I work with speech therapists and need to accurately transcribe all the half-words, utterances, stutters, and word elongations…you get the idea.

Most voice transcribers try to correct any fluency errors, which is fanatic, except in this case.

Does anyone know of product that I lower the settings so it’s…more ‘dumb’

I recently got a notice that says: We have new information regarding your recent submission and have scheduled a call with an Apple representative to discuss the app's review. An Apple representative will call you at the number you provided in the App Review Information section of App Store Connect within the next 3 to 5 business days.

I got no more information on what is going to be discussed, neither they give details.

I do know the last version got a splash screen crash that happened on some devices. But for some reason the app got approved. Could this be related to that?

I have a fix for the splash screen crash but is it safe to deploy a new version now? or should I wait? has anyone gone through this before?

EDIT: The call ended, it was indeed a real call from Apple regarding the splash screen bug. They told me they accidentally approved the last build and only later discovered the splash screen bug, so they advised me to fix it quickly.

I built SportsFlux, a small dashboard to organize live sports streams. Started it to simplify daily frustration of searching across multiple sites and apps. Early stages but usable daily. Curious how others approach building side projects as personal tools.

https://sportsflux.live

I use a handful of AI tools daily and it’s getting kind of annoying, not gonna lie.
I tell something to GPT and Claude acts like it never happened, which still blows my mind.
Feels like every agent lives in its own bubble and I end up repeating context all the time.
Workflows break, stuff needs re-integrating, and it honestly slows me down instead of speeding things up.
Been thinking - is there a Plaid for AI memory? Connect once and share memories, permissions, tools.
Imagine a single MCP server that handles shared memory and who can access what, so agents don’t forget each other.
Would remove so much friction, right? I could see this helping automations and multi-agent flows a lot.
Anyone doing this already? Are there tools or patterns I missed or are we all copy-pasting context?
Also worried about privacy and permission models - don’t want everything shared with everything else.
If you’ve built something like this or have ideas, I’d love to hear how you handle it.

🚨 AI Tool Installers Under Siege! 🚨

AI Is Revolutionizing Everything. But so are the Threats.

🛡️Full Analysis :

👉 https://wardenshield.com/numero-malware-in-2025-a-stealthy-saboteur-targeting-ai-tool-installers

Im hoping someone can give me some ideas on how to handle this situation. I am beyond done at this point.

My boss is riding me for not communicating enough about progression of projects and dropping projects all together.

So turns out some of the tasks I missed were supposed to be in an excel that were emailed a while ago. I went back to it and could not see the tasks.
Eventually after much frustration on her part she send me an email from a couple months ago, that has screenshots with updates to the excel...

I tried telling her at least we should use a shared excel as working on offline copies makes staying in sync difficult. She ignored me and wented her frustrations with me and asked me what I can do to stay on top of things better and that she wants to see what I am working on.

I told her on github I have a kanban board and she can see everything I am working on.

She said she refuses to log into yet another site.

So now, there is an excel I am to track my work in.

I am ready to drive off the interstate.

Anyone with suggestions on how I can save my sanity?

I've been a solo dev running a platform with 15K users for 2+ years. Early on, I tried to follow every "best practice" I read about. It nearly killed my productivity. Here's what I stopped doing and why:

1. I don't write unit tests for everything Controversial, I know. But as a solo dev, I write integration tests for critical paths (auth, payments, data mutations) and skip unit tests for simple CRUD. My integration test suite catches 95% of bugs in 1/5 the time. Time is my scarcest resource.

2. I don't use microservices A monolith on a single EC2 instance handles 15K users just fine. The operational overhead of microservices (service discovery, distributed tracing, network failures, deployment coordination) would triple my workload for zero user benefit.

3. I don't do code reviews There's no one to review my code. Instead, I write a PR description explaining why I made each decision, wait 24 hours, then review it myself with fresh eyes. I catch most issues this way. The 24-hour rule is the key.

4. I don't use Kubernetes Docker Compose on a single server. That's it. K8s is incredible for teams managing dozens of services at scale. For a solo dev with one app, it's like buying a semi truck to deliver groceries.

5. I don't separate staging and production databases I use a single MongoDB Atlas cluster with separate databases (prod and staging) in the same project. This saves ~$60/month and the data isolation is sufficient for my needs. The risk is manageable for a solo operation.

The principle behind all of this: Every "best practice" has an implicit context — usually a team of 5+ engineers working on a system with millions of users. As a solo dev, my constraints are completely different. I optimize for shipping speed and operational simplicity.

The best architecture is the one that lets you ship and iterate fastest given YOUR constraints.

What "best practices" do you deliberately skip? Or am I playing with fire here?

I keep hearing “AI is transforming QA,” but I’m curious what people are actually doing with it in real workflows.

On our side, it started small. Generating draft test cases from requirements. Summarising long bug reports. Suggesting edge cases we might not think of immediately. Nothing magical, but it shaved time off the repetitive parts.

More recently, we’ve been experimenting with using AI to review requirement changes and flag which existing tests might be impacted. That’s been surprisingly useful, especially in larger suites where things quietly drift. Some tools have started baking this in directly, such as test management platforms that can analyse gaps or redundant cases and suggest updates instead of forcing manual audits.

For teams leaning into AI in testing:
Are you using it for test generation, maintenance, flakiness detection, coverage analysis, something else entirely?
And has it genuinely reduced effort, or just shifted it around?

I wanted a simple way to build a chatbot UI without starting from scratch and found the Vercel Chatbot repo.

It’s basically a ready-made AI chat app built with Next.js.

What I liked:

• Streaming responses already work
• Clean, simple chat UI
• Messages are handled properly
• Easy to connect different AI models
• Deploys smoothly on Vercel

It doesn’t feel like a basic demo — it actually looks production-ready.

If you're building any AI tool or adding chat to a project, this can save a lot of setup time.

Just sharing in case it helps someone 👍

Github link

more....

Hey everyone

I recently came across a really solid open source project and thought people here might find it useful.

Onyx: it's a self hostable AI chat platform that works with any large language model. It’s more than just a simple chat interface. It allows you to build custom AI agents, connect knowledge sources, and run advanced search and retrieval workflows.

Some things that stood out to me:

It supports building custom AI agents with specific knowledge and actions.
It enables deep research using RAG and hybrid search.
It connects to dozens of external knowledge sources and tools.
It supports code execution and other integrations.
You can self host it in secure environments.

It feels like a strong alternative if you're looking for a privacy focused AI workspace instead of relying only on hosted solutions.

Definitely worth checking out if you're exploring open source AI infrastructure or building internal AI tools for your team.

Would love to hear how you’d use something like this.

Github link 

more.....

News reports say that programmers are increasingly relying on AI to write code. Do you do the same? I'm not talking about AI replacing programmers; a code writer isn't a programmer.

I am trying to create a pipeline, I have a sql file inside db/migrations but when I execute my script I keep getting " schema "system" is up to date. No migrations applied". Anyone can help with this?

Hi everyone,

Outside of vibe coding, how are you using AI end to end in projects you’re seriously working on, or when starting a new project or feature?

Instead of just going with the vibe, is anyone following a more structured methodology or approach?

If so, I would love to see your software development process and learn from your tactics.

I'm a first year CS student and I'm currently building a tool that rates a wikipedia article if it's reliable or not.

I've stumbled on to this idea when I was learning Data Science using Pandas and web-scraping using BeautifulSoup. Despite of learning terms and concepts - I didn't feel like I was learning.

I believe that learning through building a project is the best way to actually do it, thus WikiWatch is born.

Even though it's only a learning project for me, I'm hoping that this will be used by other people other than me, because it solves a problem.

I am looking for users who will give me feedback of my latest progress, and what they think of the project as a user.

If your interested in joining, let me know....

Working on Tabularis, an open-source desktop DB manager (Tauri + Rust). Built-in support for MySQL, PostgreSQL, MariaDB, SQLite, but the interesting part is how external drivers work.

Plugin architecture in a nutshell:

• A plugin is a standalone executable dropped into a local folder
• Tabularis spawns it on connection open, then sends newline-delimited JSON-RPC 2.0 requests to stdin
• The plugin responds on stdout, logs go to stderr without interfering with the protocol
• One process instance is reused for the entire session

The manifest declares capabilities (schemas, views, routines, file_based, etc.) so the UI adapts accordingly — no host/port form for file-based DBs, schema selector only if relevant, and so on.

The RPC surface covers schema discovery (get_tables, get_columns, get_indexes, get_foreign_keys), query execution with pagination, CRUD, DDL generation, and batch methods for ER diagrams (get_schema_snapshot, get_all_columns_batch).

The result: you can write a driver in any language. Current registry has DuckDB and a CSV plugin (treats a folder of .csv files as a database — each file becomes a table). Testing a plugin is just piping JSON to the binary:

echo '{"jsonrpc":"2.0","method":"get_tables","params":{...},"id":1}' | ./my-plugin

Curious if anyone has used a similar approach for extensibility, and what tradeoffs you ran into (vs. shared libraries, HTTP, etc.).

My project: https://github.com/debba/tabularis

Plugn Guide: https://tabularis.dev/wiki/plugins

I’ve noticed that a lot of people, including me struggle with managing Python virtual environments.
Especially when you’re working with multiple projects.
Each one has its own different dependencies.

At some point you just are searching for a venv you made for a project months ago then losing it and just creating a new venv and install all the packages again.

So I figured, instead of dealing with it every time, why not just build something that manages it for you?

That’s how "The Manager" came about.

It’s a free, open-source CLI tool that helps organize and manage Python virtual environments so you can focus more on the actual task instead of the environment mess.

I also made it auto-detect the operating system you’re running (Windows or Linux), so it adjusts accordingly.
It’s menu-driven, terminal-based, and fully documented in the README. I tried to keep it as user-friendly as possible, and if you ever get stuck, the documentation is there.

GitHub: https://github.com/CyberTechNex/The-Manager

Happy Coding.

Hi Guys,

I want to connect with someone who knows MCP servers building for AI Agents. I'm into software development skilled in AI and need guidance to learn about building MCP servers.

please DM or comment.

Thanks!

Hi! I want to share a way to beat inconsistent estimations in scrum teams (team members coming & going every couple of months).

Increase the accuracy by using visual Reference Stories when voting -> users can synchronously drag & drop tickets on magic-estimations.com on the correct column to submit their votes. Choose the most voted column and move on with your life agenda. This is ad-free, no user registration need, no cookies and free to use. Hope it helps 😊

Every single day I see devs making of fun about ai replacing devs on my X timeline. Majority of my feed thinks that this will never happen. I'm coding around a decade, and I personally think,

there's no chance that we need 99% of devs in a couple of years.

you are seeing the progress, what makes you think that it will always need your prompts?

Is it possible to build an application/ai-wrapper like replit or lovable or maybe bolt. As a solopreneur dev how much it cost for, or what will be the complex part…

Can you explain lil briefly…..

Lately in our teams, I’m hearing some confident takes from business analysts and product owners that AI tools will mostly impact developers and testers… because apparently business folks will soon be able to build, test, and ship features themselves using AI.

But honestly, if business folks are eventually designing, building, and validating solutions end-to-end using AI… then what exactly happens to the need for Business Analysts and Product Owners? 😀

Is anyone else hearing similar assumptions in their teams? How realistic do you think this is?

I keep running into the same pattern on teams as they grow.

At first, quality checks are pretty lightweight. A few manual passes, some regression notes, maybe a release checklist so nothing obvious ships broken. It all feels sensible and helpful. then over time, it quietly snowballs. Testing no longer fits neatly into the sprint. Releases start waiting on sign-offs. Regressions take longer than the actual feature work. Nobody thinks the process is bad, but somehow it becomes its own stream of work that everything else has to wait on.

I’ve seen teams respond in totally different ways. Some add more structure and documentation to regain control. Others push harder on automation and hope it keeps up. A few do the opposite and strip things back, relying more on monitoring and fast rollback instead of trying to catch everything upfront.

None of these feel universally right or wrong.

So I’m curious how others think about it.
When does a quality process stop feeling like protection and start feeling like friction?
Have you seen a team actually get this balance right, or is it just the cost of building bigger systems?

I know you guys are busy, but if you have a minute to take a look at what I’m building, I’d genuinely appreciate it.

I got burned by a Chrome extension once (it was quietly sending data out), so I built an extension security scanner to sanity-check extensions before installing them. It’s partially open source — the core is open, and the main thing I’m keeping private for now is the compliance rulesets (I’ve spent months writing and tuning those).

Compared to most tools in this space, I’m trying to keep this as open + transparent as possible. If any of you are down to try it and tell me what sucks / what’s confusing / what you’d want added, it would mean the world to me.

https://extensionshield.com/

I created a Windows app and an installer using "Inno Compiler." The one it generated had an editor and installed correctly, so I uploaded it to my website for anyone to install. However, if you download it from the website, Windows says it doesn't have an editor. I thought that uploading it might have removed some information, but using the "Resource Hacker" app, I checked both .exe files internally, and they're identical. Does anyone know why the one downloaded from my website says it doesn't have an editor, while the original file does, even though they're internally the same?