DEV Community: Lonnie McRorey

Colombia as a feedback-loop country for US product teams

Lonnie McRorey — Thu, 25 Jun 2026 14:29:30 +0000

Timezone overlap is weak if the team cannot close feedback loops inside the product rhythm. A US CTO or CIO does not need a louder vendor claim, they need a signal they can test inside the work loop.

The signal here is timezone overlap + product context + async review. TeamStation looks at this through role depth, review behavior, ownership, security, and delivery telemetry, bc the real risk is not finding ppl, it is trusting the wrong fit inside an AI-assisted system.

This TeamStation country page is useful bc it explains the operating logic behind colombia feedback loop signal. Read it if you want the plain-English reason this matters before building distributed engineering teams across Latin America.

https://teamstation.dev/hire/by-country/colombia

AIEngineering #EngineeringTelemetry #NearshoreEngineering #DistributedEngineering #TeamStationAI

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GitHub topic map:

Source asset:
https://teamstation.dev/hire/by-country/colombia

Brazil as a scale signal for AI engineering, not just a large talent pool

Lonnie McRorey — Wed, 24 Jun 2026 14:16:52 +0000

Brazil only helps a CTO if scale turns into delivery control, not more unmanaged headcount. A US CTO or CIO does not need a louder vendor claim, they need a signal they can test inside the work loop.

The signal here is country scale + review depth + delivery telemetry. TeamStation looks at this through role depth, review behavior, ownership, security, and delivery telemetry, bc the real risk is not finding ppl, it is trusting the wrong fit inside an AI-assisted system.

This TeamStation country page is useful bc it explains the operating logic behind brazil engineering signal. Read it if you want the plain-English reason this matters before building distributed engineering teams across Latin America.

https://teamstation.dev/hire/by-country/brazil

AIEngineering #EngineeringTelemetry #NearshoreEngineering #DistributedEngineering #TeamStationAI

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GitHub topic map:

Source asset:
https://teamstation.dev/hire/by-country/brazil

Axiom Cortex engineer vetting, cognitive delivery alignment, and why resumes do not prove loop fit.

Lonnie McRorey — Mon, 22 Jun 2026 15:54:23 +0000

Most eng hiring still tests the resume more than the work loop, and that is where a lot of bad calls start. A person can sound strong in an interview, pass a syntax test, and still fail once the real system asks for judgment, review behavior, ownership, and clean handoffs.

This is why TeamStation cares about how engineers think under delivery pressure, not only what tools they list. We look at role fit, stack depth, cognitive alignment, signal quality, and how the person behaves when the work gets messy, bc that is where AI engineering and distributed eng either hold together or drift.

The Axiom Cortex page is the source to read if you want to see how we think about engineer vetting before someone enters a TeamStation delivery system. It explains the method behind the score, the signals we care about, and why the goal is not just finding ppl, it is knowing if the person can work inside the loop.

https://teamstation.dev/axiom-cortex-engineer-vetting

AIEngineering #EngineeringTelemetry #TeamTopology #DistributedEngineering #TeamStationAI

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GitHub topic map:

Source asset:
https://teamstation.dev/axiom-cortex-engineer-vetting

Mexico as an engineering signal system for US CTOs, not only a nearshore location.

Lonnie McRorey — Mon, 22 Jun 2026 15:05:24 +0000

Mexico is not just a location choice, it is an operating signal when a US CTO needs tight feedback loops, cleaner handoffs, and eng teams that can stay close to product context. The mistake is treating country fit like a cost chart, when the better question is what kind of work the team has to survive.

When we look at engineers in Mexico, we are not only checking tools on a profile. We care about role depth, review habits, communication speed, ownership, product judgment, and whether the person can work inside AI-assisted delivery without turning speed into rework.

This TeamStation Mexico page is useful bc it shows how we frame the country as part of a distributed engineering system, not just a hiring market. Read it if you want the basic logic behind why country, role, telemetry, and team fit have to be judged together.

https://teamstation.dev/hire/by-country/mexico

DistributedEngineering #EngineeringTelemetry #NearshoreEngineering #TeamTopology #TeamStationAI

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GitHub topic map:

Source asset:
https://teamstation.dev/hire/by-country/mexico

Root-cause speed, Time to Isolation, and why debugging speed is a team topology signal.

Lonnie McRorey — Sat, 20 Jun 2026 15:01:25 +0000

Debugging tells the truth faster than a status meeting. The team either finds root cause, or it burns time circling symptoms.

That is where delivery starts leaking. One person checks logs, another checks the last deploy, another asks for context, another ships a fix that hides the symptom. The team looks busy, but the debugging horizon is too wide, and AI only adds more motion if ppl cannot reason back to root cause.

Time to Isolation is a better signal than most status reports. It shows whether the team understands the architecture, the runtime, the handoffs, and the review path. In distributed eng teams across LATAM, this matters even more, bc slow root-cause work gets multiplied by time zones, async gaps, and unclear ownership.

This article is the clean test for a CTO or CIO asking if a team can debug what it ships. It explains Time to Isolation, the debugging horizon, and why root-cause speed is one of the clearest signals of real delivery health. https://teamstation.dev/research/articles/how-fast-can-they-find-the-root-cause

EngineeringTelemetry #DistributedEngineering #TeamTopologies #DeliveryRisk #TeamStationAI

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Source article:
https://teamstation.dev/research/articles/how-fast-can-they-find-the-root-cause

When Does Fixing AI Code Cost More Than Writing It?

Lonnie McRorey — Fri, 19 Jun 2026 20:38:25 +0000

AI makes code feel cheap. Repair does not.

That is the part a lot of eng teams miss when they start adding agents into the workflow. The model can write fast, the IDE can autocomplete fast, the PR count can go up fast, but the system still has to find bad work, explain it, fix it, test it, and trust it again.

That is where the cost shows up.

The real question is not, can AI write code. It can. The better CTO/CIO question is, when does fixing AI code cost more than writing the work right the first time.

TeamStation wrote about that here:

https://teamstation.dev/research/articles/when-does-fixing-ai-code-cost-more-than-writing-it

The cheap part is not the whole system

Code output is only one step in the chain.

A useful eng system has a few other steps too:

clear acceptance rules
review depth
test behavior
architecture context
ownership
delivery telemetry
rollback logic

If those parts are weak, agent speed does not lower cost. It moves cost downstream.

One loose prompt becomes three loose files. Then review gets noisy. QA finds a symptom. A senior eng has to rebuild the idea from scratch. The team says AI moved fast, but the system paid for the speed twice.

That is not an AI problem by itself. It is a reliability threshold problem.

Reliability threshold matters more than raw speed

In simple terms, a reliability threshold is the point where work is good enough to move forward without creating more cost than value.

If the threshold is clear, AI can help. The team knows what good looks like. Reviewers know what to check. Tests know what behavior matters. Telemetry shows where the work is drifting.

If the threshold is soft, AI creates fog. The code looks complete before it is actually safe. The team starts accepting output because it is fast, not because it is right.

That is how repair cost stacks up.

You do not just fix the code. You fix the misunderstanding behind the code. You fix the test gap. You fix the review miss. You fix the trust gap. Then you fix the planning model that allowed weak work to look done.

Telemetry is the control layer

This is why engineering telemetry matters in AI engineering.

A team needs signals that show:

where AI-generated work gets rejected
where review cycles expand
where tests miss expected behavior
where senior engineers keep rescuing the same class of issue
where delivery speed turns into rework

Without those signals, leaders only see activity. They see commits, PRs, tickets, and demos. They do not see the hidden repair loop.

That hidden repair loop is where money goes.

For distributed eng teams, this gets louder. Work moves across time zones, async review, and handoffs. If the control layer is soft, every handoff adds cost. This is why TeamStation treats LATAM/distributed engineering as an operating system problem, not a staffing problem.

The point is not to slow AI down. The point is to put reliability, telemetry, and acceptance rules in front of scale.

Why this matters for CTOs and CIOs

AI engineering will make weak systems show their weakness faster.

That is good if the org can see the signal. It is bad if the org only sees speed.

The useful move is to ask better questions:

Where does AI output fail review?
Which work types create the most repair cost?
Which teams can isolate bad output early?
Which acceptance rules are still too vague?
Which delivery signals prove the work is trusted?

Those questions matter bc they turn AI from a writing tool into a governed engineering workflow.

This TeamStation article explains the reliability threshold behind that. Read it if you are trying to understand when AI code stops being cheap and starts becoming a repair-cost machine.

https://teamstation.dev/research/articles/when-does-fixing-ai-code-cost-more-than-writing-it

Source article:
https://teamstation.dev/research/articles/when-does-fixing-ai-code-cost-more-than-writing-it

The Science of Team Topologies in 2026

Lonnie McRorey — Tue, 20 Jan 2026 15:25:56 +0000

Code Became Cheap AF. Software Became Crazy Expensive.

In 2026, something confusing happened to software.

Writing code got dramatically cheaper.

Shipping software got dramatically more expensive.

This is not a contradiction. It is a systems failure that finally became visible.

AI didn’t lower the cost of software. It lowered the cost of typing. Everything else got harder.

Code Is No Longer the Scarce Resource

For decades, engineering cost models were simple.

More code meant more people.

More people meant more money.

Velocity was limited by human hands.

That constraint is gone.

In 2026, a small team with AI can generate more code in a week than a mid-sized team could in a quarter ten years ago. Syntax is not the bottleneck. Implementation is not the bottleneck.

Decision-making is.

Software Is Not Code. It Is a Coordinated System

Software exists only when many things align at once:

Product intent
Architectural consistency
Security boundaries
Operational stability
Human understanding

Code is just one artifact inside that system.

When code becomes cheap, the cost shifts to everything that cannot be automated cleanly. Coordination, interpretation, and responsibility become dominant.

This is why software feels more expensive even as output explodes.

Teams Are Systems, Not Headcount

A team can be modeled as a system with three components:

Nodes: people or agents doing cognitive work
Edges: communication and dependency paths
State: shared context required for decisions

This model matters because it exposes a hard constraint.

If a team has n members, the number of possible coordination paths is:

n(n − 1) / 2

This grows quadratically.

AI does not change this equation.

Coordination Cost Now Dominates Everything

Let:

P(n) = productive output of a team
C(n) = coordination cost
E(n) = effective output

Then:

E(n) = P(n) − C(n)

Historically, P(n) increased faster than C(n) because execution was slow and scarce.

In 2026, AI compresses execution cost toward zero. P(n) flattens.

C(n) does not.

The result is brutal and unintuitive:

Adding engineers increases cost faster than it increases value.

Why Software Got More Expensive

When code is cheap:

Teams ship more features
Systems become denser
Interactions multiply

Every new feature adds edges.

Every edge adds cognitive load.

Every overloaded node increases error rates.

The maintenance cost curve steepens while the creation cost curve collapses.

Software becomes expensive not to build, but to keep coherent.

Cognitive Load Is the True Budget

In 2026, the limiting resource is no longer time or talent.

It is cognitive load.

Let:

Lᵢ = cognitive load on individual i
Lₘₐₓ = maximum sustainable load

When Lᵢ > Lₘₐₓ, failure rates rise non-linearly. Bugs cluster. Decisions stall. Teams burn out quietly.

Bad topologies concentrate decisions.

Good topologies absorb complexity structurally.

AI accelerates overload in badly designed systems.

Entropy Explains Why Teams Drift Faster

Teams accumulate entropy over time.

Every unclear boundary, exception, or undocumented decision increases contextual disorder H.

Without active structure:

H(t + 1) > H(t)

AI increases the rate of change. Work moves faster. Drift accelerates.

Teams feel busy while alignment decays underneath them.

This is why many AI-enabled teams feel productive but unstable.

Why Platform Topologies Win in 2026

Platform teams succeed because they change the math.

They reduce the number of edges that matter.

Instead of everyone coordinating with everyone, platforms:

Centralize complexity once
Expose stable interfaces
Bound entropy growth

Stream-aligned teams then operate with smaller cognitive surfaces.

This keeps coordination cost below collapse thresholds even as execution scales.

The Threshold Nobody Models

There exists a team size n* such that:

For all n > n*

dE/dn < 0

Meaning: adding people reduces net output.

AI does not move this threshold upward.

It makes crossing it more expensive and more visible.

Why Vendor Models Are Breaking

Traditional vendors optimized for labor supply.

More people. Lower hourly cost. Faster output.

In a world where code is cheap, that model fails.

What matters now is topology design, cognitive load distribution, and entropy control. Labor alone cannot solve these.

This is why many nearshore and offshore models are quietly collapsing under AI pressure.

The Question Leaders Must Answer Now

The critical question in 2026 is no longer:

“How many engineers do we need?”

It is:

“How does work move, and where does complexity accumulate?”

If you cannot answer that formally, your software costs will keep rising no matter how cheap code becomes.

Final Thought

Code is cheap.

Software is expensive.

The difference is structure.

AI did not replace teams.

It removed the buffer that hid bad topology.

Systems that violate physics fail faster now.

Canonical doctrine, models, and extended research

https://engineering.teamstation.dev