Thu

@junchgaming I recommend going through this if you have not!

@InfernoRocket i love it! been messing with godot on and off for the last 2 years-ish and I love how lightweight it is, how free of bloat and also how intuitive the whole nodes system is. I'm a dumbass w code tho, and that's the part that still needs a bit of time to learn.

Learning how to make a 3D camera that moves freely around the player in Godot

@cloverjumpgames This is sick!

We finally got a steam page for Faewoods! Please support us by wishlisting it! Really Appreciate you ❤️ Slime Rancher Mix with Pokemon Gen 3/4 Exploration ❤️ Wishlist Here: store.steampowered.com/app/3555140/Fa… #screenshotsaturday #indiegame #gamedev #madewithunity

you guys have got to make a shitty 2 hour long rpgmaker game it is the greatest endeavor of your life

@teo_kai_xiang If you want to see games made by Singaporeans you should come down to Playtest Party happening tomorrow at Toa Payoh

singapore is the highest iq country that has never shipped anything that matters. this is my essay on why there are no great singaporean companies. we are a nation of compradors; middlemen too domesticated to do anything but serve.

Here it is, the most boring gamedev thread I've ever made. This one covers the glamorous topic of Autosaving, Void-outs, Death, and Respawning 🧵

It is 2018 Tumblr implodes Its userbase becomes nomads The internet becomes noticeably worse It is 2025 4chan implodes

@bayesiandroll what luck. she is going to grow up in a world in which she must learn to slay them

i’m genuinely gonna start bawling

Unironically one of the most believable and organic depictions of boyfriend and girlfriend I've seen in animation.

I worked a lot on my mental health and now I am no longer ambitious.

He deleted almost every app from his phone. Then he wrote 4 bestsellers in 9 years. Cal Newport's controversial take: Your phone is making you mediocre. His science-backed system for getting your brain back:

If you’re in tech, get a LLC. There are no downsides besides the yearly fee. 1) Allows you to freelance and contract easier 2) Proof of work for Full time jobs 3) if you take a gap year you can use your LLC for your own startup 4) tax write offs JUST DO IT

I've been thinking about the idea of breaking down learning into small chunks ordered as a tree, then filling it from the lowest branches, making sure to cover each chunk until proficiency ("mastery learning"). It is attractive, but many questions remain for me. I'll try to write up my current impressions. Specifically with math, I've started working more with a kid who's struggling with high school math (in an advanced math program; struggling, not failing). My previous approach was to find interesting and relatively challenging problems that are a bit above the homework/exam level but still don't require any non-curriculum knowledge, and ask her to solve them. After some time I come back and discuss how she did them. Sometimes she'd succeed, other times she'd fail, then I explain, we iterate. Over a long period of time and many relatively irregular study sessions, this did not really work. Being able to solve a more challenging problem (but not reliably!) did not translate to easy handling of more routine problems on exams. Inspired in part by the Math Academy program, Justin's large "The Math Academy Way" book and sentiments such as in the quoted post, I've recently switched to a different way of tutoring. Instead of giving problems and going away, we're sitting together, and I try to do this daily or almost daily. I'm generating simple and routine problems that should be straightforward to solve, and TK (The Kid) attempts to solve them right away. When she errs, I correct her immediately, in a maximally non-judgmental and constructive way as best I can. I continue firing off problems from different areas of math & programming ("interleaved learning") and try to come back and reinforce things after correction ("spaced repetition"). For now, I'm doing the variety bit and the spaced repetition bit manually, but I may switch to drawing plans or writing a simple program to help me manage the planning. I just started with this a few weeks ago, but lots of things feel better about this way of tutoring (which in learning theory jargon is more or less Direct Instruction as far as I can see). I can see where there are gaps in fundamentals, and we are reinforcing them. For example, solving inequalities using the method of intervals was really slow and error-prone in the beginning (even though she'd learned it thoroughly before and did a set of graduated exercizes in it a month ago), and is becoming routine and almost error-free with time. Still I hesitate to agree that everything is simply a matter of going up the ladder of prerequisites and making sure everything is filled in. Although this picture is seductive, in reality TK keeps running into problems that are not, at least on the face of it, due to not having covered previous prerequisites. I knew this before and have thought about these problems often, but now with doing really close tutoring sessions and seeing TK "in action", I'm gaining better understanding of their typology, if not necessarily knowledge how to deal with them. Most of these are problems that aren't easy for me to notice, because as a "math whiz" kid in my past I either didn't have them or dealt with them on my own quickly enough to forget about them. I think the same problem may happen with a program like Math Academy, or broadly with a way of "chunking up" the domain space and seeing it merely as a graph of prereqs. Some students have the whatever (innate ability, ways of focusing attention, memory, etc.) that allows them to navigate the graph, and then the program works for them. Others run into these problems I'm talking about (specifics below), in a way falling into the cracks between the nodes of the graph: if D depends on A,B,C and you learned A,B,C and you still can't do D reliably, what do you do? (of course, the answer *may be* "you just haven't cut the graph finely enough into A1,A2 and A3, or failed to notice additional dependencies E and F, and once you do that everything will work". Maybe! But I'm not convinced that'll always work). Some specific types of the problems I'm talking about, off the top of my head: 1. Learning to recognize a previous chunk of knowledge when it comes up in the process of solving a problem. This is a big one and happens often. TK can easily solve a system of two linear equations in two variables. But when in a process of solving something else (like a word problem) two unknown quantities come together in two different ways, and you get two equations, she wouldn't jump from that to solving a system. She would stare at the two separate equations, not sure what to do. Another example is inequalities. They come up all the time in different contexts (problems with absolute value, domains of functions etc.). But knowing how to solve an inequality and knowing to recognize something as an inequality to solve are two different skills. The former can be systematically exercised. The latter comes up in different guises all the time - it's like the "connection" between the "nodes" in the graph is dynamic and ever-shifting. Can't recall it as a personal problem. Apparently I'd built up an intuitive notion of inequality that fires at just the right time and helps me switch into a mode of "oh, let me solve this". But how did I do this? Just *talking* about it doesn't seem to help. TK is smart and she *understands*, but the understanding doesn't internalize and the concept doesn't fire next time in a week or two. Solving inequalities on their own doesn't help with this. Solving lots of problems that depend on inequalities? If they're all typical and basic, this doesn't seem to train up the concept enough. Maybe I should try to assemble a *diverse* set of very different problems that hide inequality in different ways. But that seems too clever. 2. Algebraic exploration. By this I mean a meta-algorithm that looks something like: you're given some setup with lots of interrelated thingies and some facts about them, and you need to find the numeric value of one of the thingies. Denote some of the thingies as variables and express others in terms of those variables using the facts. Work towards an equation, knowing it must come. When it comes, solve it; sometimes there'll be more than one, then solve the system. Most word problems and physics problems are like this. It's easy enough to train separate instances of the algorithm, e.g. solving the problem of a particular pattern "you throw a ball up with speed v0 from height h, when will it hit the ground?". After a while this comes easily. But how do you train the meta-algorithm, for the life of me, I don't know. There are many parts here that I suspect math-talented kids (and teachers!) just take for granted. E.g. there's a feeling you get "I must have underspecified the problem, let me re-check if I used all the facts", but what if you don't get it? Maybe the meta-algorithm is trainable for everyone/most people, but some need x100 fewer reps than others. I don't know. Sometimes the meta-algorithm is needed in a corner of a problem. You start to solve something, everything hums along without needing variables or equations, then you hit a wall and not sure how to proceed. In fact, "give the thing you don't see how to get a name, look for interconnections, write out as many expressions as possible and grope for an equation" is how to proceed. But this is meta-knowledge, not a hard pattern. TK sometimes just fails to do it. Often it's enough to say "try labelling things and looking for equations", but what if no one says it, including yourself? You need to train an inner voice that always says it, but *merely repeating this verbally DOES NOT WORK*. 3. Negative knowledge. Remembering what NOT to do, even though it seems obvious. We can solve many inequalities with a method of intervals, then after a while I throw in a simple "1/(5-x) > 4", and her first instinct is to - obviously - turn this into 1 > 4(5-x). It seems harder to train NOT to do something than to do something right. This problem does seem to be helped by doing many very simple reps interspaced, but it remains to see if the learned negative knowledge will stick. 4. Focused attention. So many small errors are just inattention. Algebraic manipulation especially. Repeated focus on fundamentals makes it better but it's not clear that it makes it go away completely. It usually helps to say "re-check your work" even without pointing out the mistake, so maybe the idea is to train *that* to happen automatically. 5. Long-term memory. This is the obvious one. Everything fades. Specific examples are the formula for roots of the quadratic equation, and the order of terms in a derivative of a fraction (f'g - fg', not the other way around). Spaced repetition can and does help with forgetting, but I also do wonder if different students have vastly different lengths of retention before they need to be reminded. 6. Sometimes TK recognizes the need to use a previous chunk of knowledge, but there's more than one, and the context which should help her select the right one doesn't help her. Say when solving a geometry problem with trigonometry, you get to a triangle, and you know some side lengths/angles, you need others. You can set up an equality with a law of sines, and it's often helpful. But if your triangle is right-angle, you don't need the law of sines. You can just do e.g. a = c*sin(alpha), because it's right there, sin(alpha) is a/c in this triangle. It's not exactly *wrong* to write a/sin(alpha) = c/sin(gamma) and then use sin(gamma)=1 because gamma=90 degrees. But it's not *right* either, and it shows lack of clarity about when to use the stored piece. Again, not easy to train. The law of sines applies everywhere and is powerful. It seems counterproductive to specifically teach "do NOT use this in a right angle triangle". Instead, the more useful piece "use sin/cos/tan directly" should come up as the more relevant one. But what if it doesn't? OK, this is enough for a first attempt to write this down. We'll see how these problems change with time. Overall I think the way I work with TK now is much better than before.

Everyone is talking about Vibe Coding (Using AI to Create Apps Only using AI) This is the most Comprehensive Guide for Vibe Coding with Cursor (By Far) 250 Minutes, All the vibe code basics of cursor, plus 4 Projects in one video! This is how I, as someone who has never written a line of code, approach building apps (every day). Part 1A Intro to Cursor, Composer, and some basics --------------------- 00:00 Intro 03:41 Downloading Cursor 06:09 What the hell is Composer? 10:47 A Note on Context and Keeping Composer Threads Small 11:38 Simple Desings with Cursor Composer From Blank Project 14:04 Editing a Simple Animation With Cursor Composer 16:35 Setting Up The Voice to Talk to Cursor Composer Whispr Flow 17:54 Lets an Early 2000's Landing Page Part 1B AI Image Generator --------------------- 23:59 Using the GitHub Template to Create a NextJS App 26:43 Template is Open, Let's Edit it 28:55 Drawing Out My Idea With Whimsical 30:11 First Prompt Using Place Holders For Image Generation 32:10 Accept All Vs Save All and Restoring in Composer (Saving your work) 33:54 Adding AI Feature (Brief Teaser, Deep Dive Later) 35:15 What is an API 37:22 Perplexity the best place to learn about API's 40:21 Api keys and running prompt for first AI Feature 42:48 Debugging, Woohoo! Learn to love this :) 43:20 Inspect - Console, In Browser Debugging Hack 48:02 AI Image Generation Works! Lets add more Part 2: Landing Page ---------------------- 51:03 Pause and Reflect, What have we done so far? 53:41 Plan for rest of video 54:34 Ok Let's Talk about (1) Designs 56:19 GitHub is like --sref for those who do image gen 58:20 Starting Cursor project from a GitHub Repo we found on Perplexity 01:00:48 Yolo Mode... Wtf is that? 01:02:38 Inspecting GitHub Repo's Examples, to use in our landing page 01:02:58 The Project We're making - A landing page 01:03:56 Landing Page from Screenshot 01:06:17 Making Changes to Landing Page 01:11:42 Making a more epic section 01:13:42 The Essence of Vibe Coding 01:15:17 Creating Cool Testimonials Section From Screenshot 01:18:18 Deploy to Vercel! But First New Repo on GitHub 01:20:45 Ok it's on GitHub... Now lets do vercel 01:21:17 Untechnical Explanation of what Vercel is Lol 01:24:18 Connecting Custom Domain (Bought on Name Cheap) To Vercel Deployment Part 3: App With Database and Authentication ---------------------- 01:27:59 Recap and Prep For The Bigger Project! 01:35:13 Getting Started from Template (Again) 01:38:52 Setting Up Database and Authentication (Firebase) 01:44:01 Back To Cursor, Let's Set up The Auth in the app 01:48:35 Switching to mermaid because compatibility issues 01:51:13 Using AI (Claude) to Generate Mermaid Diagrams 01:52:19 Adding Docs to Cursor to use AI Features over and over again 01:54:38 Let's Troubleshoot 01:56:10 Adding View Button and EDIT WITH AI 02:01:45 AI Diagram Edit Feature is DOPE 02:03:17 Using Search Feature on Cursor to find text in Codebase 02:05:55 Lets add ability to save these to Database 02:09:33 What does saved to Google Firebase even mean? 02:13:00 We can Export as PDF! 02:15:48 GitHub and Vercel Again! 02:17:27 Vercel with CLI From Cursor 02:20:52 Setting Vercel Domain as an Authorized Domain 02:27:34 How To Learn More

1. Do it 100 times. Write 100 songs, cook 100 omelettes, talk to 100 people. It never seems like a huge deal until you try it yourself. It’s manageable, and yet it stretches you, and you’ll be observably different at the end of it. Effective way to get a foothold on a new thing

new finisher animation + charged variant of the kick that does a low sweep (which can also help to dodge some attacks) #gamedev #indiegame #screenshotsaturday

Fantastic summary of the most effective learning techniques based on research study findings:

Do not start with fundamentals. This is an awful approach to learning. Start with so-called "advanced" topics and ask questions until every term/concept is understood. This is the correct, rigorous, scientific way to learn, because the advanced topics are embedded in larger, more convoluted, more abstracted constructs. This embedding is what gives the individual pieces their *meaning*. Foundational studies have removed this embedding, and present only the isolated, sterile pieces. They have no meaning. They have no context. The notion that students will piece together fundamentals into some eventual synthesis down the road is absolutely incorrect. It is literally information-theoretically obtuse. Children don't learn language using pieces. They mumble *fully*. They are never not fully embracing the complexity. It is the juxtaposition between their naive attempts and the full picture that imbues their mind with learning. Prerequisites are the dumbest approach to learning. It is utterly indefensible using any scientific argument. The basics-to-advanced directionality is diametrically opposed to how information is encoded, comprehended and used. Prerequisites are why most computer scientists and whiteboard exam-passers can't make software themselves; they can only be cogs in a company. It's why a Princeton math PhD can write the update rule for gradient descent but can't draw the actual process with circles and lines on a damn chalkboard (true story). Idiot level stuff because their learning was all basics to advanced. They never defined terms and concepts in an embedded fashion. It was all disconnected. Meaningless muscle memory with no understanding. It does not work both ways. Only pieces that are seen inside the bigger picture are understood. Do not start with fundamentals.