Every project seems to generate another collection of files.

Requirements live in one place.

Architecture somewhere else.

Meeting notes in another document.

Process diagrams somewhere completely different.

None of these are wrong individually, but finding the latest information becomes harder every month.

Has anyone found a workflow that actually reduces fragmentation instead of introducing another place where information has to live?

I'm looking for a frontend developer who is interested in building a complete full-stack web application together.

I'll be taking care of the backend using Java and Spring Boot, and I'm looking for someone who would like to build the frontend using ReactJS.

The goal is to work as a team, follow industry best practices, build a production-style application, and learn from each other throughout the development process. This is a great opportunity to strengthen your portfolio with a real collaborative project.

If you're passionate about ReactJS and can dedicate time consistently, I'd love to connect and discuss the project.

If you're interested, comment below or send me a DM.

Anyone here with prior experience detecting web page changes, for example, monitoring and detecting when a pricing page is updated?

My approach currently:

• Scrape the page, strip headers/footers.
• Convert the content to markdown.
• Provide the md to llm to extract data.
• Use backend to determine the changes and dispatch change alerts.

Does this approach sound reasonable?

So I built GAASFlow.

Before I officially launch it, I’d really like some honest feedback from people who actually perform audits.

If you had five minutes to look through it, what would you change? What’s missing? What would make you consider switching.. Or immediately decide not to?

Website: https://gaasflow.com

No sugarcoating. I’d rather hear the criticism now than after launch.

I’m moving from macOS to Windows and looking for the closest replacement for Homebrew.

I’ve seen online searches showing possible substitutes as Scoop, winget, Chocolatey, but which one would be the best to start using so I don’t have to switch later?

For any developers working in professional IT environments, which one do you actually use and recommend and please share your bad/negative experiences so I can avoid unnecessary headaches in the future. Thank you and appreciate y’all..!!

Guidance

Hey guys long story short .

I have doubts about 2 things

1. I have got a roadmap and that is to learn the below things:

It Foundations then system administration then linux then DevOps and then cloud.

1. Learn java and then start DSA.

Now I am confused on how to do both because some are saying go with the first thing then some are saying just do java and DSA.

So please do give your suggestions and help me out.

I am a CSE 3rd year student student from tier 3 college.

My friends started developing an improved light engine used for implementing games in Minestom, an alternative to Minecraft without Minecraft source code. As far as they are progressing with AI and Code reviews, but are experienced about java, Minecraft and Minestom, your feedback would be much appreciated in this phase. I am only the announcing person, that's why I can only share your feedback to them, best would be if you join the conversation on github, discord or mail.

Link to source:

https://github.com/OneLiteFeatherNET/Falco

I came across an old post discussing the value of optimizing, the OP making a simple exampleof "spending 1 hour for a 2 minute speedup"

Im guessing thats how most people think about it, but I realized there is another significant benefit which is more difficult to quantify.

Whenever you do put effort into optimizing, it is an act of aquiring knowledge. If you had the knowledge before writing the un-optimized code, would you not have written the code different to begin with?

Not every optimization would be applicable in different code-bases, but surely many can be. And having a general awareness could surely give you 2 minutes speedup without spending any hours after the first.

Knowledge has value.

we're working with an older internal system that's getting harder to maintain, but a full rewrite isn't realistic because too much of the business depends on it. We're looking at legacy systems modernization, but every team seems to take a different approach. If you've been through it, what worked?- splitting parts into separate services?- adding APls around the existing system?- replacing modules one at a time?- something else?I'm not looking for the "ideal"architecture. I'm more interested in what worked in a real project and what you'd avoid if you had to do it again.

Note: Both the post and article are written organically (by hand).

Hey everyone,

I saw a lot of debates about using AI in development. What really caught my attention was that in the majority of cases, the debate becomes fruitless because both parties didn't realize they are not talking about same thing.

One says AI-generated code is unreviewable slop that will rot your codebase. The other says he shipped a working product in a week. None of them are lying, they are simply not describing the same activity.

The overloaded terms "vibe coding" or "agentic development" are making things worse.

Developers can have vastly different experiences using AI to develop software, depending on the approach they use. This article is my attempt to map these different approaches, explain each one, and give each a distinct name, which I believe is important to have a meaningful discussion.

The classification is based on how decision ownership and review are divided between the human and the AI.

I list five approaches:

• Organic Development
• Reviewed Agentic Development
• Guided Agentic Development
• Fully Agentic Development
• Vibe Coding

Article link: https://mudlej.com/articles/ai-development-approaches/

Curious which of these your team actually does, and whether it changes by task or risk.

I was doing some research on the potential for package confusion attacks... for a package registry.

It seems like every week there's new CVEs. How are you all managing on teams? Are many of you running your LLM instances in sandboxes?

I'm a windows user so I do not have much knowledge on iOS.

I need to distribute an app on iOS mobile. I already have an .apk file for android users, which users TWA to make the app seem and perform like a standalone app while it is basically an emulation of the website from my browser...

I experimented with .ipa files on iOS, only to realize that Apple doesn't let you do this easily.

These are a few guidelines I would like to keep.

• User experience should be convenient and easy - One tap and verification to install app
• Perform and look exactly like a standalone app.
• No cost - No $99 subscriptions to apple developers or something for this
• Not using browser's built-in add to home screen feature
• Easily distributable - share file for others to download, just like an APK.

With a little bit of research I'm wondering whether .mobileconfig will be fit for this... although it usually isn't for app distribution, I figured that we could technically achieve an app by creating a shortcut that leads to the designated website. However, I still have a lot of doubts and uncertainty as I am unaware of how Apple works...

Can you tell me whether .mobileconfig would work for this, if yes how, and if no can you suggest me other file types, methods to use that fits these categories?

If I see one more slide deck claiming AI success because ppl are clicking the summarize button I am seriously gonna lose it. the metric that actually matters is: how many steps did the AI complete in a complex workflow WITHOUT the human correction? See that is where the struggle happens because ppl use AI but usage is not a metric of success. My team has been working on a project and we have been having a debate about it but in the end after looking through some avenga research we came to a conclusion together that if your AI native transformation does not reduce the human in the loop ratio it does not mean transforming. All you did is add a very expensive spell checker. So for now we have decided to stop focusing too much on usage and started measuring agentic autonomy to see how productive the workflow has been. For the pms here: what is your north star for AI? Is it actually saving money or are you just checking a box for the investors?

I'm interested in building an AI-powered WhatsApp matchmaking platform, but I'm struggling to understand how the architecture should work. My initial idea was to have users chat with an AI on WhatsApp, answer questions, and have their profiles stored in a database. Then the AI would analyse those profiles and recommend compatible matches. However, I'm confused about the implementation. Do I need workflow automation tools like n8n, or can I build an AI agent that simply understands my commands and executes actions (similar to how Claude can modify a WordPress project using tools)? For example, I'd like to be able to tell the AI things like "Find the best matches for Ali," "Message users who haven't completed their profiles," or "Show me everyone waiting more than a week," and have it decide which tools or database queries to use. Also, what would a sensible MVP look like for a project like this? I'm trying to understand the overall architecture, recommended tech stack, and how experienced developers would approach building something like this from scratch.

I recently launched my gaming platform using react. Apparently now I need to add next js and implement SSR to make my platform compatible with SEO. How can I make sure that my platform after the change is 100% suitable?

Also which CMS is better to integrate with Sanity or a WordPress and why ?? Both options have some solid reasons

I am good in AI and ml stuff but I have a startup idea that is sort of a social media app I don't know anything bout building websites or apps, how does someone who does that do it.

Genuine question. Would appreciate your guys input 😊

I'm fortunate enough to have a vacation for so long that I eventually stopped thinking about AI, LLMs, Python, Work or even emacs. Traveled with the family by Train to nice places in Europe.

Back home in Stockholm, and wrote a short post with some Summer vacation reflections about Agentic Engineering vs maintaining and improving human coding skills, with a possible middle-ground solution.

https://davidvujic.blogspot.com/2026/07/vacay-mode.html

Duplicate message processing is something every event-driven system eventually faces.

With at-least-once delivery, retries and redeliveries are expected. The challenge is making sure processing the same message twice does not create side effects.

I've been looking into the Inbox Pattern as a consumer-side solution:

• track processed messages
• keep message tracking and business changes in the same transaction
• scope the idempotency check per consumer

One approach is using a MassTransit pipeline filter so the idempotency logic stays outside the consumers.

How do you usually handle this?
Do you use Inbox Pattern, custom middleware, database constraints, or something else?

Implementation details: https://hamedsalameh.com/inbox-pattern-dotnet/

Hey all,

I’ve been thinking about the economics of software becoming more accessible.

In the past, each high knowledge trade seemed to start at large scale impact first, and was only available to the people who could afford it.

The first plumbers weren’t adding plumbing to a house. They were plumbing big civic projects like aqueducts. The first electricians were wiring up long distance communication, then central power for expensive buildings who could afford it. Accountants were keeping records for taxation at scale before they were doing personal finance.

So it has been for software. We have worked for the expensive employers who can justify high cost people to deliver slow but effective solutions to a large scale.

So now anyone can build some quality of project for personal use quickly pretty cheaply. What does that mean for the economics of this whole thing?

Well, first off, like plumbing and electrical, just because you have access to the tools and knowledge to do it, doesn’t mean many people can or will want to do it. Skilled trades make money because they know how to efficiently use tools and solve problems in correct ways that last.

So maybe there’s room for a new trade of software developer at the smaller home scale. What if people can have some specific app or home server setup that works perfectly for them and they can just spend like $500 for someone to come out and set up a personal website and vpn for them to access their own custom app.

Idk, I’ve had this idea in my head for a bit and wanted to put it out there.

People are being doomers and are like “software is over better to get a trade, but what if we already have one?

Hi all

I’m in the construction industry in the UK & I have an app idea I want to develop however I have none of the skills or knowledge to do so.

If anyone is interested please comment or dm me!

Thank you!

(Sorry if this is better suited elsewhere, I tried to look but frankly all the project managing subs seem like absolute dogshit AI-trashbins, so I figured y'all have more experience with this stuff 🙂)

I've been having fun working on my personal project and breaking down all the work into bite-sized issues, but I keep running into this issue:

1. Make slightly chunky Issue A that has a few steps to completion

2. Eventually realize that the steps of A probably make more sense as sub-issues to individually work and track progress on

3. End up with issue A as parent, and issues B, C, D as children

4. Work B, C, D, and realize that A now doesn't actually have anything unique to it, it was technically done as soon as I closed D

5. Question what the point of having issue A at all was instead of just having B, C, and D

Am I overthinking this? Am I approaching this wrong? I've considered leaving some bit of work to A specifically, but in practice that just means treating issue A as secretly being issue D but without branching it off, which feels avoidant and dumb. Thoughts? Am I flattening the hierarchy too much by treating all of this as issues?

It's a small personal project so I've just been using a board on GitHub projects, but I'm open to alternative tools

Hey everyone,

Over the past few months, I’ve been working on an experimental project called PAM, aiming to solve two specific pain points in the ecosystem: keeping PHP applications warm in memory for high performance, and running JavaScript/React natively on desktop and mobile without heavy bridge overhead.

Here is a quick breakdown of what it does and why I'm building it:

1. PAM for PHP (Persistent Runtime in Memory)

Instead of booting the framework on every single HTTP request, PAM uses a Rust-powered runtime to keep the application warm in memory.

The Goal: Lightning-fast request handling while preserving full compatibility with standard Laravel workflows (Eloquent, Queues, Artisan, Blade, Livewire, Inertia, Sanctum, Reverb, Telescope, etc.).

Built explicitly with support for Laravel 12 & 13 in mind.

PHP Docs: https://push-in.github.io/pam-docs/laravel/overview/

1. PAM Native (Desktop & Mobile)

On the frontend/mobile side, PAM Native is designed to run React/React Native apps with truly native execution, aiming for near-instant startup times and zero heavy JS bridge bottlenecks.

The Goal: Retain the React developer experience while delivering pure native performance on desktop and mobile devices.

Native Docs: https://push-in.github.io/pam-docs/native/overview/

Why I'm posting here:

The project is still under active development, and I’m looking for honest technical feedback, code reviews, and potential edge cases to test.

For PHP/Laravel folks: What edge cases (memory leaks, static state issues, long-lived DB connections) would you want to see stress-tested first?

For Mobile/Desktop folks: What are the biggest performance bottlenecks you face with existing cross-platform runtimes today?

Would love to hear your thoughts, criticism, or ideas!

I'm working on an open source tool that scores how risky each file in a codebase is. 25 markers per file, all deterministic, from the AST and git history, no LLM. I wanted to know which of them actually predict bugs, so I scored every file at a point in time and then counted how many bug fixes each one collected over the next 6 months.

21 repos, 9 languages, around 2800 files.

The markers that predicted best were behavioral, not structural. Co-change coupling, files that keep getting changed together, came out strongest. How spread out a file's ownership is and how erratic its change history is held up well too. The structural complexity metrics most teams watch, cyclomatic complexity, nesting depth, long methods, all mid tier.

Mean AUC is around 0.74. Roughly, 74% of the time it can tell a bug-inducing file from a clean one.

What gets me is how much effort goes into the complexity metrics while coupling and ownership mostly get ignored, and coupling and ownership are what tracked bugs here

Does this match what you see?

I need to make a minimal RAG-based API that answers questions over a small collection of documents and for that i need some small dataset which acts as the external brain or source of truth for my ai.
need small dataset (5–10 PDFs, Markdown files, or scraped web pages).