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Articles by Mike Dabydeen
Docker HEALTHCHECK: qué comando corre y qué no te dice
Un HEALTHCHECK de Docker contesta una sola pregunta: ¿el comando que definiste salió con código 0? Nada más. No mide si la app está "sana" en ningún sentido amplio — mide si ese comando específico, en ese momento, no falló. La documentación oficial de Docker lo dice sin vueltas: la instrucción HEALTHCHECK le dice a Docker cómo testear un contenedor para chequear que "sigue funcionando".
The thresholds are still on the agent's side of the line. Lesson 1 moves the pre-flight script, the watcher and the rollback job out of the agent's reach, but the numbers the watcher enforces come from the deploy block the agent writes. Pre-flight checks that a business metric exists, not what its threshold is, so min_relative: 0.5 on checkout_conversion_rate, or a business metric the change cannot move, passes Stage 1.
How to add mechanical enforcement to any AI coding agent
The problem AI coding agents generate code at impressive speeds. But they also commit untested code, hallucinate APIs, and forget your instructions the moment context fills up. If you've ever reviewed an AI-generated PR and thought "this looks right but I don't trust it," you've experienced the gap this framework fills. github.com/juandelossantos/another-agent-skills Prompts (CLAUDE.md, .cursorrules) work until they don't. The agent remembers until context degrades. Then it forgets.
A review contract for an agent-authored pull request
An agent-authored change should arrive with enough evidence for a reviewer to evaluate its scope and assumptions. For an integration change, I would make that requirement part of the delivery workflow. Consider a proposed fix to a partner payload mapping. The agent can inspect the relevant contract, propose a patch, and test it against fixtures. The reviewer still needs to know which input changed, which revision was tested, and what the tests leave unresolved.
What Passing Tests Leave Unresolved
A passing test tells you what the fixture covered. It does not prove that a change handled every case the contract allows. That distinction is easy to lose when a change looks small. A mapper adds a default. The existing fixtures pass. The pull request is tidy. Nothing in the test output says that the default changed the meaning of an incomplete request. Consider this illustrative integration: A partner can send shipmentId and may omit countryCode.
Verification Discipline: Catching and Correcting Hallucinations in AI Coding Agents
A Practical Framework By Jack Figliomeni · Saratoga Springs, NY · September 24, 2026 As software development shifts toward generating code from natural-language prompts, widely known as "vibe coding", the principal constraint on quality is no longer how quickly code can be produced, but how rigorously it is verified.
System One models in an agent loop: classify first, authorise in code
An agent needs a boundary between proposing an action and taking it. I would put that boundary in ordinary application code and use a fast decision model to supply structured evidence for the policy. TypeSafe announced Jev in September 2026 as its first System One model. Its public description is a model that evaluates supplied state against typed questions and returns decisions with probabilities.
System One models in an agent loop: classify first, authorise in code
The fastest model in an agent loop should usually have the narrowest authority. If a model is quick enough to call before every browser action, API request, or workflow transition, it becomes tempting to let its answer control the next step. I think the safer design keeps the model's answer separate from the permission to act. TypeSafe's Jev is an example of the new model shape. TypeSafe describes it as a System One model for structured decisions.
A 16-second clock drift falsely tripped my trading bot's kill switch. A postmortem.
"A number from one world was compared against a number from another" is the postmortem in one line, and you got there yourself, so let me push on the fix rather than the diagnosis. @_firelinks already took the taxonomy half — permanent and transient errors sharing a fallback path — so I will take the direction instead. Your day-one rule was: if the key does not validate, degrade to dry-run and carry on. That is a fail-open dressed as a safety feature.
Our System Crashed at 14:22: It Wasn't the Database
The diagnosis is the hard part and you got it: the herd was the cause and the cache was only where it surfaced. Four things about the code as posted, roughly in order of what they would cost you. The check and the acquire are not atomic. not await lock.is_locked(key) and async with lock.acquire(key) are separated by a task spawn, so under the same 300 concurrent requests a lot of them can see an unlocked key and each schedule a revalidation.
Adding OpenAPI Support to Mummy, a Nim HTTP Framework
• You asked how other people are solving this, so here is where I would press on the design, because generating the spec turns out not to be the hard part. Deriving the docs and the validator from the same schemaOf call removes drift between the doc and the validator. It does not remove drift between either of them and what the handler actually does. Request bodies get parseValidatedBody[T]. Responses get documented and then trusted.
AI Made Coding Faster—So Why Did Software Delivery Get Slower?
AI made the coding stage faster. That does not make the system faster. It raises the arrival rate at whatever stage comes next, and for most teams that stage is human review. Here is where the bottleneck actually went, and what to measure instead of coding speed. The number that should bother you The individual-productivity numbers look excellent right up until you put them next to the team numbers.
AI Made Coding Faster—So Why Did Software Delivery Get Slower?
6 hours ago Iran’s Foreign Ministry says the meeting in Oman is to ‘promote better understanding among the countries of the region’. Iran is expected to meet with Gulf states in Oman on Monday to discuss the Strait of Hormuz, the country’s Foreign Ministry announced. In a statement on Friday, the ministry said …
A Practical Checklist for an Agent-Ready API
An MCP server exposes tools. It does not repair an API that leaves side effects, retries, data limits, and recovery ambiguous. Use this checklist before exposing an endpoint to an agent. Classify the effect Every tool should identify one effect: read, draft, state_change, or irreversible_action. The calling layer, not the model, should enforce approval for consequential effects.
Your Coding Agent Has a Supply Chain, and You Probably Have Not Scoped It
We have spent two years arguing about whether AI writes good code. That argument has produced a lot of heat, a reasonable amount of evidence, and it has almost entirely skipped the operational question. A coding agent does not only write code. It acquires code. It resolves dependencies. It pulls container images. It fetches documentation, reads it, and acts on what it read. It runs build steps that reach registries you have never audited.
AI Didn't Make Your Team Faster. It Moved the Bottleneck.
This is publishing in the same month the loudest AI jobs forecasts got walked back by the people who made them. That reversal surprised a lot of commentators. It shouldn't surprise anyone who measures systems for a living, and the reason why is what this piece is about. I've been watching machine learning since 2013, back when Coursera Machine Learning courses meant running gradient descent by hand on a laptop that sounded like it was apologizing.
Hacktoberfest Stopped Counting PRs. So What Counts as a Contribution Now?
Hacktoberfest 2026 no longer rewards pull requests, and I think the change forces a useful question on all of us: if a contribution is not a merged diff, what is it? The organisers' reason is short. Their FAQ says it is "easier than ever to submit low-effort spam PRs to projects," so they listened to maintainers and stopped incentivising them.
From Code to Coaching: Embracing the Growth Mindset in Engineering Management
As an engineering manager with over two decades of experience in the industry, I've worn many hats - from network administrator to software developer to college professor. Through it all, I've learned that the most critical skill is not technical expertise, but the ability to lead and empower others. This realization has been the driving force behind my journey from writing code to coaching the next generation of engineers.
The Double-Edged Sword of Microservices: Balancing Abstraction and Complexity
As software developers, we're always looking for ways to make our systems more scalable, flexible, and resilient. One approach that's gained popularity in recent years is microservices architecture, which involves breaking down a monolithic system into smaller, independent components. But while microservices offer many benefits, they also introduce new challenges and trade-offs.
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