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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 просмотров • 12 часов назад •via X (Twitter)

Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля Jatin Garg

Jatin Garg12 часов назад

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

Фото профиля Meta Racing

Meta Racing12 часов назад

it's yapping time

Фото профиля Defi_Noal

Defi_Noal12 часов назад

finally we get a common standard for this

Фото профиля Neuni United

Neuni United12 часов назад

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

Фото профиля why

why11 часов назад

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

Фото профиля 스카이 DeFi

스카이 DeFi12 часов назад

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

Фото профиля Miano

Miano12 часов назад

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.

Фото профиля Macro Bombastic

Macro Bombastic11 часов назад

tbh the unified protocol is what I care about most here

Фото профиля tim777

tim77712 часов назад

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

Фото профиля Deep

Deep12 часов назад

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

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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

2,222,647 просмотров • 28 дней назад

"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 просмотров • 3 месяцев назад

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

69,613 просмотров • 19 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!

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Sergio Paniego

32,085 просмотров • 3 месяцев назад

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James Rochabrun

61,608 просмотров • 3 месяцев назад

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Derek Nee

15,361 просмотров • 3 месяцев назад

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The Startup Ideas Podcast (SIP) 🧃

43,523 просмотров • 1 месяц назад

The open-source community is INSANE 🤯 Claude Code routines dropped literal hours ago, and this guy's already shipped a clone. Meet Multica Autopilot: Now you can run routines entirely locally using whatever agent you want (Opencode, Codex, Hermes, OpenClaw etc). repo in 🧵↓

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Charly Wargnier ♨️

137,258 просмотров • 5 месяцев назад

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Srishti

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Yarchi

63,930 просмотров • 2 месяцев назад

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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 • 20 дней назад

How to run GLM-5.2 (Or Minimax or Qwen or Deepseek or Kimi) in Claude Code No JSON editing. No vim 😮‍💨 1. Install Claude Code npm install -g Paul Jankura-ai/claude-code 2. Grab a Fireworks API key → 3. Install FireConnect + drop in your key (one line): curl -fsSL | FIREWORKS_API_KEY="fw_..." bash 4. Flip it on: fireconnect claude on 5. Switch to GLM-5.2 and bam! Swap models with: fireconnect claude model select Docs to do the same for Pi, Opencode, and Codex:

How to run GLM-5.2 (Or Minimax or Qwen or Deepseek or Kimi) in Claude Code No JSON editing. No vim 😮‍💨 1. Install Claude Code npm install -g Paul Jankura-ai/claude-code 2. Grab a Fireworks API key → 3. Install FireConnect + drop in your key (one line): curl -fsSL | FIREWORKS_API_KEY="fw_..." bash 4. Flip it on: fireconnect claude on 5. Switch to GLM-5.2 and bam! Swap models with: fireconnect claude model select Docs to do the same for Pi, Opencode, and Codex:

Sinan Ozdemir

149,913 просмотров • 3 месяцев назад

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 🚀

27,361 просмотров • 11 дней назад

You can now run OpenClaw completely free using Qwen 3.6 Plus 🤯 Just plug Alibaba’s free API into OpenClaw, Claude Code, Hermes or Kilo Code… and suddenly you’ve got a powerful AI agent with zero cost. This might be the best free AI setup right now. 🚀

You can now run OpenClaw completely free using Qwen 3.6 Plus 🤯 Just plug Alibaba’s free API into OpenClaw, Claude Code, Hermes or Kilo Code… and suddenly you’ve got a powerful AI agent with zero cost. This might be the best free AI setup right now. 🚀

Julian Goldie SEO

31,717 просмотров • 5 месяцев назад

$HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.$

HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.

YanXbt

29,200 просмотров • 1 месяц назад

FREE 2M tokens. Opus 4.8, GPT-5.5, and every major coding model in one API key. Plugs directly into Claude Code. Inferall gives you access to the full model stack - Opus 4.8, GPT-5.5, and every other serious coding model - through one unified API. Here's the exact setup: → go to inferall(.)ai with VPN or proxy → sign in and register with any email (Google accounts work best if you hit rate limits) → create an API key - copy the key in format ifu_xxxxxxxxxxxxxxxx → go to Billing → Add a card → Activate free tier → add your card - a card with zero balance works fine(!!) → set country to United States during checkout Done. 2,000,000 free tokens loaded. Plug into Claude Code or any agent stack: BASE_URL: API_KEY: ifu_XXXXXXXXXXXXXX Works with Claude Code, OpenCode, Hermes Agent, any OpenAI-compatible setup. Opus 4.8 available immediately. 2M tokens is enough for weeks of serious coding sessions.

FREE 2M tokens. Opus 4.8, GPT-5.5, and every major coding model in one API key. Plugs directly into Claude Code. Inferall gives you access to the full model stack - Opus 4.8, GPT-5.5, and every other serious coding model - through one unified API. Here's the exact setup: → go to inferall(.)ai with VPN or proxy → sign in and register with any email (Google accounts work best if you hit rate limits) → create an API key - copy the key in format ifu_xxxxxxxxxxxxxxxx → go to Billing → Add a card → Activate free tier → add your card - a card with zero balance works fine(!!) → set country to United States during checkout Done. 2,000,000 free tokens loaded. Plug into Claude Code or any agent stack: BASE_URL: API_KEY: ifu_XXXXXXXXXXXXXX Works with Claude Code, OpenCode, Hermes Agent, any OpenAI-compatible setup. Opus 4.8 available immediately. 2M tokens is enough for weeks of serious coding sessions.

Atenov int.

25,388 просмотров • 3 месяцев назад

🚀 Introducing Open Tag A better, open-source Claude Tag. Works with any model, any agent harness, and fully custom agents. Bring any Agent to Slack, MS Teams with Generative UI, Streaming, and much more! Clone the repo and get it running in minutes Powered by the Channels SDK & AG-UI

🚀 Introducing Open Tag A better, open-source Claude Tag. Works with any model, any agent harness, and fully custom agents. Bring any Agent to Slack, MS Teams with Generative UI, Streaming, and much more! Clone the repo and get it running in minutes Powered by the Channels SDK & AG-UI

CopilotKit🪁

198,788 просмотров • 1 месяц назад

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

Om Patel

159,676 просмотров • 5 месяцев назад

Bloome is actually smart for coding workflows i added Claude Code, Codex, DeepSeek and a Project Lead agent into one group chat each agent independently built their own version one challenged the logic, one defended, one fixed missing details and the final website stayed inside the same thread no copy paste between agents no lost context just one group chat for getting work done Bloome

Bloome is actually smart for coding workflows i added Claude Code, Codex, DeepSeek and a Project Lead agent into one group chat each agent independently built their own version one challenged the logic, one defended, one fixed missing details and the final website stayed inside the same thread no copy paste between agents no lost context just one group chat for getting work done Bloome

Chetaslua

48,810 просмотров • 2 месяцев назад

OpenResearch CLI runs the entire autoresearch loop locally — give it a goal, it handles literature review through experiment analysis with parallel agents in separate worktrees. Works with Claude Code, Codex, and OpenCode. Your data never leaves your machine. Research just became something you run overnight. GitHub:

OpenResearch CLI runs the entire autoresearch loop locally — give it a goal, it handles literature review through experiment analysis with parallel agents in separate worktrees. Works with Claude Code, Codex, and OpenCode. Your data never leaves your machine. Research just became something you run overnight. GitHub:

0xMarioNawfal

45,698 просмотров • 22 дней назад