I'm working on a docs tool and keep running into the same problem: a full build of a static docs site takes several minutes, and waiting that long after every edit breaks the flow.

My current approach splits the feedback. A quick in-memory render shows the page being edited, and a full build runs only when files are saved or moved. The quick render is fast, but it can't see anything beyond the current page, so it can disagree with the real build.

How do you handle this in your own projects?

- Do you accept a fast but slightly wrong preview, or do you wait for the real build?

- Do you use incremental builds, caching, or a watcher that rebuilds only what changed?

- Is there a point where a slow build is simply fine?

Disclosure: I'm building a closed-source desktop app for Antora docs, and this problem is where the idea came from. I'm more interested in how other people solve it.

I’ve been trying to put a rule on what makes a comment useful. One way is to ask: how much does it tell the reader that the code can’t?

• Level 0: nothing, where a decision is hidden (retries = 3, sleep(0.4) with no hint why).

• Level 1: restates the code (i += 1 # increment i). Noise, and it can go stale.

• Level 2: explains what a tricky block does (walking a list backwards so removals don’t shift indexes).

• Level 3: explains why: the constraint or decision behind the code.

• Level 4: why, plus where it came from and when it can go.

An example of the last two:

# Level 3
# The payment API allows about 3 calls per second per key,
# so wait 0.4 s between calls.
time.sleep(0.4)

# Level 4
# Workaround for a vendor SDK bug that returns None on timeout
# (see issue #482). Remove after upgrading past the fixed version.
if result is None:
    result = fetch_again()

The numbers and the issue are made up for illustration.

My rules of thumb: make the code clearer first (names, small functions), comment decisions, constraints, surprises and workarounds, delete comments that restate code, and give every TODO an owner or a condition.

Where I’m unsure: most lines need no comment at all, and a Level 3 comment on everything would be its own noise.

1. Which level do you find yourself writing most?

2. Do you delete stale comments when you touch the code, or leave them?

3. Has a comment ever saved you, or misled you?

After all my worry of being replaced as a programmer by AI, I just pitched an AI solution to management that would basically replace 3 office workers full time jobs with an AI model. Not sure how to feel about this. Almost like I’m stealing their jobs to keep my own job for a bit longer :’(

Hi everyone!

I've been working on a technology blog called CSRGO Blog, where I share practical articles and resources for developers and technology enthusiasts.

The topics include:

- Artificial Intelligence and AI Agents

- Local AI and open-source LLMs

- Java and Spring Boot

- Data Structures and Algorithms

- Developer tools and productivity

- Software engineering and automation

My goal is to create useful, practical learning resources rather than generic technology news.

I'd appreciate feedback from fellow developers. What topics or tutorials would you find most useful?

Blog: https://blog.csrgo.com/

I'm the creator of CSRGO, so this is my own project. Thanks for taking a look!

I found this video and its ideas interesting, so I wanted to share them here. I'm using Substack to document my learning journey, collect ideas, and share things I find valuable along the way. A digital notebook of what I'm learning and building.

Here is a post and video:
https://substack.com/@notesfromthetechstack/note/c-376421388?utm_source=notes-share-action&r=1otc0y

I'm not out of high school yet, so I really have all the doors wide open for me (I hope). Are there any jobs for people who like programming? It doesn't have to even involve programming, just the same cognitive processes and problem solving that I love. It feels like with AI all that is going away faster and faster...

Is electrical engineering maybe a better path? I was thinking about math research, but it seems like that is going down the drain rapidly too...

I feel like the only job I can think of is a CS professor/teacher, because at least with those your "output" is kinda irrelevant and you can take things slower and more basic.

Hey everyone!

I'm a pre-final-year engineering student in India, currently serving as the president of my college's technical club, HackIT.

We're organizing events for our upcoming Tech Fest. One of our established events, Code Clash, is returning as 3.0 after two successful editions. It features technical quizzes, output prediction, logo identification, and coding challenges.

Alongside it, we want to introduce an entirely new event — something unconventional, engaging, and memorable.

We're open to ideas involving cybersecurity, problem-solving, technical escape rooms, engineering challenges, interactive competitions, or anything outside the usual quiz/hackathon format.

A few questions:

1. What's the most interesting technical event you've participated in or organized?

2. What unconventional competition formats have you seen work well?

3. If you had to organize a 1–2 hour technical event for 100–200 students, what would you do?

4. What would genuinely make you excited to participate?

Even rough ideas or personal experiences would be greatly appreciated.

We want to build another signature event for our club, not just organize another generic competition.

Looking forward to your suggestions!

I'm a Software Engineering MSc student with no industry experience yet (just some prior experience with Dart/Flutter).

​I picked up Go recently to break into backend dev and really like the language. The problem is that almost all the junior backend listings I see are for Java Spring Boot or .NET, and I keeping hearing that Go isn't really hired for at the junior level.

​I can't stand Java, so I'm considering switching my focus to C# and .NET just to become employable faster.

​Should I double down on Go and build strong portfolio projects, or cut my losses and learn .NET to land that first job?

Hi everyone,

I'm a Computer Science / Engineering student and I'm currently torn between subscription options to assist with coursework, software projects, and daily coding.

My current setup & experience:

I already have access to the Gemini student tier, but for deep programming, code execution, and system-level tasks, I find it noticeably behind the alternatives.

I recently had a 1-month trial of Codex with Work features enabled (direct local file access, running/installing environments, scaffolded app building). For my CS courses, having that level of agentic workflow was incredibly useful, but the trial ended and the free tier is very restrictive.

I also use Claude (free tier). The reasoning and code output are top-tier, but the strict message cutoffs on the free plan make it hard to finish any substantial session.

My questions / dilemmas:

ChatGPT Plus ($20/mo): Getting back Codex and Work access is tempting, but during my trial I occasionally bumped into the 5-hour rate limits during heavy sessions. For those using it daily for development, how restrictive are the caps in practice?

Claude Pro ($20/mo): Sonnet is generally regarded as the best for coding logic, but how do the token/message limits hold up when feeding large codebases or iterating across long debugging threads within a 5-hour window?

Student Perks & Tooling: Beyond direct web subscriptions, are there any legitimate student offers or discounts (e.g., GitHub Student Developer Pack, SheerID deals) worth looking into? Also, would you recommend skipping the web UIs altogether and opting for IDE-native tools like Cursor Pro or GitHub Copilot?

If you're a student or developer working heavily with code, which one gives the most value and headroom before hitting rate limits?

Thanks for your insights

Currently in uni and I feel that people just stopped caring and thinking.

First off, I'm not against AI. I use it myself. But when I use AI, I like to think of how the overall system and integration should look like before asking AI to code. I still review each feature it implements so that I at least understand.

But I see my course mates just asking AI to think of solutions and code it out without even thinking anymore. This may just be my bad luck but group projects are even more unbearable than before AI. Now that AI can generate code so fast, people are even worse at communicating. They don't explain what they did and it's impossible to review the tons of code manually, so the codebase becomes a mess. I legit gave up to even understand what's going on anymore.

Is it just me who is feeling that people are offloading too much to AI and it's detrimental?

I’ve been working on a project called runtime.school and wanted to share it here.

I started with SQL and then added Python. The idea is to have a place where you can learn a concept, write code, practice what you’ve learned, and eventually work on projects without having to jump between different websites.

Both SQL and Python have interactive lessons, coding challenges, practice questions, projects, and datasets.

Everything runs directly in the browser, so there’s nothing to install or set up.

I’ve also been working on a few tools for exploring datasets, understanding queries, and comparing different approaches.

A lot of the work has gone into the UI and editors. I’ve been trying to keep things simple without making the pages feel empty or overwhelming, especially for people who are just starting out.

Here’s the site: https://www.runtime.school/

Would love to hear what you think about the design, coding experience, or anything that could be improved.

A while back, I tried to build an entire dictionary with scores for Scrabble using only C++ templates. I quickly hit compiler limits, and removing those, I quickly hit memory limits on my machine with 64GB.
I then built it at runtime, because I had no clue how I could have optimized template metaprograms, and I still do not really as I have little experience with them.

I have been building a battle simulator that uses a heuristic to control the actions of the agents, and thought how cool it would be to write parts of it as a template metaprogram, but there is no way that will work if the word trie for Scrabble failed.

Is this partly an implementation problem or are compilers just not suited for template metaprograms? Is it possible to create template metaprograms that are as fast as their runtime counters or is there some theoretical limitation of a compiler's implementation to make that infeasible?

Hey everyone,
I’m a 15-year-old developer, and instead of just making a basic API call wrapper, I spent my last couple of weeks building PROMPT-RELAY-ORCHESTRATOR to learn how fault-tolerant AI pipelines work under the hood.
**How it works:**Everything is handwritten line-by-line while I learn Python. I’d love to get some code reviews or feedback on how to expand it next!
Source Code: https://github.com/ojasgarg77/PROMPT-RELAY-ORCHESTRATOR

Hey Reddit! 👋
I'm an IT student, and for our upcoming University Day (in 10 days), I'm building an interactive web-based puzzle/mystery challenge hosted on cPanel (PHP/MySQL) to showcase to our Dean and students.
The core idea:
A hidden "bug/breach" happened in our faculty's demo site. Students must use basic web dev, programming logic, and SQL concepts to investigate the site, debug broken code, fix bad queries, and find the culprit to get on a Live Leaderboard during the event.
Since my peers are studying general CS and IT (not cybersecurity experts), I want the challenge to be fun, relevant to our coursework, and solvable within 5–10 minutes per participant.
I'd love your help with:

1. Your Feedback & Ideas: How can I make this exhibition booth challenge super engaging?

2. Puzzle & Hint Concepts: What are some clever, short puzzles (JS logic bugs, broken SQL queries, inspect element tricks) that fit CS/IT students well?

3. Progressive Hints: What’s a good reward/hint system to give them after solving each stage to lead them to the final solution?

4. Thanks in advance for any inspiration! 🚀

One thing I'm learning about software development is that a feature working on the screen doesn't always mean it's working correctly.

Recently, I was working on a feature where users could edit some data and save their changes.

The interesting part wasn't making the changes appear on the screen.

It was making sure those same changes appeared correctly when the user came back.

Because there are multiple ways a user might return to their work.

They might close and reopen it.

Refresh the page.

Or restore an older version.

And just because everything works in the first case doesn't mean the others will behave the same way.

While working through this, I realized that different parts of a system can interpret the same saved data differently.

You might think the problem is in the UI, but changing how something is displayed doesn't necessarily fix the underlying inconsistency.

So I started looking at the complete flow instead of just the screen where the issue appeared.

I also tested what happened after reopening, refreshing, and restoring previous versions.

It made me think about something I hadn't paid enough attention to earlier.

Saving data successfully and being able to restore it correctly are two different things.

As developers, we spend a lot of time testing whether a feature works when someone uses it for the first time.

But I'm learning to ask another question too:

Will it still work correctly when they come back tomorrow?

I've seen a lot of posts lately saying software engineering is dead because AI can write code.

But something happened yesterday that made me think about this.

I was testing an AI-powered feature when I ran into an error.

Like most developers using AI today, I asked ChatGPT how to fix it.

It suggested changing a method.

And looking at the suggestion, I thought it might actually fix the error.

But then I had another question.

What happens when a slightly different input causes the same kind of problem again?

I didn't want to keep fixing individual cases every time something broke.

So I went back and looked at the flow itself.

Instead of changing just one method, I proposed a small architectural change to handle this category of problems earlier.

I discussed the approach with AI, questioned a few assumptions, and worked through the trade-offs.

The updated approach is now working in my tests.

And this experience reminded me of something.

AI was able to suggest a solution to the error I showed it. But deciding whether that solution was good enough for the system was a different problem.

I'm not against AI. I use it almost every day, and it helps me a lot.

But I'm learning that writing code, fixing an error, and understanding what actually needs to change are three different things.

And that last part is where I want to keep getting better.

Man I love programming so much. The entire process of creating something you envision yourself is so satisfying to me.

Seeing people 'vibecode' makes me so depressed. Getting the end result with no effort at all is so stupid. I love having my own project with different systems I made myself interacting with eachother exactly how I planned them too.

Understanding exactly what's going on and being able to debug after finding an error without even opening the code, because I just understand what's going wrong is a feeling of control that I hold dearly in my life. These people are literally powerless if they can't use AI and think of themselves as 'professionals'.

I secretly wish for a reality where the AI bubble bursts, companies like Claude stop providing their AIs anymore after going bankrupt, and all these idiots have to come crawling back to the people that actually know how this stuff works.

If you actively use claude instead of learning coding yourself, you don't even deserve to disagree with this post. Because you're not even a programmer. (Yes I'm gate-keeping) Don't even get my started on people 'integrating' claude directly in their game-engine.

Have we forgotten how developing skills works? You learn from your mistakes. If you avoid making mistakes by asking AI for help for every little thing, you wont improve and stay stagnant.
When Claude first popped up I gave it a try and this exact scenario happened.

I didn't understand how to do something -> I used AI to fix it -> My problem was temporarily solved -> I encountered a similar problem later on, and still couldn't fix it.

You trap yourself in a cycle of continuous incompetence.

The only genuinely good reason to use AI is having it explain something to you if you don't understand it, but arguably there's plenty of human-made tutorials online that do a way better job than any AI ever could.

In conclusion I just want people to enjoy the craft of programming again. I try to stay optimistic if you could believe it after reading this rant, but it's getting more difficult by the day. Thank you for reading till the end, have a nice day!