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Introducing Coldtea. The ADE for shipping self-driving software. Purpose-built for the entire software lifecycle. Other IDEs optimise for one thing: Build. Run more agents in parallel, ship faster, orchestrate better. But software engineering is not just about building. Coldtea AI brings your coding agents, agentic end-to-end testing, and production...

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Ohans Emmanuel

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137,236 görüntüleme • 12 saat önce •via X (Twitter)

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Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

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Introducing Giga's browser agent. Giga can now log in and completes tasks end-to-end, just like your human agents. It used to take months to get support APIs deployed for AI agents in the enterprise. Now these integrations can be built instantly.

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Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside one graph: 17:44 - Build your first AI agent 39:30 - Run agents with loop engineering 1:12:38 - Turn agent loops into graphs 1:34:26 - Build agents that throttle themselves 1:55:05 - Orchestrate the full multi-agent system Most people build one agent and stop there. Google is teaching the full stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read the full architecture below ↓

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Everyone is trying to build a support agent. Now you can build one in your CLI that resolves 90% of your support tickets. Today we're launching the Giga CLI. pip install giga-sdk Build, test, deploy, and roll back your AI agents like production software. This is the workflow our customers use to operate AI agents resolving 100,000+ support tickets every day. → CI-gated deployments → GitHub-powered knowledge base syncs → Bulk conversation analysis → Integrating Coding Agents directly into Giga Available now on PyPI.

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I built dev tools at GitHub for 7 years before AI coding became the default. AI made code gen fast, but software still depends on the human mind knowing which details matter. AI only made that harder. That's why I built Nuanced, the AI coding app I want. Nuanced is a macOS app for building software with agents around a living spec, keeping intent and implementation connected as the agent builds. Try it:
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I built dev tools at GitHub for 7 years before AI coding became the default. AI made code gen fast, but software still depends on the human mind knowing which details matter. AI only made that harder. That's why I built Nuanced, the AI coding app I want. Nuanced is a macOS app for building software with agents around a living spec, keeping intent and implementation connected as the agent builds. Try it:

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An engineer at Anthropic wrote a spec, pointed Claude at an Asana board, and went home. Claude broke the spec into tickets, spawned agents for each one, and they started building independently. When the agent is confused it runs git-blame and messages the right engineers in Slack. By Monday the agents finished the plugin feature. That's one example of how the best engineers are shipping software right now. Developers will soon orchestrate 50 AI agents in parallel and the difference between a good engineer & a great one would come down to specs. You can't write a spec that holds up at that scale without genuinely understanding what you're building at a deeper level. The next-gen developer who understands the fundamentals, can architect well and orchestrate agent is going to be a 1000x developer!
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An engineer at Anthropic wrote a spec, pointed Claude at an Asana board, and went home. Claude broke the spec into tickets, spawned agents for each one, and they started building independently. When the agent is confused it runs git-blame and messages the right engineers in Slack. By Monday the agents finished the plugin feature. That's one example of how the best engineers are shipping software right now. Developers will soon orchestrate 50 AI agents in parallel and the difference between a good engineer & a great one would come down to specs. You can't write a spec that holds up at that scale without genuinely understanding what you're building at a deeper level. The next-gen developer who understands the fundamentals, can architect well and orchestrate agent is going to be a 1000x developer!

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