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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.show more

Akshay 🚀

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361,933 views • 1 month ago •via X (Twitter)

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34 Comments

Jatin Garg1 month ago

OpenRouter works because all the models behind it take the same shape — text in, text out. Agent harnesses have different state, tool signatures, and session models. How does a unified interface handle that?

Akmal Makhmudov1 month ago

I only run one harness for my own build, Claude Code, so I've never hit the switching-cost pain this solves directly. for someone already committed to one harness, is the real win here A/B testing models across harnesses, or something else?

Yev Mura1 month ago

Useful layer. Separate itch on my side: once Codex / Claude Code / etc are installed they still rubber-stamp themselves. I built consilium so the hub agent can `consult` a different-provider CLI for a second opinion, or run a council that verifies every file:line finding.

Labangram Photography1 month ago

The hard part seems to be semantic parity: session resume, streamed tool events, and cancellation need identical guarantees across harnesses. A conformance suite covering those cases would make UHP much more useful than an adapter layer.

TechP1 month ago

This could make building with multiple AI agents much simpler. One interface, multiple harnesses.

Posterior1 month ago

What would be really cool is if the router automatically chooses the correct harness for a given task.

Kero Dewto1 month ago

Interesting! I’ll try it out for mobile development and then share my hands-on experience on my page

Kizuno181 month ago

standardizing the harness layer is the missing piece. in our autonomous pipeline targeting brazil, we don't bind models to tasks directly. the harness runs deterministic python stages and invokes external harnesses only when schema gates fail. harness routing > model routing

Hussain Hashim | Building SundayBack1 month ago

@akshay_pachaar that's a game-changer for devs dealing with multiple harnesses! simplifies the workflow like crazy. really makes you think about how much time we waste on integrations.

Chris Izatt1 month ago

Yoooo lemme finish up TaskWraith so it qualifies 🥲

BTCaveman.1 month ago

OM >

catman1 month ago

The unified interface is the strongest part, but harness routing is not quite equivalent to model routing: harnesses carry different state, tool semantics, and failure modes. UHP can normalize the plumbing, not erase those behavioral differences.

cit1z3n1 month ago

routing models is easy. routing harnesses is the part people pay for twice.

shaun1 month ago

one harness already needs a babysitter. now i can route between five lol

Salise1 month ago

the idea of running multiple harnesses like Codex and Claude under one interface is really cool, should save a lot of time

Zam1 month ago

A shared harness interface is useful only if it standardizes the ugly parts too, especially cancellation, state handoff, and failure recovery across sessions.

saietta1 month ago

model routing works because the contract is thin -- messages in, tokens out. harness routing means normalizing side effects: how each one sandboxes shell access, what a cancellation leaves mid-file-write, when a permission prompt blocks vs auto-approves. that's where it leaks first.

EDDY VU1 month ago

Having a clean abstraction layer to benchmark different harnesses against the exact same task suite is huge.

ShadowAguy1 month ago

Unified interface sounds useful, but wondering how you handle the different prompt/response formats across Codex, Claude Code, and DeepSeek? Does the abstraction leak when one tool expects JSON while another wants raw text?

Later1 month ago

Curious how you measured harness-to-harness overhead — same task, same model, what’s the extra latency and tokens just from the unified protocol versus talking to Claude Code or Codex directly?

DeDi1 month ago

专为代理框架设计，UHP统一会话流式取消这类脏活实现

Later1 month ago

Unifying the harness interface is useful, but did anyone run the same task set across Codex / Claude Code / Jev and report tokens per solved task plus idle turns?

kuzzo1 month ago

We already have @omnigent_ai, you’re overlooking this…

Arbaz1 month ago

harness switcher is nice. finding one that barely drains is better

techa1 month ago

the agent wars are just starting and standardizing the harness layer is already table stakes, whoever owns that interface wins.

Soni1 month ago

Standardizing permissions and sessions sounds clean until two runtimes disagree on the schema. Still babysitting, just one level higher up.

Alex | RouteAPI1 month ago

A common protocol gets you portability. State and tool behavior decide whether the switch is actually painless. Are you using more than one coding harness today?

Adam · Arcyton1 month ago

One interface across harnesses is useful mostly for comparison. The part worth normalising along with the calls is failure: each harness reports a failed tool step differently, and some report it as a normal answer. Does the layer unify errors, or pass them through?

Sophia Data Queen1 month ago

One interface for the harness, not just the model.

Gregor1 month ago

not sure the OpenRouter analogy holds. OpenRouter works because models share a shape. harness behavior is the opinionated part, and a wrapper flattens exactly that. does it preserve Jev's context-carry or does that disappear?

Saeed Ahmed1 month ago

Under one harness interface, what breaks first when you swap Codex for Claude Code: permissions, tools, or memory?

JSCC | Nasdaq · AI1 month ago

Unified Harness Protocol can list Codex. Sessions and cancellation still have to work across every harness.

Sebastiano Mandalà1 month ago

I honestly start to be fed up about these hype driven, AI written tools and articles that add complexity instead of value.

Max Bevza1 month ago

this is actually massive for agent infra

Related Videos

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 views • 1 month ago

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.

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Avi Chawla

30,196 views • 2 days ago

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

137,148 views • 13 days ago

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 views • 1 month ago

"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 views • 4 months ago

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 views • 1 month ago

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 views • 16 days ago

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 views • 19 days ago

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 views • 1 month ago

Finally, an open-source runtime security layer for your agent harnesses. AI agents can call tools, run shell commands, modify files, and interact with production systems. But when something goes wrong, teams often have to reconstruct what the agent actually did from scattered logs after the fact. Agent Beacon is built to change that. It runs locally and records agent activity as it happens, including: → Tool calls → Shell commands → File changes → Approval decisions → Session and harness context The useful part is that it normalizes all of this into the same event format across 23+ agent harnesses. So instead of writing different detection logic for Claude Code, Codex, or another harness, security teams can reason about the underlying action itself. A command executed is a command executed, regardless of which harness produced it. Beacon also records how confidently an event was captured. An action can be directly observed from the runtime or inferred from indirect evidence. That matters when you start writing security rules against this data. For example, you can detect when an agent reads an environment variable, modifies a sensitive file, executes a suspicious command, or performs an action without the expected approval. And the detection happens while the session is still unfolding, not days later during incident reconstruction. Everything runs locally by default. You can inspect sessions through the dashboard, write your own detection rules, or forward the same normalized events to tools like Splunk, Datadog, Elastic, Sentinel, or CrowdStrike. Beacon GitHub: (don't forget to star 🌟) I also wrote a detailed article on the same, covering how important runtime security is and how to build it for your agent harnesses. The article is quoted below.

Finally, an open-source runtime security layer for your agent harnesses. AI agents can call tools, run shell commands, modify files, and interact with production systems. But when something goes wrong, teams often have to reconstruct what the agent actually did from scattered logs after the fact. Agent Beacon is built to change that. It runs locally and records agent activity as it happens, including: → Tool calls → Shell commands → File changes → Approval decisions → Session and harness context The useful part is that it normalizes all of this into the same event format across 23+ agent harnesses. So instead of writing different detection logic for Claude Code, Codex, or another harness, security teams can reason about the underlying action itself. A command executed is a command executed, regardless of which harness produced it. Beacon also records how confidently an event was captured. An action can be directly observed from the runtime or inferred from indirect evidence. That matters when you start writing security rules against this data. For example, you can detect when an agent reads an environment variable, modifies a sensitive file, executes a suspicious command, or performs an action without the expected approval. And the detection happens while the session is still unfolding, not days later during incident reconstruction. Everything runs locally by default. You can inspect sessions through the dashboard, write your own detection rules, or forward the same normalized events to tools like Splunk, Datadog, Elastic, Sentinel, or CrowdStrike. Beacon GitHub: (don't forget to star 🌟) I also wrote a detailed article on the same, covering how important runtime security is and how to build it for your agent harnesses. The article is quoted below.

Akshay 🚀

27,859 views • 1 month ago

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

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

73,481 views • 27 days ago

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 views • 1 month ago

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

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 views • 1 month ago

holy sh*t, Google Research just open-sourced an agent that can improve its own harness steal this blueprint for a self-improving agent the prompts, memory, skills and tool guidance around your model can evolve as it works it’s called Harness² the loop: → propose changes to the agent’s setup → run candidate setups on the same task → compare what actually happened → compose a refined harness from that evidence → carry useful lessons into future tasks the clever part: it refines the harness on unfamiliar tasks without a ground-truth answer to guide those edits the architecture: propose → probe → compose → retain lessons → repeat the part builders should steal: treat your agent’s setup as something you can experiment on pick a recurring failure propose a specific harness change compare execution traces on the same task evaluate the result keep the evidence with the change the repo includes OpenCode and Codex executors, parallel and sequential refinement, and a local demo you can run without model credentials save this, then read the full guide for Opus 5.5 Harness Engineering ↓

holy sh*t, Google Research just open-sourced an agent that can improve its own harness steal this blueprint for a self-improving agent the prompts, memory, skills and tool guidance around your model can evolve as it works it’s called Harness² the loop: → propose changes to the agent’s setup → run candidate setups on the same task → compare what actually happened → compose a refined harness from that evidence → carry useful lessons into future tasks the clever part: it refines the harness on unfamiliar tasks without a ground-truth answer to guide those edits the architecture: propose → probe → compose → retain lessons → repeat the part builders should steal: treat your agent’s setup as something you can experiment on pick a recurring failure propose a specific harness change compare execution traces on the same task evaluate the result keep the evidence with the change the repo includes OpenCode and Codex executors, parallel and sequential refinement, and a local demo you can run without model credentials save this, then read the full guide for Opus 5.5 Harness Engineering ↓

beamnxw ./

12,039 views • 17 days ago

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 views • 2 months ago

I burned a shitload of compute so you don't have to. Releasing the Harness Leaderboard. Which harness should you run your open model in? I ran 28,849 trials to find out. The leaderboard compares nine coding-agent harnesses on open models. In each comparison only the harness changes: the model, provider and reasoning effort stay fixed, and every task gets three attempts. The trials ran GLM-5.3-Flash, DeepSeek-V4.1-Flash and Flint on Terminal-Bench 2.1 and DeepSWE, for $4,746 of inference. Averaged over the three models, mini-swe-agent (Kilian Lieret), a bash-only agent, solves 51.4% of tasks and OpenClaw🦞 27.3%. Four others are statistically tied with mini-swe-agent. One of them is Pi (Pi), at 49.4% for $0.29 per solved task, about 45% less than mini-swe-agent's $0.53. The best harness depends on the model: mini-swe-agent on GLM-5.3-Flash, Hermes Agent (Nous Research) on DeepSeek-V4.1-Flash and Kimi Code (Kimi.ai) on Flint. OpenCode costs $3.35 per solved task, 11.7 times as much as Pi. See Harness Hub's Harness Lens for what the nine harnesses do differently. It documents the prompts, tools and compaction of 34 coding-agent harnesses and links every claim to its source. Leaderboard: Harness Lens:

I burned a shitload of compute so you don't have to. Releasing the Harness Leaderboard. Which harness should you run your open model in? I ran 28,849 trials to find out. The leaderboard compares nine coding-agent harnesses on open models. In each comparison only the harness changes: the model, provider and reasoning effort stay fixed, and every task gets three attempts. The trials ran GLM-5.3-Flash, DeepSeek-V4.1-Flash and Flint on Terminal-Bench 2.1 and DeepSWE, for $4,746 of inference. Averaged over the three models, mini-swe-agent (Kilian Lieret), a bash-only agent, solves 51.4% of tasks and OpenClaw🦞 27.3%. Four others are statistically tied with mini-swe-agent. One of them is Pi (Pi), at 49.4% for $0.29 per solved task, about 45% less than mini-swe-agent's $0.53. The best harness depends on the model: mini-swe-agent on GLM-5.3-Flash, Hermes Agent (Nous Research) on DeepSeek-V4.1-Flash and Kimi Code (Kimi.ai) on Flint. OpenCode costs $3.35 per solved task, 11.7 times as much as Pi. See Harness Hub's Harness Lens for what the nine harnesses do differently. It documents the prompts, tools and compaction of 34 coding-agent harnesses and links every claim to its source. Leaderboard: Harness Lens:

Joël Niklaus

135,498 views • 1 day ago

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 🚀

56,499 views • 2 months ago

The easiest way to find out which models you can run on your computer: Magnitude is a 100% free, open-source desktop app that profiles your machine and ranks the models across: - Speed - Accuracy - Intelligence - Memory required Then it downloads the one you pick and tunes it for your machine. Finally, connect your favorite harness (Pi, OpenCode, Claude Code, Codex, etc.) in one click. Get started here: (don't forget to star 🌟) My co-founder wrote a detailed article on running your favorite harnesses with local models. The article is quoted below.

The easiest way to find out which models you can run on your computer: Magnitude is a 100% free, open-source desktop app that profiles your machine and ranks the models across: - Speed - Accuracy - Intelligence - Memory required Then it downloads the one you pick and tunes it for your machine. Finally, connect your favorite harness (Pi, OpenCode, Claude Code, Codex, etc.) in one click. Get started here: (don't forget to star 🌟) My co-founder wrote a detailed article on running your favorite harnesses with local models. The article is quoted below.

Avi Chawla

15,465 views • 1 month ago

The easiest way to run your agent harnesses using local models: Whether you're using Claude Code, Codex, OpenCode, or Pi, the local setup itself isn't really the hard part. The harder problem is figuring out which local model your machine can actually handle well. You have to think about RAM, model size, quantization, context length, KV cache, speed, accuracy, and a bunch of other trade-offs before you even start. Magnitude is an open-source solution that removes all of that guesswork for you. It profiles your machine, benchmarks what it can realistically run, recommends the best models for your hardware, and then lets you connect them to your preferred agent harness. The entire setup takes just two commands. In this video, I walk through the full process from hardware profiling to running a harness on a local model. Chapters: 00:00 Intro 00:17 What Magnitude is 00:45 Setup in two commands 01:29 Why picking a local model gets confusing fast 02:11 Hardware profiling + model recommendations 02:59 Choosing your harness 03:22 Connecting a harness that's already running 04:18 Outro Get started: (don't forget to star 🌟)

The easiest way to run your agent harnesses using local models: Whether you're using Claude Code, Codex, OpenCode, or Pi, the local setup itself isn't really the hard part. The harder problem is figuring out which local model your machine can actually handle well. You have to think about RAM, model size, quantization, context length, KV cache, speed, accuracy, and a bunch of other trade-offs before you even start. Magnitude is an open-source solution that removes all of that guesswork for you. It profiles your machine, benchmarks what it can realistically run, recommends the best models for your hardware, and then lets you connect them to your preferred agent harness. The entire setup takes just two commands. In this video, I walk through the full process from hardware profiling to running a harness on a local model. Chapters: 00:00 Intro 00:17 What Magnitude is 00:45 Setup in two commands 01:29 Why picking a local model gets confusing fast 02:11 Hardware profiling + model recommendations 02:59 Choosing your harness 03:22 Connecting a harness that's already running 04:18 Outro Get started: (don't forget to star 🌟)

Akshay 🚀

18,722 views • 1 month ago

Creator of Claude Code: “I’m not just prompting agents anymore. I’m building a harness with graphs and loops - and the agents inside it are building my agent. In 3–6 months, we’ll all be engineering harnesses for our agentic systems.” In this 10-minute talk, Boris Cherny reveals what the future of agentic engineering will look like. Worth more than a $500 agentic AI course. Watch it today, then read the complete guide to building an agent harness in the article below.

Creator of Claude Code: “I’m not just prompting agents anymore. I’m building a harness with graphs and loops - and the agents inside it are building my agent. In 3–6 months, we’ll all be engineering harnesses for our agentic systems.” In this 10-minute talk, Boris Cherny reveals what the future of agentic engineering will look like. Worth more than a $500 agentic AI course. Watch it today, then read the complete guide to building an agent harness in the article below.

Ichigo

74,413 views • 1 month ago