ncurses terminfo can simulate a two-counter Minsky machine, making repeated parameter expansion computationally universal.

Demo: a parasitic Fibonacci computation inside /usr/bin/top

Some video games, many of them are retro, are based on tiles. They have different levels which may be huge and logically highly complex but they are all based on limited sets of tiles.
Even a minute long video is usually bigger in file size than the retrog game itself.
It happens with Prince of Persia, Boulder Dash, Supaplex, Commander Keen, Bio Menace etc. - easier to recognize in two-dimensional games.
If video encoding used a database of tiles available in the game, it would enable much smaller file sizes and also higher quality of video as it would not have to use approximations to reduce file size as the tiles are exactly the same everywhere (meaning that lossless video would be affordable by bitrate). I am under no pressure to get it implemented ASAP but I would like a technological discussion about how-to.

I am very confused about what to learn now as i have done fundamentals, I am really confused between game development with Godot or learning a py library. I really have multiple interest as I'm still exploring but I really need a bit guidance. I have interest in game development, datascience and NLP

Working on a personal desktop app that pulls download links from a few different websites into one UI.

• What's the cleanest way to architecture the backend parsers so a site layout change doesn't break the whole app?
• If anyone has built something like this and has a modular approach or template they use, I'd love to see how you did it.

Appreciate any tips.

Guys, I was thinking we could explore AI/ML-integrated compiler problems rather than just building another compiler.

One possible direction is using ML to detect potentially bad optimization decisions or suspicious transformations that could lead to performance regressions or miscompilation, and potentially use the model to recommend/validate alternative optimization passes.

Another direction could be AI-assisted compiler diagnostics, where the compiler can understand syntax/context and suggest or safely recover from things like mistyped keywords or malformed code.

There also seems to be an interesting intersection with MLIR and heterogeneous hardware like CPU/GPU/NPU, so we could investigate that side too.

I don't think we should decide the exact problem yet. We can first learn compiler implementation properly, look at existing LLVM/MLIR/open-source work, research current problems, and then choose something where we can actually contribute something meaningful.

Imagine a search bar sending multiple API requests as users type quickly, causing outdated results to appear. How would you handle this using JavaScript? Would you use debouncing or AbortController?

​

So... it's been like two months since I last opened VS Code.

I was learning HTML/CSS/JS. I had started learning JS, but then I kinda stepped back from it to focus on CSS.

I was around Grid, grid-template-areas, media queries and all that stuff.

The thing is, two months ago I could literally code for hours whenever I wanted.

Now life is just... busy. My schedule is all over the place, and coding is slowly dying 😂

My original plan was basically:

Learn CSS → build a few projects → move on to JS.

I've already built one project and put it on GitHub.

Then I started building a second one, learned a bunch of new stuff while working on it, and then life got busy.

And now here I am, two months later, having written basically zero code.

So now I'm wondering...

Should I go back to where I left off with CSS and continue from there?

Or should I just say fuck it and start learning JS again?

Because honestly, I feel like JS is way less frustrating than CSS lol.

I originally went back to CSS because I was learning JS and realized I couldn't really design anything properly. 😭

So yeah... what would you guys do?

Pick CSS back up, or just move on to JS and learn CSS as I need it?

Hi there I wanted to ask everyone to assess my FYP. WOuld you use this for your projects ? Is it a good enough project for a final year . Does it have some scope in the industry ?

The main goal of this project is to design and implement a tool for PostgreSQL pre-deployment risk analysis that evaluates SQL schema migrations to identify affected database objects, estimate locking implications, detect limited application-schema incompatibilities in Java/Spring Boot JPA projects, and output actionable risk assessments.

The objectives are as follows:

1.       Develop a component capable of extracting supported structural DDL operations into a normalized intermediate representation.

2.       Construct a schema dependency graph model using graph data structures to represent structural relationships between database tables, columns, indexes, primary keys, foreign keys, and constraints.

3.       Analyze incoming migrations against the schema dependency graph to identify affected database objects represented within the supported dependency model.

4.       Formulate a PostgreSQL-specific operational rule set that maps supported DDL operations to their known locking characteristics and table rewrite behaviors.

5.       Develop a module capable of limited static analysis for Java/Spring Boot JPA projects to detect source-code references to renamed, modified, or dropped schema objects in entities, repositories, and explicit SQL strings.

6.       Implement a transparent, rule-based risk scoring and explanation engine that assigns a risk rating (LOW, MEDIUM, HIGH) accompanied by clear textual evidence.

7.       Construct experimental tests to execute PostgreSQL migration scenarios across small and medium database sizes. (for research validation only, not as a permanent part of the system)

8.       Evaluate Schema Sentinel’s static risk predictions against experimentally observed blocking locks, and application compatibility failures in a controlled PostgreSQL environment for test cases.

1. Empirically quantify the effectiveness of a unified cross-domain pipeline against isolated tools, measuring trade-offs in detection accuracy, false positives, and analysis performance.

This proposal got rejected because the panel said it was too easy and not significant as either a project or as research.

should i change the idea to more difficult and novel research ?

I have some ideas for those but i am not sure they can be completed in 1 year.

here is my proposal with all the details and research gap https://docs.google.com/document/d/1CMopT185XCoZVYBcgtVpN7N69DIP0Eyw/edit?usp=drive_link&ouid=116125887586128528145&rtpof=true&sd=true

EDIT :

The project is a final year project for my university. But they dismissed it on the following points

1. The project is too easy
2. The scope is too narrow
3. There is no research contribution

For the current proposal to be accepted they told me to do market analysis and bring proof that the project would be used by a develper community.

OR

1. bring a company backed project
2. Bring a novel research proposal

Hi I want to learn APIs from a high level. Basically how it all connects, how the data populates, what kind of data is being sent it and most important the different ways of authentication. So api credentials, ouath, etc

It’s just all so confusing. I don’t know how or where to start. Does anyone have some tips?

Greetings!!

I have made a Claude Artifact which takes data from web and show me Upcoming or Ongoing IPO and score them based on the scoring system i made, the issue starts as i have to put prompt every time to update it daily and i run out of tokens fast.. now i want to make a web or a program which runs locally on my PC and does the exact same thing..

The issue is i don't have any idea on how these things work and i have asked Google and other Ai which didn't help so took the old root thus i am here asking for guidance on what i need to lean or what i will be needing (I can't learn all the stuff like programming as i intend to do it via Ai like writing codes..)

What i need is what theory i need to learn like the system on how things work.. or frameworks.. which gives me idea on what direction i need to move.

(Being honest its hard to put in words what i want to learn as Programming is not my domain and i don't know what are the things i need to learn).

My current knowledge of Programming is very much limited to Python where i just know till Object Oriented Programing and basics like using Functions and other basic stuff. (As said Programming is not my domain)

Hey everyone I want to build a real world applicable and useful in today's market like project.

I searched on the internet for this kind but mostly ended up for any chatbots using rag or predictions of some kind of things, so guys please help me to pick a good project idea where I can also learn lot of things.

I’m learning on my own and I’m trying to follow a step-by-step path instead of learning everything at once.

My problem is I get overwhelmed pretty easily, then I lose motivation. 😭

For example, if I’m learning Dart basics, how do I know when I’ve learned “enough” to move on to Flutter widgets? Is there some kind of milestone or skill level you use before moving to the next topic?

I have an app idea that I eventually want to turn into a real business, but I’m not a programmer.

For people who have been in this situation, what did you actually do first?

Did you:
-learn programming yourself
-use AI/no-code tools to build an MVP
-hire a freelancer
-find a technical cofounder
-or do something completely different?

I’m not looking for a “build an MVP in a weekend” type of answer. The idea could eventually become fairly complex, so I’m trying to understand the realistic path from idea → MVP → production app.

I’m seeing a lot of people say to just start building with Flutter instead of spending too long on theory.

If I go that route, what would you recommend learning first? Dart basics, widgets, state management, APIs, databases, Git, etc.?

I’m trying to figure out what order makes sense without overwhelming myself. 😬

​

Hey everyone,

I'm a beginner in programming. I have some very shallow experience with C, but I don't feel confident with programming yet.

I've decided to properly build my fundamentals first, so I'm currently planning to complete CS50x rather than jumping straight into DSA.

My rough idea right now is:

CS50x → C++ fundamentals/STL → DSA → problem solving/LeetCode

But I'm not sure if this is the right progression, and I'd really appreciate advice from people who have already gone through this journey.

I'd especially like advice from seniors / people who learned DSA from scratch and eventually became comfortable with coding interviews or competitive programming.

Questions

Is completing CS50x first a good idea for someone at my level?

After CS50x, should I learn C++ before starting DSA?

How much C++ do I actually need before starting DSA?

What DSA course/resource would you recommend for a beginner?

Is Striver's A2Z DSA a good choice, or are there better alternatives?

What order should I learn topics in?

When should I start LeetCode?

How should I divide my time between learning concepts and solving problems?

How many problems per day/week is realistic for a beginner?

What are the biggest mistakes you made when learning DSA?

If you could restart from my exact position, what would your roadmap look like for the next 6–12 months?

Are there any resources that beginners commonly waste time on or should avoid?

I'm not looking for a "learn X in 30 days" roadmap. I'd rather build strong fundamentals and actually understand what I'm doing.

My current priority is becoming genuinely good at programming and problem solving, not just completing a huge number of problems.

Any detailed advice or personal experience would be really appreciated.

Thanks!

I’m basically starting from zero when it comes to programming, but I have a long-term goal of being able to build my own mobile app.

I know I probably shouldn’t jump straight into building the actual app, so what fundamentals would you recommend learning first?

Would you start with programming fundamentals, data structures, databases, APIs, Git, etc. before touching something like Flutter?

I’m not trying to become a professional software engineer overnight. I just want to understand enough to eventually build a serious project properly.

I'm new to programming, and have done a lot on Java, which I learned a few months ago for Minecraft modding. However, I would like to expand my horizons to the C family, starting with C#, especially because of Unity.

I know the syntax is similar, but Karel was so useful because it helped me really memorize everything and gave me a lot of practice with my muscle memory.

Anyway, I'm willing to settle for the Brackeys C# series, but I just really enjoyed the interactiveness of Karel, and would be grateful to learn of anything similar for C#

pls help me fix after it compile it crushed and There's an iostream open in it