Remote: Yes

Willing to relocate: No

Technologies: Python, Rust, Typescript, Go, Infrastructure, DevSecOps

Résumé/CV: https://www.linkedin.com/in/glenbbaker

Email: iepathos@gmail.com

Hi, I'm a Staff engineer with 14+ years of experience. Spent the last 8.5 years building an early stage startup and following it through acquisition. I supported the post-acquisition transition by training and mentoring offshore engineers and helping maintain continuity as the company shifted from startup execution to standardized enterprise delivery.

My background spans backend engineering, infrastructure, and security, with hands-on work in Python, Rust, and TypeScript. I maintain open source projects and contribute when I can to core Rust projects such as Clap and Cargo. I’m a good fit for teams that need pragmatic execution and someone comfortable owning hard problems across systems, platform, and DevSecOps.

In order to really do this, they would need to train LLMs from scratch that had no exposure whatsoever to open source code which they may be asked to reproduce. Those models in turn would be terrible at coding given how much of the training corpus is open source code.

It's like measuring productivity by how many people you pull into meetings each week. The CIA's Simple Sabotage Field Manual literally recommends holding as many meetings as possible with as many people as possible. The CIA should add "open as many PRs with AI as possible" to their list. Bonus sabotage points if the PRs are made from ambiguous "one-shot" attempts described in Slack with no follow up clarification.

"The actual correct way" is an overstatement that misses jfaganel99's point. There are always tradeoffs. EUDI is no exception. It sacrifices full anonymity to prevent credential sharing so the site can't learn your identity, but it can recognize you across visits and build a behavioral profile under your pseudonym.

If the tools get good enough to not need senior oversight, they're good enough to not need junior intermediaries either. The "juniors with jetpacks outpacing seniors" future is unrealistic and unstable—it either collapses into "AI + a few senior architects" or "AI isn't actually that reliable yet."

Technical debt, like financial debt, is a tool. The problem isn't its existence, it's unmanaged accumulation.

A few observations from my experience:

1. One-shotting - if you're prompting once and shipping, you're getting the "fast and working" version, not the "well-architected" version. Same as asking an experienced dev for a quick prototype.

2. AI can output excellent code - but it takes iteration, explicit architectural constraints, and often specialized tooling. The models have seen clean code too; they just need steering toward it.

3. The solution isn't debt-free commits. The solution is measuring, prioritizing, and reducing only the highest risk tech debt - the equivalent of focusing on bottlenecks with performance profiling. Which code is high-risk? Where's the debt concentrated? Poorly-factored code with good test coverage is low-risk. Poorly-tested code in critical execution paths is high-risk. Your CI pipeline needs to check the debt automatically for you just like it needs to lint and check your tests pass.

I built https://github.com/iepathos/debtmap to solve this systematically for my projects. It measures technical debt density to prioritize risk, but more importantly for this discussion: it identifies the right context for an LLM to understand a problem without looking through the whole codebase. The output is designed to be used with an LLM for automated technical debt reduction. And because we're measuring debt before and after, we have a feedback loop - enabling the LLM to iterate effectively and see whether its refactoring had a positive impact or made things worse. That's the missing piece in most agentic workflows: measurement that closes the loop.

To your specific concern about shipping unreviewed code: I agree it's risky, but the review focus should shift from "is every line perfect" to "where are the structural risks, and are those paths well-tested?" If your code has low complexity everywhere, is well tested (always review tests), and passing everything, then ask yourself what you actually gain at that point from further investing your time over-engineering the lesser tech debt away? You can't eliminate all tech debt, but you can keep it from compounding in the places that matter.

- Extra tokens to generate the wrapper

- New failure modes (encoding issues, exit code handling, stderr bugs)

- The same underlying tool call anyway

- No stronger guarantees - actually weaker ones, since you're now trusting both the tool AND the generated wrapper

The theoretical framing about "proofs as programs" and "semantic guarantees" sounds impressive, but the generated wrapper doesn't provide stronger semantics than rg alone, it actually provides strictly weaker ones. This is true for pretty much any CLI tool you're having the AI wrap python code around to do instead of calling battle tested tools directly.

For actual development work, the artifact that matters is the code you're building, which we're already tracking in source control. Nobody needs a "witness" of how the agent found the right file to edit and if they do agents have parseable logs. Direct tool calls are faster, more reliable, and the intermediate exploration steps are ephemeral scaffolding anyway.

I think about this often when discussing AI adoption with people. It's also relevant to this VS Code discussion which is tangential to the broader AI assisted development discussion. This post conflates tool proficiency with understanding. You can deeply understand Git's DAG model while never typing git reflog. Conversely, you can memorize every terminal command and still design terrible systems.

The scarce resource for most developers isn't "knows terminal commands" - it's "can reason about complex systems under uncertainty." If a tool frees up bandwidth for that, that's a net win. Not to throw shade at hyper efficient terminal users, I live in the terminal and recommend it, but it isn't going to make you a better programmer just by using it instead of an IDE for writing code. It isn't reasoning and understanding about complex systems that you gain from living in a terminal. You gain efficiency, flexibility, and nerd cred - all valuable, but none of them are systems thinking.

The auto-complete point in the post is particularly ironic given how critical it is for terminal users and that most vim users also rely heavily on auto-complete. Auto-complete does not limit your effectiveness, it's provably the opposite.

The objections to non-profits, OSFs, education, healthcare, and small companies all boil down to: they don't pay enough or they're inconvenient. Those are valid personal reasons, but not moral justifications. You decided you wanted the money big tech delivers and are willing to exchange ethics for that. That's fine, but own it. It's not some inevitable prostitution everyone must do. Plenty of people make the other choice.

The Google/AI distinction still doesn't hold. Anthropic and OpenAI also created products with clear utility. If Google gets "mixed bag" status because of Docs and Maps (products that exist largely just to feed their ad machine), why is AI "unquestionable cancer"? You're claiming Google's useful products excuse their harms, but AI companies' useful products don't. That's not a principled line, it's just where you've personally decided to draw it.

No, we really don't. There are plenty of places to work that aren't morally compromised - non-profits, open source foundations, education, healthcare tech, small companies solving real problems. The "we all have to" framing is a convenient way to avoid examining your own choices.

And it's telling that this framing always seems to appear when someone is defending their own employer. You've drawn a clear moral line between Google ("mixed bag") and AI companies ("unquestionably cancer") - so you clearly believe these distinctions matter even though Google itself is an AI company.