Insights
Thinking in public
We publish what we learn as we learn it. Not polished papers — working ideas, observations from the engineering floor, and the occasional strong opinion.
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June 5, 2026
·Essay · 8 min read
The Electrification Moment
AI coding tools are electric motors bolted onto a steam-age process. The gains from electrification didn't come from the motors — they came from rebuilding the factory. The same is true now.
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September 22, 2025
·Essay · 9 min read
What 'governance' means when AI writes your software
The CIO's governance concern about AI-built software is correct — but misdirected. The failure mode isn't AI-generated code. It's the absence of structured requirements management that most organizations already have.
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December 15, 2024
·LLMs
·LinkedIn
Navigating the Future of B2B SaaS: Small vs. Large LLM Models
Large Language Models are more than just the latest tech trend — they're game-changers, especially in the B2B SaaS space. This post explores the real trade-offs between small, fine-tuned models and large general-purpose ones, covering latency, cost, accuracy, and when each approach wins. The answer isn't always 'bigger is better.'
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November 15, 2024
·LLMs
·LinkedIn
A Systems Approach to Using LLMs: Bridging the Gap Between Cool Demos and Real-World Deployments
LLMs dazzle in demos but stumble in production — hallucinations and unreliability stand between a cool prototype and a deployable product. This piece reframes the LLM as a "lossy medium," drawing on how the early internet engineered reliability atop unreliable transmission, and argues that a systems-level approach — guardrails, verification, and orchestration around the model — is what actually bridges the gap to dependable enterprise software.
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October 15, 2024
·GenAI
·LinkedIn
Why is Building Production-Ready Software with Gen-AI like GPT-4 So Challenging?
Everyone is excited about generative AI, yet few startups ship production-grade products on top of it. This article breaks down the three forces working against them — output reliability and hallucinations, stale knowledge from training cutoffs, and the complexity of wiring AI into existing enterprise systems — and explains why, for now, the most successful Gen-AI applications come from established players who can fold AI into what they already run.
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September 15, 2024
·LLMs
·LinkedIn
Rethinking LLMs: A Path to Accessible and Reliable GenAI
Building bespoke models from scratch is expensive and out of reach for most teams. This post makes the case for layering — enhancing existing LLMs with additional technology layers to sharply reduce hallucinations and operational cost rather than reinventing the model — opening a route to reliable, advanced AI that small and medium businesses can actually afford and trust.
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August 15, 2024
·GenAI
·LinkedIn
Embracing GenAI: A Strategic Imperative for CIOs
For CIOs, generative AI is no longer optional — it's a strategic lever for innovation and efficiency. This article lays out how to adopt it deliberately: identifying high-impact use cases, solving hard problems faster than traditional methods, and building scalable implementation plans backed by real change management, so GenAI's content generation and automation become a durable competitive advantage.
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July 15, 2024
·GenAI
·LinkedIn
From Silos to Synergy: How Generative AI is Redefining SaaS for the Agile Enterprise
Enterprises drown in fragmented SaaS tools that don't talk to each other. This piece argues for custom, modular micro-SaaS powered by generative AI — specialized applications that stitch into one integrated system delivering real-time insight and operational visibility — and positions AI-integrated micro-SaaS as the next evolution of enterprise software, replacing one-size-fits-all with something genuinely agile.
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