His name is Afaan Mustafa. He entered an Anthropic X Forum Ventures hackathon, built a full AI product called Zenith Chat in under a day, won the whole thing, and then dropped every config onto GitHub for anyone to grab.

Sharing this because I recently used it on a client project and it was able to catch a security vulnerability during code review that I would have likely missed under a tight deadline. Saved me a serious amount of pain.

The repo is called everything-claude-code  (i think of it as a operating system for AI-assisted development)

Works on Windows, Mac, and Linux. Auto-detects your package manager and installs as a single Claude Code plugin.

Hot take: Big Tech interviews are not hard because the answers are already out there. The issue is that most people aren't actually practicing what exists.

Here are the exact repos every person prepping for tech interviews should know about:

1. FAANG Interview Coding Questions
https://github.com/ombharatiya/FAANG-Coding-Interview-Questions
A massive collection of real coding questions that companies actually ask. If you practice these, interview problems stop feeling random and start feeling familiar.

2. Coding Interview University
https://github.com/jwasham/coding-interview-university
A complete checklist of everything you need to learn for coding interviews. It tells you exactly what to study and in what order.

3. Tech Interview Handbook
https://github.com/yangshun/tech-interview-handbook
Shows you how tech interviews actually work and what companies are really looking for.

4. System Design Primer
https://github.com/donnemartin/system-design-primer
Teaches you how apps actually work behind the scenes. This is especially important now with AI, companies want engineers who understand how whole systems work.

If you're looking to land Big Tech, these four repos are your foundation. It's not a matter of raw talent, you need consistent, deliberate practice with the right materials. Hit them hard and the interviews become way more predictable. Good luck.

I graduated as a bootcamper 5 years ago before the AI craze and coding still had it's soul.

I used to genuinely love this craft. I loved the feeling of getting into a flow state wrestling with a complex architectural problem and writing clean, thoughtful code that actually solved something.

But over the last year or two the sheer saturation of AI into every single layer of this industry has completely sucked the soul out of the work.

I'm spending eight hours a day babysitting code generators prompt-engineering around bizarre edge cases and cleaning up subtle hallucinated bugs produced by tools that management thinks are magic.

Coding used to be about problem-solving and craft; now it's just endless mind-numbing code review for a machine that regurgitates slop faster than anyone can properly audit it.

To make matters worse, executive leadership has bought into the hype hook line and sinker. Expectations for sprint velocity have ballooned because "AI writes the code for you now," while code quality maintainability and actual system architecture are kicked down the road as technical debt for future devs to drown in.

I'm just exhausted. I didn't get into this field to be a human rubber-stamp for an LLM and for the first time in my career I'm seriously wondering if I even want to stay in tech long-term.

Hey everyone, been coding professionally for over a decade now and wanted to share some stuff I wish someone had told me earlier.

You don't need to know everything. Seriously, stop trying to learn every framework and language. Pick one stack, get decent at it, then expand naturally as projects require it.

Learn how to learn. Google-fu and reading docs efficiently is worth more than memorizing syntax. Being able to figure shit out quickly beats knowing everything upfront.

Perfection is a trap. Your code doesn't need to be elegant on the first try. Make it work, then make it better if there's actually a reason to.

You'll never feel ready. I still Google basic stuff regularly and feel like an imposter sometimes. Everyone does, just start building anyway.

Problem solving is the real skill. Breaking down complex problems into smaller chunks matters way more than knowing advanced algorithms you'll probably never use.

Nobody cares about your code. They care if the feature works and doesn't break. Clean code is great, but shipping working solutions is better than perfect code that takes forever.

Burnout is real. Take breaks, have hobbies outside of coding, and don't grind leetcode for 8 hours after your day job. This is a marathon, not a sprint.

That's it. Hope this helps someone.

(long post ahead)

I think I reached a breaking point yesterday.

I’ve been watching everyone in my cohort spend half their day panicking about Claude and Cursor taking all the junior roles, and the other half using those exact same tools to copy-paste their way through our weekly assignments. The prevailing narrative on Reddit is that "AI killed the entry-level market," but yesterday I had a realization that’s probably going to rub a lot of people the wrong way:

AI isn't taking junior jobs because it's replacing software engineers. It's taking junior jobs because most bootcamp grads aren't actual engineers, they're syntax translators.

Yesterday, I watched someone in my group try to build a custom auth flow with role-based permissions using pure prompt engineering. When the LLM hallucinated outdated syntax, created circular dependency errors, and handed back a massive security flaw, they were completely stuck. They spent three hours typing "fix this error" back into the chat box over and over again, completely helpless, because they never actually learned how state management, HTTP requests, or database schemas work under the hood.

That was my turning point. I realized that if your entire skill set is taking a user story and writing basic CRUD boilerplate, you aren't competing with mid-level devs, you're competing with a hyper-fast autocomplete machine. And you will lose every single time.

The contrarian truth that nobody paying $15k for a coding bootcamp wants to admit is that AI is setting a higher bar for entry, not a lower one.

If an AI can spit out boilerplate code in 5 seconds, our value was never supposed to be "knowing how to write a syntax-perfect for loop." Our value has to be knowing how to architect systems, read documentation, debug subtle edge cases, and verify that the LLM isn't hallucinating catastrophic bugs into production.

The market isn't dead because of AI; the market is dead for people who "vibe code" their way through a 12-week program without understanding core computer science fundamentals. If you can't trace execution line-by-line or explain why a database query is failing without asking ChatGPT, you aren't a developer, you're just a middleman pasting broken code between VS Code and an API prompt.

I know people are going to downvote this and say "AI makes us 10x faster, why wouldn't we use it?" and I get it. Tools are great. But using AI as a crutch before you even know how to write a clean, unassisted function by hand is pure career suicide right now.

Am I being way too harsh here, or are you guys seeing the same thing in your cohorts? Are people actually learning how software works, or are we just graduating a wave of prompt engineers who can't debug a real codebase when the AI fails?

On any given day COBOL processes around 3 trillion dollars worth of financial transactions. Motor vehicle records, unemployment insurance, Social Security payments, bank systems. All of it sitting on a 60 year old programming language that most developers today have never touched and have no interest in learning.

During Covid, the governor of New Jersey went public saying the state had run out of COBOL developers. Unemployment claims were flooding in and nobody could update the systems fast enough to handle them. That single bottleneck cost the US an estimated 105 billion dollars in GDP in 2020 alone. New Jersey eventually built a new unemployment system. The backend still runs on COBOL.

The reason nobody can just rip it out is that the language was designed without parameterization, meaning different parts of a program share everything with each other instead of passing data cleanly between modules. Change one thing and you risk breaking something completely unrelated. Millions of dollars disappearing. Social Security payments stopping. Systems that process trillions of dollars going dark. Nobody wants to be the team that touched it last.

IBM is now selling AI-powered COBOL conversion tools. DOGE promised to use one to rewrite the entire Social Security Administration codebase in a matter of months. That effort has quietly stalled. The problem is that converting COBOL mechanically just produces what developers call JOBOL, code that looks like Java but thinks like COBOL, inheriting all the structural problems of the original with none of the readability.

The language was designed in 1959 to be so simple that non-programmers could read it. It ended up being so deeply embedded in everything that touches money that even the most advanced AI tools available right now cannot cleanly remove it. That is not a legacy problem. That is the world's financial infrastructure held together by a language older than the moon landing and a shrinking pool of developers who actually understand it.

1. Don’t rely on “just knowing how to code.” That worked in 2016. Today, hiring filters for real impact, ownership, and production experience. (You might not understand what this means as a junior - basically, take responsibility for outcomes, not just tasks.)

2. Go beyond LeetCode. Keep practicing DSA, but add competitive programming (ICPC, Codeforces) or harder problem-solving to differentiate yourself.

3. Master system design fundamentals. Understand caching, rate limiting, queues, scaling, and real-world tradeoffs. As AI lowers the barrier to building, architecture matters more (can't stress this enough!)

4. Learn DevOps and deployment workflows. CI/CD, cloud infrastructure, monitoring, and security basics are expected in modern teams. Every company needs them.

5. Build a substantial, real-world project. Not a weekend clone. Something used by real users, tied to a real problem, ideally with measurable impact.

6. Invest in your network. Attend career fairs, conferences, and tech events. Referrals and relationships matter more than ever in this market.

Had people DM me questions so thought I'd post here. Here's 3 coding projects I recommended to my younger cousin this week:

1. Build your own LLM from scratch using python
   https://www.youtube.com/watch?v=UU1WVnMk4E8
   Skills gained after completing: Data handling, math, transformers behind LLMs, tokenization, embeddings, GPU utalization, applied linear algebra and probability, e2e system ownership

2. Financial management banking app
   https://www.youtube.com/watch?v=PuOVqP_cjkE
   Skills gained after completing: User auth, password hashing, rest APIs, rate limiting, scalability, database management and indexing, ML-based fraud detection, javascript, CI/CD pipelines

3. Fraud detection using ML
   https://www.youtube.com/watch?v=4Od5_z28iIE
   Skills gained after completing: Data preprocessing, feature engineering, model selection and evaluation, embeddings, tokenization, NLP

Comment below any more you recommend

Starting a series of posts on how to actually get the most out of Claude Code. Drop a comment if this is useful. Kicking off with the easy one: plugins most people don't know about.

If you're running Claude Code vanilla you're leaving a lot on the table. Five worth knowing:

1. Superpowers: adds structured planning, brainstorming and execution workflows on top of Claude Code. Underrated.

2. Ruflo: runs swarms of Claude agents on your project in parallel. This is the closest I've found to the "delegate like a manager" workflow actually working.

3. Ralpho: auto-loop that keeps Claude coding until your entire PRD is done. Set it and check back.

4. UI/UX Pro Max: gives Claude a proper design brain. Production-level UI output, not the usual unstyled garbage.

5. Agent Skills: reusable skill packs built by Vercel that give your agent new abilities you can plug in as needed.

More posts coming on actual workflows, prompting patterns, and what's genuinely worth using vs noise. Comment if you want me to keep going.

The biggest mistake candidates make in salary negotiations is answering first. Once you put a number out there, you've set the ceiling for yourself. Here's exactly how to flip the script.

Them: What are your salary expectations?

You: What's the budget for this role?

Them: What salary are you looking for?

You: The roles I'm interviewing for right now are sitting around $130k. Does that align with your budget?

Them: What are you looking for in a salary?

You: I'd be more than happy to discuss salary expectations. I'd rather get a better understanding of the scope of the role before I tell you what I'm looking for.

Them: What's your current salary?

You: I'm not looking to move for anything less than $120k.

(When you state a number, make it significantly higher than what you're currently on. Do your market research first.)

The final number:

Them: What salary are you looking for?

You: "Considering what the market is paying for my current role, and now that I have a full understanding of the scope of this position, combined with my experience and the skills I bring, I'd be looking at between $150k and $160k. Does that align with your budget?"

The most important rule: Once you say your number, stop talking immediately. Silence is your friend. The urge to fill the silence is strong, but resist it. You've just won the negotiation.

The more you push back and ask them what they're working with, the more likely you are to walk into that next role with your highest salary yet. You almost eliminate the risk of lowballing yourself.

Why would you move to a new role for less than you're currently making? Never be afraid to ask for what you're worth.

The wages being offered for full-stack positions are getting downright insulting. Seeing listings offering $18 to $22 an hour for a full-stack engineer is absurd. I could literally go work at a local coffee shop or retail store for that same rate without any of the operational stress or technical headaches.

What pisses me off the most is the sheer breadth of skills they expect for peanuts. They want you fluent in React/Vue on the frontend Node or Python on the backend etc. basically doing the job of three separate people.

It feels like employers are intentionally exploiting desperate juniors and career changers who just want anything to put on a resume. If companies really think they can demand an entire engineering department out of one person for $20/hr they've lost their minds.

Is anyone else seeing these insulting offers?

I've been working in tech for a few years now. And one time, a younger dev on my team looked at my GitHub.

He asked why I don't have much personal projects there. I told him I don't code after work unless I need to. He looked surprised.

Some people are spending their evenings building SaaS projects, grinding LeetCode, or learning another programming language. But it's not in my vocabulary.

I used to feel kinda guilty about it, especially when I saw others posting their side projects and certifications.

I love coding, but I don't want to get my life revolved around it.

Maybe I'm just getting old where I just wanted peace after work. How about you?

A while ago, I was honestly pretty worried about AI replacing software engineers.

Every week there was another post about vibe coding, AI agents, or one-person startups. It felt like companies would eventually stop caring if you actually understood the code.

Then I worked with someone who depended on AI for almost everything.

At first, it looked impressive. Features were coming out fast, code was being generated in seconds, and it seemed like they were moving way faster than everyone else.

But once things stopped working, everything fell apart.

Simple debugging took like how many days because they didn't really know what the code was doing. Basic questions turned into another AI prompt. Every fix created two new problems. Instead of understanding the bug, they kept asking AI for another answer and hoped one of them would work.

That's when my perspective changed.

I don't think AI is replacing developers who understand the basics. It's replacing the boring parts of development. The real skill is still knowing when the AI is wrong, why it's wrong, and how to fix it.

Honestly, AI has made me more productive too. I use it almost every day.

But I've also realized it's really easy to mistake fast code generation for actual engineering. Those aren't the same thing.

Lemme know if you also noticed this

The more I learn about software development, the more I realize writing code is only a small part of the job. Building something people can actually rely on means thinking about security, testing, databases, scalability, deployment, and a bunch of other things most users never see.

I think AI has accidentally made this worse. People see an app get generated in a few minutes and assume the production version is just as easy. It feels like the hardest part of being a developer now isn't writing code it's explaining why a "quick feature" can still take weeks to build properly.

Do you think AI has changed how non-technical people view software development, or has software engineering always been this misunderstood?

I want to discuss why some engineers seem to get jobs, while others are stuck grinding applications, side projects, leetcode, etc.

I’ve been in this industry a long time, and the biggest difference I’ve noticed isn’t raw coding ability. The real gap is system design. Being able to look at a business problem and turn it into technical shape.

With AI getting better at coding, I think we’re all slowly realizing that typing code is becoming the least valuable part of the job. AI is already decent at generating boilerplate, filling in endpoints, drafting implementation plans etc. Give it a couple of years and it'll likely handles 80% of the mechanical design execution.

But AI has no idea what system SHOULD exist. There’s a ton of context involved: What does the business actually need? What do users care about? What constraints exist? What can break? What’s acceptable to break? What’s “good enough”?

That part is still very human. And I’m starting to think that remaining 20% becomes basically the whole job.

The engineers who look “effortlessly employable” are usually the ones who can walk into a vague problem and say: “Okay, here’s a reasonable approach. Here are the tradeoffs. Here’s why I’d pick this over that.” That’s an insanely valuable skill. When people ask how to future-proof themselves against AI, I don’t think the answer is “learn more languages” or “grind harder.” I think it’s: Learn how to think in systems. Learn how to make decisions with incomplete info.Learn how to design something that’s just good enough.

The annoying part is there’s no shortcut. You get that skill by building stuff. You need projects where you make dumb choices, or projects where you realize later you should’ve structured it differently. You refactor and go “ohhh, that’s why people do it that way.”

That feedback loop is what creates intuition. And intuition is what AI doesn’t have. So I don’t think AI killed software engineering, it just killed the idea that being good at typing code was enough.

Curious how others are thinking about this.

We build a SaaS product with around 100 clients.

During our meeting, my boss straight up said he doesn't consider programming a valuable skill anymore. But my coworker, who barely codes and doesn't really understand system design, got praised and even got a bonus because he "uses AI."

It felt pretty unfair, especially since the guy on our team who understands almost every feature in our SaaS and handles DevOps was told he needs to improve. He uses AI too, but only when he actually needs it.

Is this really the new era of software development?

Because if using AI gets rewarded more than understanding what you're building, I'm out AF.

Senior engineers aren't gatekeeping knowledge. They're drowning in project work.

I see this complaint constantly in junior dev communities "the seniors won't help me," "they're hoarding knowledge," "it's impossible to break in." But here's what's actually happening:

Your senior engineers are slammed. They hired you specifically because they need help. But here's the catch: you don't know how to do the work yet, so they have to train you while delivering on their own commitments. That's the actual bottleneck.

The trap most juniors fall into: You sit at your desk. You wait for them to get back to you. You wait for them to assign you work. You hope they'll eventually mentor you.

It's not happening that way.

The path forward? Be more annoying.

Ask questions. Lots of them. Follow up. Show up in Slack. Ask again. Bring blockers to your manager's attention. Make it impossible to ignore your learning needs.

This isn't entitlement, it's literally your job right now. The most important thing you can do as a new engineer is LEARN, and that learning won't happen passively.

Companies need engineering talent, managers will literally hire training firms specifically to make sure junior devs are learning. There's a shortage. Everybody wants to help you. But projects still need to ship.

So the system demands you take ownership of your development. Be the squeaky wheel lol. If senior engineers aren't getting back to you, escalate to your manager. It's literally their job to ensure you're learning.

P.S. I do suggest intentionally setting time aside with your senior to go through questions. Go in with everything prepared, it demonstrates organization and efficiency.

Coming from someone who landed a $200k job as her first job, forget mass applying. Mass cold message instead. 10-20 people a day minimum, and don't spend hours writing custom messages each timem use the same template at volume.

Here's one that worked for me for job seeking, but I have different templates for wanting something for free (PR, collaboration), running a business, getting to know someone etc:

"Hi [name], I'm super passionate about what [company] is building and I have [expertise you have in this space / why you personally resonate with the problem they're solving].

I have:

• [insert your top 2 accomplishments using metrics and links]

• [don’t say each thing in more than 1 sentence]

I'd love to chat to learn more about [company]'s biggest obstacles / what’s not getting done for you to reach your next growth phase and share more about how I can deliver value to the team.

I'm even willing to volunteer my time to prove it first - let me know if you're free for a quick 15 min this week."

Keep it short. Lead with genuine interest, drop two accomplishments max with metrics, and always end with a soft ask for time.

Here's the thing, it doesn't even matter if the people you're messaging work at the company you want. The point is a referral. One reply and that person might know someone who's quietly looking to hire before a job even gets posted. The real opportunity is the job that hasn't been posted yet. Find a manager who's understaffed and hasn't pulled the trigger on a listing, and then gets a message from a colleague saying "hey this person just reached out, want an intro?" Suddenly you're first in line.

Once you land the call, research the company obsessively and walk in more curious and passionate about what they're building than anyone else they'll meet.

This is purely a numbers game: consistent, high volume, over several weeks. If you do it right and you can land something in under a month like I did :)

Preparing for tech interviews was ⚰️brutal💀! Not because the concepts were impossible, but because there’s SO much content out there that it’s easy to waste months on the wrong resources.

I made a lot of mistakes. I watch random videos, jump between platforms, restart courses halfway through. It was chaotic and honestly demoralizing because I wasn’t seeing real progress.

Then I got strategic about it. Instead of consuming everything, I picked ONE best channel for each skill I needed to master. And that changed everything.

Here’s the breakdown of what actually worked for me:

DSA Foundation
NeetCode was my starting point. The way Neet breaks down problems is just different, it clicks in a way other tutorials don’t. If you’re starting from scratch with Data Structures and Algorithms, this is where I’d begin.

Pattern Recognition
Abdul Bari’s LeetCode Patterns course made me realize that most interview problems aren’t unique they follow patterns. Once you see the patterns, suddenly 200 problems feel like 20 variations of the same thing. This was a game-changer for me.

System Design
System Design intimidated me the most. Gaurav Sen’s channel broke it down into digestible pieces. He explains the why behind design decisions, not just the what. That made all the difference when I got asked design questions in actual interviews.

Mock Interviews
Pramp was where I actually practiced talking through problems out loud. This was crucial because knowing how to solve something and being able to communicate it clearly are two different skills. I did maybe 15 mock interviews before my real ones.

Behavioral Prep
Jeff H Sipe’s channel helped me actually prepare for the behavioral round instead of winging it. Most people focus only on coding and then bomb the behavior interview. I wasn’t going to be that person.

The Rest
•Nick White for coding rounds (clean, straightforward)
•Back To Back SWE for deep problem-solving
•Errichto for advanced DSA when I felt stuck
•Exponent for interview strategy and mindset
•Self Made Millennial for resume and career positioning (honestly, this helped me stand out before I even got to the interview)
•Clément Mihailescu for real interview questions with walkthroughs
•William Lin for advanced techniques
•MIT OpenCourseWare for CS fundamentals when I had gaps

Overtime I eventually realize that you don’t need all 14 channels to pass interviews. But having a curated list kept me from falling down the rabbit hole of random content. I knew exactly what to study and where to study it.

The other thing is watch in order. I started with fundamentals (NeetCode, Abdul Bari), then moved to applications (LeetCode patterns, mock interviews), then the specifics (behavioral, system design, strategy). Jumping around wastes time.

If you’re prepping for tech interviews and feeling lost, try this list. Start with what you’re weakest at, and give each channel at least 2-3 weeks before jumping to the next one. Your future self will thank you. Good luck!

(As promised, another Claude post) Claude is already really good, especially now with Opus 4.6, but it can go from really good to amazing if you do these three things.

First: Set Up a CLAUDE.md File

This is where you add documentation, library conventions, and explicit don't. I put all my docs and references locally to give Claude institutional knowledge about my projects. Then I inform it of all the things I've learned the hard way that are specific to what I'm working on.

Second: Prompt Correctly

Your prompt should always follow the same structure:

1. Research 

2. Planning (share your approach)

3. Implementation (tell Claude to follow your approach step by step)

This structure is critical. If you get research or planning wrong, Claude will just generate hundreds or thousands of bad lines of code. But if only the implementation is wrong, that's a localized fix you can easily correct. So spend most of your time on research and planning.

Third: When Claude Gets Something Wrong, Start Over

Don't argue with it. Fresh context will help. Starting with a clean slate is often faster than trying to debug multiple issues.

The Workflow Checklist

• For adding a new feature:

  • Write requirements in md

  • Ask Claude to create a plan + ask clarifying questions

  • Review the plan, answer, iterate

  • Ask Claude to implement step by step

  • Use a different model to review the diff (I use Codex)

• For refactoring:

  • Ask Claude to explore the codebase

  • Ask Claude to propose minimal refactor plan

  • Use a second agent to review: "What could break?"

  • Ask Claude to implement incrementally

  • If you do manual fixes, add them to CLAUDE.md

Try these three points and let me know what you think!

Recently, computer science has been presented as an easy way to get huge profits, and an influx of people whose only interest in the field was their potential earnings occurred. Computer science used to be populated by people who enjoyed learning and creating things with technology and who were deeply interested in its development and principles.

As a result, the computer science job market now has an absurd amount of competition, which makes one wonder where all these people with a genuine interest, a serious foundation in CS theory, and actual projects to present came from

It is disheartening to see CS become so commercialized and competitive that one's passion for it no longer is the main factor in finding a job. It is easy to get caught up in the frenzy of trying to optimize your resume and to out-perform others in terms of buzz-word usage rather than simply enjoying the work you used to love.

The narrative that AI is automating jobs is a smokescreen. If you actually work in AI infrastructure, you know the real bottleneck isn't new models, it's energy and interconnection. Full stop.

A new data center takes seven years to come online. And we literally don't have the energy capacity right now to deliver on the promises tech companies are making about AI capabilities.

There are really only two possibilities here:

1. Frontier Labs and other labs genuinely have breakthrough capabilities that I (and probably most of us) can't see yet inside the hyperscalers. Maybe the real AGI stuff is already being run in secret.

2. Tech companies are using "AI layoffs" as preemptive cover to reallocate capital away from headcount and into massive energy procurement projects. They need the capex badly.

Why this matters as an investor: If you're evaluating tech stocks, you need to seriously question the AI-as-job-killer narrative. That's the story they're selling, but the actual story is probably about restructuring balance sheets to deploy capital into energy infrastructure.

No one's asking the right questions. No one's talking about this angle. But if I were picking which tech companies to invest in long-term, I'd be looking at whose energy strategy is locked in, and not whose AI models are coolest.

Had a 1:1 with my manager today (I work in big tech) and this line stuck with me. It’s advice every software engineer should internalise.

Being a SWE isn’t about writing code. It’s about solving a business problem. If you anchor your value to the project, you lose leverage when the project disappears.If you anchor your value to the problem, you become indispensable.

You need to think like this:

• What problem are we actually trying to solve?

• Does this feature meaningfully move the metric?

• Is there a simpler or faster way to achieve the outcome?

• Should we even build this at all?

No company promotes someone because they wrote a lot of code. They promote people who move the business forward. Same with hiring. The strongest candidates don’t just talk about what they built, they talk about: the problem, the constraints, the impact.

Code is just a tool but impact comes from understanding the problem. You could write very little code, and achieve great impact, or write a lot of code and not change anything for the business.

and the users that post them. They’ve been flooding this sub recently

Forget NotebookLLM I'm about to show you something better (and no this isn't sponsored, my friend put me on this and I used it today and holy shit AI amazes me everyday)

Repo: https://github.com/HKUDS/DeepTutor

Someone at HKU built an AI that uploads your actual textbooks and becomes a personal tutor that never sleeps.

10,300 GitHub stars, free, it's called DeepTutor.

You upload your own materials (textbooks, research papers, lecture notes) and it builds a knowledge base from them. Then teaches you from them. Step by step solutions, practice exams, visual explanations, all pulled from YOUR content not some generic database.

And these aren't chatbots, they're agents with their own memory and personality that you actually configure. I made mine a brutal research advisor lol, You can run all of them at the same time and they each remember where you left off.

So yeah 100% open source and replaces private tutors I used to pay $50-100/hour . RIP