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DeepSeek Harness is an agent harness where every component is a plugin, built on Cordis for spatiotemporal composability. Run it via `npx deepseek-ai/dsh web` to get a local Web UI at port 3080. Explore it here:

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DeepSeek DeepSeek is now live on Heurist. Open source AI just reached new heights. Access DeepSeek-R1 and DeepSeek-V3 for smarter inference at a fraction of cost. Try it via Heurist API or explore on Pondera.
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DeepSeek DeepSeek is now live on Heurist. Open source AI just reached new heights. Access DeepSeek-R1 and DeepSeek-V3 for smarter inference at a fraction of cost. Try it via Heurist API or explore on Pondera.

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DEEPSEEK ACABA DE MATAR TODA LA INDUSTRIA DEL CODING AGENT Se llama deepseek-harness Es el framework mas completo para crear agentes de codigo Open source. El plan mas completo de Claude cuesta $200 al mes. Esto es GRATIS Y viene con una idea brutal Todo es un plugin El modelo Las tools El sandbox La UI Hasta el propio loop del agente Puedes cambiar cualquiera de esas piezas solo modificando la configuracion Sin tocar el codigo base Un solo comando y tienes la interfaz web local: npx deepseek-ai/dsh web Compatible con DeepSeek, Claude, GPT, Gemini y lo que quieras enchufar Llego a mas de 160 mil estrellas en pocos dias Un laboratorio frontier acaba de soltar gratis la capa exacta que otras empresas estan vendiendo como producto premium Te dejo el repo en los comentarios
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DEEPSEEK ACABA DE MATAR TODA LA INDUSTRIA DEL CODING AGENT Se llama deepseek-harness Es el framework mas completo para crear agentes de codigo Open source. El plan mas completo de Claude cuesta $200 al mes. Esto es GRATIS Y viene con una idea brutal Todo es un plugin El modelo Las tools El sandbox La UI Hasta el propio loop del agente Puedes cambiar cualquiera de esas piezas solo modificando la configuracion Sin tocar el codigo base Un solo comando y tienes la interfaz web local: npx deepseek-ai/dsh web Compatible con DeepSeek, Claude, GPT, Gemini y lo que quieras enchufar Llego a mas de 160 mil estrellas en pocos dias Un laboratorio frontier acaba de soltar gratis la capa exacta que otras empresas estan vendiendo como producto premium Te dejo el repo en los comentarios

Jokker

246,394 görüntüleme • 20 gün önce

Building a harness to run scans with an agent. It is absolutely garbage. Completely useless. There’s literally 0 benefit to using it over plain Claude Code. Everything it can do, you can just do via Skills etc. Many were preaching the benefits of a harness, but I’m not sure.
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Building a harness to run scans with an agent. It is absolutely garbage. Completely useless. There’s literally 0 benefit to using it over plain Claude Code. Everything it can do, you can just do via Skills etc. Many were preaching the benefits of a harness, but I’m not sure.

solst/ICE of Astarte

56,130 görüntüleme • 4 ay önce

We're giving you a $10 coupon to build on Ori Harness this weekend What will you build? Run your favorite agent - Claude Code, Codex, DeepSeek, and more - with 500+ models, 70+ providers, all in one account, on OpenRouter Redeeming is easy:
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We're giving you a $10 coupon to build on Ori Harness this weekend What will you build? Run your favorite agent - Claude Code, Codex, DeepSeek, and more - with 500+ models, 70+ providers, all in one account, on OpenRouter Redeeming is easy:

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Here is an AI-native browser. You gotta see how this works! Honestly, I'm still wrapping my head around this. This web browser: • Integrates an AI agent on every page • Can navigate on autopilot • It can even use web apps for you! Check out this video:
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Here is an AI-native browser. You gotta see how this works! Honestly, I'm still wrapping my head around this. This web browser: • Integrates an AI agent on every page • Can navigate on autopilot • It can even use web apps for you! Check out this video:

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A harness turns a model into an agent. At it’s core it provides 4 things: - a system prompt - tools - an agentic loop - a translation layer across models New blog post from Earendil co-founder colin hanna on what a harness is, and how you can own yours. Full post below
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A harness turns a model into an agent. At it’s core it provides 4 things: - a system prompt - tools - an agentic loop - a translation layer across models New blog post from Earendil co-founder colin hanna on what a harness is, and how you can own yours. Full post below

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419,280 görüntüleme • 20 gün önce

You guys asked for a tutorial, so here it is. Animated Apple-style websites FULLY with AI, perfect for web and UI work. Let's break it down step-by-step below. Prompts included.
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You guys asked for a tutorial, so here it is. Animated Apple-style websites FULLY with AI, perfect for web and UI work. Let's break it down step-by-step below. Prompts included.

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Running MiniCPM5-2B locally on a 16GB MacBook 💻 It beats Qwen3.5-4B on benchmarks, so we asked it to find recent AI news, it called web search, pulled back 10 results and summarized the key developments inside Atomic Chat Run local models via
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Running MiniCPM5-2B locally on a 16GB MacBook 💻 It beats Qwen3.5-4B on benchmarks, so we asked it to find recent AI news, it called web search, pulled back 10 results and summarized the key developments inside Atomic Chat Run local models via

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Check this!! A 100% open-source toolkit to work with LLMs. Transformer Lab is an app to experiment with LLMs: - Train, fine-tune, or chat. - One-click LLM download (DeepSeek, Gemma, etc.) - Drag-n-drop UI for RAG. - Built-in logging, and more. 100% local!
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Check this!! A 100% open-source toolkit to work with LLMs. Transformer Lab is an app to experiment with LLMs: - Train, fine-tune, or chat. - One-click LLM download (DeepSeek, Gemma, etc.) - Drag-n-drop UI for RAG. - Built-in logging, and more. 100% local!

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The internet is about to change forever. Google just launched a mind-blowing AI agent. It browses the web for you. Here’s what that actually means:
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The internet is about to change forever. Google just launched a mind-blowing AI agent. It browses the web for you. Here’s what that actually means:

Patrick's AIBuzzNews

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New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.
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New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

elvis

11,303 görüntüleme • 20 gün önce

Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.
0:14

Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.

Avi Chawla

44,124 görüntüleme • 2 ay önce

You built an app, nice But no one knows about it. And the vibe is a bit... default. You need a brand. We built a skill for founders to generate distinct brands for any app. Create assets for web, social, app store, ads, and UI with a cohesive aesthetic Skill below 👇
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You built an app, nice But no one knows about it. And the vibe is a bit... default. You need a brand. We built a skill for founders to generate distinct brands for any app. Create assets for web, social, app store, ads, and UI with a cohesive aesthetic Skill below 👇

Hyperagent

903,801 görüntüleme • 3 ay önce

Claude Code is running on a remote server, along with the web app it just built. You’re on your phone. You can still open localhost:3000 in Safari or Chrome. Just create a local port forwarding rule in Termius and securely tunnel the remote port to your device. No public ports. No extra setup.
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Claude Code is running on a remote server, along with the web app it just built. You’re on your phone. You can still open localhost:3000 in Safari or Chrome. Just create a local port forwarding rule in Termius and securely tunnel the remote port to your device. No public ports. No extra setup.

Termius

132,283 görüntüleme • 2 ay önce

DeepSeek R1 is *the* best model available right now. It's at the level of o1, but you can use it for free, and it's much faster. A huge leap forward that nobody saw coming. No wonder so many people are throwing tantrums online trying to discredit the Chinese students who built this. You can use DeepSeek in Visual Studio Code right now: 1. Install the Qodo Gen AI extension 2. Select DeepSeek R1 from their list of models The Qodo team is hosting DeepSeek on their servers, so none of your data will go to China. I've been building a Tetris game using DeepSeek, and this is the most impressive model I've seen so far.
0:17

DeepSeek R1 is *the* best model available right now. It's at the level of o1, but you can use it for free, and it's much faster. A huge leap forward that nobody saw coming. No wonder so many people are throwing tantrums online trying to discredit the Chinese students who built this. You can use DeepSeek in Visual Studio Code right now: 1. Install the Qodo Gen AI extension 2. Select DeepSeek R1 from their list of models The Qodo team is hosting DeepSeek on their servers, so none of your data will go to China. I've been building a Tetris game using DeepSeek, and this is the most impressive model I've seen so far.

Santiago

1,224,381 görüntüleme • 1 yıl önce

deepseek v4 pro is basically free right now 😳 teamorouter is offering deepseek v4 pro at almost no cost you can also use deepseek v4 flash for free what you get: - deepseek v4 pro free - deepseek v4 flash free - 1M context - openai compatible api - no subscription - credits never expire why this is worth checking: > v4 pro is currently listed at $0 input and output > flash is also available at $0 > works with claude code, codex and other tools > one api gives you access to multiple models getting started: 1. go to 2. create your account 3. open the dashboard 4. create your api key base url: 5. add it to your ai coding tool 6. select deepseek-v4-pro-free worth testing while it’s available
0:16

deepseek v4 pro is basically free right now 😳 teamorouter is offering deepseek v4 pro at almost no cost you can also use deepseek v4 flash for free what you get: - deepseek v4 pro free - deepseek v4 flash free - 1M context - openai compatible api - no subscription - credits never expire why this is worth checking: > v4 pro is currently listed at $0 input and output > flash is also available at $0 > works with claude code, codex and other tools > one api gives you access to multiple models getting started: 1. go to 2. create your account 3. open the dashboard 4. create your api key base url: 5. add it to your ai coding tool 6. select deepseek-v4-pro-free worth testing while it’s available

K2S

38,998 görüntüleme • 21 gün önce

What I love about Replit ⠕. Low floor to get started. Ultra high ceiling for what you can build. I told Agent to build a native C application that runs on my desktop without a web browser - AND compile it into JavaScript to run in the web browser. Here's a video showing both running
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What I love about Replit ⠕. Low floor to get started. Ultra high ceiling for what you can build. I told Agent to build a native C application that runs on my desktop without a web browser - AND compile it into JavaScript to run in the web browser. Here's a video showing both running

jordwalke

43,765 görüntüleme • 7 ay önce

this is the best trick to maximum usage limits on chatgpt codex codex's best kept secret is that your main agent doesn't have to do everything... custom agents are just files in ~/.codex/agents, and one file gives you a second worker on deepseek v4 flash > create ~/.codex/agents/deepseek-worker.toml > set model = "opencode-go/deepseek-v4-flash" with model_reasoning_effort = "max" > keep it bounded: one task packet, no scope creep, report back ```toml name = "deepseek_worker" description = "bounded implementation, testing, and cleanup on deepseek v4 flash" model = "opencode-go/deepseek-v4-flash" model_reasoning_effort = "max" ``` then @ deepseek_worker in the composer... your root agent plans while the worker ships the implementation planning on the main model, execution on the flash lane... that's the whole trick (we run this exact file, last i checked it keeps the heavy turns off the main thread)
0:18

this is the best trick to maximum usage limits on chatgpt codex codex's best kept secret is that your main agent doesn't have to do everything... custom agents are just files in ~/.codex/agents, and one file gives you a second worker on deepseek v4 flash > create ~/.codex/agents/deepseek-worker.toml > set model = "opencode-go/deepseek-v4-flash" with model_reasoning_effort = "max" > keep it bounded: one task packet, no scope creep, report back ```toml name = "deepseek_worker" description = "bounded implementation, testing, and cleanup on deepseek v4 flash" model = "opencode-go/deepseek-v4-flash" model_reasoning_effort = "max" ``` then @ deepseek_worker in the composer... your root agent plans while the worker ships the implementation planning on the main model, execution on the flash lane... that's the whole trick (we run this exact file, last i checked it keeps the heavy turns off the main thread)

Avid

45,030 görüntüleme • 23 gün önce