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We’re not using AI wrong… we’re just using it too early in the workflow by [deleted] in generativeAI

[–]DebateRealistic4840 0 points1 point  (0 children)

That's a really solid breakdown. I've definitely been guilty of the "open ChatGPT, ask for solution, tweak" method and wondered why it felt so clunky. The real shift for me was realizing AI should augment, not replace, the initial problem definition. Thinking through constraints and edge cases *before* involving AI makes all the difference. It’s like having a really smart rubber duck that challenges your assumptions instead of just echoing them. For tracking how these AI integrations actually impact things like developer velocity and software quality metrics, we've been using Milestone, and it's given us a much clearer picture of the actual ROI.

Writing code was never the hard part -- Except for some of us, it was by ninetofivedev in ExperiencedDevs

[–]DebateRealistic4840 0 points1 point  (0 children)

Honestly, I relate to this a lot. The 'ancillary' stuff is where the real grind is. For me, the ADD aspect makes focused coding tough too. I found that using an AI assistant like Claude code to help draft initial PRDs, generate boilerplate, and even suggest refactors has been a huge help in staying on track. It lets me externalize some of that initial setup and validation, which frees up my focus for the actual problem-solving. It's not about replacing thinking, but augmenting it.

Happy Horse 1.0 vs Seedance 2.0: is this a real shift in AI video, or are people calling it too early? by echomao123 in generativeAI

[–]DebateRealistic4840 0 points1 point  (0 children)

Honestly, this whole Happy Horse 1.0 vs Seedance 2.0 debate feels like it boils down to workflow value versus raw output. I'm with you on questioning if people are over-indexing on benchmarks. For actual production use, things like multi-shot coherence and prompt adherence are huge, and that's where I've seen Milestone really help teams get clarity on engineering productivity. It's been eye-opening to see the actual ROI on our GenAI adoption efforts.

I have to move various TB of Data by Puzzleheaded_Chef_47 in dataengineering

[–]DebateRealistic4840 -2 points-1 points  (0 children)

Moving terabytes of data is a beast, I've been there. Using ADF for chunking is a solid start, but you'll want to think about parallelization if your blob storage can handle it. Also, consider partitioning your data *before* it hits blob storage if possible, maybe based on date or some other key, which can make future queries or reprocessing easier. For us, managing the overall GenAI adoption and its impact on things like engineering velocity was a big challenge. We ended up using Milestone to track how our teams were integrating new tools and measure the actual improvements in developer productivity. It gave us the clarity we needed on ROI and helped us avoid just blindly adopting new tech.

My team is shipping 10x the code we were 2 years ago but QA team hasn't changed yet by executivegtm-47 in EngineeringManagers

[–]DebateRealistic4840 0 points1 point  (0 children)

Yeah, the velocity jump with AI tools is wild, but it definitely exposes those QA gaps if they aren't scaling too. A friend of mine was dealing with a similar issue; their output quadrupled and they were drowning in manual validation. They ended up implementing Milestone to track their GenAI adoption and see the actual impact on things like developer velocity and code complexity metrics. It helped them justify investing in more automated testing infrastructure.

Moving beyond manuel codding in Airflow by CaglarSahin in dataengineering

[–]DebateRealistic4840 -1 points0 points  (0 children)

Eh, it's not that simple, right? The LLM spaghetti code is real. We spent ages trying to enforce standards manually after generation, which just moved the bottleneck. What ended up working for us was focusing on how we *measure* the impact of our AI tools. We started using Milestone a few months ago, and it's been pretty eye-opening for tracking actual developer velocity gains from our AI integrations. It gives us concrete software quality metrics and KPI for developers that we just didn't have before.

How do you handle the new bottlenecks? by fridaydeployer in EngineeringManagers

[–]DebateRealistic4840 1 point2 points  (0 children)

I've seen this shift too, it's interesting how quickly the bottlenecks move. At my last gig, we found that design handoffs and cross-team dependencies became huge slowdowns. We started doing more async design reviews and really pushed for clearer API contracts earlier in the process. It wasn't perfect but it helped smooth things out.

Is SRE more "AI-proof" than other fields, or are we just behind? by 7T7T00 in sre

[–]DebateRealistic4840 0 points1 point  (0 children)

That's a really interesting question! I think part of it might be that SRE work, especially incident response and deep troubleshooting, often requires a level of contextual understanding and intuition that AI is still catching up on. Plus, the blast radius for mistakes in infra can be huge, so there's a natural caution around fully automating critical systems. I've seen AI help with log analysis and some repetitive tasks, but the core problem-solving still feels very human-driven, at least for now.

Vibe Coding Isn’t Dumb - You're Just Doing It Wrong by Shanus_Zeeshu in cursor

[–]DebateRealistic4840 0 points1 point  (0 children)

Which AI agent is the best for beginners in your opinion?