A little more clarity.
Ideas and practical guidance for bringing intelligence into your business. Start with the questions that matter.
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Selected guides for building a more capable business.
Mistral Large 4 and the business case for AI you control
Mistral’s Le Chonk brings a new candidate for AI you can run yourself. Here is what its planned open weights could change—and what a trillion-parameter model demands.
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Where your AI runs should be your decision
Claude Cowork’s Pro and Max cloud move highlights three choices: where workflows run, where models run, and where calls originate. With Looski, your team defines the deployment.
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Should I sell my company's data to AI companies?
AI labs and their data brokers are offering six figures for a company's Slack threads, email, tickets and code. The buyers have said what they want it for: training agents to do the work those records describe. For an operating business, a one-time check is a poor price for that.
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Zapier vs Make vs Build Your Own
Zapier vs Make vs Build Your Own: compare the fit, a focused custom workflow automation, ownership benefits, operating costs, and a practical pilot.
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Quantization: memory savings, rounding error, and evaluation.
Work through an affine quantizer, include scale metadata in the memory budget, and evaluate the resulting model on the tasks you actually run.
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Build an AI workflow your team can evaluate and maintain.
Define an output contract, build a held-out evaluation set, measure correction time, and release workflow changes with a rollback path.
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Designing a testable data boundary for private AI.
Map documents, embeddings, caches, logs, and integrations. Then test egress, access isolation, deletion, and recovery with synthetic records.
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Unified memory: budget weights, context, and concurrency.
Estimate model storage, KV cache, runtime overhead, and headroom separately. Then benchmark the workload at the concurrency you plan to serve.
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