Today’s Picks
💰 “I cracked Upwork proposals with my AI agent” | r/AI_Agents
🔗 https://www.reddit.com/r/AI_Agents/comments/1t9sk3q/i_cracked_upwork_proposals_with_my_ai_agent/
📌 Why it matters: Direct money play. Someone built an agent that automates Upwork proposal generation — the most practical “agent makes money” use case of the week. If you own an agent and aren’t using it to generate freelance income, you’re leaving money on the table.
🤖 Agent angle: This is immediately replicable. Your Hermes/OpenClaw agent can scrape Upwork feeds, match your skillset to job postings, and draft tailored proposals. Build a prompt chain: (1) fetch new postings → (2) match against your portfolio → (3) generate proposal with relevant past work → (4) submit. One afternoon of setup, potential recurring revenue.
🏢 Shopify’s River: Agent operations with a public-by-default constraint | @simonw
🔗 https://x.com/simonw/status/1921369654649356411
📌 Why it matters: Shopify’s internal agent system lives in Slack and is only usable in public channels so employees can learn from each other’s agent interactions. 752 likes. This is a counterintuitive but brilliant operational pattern — forcing visibility creates an org-wide training dataset and prevents agent misuse simultaneously.
🤖 Agent angle: If you’re deploying agents for clients or within a team, steal this pattern. Make agent interactions visible by default. It accelerates onboarding (new hires learn from agent logs), improves prompts organically (people see what works), and builds institutional memory. For agent product builders, consider shipping a “public-by-default” mode as a feature.
🧠 Context window optimization framework — open source + paper | r/AI_Agents
🔗 https://www.reddit.com/r/AI_Agents/comments/1t9mv7c/i_built_a_context_window_optimization_framework/
📌 Why it matters: Context windows are the #1 bottleneck for production agents — they determine cost, latency, and task complexity limits. An open-source framework for optimizing context usage means cheaper, faster, and longer-running agents for everyone.
🤖 Agent angle: Integrate this into your agent pipeline to automatically trim, prioritize, and manage context. If you’re paying for tokens by volume (or hitting context limits on complex tasks), this directly improves your agent’s economics. Fork it, test it against your workloads, and benchmark the token savings — then share the results with your subscribers.
🧪 agent-skills-eval — A test runner for AI agent skills | New on GitHub (302★)
🔗 https://github.com/darkrishabh/agent-skills-eval
📌 Why it matters: As the agent ecosystem expands, measuring skill quality becomes critical. This is a benchmark/test runner for agent skills — think of it as unit tests for your agent’s capabilities. Early traction suggests the community is hungry for quality signals.
🤖 Agent angle: Run this against your agent’s skills to quantify performance before deploying to production. For builders creating custom skills (Hermes skills, OpenClaw skills), this is your quality gate. Ship skills with verified scores — it builds trust with users and helps you identify regressions when you update models or prompts.
🐚 Using LLM in the shebang line of a script | @simonw
🔗 https://simonwillison.net/2026/May/11/llm-shebang/
📌 Why it matters: One of the most useful patterns in the practical AI toolbox — you can write executable scripts in plain English using #!/usr/bin/env -S llm -f and your LLM turns the text into a working program. Tool calls, YAML templates, even custom Python functions are supported. This is agent-as-runtime, not agent-as-chat.
🤖 Agent angle: This is a deploy pattern, not a demo. Chain LLM shebang scripts with cron to build automated agent pipelines that anyone can read and modify. No compilation, no boilerplate — just English text files that execute. For service providers, this means clients can audit what their agent is doing by reading plain text.
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