Long-form essays on agentic coding, AI product development, voice systems, and practical workflow experiments from real projects at E-String.
The things that slowed down machine learning inside big companies are slowing down AI agents now, and small teams mostly don't have those problems. Some thoughts on where agents can help a small business first.
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How small teams can capture institutional knowledge and turn it into context their AI tools can use, from personal second brains to a shared company brain.
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For a small teams, the durable AI advantage is the loop you build around the model. The model you pick matters far less than you think.
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Running an AI model on your own hardware keeps your data in the building and out of reach when a cloud model gets pulled or repriced. Here's what local AI actually means, and why any business can now do it on a single machine.
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Privacy, cost, and AI built into the apps you already use. Apple's WWDC announcements line up with the case for going local, and that matters for small firms.
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Andrej Karpathy says you can outsource your thinking, but you can't outsource your understanding. A recent client session showed me exactly what that looks like in practice.
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Your agent is almost useless until you teach it who you are. Here is a way to do that.
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Most companies think they're using AI. The data says only 5% are getting real value from it. The AI Readiness Scorecard is a free, 5-minute assessment that shows business owners where their company actually stands, and what to fix first.
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Anthropic surveyed 80,000 people across 159 countries about AI. The findings challenge the productivity narrative and reveal what's really holding businesses back.
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The app isn't dying. It's dissolving. In the age of agents, software is becoming workflows, sandboxes, tool layers, and capability bundles rather than dashboards and screens.
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How a multi-agent analysis system stress-tests essays through stakeholder debate to reveal hidden assumptions, tradeoffs, and decision-grade conclusions.
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Why software development is shifting from manual coding to agent orchestration, and how teams can adapt with specs, context engineering, and autonomous work.
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A practical overview of modern voice AI architectures, latency tradeoffs, streaming patterns, and design choices for real-time conversational products.
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A practical developer guide to key protocols and technologies behind autonomous AI agents, from embeddings and RAG to MCP and multi-agent coordination.
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How spec-driven planning improves AI-assisted coding quality, with practical frameworks for turning vibe coding into reliable engineering workflows.
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