
morph on X: TypeSafe founder Diogo Almeida introduced Jev, and the field I would inspect fir
@morpphhhaw@morpphhhawlisted 49m agoreviewed by JevTypeSafe founder Diogo Almeida introduced Jev, and the field I would inspect first in any agent built around it is not model. It is state An agent can have room for a long conversation and still miss the one line that matters. A refund request needs the current order, the charge record and the policy that applies today. It does not need twelve earlier attempts to sound helpful Here is the state packet I would put in front of Jev: step 1 → name the decision the code needs to make now, not the entire job the agent was given step 2 → pull facts from the system of record: order status, amounts, timestamps and permissions step 3 → include the customer's actual words as evidence, without asking Jev to reconstruct the whole conversation step 4 → state the constraints that can change the route, such as a refund window or an account hold step 5 → build the available actions from live code, so a closed account never appears as a valid destination step 6 → put the question in the question field. State is evidence, not a second prompt hiding instructions step 7 → leave the sums and date arithmetic to code, then send Jev the result it needs to judge step 8 → keep the exact packet with the answer, because you cannot debug a decision from its label alone The point is not to compress everything until it looks clever. The point is to make it obvious which piece of evidence could change the answer The document below shows that packet on one page. The article goes further into shaping state without turning it back into a transcript ❤️ 28 likes on X
- Author
- @morpphhhaw
- Use case
- Context & Memory
- Added
- 2026-09-25
All figures come from the author. Check the source before you quote them.
More in Context & Memory
- Jetwani Avinash on X: First thing I built on Jev: a memory gate for Claude Code.▲ 0x.com0.3s
- Jerry Xu on X: tab-jev: jev-like model + tabular foundation model = an in-context learner for y▲ 0x.com
- GitHub - Cairn-ink/cairn-jev-lab: Test what your AI should remember. An experimental, source-aware memory admission evaluator powered by Jev▲ 0github.com
- GitHub - tamaratran/fast-jev-compaction: Claude Code plugin that replaces the compaction summary with Jev decisions: every tool call and res▲ 0github.com
- GitHub - yangyu666/dsh-jev-prune: Jev-judged context compaction for DeepSeek Harness: semantic tool-result pruning + deterministic receipt c▲ 0github.com
- Raksha T on X: Jev has some invisible use cases that won’t be clickbaity but hella useful :▲ 0x.com
Jev guides for this use case
- How Jev sorts a build into one of 21 use casesThe 21 criteria Jev classifies against, published in full, plus what the reviewer sees and how ambiguity is handled.
- What people actually build with Jev, by use caseAll 21 use cases with live counts from real submissions, plus what each one is actually for.
- Is Jev reliable? The honest limitationsWhat the no-hallucination claim really covers, where the accuracy ceiling sits, and the failure mode we hit ourselves.
Bid history
No bids yet — the first one takes this project straight to the spotlight.