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this is where REA × Kimi K3 starts to get dangerous. reverse engineering usually forces one model to keep too much of the app in context at once. REA changes the shape of the problem first. so the workflow becomes less like “read this codebase” and more like distributed...

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Ricker

11,736 subscribers

51,218 Aufrufe • vor 22 Tagen •via X (Twitter)

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20 Kommentare

Profilbild von Apex
Apexvor 22 Tagen

Look like I should research this article again

Profilbild von Ricker
Rickervor 22 Tagen

fact

Profilbild von MopOzeu
MopOzeuvor 22 Tagen

Collective intelligence is always better than a single individual’s

Profilbild von Ricker
Rickervor 22 Tagen

Great notice bro

Profilbild von rewind
rewindvor 22 Tagen

Great stuff

Profilbild von Ricker
Rickervor 22 Tagen

thanks rewind

Profilbild von Alena Chramtsova
Alena Chramtsovavor 22 Tagen

who checks the final reconstruction, another agent or a human?

Profilbild von Ricker
Rickervor 22 Tagen

It’s up to your choice fam

Profilbild von Morty
Mortyvor 22 Tagen

Nice illustration of rea work man

Profilbild von Ricker
Rickervor 22 Tagen

thanks bro

Profilbild von Alex Lee
Alex Leevor 22 Tagen

I’d give it one customer problem and a weekend. If it gets someone to pay, I’ll happily spend Monday improving an ugly first version. A polished site with no customers can wait.

Profilbild von Vipul Kumar Kewat
Vipul Kumar Kewatvor 22 Tagen

The restraint here is impressive. Nothing is overdone.

Profilbild von Hailu 🍏
Hailu 🍏vor 22 Tagen

Phân tích REA × K3 cho thấy tiềm năng mới trong kỹ thuật đảo ngược.

Profilbild von 安叫兽|Bird🕊️ 🔶 BNB
安叫兽|Bird🕊️ 🔶 BNBvor 22 Tagen

拆开查省心多了，就是最后对线索估计还得费点劲

Profilbild von Bông
Bôngvor 22 Tagen

Khá hay, REA x Kimi K3 thay đổi cách tiếp cận kỹ thuật đảo ngược.

Profilbild von HoOr 互关版
HoOr 互关版vor 22 Tagen

REA改的不是模型能力而是问题形状这点确实关键，但“distributed”具体怎么分片、分片之间怎么保证语义一致性文章没展开，实际跑起来分片边界选不好照样得把整个调用链塞回context，反而更碎

Profilbild von Emly
Emlyvor 22 Tagen

Fascinating approach to distributed AI for complex tasks.

Profilbild von 逸风·666 | 🎒
逸风·666 | 🎒vor 22 Tagen

distributed forensics framing is spot on, splitting the investigation keeps the noise out of the main loop

Profilbild von 温柔有约定
温柔有约定vor 22 Tagen

Exactly what I was thinking

Profilbild von V1nT
V1nTvor 21 Tagen

Reverse engineering is the new thing

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this is where REA × Kimi K3 starts to feel dangerous. reverse engineering normally means one model slowly reading through an app and trying to keep the whole architecture in context. REA changes that first. it breaks the app into smaller pieces that can be investigated separately - functions, dependencies, call paths, state changes, UI behavior. then K3 sends 300 agents across that map at the same time. so instead of: one model → one codebase → one long chain of reasoning you get: one app → hundreds of narrow investigations → one reconstructed system the swarm builds the understanding collectively. that’s what makes REA × K3 powerful: reverse engineering stops being limited by how much of the app one model can hold in context. full course in the article below ↓
0:40

this is where REA × Kimi K3 starts to feel dangerous. reverse engineering normally means one model slowly reading through an app and trying to keep the whole architecture in context. REA changes that first. it breaks the app into smaller pieces that can be investigated separately - functions, dependencies, call paths, state changes, UI behavior. then K3 sends 300 agents across that map at the same time. so instead of: one model → one codebase → one long chain of reasoning you get: one app → hundreds of narrow investigations → one reconstructed system the swarm builds the understanding collectively. that’s what makes REA × K3 powerful: reverse engineering stops being limited by how much of the app one model can hold in context. full course in the article below ↓

Ricker

69,545 Aufrufe • vor 13 Tagen

this is where REA × Kimi K3 starts to look like cheating. reverse engineering usually means slowly tracing functions REA flips that process. the workflow becomes: find a function → collect evidence → infer behavior → cross-check it → connect it back to the system instead of: one model trying to understand the whole app it turns the codebase into hundreds of smaller investigations and sends 300 K3 agents across them in parallel. so while one group is tracing a function, others are already checking its callers, dependencies, behavior and evidence. and every new finding makes the map more complete. hundreds of agents are reconstructing different parts of it at the same time. full REA × Kimi K3 course below ↓
0:39

this is where REA × Kimi K3 starts to look like cheating. reverse engineering usually means slowly tracing functions REA flips that process. the workflow becomes: find a function → collect evidence → infer behavior → cross-check it → connect it back to the system instead of: one model trying to understand the whole app it turns the codebase into hundreds of smaller investigations and sends 300 K3 agents across them in parallel. so while one group is tracing a function, others are already checking its callers, dependencies, behavior and evidence. and every new finding makes the map more complete. hundreds of agents are reconstructing different parts of it at the same time. full REA × Kimi K3 course below ↓

Ricker

11,012 Aufrufe • vor 12 Tagen

8 BOTS JUST REVERSE-ENGINEERED A $400K/YR SAAS IN ONE NIGHT. watch the clip. that ring of 8 grok bots around the rea core is one closed app being torn apart in real time. trace, xref, read, site, spec, build, test, review. by the end of the loop the screen shows: - 2,418 files parsed - 23 blind calls - 16 pieces of evidence, 6 open assumptions - 9 modules reconstructed, 1 confirmed - 214 out of 214 observed behaviors matched that is not a stylised dashboard. it is literally the rea workflow: app → rea → grok bot → spec.md → build → test the numbers behind the hype are insane: - rea repo: 11k to 200k github stars in 5 days, global rank #161 - aegis authenticator: 3,077 classes, 133 test cases matched - dx-ball 1996: sound-panning function recovered, 3,205 behavioral tests passed - a steam game fully decompiled into unity in under 30 minutes - cost to understand a closed app: $0 and one evening the opportunity is wilder. pentest shops charge $8k to $50k per audit. reverse engineers bill $200 an hour and wait weeks. one mcp server + a bot with its own computer runs the same investigation while you sleep. one operator + grok bot + rea + one night = a reverse engineering firm. save the post, watch the terminal, then open the breakdown below.
1:12

8 BOTS JUST REVERSE-ENGINEERED A $400K/YR SAAS IN ONE NIGHT. watch the clip. that ring of 8 grok bots around the rea core is one closed app being torn apart in real time. trace, xref, read, site, spec, build, test, review. by the end of the loop the screen shows: - 2,418 files parsed - 23 blind calls - 16 pieces of evidence, 6 open assumptions - 9 modules reconstructed, 1 confirmed - 214 out of 214 observed behaviors matched that is not a stylised dashboard. it is literally the rea workflow: app → rea → grok bot → spec.md → build → test the numbers behind the hype are insane: - rea repo: 11k to 200k github stars in 5 days, global rank #161 - aegis authenticator: 3,077 classes, 133 test cases matched - dx-ball 1996: sound-panning function recovered, 3,205 behavioral tests passed - a steam game fully decompiled into unity in under 30 minutes - cost to understand a closed app: $0 and one evening the opportunity is wilder. pentest shops charge $8k to $50k per audit. reverse engineers bill $200 an hour and wait weeks. one mcp server + a bot with its own computer runs the same investigation while you sleep. one operator + grok bot + rea + one night = a reverse engineering firm. save the post, watch the terminal, then open the breakdown below.

shmidt

15,024 Aufrufe • vor 25 Tagen

People are wiring Kimi K3 straight into Codex to skip its own app The model is elite. The Kimi app is the weak part. So builders connect their Kimi subscription to Codex through OAuth instead 👇 ▫️ K3 shows up in the model picker next to Sol 5.6 ▫️ No API key, no extra subscription ▫️ Sign in with OAuth, done The frontier model, running inside the coding tool people actually like using. When the model outgrows the app, users just route around it
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People are wiring Kimi K3 straight into Codex to skip its own app The model is elite. The Kimi app is the weak part. So builders connect their Kimi subscription to Codex through OAuth instead 👇 ▫️ K3 shows up in the model picker next to Sol 5.6 ▫️ No API key, no extra subscription ▫️ Sign in with OAuth, done The frontier model, running inside the coding tool people actually like using. When the model outgrows the app, users just route around it

Ruben

58,625 Aufrufe • vor 3 Monaten

300 agents without context engineering is just 300 ways to lose track of the answer. a serious agent system starts looking less like: 1 model → 1 prompt → 1 answer and more like: 1 planner → N parallel agents → verifier → merge → memory → human gate once you have 10, 50, or 300 agents, the number of possible relationships explodes. 100 entities = 4,950 possible pairwise relationships. 300 = 44,850. 1,000 = 499,500. that’s why “just give the model more context” stops working surprisingly fast. you need different agents seeing different slices of the graph. the architecture around the model is becoming the product. full article below ↓
0:40

300 agents without context engineering is just 300 ways to lose track of the answer. a serious agent system starts looking less like: 1 model → 1 prompt → 1 answer and more like: 1 planner → N parallel agents → verifier → merge → memory → human gate once you have 10, 50, or 300 agents, the number of possible relationships explodes. 100 entities = 4,950 possible pairwise relationships. 300 = 44,850. 1,000 = 499,500. that’s why “just give the model more context” stops working surprisingly fast. you need different agents seeing different slices of the graph. the architecture around the model is becoming the product. full article below ↓

Ricker

32,998 Aufrufe • vor 2 Monaten

Kimi K3 is the first open source model to earn the BridgeMind stamp of approval. One shot Horror House game. One shot full-stack app. Beating Fable 5 in early BridgeBench arena matches. #3 on the Intelligence Index. Moonshot AI just caught the frontier. Full Kimi K3 review below.
17:39

Kimi K3 is the first open source model to earn the BridgeMind stamp of approval. One shot Horror House game. One shot full-stack app. Beating Fable 5 in early BridgeBench arena matches. #3 on the Intelligence Index. Moonshot AI just caught the frontier. Full Kimi K3 review below.

BridgeMind

32,023 Aufrufe • vor 3 Monaten

this guy just wired kimi k3 directly into codex. the model is insanely good, but the app isn’t. so instead of using the kimi app, he connected his kimi subscription to codex through oauth. now kimi k3 shows up right inside the model picker alongside sol 5.6. no api key needed. no extra subscription either. just sign in with oauth through kimi code, and it’s ready to use. simple guides in the video and links below 👇
0:40

this guy just wired kimi k3 directly into codex. the model is insanely good, but the app isn’t. so instead of using the kimi app, he connected his kimi subscription to codex through oauth. now kimi k3 shows up right inside the model picker alongside sol 5.6. no api key needed. no extra subscription either. just sign in with oauth through kimi code, and it’s ready to use. simple guides in the video and links below 👇

MIKE

58,904 Aufrufe • vor 3 Monaten

Kimi K3's frontend work here is insane. Someone asked a Kimi K3 Max agent swarm to recreate macOS 27 as a web app. It looks so much like a real Mac that I kept forgetting the whole thing was running in a browser.
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Kimi K3's frontend work here is insane. Someone asked a Kimi K3 Max agent swarm to recreate macOS 27 as a web app. It looks so much like a real Mac that I kept forgetting the whole thing was running in a browser.

Kai

271,192 Aufrufe • vor 3 Monaten

Anthropic just shared one of their most expensive systems, a $4.2M one, for Claude Code It rewrites a codebase agencies quote at $240,000 in 7 minutes for 88 cents. most rewrites die the same way: you dump 212 files of old logic into the model and it drowns REA reverse-engineers the old system once and writes a spec instead, every endpoint, every rule, every edge case 12 Sonnet 5.5 agents build from that spec, one module each, and none of them touches the old code Opus 5.5 writes the plan, then sits idle the whole run, so you pay almost nothing at the end REA replays the new system against the old one, 148 of 148 behaviours match Opus plans. REA reverse-engineers. Sonnet builds. save it, tomorrow this will be sold in $500 courses ↓
0:22

Anthropic just shared one of their most expensive systems, a $4.2M one, for Claude Code It rewrites a codebase agencies quote at $240,000 in 7 minutes for 88 cents. most rewrites die the same way: you dump 212 files of old logic into the model and it drowns REA reverse-engineers the old system once and writes a spec instead, every endpoint, every rule, every edge case 12 Sonnet 5.5 agents build from that spec, one module each, and none of them touches the old code Opus 5.5 writes the plan, then sits idle the whole run, so you pay almost nothing at the end REA replays the new system against the old one, 148 of 148 behaviours match Opus plans. REA reverse-engineers. Sonnet builds. save it, tomorrow this will be sold in $500 courses ↓

Noisy

37,632 Aufrufe • vor 22 Tagen

I'm sharing with you one of the best papers I've read on Harness Engineering it takes apart Claude Code (Opus 5.5), Codex, Gemini CLI and more, and boils all of them down to one line: agent = model + harness what you walk away with: > what a harness actually is > how today's coding agents are built under the hood > the patterns every one of them repeats > where the whole category is heading > their checklist for building your own the model gets the headlines. the harness decides whether the agent works loop and graph engineering on top of it - in my article below
0:24

I'm sharing with you one of the best papers I've read on Harness Engineering it takes apart Claude Code (Opus 5.5), Codex, Gemini CLI and more, and boils all of them down to one line: agent = model + harness what you walk away with: > what a harness actually is > how today's coding agents are built under the hood > the patterns every one of them repeats > where the whole category is heading > their checklist for building your own the model gets the headlines. the harness decides whether the agent works loop and graph engineering on top of it - in my article below

Mr. Buzzoni

84,277 Aufrufe • vor 1 Monat

This is f*cking insane. A Chinese lab just published a paper that ends a forty-year profession in one title: "The End of Reverse Engineering". The claim fits in one line: a stripped binary is not hidden from a model, it is simply unread. An analyst reads one function at a time. The program has forty thousand of them. That is why the job took weeks and the report was stale before anyone opened it. A model reads the whole artifact in one pass: binary, minified bundle, running app, packet capture. Six fixed steps, inventory to verify, and every claim carries the line it came from. Then the only test that matters: rebuild it from the spec and diff against the original. Empty diff or back to step three. Weeks of guessing cannot compete with one pass of reading. So you stop being the person in the disassembler. You become the person who points: you choose what gets read and you check the diff. Analysts turn disposable. The artifact turns into the spec. That is the exact loop I run with REA + Kimi K3 and documented below: 26 binaries, 26 specs that rebuilt, 23 of them better than the official docs, zero README opened. The whole paper in one line: read the binary, not the README ↓
0:28

This is f*cking insane. A Chinese lab just published a paper that ends a forty-year profession in one title: "The End of Reverse Engineering". The claim fits in one line: a stripped binary is not hidden from a model, it is simply unread. An analyst reads one function at a time. The program has forty thousand of them. That is why the job took weeks and the report was stale before anyone opened it. A model reads the whole artifact in one pass: binary, minified bundle, running app, packet capture. Six fixed steps, inventory to verify, and every claim carries the line it came from. Then the only test that matters: rebuild it from the spec and diff against the original. Empty diff or back to step three. Weeks of guessing cannot compete with one pass of reading. So you stop being the person in the disassembler. You become the person who points: you choose what gets read and you check the diff. Analysts turn disposable. The artifact turns into the spec. That is the exact loop I run with REA + Kimi K3 and documented below: 26 binaries, 26 specs that rebuilt, 23 of them better than the official docs, zero README opened. The whole paper in one line: read the binary, not the README ↓

slash1s

28,560 Aufrufe • vor 21 Tagen

It worked!! Kimi k3 Oauth is now wired directly into codex. The Kimi k3 model is insane but their app is not. So I figure a better way to use k3 by hooking my sub straight into codex instead. K3 now sits in the model picker next to Sol 5.6. Same subscription i was already paying for, no api key, just have to do OAuth in Kimi Code. Links down below 👇
0:40

It worked!! Kimi k3 Oauth is now wired directly into codex. The Kimi k3 model is insane but their app is not. So I figure a better way to use k3 by hooking my sub straight into codex instead. K3 now sits in the model picker next to Sol 5.6. Same subscription i was already paying for, no api key, just have to do OAuth in Kimi Code. Links down below 👇

Ziwen

277,302 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 1 Monat

Reverse engineering with REA + Opus 5.5 takes any program apart piece by piece, and it's f**king unreal websites, games, apps what took years, REA now takes apart in one evening and copies everything without you writing a single line of code this file turns Claude into a system that knows everything: 1,143 files, 7,449 functions, every button first it reads the whole thing, over 1,000 files and thousands of pieces of code, and marks everything that still needs checking Opus 5.5 plans, Sonnet 5.5 does most of the work and a cheap Haiku finds the right spots in the code, so the expensive model only switches on when it has to think then REA opens the program itself, clicks the buttons and watches what happens on screen a second Opus 5.5 checks every conclusion and only lets through what's proven findings are saved between sessions, so tomorrow it picks up right where it stopped REA doesn't copy anyone's code. it explains how every button works, helps you write your own version from scratch and tests it against the original save it before you pay for your next tool ↓
0:49

Reverse engineering with REA + Opus 5.5 takes any program apart piece by piece, and it's f**king unreal websites, games, apps what took years, REA now takes apart in one evening and copies everything without you writing a single line of code this file turns Claude into a system that knows everything: 1,143 files, 7,449 functions, every button first it reads the whole thing, over 1,000 files and thousands of pieces of code, and marks everything that still needs checking Opus 5.5 plans, Sonnet 5.5 does most of the work and a cheap Haiku finds the right spots in the code, so the expensive model only switches on when it has to think then REA opens the program itself, clicks the buttons and watches what happens on screen a second Opus 5.5 checks every conclusion and only lets through what's proven findings are saved between sessions, so tomorrow it picks up right where it stopped REA doesn't copy anyone's code. it explains how every button works, helps you write your own version from scratch and tests it against the original save it before you pay for your next tool ↓

Noisy

13,006 Aufrufe • vor 26 Tagen

94% OF ENGINEERING ORGS USE AI. ONLY 6% SCALE IT The missing link is context and governance. That’s exactly what Atlassian is going after with its new agentic engineering stack: Code Context + governed agent loops in Atlassian Jira + DX measurement. The idea is simple: make agents a native part of your engineering system instead of a side quest in a terminal. > Code Context grounds them in your architecture > Governed loops take work from backlog to pull request > DX measures the impact And the data backs up the context thesis: DX found context-rich AI teams shipped ~64% more per developer 👊 A pretty strong signal that context is becoming one of the biggest leverage points in agentic engineering.
1:00

94% OF ENGINEERING ORGS USE AI. ONLY 6% SCALE IT The missing link is context and governance. That’s exactly what Atlassian is going after with its new agentic engineering stack: Code Context + governed agent loops in Atlassian Jira + DX measurement. The idea is simple: make agents a native part of your engineering system instead of a side quest in a terminal. > Code Context grounds them in your architecture > Governed loops take work from backlog to pull request > DX measures the impact And the data backs up the context thesis: DX found context-rich AI teams shipped ~64% more per developer 👊 A pretty strong signal that context is becoming one of the biggest leverage points in agentic engineering.

Charly Wargnier ♨️

29,622 Aufrufe • vor 1 Monat

Head of Anthropic Labs: "Fable is one of those models you'll just remember - like Sonnet 3.5, Opus 4, Opus 4.5. Fable is way smarter than me. It finishes a task, lists its trade-offs, and I have to ask it to explain them to me." in 20-minute talk, the Anthropic head reveals the full capabilities of the Fable 5 model in practice. Watch it now, then read how to use this frontier model in the article below
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Head of Anthropic Labs: "Fable is one of those models you'll just remember - like Sonnet 3.5, Opus 4, Opus 4.5. Fable is way smarter than me. It finishes a task, lists its trade-offs, and I have to ask it to explain them to me." in 20-minute talk, the Anthropic head reveals the full capabilities of the Fable 5 model in practice. Watch it now, then read how to use this frontier model in the article below

Movez

33,774 Aufrufe • vor 4 Monaten

REA + Opus 5.5 might be the most dangerous combo on the internet right now and 99% of builders have no idea what to do with it you're paying every month for apps whose features are already sitting on your laptop send this one prompt to Opus 5.5 it reverse-engineers any paid closed-source app for you the engineer who brings a working internal copy of a $500/seat tool to Monday standup is the one who ends the quarter with a new title full guide on becoming a REA engineer from scratch below ↓
0:24

REA + Opus 5.5 might be the most dangerous combo on the internet right now and 99% of builders have no idea what to do with it you're paying every month for apps whose features are already sitting on your laptop send this one prompt to Opus 5.5 it reverse-engineers any paid closed-source app for you the engineer who brings a working internal copy of a $500/seat tool to Monday standup is the one who ends the quarter with a new title full guide on becoming a REA engineer from scratch below ↓

Mr. Buzzoni

34,637 Aufrufe • vor 24 Tagen

Next up on First Pass: A conversation with Simon Mo from Inferact on Kimi K3. We discuss: - The basics on the K3 model (it's huge!) - Why open weight models are important - The engineering complexity of working with a model this large - Innovative licensing models And more!
14:31

Next up on First Pass: A conversation with Simon Mo from Inferact on Kimi K3. We discuss: - The basics on the K3 model (it's huge!) - Why open weight models are important - The engineering complexity of working with a model this large - Innovative licensing models And more!

Jamin Ball

13,707 Aufrufe • vor 3 Monaten

Boris Cherny (creator of Claude Code, Anthropic): "My entire CLAUDE.md is two lines. With every new model you need less instruction, not more, most people are over-engineering the one thing that should stay tiny." in a 7-minute interview, Boris Cherny breaks down why the smallest AI-coding setup wins, and the one moment you should delete your config and start from scratch most engineers do the opposite: they pile on rules, build scaffolding around the model… then the next model ships and makes all of it useless. what he actually keeps in those two lines, and why he tells his team to throw the rest away. Watch the talk, then read the article below. That’s worth more than a $800 course on agent engineering.
7:13

Boris Cherny (creator of Claude Code, Anthropic): "My entire CLAUDE.md is two lines. With every new model you need less instruction, not more, most people are over-engineering the one thing that should stay tiny." in a 7-minute interview, Boris Cherny breaks down why the smallest AI-coding setup wins, and the one moment you should delete your config and start from scratch most engineers do the opposite: they pile on rules, build scaffolding around the model… then the next model ships and makes all of it useless. what he actually keeps in those two lines, and why he tells his team to throw the rest away. Watch the talk, then read the article below. That’s worth more than a $800 course on agent engineering.

Prometheus

238,143 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 1 Monat