Jim Murphy

AI copilots aren’t just for engineers anymore. 300,000+ plumbers, landscapers, and electricians are now using them to run their businesses. I sat down with Forrest Zeisler, CTO of Jobber, to unpack how the trades went from paper invoices and binders to AI copilots that actually get stuff done: - Booked 200,000+ after-hours calls (no more missed opportunities) - Helped pros to win more work with better proposals - Replaced paperwork headaches with workflows that just work - Proved that trust (not hype) drives real AI adoption The big takeaway: AI usage is exploding across the trades, and fast. Forrest’s story is live now - listen wherever you get your podcasts. — P.S. We’re digging into more AI trends in this month’s AI Leaders Lab: Candid, tactical conversations with execs scaling AI where every tool has to prove its worth. Request an invite here: https://lnkd.in/gJT-RZHR

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We were sure we did not want to be another GPT wrapper when we were building Composio. In the early days, there were plenty of tempting paths. Chasing the hype, building for markets we didn’t live in or betting on futures that looked exciting but weren’t real yet. Unlike several other founders, we kept coming back to the same principle: If we didn’t feel the pain ourselves, we weren’t going to build the solution. So we built for the problems we actually faced as developers. Real, immediate and something that could stand on its own. That’s how Composio became what it is today: Still solving integrations, but for the new generation of software - agents. Looking back, that conviction - not to be “just another wrapper” - quietly defined the company we’ve become.

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🚀 OpenAI 𝐣𝐮𝐬𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 (𝐚𝐠𝐚𝐢𝐧) with the launch of 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭 on Dev Day 2025 - and the implications for the agent ecosystem are massive. For years, building true “agents” has required stitching together frameworks like LangChain, CrewAI or 𝐀𝐃𝐊𝐬 - each great in its own right, but all requiring manual plumbing, action wiring, and environment setup. AgentKit flips that model. It brings the first 𝐧𝐚𝐭𝐢𝐯𝐞 𝐚𝐠𝐞𝐧𝐭 𝐫𝐮𝐧𝐭𝐢𝐦𝐞 directly inside OpenAI’s ecosystem - one where GPTs can 𝐞𝐱𝐞𝐜𝐮𝐭𝐞 𝐭𝐨𝐨𝐥𝐬, 𝐜𝐚𝐥𝐥 𝐀𝐏𝐈𝐬, 𝐚𝐧𝐝 𝐩𝐞𝐫𝐬𝐢𝐬𝐭 𝐬𝐭𝐚𝐭𝐞 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬𝐥𝐲. This is OpenAI’s most serious move yet toward agents that can actually do things, not just talk about them. But here’s the twist: 👉 Frameworks/ Toolkits like 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧, 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭 and 𝐀𝐃𝐊𝐬 still depend on developers to manually define the “what” (tools, APIs, and actions). 👉 Adopt AI 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐬 𝐭𝐡𝐚𝐭 𝐥𝐚𝐲𝐞𝐫 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲. With 𝐀𝐝𝐨𝐩𝐭 + 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭, you get the best of both worlds: ⚙️ 𝐀𝐠𝐞𝐧𝐭𝐊𝐢𝐭 gives your LLMs execution and persistence. 🧠 𝐀𝐝𝐨𝐩𝐭 auto-discovers APIs, generates actions, and makes any application—legacy or modern—agent-ready instantly. Together, they collapse the 𝟎 → 𝟏 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 of building agents that can execute actions across any application into hours instead of months. This isn’t just another framework integration. It’s a step toward 𝐬𝐞𝐥𝐟-𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐢𝐧𝐠, 𝐬𝐞𝐥𝐟-𝐮𝐩𝐝𝐚𝐭𝐢𝐧𝐠 𝐚𝐠𝐞𝐧𝐭𝐬 that can reason, act, and evolve—without manual specs or scaffolding. ✨ 𝐓𝐡𝐞 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐞𝐫𝐚 𝐣𝐮𝐬𝐭 𝐠𝐨𝐭 𝐚 𝐦𝐚𝐣𝐨𝐫 𝐮𝐩𝐠𝐫𝐚𝐝𝐞. #AdoptAI #AgentKit #OpenAI #DevDay #AIagents

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AI’s evolving role in software development has created a tension between rapid code generation and quality. In the real world, true disruption is more than just velocity; it’s about delivering intelligent, reliable software at scale. Join me at TechCrunch Disrupt 2025 for a discussion on how AI built for developers is reshaping the industry, and why elevating code quality, not just speed, will become the new measure of success. https://lnkd.in/eHNA_43y

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As we work with more and more customers at bitdrift, one of the biggest usability problems that customers face that is inherent in real-time observability is the difficulty of understanding whether workflows (the finite state machines we send to clients) are actually going to match the expected event sequence once deployed. It is extremely frustrating to deploy a workflow only to have no matches due to a typo or not fully understanding the shape of logs that are actually generated on the device. Enter the workflow debugger. This is the culmination of a ton of work, tying together our various systems into a unified real-time debugging experience driven directly from the browser. Just watch the video. Your 🧠 will explode. Shipping shortly. 🚀

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