Harness
24 posts

Harness
@harnessoperator
Signal for the agent layer. By @facundofranco_

"We used to say build the MVP. Now you should build the system that builds the MVP first." Here's my new episode with @ryancarson where he shared how he runs his startup solo with AI agents: ✅ OpenClaw as his AI chief of staff to triage emails, book meetings, and do sales outreach ✅ Codex and Devin as his AI eng team to ship features while he sleeps Some quotes from Ryan: "Spend a lot of time upfront setting up your skills + documentation. Then you've suddenly unlocked the work of 10 people." "Treat your agent like a real employee. Give it a real email address, calendar access, and GitHub account." "Pay a designer to set up your design system and brand. After that, you can use AI to generate on-brand assets." 📌 Watch now: youtu.be/IDqdVZwAwjw Thanks to our sponsors: @WisprFlow: Don't type, just speak ref.wisprflow.ai/peteryang @linear: The AI agent platform for modern teams linear.app/partners/behin…

Demo crashed while people were watching. Fixed it live. Three root bugs, Claude Code, maybe 20 minutes. That's the job. whatsapp-sales.vercel.app

My simple secret to agentic coding forbes.com/sites/josipama…

We’re sharing our internal AI job board Every company will have internal ops and engineers building AI agents Discover roles from @Box, @tryramp, @DecagonAI, @baseten, @WeAreLegora, and 150+ companies And if you’re an AI lead driving internal transformation, join our leaders community below internal-ai-jobs.concept.site


We’ve automated every single thing we can @every with AI agents. And yet there’s way more human work to do than ever. We’ve gone from 4 -> 30 human employees since GPT-3. I wrote a report on the structural reasons: how AI makes expert competence cheap, why that drives up demand for experts, and why the dynamic only intensifies as we approach AGI. After Automation: every.to/p/after-automa…


Understanding the distinction between an agent SDK and a managed agent service could save you from a lot of confusion before signing up. An agent SDK is a harness. You bring your own compute, choose your sandbox provider, can swap model providers, and basically control the full stack. A managed agent service is the model, the harness, and the sandbox all wrapped together and served to you. You control and operate less and get more simplicity by trading the flexibility layer. Neither is necessarily better. They're just different products for different builders.






The package layer for agents is still barely built. Npm, PyPI and Maven Central account for more than 23.2 million packages. Agent native MCP listings are still around 15.6K, roughly 0.07% of the compared package universe. That gap matters because discovery is only the first step. If agents are going to install and use software in real workflows, they need trust, verification and control built into the package layer itself. That is why @nipmod exists.

An agent is just an LLM calling tools in a loop. The harness is everything around it. I put together a reading list on agent harnesses: the tools, memory, evals, hooks, and orchestration patterns around modern AI systems. It collects a bunch of conversations and collaborations I’ve been lucky to have recently, including: - building self-extending agents with @ivanleomk Leo (Google DeepMind, ex-Manus) - harness engineering + context limits with @jeffreyhuber (Chroma) - agentic search architectures with @softwaredoug (led Search at Reddit and Shopify) - context engineering patterns with @RLanceMartin (Anthropic, ex-Langchain) It also includes some of my other favourite posts: - Pi: The Minimal Agent Within OpenClaw by @mitsuhiko - How do I evaluate agentic workflows? by @HamelHusain - Effective harnesses for long-running agents by the @AnthropicAI team - Demystifying evals for AI agents by the @AnthropicAI team Get the full reading list here: maven.com/p/87a912/build… Would love to know what you build with these skills!