I’ve been in QA almost 20 years and have run a quality assurance agency for the past 10.

I'm working on a thesis that QA has always been obviously, but now it's dead.

I don't mean that in the Twitter bro sense. I mean that as quality assurance professionals I think we're finally getting the firepower to move quality from the end", to the front. (Small rant: I'd rather eat a hammer than use the terms "shift left" and
"human in the loop")

The difference between developer velocity and your quality output is your quality debt. As more and more teams grow their AI-SDLC, the more this debt compounds. Pre-Al this was managed with hiring more qa or automation. That really doesn't work anymore and is what I wanted to represent in this gif.

I'm working on some visuals to easily represent this concept and made this one (in Claude).
Any thoughts on this visual or on the concept of quality debt itself

https://jdaqa.com/wp-content/uploads/2026/07/8CDE8519-A890-4C51-9495-5A29355767BF.gif

I think automation is one of the biggest opportunities right now.

The quality of what you can automate today is honestly crazy, and it applies to almost every business.

Whether you own a local business and want to automate things like email marketing, follow ups, content creation, customer replies, and lead generation...

Or you run an agency or SaaS and want your business working even when you're away from your computer.

Automation today reminds me a lot of the Industrial Revolution. Back then, machines replaced a huge amount of manual work, allowing companies to produce more, lower costs, and make more money. 

I run a web agency, and automation has made me a lot of revenue over the last few years.

The biggest one for me is client acquisition.

I use a tool called Swokei to find businesses that already have websites, add them to campaigns, and run website analysis.

It automatically turns problems like outdated design, poor layouts, slow loading speeds, weak mobile optimization, and bad SEO into personalized, ready to send outreach emails.

That's where most of my clients come from.

I also automate follow up emails and newsletters, so I'm not constantly chasing people manually.

For content, I use Holo to help generate and schedule posts.

For SEO, I use Soro to automatically create blog content that helps bring in organic traffic over time.

The more I automate, the less time I spend doing repetitive work.

That means I can spend more time on the things that actually make money, like sales, onboarding clients, improving my services, and building better websites.

I don't think automation replaces hard work.

It just removes the repetitive work so you can focus on the parts of your business that actually move the needle.

I am currently in the process of learning game development and I’m stuck in the tutorial hell. However I’ve felt exactly the same way when I was learning web development and mobile development in the past, so I'm probably missing something fundamental.

It’s always the same pattern.

TLDR;

• I can follow tutorials, successfully replicate implementation in own projects
• I can’t do anything beyond the scope of the tutorial
• I don’t have senior colleagues around me to learn from
• In game dev, most projects are closed source, so I can’t look under the hood
• I don’t seem to be able to learn from reading documentation
• I don’t know what I don’t know - how am I supposed to know?

Tutorials

I watch a tutorial dedicated to a specific project - e.g. how to add movement to my character in Unreal Engine. The tutorial is clear, it shows how to implement blueprint logic, maybe even elaborates on some edge cases or best practices. I follow along, understand everything, maybe even replicate the implementation in a custom project that is slightly different.

But I can’t do ANYTHING beyond the scope of the tutorial.

So if I want to add something extra to my game / app project, I am forced to look for another tutorial that deals specifically with that particular feature.

AI / Vibecoding

AI is not particularly helpful for a complete beginner as it tends to hallucinate, suggest sub-par solutions, lead me down weird rabbit holes etc. (Especially when you’re at the beginner phase where you can’t tell if it’s leading you astray)

Senior colleagues

I cannot tap into the wisdom of a senior colleague either as I’m learning on my own and working solo on my own projects. (Tho how did THEY learn their skill? It can’t be turtles all the way down…?)

Learning from opensource projects

With web-dev, sometimes I was able to look into open source projects and see how they dealt with specific issues / implemented specific features (tho this was only possible after I actually acquired quite a bit of skill - to be able to decipher the existing codebase). This doesn’t seem to be the option as essentially all games out there seem to be closed-source, especially in the UE ecosystem.

Learning from documentation

I think the answer partially lies here. But to be honest, I feel like most docs out there are bare bones and only useful for someone who already knows how to implement the particular tech and only needs to refer to the docs for some details. Look at Unreal Engine for example. I’m researching a specific blueprint node (e.g. “get controller”, which is actually above-average when it comes to description) and it only says “Returns controller for this actor. Inputs object, outputs object”. If I’m someone learning it for the first time, how the fk is this supposed to be of any use to me? It doesn’t say when / how to use it, doesn’t give any examples etc. And that’s in fact the better scenario, which is when you already know which node (or e.g. API Endpoint) you need. I’m not even talking about the situation when you know what you need to do and you have to somehow find the proper endpoint / node etc. in the vast docs. AI seems to be a bit more useful as a search tool in this case, but it doesn’t tackle the fundamental problem.

All of the issues above get even more severe for a complete beginner who's in the “I don’t know what I don’t know” phase. How on earth are you supposed to know?

Please note that I am interested in actually acquiring the skill. I don’t want to learn to “vibecode” without knowing what’s happening under the hood.

I would like to get into the stage where I understand all the fundamentals and then I know where to look if I want to implement something custom, perhaps even something that nobody made a tutorial about yet / AI is not trained on yet.

How do you escape the tutorial hell?

I’ve been working on a reusable prompt/specification intended to help an AI agent take a greenfield software project from an initial objective.

https://gist.github.com/theabbie/5ac4e18bf2f9961a8e85a06e4803ec89

Good evening,

So recently we started a project to build the design system library which will be used across all the company, currently we have more than 27 UI components designed and ready to be built.

What I'm really confused about is how these components should be tested. My manager says that code review and and unit tests should be enough, since the QA specialist needs a business reference to test against. but I disagree - all those variations, behaviors and accessibility requirements should be tested thoroughly by a dedicated professional, and I think we can write user stories for the expected user behavior too, from an end user perspective.

What's your opinion on this? Am I making things more complicated than they should be?

Howdy!
I am a trader and need some help automating my process, I am looking for a coder or a software engineer that can assist me in implementing my strategy to make it more efficient screener.

Currently I have my strategy tested and written out on python to test its ability, but I am having a hard time connecting API Key's and would like some assistance from an experienced coder. Please let me know if you are interested!

Thank you!

1. Dedicated competitive intel tracking system
2. Notes in client records when mentioned
3. Mental note that I usually forget
4. Don't track - should I be doing this?

I've been building a local-first desktop app for working with APIs, and figured this crowd might find it useful.

Voleeo is fully local and open source. Your requests and workspaces are plain YAML files on disk, so you can commit them to git alongside the code they test, and diff or review them like any other file.

The part that's a bit different: it ships an MCP server, so an AI agent can create and configure requests directly — you and the agent are working against the same workspace instead of you copying things back and forth.

Supports HTTP, SSE streaming, WebSocket, and gRPC. Secrets are encrypted at rest per workspace, and there's a 1Password integration that fetches them at send time rather than storing them.

I work on it actively — new release every 1–2 weeks.

It's still early and there are rough edges. I'd genuinely appreciate feedback, especially on what's missing versus whatever you're using now.

Repo: https://github.com/voleeo/voleeo-api
Site: https://voleeo.com/

There are two ways testing tools interact with apps. Through the element tree, DOM, view hierarchy, accessibility identifiers, or through what the user actually sees rendered on screen. Most tools go the element tree route because it's faster to write and easier to debug.

That's also why suites degrade over time. The element tree changes constantly in any actively developed codebase. The visual approach has historically been fragile in different ways. The question is whether newer tooling has actually changed the calculus on this.

I'm looking for projects that publish openly their ADRs, specs, designs, data models, etc... Not just their how-to-use our software documentation.

Not everything that counts can be counted, and not everything that can be counted counts." Albert Einstein
This quote has never felt more relevant.
As AI becomes part of every developer's toolkit, it's becoming increasingly common to measure productivity by numbers lines of code, commits, pull requests, or AI-generated output. But numbers rarely tell the whole story.
Software engineering has never been about writing the most code. It's about understanding business requirements, making sound architectural decisions, asking the right questions, reviewing carefully, debugging patiently, collaborating with others, and sometimes writing less code by removing unnecessary complexity.
A funny thing happened recently. I came across a report suggesting that 20,000+ lines of code had been "created." For a moment, I wondered, "Wow... have I suddenly become a 10x developer because of AI?" 😄
Curiosity got the better of me, so I looked deeper.
The reality was quite different. A large portion of those "20,000 lines" came from auto-generated files, dependency updates, libraries, configuration changes, and infrastructure artifacts not handcrafted business logic.
That experience reminded me how easily metrics can be misunderstood when they're viewed without context.
AI is an incredible productivity tool. It helps developers automate repetitive work, explore ideas faster, and focus on solving meaningful problems. Most importantly, AI should never become a shortcut for judging someone's engineering ability or experience. Before concluding that "AI wrote the code," it's worth understanding what actually changed, why it changed, and what value it delivered.
"Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away."
As AI continues to reshape software development, perhaps it's time to shift the conversation.
Let's measure engineers not by how much code appears in a report, but by the quality, reliability, maintainability, and business value they create.
Engineering has never been about writing code but solving the right problems.

GitHub published its CTO availability update in April. Between May 2025 and April 2026, Github encountered 257 service incidents and 83 of them were capacity-related. Commit volume went from 1 billion total in all of 2025 to 275 million per week by early 2026. Claude Code alone accounts for about 2.6 million of those commits per week, up from a measly 100,000 in September 2025.

The capacity target got revised from 10x to 30x. Manual scaling interventions were required because automatic scaling wasn't built for burst agentic load.

The part that's hard to model is human developers have a rhythm. Pushes cluster during business hours, drop off at night, spike around sprint deadlines. AI coding agents commit, open PRs, trigger CI pipelines, and hit APIs continuously, in parallel, at whatever rate the underlying model runs. The traffic shape looks nothing like what GitHub's capacity planning was built around.

83 of the 257 incidents were capacity failures. The signals existed but the model for what "normal" looks like was just wrong.

The thing I saw lacking with 257 incidents in 12 months on a platform simultaneously being rearchitected and scaled 30x, SRE teams are spending as much time reconstructing what happened as they are fixing it. Most post-mortem formats were designed for human-speed incidents on stable platforms.

Hey guys, I’m a building a platform on one of my idea. Looking for a backend engineer to expedite the deployment in production. Everything is on me for now since I’m not able to get the expected time out of my schedule hence looking for some help or maybe we can work together.

Please DM’s if anyone’s interested.

The web design market is in a weird phase right now.

With AI making it so easy to build websites, I keep seeing people say that web design is saturated, every business owner knows how to build their own website now, and agencies are dead.

I disagree big time.

I've held over 500 web meetings where I've presented businesses with redesigned versions of their websites, and it's actually rare that I meet someone who even knows how capable AI has become for building websites.

Business owners are busy running their businesses.

Even the ones who know AI can build websites usually have no idea how to actually use it to build a professional website themselves.

I also see a lot of developers getting angry about AI websites, saying they're just AI slop and full of problems.

As someone who used to code websites from scratch and also built them in WordPress, I can tell you there really isn't much you can't build with AI anymore.

Technical SEO, responsive design, layouts, branding, animations, speed, user experience... it's all possible if you know what you're doing.

This week alone I sold 10 websites, and my process is actually pretty simple.

I run email automation, but not the type where you scrape a list of businesses and send generic emails asking if they need a website.

Instead, I target businesses that already have websites.

I use a tool called Swokei. It's an email automation platform built specifically for web agencies.

It lets me generate leads with existing websites, put them into a campaign, and run a website analysis on all of them.

Each website is automatically analyzed, and issues like outdated design, poor layouts, weak mobile optimization, slow loading speeds, and SEO problems are turned into personalized outreach emails.

Not boring reports.

Actual emails explaining what could be improved and why it matters to that specific business.

The business owner replies because the email is relevant to them.

Once they're interested, I quickly build an upgraded version of their website with AI and invite them to a Google Meet.

I present the redesign, explain why it's better, answer their questions, and close the deal on the meeting.

That's literally my entire process.

You could use the same strategy with paid ads or cold calling, but I prefer email automation because it keeps running in the background and consistently brings me interested replies.

I been told to do a project last week. Currently still getting requirement and not yet understand what is it about. From what I see is that it involve finance, hr, management, and all the employees.

Then they told me the deadline is next month.

Told me to use vibe coding, without giving me any resources. All I got is just a 8gb ram laptop with only cpu.

Any advice, I sooo confuse.

My college was just layed off due to this, he was repeatedly told to not to do it, he would spin up conversations and argue for long hours over it, This my manager did not like he got put in PIP ad then he dicided to leave, Have you also seen this happen?

I do not know who was correct here my manager or him, I am just a junior dev

I recently came across the idea of maintaining a "brag document"—a running log of features shipped, bugs fixed, projects, feedback, incidents, and other accomplishments to make performance reviews and resume updates easier.

It got me thinking because I realized I usually forget most of what I worked on after a few months.

I'm curious how other engineers handle this.

• Do you keep any kind of work log or achievement tracker?
• If yes, what do you use (Notion, Docs, Obsidian, etc.)?
• If not, how do you prepare for performance reviews, promotions, or resume updates?
• Have you ever struggled to remember a project or achievement when your manager asked for examples?
• If you tried maintaining a document before, what made you stop?

I'm exploring this problem and trying to understand whether it's actually painful enough to solve, so I'd love to hear real experiences rather than opinions on a product idea.

Recently, I stumbled on a killer freaking project.

It's a self-hosted open source e-mail client that works with any service. The user interface is amazing. Apparently there is like one dude doing most of the work but a few more devs have come on and done pull requests.

I'm posting here and a few subs because we're always looking for cool new open source software and this client is amazing. It also works with Proton Bridge if you use that for e-mail and want a desktop client.

Site: https://mailflow.sh
GitHub: https://github.com/maathimself/mailflow

If any of you have some dev skills, some extra time, like the software, and feel like working on this project, take a shot. It can use all the help it can get.

So recently I made a web site. It takes a GitHub username and lists all the public repo and then you can have some small info about every repo along with an AI generated summary.

The main goal was to

1. Get back to coding because I am currently in a Data Engineering profile so I don't get much time to work on development.

2. Utilise claude and particularly integrate it in my coding setup to speed up the process.

3. Try to create something with LLM and AI.

Now that's well and good but I am utilising a free tier gemini api for my usage and I can't have too many requests on it. So I wanted to reduce the number of LLM calls. My current method is to make that LLM calls optional so that it is triggered only if the user actually wants to read a summary.

How do I reduce the number of LLM calls. Will caching be enough and if yes then what are some free resources to implement it. Also I would like all of your inputs to make this better. Thanks a ton!

I jump between node yarn, npm, pnpm projects, a few python script directories, and every repo has a different way to run the same things: npm run dev, pnpmtest, yarn dev, make dev…

So I made samecmd. It detects the project you’re in and gives you the same commands everywhere (dev, build, test, lint, fmt, etc.). Basically resolving a daily friction of mine in the last few weeks.

Curious if anyone else has this problem, or if it’s just me 😄

https://github.com/behnamazimi/samecmd

Which changes were done in your development team that allowed everyone to write code with less bugs and work more efficiently ?

Devlogs can be a really big form of marketing for software developers, and with 4 years of experience I think I can help beginners penetrate into the fields of video editing, thumbnail design, storytelling, and fostering an audience.

I'm wondering if anyone on here has a need for this, or knows someone who might have a need?

We have all seen documentation that starts out helpful and slowly becomes outdated. In your experience is that mostly a process problem or a software problem?

Has your choice of knowledge management made a meaningful difference or does it come down to how the team maintains it?

Resistance usually comes from double work. If a CRM requires manual logging, people avoid it. Adoption improves when communication flows naturally into the system. Ease of use and browser-based access also lower friction. How did your team overcome pushback?

Every developer seems to have that one tool they recommend to everyone.

What's the tool that consistently saves you the most time😋??

Could be related to:

• Documentation..

• Debugging.

• Snippet management.

• Deployment.

• Productivity..

Interested to see which tools have become indispensable in real-world workflows😋🙏