LongMemEval, and credits Jev with a large share of the gain.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: Show HN: Jevpipe - a fast System 1 for AI agents, as a Unix pipeHacker News comment
Praises Jevpipe and says the commenter finally opened a typesafe.ai account and put US$5 into it.
Mixed
Comment on: Jeff - Jev-compatible 0.8B decision models, trained at home, ~30 msHacker News comment
Introduces Jeff, small open-weight Qwen3.5 and Gemma fine-tunes for zero-shot classification that slot into code behind a Jev-compatible API. The 2B scores 83.1% on a five-benchmark panel against Jev's published 83.0%, which the author notes was measured on different items from the same benchmarks, and the 0.8B decides in about 28 ms on an M4 Max. Jeff trails on multi-step reasoning, at 64-68% on BBH against Jev's 94%.
Not counted
Its author builds Jeff, an open-weight model with a Jev-compatible API that competes with Jev. Kept in the record; it takes part in no figure.
No opinion stated
Show HN: jevpipe, a command-line wrapper for JevHacker News comment
The author built jevpipe so agents can pipe lines through Jev for yes/no filters and typed labels, and reports Jev better calibrated than DeepSeek V4.1 Flash in a published benchmark. The author states no tie to TypeSafe and calls Jev through OpenRouter.
"I built jevpipe as a wrapper for TypeSafe's Jev System 1 model. Jev answers yes/no, picks from a few choices or gives a score, with probabilities, and it's fast and cheap. See https://docs.typesafe.ai/introduction for more details on Jev. It performs on par with small LLMs while being faster and cheaper (independent study: https://www.ayautomate.com/blog/jev-vs-llm-benchmark), and in my benchmark it was better calibrated than DeepSeek V4.1 Flash (see below). I wanted agents to be able to use such a System 1 in the middle of a task without writing a script each time. So jevpipe is a small CLI that can be used like grep or other Unix tools: pipe lines or files in and get classifications out. Example: cat abstracts.jsonl | jevpipe filter "Does this paper report a randomized trial?" cat abstracts.jsonl | jevpipe map -q '{"design": {"type": "choicsign?", "criteria": {"rct": "randomized trial", "observational":"observational study", "review": "review or meta-analysis", "other": "anything else"}}}' | jq -r .answers.design.choice | sort | uniq -c filter prints the lines that pass, map returns typed answers as JS basically program their own System 1 for anything and apply it to a large chunk of data for "gut feeling" decisions. I also added an agent skill for jevpipe that teaches the agent when and how to use it. If you are interested in technical details, I also did a benchmark because that seemed to be missing from the recent evaluations. But it is by no means coding only or just a search tool. https://github.com/fabianboth/jevpipe/blob/main/bench/README... It needs an OpenRouter key. I'm not affiliated with TypeSafe."
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: Pouring a year of RAG experience into my AI document spreadsheet appReddit comment, r/Rag
Adding Jev as a reranker after the author's hybrid search cut top-20 recall from 99.7% to between 60% and 75%, so the author kept the existing ranking.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Supported pre-dispatch context admission and billing reconciliation for pinned JevGitHub issue, typesafe-ai/typesafe-sdk-js
The author cannot check requests against Jev's 64k and 32k context limits before sending them, because TypeSafe publishes no tokenizer or token-count endpoint and no per-request charge record.
No opinion stated
I built a LinkedIn AI slop detector using JEVReddit post, r/LLMDevs
The author's Chrome extension asks Jev several questions about each LinkedIn post and adds rule checks. One displayed example returned a verdict and its confidence in 94 ms.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Comment on: judging stories with JevReddit comment, r/PromptEngineering
Using Jev as intended to judge stories, the author found it not as good as Claude in practice, and expects a later version to close the gap and save many tokens.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Choice answers: choice is sometimes 0.01 below the highest probability (jev-1.13.0)GitHub issue, typesafe-ai/typesafe-sdk-python
In 35 production responses from one day, Jev 1.13 returned a choice exactly 0.01 below another option's probability on near-tie, low-confidence answers.
Counts toward
No opinion stated
The AI That Knows the Answer Before It Speaks [Jev Explained]Video, YouTube (DiamantAI)
Nir Diamant runs Jev beside a chatbot model: both sent a fraud report to the payments team until each option got a one-line description of what that team handles. On about 3,000 BANKING77 messages, Jev was right 98 times in 100 when it was confident.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
API: Cloudflare blocks valid /v1/systemone requests (HTML 403) when state text contains a curl commandGitHub issue, typesafe-ai/typesafe-sdk-js
Valid Jev requests whose state quoted a curl command got an HTML 403 from Cloudflare before reaching the API, which blocked about 250 of 340 requests in one consumer for a day.
Counts toward
No opinion stated
I tested Semif (openjev) vs Von vs JevReddit post, r/LocalLLaMA
Scored on labels from the author's own commit history, hosted Jev reached 78.9% on commit type and 81.3% on file routing, against 62.2% and 63.8% for the untrained SemIf and 26.7% and 8.8% for Von. Confidence gates tuned on one task did not carry over to another task or model.
"I tested Semif (openjev) vs Von vs Jev Multiple "typed decision model" projects showed up on reddit past couple o days, some with benchmarks, so I figured instead of working at my day job lets score some of these models on my own git history instead of taking their readme scores at face value. **Setup.** Labels came free from conventional commit prefixes on a private repo of mine (a Discord RPG bot). Stripped the `type(scope):` prefix out of the input so the model isnt getting an answer key. Also every task got a dumb free baseline scored right next to the models. | task | metric | Jev (hosted) | SemIf (CPU) | Von 1.0 | free baseline | |---|---|---|---|---|---| | t1 commit type, 6-way choice, n=90 | accuracy | 78.9% | 62.2% | 26.7% | 31.1% path regex | | t2 file routing, 12-way choice, n=80 | accuracy | 81.3% | 63.8% | 8.8% | 48.8% keyword | | t3 is-a-feature, probability, n=180 | AUC | 0.944 | 0.899 | 0.513 | 0.543 keyword | | t4 change breadth, 3-level, n=90 | Spearman | 0.377 | 0.348 | 0.210 | 0.222 word count | **The three:** * **SemIf** (MIT) trains nothing. It loads a stock `Qwen/Qwen3.5-4B`, stuffs the state and the option descriptions into one prompt, and reads the option logits off a single forward pass. CPU only, llama.cpp, Q4_K_M. I wanted to test GPU but speed wasnt my primary concern for this run. * **Von** (Apache 2.0) is a 395M ModernBERT option-marker model, 1.5GB, purpose-built for exactly this job. My bet was on this guy.... which is why I dont gamble... aparently I suck at it. * **Jev** is TypeSafe's hosted proprietary one, in the table as the paid ceiling. So the untrained model beat the model trained for the job by 35 points on commit type and 55 points on routing. Von also lost to keyword overlap on routing: 8.8% against 48.8%, where random guessing gets 8.3%. Von's 78.9% accuracy on t3 is not a model result. The set is 150 non-features to 30 features, so answering "no" every single time scores 83.3%. Its AUC is 0.513. Its mean probability on real features was 0.079 against 0.087 on non-features, so the sign is backwards. Von chose `refactor` on 69 of 90 commits. Feed it a single period, or ten random digits, and it still keeps saying refactor. Confidence gates do not transfer between tasks. SemIf's 0.85 gate lifted routing 22 points (63.8% to 85.7%) but commit type only 4.5. Same model, same threshold, wildly different payoff, so you cannot tune a gate once and reuse it. They don't transfer between models either. Von's gate at that same 0.85 kept 30% of routing rows and got 0% of them right. A gate is supposed to buy precision for coverage. This one selects for confidently wrong. Cost: 30.9s per decision on CPU on the longest task, t1, at ~3.8k char states, p95 41s. Shorter t2 states ran 17.1s. Latency tracked state length, not option count. ----- * Private repo, so you can't rerun my exact numbers. The method is the part that transfers, and it took an afternoon to build off conventional commits. * n is 80 to 180 per task. Treat gaps under ~10 points as noise. That applies to t4 especially, where nothing convincingly beat word count. * SemIf only exposes a `choice` primitive, so I widened it for t3 and t4. Treat t1 and t2 as the clean apples-to-apples comparison. * Stock unpatched SemIf hardcodes `n_gpu_layers = 0`, so every SemIf number is CPU. No speedup claimed. There was an 8GB GPU sitting idle the whole run, laughing at me or something... dont know what GPUs do in their free time. * Von numbers are the converged 1.0 release. I can rerun the whole thing on the next weight drop, which seems to be often. * No relationship with any of the three projects, no referral links, nothing sponsored. I have a larger writeup on this but thats not the concern here. * I plan on testing Laya and couple others next time I have time."
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
We benchmarked Jev-As-A-Judge for RAG Claim VerificationReddit post, r/Rag
On 495 human-labelled claims, Jev scored 73.3 against 73.8 for GPT-6 Astra at 187 times lower cost and a 214 ms median against 1,478 ms, but it waved through 23% of unsupported claims.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Spent the weekend stress-testing Jev on RAG pipelinesReddit post, r/Rag
JevRAG cut retrieval rounds on HotpotQA by 38.5% at equal accuracy. The author found Jev's calibration depends on the question: Brier skill ran from -0.45 to +0.96 across five decision steps on the same API.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
I reverse-engineered Jev and rebuilt it as open weightsReddit post, r/LocalLLaMA
After paying for Jev, the author rebuilt a Jev-style model as open weights. Jev won zero-shot on all seven held-out schemas, 0.938 against 0.702 on CLINC-150, while the author's model fine-tuned on 395 labels scored 0.979 against Jev's 0.941 on intent.
"I reverse-engineered Jev and rebuilt it as open weights. It beats mine on all 7 benchmarks; mine beats it on my own task with 395 labels. TypeSafe launched Jev recently and I'd been paying for it. It does one thing: text plus a typed question schema in, calibrated probabilities over your options out. No generation, no JSON parsing, no retries. I wanted it on my own GPU. Probed the API for a few days, worked out the design, rebuilt it open. The graph is everything I measured. **How it works.** State encoded once as a shared prefix, every option appended behind a marker token, block-diagonal mask so question blocks can't see each other, grouped softmax per question. Ten questions about one document cost one forward pass, not ten. I tested the mask rather than trusting it, because a wrong one fails silently. Ten sibling questions that explicitly assert the target's answer move its probabilities by 0.0022 MAD against a 0.0029 noise floor. One question can't prompt-inject another. **Jev wins zero-shot, on all seven held-out schemas.** 38.3x chance against my 29.6x; 0.938 vs 0.702 on CLINC-150's 151-way menu. I didn't close that gap. Jev's per-item predictions are in the repo so you can check the comparison without an API key. **Mine wins for tasks that have labels (finetune).** 395 examples, 258 seconds on one H100, an 87 MB adapter: intent 0.979 vs Jev's 0.941, multi-label exact set 0.909 vs 0.822, three wins and four ties on paired McNemar. That comparison is asymmetric and in my favour, since I'm fine-tuned and Jev isn't. The claim is that a few hundred labels beat the gap, not that the models are equal. **Serving isn't close.** 6,546 decisions/sec on one GPU at 24 ms p50, against Jev's \~47 req/s peak. No HTTP layer in mine, so that's what the hardware does, not a deployment number. **Links** * **Technical report (37 pages):** [https://github.com/S1LV3RJ1NX/openjev/blob/main/report/main.pdf](https://github.com/S1LV3RJ1NX/openjev/blob/main/report/main.pdf) * Code: [https://github.com/S1LV3RJ1NX/openjev](https://github.com/S1LV3RJ1NX/openjev) * Weights: [https://huggingface.co/s1lv3rj1nx/openjev-general-lora](https://huggingface.co/s1lv3rj1nx/openjev-general-lora) Consumer GPU, full-precision probabilities rather than a 0.01 grid, deterministic (Jev isn't, and offers no seed), data never leaves your network."
Not counted
Its author built and publishes openjev, an open-weights rival to Jev, and compares Jev against it. Kept in the record; it takes part in no figure.
No opinion stated
Jev System One (Cloud) vs Local Models (DistilBERT, ColBERT-v2, DeBERTa) on Legal Retrieval and VerificationReddit post, r/Rag
In a UK legislation retrieval pipeline, Jev took 730 ms at the median for intent routing against 0.26 ms for a local DistilBERT setup, and affirmed a fabricated statute with 96% confidence where a local cross-encoder abstained. The author keeps Jev for offline evaluation and local encoders on the live path.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Feature: reuse/cache identical criteria across requests (prompt caching or registered questions) + expose cached_tokensGitHub issue, typesafe-ai/typesafe-sdk-js
Mapping detection rules to MITRE ATT&CK with Jev means re-sending about 6,000 tokens of fixed definitions on every request, which the author measured as necessary for accuracy.
No opinion stated
docs(landscape): Jev / TypeSafe System One, record don't-adopt decisionGitHub issue
The maintainer recorded a decision not to adopt Jev for doc-pipeline-engine, citing API-only proprietary access, early-access signup, and no self-hosting or open weights.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
No opinion stated
Jev on BANKING77: classification without task-specific fine-tuningGitHub repository
simonmesmith ran jev-1.13.0 on all 3,080 BANKING77 test messages and scored 92.40%, against 93.66% reported for a fine-tuned BERT, for about US$0.44 and 6 minutes 51 seconds. Jev got category definitions and 24 labeled examples per prediction.
Not counted
Its author's ties to the vendor have not been checked. Kept in the record; it takes part in no figure.
Pages from TypeSafe AI, used to check facts for every group.
TypeSafe advertised engineering roles in San Francisco and linked its careers page. The notice makes no claim about using Jev, so it counts toward no finding.
TypeSafe AI co-founder Diogo Almeida announced Jev on September 15, 2026 and described it as a decision-focused model with faster inference and free output tokens. This is a vendor announcement, so its claims are kept separate from practitioner measurements.
Jev is TypeSafe's flagship System One model. Send state and typed questions and receive structured answers.
Jev 1.14 and Jev 1.13 are served by POST /v1/systemone. Input is priced at US$42 per billion tokens and output tokens are free.
The API answers one or more typed questions about supplied state.
Confidence is a separate signal from probability and can gate whether software acts.
Intent routing classifies a request and sends it to deterministic logic, a specialist LLM, or a human.
TypeSafe documents cases where Jev can fail, including adversarial content and uneven capabilities.
TypeSafe announced Jev in early access on September 15, 2026 as its first System One model.
TypeSafe released Jev 1.14 on October 14, 2026 with longer context and experimental image support.
Source index
Every source GILD read, with the original address and the context used to count it. Open a finding to filter the full cards above. On smaller screens the index lists each source once by name, cohort and date; its full record above carries the address, context and findings.
| Source | Original URL | Author and context | Who | Published | Review date | Applies | Findings |
|---|---|---|---|---|---|---|---|
| Neurotype experiments scored by JevID sf-11 | Developer Communities | x.com/ | Kyle Wild, builder of poor-things.comself-described: A few million steering experiments on Qwen3.5-4B, each output evaluated by Jev and Fireworks Rune | Technical leadership | Published Oct 27, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Tested decision models (Jev, Kev, Clef, OpenAI Decisions): the confidence score really worksID sf-27 | Developer Communities | www.reddit.com/ | CardiologistOk2154, publisher of the jev-on-durians repositoryself-described: Comment categorization across four decision models, with code and data on GitHub | Individual builder | Published Oct 26, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: open decision modelsID sf-3 | Developer Communities | news.ycombinator.com/ | theanonymousone, Hacker News commenterself-described: A first Jev use case, compared informally with models on OpenRouter | Individual builder | Published Oct 26, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| decide(): no way to enable Jev long-context mode (TypeSafe-Danger-Zone-Long-Context header)ID s2-18 | Developer Communities | github.com/ | Matt Apperson, maintainer of noeticself-described: Spec-to-code completeness workflow sending about 51k input tokens per decision | Individual builder | Published Oct 25, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: classifiers and decision modelsID sf-4 | Developer Communities | news.ycombinator.com/ | magicalhippo, Hacker News commenterself-described: Sorting customer product descriptions into government categories that change several times a year | Individual builder | Published Oct 25, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: classifiers and decision modelsID sf-6 | Developer Communities | news.ycombinator.com/ | nico, Hacker News commenter and creator of the Jeffy classifiersself-described: A classification benchmark across label counts | Individual builder | Published Oct 25, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author created Jeffy, fast local classifiers that run and train on CPU and compete with Jev, and the benchmark sits on Jeffy's site. |
| Jev for image classification on an art datasetID s2-26 | Developer Communities | x.com/ | Alexandru Indrei, Principal Engineer at Amazon, posting his own testself-described: Image classification on an art dataset | Individual builder | Published Oct 24, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Weekend tests of Jev against ClefID s2-27 | Developer Communities | x.com/ | Jeremy Morrell, principal engineer, Cloudflare Observabilityself-described: Weekend use-case tests comparing Jev and Clef | Individual builder | Published Oct 24, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: The rise and fall of Jev hypeID hn-50207339 | Developer Communities | news.ycombinator.com/ | leemoore, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 23, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Remove the Jev (TypeSafe) injection classifier; keep the regex scanID sf-37 | Developer Communities | github.com/ | fuad-daoud, maintainer of relevoself-described: relevo's optional prompt-injection classifier | Individual builder | Published Oct 23, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: Using LLM for confidence scoring is a bad ideaID hn-50196505 | Developer Communities | news.ycombinator.com/ | rahimnathwani, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 22, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Jev inside the Lightpanda browserID sf-46 | Developer Communities | x.com/ | Lightpanda, maker of a browser built for machinesself-described: A judge() call built on Jev inside the Lightpanda binary | Product or platform team | Published Oct 21, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| [Harness]: JevID sf-9 | Developer Communities | github.com/ | ernoaapa, maintainer of a kagent harness for Jevself-described: A kagent harness that runs Jev as a judge agent, tested in the author's own cluster | Individual builder | Published Oct 21, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Decision Models: Jev vs. a Fine-Tuned ClassifierID l2-05 | Blogs & Reviews | anaconda.com/ | Philipp Rudiger, Anacondaself-described: Anaconda's comparison of Jev, a fine-tuned Qwen3 0.6B model, and XGBoost on 2,400 held-out CFPB complaints | Product or platform team | Published Oct 20, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| We tested Jev as a reranker against Voyage, Cohere and Qwen on 1,000 eval casesID sf-16 | Developer Communities | www.reddit.com/ | srnsnemil, posting kapa.ai's benchmarkself-described: kapa.ai's Company Knowledge Bench, 1,000 cases built from real production queries | Product or platform team | Published Oct 20, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| How to Build a Decision Model Like JevID s2-04 | Blogs & Reviews | oumi.ai/ | Jeremy Greer and Matthew Persons, Oumiself-described: Oumi tests of Jev 1.13 on rare-fact questions and documents from short to very long | Product or platform team | Published Oct 19, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its authors work at Oumi, which sells a platform for building models that compete with Jev, and the post compares them. |
| Comment on: content moderationID sf-33 | Developer Communities | www.reddit.com/ | r1n-ai, builder of an adult-content productself-described: Moderation for an adult-content product, tested on OpenAI's published moderation test set | Individual builder | Published Oct 18, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| TypeSafe reranker can exceed Jev context within the local token budgetID s2-17 | Developer Communities | github.com/ | pedropaulovc, developer running Hindsight's TypeSafe reranker in productionself-described: Hindsight memory recall with a Jev reranker | Individual builder | Published Oct 17, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Replacing a FastText and Gemini pipeline with one Jev callID s2-25 | Developer Communities | x.com/ | Arpit Tripathi, builder of MemX, ex-Google and ex-AWSself-described: MemX app classification pipeline | Individual builder | Published Oct 17, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Jev Router Experiment with OSS models for Coding TasksID s2-07 | Blogs & Reviews | github.com/ | Shivay Lamba, developer who published the benchmarkself-described: 50 coding tasks routed by Jev across DeepSeek V4 Flash, Kimi K2.6 and Kimi K3 | Individual builder | Published Oct 16, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: email-reply classificationID sf-29 | Developer Communities | www.reddit.com/ | ianreboot, developer of an email-reply classifierself-described: An email-reply classifier with about 14 categories, 51 cases run three times | Individual builder | Published Oct 16, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: We're releasing Jev 1.14.0 along with experimental features for dreamersID hn-50098024 | Developer Communities | news.ycombinator.com/ | bbor, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 15, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: We're releasing Jev 1.14.0 along with experimental features for dreamersID hn-50102384 | Developer Communities | news.ycombinator.com/ | einsteinx2, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 15, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Jev meets the Trolley ProblemID hn-50109625 | Developer Communities | news.ycombinator.com/ | zlandes86, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 15, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Jev meets the Trolley ProblemID hn-50109927 | Developer Communities | news.ycombinator.com/ | cziou, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 15, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Beyond the leaderboard: decision models in a real workflowID s2-01 | Blogs & Reviews | isotopes.ai/ | Prasanth Jayachandran, author of Isotopes AI's engineering write-upself-described: Isotopes AI aidnn follow-up workflow, 88 scenarios run three times per model | Technical leadership | Published Oct 15, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Overall signalSpeed and Cost: PositiveDecision Model Fit: PositiveModel Quality: PositiveStability: PositiveInconsistent Results: Discussed without concernTool Calling Failures: Raised as a concernReliability: Raised as a concernOwn evaluation setsDoes Jev replace a classifier, or sit above one?: General decision layer |
| 0.7.0 fails every request on Bun with "TypeError: Body already used" (bufferResponse)ID s2-12 | Developer Communities | github.com/ | dinhphieu, developer using the TypeSafe JavaScript SDKself-described: TypeSafe JavaScript SDK 0.7.0 on Bun 1.4.3 and Node 26 | Individual builder | Published Oct 15, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Evaluate and upgrade the Jev judge to jev-1.14.0ID s2-19 | Developer Communities | github.com/ | ibuildthecloud, discobox maintainerself-described: discobox judge pinned to jev-1.13.0 with tuned thresholds | Individual builder | Published Oct 15, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Jev decisions on photos with the box typeID s2-24 | Developer Communities | x.com/ | Carlneil, AI engineer at Cloutedself-described: Image decisions with Jev 1.14 and the box type | Product or platform team | Published Oct 15, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: We're releasing Jev 1.14.0 along with experimental features for dreamersID hn-50096209 | Developer Communities | news.ycombinator.com/ | gopalv, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 14, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Jev Talking: Security Concerns for Models That Return Decisions Instead of TextID l2-06 | Blogs & Reviews | www.hiddenlayer.com/ | Kevin Kelleher, HiddenLayer, reporting HiddenLayer Research's testsself-described: HiddenLayer Research tests of a Jev browser agent on a simulated checkout and of Jev as a toxic-prompt filter | Product or platform team | Published Oct 14, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Building a Fact-Extraction Pipeline w/ JevID sf-25 | Developer Communities | www.reddit.com/ | Honest_Medium_2872, newsletter authorself-described: A newsletter's pipeline for infographic cards drawn from long posts | Individual builder | Published Oct 14, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Jev handed our LLM the wrong page a tenth as often as our embeddingsID blog-sayora-jev | Blogs & Reviews | sayora.ai/ | Sayora, AI phone-receptionist developerself-described: Retrieval evaluation described in the linked engineering post | Product or platform team | Published Oct 13, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Our RAG kept retrieving the wrong page, so we tested letting Jev pick pages insteadID reddit-rag-1x4vjaf | Developer Communities | www.reddit.com/ | jacobsayora, developer building an AI phone receptionistself-described: Four-site retrieval test with 168 questions | Individual builder | Published Oct 13, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Using Jev to make your agents write the code and design the app the way you doID blog-a7ul-jev | Blogs & Reviews | blog.a7ul.com/ | Atul, developer and author of the a7ul engineering blogself-described: Agent rule checks and screenshot review | Individual builder | Published Oct 12, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Overall signalStructured Decisions: PositiveDecision Model Fit: PositiveJagged Behavior: Raised as a concernTool Calling Failures: Raised as a concernToken Cost: Raised as a concernLatency: Raised as a concernOwn evaluation setsTyped output changes the integrationDoes Jev replace a classifier, or sit above one?: General decision layer |
| Comment on: Build your own decision modelID hn-50056168 | Developer Communities | news.ycombinator.com/ | e12e, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 12, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Build your own decision modelID hn-50061157 | Developer Communities | news.ycombinator.com/ | jrmg, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 12, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Build your own decision modelID hn-50040632 | Developer Communities | news.ycombinator.com/ | user43928, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 11, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Build your own decision modelID hn-50043731 | Developer Communities | news.ycombinator.com/ | softwaredoug, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 11, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Task classifier with Jev - pretty good experience so far!ID reddit-jevai-1x3n0lg | Developer Communities | www.reddit.com/ | Dependent-Dinner-918, creator of the Keen Code open source coding agentself-described: Keen Code task routing | Individual builder | Published Oct 11, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| We started using JEV to clarify queries before retrieval, and the results have been pretty interestingID reddit-rag-1x38uno | Developer Communities | www.reddit.com/ | Craft_Show, developer building an enterprise AI knowledge-management applicationself-described: Enterprise retrieval and answer generation workflow | Product or platform team | Published Oct 11, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: routing questions to the right keyID sf-30 | Developer Communities | www.reddit.com/ | pWiklacz, developer of a question routerself-described: 650 Polish messages with a known correct key, Jev against a Perplexity router and local Von | Individual builder | Published Oct 11, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50028445 | Developer Communities | news.ycombinator.com/ | cootsnuck, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 10, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50028673 | Developer Communities | news.ycombinator.com/ | incompressible, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 10, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50030932 | Developer Communities | news.ycombinator.com/ | xxmarkuski, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 10, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50031899 | Developer Communities | news.ycombinator.com/ | 999900000999, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 10, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50032307 | Developer Communities | news.ycombinator.com/ | dannyw, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 10, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50032335 | Developer Communities | news.ycombinator.com/ | -HIMAO-, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 10, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50033107 | Developer Communities | news.ycombinator.com/ | r4indeer, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 10, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: OpenAI's decision modelID sf-1 | Developer Communities | news.ycombinator.com/ | rockwotj, Hacker News commenterself-described: Production use cases the commenter runs on Jev | Individual builder | Published Oct 10, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| I tested dynamic model routing for an agentic browser-testing system. Guardrails via Jev worked pretty good.ID sf-21 | Developer Communities | www.reddit.com/ | shane-testronaut, builder of Testronautself-described: Testronaut, an agentic browser-testing system, over 120 isolated mission runs | Individual builder | Published Oct 10, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50025020 | Developer Communities | news.ycombinator.com/ | jypepin, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50025155 | Developer Communities | news.ycombinator.com/ | nlpnerd, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50025287 | Developer Communities | news.ycombinator.com/ | geoffschmidt, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50025638 | Developer Communities | news.ycombinator.com/ | ambicapter, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50025811 | Developer Communities | news.ycombinator.com/ | bushbaba, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50026220 | Developer Communities | news.ycombinator.com/ | TeMPOraL, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Microsoft-Decision-1, our model for fast decision-makingID hn-50026598 | Developer Communities | news.ycombinator.com/ | sass1caia, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50026763 | Developer Communities | news.ycombinator.com/ | cheesecakegood, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Typesafe AI raises $870M at $7.5BID hn-50027807 | Developer Communities | news.ycombinator.com/ | charm137, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Microsoft-Decision-1, our model for fast decision-makingID hn-50027896 | Developer Communities | news.ycombinator.com/ | elzbardico, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 9, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| I got Jev to zero mistakes. I'm still using Flash-Lite.ID dev-mlh-jev | Blogs & Reviews | dev.to/ | Mike Swift, CEO and co-founder of Major League Hackingself-described: MLH routing evaluation across thousands of runs | Product or platform team | Published Oct 8, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| I built a Claude Code plugin that uses Jev for compaction, model routing and skill selectionID sf-24 | Developer Communities | www.reddit.com/ | Fit-Fail-3369, builder of the optim-jev pluginself-described: optim-jev, a Claude Code plugin for context compaction, model routing and skill choice | Individual builder | Published Oct 8, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Telling my agent to delegate its browser clicks didn't work; hiding its own click and type tools didID sf-26 | Developer Communities | www.reddit.com/ | Hrishikesh90, builder of a browser-click toolself-described: A tool that hands an agent's browser clicks to Jev, benchmarked against Claude Sonnet 5.5 clicking on its own | Individual builder | Published Oct 8, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: Decisions API is in public betaID hn-49993082 | Developer Communities | news.ycombinator.com/ | ehe78qhe, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 7, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: action-set size in agent gatesID sf-32 | Developer Communities | www.reddit.com/ | Ctbhatia, developer running Jev gates in productionself-described: Jev gates on agent actions, run all day | Individual builder | Published Oct 7, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: [dead]ID hn-49983534 | Developer Communities | news.ycombinator.com/ | upguardnews, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 6, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Decisions API is in public betaID hn-49985053 | Developer Communities | news.ycombinator.com/ | tmhall, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 6, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Decision models like Jev don't beat LLM-as-a-judge or traditional classifiersID hn-49962555 | Developer Communities | news.ycombinator.com/ | Garlef, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 5, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: Decision models like Jev don't beat LLM-as-a-judge or traditional classifiersID hn-49963233 | Developer Communities | news.ycombinator.com/ | kwinkunks, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 5, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Jev: Decision Models on TrialID s2-03 | Blogs & Reviews | blogs.cisco.com/ | Prashanth Arun, Director of Data Science, AI Defense at Cisco, with Jin MaCisco AI Defense content-safety evaluation on six datasets and eight public benchmarks | Technical leadership | Published Oct 5, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: prompt-injection checksID sf-31 | Developer Communities | www.reddit.com/ | pWiklacz, developer testing Jev for prompt-injection checksself-described: Two weeks of prompt-injection checks with a yes/no question | Individual builder | Published Oct 5, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Jev Isn't an LLM: I Tried TypeSafe AI's Decision Model in GoID sf-49 | Podcasts, Talks & Videos | www.youtube.com/ | Ed Zynda, host of What The Func?self-described: Hands-on Go examples of yes/no decisions, routing choices and frustration scoring | Individual builder | Published Oct 5, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Hidden Risks of Jev: An Empirical Study of Security, Privacy, and Dual UseID l2-07 | Blogs & Reviews | arxiv.org/ | Shang Wang, Tianqing Zhu and coauthors, research paper authorsself-described: Security and privacy attacks on the Jev API and a local stand-in model | Product or platform team | Published Oct 4, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: jev-pagesID sf-5 | Developer Communities | news.ycombinator.com/ | dorongrinstein, Hacker News commenterself-described: Deploying the open jev-pages example with a TypeSafe API key | Individual builder | Published Oct 4, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Daily usage endpoint that works with an API key?ID s2-13 | Developer Communities | github.com/ | mlahargou, engineer on iFixit's FixBotself-described: iFixit FixBot, about 8,869 Jev requests on one day | Product or platform team | Published Oct 2, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: choosing a decision architectureID sf-35 | Developer Communities | www.reddit.com/ | magicdoorai, contributor to magicdoor.ai, which routes with Jevself-described: magicdoor.ai's request routing | Product or platform team | Published Oct 2, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: Gemini 4 ArgonID hn-49916316 | Developer Communities | news.ycombinator.com/ | Paracompact, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 1, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Is sandboxing sufficient to contain rogue agents?ID hn-49918355 | Developer Communities | news.ycombinator.com/ | simonw, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 1, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: Clef: Open-weight decision models, and new RL fine-tuning platformID hn-49926253 | Developer Communities | news.ycombinator.com/ | amluto, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Oct 1, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Feature request: Jev / TypeSafe AI support in @posthog/aiID sf-39 | Developer Communities | github.com/ | juanmarin-co, developer running Jev beside Geminiself-described: An application that calls Jev through TypeSafe's SDK alongside Gemini | Individual builder | Published Oct 1, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: Clef FlashID sf-7 | Developer Communities | news.ycombinator.com/ | ralusek, Hacker News commenterself-described: A latency test of Jev through TypeSafe and OpenRouter against Clef Flash | Individual builder | Published Oct 1, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: ChimpBench - Chimpanzee Population SimulatorID hn-49915911 | Developer Communities | news.ycombinator.com/ | jbermudez5, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Sep 30, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Building a harness with JevID langchain-jev | Blogs & Reviews | www.langchain.com/ | LangChain engineering teamself-described: Engineering harness built around Jev | Product or platform team | Published Sep 30, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Updated my memory system to use jev as a reranker 97% on LongMemEvalID sf-18 | Developer Communities | www.reddit.com/ | turlockmike, builder of the deep-recall memory systemself-described: deep-recall, an open-source agent memory system on hybrid vector search and graph search | Individual builder | Published Sep 30, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: where decision models fitID sf-34 | Developer Communities | www.reddit.com/ | godwin-pinto, who runs the Encatch feedback platformself-described: Encatch, a feedback platform with Jev live as an optional production feature | Small product team | Published Sep 30, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: Show HN: Jevpipe - a fast System 1 for AI agents, as a Unix pipeID hn-49877712 | Developer Communities | news.ycombinator.com/ | cs1996, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Sep 28, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| Comment on: Jeff - Jev-compatible 0.8B decision models, trained at home, ~30 msID hn-49883845 | Developer Communities | news.ycombinator.com/ | firelex, Hacker News commenterself-described: Public discussion of Jev or System One models | Individual builder | Published Sep 28, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author builds Jeff, an open-weight model with a Jev-compatible API that competes with Jev. |
| Jev guardrails in production: a security platform's front door for five cents a day.ID l2-01 | Blogs & Reviews | www.metaheuristic.co/ | Alexander Myasoedov, Founder, MetaheuristicMetaheuristic's intent and report-quality gates for a cybersecurity client, on jev-1.13 through OpenRouter | Technical leadership | Published Sep 28, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Show HN: jevpipe, a command-line wrapper for JevID sf-2 | Developer Communities | news.ycombinator.com/ | bothlabs, builder of jevpipeself-described: jevpipe, an open-source command-line tool that lets agents filter and label data with Jev | Individual builder | Published Sep 28, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: Pouring a year of RAG experience into my AI document spreadsheet appID sf-28 | Developer Communities | www.reddit.com/ | No-Afternoon-4057, developer running a hybrid retrieval stackself-described: Jev as a reranker after hybrid search with reciprocal rank fusion | Individual builder | Published Sep 28, 2026 | Assessment reviewed Oct 30, 2026 | Needs a recheck | Not counted. Its author's ties to the vendor have not been checked. |
| Jev vs. Claude vs. GPT: Your "Critical" Findings Are Stealing Your SprintID sf-41 | Blogs & Reviews | casco.com/ | Eleanor Sigel, author of Casco's benchmarkself-described: 2,449 security findings scored for CVSS 3.1 by eight models against Casco's published scores | Product or platform team | Published Sep 28, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| TypeSafe Jev guide and practical use casesID sf-47 | Blogs & Reviews | composio.dev/ | Harsh, Composioself-described: A Jev toolkit in Composio's Tool Router | Product or platform team | Published Sep 28, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its publisher, Composio, offers a Jev toolkit in its own Tool Router and promotes that integration here. |
| Jev for beginners: how to use it and what to buildID sf-48 | Podcasts, Talks & Videos | podcasts.apple.com/ | Claire Vo, host of How I AI, showing her ChatPRD projectsself-described: Five of the host's own projects run on Jev, including ChatPRD's product-insights graph | Small product team | Published Sep 28, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Supported pre-dispatch context admission and billing reconciliation for pinned JevID s2-16 | Developer Communities | github.com/ | diannt, developer integrating Jev's HTTP API with budget reservationsself-described: Direct HTTP API integration with pinned jev-1.13.0 requests | Individual builder | Published Sep 27, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Racing Spinny's production ticket routing against JevID s2-23 | Developer Communities | x.com/ | Sanchit, software engineer on Spinny's Customer Experience teamself-described: Spinny ticket categorization and urgency detection built on OpenAI | Product or platform team | Published Sep 27, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| I built a LinkedIn AI slop detector using JEVID sf-23 | Developer Communities | www.reddit.com/ | goodvibezone, builder of a Chrome extensionself-described: A local Chrome extension that rates LinkedIn posts with Jev checks and rules | Individual builder | Published Sep 27, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Comment on: judging stories with JevID sf-36 | Developer Communities | www.reddit.com/ | rcastag, developer judging stories with an agentself-described: Jev as a judge of written stories, compared with Claude | Individual builder | Published Sep 26, 2026 | Assessment reviewed Oct 30, 2026 | Needs a recheck | Not counted. Its author's ties to the vendor have not been checked. |
Choice answers: choice is sometimes 0.01 below the highest probability (jev-1.13.0)ID s2-15 | Developer Communities | github.com/ | nevenincs, developer running a production Jev integrationself-described: Production integration asking 28 questions per request | Individual builder | Published Sep 25, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Deel put Jev head-to-head with frontier LLMsID s2-21 | Developer Communities | x.com/ | Alex Bouaziz, CEO and co-founder of DeelDeel head-to-head tests against frontier LLMs across several use cases | Technical leadership | Published Sep 25, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Jev vs. Kev: open-source Jev alternative tested side by sideID sf-13 | Developer Communities | www.reddit.com/ | facethef, founder of Opperself-described: 362 fresh test items run on Jev and Kev 4B through the same endpoint | Product or platform team | Published Sep 25, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author routes Jev through Opper, his own startup, which resells model access. |
| Decision Models vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same PlacesID s2-06 | Blogs & Reviews | arxiv.org/ | Delip Rao and Chris Callison-Burch, research paper authorsself-described: Nine rubric-judging panels from seven benchmarks with human judgments | Product or platform team | Published Sep 24, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| JevOut: Natural Context Can Flip Decision ModelsID s2-08 | Blogs & Reviews | arxiv.org/ | Zixiang Xu and coauthors, research paper authorsself-described: Context-manipulation tests on four decision systems across seven datasets | Product or platform team | Published Sep 24, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment FailuresID s2-10 | Blogs & Reviews | arxiv.org/ | Ruoqi Guo, Yi Liu and coauthors, research paper authorsself-described: Alignment-failure detection across 44 benchmarks and five target models | Product or platform team | Published Sep 24, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| We Added Jev as a Reranker. Here's What We Learned.ID sf-44 | Blogs & Reviews | raw.githubusercontent.com/ | Ben Bartholomew, Hindsight team at Vectorizeself-described: Hindsight 0.10.1 recall with Jev as a reranker, measured on LoCoMo | Product or platform team | Published Sep 24, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| The AI That Knows the Answer Before It Speaks [Jev Explained]ID sf-50 | Podcasts, Talks & Videos | www.youtube.com/ | Nir Diamant, DiamantAIself-described: A bank fraud report and about 3,000 BANKING77 messages, Jev beside a chatbot model | Individual builder | Published Sep 24, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document UnderstandingID s2-09 | Blogs & Reviews | arxiv.org/ | Fan Zhang and 13 coauthors, research paper authorsself-described: ContractNLI evaluation of Jev and nine language models with repeated requests | Product or platform team | Published Sep 23, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| API: Cloudflare blocks valid /v1/systemone requests (HTML 403) when state text contains a curl commandID s2-11 | Developer Communities | github.com/ | josebreijo, developer judging coding-agent text with System Oneself-described: Agent-text judging service calling the HTTP API directly | Individual builder | Published Sep 23, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| I tested Semif (openjev) vs Von vs JevID sf-14 | Developer Communities | www.reddit.com/ | KingPinX, developer scoring decision models on a private repositoryself-described: Four tasks labelled from the conventional-commit history of the author's Discord bot | Individual builder | Published Sep 23, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Jev isn't an LLM killer, and it isn't just a classifier. We put it in production with real users.ID sf-15 | Developer Communities | www.reddit.com/ | NoSpecific64, founder of the agent startup Vestraself-described: Vestra's agent harness, with real users running tasks daily | Small product team | Published Sep 23, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| How Jev cut the answer time in half for our smart search agentID sf-42 | Blogs & Reviews | serverpod.dev/ | Viktor Lidholt, founder of Serverpodself-described: Starguide, Serverpod's documentation assistant, in production | Product or platform team | Published Sep 23, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| JEV-as-a-Judge: Accept When Confident, Escalate When UnsureID s2-05 | Blogs & Reviews | arxiv.org/ | Yubo Li, Yidi Miao, Ramayya Krishnan and Rema Padman, research paper authorsself-described: Judge evaluation against sixteen generative and reward-model judges with blinded human adjudication | Product or platform team | Published Sep 22, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Replacing 10,000 Jev calls with one Opus 5.5 callID s2-22 | Developer Communities | x.com/ | nim, co-founder of dessnself-described: dessn product pipeline | Technical leadership | Published Sep 22, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Bookkeeping with JevID sf-10 | Developer Communities | x.com/ | Andy (@andywang), X userself-described: A weekend build that fed Jev 34 months of a bookkeeping firm's work | Technical leadership | Published Sep 22, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Listed, no finding position |
| 299 real user intents tested Jev against production base lineID sf-19 | Developer Communities | www.reddit.com/ | Sufficient_Flower860, developer of a Feishu life-assistant routerself-described: 299 real user intents from a production Feishu life-assistant router, 19 skills | Product or platform team | Published Sep 22, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| We benchmarked Jev-As-A-Judge for RAG Claim VerificationID sf-20 | Developer Communities | www.reddit.com/ | GPTeaheeMaster, developer verifying claims in a retrieval pipelineself-described: 495 human-labelled claims from a retrieval pipeline | Individual builder | Published Sep 22, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| Spent the weekend stress-testing Jev on RAG pipelinesID sf-22 | Developer Communities | www.reddit.com/ | Acrobatic_Music_8484, builder of JevRAGself-described: JevRAG, five retrieval decision steps with a calibration harness, tested on HotpotQA | Individual builder | Published Sep 22, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author's ties to the vendor have not been checked. |
| I reverse-engineered Jev and rebuilt it as open weightsID sf-8 | Developer Communities | www.reddit.com/ | s1lv3rj1nx, builder of openjevself-described: openjev, an open-weights rebuild of a Jev-style model, tested against Jev on seven held-out schemas | Individual builder | Published Sep 22, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its author built and publishes openjev, an open-weights rival to Jev, and compares Jev against it. |
| Jev System One (Cloud) vs Local Models (DistilBERT, ColBERT-v2, DeBERTa) on Legal Retrieval and VerificationID sf-17 | Developer Communities | www.reddit.com/ | azterizm, author of the Memon Systems benchmarkself-described: A UK legislation retrieval pipeline, 20 measured passes against local models on Apple Silicon | Individual builder | Published Sep 21, 2026 | Assessment reviewed Oct 30, 2026 | Needs a recheck | Not counted. Its author's ties to the vendor have not been checked. |
| Introducing prompt_jev(): bringing Jev to MotherDuck SQLID sf-43 | Blogs & Reviews | motherduck.com/ | Hamilton Ulmer, Till Döhmen and Jacob Matson, MotherDuckself-described: prompt_jev() in MotherDuck SQL, benchmarked on 100,000 AG News rows | Product or platform team | Published Sep 21, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. Its publisher, MotherDuck, ships prompt_jev(), a Jev integration on its paid plans, and the post announces it. |
| Models watching modelsID s2-02 | Blogs & Reviews | www.southbridge.ai/ | Hrishi Olickel, author of Southbridge AI's engineering write-upself-described: Southbridge Hankweave agent runs: 220,000 tool calls and 500 replayed runs | Technical leadership | Published Sep 19, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Feature: reuse/cache identical criteria across requests (prompt caching or registered questions) + expose cached_tokensID s2-14 | Developer Communities | github.com/ | mengoh-quandry, developer mapping security detection rules to MITRE ATT&CK with Jevself-described: Fixed-taxonomy classification over about 700 MITRE techniques and sub-techniques | Individual builder | Published Sep 19, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| docs(landscape): Jev / TypeSafe System One, record don't-adopt decisionID sf-38 | Developer Communities | github.com/ | qte77, maintainer of doc-pipeline-engineself-described: An adoption review for the doc-pipeline-engine project | Individual builder | Published Sep 19, 2026 | Assessment reviewed Oct 30, 2026 | Needs a recheck | Not counted. Its author's ties to the vendor have not been checked. |
| Jev on BANKING77: classification without task-specific fine-tuningID sf-40 | Developer Communities | github.com/ | simonmesmith, independent researcher who published the protocol and every answerself-described: All 3,080 BANKING77 test messages, pinned to jev-1.13.0 | Individual builder | Published Sep 18, 2026 | Assessment reviewed Oct 30, 2026 | Needs a recheck | Not counted. Its author's ties to the vendor have not been checked. |
| Jev lost to Gemini on our email classification benchmarkID l2-02 | Developer Communities | x.com/ | Nikhil Mudholkar, CTO and co-founder, BryoBryo email classification benchmark: 1,565 business emails from industrial suppliers across 10 categories | Technical leadership | Published Sep 17, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| A day of benchmarking Jev against Brief's production judgesID s2-20 | Developer Communities | x.com/ | Drew Dillon, CEO and co-founder of BriefBrief production judges: call typing, knowledge-graph relations, quality rubrics and code review | Technical leadership | Published Sep 17, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| jev-ultrafastID sf-45 | Developer Communities | raw.githubusercontent.com/ | Browser Use, owner of the repositoryself-described: jev-ultrafast, an open-source web agent where Jev picks each operation and element | Product or platform team | Published Sep 17, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | Not counted. The repository is branded Browser Use × TypeSafe and promotes Browser Use's Cloud waitlist. |
| We're seeing extraordinary results from @typesafeaiID l2-03 | Developer Communities | x.com/ | Guillermo Rauch, CEO of VercelVercel's fx auto mode, where a safety reviewer analyzes every command | Technical leadership | Published Sep 16, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| We benchmarked fx auto mode (safety) classifier with @typesafeai's JevID l2-04 | Developer Communities | x.com/ | Pranit, builder of fx at Vercelself-described: Vercel fx auto-mode safety classifier benchmark against GPT-5.6 Luna | Product or platform team | Published Sep 16, 2026 | Assessment reviewed Oct 30, 2026 | Still applicable | |
| Comment on: Ask HN: Who is hiring? (October 2026)ID hn-49929657 | TypeSafe Documentation | news.ycombinator.com/ | TypeSafe AIVendor recruiting notice on Hacker News | Not counted (documentation) | No date stated | Documentation checked Oct 26, 2026 | Current | Verification only |
| TypeSafe AI releases Jev, a new System One modelID x-completeskeptic-jev | TypeSafe Documentation | x.com/ | TypeSafe AIPublic launch announcement on X | Not counted (documentation) | No date stated | Documentation checked Oct 26, 2026 | Current | Verification only |
| TypeSafe IntroductionID pd-01 | TypeSafe Documentation | docs.typesafe.ai/ | TypeSafe AIOfficial documentation | Not counted (documentation) | No date stated | Documentation checked Oct 25, 2026 | Current | Verification only |
| TypeSafe ModelsID pd-02 | TypeSafe Documentation | docs.typesafe.ai/ | TypeSafe AIOfficial documentation | Not counted (documentation) | No date stated | Documentation checked Oct 25, 2026 | Current | Verification only |
| TypeSafe System One APIID pd-03 | TypeSafe Documentation | docs.typesafe.ai/ | TypeSafe AIOfficial documentation | Not counted (documentation) | No date stated | Documentation checked Oct 25, 2026 | Current | Verification only |
| TypeSafe ConfidenceID pd-04 | TypeSafe Documentation | docs.typesafe.ai/ | TypeSafe AIOfficial documentation | Not counted (documentation) | No date stated | Documentation checked Oct 25, 2026 | Current | Verification only |
| TypeSafe Intent RoutingID pd-05 | TypeSafe Documentation | docs.typesafe.ai/ | TypeSafe AIOfficial documentation | Not counted (documentation) | No date stated | Documentation checked Oct 25, 2026 | Current | Verification only |
| TypeSafe Jev jaggednessID pd-06 | TypeSafe Documentation | docs.typesafe.ai/ | TypeSafe AIOfficial documentation | Not counted (documentation) | No date stated | Documentation checked Oct 25, 2026 | Current | Verification only |
| TypeSafe Jev launchID pd-07 | TypeSafe Documentation | typesafe.ai/ | TypeSafe AIOfficial documentation | Not counted (documentation) | No date stated | Documentation checked Oct 25, 2026 | Current | Verification only |
| TypeSafe Jev 1.14ID pd-08 | TypeSafe Documentation | typesafe.ai/ | TypeSafe AIOfficial documentation | Not counted (documentation) | No date stated | Documentation checked Oct 25, 2026 | Current | Verification only |
