5 Things I Stopped Doing as a Lead

by
Susiloharjo

I led a small engineering team for two years. We shipped on time, the code was clean, and the standups were short. By every metric I cared about, the team looked fine. Then three of my best engineers quit within a month. None of them had a single conversation with me before they handed in their notice.

The exit interviews told me what I should have seen earlier. They were not burned out by the work. They were burned out by the way I gave it to them.

I spent the next six months rebuilding how I delegated. I read four books on engineering management. I ran a one-on-one feedback survey every Friday for twelve weeks. I shipped a working team again, and the difference was not subtle.

Here are the five delegation habits I had to kill. If you are a tech lead and any of these feel familiar, the exit interview pattern will too.

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Why a Simple If-Else Can Beat an LLM

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Code logic: simple if-else flow chart contrasting with LLM API endpoint

Why a Simple If-Else Can Beat an LLM

TL;DR: When you can describe the inputs and the expected outputs in advance, you don’t need a model — you need a function. Here’s the principle, the proof, and the one case where the principle breaks.

A teammate burned $47 of API credits last quarter on a “smart” classifier. The job: sort incoming support emails into four buckets (billing, technical, account, other) and route them to the right Slack channel. The model nailed it about 91% of the time. The remaining 9% it was confidently, hilariously wrong — sending a billing dispute to the technical channel, an outage report to “other.”

I replaced it with a 40-line Python script using if and a handful of keyword checks. It runs in 12 milliseconds per email, costs $0, and gets the same 91% — except the 9% it gets wrong are predictably wrong, so we know to watch them. The classifier used to hallucinate categories that didn’t exist. The script never invents a fifth bucket.

That’s not an edge case. That’s the principle: when the parameters are already known, deterministic code is the right answer. The question is why this works, and when it stops working.

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Anthropic Shipped Two New Models. They’re the Same Model.

by
Susiloharjo

TL;DR: Anthropic launched Claude Fable 5 (general availability) and Claude Mythos 5 (restricted, cyber defenders only) on June 9, 2026. Same underlying model. Different safety posture. $10/M input tokens and $50/M output — less than half the price of Mythos Preview. Part 1 of 2: specs and use cases. Part 2 will be the review after I have run something real on it.

I opened Anthropic’s newsroom on Tuesday and saw two new flagship models. Then I read the second paragraph and realized they were the same model.

That is the structural fact nobody is writing about. The release notes, the benchmark table, the customer quotes — all of that is downstream of one decision Anthropic made: a single frontier model, packaged twice. Once for everyone. Once for the people with a clear reason to be dangerous.

The packaging is the product. Let me walk you through what’s actually shipping, what the safeguards mean in practice, and what I want to test once the access thaws.

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Design Thinking Is 80% Theater. Here’s the 20% That Works.

by
Susiloharjo

Last quarter I ran a design thinking sprint on an AI agent project. Three weeks in, the only thing I’d produced was a wall of Post-it notes, two empathy maps, and a definition statement nobody on the engineering team could repeat. The agent itself had not moved one line of code forward.

Then I threw out 80% of the framework and kept the 20% that actually shipped the project.

Design thinking, stripped of consultant-speak, is a debugging loop for the gap between “what we think the user needs” and “what the user actually needs.” Most of what gets taught in corporate workshops is theater. The 20% that matters is something engineers have been doing for decades under a different name. They called it “writing tests against user behavior” or “asking the customer before shipping.”

This post is the 20%.

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I Replaced 3 Paid Monitoring Tools With a Homelab at $0/Month

by
Susiloharjo

I was paying $25/month across three monitoring services for the same thing: knowing when my homelab services go down. Better Uptime ($5), UptimeRobot ($8), and Grafana Cloud ($12 for metrics retention). Last month I replaced all three with a single Docker Compose stack running on the same ThinkCentre it’s monitoring. Three months in, it’s caught … Read more