u/Dazzling_Cash_6790

On a senior dev / tech lead role. I work in an industry were failure can mean significant monetary losses either indirect or direct.

For the past few months I have gone from advocating "do not trust LLMs, use it for ideas" to sending emails about "we should have % code written by AI and we focus our attention to XYZ". with the % changing in different emails from 50% to 33% to 75%, depending on the period and the results I would see from latest models etc (sometimes the results were degraded depending on overload or latest models or whatever).

The only common thing I kept, non-negotiable, is that automated tests, QA etc should be done in the traditional way, and, if someone wants to introduce AI for those it should come as an extra step in the pipeline and not a replacement of the existing processes / way of doing things.

All these were influenced heavily by whatever the hype was, my personal outcomes from usage, company announcements and direction, and things I was seeing other people using online / peers in other companies or regions etc.

What I am looking for is how other folks are working / seeing things moving forward / workflows and would like to hear mostly from people who work with software that, if mismanaged, can have serious consequences.

In short, I am not interested on hearing that LLMs can vibecode a CRUD app or whatever along these lines.

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The workflow I result to therefore is, mainly for implementing features:

1. Plan in a traditional manner the layers that should be affected for a feature implementation. No AI here and I expect my team to have solid understanding of the architecture.
2. Break down the tasks, again without AI. Keep critical business logic / critical parts for implementing without AI (and clearly define what these tasks are ).
3. Feed 1 and 2 into AI along with ADR context and .MD files that describe the project's architecture etc.
4. Review & refine result.
5. Feed AI with the overall context and start implementing task by task.
6. Review, refine, test, repeat.
7. Small PRs.

The reasons I result in the above workflow is because:

- I saw, otherwise excellent software engineers, become "rusty" and ship things that they wouldn't, along with an overall spirit of discouragement / loss of interest.
- Comments on PRs that are LLM "thinking" or "for now we will keep it in this X way" probably because LLM didn't have some context and assumed. This means that whoever opened the PR, probably didn't review at all or minimum.
- QA finding unexplainable "bugs" in a sense of something having moved, or something small added etc, which is not acceptable at all for the kind of software we are working on.
- Last but not least, I don't want to have a team of engineers (and don't want this for myself also ) to solely rely on LLMs for doing the job. Down the line this means a huge dependence on LLMs, skills fading and probably something bad happening or us being unable to review / understand what's happening after a point (ok this might be a bit dramatic, or is it).

Do the above reasonate ? Does the workflow feel like a bottleneck / not efficient ? Any suggestions on improving it ?

Senior dev / tech lead. The workflow I default lately, for various reasons, and advise my team to follow also is:

1. Plan in a traditional manner the layers that should be affected for a feature implementation. No AI here and I expect my team to have solid understanding of the architecture.
2. Break down the tasks, again without AI. Keep critical business logic / critical parts for implementing without AI (and clearly define what these tasks are ).
3. Feed 1 and 2 into AI along with ADR context and .MD files that describe the project's architecture etc.
4. Review & refine result.
5. Feed AI with the overall context and start implementing task by task.
6. Review, refine, test, repeat.
7. Small PRs, testing etc.

The biggest reason is that we work in critical software, and the past few months I saw unimaginable things in PRs.

The above workflow gives us somewhere around 25-35% improved efficiency while keeping the quality in a good level. I imagine companies that work on less critical software could go in a bigger % than that.

With these amounts of improved efficiency, which means less people needed, is there any direct impact in outsourcing ?

Asking on this space since India is the biggest player in this business right now.

On a senior dev / tech lead role. I work in an industry were failure can mean significant monetary losses either indirect or direct.

For the past few months I have gone from advocating "do not trust LLMs, use it for ideas" to sending emails about "we should have % code written by AI and we focus our attention to XYZ". with the % changing in different emails from 50% to 33% to 75%, depending on the period and the results I would see from latest models etc (sometimes the results were degraded depending on overload or latest models or whatever).

The only common thing I kept, non-negotiable, is that automated tests, QA etc should be done in the traditional way, and, if someone wants to introduce AI for those it should come as an extra step in the pipeline and not a replacement of the existing processes / way of doing things.

All these were influenced heavily by whatever the hype was, my personal outcomes from usage, company announcements and direction, and things I was seeing other people using online / peers in other companies or regions etc.

What I am looking for is how other folks are working / seeing things moving forward / workflows and would like to hear mostly from people who work with software that, if mismanaged, can have serious consequences.

In short, I am not interested on hearing that LLMs can vibecode a CRUD app or whatever along these lines.

---

The workflow I result to therefore is, mainly for implementing features:

1. Plan in a traditional manner the layers that should be affected for a feature implementation. No AI here and I expect my team to have solid understanding of the architecture.
2. Break down the tasks, again without AI. Keep critical business logic / critical parts for implementing without AI (and clearly define what these tasks are ).
3. Feed 1 and 2 into AI along with ADR context and .MD files that describe the project's architecture etc.
4. Review & refine result.
5. Feed AI with the overall context and start implementing task by task.
6. Review, refine, repeat.
7. Small PRs.

The reasons I result in the above workflow is because:

- I saw, otherwise excellent software engineers, become "rusty" and ship things that they wouldn't, along with an overall spirit of discouragement / loss of interest.
- Comments on PRs that are LLM "thinking" or "for now we will keep it in this X way" probably because LLM didn't have some context and assumed. This means that whoever opened the PR, probably didn't review at all or minimum.
- QA finding unexplainable "bugs" in a sense of something having moved, or something small added etc, which is not acceptable at all for the kind of software we are working on.
- Last but not least, I don't want to have a team of engineers (and don't want this for myself also ) to solely rely on LLMs for doing the job. Down the line this means a huge dependence on LLMs, skills fading and probably something bad happening or us being unable to review / understand what's happening after a point (ok this might be a bit dramatic, or is it).

Do the above reasonate ? Does the workflow feel like a bottleneck / not efficient ? Any suggestions on improving it ?

Basically what the title says. My total savings are 150k EUR. 50k EUR out of the 150k EUR are invested in stocks and bitcoin (no ETFs). 100k EUR are sitting on a savings account with 3% rate.

Is this the optimal allocation ? Should I be looking to invest more out of the 100k EUR that are in the savings account, and, is it the right time to do so with all the talk going around about market/economy crash in 2026 ?

Note: I am 33yo in case that matters.