u/wardrox

I found employing EEMUA 191 pretty solid.

"EEMUA's foundational rule is that an alarm is a signal that requires the operator to do something. If a condition needs no response, it is by definition not an alarm; putting it in the alarm stream is a fault, not a feature. A large fraction of real-world alarm-system failures trace back to violating this single rule — the stream fills with things that are merely visible until the operator stops reading it.

This is the acceptance test for the top of the chain:

Does this genuinely require the human, right now — or is it just something the human might like to see?"

https://harbour.cat/library/doc/escalation-philosophy

Agents will build patches on patches, where a human would step back and consolidate.

It's why the one-shot works, then later updates drag.

I made a framework which cost effectively uses AI to do a lot of development tasks for me, leaving me to focus on the big picture; product, architecture, quality etc.

I either drive Claude by hand, or pop a task through Harbour, and it's working well.

I make the code easy to understand and maintain.

It's not "I have no idea how this works" risky, but it's also not "I remember every line from my Mind Palace". It's just making it safe and reliable for other people to pick up and carry on with a project, and reducing the Bus Number.

I noticed a while back that Claude was worse on Fridays. I spent a while trying to prove it was the model.

I successfully proved the subtle additional stupidity from myself was the main difference.

Garbage in, garbage out, as they say.

No, out of touch because if you just did [big impractical shopping list of tasks making the codebase effectively flawless] then the AI can totally do it. Except the parts it can't.

More people in the country improves our public services, this is well studied and well understood by other people. But, to know this requires you to actually understand how things work, and not believe racist nonsense.

Fun fact, even being given this information won't change your view. This, ironically, is also well studied and well understood.

How many successful businesses have been created with it?

I use an orchestration framework and harness which manages it nicely. In practice the largest fan out I've seen was 50 concurrent sessions, but for the work I want done and the speed I need it, 5 to 10 is more than enough per project. Usually the right solution is pretty simple.

If people skip planning and design and plan review and code review and retros and design reports and analysis... Yeah, that's a slop grenade, good luck.

$200 of Claude gets you around $6k of tokens, per month. Unless you're comfortable with cheaper models (hello Open Code), it's one of the more efficient routes.

"Cost per verified task done" - including human time and follow up work

Well, if we way to get hoity toity: myself, with two degrees in computer science and computer engineering, with a masters in emergent neural nets and AI, and 30 years in the industry, currently working with frontier models and fairly advanced processes for my job.... Sure? I guess?

AGI has whatever definition you'd like, but I can't currently ask AI to solve problems in a general sense; I have to set it up to solve a specific problem, within a niche, with trial and error, and then it does a pretty good job. It can't decide when most tasks are done, it needs human intervention, and it can't decide what's worth doing.

AGI isn't a "feeling", it's either a marketing term meaning "it pleasantly surprised me", or a scientific term. LLMs are the former, and it's useful to know the definition/goalposts are pretty moveable.

"Big software" is 10% code. A lot of people have just made their own versions of stuff that suite them better; disposable private software is a treat.

Code review by AI already vastly outperforms the majority of previous human driven versions, and people who find that success often go through rounds of tuning, evals, and retros.

Honestly, it's kinda lovely. I keep my head in the architecture, direction, problem solving. AI gives extra capacity for large parts of that.

Building the AI system; an afternoon

Knowing which AI system to build so it solves their problem and won't get outdated in a month; priceless

Yes: a thorough multi step review & close-out ledger + escalation and clear definitions for human intention. Standard software development process, just crammed in a box with the agents.

A second session runs and adversarial review.

Fascinating! Have you got any benchmark or eval data to compare the quality of output you get against other models without your improvement?

It's because I've 30 years of experience that I can avoid writing code now (and put my time and efforts into deciding what's worth doing, and what counts as done).

90% of my AI use last year was experiments to find the limits, how to measure success, and the various foot-guns AI introduces both for the development process, and the wider team. I'm lucky that it's my job to do this.

If I didn't have those experiences, AI would simple accelerate me off a cliff before I could notice.

Or, put another way:

The old world is dying, and the new world struggles to be born: now is the time of monsters.

Few people go to a therapist because the think they need to go, and you don't need to wait for a full breakdown or burnout. It's more like a really effective stress relief valve for the processes you already know are putting pressure on you, which over time helps the stressful things become easier to work with or around.

It fundamentally turned both my life, and by extension my career, around, and I can't recommend it enough.

For a typical web stack my preference is: Lots of e2e tests (at least every happy path and one person class of error, ideally more) with mocked third parties, comprehensive API tests acting as integration tests, module tests as needed, unit tests on the expensive stuff.

What's different compared to a regular code review step at that stage?

Have you got Ops now, and was anyone watching the error logs?

Sounds like you're close to ideal for an Engineering Lead type role. Having decades of experience puts you in the top 1% (or higher) currently hireable staff. Little bit of AI upskilling (if just to sharpen your skills and play with the tools du-jour), and you're golden!