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Finally, an OpenRouter for agent harnesses! (including System One by Jev) Devs just open-sourced plug-and-play infrastructure layer that lets you run any harness under a single interface for free, like: - Codex - Claude code - Hermes - DeepSeek Harness - System One, powered by Jev - And 9... more agent harnesses This means you can bring Jev into the same product that already uses Codex, Claude Code, or another supported harness, without writing another implementation for sessions, streaming, files, cancellation, and failure handling. Here's the repo: The harnesses run locally, and the Unified Harness Protocol (UHP) defines the common task interface with an OpenAI Responses-compatible API. If you want to dive deeper, the article below explains how to set up Jev from scratch, check it out ↓show more

rody

3,583 subscribers

86,278 просмотров • 3 дней назад •via X (Twitter)

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Комментарии: 20

Фото профиля Featherless AI

Featherless AI3 дней назад

wanna come hack on Jev with us in SF this friday?

Фото профиля J Huang

J Huang3 дней назад

OpenRouter but for harnesses...... yeah that was overdue

Фото профиля Morlex

Morlex3 дней назад

wow, that is really good breakdown

Фото профиля rody

rody3 дней назад

thanks mate

Фото профиля Yuri | Queiroz

Yuri | Queiroz3 дней назад

I use Codex, OpenCode and DeepSeek Harness daily. The biggest friction is duplicating sessions, streaming, cancellation and failure handling. Responses compatibility feels like the right abstraction. Switching harnesses should be config, not a rewrite. How does UHP handle features that are specific to each harness?

Фото профиля rody

rody3 дней назад

Well, UHP keeps harness-specific features in documented additive places (like metadata, a few extra request fields) and doesn't change the meaning of an existing field, so even a client that ignores every extension still gets a working task. I'm not quite a UHP expert but that's how it's supposed to work

Фото профиля Dominik

Dominik2 дней назад

the missing layer in most agent stacks is exactly this routing contract. if sessions, streaming, cancellation, and failure handling stay portable, continuity can survive a model switch instead of being rebuilt at every boundary.

Фото профиля Tanguy

Tanguy2 дней назад

routing is the easy win. the real cost is when every tool dump still lands on the frontier seat

Фото профиля tim777

tim7772 дней назад

jev setup amazing

Фото профиля Brian Hadu

Brian Hadu3 дней назад

this could simplify integration for devs managing multiple agents under one roof

Фото профиля magsimich

magsimich2 дней назад

That is a serious overnight runOne interface for all these tools

Фото профиля Sercan

Sercan3 дней назад

OpenRouter for models was obvious. OpenRouter for harnesses was overdue. If Jev sits behind the same UHP interface as Codex and Claude Code, you stop treating it as a side experiment and start routing real tasks to it.

Фото профиля 安叫兽|Bird🕊️ 🔶 BNB

安叫兽|Bird🕊️ 🔶 BNB2 дней назад

换框架的时候，之前的上下文能一起带过去吗？

Фото профиля isunny

isunny2 дней назад

@grok 这个动画效果是怎么做的？

Фото профиля Deniska

Deniska2 дней назад

This could save teams from rebuilding the same plumbing

Фото профиля Brjan | AI Builder

Brjan | AI Builder2 дней назад

open-sourcing that infrastructure layer is a big step, but integration challenges remain

Фото профиля Vishnudhat N

Vishnudhat N2 дней назад

Does it work with openjev

Фото профиля Saleh

Saleh3 дней назад

Thanks, How do you make those videos in your posts? They look awesome. What tools or workflow do you use?

Фото профиля rody

rody3 дней назад

Well, first I create a script using Claude, it runs a small simulation. The drawing itself is vector work in Cairo, rendered frame by frame and piped into ffmpeg. So there is basically no screen recording, every frame is drawn from code. I use capcut for minor changes tho

Фото профиля Sophia l

Sophia l2 дней назад

how’s the latency compared to running claude code natively

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Finally, an OpenRouter for agent harnesses! (including System One by Jev) Devs just open-sourced a plug-and-play infrastructure layer that lets you run any harness under a single interface, like: - Codex - Hermes - Claude code - DeepSeek Harness - System One, powered by Jev - And 9 more agent harnesses This means you can bring Jev into the same product that already uses Codex, Claude Code, or another supported harness, without writing another implementation for sessions, streaming, files, cancellation, and failure handling. Here's the repo: (don't forget to star it ⭐ ) The harnesses run locally, and the Unified Harness Protocol (UHP) defines the common task interface with an OpenAI Responses-compatible API. If you want to dive deeper, my recent article explains why model routing is not the same as harness routing, and what it takes to support multiple harnesses. It also covers UHP, the full local setup, a working API call, and how sessions and files work. Read it below.

Finally, an OpenRouter for agent harnesses! (including System One by Jev) Devs just open-sourced a plug-and-play infrastructure layer that lets you run any harness under a single interface, like: - Codex - Hermes - Claude code - DeepSeek Harness - System One, powered by Jev - And 9 more agent harnesses This means you can bring Jev into the same product that already uses Codex, Claude Code, or another supported harness, without writing another implementation for sessions, streaming, files, cancellation, and failure handling. Here's the repo: (don't forget to star it ⭐ ) The harnesses run locally, and the Unified Harness Protocol (UHP) defines the common task interface with an OpenAI Responses-compatible API. If you want to dive deeper, my recent article explains why model routing is not the same as harness routing, and what it takes to support multiple harnesses. It also covers UHP, the full local setup, a working API call, and how sessions and files work. Read it below.

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353,160 просмотров • 5 дней назад

HarnessRouter just open sourced a plug-and-play infrastructure layer that runs Codex, Claude Code, Hermes, DeepSeek Harness, System One powered by Jev and 9 more agent harnesses under one interface, with a Unified Harness Protocol and OpenAI Responses-compatible API. → Bring Jev into the same product already using Codex or Claude Code without writing another implementation → Handles sessions, streaming, files, cancellation and failure handling in one place → Harnesses run locally with the UHP defining the common task interface

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Another insane Jev use case! Jev is making it dramatically cheaper to evaluate what actually happened inside an agent run. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode, and 20+ more Beacon by Asymptote Labs continuously captures your agent history across harnesses and uses Jev to identify which runs are actually worth learning from. It then turns the highest-signal workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐ ) Beacon preserves the complete session history. But preserving a run and learning from it are two different things. Most coding-agent sessions contain routine exploration, failed commands, and fixes that only apply to one task. The trace can remain available for inspection without turning every detail into guidance for future agents. Jev scores each run for evidence, reuse potential, and human correction signals. An application policy then decides whether to promote, review, or discard it. The recording shows this in action. Claude receives a coding task, modifies the implementation, and runs the tests. I then provide an edge-case correction, so Claude updates the code and adds regression coverage. Beacon automatically captures the complete session. Jev evaluates whether the correction contains a reusable engineering lesson. Once approved, that lesson becomes available to other coding agents working on the project. Since it works across harnesses: - Claude Code sessions can teach Codex. - Cursor debugging can improve OpenCode. So a problem solved by one agent should not need to be learned from scratch by another. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.

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286,777 просмотров • 5 дней назад

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Akshay 🚀

114,073 просмотров • 3 дней назад

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27,859 просмотров • 16 дней назад

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unicode

20,745 просмотров • 3 дней назад

I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching (AgentSky), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.

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Derek Nee

15,361 просмотров • 3 месяцев назад

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Ichigo

100,458 просмотров • 22 дней назад

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The Startup Ideas Podcast (SIP) 🧃

43,523 просмотров • 2 месяцев назад

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Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:

Roan

119,605 просмотров • 6 дней назад

OpenEnv has a new home: starting today, it's coordinated by a committee that includes Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, and Hugging Face frontier labs train their models and their harnesses together. Claude knows Claude Code. GPT-5.5 knows Codex. that's not an accident, it's training. open-source models deserve the same magic, but pulling that off requires infrastructure that belongs to everyone, not one lab OpenEnv is that layer. one api, any harness, any trainer, any environment rewards and training loops stay in TRL, Unsloth, wherever you already work. OpenEnv is the socket they all plug into get involved!

OpenEnv has a new home: starting today, it's coordinated by a committee that includes Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, and Hugging Face frontier labs train their models and their harnesses together. Claude knows Claude Code. GPT-5.5 knows Codex. that's not an accident, it's training. open-source models deserve the same magic, but pulling that off requires infrastructure that belongs to everyone, not one lab OpenEnv is that layer. one api, any harness, any trainer, any environment rewards and training loops stay in TRL, Unsloth, wherever you already work. OpenEnv is the socket they all plug into get involved!

Sergio Paniego

32,085 просмотров • 3 месяцев назад

Thanks to Jev, you can now write workflows in plain English and run them instantly like code. I spent the day feeding it descriptions of tasks and it spit out workflows across hundreds of apps faster than I could read them. An agent could do this already, in 30 seconds, for a dollar, differently every run. Jev does it in two seconds, for free, the same way every time.

Thanks to Jev, you can now write workflows in plain English and run them instantly like code. I spent the day feeding it descriptions of tasks and it spit out workflows across hundreds of apps faster than I could read them. An agent could do this already, in 30 seconds, for a dollar, differently every run. Jev does it in two seconds, for free, the same way every time.

Gil Feig

60,209 просмотров • 6 дней назад

We just launched Agentsky AgentSky, world’s 1st Agent Market! OpenRouter is for models. AgentSky is for agents. Use 40+ agents—Claude Code, Codex, OpenCode, Hermes, Pi in your browser(even your phone!) or via one API. All without installing or setting up anything. Hit Codex Astra’s weekly limit? Hand off to another agent such as OpenCode + DeepSeek V4.1 in browser without losing any context. You can compare any agent + model directly in browser and that's how I found Astra costs $5.3 while deepseek v4.1 cost $0.12 on the same dashboard task. (I actually preferred deepseek) Try it at If you can try any agent easily without install, which agent do you want to try next? Type the agent name and I will give $20 credit (you can use it for any model)

We just launched Agentsky AgentSky, world’s 1st Agent Market! OpenRouter is for models. AgentSky is for agents. Use 40+ agents—Claude Code, Codex, OpenCode, Hermes, Pi in your browser(even your phone!) or via one API. All without installing or setting up anything. Hit Codex Astra’s weekly limit? Hand off to another agent such as OpenCode + DeepSeek V4.1 in browser without losing any context. You can compare any agent + model directly in browser and that's how I found Astra costs $5.3 while deepseek v4.1 cost $0.12 on the same dashboard task. (I actually preferred deepseek) Try it at If you can try any agent easily without install, which agent do you want to try next? Type the agent name and I will give $20 credit (you can use it for any model)

Xiaoyin Qu

2,693,999 просмотров • 8 дней назад

Google Brain founder Andrew Ng: "Prompting will be dead in 6 months Agent harnesses built with loops and graphs will replace it" Agent → Harness → Feedback → Loops → Graphs → Self-Improving Systems In this 1-hour Stanford lecture, he explains what the best engineers are building instead and how you can start today Prompt → Run → Verify → Improve The first 20 minutes teach what most $1,500 courses try to sell you For free Bookmark and watch it today Then read the guide below on building an agent harness that improves itself

Google Brain founder Andrew Ng: "Prompting will be dead in 6 months Agent harnesses built with loops and graphs will replace it" Agent → Harness → Feedback → Loops → Graphs → Self-Improving Systems In this 1-hour Stanford lecture, he explains what the best engineers are building instead and how you can start today Prompt → Run → Verify → Improve The first 20 minutes teach what most $1,500 courses try to sell you For free Bookmark and watch it today Then read the guide below on building an agent harness that improves itself

rari

127,666 просмотров • 7 дней назад

This GitHub repo is gold for anyone running Claude Code/ Codex / Any coding agent at all. My usage limits almost last double since I started using this. An open source context layer that stops your agent from reading 40 files to answer one question. No API key, no billing, no rate limit tiers. Benchmarked against Claude Code baseline: 27x token efficiency, 36% lower cost, 89% fewer file reads, 49% fewer tool calls. Every number is reproducible, the harness is in the repo. 3.5k+ stars. 50k+ pip installs, 30+ contributors, works with Claude Code, Cursor and VS Code out of the box. pip install repowise and point it at your codebase.

This GitHub repo is gold for anyone running Claude Code/ Codex / Any coding agent at all. My usage limits almost last double since I started using this. An open source context layer that stops your agent from reading 40 files to answer one question. No API key, no billing, no rate limit tiers. Benchmarked against Claude Code baseline: 27x token efficiency, 36% lower cost, 89% fewer file reads, 49% fewer tool calls. Every number is reproducible, the harness is in the repo. 3.5k+ stars. 50k+ pip installs, 30+ contributors, works with Claude Code, Cursor and VS Code out of the box. pip install repowise and point it at your codebase.

Srishti

12,998 просмотров • 2 месяцев назад

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 is showing. one state enters. Jev turns it into structured decisions. those decisions fan out across dozens of routes, agents and tools. then everything collapses back into one verified state. so instead of: LLM → agent → tool → another LLM → another guess you get: state → Jev decision layer → parallel routes → execution → verification → next state that’s the point of Jev Engineering: more parallelism without giving every branch permission to improvise. full breakdown in the article below ↓

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 is showing. one state enters. Jev turns it into structured decisions. those decisions fan out across dozens of routes, agents and tools. then everything collapses back into one verified state. so instead of: LLM → agent → tool → another LLM → another guess you get: state → Jev decision layer → parallel routes → execution → verification → next state that’s the point of Jev Engineering: more parallelism without giving every branch permission to improvise. full breakdown in the article below ↓

Ricker

44,494 просмотров • 5 дней назад

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Simplifying AI

68,025 просмотров • 1 месяц назад

run agent harnesses 100% private & offline. (no token costs, no API keys, 100% open-source) your agent runs locally. the model doesn't. every prompt, every file, and every secret still leaves your machine before the agent does anything with it. Magnitude fixes that. it's an open source inference server that runs models on your own hardware and plugs into the coding agent you already use. setup is one command. it profiles your machine, measures the memory bandwidth that sets your token rate, and hands back complete configurations instead of a list of models. each one names a model, a compression level, a context size, and a speed range you can expect. pick one and start working. it doesn't replace your harness. setup asks which one you want and writes that config for you. Pi, OpenCode, Claude Code, Codex, and Cline all work, and there's a built-in one tuned for local models if you don't have a harness yet. that one uses your shell, edits files, and runs scripts out of the box. add skills and it handles Excel, PowerPoint, PDFs, or Chrome. everyday work it covers: → analyze sensitive data → manage private notes → review code and logs → search and organize files → build docs or slides Apache 2.0. no rate limits, and nothing leaves the machine. 𝗻𝗽𝗺 𝗶 -𝗴 @𝗺𝗮𝗴𝗻𝗶𝘁𝘂𝗱𝗲𝗱𝗲𝘃/𝗰𝗹𝗶 the repo is here: (don't forget to star 🌟) i wrote the full breakdown of why picking the configuration is the hard part. the article is quoted below.

run agent harnesses 100% private & offline. (no token costs, no API keys, 100% open-source) your agent runs locally. the model doesn't. every prompt, every file, and every secret still leaves your machine before the agent does anything with it. Magnitude fixes that. it's an open source inference server that runs models on your own hardware and plugs into the coding agent you already use. setup is one command. it profiles your machine, measures the memory bandwidth that sets your token rate, and hands back complete configurations instead of a list of models. each one names a model, a compression level, a context size, and a speed range you can expect. pick one and start working. it doesn't replace your harness. setup asks which one you want and writes that config for you. Pi, OpenCode, Claude Code, Codex, and Cline all work, and there's a built-in one tuned for local models if you don't have a harness yet. that one uses your shell, edits files, and runs scripts out of the box. add skills and it handles Excel, PowerPoint, PDFs, or Chrome. everyday work it covers: → analyze sensitive data → manage private notes → review code and logs → search and organize files → build docs or slides Apache 2.0. no rate limits, and nothing leaves the machine. 𝗻𝗽𝗺 𝗶 -𝗴 @𝗺𝗮𝗴𝗻𝗶𝘁𝘂𝗱𝗲𝗱𝗲𝘃/𝗰𝗹𝗶 the repo is here: (don't forget to star 🌟) i wrote the full breakdown of why picking the configuration is the hard part. the article is quoted below.

Akshay 🚀

55,693 просмотров • 24 дней назад

You lose customers every time an agent like Codex or Claude Code uses your product and fails to complete a task. At Scope (YC P26) we built a feature to find those errors for you and fix them. Let’s use Vercel as an example to show you how it works! Want us to run a workflow for you? Drop it in the comments below :)

You lose customers every time an agent like Codex or Claude Code uses your product and fails to complete a task. At Scope (YC P26) we built a feature to find those errors for you and fix them. Let’s use Vercel as an example to show you how it works! Want us to run a workflow for you? Drop it in the comments below :)

Lin

16,349 просмотров • 16 дней назад