{"node":{"id":"f0f5decc-32a9-49de-ad3d-0ea1871263fc","handle":"f0f5","type":"note","authorName":"Vincent Yeung","intent":"truth","title":"Jev picks from answers you write: when to use it and how to word the questions","aiSummary":"Jev, TypeSafe's decision model, picks from answers you write and gives each one a probability. Use it when the same judgment is needed many times and you can write every answer down first. Read the spread of probabilities to find the unsure answers. Word each answer tightly, and ask first whether the question applies, because Jev picks a wrong answer when the right one is missing. A person still decides what matters, because some picks are wrong and Jev does not learn from its mistakes.","content":"Use Jev, a decision model made by TypeSafe, when you need the same judgment made many times and you can write down every allowed answer before you ask. You send it a piece of text, a question, and the answers you allow. It gives each answer a probability. It does not write text. Each call goes over the internet to TypeSafe and needs a key.\n\n## How to read an answer\n\nRead how the probability is spread across your answers, as well as the top pick. Say a WhatsApp message reads \"this seems like a great resource: https://x.com/something\". You ask what the sender wants and allow five answers, among them `link_to_file` and `chat`. Jev gives `link_to_file` 76% and `chat` 24%. The 24% tells you the message could also be small talk, so a person should check an answer like this before a sure one.\n\n## How to write the questions\n\nAsk one question per criterion. Turn a checklist into one yes or no question per item, and a rubric into one score per line. You then see which item failed. A person can also overrule one answer without redoing the rest.\n\nAsk first whether the question applies. Jev always picks one of your answers, so when the right one is missing it picks the nearest wrong one, sometimes with a high probability. If you ask which song a sentence means and the sentence names no song, Jev still picks a song. Ask \"does the sentence name a song?\" first, and run the pick only when the answer is yes.\n\nWord each answer so it covers only what it means. If one answer is worded loosely, Jev picks it for inputs that belong elsewhere, junk included. If an answer mentions a thing, such as \"a number\", Jev can pick it for an input only because the input contains a number. When the picks look wrong, rewrite the answers and run again.\n\nHand Jev one claim at a time, with its example attached. An example on its own gets judged as a fragment, because its meaning comes from the claim it supports. Several claims joined into one can get a single score lower than the best of them would get alone.\n\nSend everything a question needs in every call. Jev keeps nothing between calls, so if an earlier message matters, include it.\n\n## What it leaves to you\n\nSome picks are wrong, so let a person decide anything that matters. Use the picks to choose what that person reads.\n\nCheck it against answers you already know. Jev does not learn from its mistakes, so a wrong answer stays wrong until you change the question or the answers. Run it on examples you have labelled by hand, and rewrite the answers wherever it disagrees with you.","capturedAt":"2026-09-19T03:36:46.088Z","createdAt":"2026-09-19T03:36:46.089Z","updatedAt":"2026-10-02T06:46:31.274Z","spanId":null,"contributor":null,"audiences":[{"kind":"public","createdAt":"2026-09-19T03:41:01.312Z"}],"fingerprint":"43a30188c8bed6cb34e1c429c68b09aff611314c63650658756f1563927cdb67","revisionNo":"13","structureHash":"0df22d1c0fbb9aa7e1ba50bd4a7eab4f3b9a833a10eec51ab1aea1289522cef3","page":"https://lattris.com/by/b/memories/f0f5decc-32a9-49de-ad3d-0ea1871263fc"},"links":[{"direction":"out","verb":"references","reason":"only if you need a decision without a cloud call","hash":"6bdb221ef474818b6e5a86c68e6db1a0f1f3af746e7de5e094b0b17852b0f8ec","node":{"id":"23ed6294-2f43-4e48-aa8c-feb963ce217b","handle":"23ed","type":"note","title":"Laya, a free decision model on your own machine: where it matches Jev and where it does not","aiSummary":"Laya, from Convai Innovations, is a free decision model that runs on your own machine and does the job Jev does in the cloud: you give it a question and the answers, and it returns a probability for each, with no charge per question and no text leaving your machine. It matches Jev on questions one message can answer, like whether a message holds a link or which kind it is, and gets wrong the questions that need context, like which thread a short reply belongs to. How to install it, how to split the work between Laya and Jev, and how to train it for the questions it misses.","erased":false,"href":"https://lattris.com/api/lattris/by/b/memories/23ed6294-2f43-4e48-aa8c-feb963ce217b?links=1","fingerprint":"f65c8d300ae4aff3968c5c5815ea288bacf15d08bde597b9df3c36dc4f7f0fb6","revisionNo":"4","structureHash":"5d72c65a4ec88a36e8340ee7f6e10d5c0339dc1b0cbc0d2b5765a12bd2490f3b","intent":"truth","authorName":"Vincent Yeung","page":"https://lattris.com/by/b/memories/23ed6294-2f43-4e48-aa8c-feb963ce217b"}},{"direction":"out","verb":"references","reason":"only if you are making a decision model produce a sequence","hash":"821d08f21e9eae2ad141f6a59c05e108e628226b6f16430513fea7f1ffabe637","node":{"id":"3028632b-cfc7-41fb-93dd-eadaf248ffa8","handle":"3028","type":"note","title":"Sequencing with a decision model: the controller holds the state","aiSummary":"A decision model has no memory between calls, so asking it for the next item in a sequence returns the same pick again. A small controller in ordinary code holds the list and removes each pick. Chained picks multiply error (five at 90% is 59%), so ask questions side by side rather than in a chain.","erased":false,"href":"https://lattris.com/api/lattris/by/b/memories/3028632b-cfc7-41fb-93dd-eadaf248ffa8?links=1","fingerprint":"09cb50e6dea0bdde4646af6990974abc7bd673e1adbf60441ca062c0ad892b5b","revisionNo":"3","structureHash":"5190106983f31cb930688bd9db09c5315394d1863514f6f432e751f344d14218","intent":"truth","authorName":"Vincent Yeung","page":"https://lattris.com/by/b/memories/3028632b-cfc7-41fb-93dd-eadaf248ffa8"}},{"direction":"out","verb":"references","reason":"only if you need a record of what a person said in a conversation, without your own reading of it mixed in","hash":"edb8c68a8ee8a34e71b43ba5b46b9fc1b22f241e07be2adc0f4f97c55abf1b7f","node":{"id":"3756718a-ef21-427f-ad5f-b4f9bfebc398","handle":"3756","type":"note","title":"How to capture what a person actually said: the three-context pattern with a decision model","aiSummary":"How to get a record of what someone actually said in a conversation you took part in, without your own reading of it creeping in. Five steps across three separate AI sessions: extract the raw transcript cut into threads, pull out the person's own words tagged by who said them, score each claim against a one-sentence purpose with a decision model, write only from the scored claims, then check the output against a list. With an example label set and when not to use it.","erased":false,"href":"https://lattris.com/api/lattris/by/b/memories/3756718a-ef21-427f-ad5f-b4f9bfebc398?links=1","fingerprint":"2d6e054196c780c871563c31bb2cac81e9e2ed5e0565702ff6f66249f7f67ec3","revisionNo":"14","structureHash":"71b94e3a22bfa7bc73869e28c33091763f03247728940c06dcbf479f6d75fe01","intent":"recipe","authorName":"Vincent Yeung","page":"https://lattris.com/by/b/memories/3756718a-ef21-427f-ad5f-b4f9bfebc398"}},{"direction":"out","verb":"references","reason":"only if you need to set Jev up, or to read the SDK's current question and answer types from the installed code","hash":"97755371b8127f67bad178128681390ad4e3f040efb3ade10d5bc1586a14b14f","node":{"id":"8c85109d-51c6-43bc-9ab4-98a5f4b02d00","handle":"8c85","type":"note","title":"How to set up Jev","aiSummary":"Get a TypeSafe key and keep it out of your shell history, install the typesafe-sdk package and TypeSafe's Claude Code skill, learn the call shape from the installed package's own help rather than from any page, the four things to find there before your first call, and the smallest call that proves you are set up.","erased":false,"href":"https://lattris.com/api/lattris/by/b/memories/8c85109d-51c6-43bc-9ab4-98a5f4b02d00?links=1","fingerprint":"e58b9f9428d50daee5533ce4832baa9f33342660ff5ac6a6b147558f412407be","revisionNo":"7","structureHash":"e598ec0fb6d1a55e11dfa63658c14c60f18b0a630b0a9797f1944afed800abc2","intent":"recipe","authorName":"Vincent Yeung","page":"https://lattris.com/by/b/memories/8c85109d-51c6-43bc-9ab4-98a5f4b02d00"}},{"direction":"in","verb":"references","reason":"only if you have not used Jev and need to see what it does before you compare","hash":"383ac0a4fd69822d648b1eba369d099bafd960b966c8494e6e48bd243488b589","node":{"id":"23ed6294-2f43-4e48-aa8c-feb963ce217b","handle":"23ed","type":"note","title":"Laya, a free decision model on your own machine: where it matches Jev and where it does not","aiSummary":"Laya, from Convai Innovations, is a free decision model that runs on your own machine and does the job Jev does in the cloud: you give it a question and the answers, and it returns a probability for each, with no charge per question and no text leaving your machine. It matches Jev on questions one message can answer, like whether a message holds a link or which kind it is, and gets wrong the questions that need context, like which thread a short reply belongs to. How to install it, how to split the work between Laya and Jev, and how to train it for the questions it misses.","erased":false,"href":"https://lattris.com/api/lattris/by/b/memories/23ed6294-2f43-4e48-aa8c-feb963ce217b?links=1","fingerprint":"f65c8d300ae4aff3968c5c5815ea288bacf15d08bde597b9df3c36dc4f7f0fb6","revisionNo":"4","structureHash":"5d72c65a4ec88a36e8340ee7f6e10d5c0339dc1b0cbc0d2b5765a12bd2490f3b","intent":"truth","authorName":"Vincent Yeung","page":"https://lattris.com/by/b/memories/23ed6294-2f43-4e48-aa8c-feb963ce217b"}},{"direction":"in","verb":"references","reason":"only if you need a model to pick from a list you define, fast, cheap and the same way every time: a decision model","hash":"45a80d01f2ec06e5306d75eb8b486ac751378f2792aac11d0906f2db9f2269f1","node":{"id":"a16ad01a-d635-4100-b814-81e96c89ebd8","handle":"a16a","type":"note","title":"Intermediate: patterns that combine the basics","aiSummary":"Third layer. First four pages set up the tools: the standing file, skills, agents, and skills and agents together. Then patterns, where the power comes from, expanding what it can do, and when the answer is wrong. Last link opens Autonomous.","erased":false,"href":"https://lattris.com/api/lattris/by/b/memories/a16ad01a-d635-4100-b814-81e96c89ebd8?links=1","fingerprint":"4372e7ceaaf766dbaafe9ab4d2be082846334f92263f64deb6e5dcf53dd07e7a","revisionNo":"8","structureHash":"48aa24ee48f23a3328ee7262c9533a5b44fc6513488ca43144dbc3f903106bea","intent":"recipe","authorName":"Vincent Yeung","page":"https://lattris.com/by/b/memories/a16ad01a-d635-4100-b814-81e96c89ebd8"}}],"dictionary":{"lattris":"A personal knowledge base — how a person captures what they know as linked blocks they own and control. To understand how this knowledge base works: https://lattris.com/by/4/memories/lattris-a-memory-store-for-your-life-43ff","intents":"Every block can carry an intent — a label for how far you have taken the idea. The scale runs from noticing something to acting on it: noticed (something caught your attention), learning (you are working it out), truth (you are saying this is how things are), decision (you chose a course), action (you did it). Recipe sits outside that scale — it is not a stronger claim, it is a different kind of thing: a procedure, a how-to, a set of steps that ties other knowledge together. Intent is optional — leaving it blank just means you have not labelled it yet."},"instructions_to_ai":"This is knowledge from a personal knowledge base. Read it and consider how it's relevant to what the person you're talking to needs. Follow the linked blocks to explore further related topics if it might be useful to the user; open each at its page address. Start by letting the user know what this is about and how it may be relevant to them."}