Tag · 23 essays
Writing tagged "Product strategy".
Coding agents are changing developer experience. SaaS platforms should rethink SDK investments and prioritize APIs, OpenAPI specs, documentation, and agent tooling.
Cursor's $60 Pro+ plan is now a legacy subscription, replaced by a $100 Super plan that bundles Grok and bills Claude, GPT, and Gemini at API rates from the first request. Inference is getting cheaper, but agentic workflows and ecosystem pricing are sending the bill the other way.
How the new MCP Skills Extension works, from skill discovery and on-demand loading to content verification, workflow guidance, and managed profiles.
This article argues that a single, monolithic MCP server per platform is the wrong abstraction, especially for complex systems like Contentstack. Instead, it introduces managed profiles as the right unit of configuration, aligned to jobs, teams, or access boundaries rather than product catalogs. Each managed profile bundles a curated set of tools, Automations, and agents, enriched with account context, while still respecting individual user identities and permissions. Profiles can only narrow what a user can do, never expand their underlying platform permissions. This makes configurations easier to reason about, review, audit, and replicate across environments, and offers a scalable pattern for enterprise platforms with many roles and capabilities.
Introducing MCP Profile Hub. I explain why enriching tool definitions with tenant-specific context dramatically improves agent performance. Instead of exposing generic CMS tools that force models to discover content types, environments, locales, and branches through multiple lookup calls, Profile Hub injects real account data directly into JSON Schemas as enums, defaults, and descriptions. This reduces tool calls, latency, and reasoning tokens while avoiding misleading examples and invalid defaults. The piece also covers the production engineering behind enrichment, how Automations and Agent OS agents are exposed as high-level deterministic tools, and why reusable HTTP-based tool definitions let teams run their own MCP runtimes. The core takeaway is that fewer, richer, context-aware tools beat large generic catalogs for real-world agent workflows.
This article argues that AI security risks extend far beyond model jailbreaks and prompt engineering, into every piece of text an AI can read. Natural language now behaves like soft code, where logs, documentation, commit messages, wikis, support tickets, web pages, PDFs, and search results can all carry hidden instructions for agents. The author walks through ten concrete scenarios where ordinary text fields become attack surfaces, often with delayed or indirect activation through internal tools and multi agent pipelines. The core message is that information and instruction have blurred, and any text accessible to AI must be treated as part of the security model. Teams need to rethink access, editing rights, retrieval, and agent capabilities accordingly.
This article argues that the real risk of corporate AI is not rogue superintelligence but how large organizations deploy it, usually in service of cost-cutting rather than creating new value. Big enterprises buy AI like office furniture, wrapped in committees, procurement, and risk matrices, so it ends up optimizing ticket deflection and headcount instead of enabling innovation. The real bottleneck is bureaucracy, not intelligence. In contrast, a roughly 500-person company has enough depth to build serious systems but short enough communication paths that the person with the problem can help build the solution. AI lets domain experts prototype directly, shortening the loop between friction and fix. The key is governance that enables safe experimentation instead of vetoing it, using AI to expand reach rather than just reduce costs.
Two free skills can strip the AI tells out of your writing in seconds. Nobody uses them. So every feed is now a slop fest of em dashes, rule-of-three lists, and It's not just X, it's Y. Here is how to spot the patterns and stop shipping them.
Supply-chain attacks like Mini Shai-Hulud will make teams distrust third-party packages. AI makes it easy to react by generating internal replacements for dependencies, SDK helpers, workflow tools, and glue code. That feels safer, but a lot of that code will be vibe coded without threat models, tests, update paths, or security review.
Vibe coding has made it trivially easy to spin up apps that collect user data, handle logins, and store sensitive information. The problem is that most people building these apps have no idea how authentication, database security, or data protection actually work. Defaults on services like Supabase and Firebase are dangerously permissive, GDPR obligations are being ignored entirely, and users are handing credentials to apps built by people who have never heard of row level security. My personal rule now is simple. If it looks vibe coded and asks for username and password, I walk away.
CMS migrations fail not because of the new platform but because nobody cleaned up the mess before packing it into boxes. Bad content models get locked in, integrations get reverse-engineered at midnight, legacy logic resurfaces as unexplainable bugs, and composable architecture without discipline becomes distributed chaos. The only thing worse than a painful migration is a successful one that preserved all your worst decisions.
This article explores how AI changes the balance between research-heavy processes and intuition-driven building, especially in product and technical work. The author reflects on a career of moving faster than surrounding teams, where strong intuition (really compressed experience from shipping many similar things) often clashed with expectations for lengthy research and documentation. As AI makes implementation and iteration dramatically cheaper and faster, the true bottleneck shifts from building to deciding what is worth building. Research still matters for those without established mental models, and for environments where stakeholders need evidence and paper trails. But when iteration costs hours instead of weeks, over-indexing on analysis can become the real drag. The piece argues that, in an AI-enabled world, experienced intuition is not anti-process; for the right people and problems, it is the process.
After twenty years of engineering, I accidentally ended up running multiple product lines. My approach (build first, spec later, let AI handle the translation) isn't traditional PM work. But it might be where the role is heading. Here's why engineers who produce before they plan might have an unfair advantage in the age of AI.
AI coding tools have removed the old excuse that test driven development is too slow or too costly. When AI can generate both implementation and test scaffolding in minutes, the time cost of writing tests first collapses, turning TDD into an obvious quality and productivity win. The real risk now is vibe coding, where developers ship AI generated code that looks fine but crumbles under real edge cases. By using AI for planning, then encoding that thinking as tests and letting the AI implement against them, teams get faster feedback, more reliable code, and fewer hotfixes. In an AI assisted world, the competitive advantage shifts to defining behavior and edge cases up front, and TDD becomes the discipline that makes that thinking explicit.
This article explores how AI has radically reduced the cost of producing things, creating an intoxicating sense of limitless output for ambitious people. But when building becomes almost frictionless, the real bottleneck shifts from implementation to judgment. The author argues that taste (the ability to choose what is worth building) and genuine rest become the true sources of leverage. AI is an exceptional how engine, but deciding what and why remains a deeply human responsibility. Without rest, our judgment degrades, and we risk building more but meaning less. The piece encourages pairing curiosity and experimentation with discipline, restraint, and strategic downtime.
I released my Cursor rules and commands as a public repo because Cursor is not magic, it is leverage. By turning it into a constrained system with explicit rules, reusable commands, and real project context, you can make it behave like a senior engineer on SaaS projects. The real win is not speed, but trust at speed.
AI is dissolving silos and creating a new kind of product person who thinks like a system, connects disciplines, and prototypes ideas before lunch.
Exploring the misconception of modern CMS platforms as monoliths and clarifying the distinctions between true monolithic systems and today's flexible, API-driven content management solutions.
People's choices, ability to roll with the punches, and how well they play in the sandbox together bring the magic of headless technology to life.
Explore the differences between page builder and data modeler CMSs, their unique features, and how they cater to varying organizational needs.
After an exciting journey at Uniform, it's time for a new adventure. At Uniform, we thrived during...
Building outstanding user experiences takes, first and foremost, effective collaboration between...
Calling for a revolution in how agencies run tech projects I spent a lot of time working...