Signals

// signal vs noise

Signals is the studio’s writing: what I’m building, what broke, what the fix cost, and what it taught. It’s written for people who build, buy, or back software, in first person, with the business outcome next to the technical fact. No SEO filler, no hype.

Read the Signals →

What a reader gets

  • AI in practice, not in theory. Agents, MCP servers (the interfaces agents call tools through), and local models: how they actually ship, what broke on the way, and what the fix cost. When a post cites code, it points at one of 165+ repos on GitHub (RandomSynergy17).
  • The method, with receipts. The studio runs on one loop (build once, prove cheaply, graduate what works), and posts here show it operating on real work: studio products, plus advising teams like Levels AV on AI integration and innovation.
  • Three technology eras, one perspective. I worked the last two cycles from the inside: content systems 20 years ago, hospitality tech ~10 years ago. Posts read this era against the two before it.
  • Studio notes and UAE context. The view from the ground: I run funding research and go-to-market for Abu Dhabi, UAE, and international programmes (Fortune 500 clients and small teams alike), and some of what that teaches ends up here.

Cadence

Honest answer: there is no content calendar. A post ships when the work produces signal: something learned, measured, or broken in an instructive way. If it’s quiet here, the building was louder than the writing.

Latest

  • Brain-first: the memory every agent checks before it answers

    Agent sessions start from zero and re-derive the same decisions endlessly. GBrain is the self-hosted memory server every agent I run checks before it answers, and writes back to after it works.

  • Three eras, one method

    Why RandomSynergy isn’t an AI studio, and what two prior technology cycles taught me about this one.

  • One week, 1,189 tests: building The Foundry cockpit

    My portfolio intelligence kept freezing in static handover docs, so I built a system that keeps it live: profiled, scored, and routed to a decision. It took one week and 1,189 tests.

  • Deterministic first, LLM second

    The reliable way to ship an LLM feature is to make the model the last layer, not the first. Here’s how MeetKlay’s diagnosis is built.

  • Playwright can’t tap: giving an agent a real iOS device

    CI runs Playwright, which renders Chromium, not the WebKit an iPhone actually uses. iPhone-only bugs never fail where you’d catch them, so I built an engine that drives the real device.

  • Generation belongs in an MCP server

    An agent shouldn’t break flow to open a design tool when it needs an image. Generation should be a service any agent can call, which is what I built RNSNB to be.

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