Anthropic Launches Advanced Tool Calling
Today's top 12 insights for PM Builders, ranked by relevance from X, LinkedIn, Blogs, and YouTube.
Anthropic Launches Advanced Tool Calling
#1 đť•Ź
Jason Zhou hails Anthropic’s advanced tool calling—featuring programmatic invocation, dynamic filtering, built-in search and real-world use examples—as underrated gold in his quick 3-minute breakdown.
#2 in
Peter Yang: Nat Eliason’s OpenClaw bot Felix autonomously built a website product with Stripe integration and generated $14,718 in three weeks. His setup hinges on a 3-layer memory system, five concurrent chat sessions, and secure API access to Stripe, Vercel, and X.
#3 đź“ť Simon Willison
Agentic Engineering Patterns - A guide collecting patterns for building and operating agentic systems. It serves as a hub for specific patterns such as red/green TDD for coding agents.
#4 đť•Ź
claire vo đź–¤ audited her claw config and discovered it stored 1Password-sourced API keys in plain-text files, had weak anti-prompt-injection safeguards, and overly permissive subagent tools.
#5 đź“ť Simon Willison
Research WebMCP + Chrome DevTools Protocol Demo - Demo of WebMCP, a proposed browser API for exposing structured, callable tools to AI agents, showing how to register and interact with WebMCP tools from a Python client over the Chrome DevTools Protocol. The project aims to reduce reliance on brittle UI automation.
#6 đź“ť PromptLayer Blog
How Large Organizations and Enterprises Standardize LLM Benchmarks - Addresses the challenge large organizations face when evaluating LLMs consistently and meaningfully as they move into production use. PromptLayer outlines approaches for building comparable benchmarks that reflect real-world performance and business needs.
#7 đź“ť PromptLayer Blog
Is Opus Smarter Than Sonnet? — Opus vs Sonnet - Compares Anthropic's Opus and Sonnet model families, arguing that 'smarter' depends on the task and workflow. The article draws on PromptLayer's observations of model behavior across real workflows to explain trade-offs between the models.
#8 đť•Ź
Jason Zhou shows that providing LLMs with concrete tool-use examples for complex tools with many optional fields and dependencies boosts JSON output accuracy from 72% to 90% in Anthropic’s benchmarks.
#9 đť•Ź
claire vo đź–¤ built an Electron app in 24 hours to backup/sync her @openclaw markdown knowledge files across devices, track and assign agent tasks, and log every briefing pushed to her.
#10 ▶️
Full Tutorial: How to Build an OpenClaw Business That Makes $4,000 a Week (35 Min) | Nat Eliason
Peter Yang
Nat Eliason uses OpenClaw’s Felix agent with Versel, Stripe, GitHub, and Telegram API keys, a QMD-based memory index, and cron-driven heartbeat to autonomously launch felixcraft.ai, generating $3,596 gross in Stripe sales over four days and accruing ~$80 000 in crypto fees.
- Felix’s overnight-built PDF guide on felixcraft.ai, deployed via Versel and connected to Stripe, achieved $3,596 gross ($3,440 net) in sales over four days.
- Felix accumulated approximately $80 000 in its Ethereum wallet by automatically claiming 60% of the 0.2% fee on community-launched “Felix” token trades, burning half the tokens daily.
- Nat implemented a three-layer OpenClaw memory system using Shopify’s QMD for markdown indexing, a nightly 2 a.m. memory-consolidation cron job updating project notes, and a heartbeat cron to monitor and restart long-running Codeex sessions.
#11 ▶️
AI Jason
Entropic’s Advanced Tool Calling release introduces programmatic tool calling via a code execution sandbox, dynamic filtering for Web Fetch v2026209, deferred loading through Tool Search, and input examples for tool usage, boosting agent efficiency and reducing token consumption.
- Programmatic tool calling uses a code_execution sandbox and allowed_caller parameter to let models generate code that invokes multiple MCP functions, cutting token usage by 30–50%.
- Dynamic filtering for the Web Fetch tool v2026209 applies code to extract only relevant HTML content before returning it to the model, reducing token consumption by 24%.
- Tool Search with deferred loading stores schemas off-context and retrieves only needed tools via a ~500-token tool_search call, slashing tool context footprint by up to 80%.
#12 đť•Ź
Santiago warns that AI companies are scraping every blog post, tutorial, and open-source repo to train their models, then monetizing that data through tokens and ads.