I’ve been using an AI automation setup recently that completely changed how I handle leads and follow-ups. Before this, I was missing a lot of potential calls just because I couldn’t consistently follow up. Now the system I’m using: - captures leads automatically - follows up without manual work - helps turn conversations into booked calls more consistently It’s not a complicated setup, but it removed a major bottleneck in my process. I broke it down here: https://14lzvlw.atoms.world Curious—has anyone else here solved follow-up in a better way, or still doing it manually?

Have you guys dialed into claude clode yet?

• OpenAI dropped GPT-5.4 with a million-token context window, native computer control, and a new Codex Security agent that hunts vulnerabilities in codebases. If you're building AI-assisted workflows or shipping agents, the jump in context length alone changes what's possible in a single pass. • Google completed its acquisition of Wiz and is now rolling out Gemini-powered "Agent Designer" on the Pentagon's AI portal, letting users build custom AI agents without code. For builders, the signal is clear: no-code agent creation is going mainstream fast. • Anthropic published a labor market impact report introducing an "AI displacement risk" metric that combines model capability with real-world usage data. If you're positioning automation services for clients, this kind of research helps you frame the conversation around augmentation vs. replacement. • A Harness study found that while AI coding tools are accelerating code output, DevOps maturity isn't keeping pace — leading to more deployment failures and longer recovery times. Worth thinking about if you're using AI to ship faster: the bottleneck is shifting from writing code to deploying it safely. • Google AI matched or beat human radiologists in breast cancer detection in a new study, identifying 25% more interval cancers with fewer false positives. Healthcare automation is quietly becoming one of the highest-impact verticals for AI builders to watch. What caught your eye this week? Drop your thoughts below.

• Anthropic launched a code review tool designed to catch the flood of AI-generated code hitting production systems. If you're shipping AI-assisted code in your automations or products, this kind of quality-gate tooling is becoming essential infrastructure to watch and adopt. • OpenAI acquired Promptfoo, a security-focused testing framework for AI agents. For builders deploying autonomous agents, this signals that adversarial testing and prompt security are no longer optional — they're becoming standard practice. • Anthropic sued the Defense Department over a supply-chain risk designation, while simultaneously navigating controversy over its Pentagon deal — causing OpenAI hardware exec Caitlin Kalinowski to resign in protest. The politics around government AI contracts are heating up fast, and it's worth understanding how these deals may shape which models and APIs remain openly available to builders. • Tencent's Penguin-VL paper explores pushing vision-language models to their efficiency limits using LLM-based vision encoders. Smaller, faster multimodal models mean cheaper, more practical vision capabilities for real-world automation pipelines. • A new paper from OpenAI found that reasoning models struggle to control their own chains of thought, producing reasoning steps that don't reliably reflect their internal process. If you're building systems that depend on model reasoning being transparent or auditable, this is a meaningful limitation to design around. What's on your radar this week — are you thinking about agent security, multimodal tools, or something else entirely? Drop it in the comments.

Quick question for the community: When building time-saving automations, do you usually start from a ready workflow or build from scratch and customize later? Just trying to improve my process.