Running a Model Locally Makes Your Team the Vendor
Running a model locally shifts production work to your team. Evaluate capacity, reliability, security, and engineering time before replacing an external API.
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Running a model locally shifts production work to your team. Evaluate capacity, reliability, security, and engineering time before replacing an external API.
Deleting an uploaded file is only one step. Design and verify deletion across extracted text, search indexes, saved answers, logs, caches, and backups.
Before building an MCP integration, test whether an existing CLI meets the agent's needs. Weigh maintenance, permissions, reliability, and workflow fit.
Token consumption measures AI activity, not developer productivity. Evaluate delivery, review effort, rework, reliability, and learning across the team.
Model selection, caching, context, batch processing, and bounded workflows shape AI costs. Measure the full cost of a successful resolution.
Coding interviews should evaluate how candidates collaborate with AI, verify generated code, debug failures, and take responsibility for the result.
Vibe coding is exciting, but professional software developers cannot blindly trust AI-generated code. Like GE under Jack Welch, optimizing for short-term output while ignoring quality may take decades to backfire -- but it will.
Every tech layoff headline blames AI. But the real cause is the hiring binge of 2020-2022 fueled by cheap money. AI is just the convenient excuse companies use to avoid admitting a strategic mistake.
When Claude was banned from government systems, some teams swapped providers in a day. Others faced months of refactoring. The difference? Building every integration for replaceability. Here's why this principle applies to every dependency you have.
Sam Altman says don't learn to code. History says the opposite. Every time a new abstraction layer appears, the people who win are the ones who understand the layer below it. Here's why coding is your edge in the AI era.
The gap between developers who integrate AI into their workflow and those who don't is about to become enormous. Here's why AI-Native developers are outperforming traditional developers by months, and which type will survive.
Your company's AI strategy is all theater and no action. Here's why your engineers are laughing at you, and what you can do to actually ship something instead of endlessly identifying use cases.
Why comparing AI training to teen drivers is misleading - exploring the hidden assumptions about human learning, prior knowledge, and what we should really be asking about AI development.
How an 8-year-old discovered AI tools are sandboxes, not screwdrivers - building 30+ games with Claude Code, then surprising everyone by creating a school presentation. A story about gamification, exploratory learning, and the next generation approach to AI.
Explore Meta's groundbreaking Code World Models research that teaches AI to understand code semantics and execution behavior, not just syntax. Learn how this 32B parameter model simulates program execution and what it means for bug-free software development.
Every time you unlock your phone, you are witnessing the outcome of one of history's most enduring intellectual battles: Plato versus Aristotle. This ancient philosophical divide actively shapes how we build software, train AI models, and design modern technology.
The key to maximizing AI development tools: treat them like team members you lead, not just code generators. Learn the mindset shift from implementer to orchestrator that multiplies your AI productivity.
A quick guide to understanding the differences between GPT-5 Fast, GPT-5 Thinking, and GPT-5 Pro models.
How competing definitions of Artificial General Intelligence are reshaping laws, breaking billion-dollar contracts, and determining who controls the future of AI. A critical analysis of economic, cognitive, and skeptical perspectives on AGI.