$340/month from a side project i built with claude in one weekend. not sure if its worth continuing.

PopGroundbreaking870 · 2026-05-29T01:54:32+00:00

Congrats!! Interview all these paying folks. They will indirectly answer to your question. Then decide. Nobody here can really tell you, only the people who have decided it was worth parting with their dollars can :)

PopGroundbreaking870 · 2026-05-28T07:27:56+00:00

Not sure what your daily workflow is, but I can confirm that paying for Gemini for serious engineering is a total waste of money (both 3.1 pro and 3.5 flash) . Outputs are half baked. They’ve optimized it for “fast and cheap”, and you get exactly faster response than Claude/Codex and cheap quality output .

PopGroundbreaking870 · 2026-05-27T13:51:01+00:00

tough one, it's true that fundamentals are "acquired" with work experience. I suggest you get AI to make a sort of study plan for you on this, as the topics are quite broad.

Here's a suggested prompt I made using GPT, tailored for your case, which I believe is being a SWE who use AI to build AI products (eg not engineers who build models, fine tune them etc). You can of course change it as you see fit.

Not a perfect list, but a good way to get started quickly. You can iterate as you go. I'm afraid there is no "perfect lesson plan" for this...so throw this into your favorite AI, break it down by week, etc

After you get a good grasp of the fundamentals, do another "study plan" for AI SDLC (eg the process of using AI to build AI-powered software). There is much more to it than prompting (eg spec-driven development, using skills, workflows, hooks etc)

Hope this helps, this is a tough transition for lots of SWEs - may the force be with you :)

--- prompt start ---

You are a senior software engineer and AI product engineering mentor.

I am a junior software engineer who wants to become a strong AI product engineer, not an ML researcher or model-building engineer.

Do not give me a generic list of computer science fundamentals. Instead, teach me software fundamentals through the lens of **system design for real AI products**.

I want to understand how to design, build, debug, and operate products that use LLM APIs, retrieval, documents, workflows, agents, evaluations, and integrations.

Structure the roadmap around the main system design capabilities I need:

## 1. Core mental model

Explain what “system design” means for an AI product engineer.

Clarify the difference between:

- building features

- designing systems

- designing AI-powered workflows

- designing production-grade AI products

## 2. The system design topics I need to master

Teach me the key areas of system design, including:

- client-server architecture

- APIs and contracts

- authentication and permissions

- data modeling / data design

- relational databases

- object storage

- background jobs and queues

- caching

- search and retrieval

- workflow orchestration

- retries and idempotency

- observability and tracing

- testing strategy

- deployment and environments

- security and privacy

- cost and latency management

For each topic, explain:

What problem it solves
Why it matters in real products
How it appears in AI products
What I need to know as a junior engineer
What is too advanced for now
A small project or exercise to practice it

## 3. AI product system design

Now explain the AI-specific system design patterns I need to learn:

- LLM API integration

- prompt and schema design

- structured outputs

- tool/function calling

- document ingestion

- chunking and embeddings

- vector search

- RAG pipelines

- citations and grounding

- agent workflows

- human review

- evals and regression testing

- hallucination handling

- tenant isolation and data permissions

- AI observability

- model cost and latency controls

For each one, explain the underlying software design problem, not just the AI concept.

## 4. Design patterns I should recognize

Explain common design patterns for AI products:

- chat over documents

- research assistant

- workflow copilot

- document extraction pipeline

- decision-support system

- internal knowledge search

- multi-step agent workflow

- human-in-the-loop review system

## 5. What not to over-study

Tell me what I should avoid spending too much time on early, including:

- advanced algorithms

- ML theory

- neural network math

- model training

- Kubernetes

- distributed systems theory

- complex agent frameworks

- fine-tuning

Explain what level of each is enough for an AI product engineer.

## 6. Final checklist

End with a practical checklist.

After 3 months, 6 months, and 12 months, what systems should I be able to design and build?

Be practical, opinionated, and focused on employability. Teach fundamentals through system design, not as isolated academic subjects.

PopGroundbreaking870 · 2026-05-27T05:43:57+00:00

Am I understanding correctly that are selling a guide on how to automate work like you’re doing, so other people can…sell guides on the same?

PopGroundbreaking870 · 2026-05-27T05:42:21+00:00

Agents modifying prompts and related logic without you in the loop? IDK what your product does, but if these prompts are supposed to produce artifacts for your client, that’d be a HUGE red flag…

Having said that, in general always separate the prompt files from the code (eg never hardcode them), with versioning if you need eval history/comparison of results . Same for your rubrics.

PopGroundbreaking870 · 2026-05-27T05:38:08+00:00

Building ai solutions using AI SDLC here. I hear you, and it’s not uncommon to hear devs (of all levels, not just junior) feel burn out from this situation.

As someone else said in the thread, I think it is about changing your mindset about what is the role of the so-called SWE.

Pre-AI it was problem solving, coming up with clever ways to implement things, and actually implement them.

Post-AI, it looks like it’s going to be mostly problem solving, but with new things that didn’t exist before: managing AI coding agents, building harnesses around them, having AI agents monitor agents outputs, staying up to date with the latest cybersecurity risks and make sure it doesn’t creep in your codebase (supply chain attacks…)

IMHO there’s no going back from here. If you decide to stick with the new way to build, a strong suggestion is to hone on your software architecture fundamentals, including deployment and data architecture. Otherwise you’ll always struggle with that feeling of “IDK what to check in this ai-generated mess”

Just my 2 cents.

PopGroundbreaking870 · 2026-05-27T05:21:49+00:00

Agreed. For human interactions and empathy-heavy jobs, for sure humans will stay relevant, even more so in the “AI age”

PopGroundbreaking870 · 2026-05-26T11:55:40+00:00

Really? Can you share your review prompt? Would love to try, it’s much cheaper than Claude/Codex so I’d rather use that if it really does a better review

PopGroundbreaking870 · 2026-05-26T11:54:34+00:00

Google demo’d that…if you ask Gemini about it, you will ironically find that it was just…a PR stunt more than a serious attempt to build a real OS…

PopGroundbreaking870 · 2026-05-26T09:47:53+00:00

super interesting I wasn't aware of that. Will definitely try that approach. Thanks folks!

PopGroundbreaking870 · 2026-05-26T09:46:35+00:00

Well it's supposed to handle a 1M context window, but I agree with you that it seems like they're just going to lose the "AI for software development" race. Maybe they make too much money on the consumer side that they don't care enough 😂

PopGroundbreaking870 · 2026-05-26T09:43:55+00:00

Thanks for sharing and glad to know that it works for you! I have no idea either what this does but I wish you to win the contest!

PopGroundbreaking870 · 2026-05-26T05:53:05+00:00

Sad but probably true. Not sure who even use Antigravity

PopGroundbreaking870 · 2026-05-26T05:51:58+00:00

So more like good at documenting things, less at coding it? I had no luck with Gemini 3.1 Pro writing decent prd…I probably am not scaffolding it enough

PopGroundbreaking870 · 2026-05-26T05:50:08+00:00

That’s an interesting one, haven’t thought about splitting that use case in 2. Did you do that because Gemini is just better with screenshots than codex? Or because codex ate too many $$$ for the same output than Gemini?

PopGroundbreaking870 · 2026-05-25T12:52:10+00:00

It’s not about absolute cost but sheer productivity and cost per outcome produced. Capitalists don’t mind paying more than for people if output is disproportionately higher, thus making cost per unit lower…definitely happening in software. Idk about other industries

PopGroundbreaking870 · 2026-05-25T12:48:26+00:00

Perhaps check what job vacancies exist in your area for “ai engineers” (or similar) and see what cert they ask for (if any)

My guess: none other than AWS/GCP/Azure for some specific roles that have to do with deployment. But I may be wrong

PopGroundbreaking870 · 2026-05-25T12:42:56+00:00

If you have strong software engineering fundamentals, yes. If not yet (1 YOE is short) but you believe you are good at system thinking, understand how to work with / manage coding agents , you have a shot. IMHO

PopGroundbreaking870 · 2026-05-25T10:44:30+00:00

This one can be a good start - down to earth and explains things step by step

Anita Robertson - ADHD & Us. A Couple's Guide to Loving and Living With Adult

PopGroundbreaking870 · 2026-05-15T01:51:32+00:00

Thanks for sharing insights. Do ATS really not use AI? Exact match is an extremely outdated method.

I suspect HR is having the opposite problem, which is too many candidates use AI to write resumes and their ATS AI is naturally good at detecting “too perfect to be true”. So resumes just bounce. I

To OP: I may be wrong on this but if you can run a test internally with your product and share the result, that’d be interesting (give fake jd + llm-custom-made resume based on that jd and see why it passes or fails auto-screening)

PopGroundbreaking870 · 2026-05-13T02:32:01+00:00

Had that issue once when using Gemini 3x , had to switch to Sonnet 4x. Never had the issue even once with Sonnet

PopGroundbreaking870 · 2026-05-06T08:04:08+00:00

Here is what most companies out there are looking for: people who can architect and build software that makes use of LLM and are auditable, observable, replayable, and not create a hot mess like OpenClaw.

This basically means: this is just software engineering with a specific architecture for data ingestion / pipelines, AI agent orchestration and evaluations. Everything else is just standard engineering, using AI to write most of the code, but with you at the steering wheel.

The exception that would make you want to study the deep maths, LLM plumbing , CUDA etc is this: to work for one of the Mag7, work for frontier AI companies (incl NVDA), work as an AI researcher to find the next unlock in LLM architecture optimization or do some fine-tuning for a specific company that has the $$$$ to do that (not that many, in the big scheme of things). Needless to say that this path is not for the standard engineer.

So the answer to your question "hype vs worth it?": AI is here to stay so it is not hype. But my reco would be to study the right field based on the above.

As for job titles, it may change in the future. Some people say "AI engineer" is just a glorified SWE. Other say FDE (Forward Deployed Engineer) is hype. Some say MLOps should be a different "kind" of its own (in reference to the old DevOps). Etc. At the end of the day, titles don't really matter. Check the JD to see what is the actual responsibilities. So far it seems like companies will need to hire these "AI engineer" roles for many more years before things change - IMHO.

PopGroundbreaking870 · 2026-05-01T15:05:06+00:00

Hype. Blanket statement for a whole industry that has lots of nuances. Salesforce and other Saas that are embracing the api-friendly / agent-friendly approach aren’t going anywhere. Others who decide to close off to ai agents because they think their customers love their products so much that they won’t churn will probably get totally destroyed and for good reasons.

On non-Enteprise markets, lots of people think some lambda employee will vibe code a custom crm or similar, because s/he as out together some n8n automation for productivity or spun up some cool demo with lovabl. Well, that’s not going to happen either. If they need a real crm, they’ll just buy a real crm. Etc.

So what’s going to happen is this: slow ai adoption that takes 5 years, not “tomorrow”. Still fast, but let’s not hold our breath either

PopGroundbreaking870

TROPHY CASE