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Keeping a fast-growing product organisation's engineering foundation current, without disrupting the teams building on top of it, requires more than adopting new frameworks. Three of the architects behind twimbit's technology modernisation discuss the principles guiding their decisions, including the shift to Next.js and the reasoning behind consolidating projects into a monorepo structure.
What automating continuous integration and deployment pipelines has meant for a small, fast-moving engineering team comes up, along with where friction has shown up most during the transition. Participants also reflect on how a young technology team builds the habits, documentation practices and decision-making processes needed to keep pace as both the codebase and the wider organisation continue to scale, including how ownership is distributed once a monolith gives way to a more modular structure.
An honest look at the mistakes teams commonly make when modernising legacy systems closes things out, offering practical lessons rather than a polished account of a finished architecture, along with a candid take on what they would do differently with hindsight.
<ul><li>How a monorepo structure and CI/CD automation support a scaling engineering team</li><li>Common pitfalls to avoid when modernising a technology stack</li></ul>
<blockquote>What does it take to modernise a technology stack in a way that actually holds up as an organisation scales?</blockquote>

Modernising tech at twimbit

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AI is becoming standard in CX; execution is becoming the differentiator. The competitive gap is shifting from who has the most AI features to who can connect intelligence across customer context, enterprise systems and workflows to resolve outcomes end to end.

The Twimbit AI-CX Vendor Landscape 2026 therefore evaluates vendors across both AI capability and innovation and market execution and adoption, revealing who can turn AI from impressive technology into scalable enterprise impact.

AI-CX Vendor Landscape 2026

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Managed access to Chinese frontier AI models is reshaping enterprise AI strategy. The arrival of Moonshot AI’s Kimi K3 on Amazon Bedrock signals a shift in how large organizations approach AI model selection, procurement, and governance. Enterprises can now leverage advanced, open-weight Chinese AI through familiar AWS APIs, billing, and security frameworks, reducing operational friction and enabling new options for cost management and deployment flexibility.Kimi K3’s integration with Amazon Bedrock introduces prompt caching and supports multi-model architectures, allowing organizations to distribute workloads between open-weight and proprietary models based on cost, complexity, and business needs. This development prompts a re-examination of AI infrastructure, balancing performance, cost efficiency, and governance across diverse use cases.Key considerations for CIOs, CFOs, and CISOs include model provenance, licensing, regulatory compliance, and data residency—especially as regional availability and legal obligations differ across the United States, Europe, and Asia Pacific. The managed environment provides strong infrastructure-level security, but enterprises must still evaluate the governance of Chinese-origin models before adoption.<ul><li>Explores managed deployment options for open-weight AI on AWS</li><li>Assesses security, compliance, and procurement implications</li><li>Compares regional constraints and opportunities for global enterprises</li></ul><blockquote>How will the availability of Chinese frontier AI models like Kimi K3 on Amazon Bedrock change enterprise AI procurement and governance strategies?</blockquote>

The Managed Front Door to Chinese Frontier AI: What Kimi K3 on Amazon Bedrock Means for Enterprise Buyers

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AI compute and chips are undergoing rapid transformation as enterprises shift from a focus on peak training performance to a more nuanced approach that weighs workload requirements, utilisation economics, and infrastructure flexibility. The landscape now extends beyond general-purpose GPUs to include hyperscaler-designed silicon, specialised processors, and regionally controlled architectures. This evolution is driven by growing demand for inference, AI agents, real-time applications, and distributed deployment models, all of which are reshaping how compute is sourced and deployed.Key considerations now include not only processing power, but also cost per workload, energy efficiency, software compatibility, supply resilience, and long-term adaptability. Enterprises must evaluate chip architectures and suppliers across technical, financial, strategic, and operational dimensions, with an increasing emphasis on matching infrastructure to specific application needs rather than standardising on a single chip type.<ul><li>Coverage of general-purpose GPUs, custom cloud silicon, inference specialists, and regional AI chip providers</li><li>Analysis of workload-driven procurement strategies for AI infrastructure</li><li>Examination of supplier risk, software lock-in, and multi-chip strategies</li></ul>For CIOs and technology leaders, the central question is no longer which chip is the fastest, but which combination of compute, software, and supplier ecosystem best supports evolving business and AI workload requirements.<blockquote>How are enterprise AI infrastructure decisions shifting from performance-led to workload-led strategies, and what forces are redefining the AI chip market?</blockquote>

AI Compute & Chips​: Trends and analysis in a fast-evolving market​

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Keeping a fast-growing product organisation's engineering foundation current, without disrupting the teams building on top of it, requires more than adopting new frameworks. Three of the architects behind twimbit's technology modernisation discuss the principles guiding their decisions, including th

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Modernising tech at twimbit

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