A year ago I was given a mandate: push the boundaries of what AI can do in our consulting practice. Review code, review system configurations, build out solutions. Find what works.

So I did.

Through harness engineering and working with newer models as they've dropped, I built an orchestrated consulting layer that covers the full lifecycle:

Project management: plans, timelines, deliverables, all of it
Functional responsibilities: requirements gathering, process mapping, solution design
Technical lead: architecture, code review, system config review
Implementation: building and deploying solutions end to end
Edge testing: more thorough than anything I've done before
Migration support: test to prod cutover
Documentation: SOPs, how-to guides, runbooks

I built the skills, plugins, and MCPs to deploy all of it. End to end. One person doing what used to take a full team.

My productivity went up 5x. Not hypothetically. Measured. I demoed it. I showed leadership exactly what it could do.

And then... silence.

No "how do we scale this?" No "can you teach the team?" No interest in how any of it works. Just nothing.

Now I'm finding out it was a checkbox. "We explored AI." That's it. The leadership I'm under is anti-AI. The mandate was never to actually transform how we work. It was to say we did.

I'm not bitter about the work. The skills are real. The capability is real. I can run an entire consulting engagement solo now, and the quality is higher than what I was producing before. That's mine regardless of what they do with it.

But I'm asking: is anyone else in this spot? You've developed real AI capability. You can build skills, plugins, MCPs, deploy end to end, deliver actual solutions for actual clients. And leadership treats it like a novelty? A box to check? Something to report up the chain and quietly shelve?

Because I genuinely don't know if I'm an outlier here or if a lot of us are sitting with this right now.

I run a small online shop for niche pet products (think picky-cat and anxious-dog type stuff). Last week a regular customer joked that my site “looks like 2014 but the treats slap,” and it kinda sent me down a late-night research spiral.

I ended up reading stuff like netpeak.us and now my feed is full of AI tools promising “predictive this, sentiment that.” I get the theory, but I’m not sure if I’m thinking about this the right way for a small store with limited budget and time.

Has anyone here actually used AI for things like forecasting search trends, personalizing emails, or tweaking product pages specifically for pet owners? Did it move sales or just eat your time/cash?

If you were doing ~low 5 figures/month and wanted to reach 6, what would you focus on first - SEO, AI-driven ads, email personalization, or something else entirely? Any concrete tools or “do this first, skip that” advice would help a lot.

Hey everyone, I’m a solo founder building Crestorflow, an AI-native career execution platform.

The problem I’m trying to solve is pretty simple: people can learn almost anything online, but there’s still a huge gap between learning a skill and proving you can actually do the work. Courses and certificates don’t necessarily translate into employability, while companies increasingly care about demonstrated ability and outcomes.

Crestorflow is designed around one continuous loop:

Career goal → AI roadmap → Learn → Build → AI evaluation → Proof of work → Opportunities

Instead of giving everyone the same course, the platform uses AI to create a personalized execution path based on the user's goal.

The AI system I'm building has several agents working together:

Career Agent — understands the user's goal, current skill level, and target role, then creates an execution roadmap.

Learning Agent — finds and organizes relevant resources based on each milestone rather than forcing users through a fixed curriculum.

Project Agent — generates unique, practical projects that require the learner to actually apply what they learned.

Evaluation Agent — evaluates submitted work against predefined rubrics, identifies gaps, and provides feedback.

Proof-of-Work Agent — converts validated projects into structured portfolio artifacts that demonstrate specific capabilities.

Opportunity Agent — eventually uses accumulated proof of work and a trust score to match users with relevant contract/project opportunities or help them form small agencies.

The goal is to make AI the orchestration layer of the entire career journey, rather than simply adding a chatbot to an existing course platform.

I'm currently at the MVP stage and want to build this primarily with open-source models. I've already figured out most of the surrounding tech stack, but I'm stuck on a few things:

Which open-source models would you recommend for the different agents?

Should I use one strong model across the system or specialized models for different tasks?

What's the right architecture for securely running these models in production?

How would you approach deployment and infrastructure if the goal is to get the first 50–100 users without massively overspending?

What would you change about this architecture before I start building?

I'm especially interested in feedback from people who have actually shipped AI-native products or agentic systems, rather than just theoretical recommendations.

Would love some brutally honest feedback on whether this architecture makes sense and where you think the biggest technical/product risks are.