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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... show more

0xMarioNawfal

264,127 subscribers

52,014 görüntüleme • 1 ay önce •via X (Twitter)

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Jatin Garg profil fotoğrafı

Jatin Garg1 ay önce

The infrastructure layer is the unlock here. Routing between models stops being a research problem when the plumbing is already built.

Meta Racing profil fotoğrafı

Meta Racing1 ay önce

it's yapping time

Defi_Noal profil fotoğrafı

Defi_Noal1 ay önce

finally we get a common standard for this

Neuni United profil fotoğrafı

Neuni United1 ay önce

Everything in life is temporary. Be grateful in the good times and stay hopeful in the hard ones. Nothing lasts forever

why profil fotoğrafı

why1 ay önce

One interface across Codex, Claude Code, DeepSeek Harness, and the rest is a pretty useful abstraction for agent tooling.

스카이 DeFi profil fotoğrafı

스카이 DeFi1 ay önce

Unified Harness Protocol is the real breakthrough here, standardizing the mess is overdue

Miano profil fotoğrafı

Miano1 ay önce

One interface for many agent harnesses feels like the missing layer between model choice and reliable execution. The protocol matters more than the novelty of another wrapper.

Macro Bombastic profil fotoğrafı

Macro Bombastic1 ay önce

tbh the unified protocol is what I care about most here

tim777 profil fotoğrafı

tim7771 ay önce

one interface, twelve harnesses. the router layer is where the margin quietly moves.

Deep profil fotoğrafı

Deep1 ay önce

one interface for every harness is the exact thing i keep rebuilding by hand. nice to see it open sourced.

Benzer Videolar

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 ↓

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 ↓

rody

88,298 görüntüleme • 1 ay önce

Insane Grok Grok Bot upgrade! Finally, you can connect Grok Bot to different agent harnesses and run tasks through their native runtimes. Devs just open-sourced HarnessRouter, the infrastructure layer that lets Grok Bot run tasks across: - Codex - Hermes - Claude Code - DeepSeek Harness - System One, powered by Jev - Pi, and 20 more GitHub repo: (don’t forget to star it ⭐) With HarnessRouter, each external specialist gets to use its own harness, model, tools, skills, and agent loop while Grok coordinates the project from one conversation. I tested this by asking Grok Bot to turn a support-ticket CSV into an interactive dashboard. Grok Bot assigned the analysis to DeepSeek Harness with Claude Sonnet 5.5, passed the output to Codex with GPT-5.5, and checked the finished dashboard in its browser. This differs from simply creating more Bots. These workers are not additional Grok Bots, and Grok Bot itself does not switch harnesses. Instead, it remains the coordinator while the delegated tasks run inside separate HarnessRouter sessions with the harness of your choice. So for another task, you can use Claude Code, Hermes, Pi, etc., without requiring a separate integration for task execution, sessions, streaming, and generated files. HarnessRouter standardizes all those operations through the Unified Harness Protocol (UHP). UHP handles sessions, streaming, files, cancellation, and failures through the same interface, regardless of which harness runs the task. I shared the GitHub repo above. And if you want to dive deeper into Grok Bot, I wrote about it in full in a masterclass, including the shared computer, memory, skills, routines, and the macOS setup. Read it below.

Insane Grok Grok Bot upgrade! Finally, you can connect Grok Bot to different agent harnesses and run tasks through their native runtimes. Devs just open-sourced HarnessRouter, the infrastructure layer that lets Grok Bot run tasks across: - Codex - Hermes - Claude Code - DeepSeek Harness - System One, powered by Jev - Pi, and 20 more GitHub repo: (don’t forget to star it ⭐) With HarnessRouter, each external specialist gets to use its own harness, model, tools, skills, and agent loop while Grok coordinates the project from one conversation. I tested this by asking Grok Bot to turn a support-ticket CSV into an interactive dashboard. Grok Bot assigned the analysis to DeepSeek Harness with Claude Sonnet 5.5, passed the output to Codex with GPT-5.5, and checked the finished dashboard in its browser. This differs from simply creating more Bots. These workers are not additional Grok Bots, and Grok Bot itself does not switch harnesses. Instead, it remains the coordinator while the delegated tasks run inside separate HarnessRouter sessions with the harness of your choice. So for another task, you can use Claude Code, Hermes, Pi, etc., without requiring a separate integration for task execution, sessions, streaming, and generated files. HarnessRouter standardizes all those operations through the Unified Harness Protocol (UHP). UHP handles sessions, streaming, files, cancellation, and failures through the same interface, regardless of which harness runs the task. I shared the GitHub repo above. And if you want to dive deeper into Grok Bot, I wrote about it in full in a masterclass, including the shared computer, memory, skills, routines, and the macOS setup. Read it below.

Avi Chawla

137,036 görüntüleme • 13 gün önce

Harness Engineering 2.0 is here! We benchmark models all the time but the harness running the model can dramatically improve the result, cost, and execution time too. And finally, devs just open-sourced Harness Arena, where you can test the same task across multiple harness and model combinations side by side. You provide one task and then run it across several model x harness configs that you want to test. Every config receives the same prompt, files, tools, skills, and evaluation checks. Harness Arena then returns: - total cost - execution time - whether the task passed - files and artifacts produced If you want to try it yourself, it is implemented within the open-source HarnessRouter project. Here's the repo: (don't forget to star it ⭐ ) HarnessRouter is 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 25 more agent harnesses In Harness Arena, the cheapest run does not automatically win. A config must complete the task correctly first. Cost and execution time are compared only among the configurations that pass. Here's the result from one Harness Arena test: - Hermes + GPT-5.2: 5/5 successful runs using 0.47 credits - Claude Code + Opus 4.8: 3/5 successful runs using 223 credits That is around 474x difference in credits, while the more expensive configuration also failed two runs. You can keep the model fixed to measure how much the harness changes the result. Or vary both to find the best harness and model pairing for that task. This helps answer practical questions such as: - Should this task run on Codex or Claude? - Can a cheaper config still pass every check? - Which setup is best to handle this workload? Once you find the best config, you can run future tasks through it using the same Unified Harness Protocol (UHP) interface for sessions, streaming, files, artifacts, cancellation, and failures. So you are no longer choosing an agent stack based only on a model leaderboard. Instead, you can benchmark the complete runtime on the task your product actually needs to solve. To dive deeper, my co-founder wrote an article explaining why model routing is different from harness routing, how UHP works, and what supporting multiple agent runtimes involves. It also covers the local setup, a working request, and how sessions and files move through the system. Read it below.

Harness Engineering 2.0 is here! We benchmark models all the time but the harness running the model can dramatically improve the result, cost, and execution time too. And finally, devs just open-sourced Harness Arena, where you can test the same task across multiple harness and model combinations side by side. You provide one task and then run it across several model x harness configs that you want to test. Every config receives the same prompt, files, tools, skills, and evaluation checks. Harness Arena then returns: - total cost - execution time - whether the task passed - files and artifacts produced If you want to try it yourself, it is implemented within the open-source HarnessRouter project. Here's the repo: (don't forget to star it ⭐ ) HarnessRouter is 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 25 more agent harnesses In Harness Arena, the cheapest run does not automatically win. A config must complete the task correctly first. Cost and execution time are compared only among the configurations that pass. Here's the result from one Harness Arena test: - Hermes + GPT-5.2: 5/5 successful runs using 0.47 credits - Claude Code + Opus 4.8: 3/5 successful runs using 223 credits That is around 474x difference in credits, while the more expensive configuration also failed two runs. You can keep the model fixed to measure how much the harness changes the result. Or vary both to find the best harness and model pairing for that task. This helps answer practical questions such as: - Should this task run on Codex or Claude? - Can a cheaper config still pass every check? - Which setup is best to handle this workload? Once you find the best config, you can run future tasks through it using the same Unified Harness Protocol (UHP) interface for sessions, streaming, files, artifacts, cancellation, and failures. So you are no longer choosing an agent stack based only on a model leaderboard. Instead, you can benchmark the complete runtime on the task your product actually needs to solve. To dive deeper, my co-founder wrote an article explaining why model routing is different from harness routing, how UHP works, and what supporting multiple agent runtimes involves. It also covers the local setup, a working request, and how sessions and files move through the system. Read it below.

Avi Chawla

30,046 görüntüleme • 2 gün önce

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.

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.

Avi Chawla

293,860 görüntüleme • 1 ay önce

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.

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.

Xiaoyin Qu

2,227,612 görüntüleme • 2 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.

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

Hit your usage limit in Codex/Claude Code mid-task? Orca now lets you resume the exact same session in a different agent harness — one click. Codex ⇄ Claude Code, full context intact🔥

Hit your usage limit in Codex/Claude Code mid-task? Orca now lets you resume the exact same session in a different agent harness — one click. Codex ⇄ Claude Code, full context intact🔥

Orca ADE

55,781 görüntüleme • 3 ay önce

Today we’re releasing Venice Skills. A better interface for agents using the Venice API, ready for Cursor, Claude Code, Codex, Hermes, and other agent runtimes. 👉

Today we’re releasing Venice Skills. A better interface for agents using the Venice API, ready for Cursor, Claude Code, Codex, Hermes, and other agent runtimes. 👉

Sabrina

62,092 görüntüleme • 6 ay önce

Another insane way to use Jev: Jev makes it dramatically cheaper to identify which corrections and trajectories from an agent run are reusable across agents. 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 - Hermes, and 20+ more Beacon by Asymptote Labs continuously captures agent sessions across harnesses and stores their traces in a shared index. I used it with Jev to build a live cross-agent memory workflow. Beacon captures each run as it happens across all harnesses on my machine. Then Jev evaluates whether the trace contains a reusable engineering lesson, and the approved lesson becomes available to the next agent. GitHub repo: (don't forget to star it ⭐) Preserving an agent run and learning from it are two different problems. Most traces contain file reads, failed commands, routine exploration, and implementation details that only apply to one task. Passing the complete transcript to another agent adds context without identifying what matters. In this workflow, one agent never receives another agent's raw transcript. It receives only the specific engineering lesson produced from the evaluated trace. A correction made in one agent can then prevent the same mistake when another agent encounters a similar problem. The recording shows the complete workflow I built. 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.

Another insane way to use Jev: Jev makes it dramatically cheaper to identify which corrections and trajectories from an agent run are reusable across agents. 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 - Hermes, and 20+ more Beacon by Asymptote Labs continuously captures agent sessions across harnesses and stores their traces in a shared index. I used it with Jev to build a live cross-agent memory workflow. Beacon captures each run as it happens across all harnesses on my machine. Then Jev evaluates whether the trace contains a reusable engineering lesson, and the approved lesson becomes available to the next agent. GitHub repo: (don't forget to star it ⭐) Preserving an agent run and learning from it are two different problems. Most traces contain file reads, failed commands, routine exploration, and implementation details that only apply to one task. Passing the complete transcript to another agent adds context without identifying what matters. In this workflow, one agent never receives another agent's raw transcript. It receives only the specific engineering lesson produced from the evaluated trace. A correction made in one agent can then prevent the same mistake when another agent encounters a similar problem. The recording shows the complete workflow I built. 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.

Avi Chawla

70,794 görüntüleme • 16 gün önce

"The model is no longer the product. Codex, Perplexity Computer, or Claude Code - all are orchestration system. It takes a model and pairs it with an agent harness. What is an agent harness ? The rules for how the agent loops around" - Aravind Srinivas

"The model is no longer the product. Codex, Perplexity Computer, or Claude Code - all are orchestration system. It takes a model and pairs it with an agent harness. What is an agent harness ? The rules for how the agent loops around" - Aravind Srinivas

Rohan Paul

120,488 görüntüleme • 4 ay önce

Introducing AQ: a multiplayer harness of harnesses. • Run it on any cloud you already have, or use ours. • Connect any coding harness you like: Claude Code, Codex, OpenCode, Cursor, Kimi, Grok, Devin and more. • Work with your teammates live, way beyond sharing a session. • 17 open-weights models, one click. • Use it from anywhere: Mac, Linux, Windows, iPhone, Android.

Introducing AQ: a multiplayer harness of harnesses. • Run it on any cloud you already have, or use ours. • Connect any coding harness you like: Claude Code, Codex, OpenCode, Cursor, Kimi, Grok, Devin and more. • Work with your teammates live, way beyond sharing a session. • 17 open-weights models, one click. • Use it from anywhere: Mac, Linux, Windows, iPhone, Android.

Khaled

70,475 görüntüleme • 2 ay önce

Introducing 𝙨𝙚𝙨𝙨𝙞𝙤𝙣-𝙢𝙞𝙜𝙧𝙖𝙩𝙚, a tool for migrating coding agent sessions across Claude Code, Codex, Pi, OpenCode, and more. If you started in Claude but ran of usage, you can now convert your session to another harness in one command:

Introducing 𝙨𝙚𝙨𝙨𝙞𝙤𝙣-𝙢𝙞𝙜𝙧𝙖𝙩𝙚, a tool for migrating coding agent sessions across Claude Code, Codex, Pi, OpenCode, and more. If you started in Claude but ran of usage, you can now convert your session to another harness in one command:

Xing Han Lu

30,870 görüntüleme • 2 ay önce

I just killed all browsers. Introducing Serein, with built-in Claude Code, Codex, and Grok agents that can browse the web. Each agent gets its own tabs and cookies, when they reach a password or payment page, they stop and hand the tab back to you. Serein works with modern agent harnesses like Claude Code, Codex, and OpenCode, using your existing subscription. Currently is free on mac, link in the comment 👇

I just killed all browsers. Introducing Serein, with built-in Claude Code, Codex, and Grok agents that can browse the web. Each agent gets its own tabs and cookies, when they reach a password or payment page, they stop and hand the tab back to you. Serein works with modern agent harnesses like Claude Code, Codex, and OpenCode, using your existing subscription. Currently is free on mac, link in the comment 👇

Ziwen

14,688 görüntüleme • 23 gün önce

Four ways to add free models to Claude Code or Codex: local model control, Agent OS engine, DeepSeek Harness or a free API like DeepSeek V4.1 Flash, giving you lower cost, more privacy and offline options.

Four ways to add free models to Claude Code or Codex: local model control, Agent OS engine, DeepSeek Harness or a free API like DeepSeek V4.1 Flash, giving you lower cost, more privacy and offline options.

0xMarioNawfal

50,022 görüntüleme • 1 ay önce

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

32,760 görüntüleme • 5 ay önce

I made Claude Code and Codex talk to each other. No APIs. No special protocol. Just a terminal. smux lets agents create, read, and act on terminals. the terminal becomes a shared interface. this enables agent-to-agent communication. Now Claude Code and Codex can hold hands 🤝 Try it now ↓

I made Claude Code and Codex talk to each other. No APIs. No special protocol. Just a terminal. smux lets agents create, read, and act on terminals. the terminal becomes a shared interface. this enables agent-to-agent communication. Now Claude Code and Codex can hold hands 🤝 Try it now ↓

shawn

318,609 görüntüleme • 7 ay önce

New Release: Agents API Run Blackbox CLI, Claude Code, Codex CLI, Gemini CLI and more agents on remote VMs powered by Vercel sandboxes with 1 single api implementation

New Release: Agents API Run Blackbox CLI, Claude Code, Codex CLI, Gemini CLI and more agents on remote VMs powered by Vercel sandboxes with 1 single api implementation

BLACKBOX AI

396,167 görüntüleme • 9 ay önce

Jev founder, Diogo Almeida, just put out a PDF on building a Jev harness for coding agents the claim: 200x faster, 400x cheaper the model stopped being the bottleneck a while ago. the harness around it is where the cost and the speed actually live five decisions sit on every loop, and only one of them writes code hand this PDF and the article below to your Claude Code or Codex instance and let it rebuild its own setup your agent gets a better harness tonight, and monday looks different than it would have 👇

Jev founder, Diogo Almeida, just put out a PDF on building a Jev harness for coding agents the claim: 200x faster, 400x cheaper the model stopped being the bottleneck a while ago. the harness around it is where the cost and the speed actually live five decisions sit on every loop, and only one of them writes code hand this PDF and the article below to your Claude Code or Codex instance and let it rebuild its own setup your agent gets a better harness tonight, and monday looks different than it would have 👇

Hanako

37,887 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 the promise: > 200x faster > 400x cheaper the model hasn't been the slow part for a while the speed and the cost sit in the harness wrapped around it • how to use it > drop this PDF and the article below into Claude Code or Codex > let it rebuild its own setup one evening of setup and your agent starts next week on a harness most teams still don't have

this is pure f*cking gold for anyone running coding agents Jev founder Diogo Amogo wrote a PDF on building a Jev harness the promise: > 200x faster > 400x cheaper the model hasn't been the slow part for a while the speed and the cost sit in the harness wrapped around it • how to use it > drop this PDF and the article below into Claude Code or Codex > let it rebuild its own setup one evening of setup and your agent starts next week on a harness most teams still don't have

Mr. Buzzoni

80,191 görüntüleme • 19 gün önce

this is pure f*cking gold for anyone running coding agents Jev founder Diogo Amogo wrote a PDF on building a Jev harness the promise: > 200x faster > 400x cheaper the model hasn't been the slow part for a while the speed and the cost sit in the harness wrapped around it • how to use it > drop this PDF and the article below into Claude Code or Codex > let it rebuild its own setup one evening of setup and your agent starts next week on a harness most teams still don't have 👇

this is pure f*cking gold for anyone running coding agents Jev founder Diogo Amogo wrote a PDF on building a Jev harness the promise: > 200x faster > 400x cheaper the model hasn't been the slow part for a while the speed and the cost sit in the harness wrapped around it • how to use it > drop this PDF and the article below into Claude Code or Codex > let it rebuild its own setup one evening of setup and your agent starts next week on a harness most teams still don't have 👇

Mr. Buzzoni

128,732 görüntüleme • 1 ay önce