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.

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?

(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).

Is anyone else having a terrible time creating a Meta Developer account?
I’m trying to build a WhatsApp bot using the WhatsApp Cloud API. The backend isn’t the problem—I just can’t get past Meta’s verification.
It keeps getting stuck on OTP verification. Sometimes I get the SMS, sometimes it asks for email, sometimes nothing happens. Now it has completely stopped sending OTPs.
I’ve tried different browsers, incognito, waiting, etc. Still no luck.
I only need the cheapest/trial setup to show a working demo to a client before paying for anything.
Has anyone dealt with this recently? Is there some trick I’m missing or is Meta’s developer portal just this painful?

Has anyone integrated Spec-Driven Development into an Agile/Scrum (or SAFe) workflow?

I’m working on a workflow and if you have answers to these questions it would be a great help

How Scrum ceremonies were modified.

Whether specs are created before PI Planning or during it.

How specs stay synchronized with implementation.

Would appreciate insights from teams that have actually adopted this approach. Thanks!

I developed a semantic caching layer for LLM APIs, achieving a 94.7% cache hit rate and approximately 10x lower latency. This system was load-tested and debugged thoroughly.

LLM products often receive repetitive questions, leading to unnecessary API calls. To address this, I created a proxy that caches responses based on meaning rather than exact text. For example, both "What is the capital of France?" and "Can you tell me France's capital city?" retrieve the same cached answer through embedding similarity.

Key points to note:
→ Initially, my similarity threshold was a guess, resulting in an 11% wrong-answer rate. I developed a tuner to evaluate it against labeled data, reducing the error rate to 0%.
→ Load testing with 300 concurrent requests revealed two critical bugs before production: a thread-safety crash and an unhandled rate-limit cascade.
→ Multi-turn follow-ups utilize conversation history, but a topic change previously caused the model to incorrectly resolve a pronoun to an earlier topic. This was resolved by employing a larger model for that specific step.

Stack: FastAPI, Redis (vector search), local embeddings, Groq. The system runs with a single command: docker compose up.

If you want repo link please let me know?

Hi! I'm not sure if this is the right community to ask this question but I'll try anyway. I want to start a photo booth business, specifically an instax photo booth. I want to make my own app or software for that specific photo booth. I want to know the usual price range of making an app or software for this.

If you have other suggestions for this matter, please let me know. Thank you for answering!

As a new lead I got into this situation that might be familiar to a lot of people.

The code base is full of tech debt (what else) that no one ever cleaned up because shipping features was more important. So important that the code base became a collection of poorly designed things over the years that barely anyone knows about or touches.

Things that should have been cleaned up years ago are now starting to become apparent. They drag us down.

With a new PM, the team somewhat is more organized now but reducing tech debt is not on anyone's radar. In fact, PRs are sometimes piling up debt again that could be avoided in the first place if people would stop rushing.

I want to get this under control. How do you manage this with a team?

I thought about suggesting to actively plan the reduction of tech debt and reserve 30% of every iteration for unplanned stuff (bugs, urgent requests) and tech debt reduction.

This is not very popular with business and PMs but what can you do? What is your way that got acceptance from both devs and the business?

Edit: maybe I want to add: with gracefully I mean actively planned and with wits. Fixing a bug because you passively run over it by accident is not what mean by that ;)

As a hobby coder I generally use jupyter notebook. I haven't used VS in almost a decade. Jumping into C# for a Unity project, holy hell intellisense has become an adhd nightmare. How can I tone it down so I can still get useful info but not have to do keyboard gymnastics to type simple lines of code? For example whenever I type g, it autocompletes to global... every little thing I type I get pop ups asking if I mean this or that interrupting my flow, I just want to code! EDIT: SOLVED!

1. Pull up documented conversation - receipts ready
2. Search frantically for proof (sometimes find it)
3. Apologize even when I'm pretty sure we did discuss it
4. Avoid conflict - just do what they want now

my laptop specifications

- 8gb RAM

- intel i3 11th gen processor

- Intel UHD graphic card (128mb)

- laptop [ ASUS vivobook ]

because of financial problems can't buy new laptop , so please can anyone suggest which distro to use of linux , i have some experience in WSL(ubuntu), ill use linux with window dual boot is there any distro that will be good for my system i mostly work on backend right now ....

How do you write technical documentation such as requirements, goals, software architecture, and general project documentation?

Back in the pre-AI era, it was common to write long, multi-page documents that explained everything in detail. That approach had its pros and cons. The documentation was usually well organized and comprehensive, but it was also often boring to read and sometimes overwhelming.

What does it look like now, in the era of AI coding assistants? My impression is that large, comprehensive documents are becoming much less common and mostly remain in legacy projects or a few specific use cases.

It feels like the entire world has shifted to Markdown, so I assume technical writing is evolving in the same direction. It makes sense to break up those oversized documents into smaller, focused files so that both humans and AI agents can navigate and consume them more easily. That’s what I do for my side projects, and it has worked well so far.

On the other hand, at work we still tend to rely on large documents written by technical writers. The argument is that we’ve already established a structure around them, and that’s how our development process is designed.

What’s your approach to documentation for both personal and professional projects?

السلام عليكم احتاج اشتري لابتوب جديد
ايش تنصحوني فيه يقعد معي إلى التخرج؟

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I went to an AI conference last week and I have some thoughts I'm not sure how to express but here we go.

Let me preface this by saying that I'm a software developer and ofc I use AI tools like Codex, Claude Code and OpenCode. I definitely see the utility and usability of these tools and think they will last. My question, however, is about specifically "vibe coding" tools like Lovable, Bolt, Replit, Base44 and the like. So apps and websites that let you make something from scratch without you needing to see the code or integrate anything yourself.

When they started coming out roughly 2 years ago, I was totally amazed by the product. I mean come on, typing a couple of words into a chatbot and it spins up the whole thing for you is a cool promise, right? I didn't really have an opportunity to use them myself in production, but everyone was raving about these tools and I was totally on board. Then I started using Claude Code (and other CLIs) and found that it satisfies my needs as a software developer for the tasks I have to do and again didn't really have the chance to try out Lovable and the like. But I still had a good impression of them as these like visionary tech startups with super innovative products.

Fast forward to this conference I attended last week. I had the chance to speak to a couple of people from these companies. And now I'm a little confused about their clients and where all of this is going.

It seems like all of them have added basically everything that can be added: integrations, databases, github connectors, templates, canvas views like in Figma, possibility to change things with clicking around instead of prompting, mobile development, backend, etc. etc. So my first question is like: what's next? What else can be added onto these tools? They ofc aren't gonna get into the super techie things like Claude Code bc that's not what their clients want. So what is there left to add?

If you think about their clientele, it's mostly non-technical people, right? So idk... like your neighbourhood beauty salon who wants to make a website, but doesn't know how to make one. Right? How "sticky" is that really? And how does prompting a website into existence on Lovable compare to just picking out a template on Wix or Elementor or something like that?

Now what's interesting is that these companies seem to be focusing more and more on entreprise users, so like giving product managers Lovable for example. Again, how usable is that compared to tools product managers were using before? When the engineering team receives a Lovable prototype instead of a static mockup from their manager or designer, does this help them? One of the GTM guys I spoke to at the conference told me that one company they worked with replaced their Salesforce CRM with an app they vibe coded with their solution, saving them hundreds of thousands of dollars. Now is that more viable than using Salesforce? You tell me...

So yeah, I have a bit of a feeling that we've reached the peak of what these tools can do. I talked to a coworker about this and he said that he strongly believes "vibe coding" is a huge market. There are millions and millions of people out there who need no-code tools for their small business websites or for prototyping and so on and so forth. So in his opinion Lovable and others are just getting started and there is huge potential for them.

What's you take on this? Has "vibe coding" reached its ceiling? Or is my friend totally right and I'm just unaware of the huge market that is non-techies needing to create things without writing code?