Jev + Grok Bot... i can't understand why everyone isn't wiring this up yet the forks alone drop from about $110 a day to $4 once jev takes them over. right now it's the best way i know to run grok what jev handles: -> scores which project notes matter before the context even loads -> hands the easy steps to a faster worker -> picks a recovery path when a tool fails, so nothing retries blind -> runs the focused checks before the full test suite Grok does the hard reasoning. jev chooses from options the harness already prepared and validated that's jev engineering in one line: the expensive model thinks, the cheap one decides Ckeck out the full guide on Jev in the article below ↓ ↓

call me insane but.. i will keep saying that: grok bot + jev is going to create more millionaires in 2026 than crypto ever did. idea → grokbot → jev decisions → grokbot execution → $$$$$ don’t scroll past this if it hits your timeline. just feed this entire thing into grok bot. thank me later.

call me insane but.. i will keep saying that: grok bot + jev is going to create more millionaires in 2026 than crypto ever did. idea → grokbot → jev decisions → grokbot execution → $$$$$ don’t scroll past this if it hits your timeline. just feed this entire thing into grok bot. thank me later.

REA, an open-source repository, allows Claude Code to analyze any application on your device all the way down to the binary level What it has already reconstructed: > an authenticator application, with all 133 tests passing > a Touhou game, with 696 out of 697 functions matching perfectly, completed in roughly 12 days > a sound function from a game released in 1996, rewritten in C and successfully passing 3,205 original test cases. - How to start using it: > install REA into Claude Code using a single setup command > inquire about how a feature operates in an application you use daily > have it draft a specification, then create your own version based on that. After just one evening with REA, your perspective on subscriptions will change completely Full guide is in the article below:

call me crazy but.. I will keep saying this… Grok Bot + Jev is going to make more millionaires in 2026 than crypto ever did. prompt → GrokBot → Jev SEO decision → GrokBot action → $$$$$ don’t bookmark this if it shows up on your feed. Just paste this entire thing into Grok Bot. thank me later.

call me crazy but.. I will keep saying this… Grok Bot + Jev is going to make more millionaires in 2026 than crypto ever did. prompt → GrokBot → Jev SEO decision → GrokBot action → $$$$$ don’t bookmark this if it shows up on your feed. Just paste this entire thing into Grok Bot. thank me later.

I was absolutely shocked by this. The Stanford AI research group has given Claude Code a configuration that mimics a human brain. JEV functions as the quick gut brain. It evaluates everything in mere milliseconds and makes simple choices. Claude is the more methodical brain that engages only for complex problems. The following are the commands that link the two systems: /plugin marketplace add rashedInt32/jev-mcp /plugin install jev@jev-mcp -> Claude receives Jev's tools: classify, score, check. npm install -g @dzhng/jevgrep jg "how are payments retried?" ./my-project -> ask what the code accomplishes and obtain the relevant files. The author recorded approximately a 30% decrease in costs for the same tasks. npx skills add altryne/jevify --skill jevify -> trains your agent to autonomously manage the bulk of yes/no tasks with Jev. > and fast-jev-compaction substitutes Claude Code's summary with Jev's decisions: it discards outdated tool calls while keeping all other content intact. Quicker operations, reduced expenses, and minimal compute wasted. Ensure to save this before your next session.

I was absolutely shocked by this. The Stanford AI research group has given Claude Code a configuration that mimics a human brain. JEV functions as the quick gut brain. It evaluates everything in mere milliseconds and makes simple choices. Claude is the more methodical brain that engages only for complex problems. The following are the commands that link the two systems: /plugin marketplace add rashedInt32/jev-mcp /plugin install jev@jev-mcp -> Claude receives Jev's tools: classify, score, check. npm install -g @dzhng/jevgrep jg "how are payments retried?" ./my-project -> ask what the code accomplishes and obtain the relevant files. The author recorded approximately a 30% decrease in costs for the same tasks. npx skills add altryne/jevify --skill jevify -> trains your agent to autonomously manage the bulk of yes/no tasks with Jev. > and fast-jev-compaction substitutes Claude Code's summary with Jev's decisions: it discards outdated tool calls while keeping all other content intact. Quicker operations, reduced expenses, and minimal compute wasted. Ensure to save this before your next session.

Jev Engineering turns an agent stack into an actual control system and moves the expensive model out of every decision loop. and up to 193x faster and 444x cheaper in tests. the model shouldn’t decide everything. in this setup: request → structured state → Jev router → cheapest capable model → worker → relevance / approval checks → execution gate → tool the important part is that the decision layers don’t generate text. they route → score → block → approve. so expensive models only get called when the task actually needs them. that’s the point of Jev Engineering: separate reasoning from decision-making, then make the decision layer measurable, cheap and fast. full breakdown in the article below ↓

Jev Engineering turns an agent stack into an actual control system and moves the expensive model out of every decision loop. and up to 193x faster and 444x cheaper in tests. the model shouldn’t decide everything. in this setup: request → structured state → Jev router → cheapest capable model → worker → relevance / approval checks → execution gate → tool the important part is that the decision layers don’t generate text. they route → score → block → approve. so expensive models only get called when the task actually needs them. that’s the point of Jev Engineering: separate reasoning from decision-making, then make the decision layer measurable, cheap and fast. full breakdown in the article below ↓

Jev Engineering is what turns an agent stack into an actual control system and moves the expensive model out of every decision loop and up to 193x faster and 444x cheaper in tests. the model shouldn’t decide everything. in this setup: request → structured state → Jev router → cheapest capable model → worker → relevance / approval checks → execution gate → tool the important part is that the decision layers don’t generate prose. they route → score → block → approve. so expensive models only get called when the task actually needs them. that’s the point of Jev Engineering: separate reasoning from decision-making, then make the decision layer measurable, cheap and fast. full breakdown in the article below ↓

Jev Engineering is what turns an agent stack into an actual control system and moves the expensive model out of every decision loop and up to 193x faster and 444x cheaper in tests. the model shouldn’t decide everything. in this setup: request → structured state → Jev router → cheapest capable model → worker → relevance / approval checks → execution gate → tool the important part is that the decision layers don’t generate prose. they route → score → block → approve. so expensive models only get called when the task actually needs them. that’s the point of Jev Engineering: separate reasoning from decision-making, then make the decision layer measurable, cheap and fast. full breakdown in the article below ↓

Jev + Claude Opus 5.5... i can't understand why everyone isn't wiring this up yet the forks alone drop from about $110 a day to $4 once jev takes them over. right now it's the best way i know to run opus what jev handles: -> scores which project notes matter before the context even loads -> hands the easy steps to a faster worker -> picks a recovery path when a tool fails, so nothing retries blind -> runs the focused checks before the full test suite opus does the hard reasoning. jev chooses from options the harness already prepared and validated that's jev engineering in one line: the expensive model thinks, the cheap one decides Ckeck out the full guide on Jev in the article below ↓ ↓

Opus 5.5 + Jev combo is extremely powerful. It's literally 200x faster and 400x cheaper than running Opus alone. Send this prompt to Claude, and it will tell you exactly how to set it up:

The guy who built Jev realized we’ve been using our smartest AI for the dumbest possible jobs your $25-$250/mo frontier model is researching, writing code, planning… and then you’re paying the same brain to decide YES / NO. Jev flips that. it’s a tiny decision model that sits in front of the expensive stuff and handles the boring forks: -> which agent goes next? -> is this worth researching? -> reply or ignore? -> publish, wait or escalate? -> BUY / SELL / HOLD? -> click this or keep looking? then I saw the numbers people are reporting and the whole architecture started making a lot more sense. ~2,000 papers classified for around $0.05 ~800 emails sorted for around $0.028 browser-agent loops demonstrated around 6 seconds for ~$0.0029 and at the quoted pricing, roughly 20,000 ~2K-token decisions comes out around $0.62. that’s not because Jev became smarter than Claude. it’s because nobody asked it to be Claude. the setup is stupidly simple: -> big LLM gets the hard thinking, research and writing -> Jev gets the thousands of tiny decisions between those steps -> code actually clicks, saves, sends and executes and suddenly you start noticing these little decisions everywhere. > your inbox. > X feed. > browser agents. > support queue. > meetings. > leads. > video clips. > even paper-trading loops. that’s the part I think most agent builders are missing. the expensive model doesn’t need to touch every step just because it’s capable of touching every step. > let Claude think. > let Jev decide what deserves Claude. > let code do the actual work. I broke down 10 builds you can wire into an agent today in the article below ↓ ↓

The guy who built Jev realized we’ve been using our smartest AI for the dumbest possible jobs your $25-$250/mo frontier model is researching, writing code, planning… and then you’re paying the same brain to decide YES / NO. Jev flips that. it’s a tiny decision model that sits in front of the expensive stuff and handles the boring forks: -> which agent goes next? -> is this worth researching? -> reply or ignore? -> publish, wait or escalate? -> BUY / SELL / HOLD? -> click this or keep looking? then I saw the numbers people are reporting and the whole architecture started making a lot more sense. ~2,000 papers classified for around $0.05 ~800 emails sorted for around $0.028 browser-agent loops demonstrated around 6 seconds for ~$0.0029 and at the quoted pricing, roughly 20,000 ~2K-token decisions comes out around $0.62. that’s not because Jev became smarter than Claude. it’s because nobody asked it to be Claude. the setup is stupidly simple: -> big LLM gets the hard thinking, research and writing -> Jev gets the thousands of tiny decisions between those steps -> code actually clicks, saves, sends and executes and suddenly you start noticing these little decisions everywhere. > your inbox. > X feed. > browser agents. > support queue. > meetings. > leads. > video clips. > even paper-trading loops. that’s the part I think most agent builders are missing. the expensive model doesn’t need to touch every step just because it’s capable of touching every step. > let Claude think. > let Jev decide what deserves Claude. > let code do the actual work. I broke down 10 builds you can wire into an agent today in the article below ↓ ↓

this is pure f*cking treasure a team of researchers found how to orchestrate Claude Opus 5.5 and Jev together, and it completely breaks the agent scaffolding wall most developers try to make an agent reliable by routing every decision back through the model. you burn tokens on choices that have an exact rule and freeze your routing logic into a prompt that drifts within 5 edits this architecture eliminates human-written routing prompts and splits the cognitive stack: search director: Claude Opus 5.5 plans the work, querying a persistent decision graph where routing and scoring reduce to single typed calls typed router: Jev answers every fork in code, returning an option with a probability instead of prose zero context bleeding: a routed fork never enters the chat context, so a thousand decisions cost the same context as zero evaluator isolation: candidate code never touches the scoring process, stopping reward hacking and test leakage cold handoff contract: every step passes task, inputs, done-condition and confidence as a file, so a crashed session loses nothing the metrics outclass single-context agent loops: 7x lower model spend than running the same loop through one context window 0 tokens generated on routed decisions sub-500ms on forks that used to cost a full frontier round trip 0% reward hacks: candidate-controlled metric tampering eliminated Opus directs the plan. Jev picks the branch you stop writing rigid routing prompts. you let the type system own the decision loop I'll leave some ready-to-copy Jev builds in the article below ↓ ↓

this is pure f*cking treasure a team of researchers found how to orchestrate Claude Opus 5.5 and Jev together, and it completely breaks the agent scaffolding wall most developers try to make an agent reliable by routing every decision back through the model. you burn tokens on choices that have an exact rule and freeze your routing logic into a prompt that drifts within 5 edits this architecture eliminates human-written routing prompts and splits the cognitive stack: search director: Claude Opus 5.5 plans the work, querying a persistent decision graph where routing and scoring reduce to single typed calls typed router: Jev answers every fork in code, returning an option with a probability instead of prose zero context bleeding: a routed fork never enters the chat context, so a thousand decisions cost the same context as zero evaluator isolation: candidate code never touches the scoring process, stopping reward hacking and test leakage cold handoff contract: every step passes task, inputs, done-condition and confidence as a file, so a crashed session loses nothing the metrics outclass single-context agent loops: 7x lower model spend than running the same loop through one context window 0 tokens generated on routed decisions sub-500ms on forks that used to cost a full frontier round trip 0% reward hacks: candidate-controlled metric tampering eliminated Opus directs the plan. Jev picks the branch you stop writing rigid routing prompts. you let the type system own the decision loop I'll leave some ready-to-copy Jev builds in the article below ↓ ↓