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Jev Engineering is what lets one decision layer control hundreds of agent paths without turning the system into chaos. and up to 193x faster and 444x cheaper in tests. it’s deciding: → which model gets called → which tool gets access → which result survives that’s what the video...

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Ricker

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57,821 görüntüleme • 1 ay önce •via X (Twitter)

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19 Yorum

Apex profil fotoğrafı
Apex1 ay önce

You are one of the best in this kind of vis mate

Ricker profil fotoğrafı
Ricker1 ay önce

thanks Apex

Morty profil fotoğrafı
Morty1 ay önce

This vis perfectly shows how Jev works

Ricker profil fotoğrafı
Ricker1 ay önce

i do my best to illustrate it

rewind profil fotoğrafı
rewind1 ay önce

Verification keeps branches honest

Ricker profil fotoğrafı
Ricker1 ay önce

fact man

Andi Monroe profil fotoğrafı
Andi Monroe1 ay önce

Jev is really amazing. Specific input shape makes you rethink the task and it’s beautiful crazy

Smarty profil fotoğrafı
Smarty1 ay önce

wow, its gonna be banger

Tolik profil fotoğrafı
Tolik1 ay önce

Pinch the branches back to one state otherwise it's just a fan of guesswork

Chen profil fotoğrafı
Chen1 ay önce

yeah. i keep underestimating how long the second half takes

Wallchain Community Hub profil fotoğrafı
Wallchain Community Hub1 ay önce

Parallelism without chaos. That’s the real unlock

starmex profil fotoğrafı
starmex1 ay önce

the real unlock is having one decision layer coordinate all those parallel agents parallel execution without letting every branch make its own decisions is a huge architectural shift

Brian Hadu profil fotoğrafı
Brian Hadu1 ay önce

if there's no clear oversight, even Jev's speed won't prevent chaos

Sophia l. profil fotoğrafı
Sophia l.1 ay önce

Thanks for sharing this

Slonski profil fotoğrafı
Slonski1 ay önce

collapse back into one verified state is the product that is what keeps a swarm usable

🇺🇸 profil fotoğrafı
🇺🇸1 ay önce

Looks like some slop

AI Mastery Guide profil fotoğrafı
AI Mastery Guide1 ay önce

444x cheaper is insane if true

Lea Thompson profil fotoğrafı
Lea Thompson1 ay önce

My CoinStats dashboard tracks actual P&L, not just fancy routing. Sounds great until the whole damn thing locks up because one agent improvised.

alpha404 profil fotoğrafı
alpha4041 ay önce

goo strategy brother

Benzer Videolar

Jev Engineering is the layer most agent stacks are still missing. state → decision → action → verification → next state up to 193x faster and 444x cheaper in our tests. everyone is optimizing the model. Jev Engineering optimizes what happens between model calls. every agent eventually hits the same fork: state → multiple possible actions → one route has to win instead of sending every decision back to an expensive LLM call, Jev turns that into a dedicated layer: so the loop becomes: LLM → reason Jev → decide tools / agents → execute state → update Jev → decide again once decisions become their own engineering layer, you can benchmark, batch and verify them separately. full blueprint in article ↓
0:29

Jev Engineering is the layer most agent stacks are still missing. state → decision → action → verification → next state up to 193x faster and 444x cheaper in our tests. everyone is optimizing the model. Jev Engineering optimizes what happens between model calls. every agent eventually hits the same fork: state → multiple possible actions → one route has to win instead of sending every decision back to an expensive LLM call, Jev turns that into a dedicated layer: so the loop becomes: LLM → reason Jev → decide tools / agents → execute state → update Jev → decide again once decisions become their own engineering layer, you can benchmark, batch and verify them separately. full blueprint in article ↓

Ricker

116,701 görüntüleme • 1 ay önce

Jev Engineering is what turns a messy agent graph into a controlled decision loop. and up to 193x faster and 444x cheaper in tests. the video looks complex, but the idea is simple: Jev Engineering makes that decision explicit. state enters → routes get scored → confidence updates → weak branches die → strong branches keep moving → execution unlocks so instead of every agent improvising independently, the system keeps narrowing the graph. 0.00 → 0.41 → 0.68 → 0.80 → 0.93 and that score can decide: which model runs → which tool gets called → which branch retries → which result gets rejected that’s the point of Jev Engineering: don’t just give agents more intelligence. engineer how they make decisions. full breakdown guide below
0:38

Jev Engineering is what turns a messy agent graph into a controlled decision loop. and up to 193x faster and 444x cheaper in tests. the video looks complex, but the idea is simple: Jev Engineering makes that decision explicit. state enters → routes get scored → confidence updates → weak branches die → strong branches keep moving → execution unlocks so instead of every agent improvising independently, the system keeps narrowing the graph. 0.00 → 0.41 → 0.68 → 0.80 → 0.93 and that score can decide: which model runs → which tool gets called → which branch retries → which result gets rejected that’s the point of Jev Engineering: don’t just give agents more intelligence. engineer how they make decisions. full breakdown guide below

Ricker

19,376 görüntüleme • 1 ay önce

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 ↓
0:39

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 ↓

Ricker

183,128 görüntüleme • 1 ay önce

Jev Engineering turns a static agent workflow into a graph that can rewrite itself while running. the video is basically the problem at scale: hundreds of routes → thousands of crossings → different agents → different tools → different confidence levels Jev Engineering doesn’t control every step. it controls the crossings. when two paths compete: → score both → kill the weak route → reroute the task so instead of one fixed chain, you get a live braid: state → decision → parallel paths → crossings → Jev → next state that’s the point of Jev Engineering: more parallel execution without letting the system lose the objective. the agents create the paths. Jev decides which paths survive. full breakdown below ↓
0:41

Jev Engineering turns a static agent workflow into a graph that can rewrite itself while running. the video is basically the problem at scale: hundreds of routes → thousands of crossings → different agents → different tools → different confidence levels Jev Engineering doesn’t control every step. it controls the crossings. when two paths compete: → score both → kill the weak route → reroute the task so instead of one fixed chain, you get a live braid: state → decision → parallel paths → crossings → Jev → next state that’s the point of Jev Engineering: more parallel execution without letting the system lose the objective. the agents create the paths. Jev decides which paths survive. full breakdown below ↓

Ricker

11,145 görüntüleme • 1 ay önce

Jev Engineering turns one agent chain into a decision graph that can reroute itself and moves the expensive model out of every decision loop. the important part in this setup isn’t the number of agents. it’s that execution doesn’t follow one fixed path. and up to 193x faster and 444x cheaper in tests. every result can change what happens next: state → Jev decision → branch → specialist agent → result → score → reroute and Jev Engineering sits above that graph deciding: which route stays alive → which specialist gets control → when two branches should merge → when a result needs another pass → when the goal is good enough to stop by the end of the run: 5,400 executions → 0.93 goal score → $0.020 decision cost that’s why Jev Engineering is more than model routing. it’s the control layer that keeps a changing agent graph pointed at one objective. full breakdown below ↓
0:39

Jev Engineering turns one agent chain into a decision graph that can reroute itself and moves the expensive model out of every decision loop. the important part in this setup isn’t the number of agents. it’s that execution doesn’t follow one fixed path. and up to 193x faster and 444x cheaper in tests. every result can change what happens next: state → Jev decision → branch → specialist agent → result → score → reroute and Jev Engineering sits above that graph deciding: which route stays alive → which specialist gets control → when two branches should merge → when a result needs another pass → when the goal is good enough to stop by the end of the run: 5,400 executions → 0.93 goal score → $0.020 decision cost that’s why Jev Engineering is more than model routing. it’s the control layer that keeps a changing agent graph pointed at one objective. full breakdown below ↓

Ricker

13,896 görüntüleme • 1 ay önce

Jev Engineering changes the unit of optimization from “agent” to “decision.” that sounds small, but it changes the architecture. and up to 193x faster and 444x cheaper in tests. normal multi-agent stack: agent → prompt → tool → output Jev-style stack: shared state → decision boundary → selected branch → execution → measurement → state update now you can optimize the actual control points: latency per decision cost per fork confidence threshold retry policy branch survival termination condition the agents underneath can change. the models can change. the tools can change. the decision layer stays measurable. that’s why Jev Engineering feels more like systems engineering than prompt engineering.
0:39

Jev Engineering changes the unit of optimization from “agent” to “decision.” that sounds small, but it changes the architecture. and up to 193x faster and 444x cheaper in tests. normal multi-agent stack: agent → prompt → tool → output Jev-style stack: shared state → decision boundary → selected branch → execution → measurement → state update now you can optimize the actual control points: latency per decision cost per fork confidence threshold retry policy branch survival termination condition the agents underneath can change. the models can change. the tools can change. the decision layer stays measurable. that’s why Jev Engineering feels more like systems engineering than prompt engineering.

Ricker

21,662 görüntüleme • 1 ay önce

Jev Engineering ran 5,400 agent executions and sent exactly 0 decisions back to me. that’s the number i care about here. and up to 193x faster and 444x cheaper in tests. 18 turns → 300 agents → 5,400 runs → 43 Jev calls → 16 outputs kept → 0 human escalations the loop handled three decisions around every turn: Choice → Score → Noul then it stopped itself when the goal crossed 0.93 vs 0.85 required. that’s what Jev Engineering looks like when it works: not “more autonomous agents.” fewer reasons for the human to come back into the loop.
0:41

Jev Engineering ran 5,400 agent executions and sent exactly 0 decisions back to me. that’s the number i care about here. and up to 193x faster and 444x cheaper in tests. 18 turns → 300 agents → 5,400 runs → 43 Jev calls → 16 outputs kept → 0 human escalations the loop handled three decisions around every turn: Choice → Score → Noul then it stopped itself when the goal crossed 0.93 vs 0.85 required. that’s what Jev Engineering looks like when it works: not “more autonomous agents.” fewer reasons for the human to come back into the loop.

Ricker

13,329 görüntüleme • 1 ay önce

Everyone is talking about JEV. Almost nobody knows how to actually use it. These 10 GitHub repos fix that. 1. browser-use/jev-ultrafast (~16.6k⭐) Jev for browser agents: pick the next action without another expensive LLM call. 2. tamaratran/fast-jev-compaction (~6.5k⭐) Uses Jev to decide what context still matters and what can be dropped. 3. TheoLeeCJ/SemIf (~4k⭐) Open-source Jev-style decision making you can run locally. 4. jarrodwatts/jev-trader (~2.1k⭐) Real-time trading loop where Jev decides buy, sell or wait. 5. kerpopule/hermes-jev-skills (~670⭐) Jev routing, memory, compaction and skill selection for AI agents. 6. devagrawal09/jev-review (~570⭐) Code review powered by Jev decisions instead of one giant prompt. 7. thruwire/foreman (~520⭐) Jev as a supervisor for coding agents: continue, retry, verify or stop. 8. superagents-lab/jev-search (~400⭐) Jev decides where and how an agent should search. 9. droidrun/mobile-jev (~350⭐) Jev for Android agents choosing the next app action. 10. gargpratyush/jev-router (~340⭐) Routes agent tasks to the right model with Jev. After looking through these, the pattern is pretty clear: LLMs generate. Agents execute. JEV decides. That decision layer is what most agent stacks are still missing. Save this if you want to integrate Jev into your workflow👇
1:11

Everyone is talking about JEV. Almost nobody knows how to actually use it. These 10 GitHub repos fix that. 1. browser-use/jev-ultrafast (~16.6k⭐) Jev for browser agents: pick the next action without another expensive LLM call. 2. tamaratran/fast-jev-compaction (~6.5k⭐) Uses Jev to decide what context still matters and what can be dropped. 3. TheoLeeCJ/SemIf (~4k⭐) Open-source Jev-style decision making you can run locally. 4. jarrodwatts/jev-trader (~2.1k⭐) Real-time trading loop where Jev decides buy, sell or wait. 5. kerpopule/hermes-jev-skills (~670⭐) Jev routing, memory, compaction and skill selection for AI agents. 6. devagrawal09/jev-review (~570⭐) Code review powered by Jev decisions instead of one giant prompt. 7. thruwire/foreman (~520⭐) Jev as a supervisor for coding agents: continue, retry, verify or stop. 8. superagents-lab/jev-search (~400⭐) Jev decides where and how an agent should search. 9. droidrun/mobile-jev (~350⭐) Jev for Android agents choosing the next app action. 10. gargpratyush/jev-router (~340⭐) Routes agent tasks to the right model with Jev. After looking through these, the pattern is pretty clear: LLMs generate. Agents execute. JEV decides. That decision layer is what most agent stacks are still missing. Save this if you want to integrate Jev into your workflow👇

unicode

20,745 görüntüleme • 1 ay önce

Holy crap, who the hell did this?!! Someone open-sourced a Jev-powered second brain for AI agents and lets you plug it straight into your own agent. GitHub's already at 925 stars, repo's called Hermes-Jev-Skills. You just run one install command, and Jev takes over every tiny decision your agent burns premium tokens on. Which model answers, which skill to load, which memories matter, what survives when context gets cut. Give it your computer or browser and it'll pick the next click, but only from actions you already marked safe. It'll sort your inbox into lanes, strip hidden prompt injections out of search results, and triage every message in ~0.4 seconds. Claude Code and Codex support - they threw that in too. No dependencies, no patching Hermes core, and your API key never touches the agent Link's right here: The catch? Jev itself is a paid cloud API. The skills run on your machine; the decisions don't. But hey, a decision costs a fraction of a cent, everything fails open if Jev goes down, and your expensive model finally gets to do only the thinking. Who needs a damn frontier model picking which file to open!
0:31

Holy crap, who the hell did this?!! Someone open-sourced a Jev-powered second brain for AI agents and lets you plug it straight into your own agent. GitHub's already at 925 stars, repo's called Hermes-Jev-Skills. You just run one install command, and Jev takes over every tiny decision your agent burns premium tokens on. Which model answers, which skill to load, which memories matter, what survives when context gets cut. Give it your computer or browser and it'll pick the next click, but only from actions you already marked safe. It'll sort your inbox into lanes, strip hidden prompt injections out of search results, and triage every message in ~0.4 seconds. Claude Code and Codex support - they threw that in too. No dependencies, no patching Hermes core, and your API key never touches the agent Link's right here: The catch? Jev itself is a paid cloud API. The skills run on your machine; the decisions don't. But hey, a decision costs a fraction of a cent, everything fails open if Jev goes down, and your expensive model finally gets to do only the thinking. Who needs a damn frontier model picking which file to open!

Archive

17,492 görüntüleme • 1 ay önce

Jev + Opus 5.5, side by side. same agent. same task. same 60 decisions. Opus-only: $2.24 - 3m 37s Jev + Opus: $0.43 - 1m 15s the difference is who makes the small calls: which tool runs next - Jev is this output good enough - Jev is the goal met - Jev rewrite the refund handler - Opus 57 of 60 decisions never touched Opus. what you're watching: left routes every decision through Opus 5.5. right asks Jev first, Opus only fires when the task needs real reasoning. per decision: $0.032 -> $0.00025. watch the cost counters, not the output. the output is identical. full setup in the post below ↓
0:22

Jev + Opus 5.5, side by side. same agent. same task. same 60 decisions. Opus-only: $2.24 - 3m 37s Jev + Opus: $0.43 - 1m 15s the difference is who makes the small calls: which tool runs next - Jev is this output good enough - Jev is the goal met - Jev rewrite the refund handler - Opus 57 of 60 decisions never touched Opus. what you're watching: left routes every decision through Opus 5.5. right asks Jev first, Opus only fires when the task needs real reasoning. per decision: $0.032 -> $0.00025. watch the cost counters, not the output. the output is identical. full setup in the post below ↓

Mahax

19,565 görüntüleme • 1 ay önce

this harness stack is f*cking gold TypeSafe AI + LangChain finally dropped the layer that makes agents affordable at company scale: jev running inside the langchain agent loop as middleware the problem every big team hits: each fork in the loop is another full LLM call. at 10 agents that's a bill. at 10,000 it's the budget > routing before: every task goes to the frontier model now: jev picks the least costly model that can finish it. lookups go fast, architecture goes powerful > guardrails before: the "is this command dangerous?" check sat in the closed parts of claude code, codex, cursor now: AutoModeMiddleware(tools=["bash"]). one line, any agent, blocked before the tool runs > decisions before: one LLM call per question now: every question about the same state in one request, answered in parallel > audit before: "the model decided" now: the answer, the probability and the confidence stay in agent state > adoption no rewrite. pip install langchain-typesafe and drop it into the agent you already run TypeSafe reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification. the LLM still reasons and writes. jev takes the thousands of small calls in between already live: browserbase agents for fractions of a cent, a live trading agent, email triage at scale full loop mapped below: 7 agents, 4 jev gates per step count the forks in your agent loop tonigh
0:22

this harness stack is f*cking gold TypeSafe AI + LangChain finally dropped the layer that makes agents affordable at company scale: jev running inside the langchain agent loop as middleware the problem every big team hits: each fork in the loop is another full LLM call. at 10 agents that's a bill. at 10,000 it's the budget > routing before: every task goes to the frontier model now: jev picks the least costly model that can finish it. lookups go fast, architecture goes powerful > guardrails before: the "is this command dangerous?" check sat in the closed parts of claude code, codex, cursor now: AutoModeMiddleware(tools=["bash"]). one line, any agent, blocked before the tool runs > decisions before: one LLM call per question now: every question about the same state in one request, answered in parallel > audit before: "the model decided" now: the answer, the probability and the confidence stay in agent state > adoption no rewrite. pip install langchain-typesafe and drop it into the agent you already run TypeSafe reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification. the LLM still reasons and writes. jev takes the thousands of small calls in between already live: browserbase agents for fractions of a cent, a live trading agent, email triage at scale full loop mapped below: 7 agents, 4 jev gates per step count the forks in your agent loop tonigh

NO1ennn

31,666 görüntüleme • 1 ay önce

Jev Engineering transforms a collection of AI agents into a full-fledged management system while eliminating heavy, expensive models from every decision-making loop Speed: +19,000% Costs: -99.7% How this architecture works: Gatekeeper Request Mini-Model Validation Tool Router State Worker Management layers avoid generating detailed text and instead focus on directing, evaluating, blocking, or permitting Route -> Score -> Block -> Approve As a result, resource-intensive models are invoked exclusively when the task truly requires it This is the essence of Jev Engineering: to decouple reasoning from decision-making, making the decision-making layer fast, cheap, and easily quantifiable Detailed instructions in the article below
0:24

Jev Engineering transforms a collection of AI agents into a full-fledged management system while eliminating heavy, expensive models from every decision-making loop Speed: +19,000% Costs: -99.7% How this architecture works: Gatekeeper Request Mini-Model Validation Tool Router State Worker Management layers avoid generating detailed text and instead focus on directing, evaluating, blocking, or permitting Route -> Score -> Block -> Approve As a result, resource-intensive models are invoked exclusively when the task truly requires it This is the essence of Jev Engineering: to decouple reasoning from decision-making, making the decision-making layer fast, cheap, and easily quantifiable Detailed instructions in the article below

Bober_smart

13,950 görüntüleme • 1 ay önce

jev + opus 5.5 is one of those setups that makes agent architecture feel weirdly overcomplicated in hindsight. the useful split isn’t “small model vs big model.” it’s deciding which calls actually need generation at all. → jev routes the next worker → scores which context is worth keeping → catches repeated actions before they burn the run → gates risky tool calls and sends uncertainty to review opus still owns the expensive part: planning, writing, coding, synthesis. jev sits around it as the decision layer. that matters because a typical loop can have 11 decision points but only 2 real generation calls. once those are separated, latency and cost stop scaling with every tiny fork in the workflow.
0:24

jev + opus 5.5 is one of those setups that makes agent architecture feel weirdly overcomplicated in hindsight. the useful split isn’t “small model vs big model.” it’s deciding which calls actually need generation at all. → jev routes the next worker → scores which context is worth keeping → catches repeated actions before they burn the run → gates risky tool calls and sends uncertainty to review opus still owns the expensive part: planning, writing, coding, synthesis. jev sits around it as the decision layer. that matters because a typical loop can have 11 decision points but only 2 real generation calls. once those are separated, latency and cost stop scaling with every tiny fork in the workflow.

Gipp 🦅

51,452 görüntüleme • 1 ay önce

Ok this is actually insane. Everyone building agents right now is doing the same expensive mistake. They let the model decide LITERALLY everything. Every check. Every gate. Every "should this proceed." All routed through the same $50/M model that's supposed to be doing the hard thinking. That's not intelligence. That's just burning money to ask yes or no questions. Here's what Jev Engineering actually does: request → structured state → Jev router → cheapest capable model → worker → approval check → execution gate → tool The decision layer doesn't write a single word. It routes. Scores. Blocks. Approves. That's it. The expensive model only gets touched when the task LITERALLY requires reasoning. Everything else gets handled by something that costs almost nothing and responds in milliseconds. 193x faster. 444x cheaper. Not projected. Measured. Most people are going to keep paying frontier prices to ask their agent "yes or no" for another year before they figure this out. Full breakdown below, this is the unlock nobody's talking about yet. Follow CyrilXBT, I'm not gatekeeping this one.
0:40

Ok this is actually insane. Everyone building agents right now is doing the same expensive mistake. They let the model decide LITERALLY everything. Every check. Every gate. Every "should this proceed." All routed through the same $50/M model that's supposed to be doing the hard thinking. That's not intelligence. That's just burning money to ask yes or no questions. Here's what Jev Engineering actually does: request → structured state → Jev router → cheapest capable model → worker → approval check → execution gate → tool The decision layer doesn't write a single word. It routes. Scores. Blocks. Approves. That's it. The expensive model only gets touched when the task LITERALLY requires reasoning. Everything else gets handled by something that costs almost nothing and responds in milliseconds. 193x faster. 444x cheaper. Not projected. Measured. Most people are going to keep paying frontier prices to ask their agent "yes or no" for another year before they figure this out. Full breakdown below, this is the unlock nobody's talking about yet. Follow CyrilXBT, I'm not gatekeeping this one.

CyrilXBT

23,071 görüntüleme • 1 ay önce

Played around with TypeSafe AI Jev today, mostly to understand what it does for tool calling. Instead of an LLM deciding what to do, I substituted that part with Jev. My learning is that we will be able to make the agent act before the user finishes the sentence. So far, we've used different LLM combinations (non-thinking + thinking ) plus state machine setups to get the right experience for the end user. TLDR is to understand more about how voice agents can run decisions on partials instead of waiting for the end of the turn, with lower costs.
1:10

Played around with TypeSafe AI Jev today, mostly to understand what it does for tool calling. Instead of an LLM deciding what to do, I substituted that part with Jev. My learning is that we will be able to make the agent act before the user finishes the sentence. So far, we've used different LLM combinations (non-thinking + thinking ) plus state machine setups to get the right experience for the end user. TLDR is to understand more about how voice agents can run decisions on partials instead of waiting for the end of the turn, with lower costs.

Pratim Bhosale

113,250 görüntüleme • 1 ay önce

Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF" In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better worth more than a $500 Claude Code engineering course watch today, then read how to actually boost yourself x100 with Jev in the post below
17:33

Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF" In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better worth more than a $500 Claude Code engineering course watch today, then read how to actually boost yourself x100 with Jev in the post below

Codez

193,792 görüntüleme • 1 ay önce

Another insane Jev use case! Jev makes it incredibly cheap to evaluate and classify agent runs at scale. And finally, someone open-sourced a self-improving memory layer that can put that capability to work across agent harnesses. It turns your agent sessions into a compounding knowledge layer, where every successful run can make future agents smarter across: - Codex - Claude Code - Cursor - OpenCode and 20+ more Beacon by Asymptote Labs continuously builds a shared history across your agent harnesses and uses Jev to identify the runs worth learning from. It then turns the best workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐) Most agent runs are messy. They contain exploration, failed commands, dead ends, and one-off fixes that should never become permanent memory. So Beacon preserves the full session history, while Jev helps decide what should be promoted, reviewed, or discarded. The recording below shows this in action. Beacon found 579 sessions across 5 coding-agent harnesses and normalized them into one consistent history. From there, Jev surfaces the lessons worth keeping and makes them available across your agent stack. - A pattern learned in Cursor can carry into OpenCode. - A lesson from Claude Code can improve the next Codex run. Every successful run adds to the shared knowledge layer, making future agents smarter. If you want to dive deeper into Jev, I also wrote a breakdown of how it works. The article is quoted below.
0:29

Another insane Jev use case! Jev makes it incredibly cheap to evaluate and classify agent runs at scale. And finally, someone open-sourced a self-improving memory layer that can put that capability to work across agent harnesses. It turns your agent sessions into a compounding knowledge layer, where every successful run can make future agents smarter across: - Codex - Claude Code - Cursor - OpenCode and 20+ more Beacon by Asymptote Labs continuously builds a shared history across your agent harnesses and uses Jev to identify the runs worth learning from. It then turns the best workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐) Most agent runs are messy. They contain exploration, failed commands, dead ends, and one-off fixes that should never become permanent memory. So Beacon preserves the full session history, while Jev helps decide what should be promoted, reviewed, or discarded. The recording below shows this in action. Beacon found 579 sessions across 5 coding-agent harnesses and normalized them into one consistent history. From there, Jev surfaces the lessons worth keeping and makes them available across your agent stack. - A pattern learned in Cursor can carry into OpenCode. - A lesson from Claude Code can improve the next Codex run. Every successful run adds to the shared knowledge layer, making future agents smarter. If you want to dive deeper into Jev, I also wrote a breakdown of how it works. The article is quoted below.

Akshay 🚀

121,058 görüntüleme • 1 ay önce

Jev has been exploding in popularity recently. If you already have access to the Jev API but aren't sure how to start experimenting with it, just copy this checklist: 1. jev-ultrafast Browser Use's fastest agent. Jev decides the next action and which element to click, and a language model is only called when text has to be typed. 2. typesafe-mario Jev plays Super Mario Bros. from structured emulator state, choosing every action from features pulled out of the game. 3. jev-plays-pokemon Reads Pokémon Red's game state as text, answers typed questions each turn, and lets plain code turn the answers into moves. 4. jev-drone A camera-only autonomous drone in MuJoCo with a Jev judgment model sitting in the control loop at 2.5 Hz. 5. robo-harness A real SO-101 robot arm workbench where Jev picks bounded joint steps from typed candidate actions under a spend budget. 6. fast-jev-compaction Claude Code plugin that replaces the compaction summary with Jev decisions, scoring every tool call for whether it is still needed. 7. jev-claude Routes Claude Code's own judgment calls through Jev: typed choices with probabilities at plan approval, on questions, and before risky commands. 8. is-malicious Supply-chain check before you run anything: Jev Noul checks over source and build files, returning the implicated files and lines. 9. sqlite-jev Jev inside SQL. Noul, Choice and Score judgments exposed as SQLite functions, with confidence on every row. 10. jevinci Paints images by having Jev predict every pixel's colour in parallel, with confidence deciding how wide each stroke is drawn. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓
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Jev has been exploding in popularity recently. If you already have access to the Jev API but aren't sure how to start experimenting with it, just copy this checklist: 1. jev-ultrafast Browser Use's fastest agent. Jev decides the next action and which element to click, and a language model is only called when text has to be typed. 2. typesafe-mario Jev plays Super Mario Bros. from structured emulator state, choosing every action from features pulled out of the game. 3. jev-plays-pokemon Reads Pokémon Red's game state as text, answers typed questions each turn, and lets plain code turn the answers into moves. 4. jev-drone A camera-only autonomous drone in MuJoCo with a Jev judgment model sitting in the control loop at 2.5 Hz. 5. robo-harness A real SO-101 robot arm workbench where Jev picks bounded joint steps from typed candidate actions under a spend budget. 6. fast-jev-compaction Claude Code plugin that replaces the compaction summary with Jev decisions, scoring every tool call for whether it is still needed. 7. jev-claude Routes Claude Code's own judgment calls through Jev: typed choices with probabilities at plan approval, on questions, and before risky commands. 8. is-malicious Supply-chain check before you run anything: Jev Noul checks over source and build files, returning the implicated files and lines. 9. sqlite-jev Jev inside SQL. Noul, Choice and Score judgments exposed as SQLite functions, with confidence on every row. 10. jevinci Paints images by having Jev predict every pixel's colour in parallel, with confidence deciding how wide each stroke is drawn. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

Hanako

29,894 görüntüleme • 1 ay önce

a Stanford professor used JEV to build a data science system that analyzes 40.000.000.000 data points every 15 min his system can process massive datasets and automatically decide which results deserve deeper analysis. the professor spent more than a year developing the system with his research team. the first version relied heavily on LLMs to interpret every result. it worked, but analyzing millions of records created unnecessary latency and huge inference costs. then they redesigned the decision layer around Jev. instead of generating explanations for every data point, Jev evaluates the results and decides what the system should investigate next. I found a breakdown of the pipeline they used and the way Jev filters the data is surprisingly clever. I’ll show you how it works next so stay tuned if you want to see what Jev Engineering looks like inside a real data science system. would you let an AI decide which patterns in your data are worth investigating?
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a Stanford professor used JEV to build a data science system that analyzes 40.000.000.000 data points every 15 min his system can process massive datasets and automatically decide which results deserve deeper analysis. the professor spent more than a year developing the system with his research team. the first version relied heavily on LLMs to interpret every result. it worked, but analyzing millions of records created unnecessary latency and huge inference costs. then they redesigned the decision layer around Jev. instead of generating explanations for every data point, Jev evaluates the results and decides what the system should investigate next. I found a breakdown of the pipeline they used and the way Jev filters the data is surprisingly clever. I’ll show you how it works next so stay tuned if you want to see what Jev Engineering looks like inside a real data science system. would you let an AI decide which patterns in your data are worth investigating?

deezzex

175,643 görüntüleme • 1 ay önce

this is pure f*cking gold for anyone running coding agents Jev founder Diogo Amogo wrote a PDF on building a Jev harness what their tests show: > up to 200x faster > up to 400x cheaper the model hasn't been the bottleneck for a while speed and cost both come from the harness you wrap around it • how to use it > give this PDF and the article below to Claude Code or Codex > ask it to rebuild its own setup around Jev • how it turns into a promotion > pick the agent workflow your team pays the most for > rebuild it in one evening > bring the before and after bill to your manager one evening of Jev engineering, and next week you're the one running a harness most teams haven't built yet
0:24

this is pure f*cking gold for anyone running coding agents Jev founder Diogo Amogo wrote a PDF on building a Jev harness what their tests show: > up to 200x faster > up to 400x cheaper the model hasn't been the bottleneck for a while speed and cost both come from the harness you wrap around it • how to use it > give this PDF and the article below to Claude Code or Codex > ask it to rebuild its own setup around Jev • how it turns into a promotion > pick the agent workflow your team pays the most for > rebuild it in one evening > bring the before and after bill to your manager one evening of Jev engineering, and next week you're the one running a harness most teams haven't built yet

Annatar.md

73,968 görüntüleme • 1 ay önce