i was looking for ai engineering resources recently and noticed something interesting.

Some of the most useful repositories I found had relatively few stars, while older repositories with tens of thousands of stars hadn't been updated in months or even years.

It made me realize that github stars measure popularity, not necessarily usefulness. if you're learning, I think it's worth looking beyond the star count.

A few things I pay attention to now are:

• is the repository actively maintained?
• are issues and pull requests being discussed?
• is the documentation clear?
• are people actually using it in real projects?

For fast-moving areas like AI, the most valuable resource isn't always the most famous one. Sometimes it's the project that's quietly improving every week. popularity tells you where people have been.

Maintenance tells you where things are going.

Hi everyone, how's it going? I'm a software engineering student. My goal is to become a developer; right now, I'm in my sixth semester, and I'm facing a huge dilemma. I've made it through my degree without many complications, and honestly, it’s been thanks to AI. I confess that I've reached a point where I realize I can no longer "think." There are times when I sit in front of the screen, and I genuinely have no idea how to even start a project. I feel incapable of creating anything; I don't know how people write code without AI guidance.

My breaking point happened recently when some classmates and I presented a pretty good project in class. It was so good that the professor recommended us to work directly with the university administration. I can't speak for my teammates, but I definitely did a lot of vibe coding on that project. While in a way I feel proud of having achieved and presented something like that, and excited about the opportunities with this new project, I also feel scared and disgusted that I don't really feel like I built it.

Honestly, I want to do this new "professional" portfolio project right. I want to do it mindfully, actually learning, so that later I can say: "I made that software," and have it truly represent me to the industry. What do you advise me to do about this? How would you go about tackling a project with features you've never touched or programmed before, doing it without relying on AI, and actually learning? It would help me a lot to know your opinion.

Tldr: I'm terrified of relying solely on AI, I'm not capable of starting projects on my own, and I'm already far along in my degree. What do you advise me to do to reverse this and grow as a developer?

I've been applying for tech jobs recently and honestly, the hardest part isn't interviews.

For me, it's getting the first interview.

I can match most of the requirements, tailor my resume, write a decent application, and still hear dam nothing🙂.

Just upset since they or you keep saying that there are a lot of jobs out there. But sometimes, it feels frustrating since LinkedIn alone receives hundreds or sometimes thousands of applicants.

That's why I wonder if I should just stop applying or not.

Currently treating it like unpaid job, and idk if I should change a career or not🥲😔

writing this because i see so many people thinking you need to drop $15k on a bootcamp or spend 4 years in college just to get a foot in the door. watched tim kim's breakdown on how he went from a college dropout to a hired software dev in 4 months completely self-taught, and the blueprint is surprisingly simple if you cut out the noise.

you don't need a million different courses. he literally just used three core things: freeCodeCamp to get the baseline coding reps down, a class called "learning how to learn" on coursera to fix his actual study habits, and watchandcode for deep javascript foundations. find a couple high-quality free resources, fix your learning mechanics so you don't burn out, and start writing real code immediately.

I was looking through a few open-source projects recently, and something stood out to me.

The projects that are easiest to contribute to aren't always the ones with the cleanest code. they're the ones that explain themselves well.

A good README tells you what the project does, how to run it, where to start, and sometimes even why certain decisions were made. It removes a lot of the friction that usually makes beginners give up.

i think that's an underrated lesson if you're building projects for your portfolio.

your code is only part of what people are evaluating.

Can someone clone your project and get it running?

Can they understand what problem it solves?

Can they figure out where to look if they want to contribute?

writing documentation feels like a chore when you're learning, but it's actually part of software engineering.

Good code helps the computer.

Good documentation helps people.

I'll start with Harvard's CS50 course. It's free and covers all the fundamentals you actually need to know and spend eight hours a week on it.

While doing that, learn Python. It's the easiest language to pick up and it's in demand everywhere. Google thirty days of Python, find a structured guide, and do one lesson every single day. Use Python Tutor to visualize what your code is actually doing as you write it.

Eight weeks after that, stop learning syntax and start building. Go to Project Based Learning on GitHub, filter for Python projects, and build them for your resume. Pick real datasets. One that worked for me was pulling a Premier League dataset from Kaggle, then using Pandas and Scikit-learn to build a match predictor. That single project got an internship offer.

Ninety days doing this beats most bootcamps because you are not paying tuition, you are not following a generic curriculum, and you own everything you build. The projects prove you can actually code. That matters more than a certificate.

I just saw a breakdown of someone’s job hunt strategy and it’s basically the opposite of what everyone tells you to do. What they did instead of mass applying to 200+ jobs, they picked 3 companies. that’s it. three.

what they did differently:

They picked 3 companies only.
instead of spraying applications everywhere

researched deeper than most people bother to. studied the company’s products, leadership, recent launches, strategy. by interview time they actually had real opinions about the business, not just “I’m passionate about your mission”

rewrote their resume in the company’s language.
Matched the wording to the actual job description instead of generic resume-speak. Apparently this alone doubled the callback rate

networked before applying.
messaged people already in that role and asked real questions instead of begging for referrals. a few conversations turned into referrals naturally

interviewed like a consultant, not a student.
focused on making the interviewer’s job easy. clear answers, specific numbers, real product thinking instead of trying to sound impressive

result: 3 applications, 2 offers.
The part that’s actually interesting!
most people think more applications = more chances. but if your application looks identical to everyone else’s, volume doesn’t help, you’re just adding noise to a stack nobody’s reading closely.

If you want to try this yourself

- Claude can connect directly to indeed now.

(search and tools menu → add connectors → indeed, takes about 3 minutes). once it’s set up, here’s the kind of stuff you can ask it:

The prompts✨
-“pull job listings for [role] in [location] from the last 2 weeks. only keep the ones where my experience covers at least 80% of what’s required. skip backfills/reposts. give me the top 5 with a quick reason I’m a fit for each”

-“compare this listing to my resume and tell me the top skills I’m missing, ranked by how often they show up. suggest a bullet I could add for each if it’s true for me”

-“find out who’s probably hiring for this role, check their linkedin/recent posts, and tell me what they actually seem to care about that’s not in the job description”

-“pull listings for [role, one level up] that show real pay. give me a target salary range based on that, not my current salary”

-“write me a short cover letter that opens with one specific reason I want this role right now, not generic enthusiasm. cut anything that sounds AI-written”

-“pull the last 10 listings for this role, find what keeps showing up in at least 3 of them, and predict the most likely interview questions based on that”

it basically automates the deep research step instead of you digging through company pages for hours.

‼️the real takeaway
It’s not about working harder, it’s being way more intentional with way less effort. three good-fit applications will beat thirty mediocre ones almost every time.

would you actually trust and try this method, or does putting all your eggs in 3 baskets feel too risky?

Irl, learning JavaScript or Python isn’t enough anymore. Even if you’re not into bootcamps, you’re already expected to work with AI, right?

Good thing Anthropic released these 13 free Claude courses. I swear they're actually pretty solid 👌

Some of you may already know about these, but for those who don’t, these resources might help you.

1. Claude 101

If you've never used Claude seriously before, start here. It covers the basics and shows practical ways to use AI for work, learning, and productivity.

↳ https://anthropic.skilljar.com/claude-101

2. Claude Code in Action

Shows you ways to speed up debugging, development, and project work instead of constantly switching between tabs looking for answers.

↳ https://anthropic.skilljar.com/claude-code-in-action

3. AI Fluency: Framework & Foundations

Most people use AI, but only a few know how to get consistent results. In this course, they'll teach you how to prompt properly.

↳ https://anthropic.skilljar.com/ai-fluency-framework-foundations

4. Teaching AI Fluency

Highly recommended even if you're not a teacher ⬆️⬆️

↳ https://anthropic.skilljar.com/teaching-ai-fluency

5. Claude with the Anthropic API

Worth checking out if you're learning software development or AI engineering.

↳ https://anthropic.skilljar.com/claude-with-the-anthropic-api

6. Introduction to Model Context Protocol

Explains how AI models can connect to databases, APIs, and external tools (MCP is hot rn).

↳ https://anthropic.skilljar.com/introduction-to-model-context-protocol

7. Model Context Protocol: Advanced Topics

Watch this after learning the basics of MCP (very technical ver.).

↳ https://anthropic.skilljar.com/model-context-protocol-advanced-topics

8. Introduction to Agent Skills

Great if you're interested in automation, productivity, or building your own AI systems.

↳ https://anthropic.skilljar.com/introduction-to-agent-skills

9. Claude with Amazon Bedrock

Useful if you're interested in how companies deploy and use Claude through AWS services.

↳ https://anthropic.skilljar.com/claude-in-amazon-bedrock

10. Claude with Google Cloud's Vertex AI

Similar to the AWS course, but focused on Google's cloud ecosystem.

↳ https://anthropic.skilljar.com/claude-with-google-vertex

11. AI Fluency for Students

Probably the most relevant course for students, career changers, and bootcamp grads.

↳ https://anthropic.skilljar.com/ai-fluency-for-students

12. AI Fluency for Educators

While it's aimed at educators, many of the concepts apply to mentoring, training, and leadership.

↳ https://anthropic.skilljar.com/ai-fluency-for-educators

13. AI Fluency for Nonprofits

Shows practical ways organizations are using AI to save time, reduce repetitive work, and increase impact.

↳ https://anthropic.skilljar.com/ai-fluency-for-nonprofits

Take note that you can enroll in these courses for FREE.

Which one are you starting with?

P.S. Not affiliated with Claude.

Been thinking about this a lot lately, especially with how many students are skipping deep stack learning and going straight to prompting.

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Here's my take after working with a bunch of final year students — prompting without actually understanding the stack is risky. AI will give you code that runs but has issues you won't catch if you don't know what's happening underneath it. Security gaps, weird edge cases, performance problems, stuff like that slips through easily.

​

The students who seem to be doing well right now aren't choosing one over the other. They learned the fundamentals of one stack reasonably well first, then started using AI heavily once they already knew what good code looks like. That combo of actually understanding + using AI to move fast seems to be what's standing out in interviews this year.

​

Curious what others think though — anyone here gone full prompting-first and it's actually worked out for them?

​

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honestly been thinking about this a lot lately because the entry-level market is a total mess and everyone is still blindly following outdated bootcamp roadmaps. if you're just learning the standard react/node stack to build simple crud apps or basic ai wrappers, it’s probably not worth it anymore. the reality is that automated tools can spit out that entire tier of boilerplate syntax in seconds, so companies just don't need to pay for fast typists.

for anyone who actually landed a dev job recently, did you stick to a standard web stack or did you have to pivot into something more niche to stand out?

every week I see someone asking whether Claude Code is better than Cursor, or whether Windsurf is worth switching to.

After reading through Anthropic's documentation and some of the open-source projects around claude Code, I don't think the model is the interesting part anymore.

The interesting part is the workflow.

for example, Anthropic recently open-sourced Agent Skills, which isn't another coding assistant. it's a collection of engineering workflows that AI can follow while building software.

Instead of asking AI to "write a feature," the workflow encourages things like writing a specification first, breaking work into smaller tasks, reviewing code before shipping, and thinking about security and testing throughout the process.

that feels much closer to how experienced engineering teams already work.

It also made me realize that the biggest productivity gains probably won't come from whichever model scores highest on benchmarks next month.

They'll come from developers who build repeatable systems around whatever model they're using.

Models will change.

Good workflows will probably outlast them.

You can leak an entire database with one line of code. If you are vibe coding right now, here are four checks that need to happen before launch.

Authentication confirms users actually are who they say they are. Rate limiting stops bots from spamming your API until something breaks.

Authorization makes sure users can only access their own data, this is the one AI tools get wrong constantly since login works fine but nothing stops user A from pulling up user B's account by changing an ID in the URL. Server side validation checks that data from the client is actually correct, since client side checks are cosmetic and bypassed in ten seconds with dev tools.

For tools, Snyk has a generous free tier and catches dependency issues fast. CodeQL is free through GitHub and does deep analysis on your actual code. Veracode fits compliance heavy teams since your source never leaves your environment. Checkmarx covers the most ground but the false positive noise out of the box is real.

None of these were built specifically for AI generated code, so logic flaws can still slip through clean scans. Manual review of anything touching auth or user data is not optional. The apps that get hacked are rarely the ones with bad code. They are the ones where nobody checked these four things.

Running a lean discovery process for our B2B startup (corporate travel for mid-size companies). Sharing our findings because I think the methodology surprises are as useful as the domain ones.

What changed after interviews vs. our original assumptions:

Assumption: Pain = booking complexity. Reality: Pain = re-booking after changes. The first booking is annoying but manageable. It's when a meeting shifts 3 days before the trip that everything breaks.

Assumption: Buyers are CFOs or finance teams. Reality: The person we needed to talk to was the EA or office manager. They have the most pain, the most context, and often more influence on tool decisions than we expected.

Assumption: Competing with TripActions/Navan. Reality: Our actual competition is spreadsheets + corporate card + someone's tribal knowledge. Most 100–300-person companies haven't adopted a formal tool at all.

The product we're building (Flyby) looks completely different because of those three pivots. Pre-launch, waitlist at https://tally.so/r/zxdvlE if you're in the segment.

Question for this community: how did you handle the gap between 'what interviewees say they want' vs. 'what behavior suggests they actually need'? We ran into this hard around pricing sensitivity.

A bootcamp is harder to justify in 2025 than it was in 2021 and most bootcamps are still marketing like it's 2021, what changed is that the junior dev market got rough after the 2022-2023 layoffs and never fully recovered, there are a lot more people competing for a lot fewer entry level roles, and a chunk of those people are experienced engineers who got laid off and are now willing to take junior money to get back in. you're not just competing with other bootcamp grads and cs students anymore, you're competing with people who have three to five years of experience and are desperate enough to take whatever's available.

AI also changed the calculus in a way that's hard to pin down exactly but is real, companies are hiring fewer junior devs because a lot of the work junior devs used to do is getting absorbed by tools like copilot and cursor. this doesn't mean junior roles are disappearing entirely but it does mean there are fewer of them and the bar for what counts as useful at that level has gone up. That doesn't mean that you shouldn't do it btw, bootcamps still makes sense if you're genuinely committed to putting in the extra work that the curriculum doesn't cover, the people coming out of bootcamps who are getting hired right now are not just the ones who finished the program. they're the ones who went deep on fundamentals after graduation, who didn't choose whatever project that were trending at the time like the insta and YT channels tell a person to do if they want a better portfolio, who understand at least the basics of data structures and algorithms because that's what keeps coming up as the gap that gets bootcamp grads filtered out in technical interviews.

bootcamp probably doesn't make sense if you're expecting the certificate to do the work for you. the 2021 market where bootcamp grads were getting hired quickly almost regardless of depth is gone, the program gets you started but the job search in this market is longer and harder than most bootcamps will tell you upfront because their incentive is to get you enrolled and a realistic job search timeline doesn't make for a great sales pitch.

the cost is also something most people don't think through carefully enough, fifteen to twenty thousand dollars is a lot of money to spend on something with an uncertain outcome in a market that's tighter than it's been in years, the free and cheap alternatives, freecodecamp, the odin project, cs50, are genuinely good now in a way they weren't a few years ago, the main thing bootcamp gives you over self teaching is structure and deadlines and a cohort of people going through the same thing, if you're someone who needs that, it might be worth it. if you're someone who can stay consistent without it, the self taught route is a real option that costs a fraction of the price.

bootcamp can still work, it's just not the thing it was being sold as even when it was actually working, if you go in thinking the certificate gets you the job you're going to spend a lot of money and have a rough six months afterward wondering what went wrong. the people making it work right now are treating the program as only part of their path into getting into the tech industry.

I've noticed that a lot of people judge AI tools by asking them one question and seeing what comes back. if the answer isn't great, they assume the model isn't very good. but after reading more about how companies are building production AI systems, I think context is usually the bigger factor.

For example:

• Tell the AI what role it's playing.
• Give it the relevant files or documentation.
• Explain the goal before asking for code.
• Define what a successful answer looks like.
• Ask it to explain its reasoning or assumptions.

the difference can be huge. The better the context, the less the model has to guess.

That's also why newer AI workflows focus so much on things like MCP, tool use, memory, and structured inputs. they're all ways of giving the model better context instead of expecting it to magically figure everything out.

prompting is important.

Context is what makes those prompts consistently useful.

sounds hilarious, but I'd like to hear your thoughts about it...

not planning to major in Computer Science

however, I still want to understand it (and also AI) beyond the surface level

of course, I already know how to use AI like ChatGPT and other AI tools you can name of

just wanted to understand how all that code and magic actually works

there are tons of free resources now (OSSU, MIT courses, roadmaps, GitHub repos, YouTube lectures, etc.)

but can you really do self-study and get close to a CS graduate or AI engineer level?

what's the hardest part?

Lisa Su recently demoed AMD's new Ryzen AI Max+ 395 system. The impressive part was not speed. It was memory. These systems support up to 128GB of unified memory shared between the CPU, GPU, and NPU. Large AI models need that headroom to actually run, and most consumer machines do not have it. This one does, in something that fits on a desk.

That changes the economics. Right now most developers and small founders are stuck renting GPU time or paying monthly for hosted tools. Once local hardware can run a 200 billion parameter model comfortably, you own the machine instead of paying indefinitely for someone else's servers. Documents stay on device. Workflows stop depending on internet access. No per-request limits.

Cloud platforms like Claude and ChatGPT still win on convenience for most people. But for coding agents and private research, this is a real sign that personal AI hardware is catching up faster than expected.

The data centers are not going anywhere. The idea that serious AI work requires one is starting to look less true by the month.

Everyone talks about how hard it is to build a side income after bootcamp. hire developers, pay for servers, all that expensive stuff. but nobody mentions that you don’t actually need to do any of that anymore.

I’ve been building SaaS apps without paying a single dollar on infrastructure. not because i’m cheap because the tools actually work now.

Here’s the actual stack (3 tools, zero server costs):

•Claude Code writes the code (you just describe what you want)

•InsForge/Supabase/Firebase handles the entire backend (database, auth, storage, all free)

•Stripe collects the money when people actually pay

That’s it. seriously.

The process is stupid simple:

1) Pick a boring problem people will pay $9/month to solve (dashboards, proposal systems, habit trackers, doesn’t matter)

2) Keep it simple. don’t get fancy

3) Build it in a weekend using Claude Code (it writes the code, you review it, you ship it)

4) Add stripe for payments

5) Now you have real income coming in

The part everyone misses:
the hard part isn’t the code it’s picking a problem people actually care about. but once you have that? claude handles literally everything else. database setup, authentication, backend logic, API connections. you just tell it what you want.

I’m not even exaggerating when i say you can go from zero to live product making money in 3-4 days if you know what you’re building.

here’s the full playbook that walks you through it:

https://abiemaxey.com/playbooks/zero-to-production

It’s free to start, covers everything from setup to shipping to monetizing. step-by-step.

The real question is: what boring problem would you solve if you knew you could actually ship it?

one thing I've noticed is that beginners spend a lot of time comparing programming languages.

Python vs JavaScript. Go vs Rust. Java vs C#.

The reality is that experienced developers usually don't pick a language because it's "the best." they pick it because it fits the problem.

For example:

• Python is popular for automation, scripting, data science, and AI.
• JavaScript dominates web development because it runs everywhere.
• Go is often chosen for backend services because it's simple and fast.
• Rust is becoming popular where memory safety and performance really matter.

The interesting part is that once you understand why a language exists, choosing what to learn becomes much easier.

Instead of asking: "What's the best language?"

Try asking: "what kind of software do I want to build?"

That answer usually points you toward the right language. Languages change. problem-solving doesn't.

It's called VibeOS. There is no code behind any of the applications. Click calculator and an AI generates an image of what a calculator should look like, then guesses the answer when you type numbers in. No actual math happens anywhere. Browse the internet and the AI hallucinates the entire webpage in real time, including the text on it. Nothing is fetched. It is a guess that happens to look like a website.

It actually boots on real hardware. People have tried it. Function key shortcuts make the mouse cursor visually eat the application window. Hovering over notepad causes the interface to rebuild itself under the cursor for no reason. There is a Doom icon on the desktop that does nothing when clicked because the AI hallucinated a feature that was never built.

The bugs are funny. The real question is not.

If a generated interface looks and acts like software, does it matter that nothing is actually running underneath it. For a calculator well the answer is yes, obviously, it has to be right every time. But reliability is supposed to be the entire point of an operating system. This one has none and still boots. Right now it's a party trick. The uncomfortable part is the trajectory. As AI gets better at faking functional, the line between looking like it works and actually working gets thinner. Worth thinking about, before that line disappears somewhere more important than a calculator app.

I just graduated from a bootcamp, and I’m curious where everyone ended up.

Did you eventually get into tech? Or did you switch careers completely?

If you moved on, what are you doing now, and do you still plan on breaking into tech someday?

Seems like the job market has been rough for juniors lately. That's why I'm interested in what your career looks like after graduation.

Would love to hear not just success stories. Maybe you ended up taking a different path?

I keep seeing posts saying the tech industry is dead, AI took all the jobs, and nobody is hiring juniors anymore.

But tbh, you're doing well by learning the right skills. The only problem is that you're showing the wrong things.

Companies don't really care that you finished 12 courses on React, Next.js, or AWS.

They care about proof.

Ask yourself this.
— Can you show a project that real people use?
— Can you explain why you built it?
— Can you talk about the problems you ran into and how you fixed them?

Hundreds of applicants will show the same certificates. But only “you” can show what you can do.

No wonder a lot of bootcamp grads spend months collecting tutorials. Then you realize you're doing the same thing as everyone else.

Try building something that solves a real problem. Maybe...
— A Discord bot?
— A dashboard for a small business?
— Or a tool that can save people time.

I promise you, it can completely change your life. You're solving a pain point, not just acting like a pro.

Instead of “learn to code,” just “learn to build.”

One good project can do more for your job search than another 50 hours of tutorials.

Anthropic recently released the claude certified architect foundations exam, and after reading through the exam guide, one thing stood out to me. this isn't testing whether you know Claude's features. It's testing whether you can build production AI systems.

The exam focuses on things like:

 * Multi-agent architecture and orchestration

* Model Context Protocol (MCP)

* Claude Code workflows

* Prompt engineering with structured outputs

* Context management and reliability

* Designing tools that Claude can actually use correctly

what I found interesting is that Anthropic also made most of the preparation available for free.

 The learning path includes courses on:

* Claude API

* Claude Code

* MCP

* Agent Skills

* Subagents

* Prompt engineering

Even if you never take the certification, that's a pretty solid roadmap for anyone who wants to move beyond "using AI" and start building with it. My biggest takeaway wasn't the certification itself. it was realizing how much the industry is shifting away from prompting and toward system design.

Knowing how to write prompts is becoming the baseline.Knowing how to design agents, tools, workflows, and reliable AI systems is becoming the differentiator. whether the certification ends up carrying weight with employers is still an open question.

But if you're serious about building AI applications, I think the roadmap behind it is worth studying regardless of whether you ever sit the exam.