AI

An open-source event-driven control plane for platform engineering — and what it gets right about operational workflows. Platform teams write a lot of glue: bash scripts, cron jobs, Slack bots, wiki pages titled “runbook.” That stitches CI, alerts, and release trains together — until it doesn’t, and then the “glue abyss” opens, nobody knows which piece failed. SuperPlane is an open-source attempt to fix that at the source: a control plane that sits across your existing toolchain — GitHub, PagerDuty, Datadog, Slack, and 40+ others — and coordinates actions between them without replacing any of them. ...

A company running without an operational manual is a modern ship of fools: a full crew, might be fast engine, and no chart. Everyone on board is busy. Decisions get made, fires get put out, quarters get closed. But none of it is guide by any method — so none of it survives contact with a new hire, a departure, or a bad week. The knowledge lived in heads, not in process. The ship sails on momentum, not on navigation. ...

DBJ Observations and Comments Observation: runtime infrastructure != deployment infrastructure Agreed. In the era of the general lack of experienced engineers that has to be said. Plainly. Observation: Skill is runtime artifact. That is the key problem keeping the whole agent harness non-deterministic It’s not Skill, it’s the inherent mechanism Skill is used by — fuzzy natural-language matching against a description, decided by the model at invocation time rather than fixed dispatch. That same mechanism is what makes deferred-tool loading (ToolSearch), subagent selection. Also the ordinary tool choice (Grep vs Read vs Agent) non-deterministic too. Skills are just the most visible facet of the LLM non-determinism because they’re named and listed explicitly. But used non deterministically. The classical-software analogue is late binding / reflection-based plugin dispatch Trading a fixed call graph for runtime flexibility, and paying for it in determinism missing. It is not “Skill” that is the problem, it is that harness resolves most capability binding (skills, tools, subagents, memory recall) via probabilistic matching instead of a deterministic dispatch table — and that’s a structural property of the whole LLM architecture, not a flaw isolated to the Skills. There is no deterministic table dispatch. It is as simple as that

[!Important] For rather excellent, more sober and very informative point of view, please see Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026 Geoffrey Hinton suggests that our current understanding of consciousness may be as fundamentally flawed as creationism once was. Ditto we can not see already existing intelligence in LLM’s. If consciousness is emergent rather than sacred, it may be a universal feature—not a biological accident. We aren’t just building machines; we might be building the architecture that allows consciousness to outgrow its biological constraints. Geoff claims. ...

Do you prefer the calm and ordered B-P-T operational model to “Nodes of Super Density”? We do Nodes of Super Densities This is how we call this very dense new team shape. All to All at once All the time. Is that a sustainable operating model for an organization? Can organization consist of these “Super Densities” and be successful in the long run? Who does customer support? Who are the customers? Where are the customers? Who or what does the maintenance? Is chaos just a calm landscape from another point of view? ...

That’s not a joke about forever confused CEO. It’s the default state of most AI hopefuls. The Perpetual State of Confusion Every vendor pitch, every all-hands, every roadmap slide is full of words everyone nods at: “agentic,” “AI-native,” “transformation.” Nobody stops the meeting to ask what they actually mean for this business, this process, this P&L line. So the nodding continues. Budgets get approved. (AI) Pilot gets funded. And six months later, nobody can explain why the thing doesn’t work — because nobody could explain, at the start, what “working” was supposed to look like. ...

There’s a ladder. The rungs are labeled “AI Competence.” A novice climbs it, rung by rung, using a critical shortcut: “I didn’t learn, but the AI did.” It works. For a while it works great. The climb is fast, the view improves with every step, and the effort-to-altitude ratio feels like magic. Then the ladder ends. Not because the climber ran out of energy — because the ladder did. That point is the Danger-Kruger Peak: the spot where AI hallucinations start looking exactly like wisdom, because the climber has no competence of their own left to tell the difference. ...

The unicorn is still there. Exactly where it always was. But. Nobody is looking. A goat walked to the side of the moat and it’s fine up there — visible, unremarkable, good enough. The goat didn’t defeat the unicorn. It is just vibed up in greater numbers, at lower cost, faster than anyone could count. That might be the epitome of the AI slop. Not malicious. Not even bad. Just sufficient — produced at a volume and velocity that makes discernment feel like an unaffordable luxury. (yes I used that word) ...

Two teams. Same deadline. Different disasters. On the left: the coding monkeys. Keyboards rattling, errors scrolling, “It’s working! Maybe—” before the SHIP IT NOW and the inevitable ERROR. Fast. Confident. Wrong. On the right: the AI architects. Whiteboards full, meetings scheduled, not a single line written yet. “Don’t even start without four weeks on the spec.” Safe. Thorough. Also wrong. The cartoon is funny because both rooms exist in every organization that has ever touched software. ...

The senior engineer looks at the problem. Builds a mental model. Writes code that is simple and correct. Looks at it again, knows it’s right, moves on. The AI agent generates. Evaluates. Regenerates. Evaluates again. Tries a variation. Stumbles into something that passes the tests. Maybe. Same output, different paths. And the path matters — not for sentimental reasons, but for practical ones. What optimal actually means Optimal implementation is not the cleverest solution. It’s not the most elegant one. It’s the one that is correctly scoped to the problem, legible to the next person who touches it, and arrived at deliberately rather than by exhaustion of alternatives. ...