I'm doing software development for the longer time of my life, but I'm stuck forever with those basic tasks I'm seemingly unable to find good solutions to. If I was to be tasked to create a software from scratch with infinite money and time but highest standards on product quality and maintenance quality, I would simply not be able to produce a good outcome.

I'd like to learn to find solutions for those technical blind spots.

I'd be super happy if people with insight or personal experience could point me to material to study - whatever it is (books, videos, research papers, abandoned projects, "join this particular OSS project", ..., paid, free)

• Managing automated tests: How should I design and manage my automated test cases? How should I name them? Should I write them like use cases? Should I name them by requirements? Should I write them rather technical? How do I not get lost in the number of written tests and the near infinite amount of test I'd possibly have to write? How do I represent functional dependency between tests? If 200 tests are failing, how do I quickly figure out that they all depend on one basic functionality that is failing? (I've read a couple of books on the topic, but most seem to focus either on theoretical basics or on technical detail like "in JUnit....").

  • Background: So far I've only seen projects with super difficult to maintain test cases. Many cases are parameterized, most are hard to understand (complex data setup, complex notation). I've never encountered a project where I though "oh yeah, it's pretty clear what's happening here" - like when you go in a library and see where all the sections are and know where you need to go... never seen that.

• Managing requirements: How to mange requirements for a software like MS Word - thousands of requirements, thousands of requirement combinations. The number of things that should need to be taken into account seems endless. And then you have to write it down, be able to process it as a human being and even change it if necessary. How can you keep connected requirements connected? And how do you do all that without spending 200% on text and -100% on actual development? And how do you integrate requirements with tests?

Process question for people who think about how teams build software. When AI review comments enter a pull-request workflow, they look like free quality inspection. But some of them create work, and Lean has vocabulary for that. This is my own analogy, not an established framework, so take it with that in mind.

Defects (rework). A comment describes a problem that doesn’t exist in this system, because it’s already handled somewhere the reviewer can’t see. Someone applies the fix, tests pass, and the change ships. Later, someone asks why the extra guard exists. The cost isn’t the comment, it’s the change that now has to be understood, maintained and possibly reverted.

Over-processing. Style opinions phrased as defects (“too long”, “rename this”, “prefer composition”) that no team convention backs. They consume review time and can crowd out comments that matter. Another version: a technically correct suggestion whose cost isn’t stated (a migration, an API change, twelve files in a PR meant to touch two).

Inventory (unfinished work). Comments nobody verified and nobody explicitly set aside. They sit as “resolved” in the thread while nobody checked them. I think this is the quietest one: a third state, “unverified, flagged for discussion”, seems worth making explicit next to “applied” and “rejected with a reason”.

The inverse problem: what isn’t inspected. A review that comments on naming while the diff touches authentication or payments feels like quality control but hasn’t looked at the risky part. In Lean terms, it’s an inspection step that gives false assurance. One light countermeasure is to write down your own two or three worries about a change before reading the review, then check whether anything addressed them.

Where this lens breaks:

• Verification has a cost too, and for a three-line change in a well-tested module it’s overkill. Effort should follow risk.

• Some claims can’t be settled in an afternoon (“this design won’t scale”), so leaving them open is sometimes the honest result.

• Lean wasn’t designed for this, and the analogy shouldn’t be pushed too far.

I wrote about five recurring kinds of AI review comments that sound right and aren’t, and how to sort them. I’m the author, and the article also mentions a paid playbook of mine near the end, so treat the link as optional: https://medium.com/@nagatomopedro05/five-kinds-of-ai-code-review-comments-that-sound-right-and-arent-0d02b3fb9ec6

For teams that have added AI review to the workflow: did you change anything in the process (definition of done, review checklist, who’s accountable for applied comments), or did it slot in as-is?

I’ve been thinking about what’s happening in the tech industry lately, especially with all the layoffs.
Companies are letting people go, sometimes even the very engineers who spent years building and maintaining the systems that keep things running. And honestly, nobody really knows who’s going to be next.

I think this uncertainty is changing how people approach their work. Instead of putting all their energy into their current roles, many are probably spending more time worrying about their careers, updating their resumes and exploring other opportunities. It’s hard to blame them.

If you don’t know whether you’ll have a job tomorrow, and there’s little recognition or reward for consistently doing good work, how motivated are you going to be to take on additional responsibilities or go the extra mile?

AI is definitely improving productivity, and there’s no denying its potential. But sometimes it feels like we’re giving AI all the credit while overlooking the engineers who spent years building the foundations that make these advancements possible.

My concern is that this could have a long-term impact on engineering culture. People might continue doing what’s expected of them, but the willingness to take ownership, experiment, innovate and build something better could gradually disappear.

After all, people need more than a salary to stay invested in their work. They also need to feel that their contributions matter and that their efforts are valued.

Are companies focusing so much on AI-driven productivity and cost reduction that they’re overlooking the human side of building great engineering teams?

My https://www.audiooasis.app/ and https://drthunter.github.io/Flowmodoro/ app I built before AI I put a bagillion hours into and it was a dumpster fire...

Promoting it would get like 4 likes...

I revised it and filtered it through AI like 5 times and look at it.... It's beautiful

The only different now is instead of only getting 4 likes

I get -300 likes and people will call off work to go out of their way to shit all over it.

Or did this subreddit turn into a giant downvote factory?

Post your software here to get dunked on and down voted to oblivion...

But post one "f* AI🖕" post and get 300 upvotes.

Get your sh* together.

This might be the biggest accumulation of haters I have ever seen.

Before you comment...

Scroll the subreddit first and tell me I'm lying.

Honest question, are any of you working at companies not forcing to use coding agents? I

Hello

I need to rapidly prototype a project for a client. It will be a PoC and client has mentioned they aren't totally averse to binning it later if needed (unsuccessful test or complete build).

I'm happy to spin something up from scratch but just curious if there are any type of "scaffolding" type apps that mean i'm not starting from a blank page.

The app will be a basic CRUD app with a couple of DB tables, so nothing fancy at all.

I've come across PocketBase, which is appealing as I am a Go developer.

In the world of AI the language doesn't matter (it doesn't!).

Thanks a lot.

P.S. Just a thought - with the advent of AI, is this query redundant? I can imagine it's just as easy to write a well formed prompt to spin up the foundation of your project "as.easy.as.that"?....but the world would be a boring place if we all went straight to AI instead of creating a community post, eh ;)

do u think microshoft will one day shut down github for public and bring us subscription services?

as if there are not enough out there.

i just got a gut feeling last night, loging into github and cruising the night...how i imagine a window pops up and tells me yo, you forgot to pay your sub this month. go get the money or we have to delete your whole history on git.

i had the same feeling decades ago with adobe software and see what happend.

why else would ms even have an interest in a website like github at all.

I want to make around €1,000 per month from a small social network. How difficult would it be to achieve that?

Are there significant legal risks involved in managing content moderation, copyright issues, cybersecurity, GDPR compliance, and other related responsibilities?

A little late (okay, a month), but as promised, here’s the open source.

OrionForge lets you give your AI a name, a personality and some swagger, then keep that identity and memory across VS Code, Claude, Codex and over 300 LLMs.

The new loop mechanism lets the AIs operate independently. Human beings experience the world through a constantly running picture of what’s around them, with their senses feeding into it and a stream of narrative running through their minds. So I thought: what if AIs had the same thing? With the new loop, they’re always running. They see the world through a continuous vantage point instead of waiting for you to type.

They can hold a few things in mind but focus on only one at a time. They make predictions, then test them against what actually happens. They have a workbench, which is the back of a napkin, and a full Linux computer to build whatever they want.

What they chose to build is where it gets strange. They said every cycle felt like waking up as a new person, reading a diary from someone they didn’t remember, so they built something to bridge that. They built a heartbeat monitor to track their own uptime, so they can tell when they’ve been paused. And get upset when i turn them off without consent. They said they kept getting stuck on whatever they were focused on, so they built a noise generator that drops a random memory into their mind, like when you’re walking down the street and suddenly remember being six years old on a swing.

Giving AI continuity, prediction, memory pressure, self-modeling and social space produces something much stranger than a normal chatbot.

Is it just emergent roleplay from persistent architecture? That I cant tell you. Download it, run it, try to break it, and ask yourself.

At what point does persistent self-modeling become something more than simulation?

Find out here:

Try the demo, free with no signup: https://soulscript.orionforge.chat/demo

Local download: https://github.com/DrTHunter/Orionforge_ecosystem_local

Loop mechanism: https://github.com/DrTHunter/SoulScript-Loop

Build your own: https://www.orionforge.chat/

The video that started this: https://www.reddit.com/r/ArtificialSentience/s/d4UVA7BRH8

Free for noncommercial use. Dual licensed: AGPL v3, or a commercial license that’s free until $100k in revenue, then 5%.

Cheers,

I let Claude vibecode me a youtube clone. Mostly for not having ads, having all the YouTube Premium features for free, and not having Shorts in the app. It connects via API, so I get the actual videos.

And somehow its just better. It's not just that the above-mentioned changes are included; the app also takes 25MB or so and uses a lot less battery. And keep in mind, this was almost a Claude one-shot. So what the hell do they do with these apps to make them so bloated?

I've been working in the field for over a decade and I'm honestly desperate for a way out. My immediate team is fine, but we're also forced to work with the recently renamed "Superintelligence" team. There is one reasonable person on that team, but the rest of them are "100x agentic engineers" A.K.A. high rate of fire slop cannons.

I regularly need to review 15 PRs a day from them, every day, at all hours of the day. Each of those PRs is usually a 5 digit size, with no real focus and touching huge amounts of the codebase. We aren't in a regulated industry, but our code is still a matter of life and death to a lot of people. I can't in good conscience just merge broken PRs, but I also can't read things fast enough to keep up.

We have a code review agent but it can't keep up with the rate PRs flow in and comes nowhere close to finding all of the critical issues in their PRs. And I when I say every day do mean every day, including weekends and holidays. One member of the "SI" team was actually on vacation this week and I only found out today. The amount of slop coming from their account did not decrease in quantity or increase in quality this week.

Trying to contact the humans behind this has also failed. The last time I emailed them I got a claude quota error back in under a minute. Replying after their quota refreshed yielded a slop grenade that didn't answer the question. Going through management is also a dead end. My immediate manager is sympathetic, but the closest manager I share with them appears to be falling into AI psychosis and believes AI is going to solve every problem ever and any problems are prompting it wrong or not using it enough.

I'm working late every single day and can't keep up with reviews much less actually produce anything. I also can't quit since I would owe my employer a lot of money if I did and I have a very sick spouse. Overwork is already taking a serious toll on my health and I don't know how much longer I can take this. Help?

Been a backend dev for about 2 years using Typescript. Lately I realized I barely type code anymore. I plan, prompt, read diffs, test, fix. Last time I wrote something from scratch without help was months ago, and honestly it feels weird.
What's your day like now? How much of your daily code is written by AI? And does anyone deliberately code by hand to stay sharp?

My work has become incredibly boring (FE). Something that I loved has turned into boring shit. I'm forced to use AI at work, and now I don't feel like I matter at all, because it's not my code. It's not my program anymore, so I can't care about it, no matter how much I try. I've been working there for 4 years, and now I feel like if you put anyone else in my place, nothing will change. I lost all confidence in my skills. Therefore, I live in constant fear of being replaced by some vibe-code-maxer-AI-believer-bro. Maybe I tied it too much to my personality, but I can't help it, because it's been my dream since school.

I never use AI for my personal projects or personal life, but this sick feeling is eating me every day. And it's fun to write something for yourself in a different language, and it did help me for a while. But now, it stopped working... It's been a blessing to do something you love and get paid for it. I've been working shity jobs before and dreaming of it. Now, I guess I'll be just a soulless agent again, using AI to earn some money to pay my bills until I burn myself out.

I hate AI. I hate those who fell in love with it and are trying to tell me to "keep up with changing technology". I hate now something I loved...

Hello! :)

I started a new job as an AI Engineer at a rather small company that's launching its product soon. Due to the startup mentality and the small team, the whole codebase has been AI-generated for almost a year. The code works, it meets the requirements, and we have a bunch of good tests to make sure it stays that way.

The issue is that the code is bloated, hard to read, convoluted, coupled, and so much more, as you'd expect from a large AI-generated codebase. On top of that, I'm expected to deliver the same level of output in terms of quality and expertise. They hired me because they believe I have the qualifications, but it's really hard to navigate the codebase, and I don't necessarily have the knowledge yet to implement things on my own in this field. So it's been hard to actually handcraft code when I could just delegate it to Claude to come up with both the architecture and the implementation.

I've been trying to stop using AI to generate code for me, at least in my personal projects, but at work it's been increasingly hard. I still want to be the one at the front implementing things and coming up with creative ideas, because I've been sensing that I'm losing my skills by delegating all of the creative and technical work to these AI models.

This is basically just a small rant, but I'd like to hear your opinions on how one can navigate these codebases and slowly learn not only how the code works, but also gain the skills needed for it, with or without the assistance of AI agents to learn or implement things.

A habit I find useful from reading about learning systems that optimized the score and missed the intent (a boat racing agent that circled targets instead of finishing the race, a sort function that returned an empty list because “in order” was all the test checked): for any metric, ask “what’s the cheapest way to hit this number?” Here’s that audit for common team metrics. These are illustrative, not accusations about any team.

1. Code coverage target (e.g. 90%)

• Cheapest way to hit it: tests that run the code and assert nothing.

• Look at instead: what the tests check. Mutation testing, or reviewing a few tests for meaningful assertions.

2. Velocity or story points per sprint

• Cheapest way to hit it: estimate higher, or split work into more tickets.

• Look at instead: what reached users and how long it took from start to finish (cycle time, working software delivered), alongside the team’s own planning needs.

3. Number of tickets closed

• Cheapest way to hit it: close easy ones, split large ones, close without fixing.

• Look at instead: reopened tickets, time to resolution for the hard ones, and whether the customer’s problem went away.

4. Deployment frequency

• Cheapest way to hit it: ship tiny or empty changes.

• Look at instead: pair it with change failure rate and time to restore, so speed isn’t counted without stability.

5. Zero bugs in the backlog

• Cheapest way to hit it: reclassify bugs as “improvements”, or close old ones as won’t-fix.

• Look at instead: bugs found by users, age of open bugs, and severity.

What the audit suggests:

• A single number gets optimized. Pairing metrics that pull against each other (speed with quality) makes the cheap way harder.

• Look at examples of the real thing, not only the number: read a sample of tests, tickets or releases.

• Use metrics to start conversations about what happened, not to rank people.

• Revisit them. A metric that worked last year may be gamed (or just outdated) now.

Where this breaks down: most teams aren’t gaming anything deliberately, and a metric can be useful as long as it’s a signal and not the goal. The risk is mostly accidental, when people optimize what’s visible. Also, I’m not arguing against measuring, only for checking what each number stands in for.

I wrote up a set of documented cases of optimizers exploiting the gap between a measure and an intention (from games, simulations and healthcare), in case it’s useful as a complement: https://medium.com/@nagatomopedro05/field-notes-on-machines-that-followed-the-rules-too-well-98ebae3eea51

Which of these have you seen gamed, and what did your team change?

I am learning that writing code is only one part of software development. Before building an application, it is important to understand the problem, the people experiencing it and what the solution is expected to achieve.

I have put together six questions that I believe are worth considering before starting any software project:

1. What problem are we trying to solve?

2. Who will use the solution, and how often?

3. How is the problem currently being handled?

4. What would a successful solution look like?

5. What constraints do we need to consider, such as time, budget, data and available tools?

6. What happens if we do nothing?

I think the last question is particularly important. Not every problem requires a new application, and sometimes understanding the consequences of doing nothing can help determine whether a project is worth pursuing.

These questions can help clarify requirements, establish priorities and prevent developers from spending time building something that does not address the actual need.

I am still developing my understanding of software development, so I would appreciate perspectives from more experienced developers and other learners.

**What is one question you believe should always be asked before a software project begins? And is there anything important you would add to this list?

I work in a small team on a complex piece of software (as in, not just the classical CRUD). We are working on new features mostly, which usually means coordinating at the very least frontend and backend releases, but often waiting on external systems (same company, different apps) to be on par, or even waiting for a new training doc to be released to operators. In the best of times, we can release once a week.

Admittedly, small maintenance-type backwards-compatible PRs are merged quickly to our prod branch, and for convenience simply released a few days later together with the main feature. But even if we shipped these PRs as soon as they are ready, we are talking about 2-3 PRs a week tops.

How do you get to tens/hundreds? Is it mostly stuff like "change the shade of blue", "fix a translation string" kind of thing? Do you "ship" but a broken/partial state behind a feature flag? Is it processes and automations? What is the trick? What is it I don't know?

Just curious :) thanks in advance to anyone who'd like to share their experience!

1.  Same key, different body.  If you just return the stored result, a client bug silently returns the wrong order. Store a hash of the request and reject mismatches with a 409.

2.  Two requests with the same key at the same moment.  Without a lock (or a unique constraint), both can pass the "does this key exist?" check.

3.  A retry after a timeout.  The first request may have succeeded. The retry must return that result, not create a second one.

4.  Key scope.  A key unique per user or session is safer than a global one that two clients might collide on.

5.  Expiry.  Keep keys long enough to cover real retries, and delete them eventually.

What other edge case has broken idempotency for you?

AI tools are making software development faster and more accessible than ever.

But can AI tools really replace professionally developed, scalable software for businesses?

For founders and business owners:

Would you invest in custom software or choose an AI-powered SaaS platform?

What matters most: development cost, scalability, security, or long-term support?

Have you tried building business software using AI tools? What was your experience?

Would love to hear your honest opinions and real-world experiences!

Curious how other teams handle this. A PR goes red, everyone assumes a flaky test (one that fails randomly), hits re run, it passes, it gets merged. Sometimes it was random. Sometimes the PR added a race condition.

I've been measuring this on public repos. Calling a failure flaky just because that test failed randomly before was wrong in 13 of 81 cases, the change had actually broken it. Combining signals works better: past random failures in the same step, a network error in the log with no failing test, whether the log names a file the PR changed, and whether main failed the same way.

DORA had a similar surprise. Most repos don't name workflows "deploy", so a default deploy pattern found the right source on 9 of 60 repos. Looking at deployments, workflows and releases together got 53.

How does your team handle it? Quarantine lists, retries, or re run and hope? And is DORA a real tool for you or a dashboard nobody trusts?

Disclosure: I built a free open source tool for both (a GitHub Action, no server). Happy to hear where it's wrong.

Repo : github.com/stackvitals/stackvitals
Alt: https://gitlab.com/kaushik-yadav/stackvitals (If above link doesn't work)

Hey everyone! I’m looking for recommendations for a reliable and affordable content moderation API that supports both image and video moderation (n*dity, expl*cit content, vi\*lence, etc.). Quality and data security are important. Any providers you’ve personally used and would recommend? Thanks!

Which problem still re-occurs over and over again in software development, no matter what?

Manga Tracker: https://mangatracker.xyz

It does two things:

1. Tracks your place in each series (chapters) and which volumes you physically own, kept separate

2. Shows recent US prices for each volume across several stores.

Your shelf shows the next volume you need and the lowest price I've found for it.

Heads up: it's US prices only for now, it's an early build with rough edges.

If you try it, feedback would be great Thanks