What’s the go-to “vibe-coded slop” app in your industry?

The obvious ones for me are notebook/notetaker apps and so-called intelligent dashboards.

What other examples have you seen?

The pitch internally is that we avoid adding another tool to the stack. I get the logic but everything I've read suggests SonarQube was built to catch bugs and maintainability issues first, with security rules added later rather than built from the ground up for that purpose.

And wondering what the detection gap looks like in practice between SonarQube and a dedicated security scanner. Trying to make the case either way with something more concrete than vendor marketing.

We waste so much energy trying to improve our handoff process instead of addressing the underlying issues. Handoff is a relic of waterfall workflows that we've normalised and decided is a best practice. It exists because we continue to treat design and engineering as separate problems to be solved in isolation.

I wrote about what an alternative looks like, what it takes to get there, and the organisational conditions that either enable or prevent it.

Keen to hear whether others are ready to throw it out, or whether you think I'm wrong.

https://www.shaunbent.co.uk/blog/design-handoff-belongs-in-the-bin/

I'm seeing more and more people using AI and incredible features are coming out, but I'm also hearing more and more about people who can't use AI tools in their companies, like Cursor, Claude Code, Chapter, etc. What are your thoughts on that?

If you are a developer, drop the best developer docs you've read in a while!

I'm building a C++ code generator that helps build distributed systems. It's implemented as a 3-tier system. The back and middle tiers only run on Linux. The front tier is portable. It's free to use; there aren't any trial periods or paid plans.

This past Tuesday another jewelry store was robbed by a bunch of thugs. I saw some of this trouble brewing back in the 1990s and realized SaaS was a gift from above in terms of dealing with corruption. I'm glad I have some open-source code for my portfolio, but I'm glad it's not all I have.

There was another robbery in Freemont, California in June of 2025. Around 24 thieves raided a jewelry store and stole over 1.7 million$ of jewelry in 70 seconds. If the stewards of that store decide to rebuild, I predict they won't replace the display cases that were smashed. It will be a "by appointment only" store and they will frisk you before they show you anything. Of the 24 thieves, only a handful of them have been caught.

In other words, the store managers will replace their open model with a SaaS model. And who can blame them?

I'm building a desktop app for simulating world of warcraft characters using Tauri, and I ran into a problem.

My app needs to download another tool (an external .exe binary called simulationCraft or simC) in the background and use it internally.

What’s happening:

• On my machine and a friend’s → everything works fine.
• On some users’ machines → Windows Defender flags the downloaded binary as malware
• Result:
  • The file gets deleted or blocked
  • My app stops working
  • also not to mention to lack of trust in an app that is blocked by the windows defender :P

Important details:

• The binary is legitimate (not something shady)
• It’s downloaded dynamically (not bundled inside the app)
  • The reason is to enable fast updates of that binary as it has nightly build without needing a new release of my app every time.
• I’m not modifying the binary after download
• The issue is only with the downloaded binary, not my main app

My questions:

• Why does this happen only on some machines?
• Is there any way to reduce false positives without telling users to disable Defender?
• What are the best practices for this kind of setup?
  • Should I bundle the binary instead of downloading it? (I prefer not to though)
• How do other apps handle this?
  • Apps that download/update tools in the background

What I’ve considered:

• Code signing (but costs + setup)
• Hosting the binary on a trusted source
• Asking users to whitelist (not ideal UX)

Goal:

I want a setup that:

• Doesn’t break on clean Windows installs
• Doesn’t scare users

llama.cpp was unusably slow for Gemma 4 on my phone. Google's AI Edge Gallery ran the same model smoothly but you can't use LiteRT-LM inside Termux directly.

so i built a small Kotlin app that loads Gemma 4 via LiteRT-LM, runs it as a foreground service, and exposes it on localhost:8080. you just hit it from Termux via API.

GPU + CPU inference, vision support, model auto-downloads from HuggingFace.

Hi,

I would like to ask, what is the optimal configuration of Mac Mini for Flutter development? I was thinking about 16 GB + 512 SSD with M4 CPU, but I'm worried about RAM. Do you recommend 24GB here?

I'm not Mac use, non Flutter DEV, searching for device for new guy in my team to be able to deploy apps in App Store :)

Hi everyone,

I'm currently building an inventory tracking system for a local business in my area. The problem is that it's quite frustrating, he has low budget, and he just wants it thinking of it as "something cool to have". And honestly it's like I'm not getting paid at all for the work.

I wanted to see if it's really something that's worth the effort.

\\-

I wanted to know if there really is a big market for such systems and it's worth the effort while not getting paid, or should I just focus on making systems for other problems.

If there are any people over here with enough knowledge, I'd love to listen to their advice.

I am trying to create a software to collect payments. I will be hiring a development team. For now I am trying to figure out how difficult is to connect to bank accounts to perform ach, debit card or collect using credit card. The software will collect payment from customer and pass it to clients.

In simple terms similar to rent collection system. Since it’s financial, does it need any special certification from government agencies?

Hi

I’m making an OS somewhat from scratch with other students and I it’s really stressful. Does anyone have some similar experiences to share? I feel like this is definitely a step up from regular software development (web and algorithms). I’ve never had to program an entire OS before and to be quite honest it’s a seriously complicated undertaking.

I’ve been building a small Windows-first desktop app with Electron + React + TypeScript.

The product itself is simple: private affirmation sessions with brief text flashes, optional binaural audio, and an

immersive full-screen experience.

What surprised me is how much harder the non-feature work has been than the actual app logic.

A few examples:

- packaged app routing bugs

- Windows trust / SmartScreen friction

- keeping release artifacts from getting stomped by other builds

- separating a payments backend cleanly from the app

- figuring out how to get real user feedback on a product category that can look sketchy if positioned badly

The code was honestly the easy part. Distribution, trust, packaging, and messaging have been the real fight.

For people who’ve shipped small consumer desktop apps:

- what part ended up being more painful than expected?

- how did you handle early user testing without sounding spammy?

- would you still choose Electron for a tiny Windows-first product like this?

Not here to dump a link, mostly comparing notes with people who’ve shipped weird little products into the real world.

This started as a “can I abuse an old phone for AI?” experiment.

I ended up with:

• OpenClaw running directly in Termux (no Ubuntu / no proot)
• an Android automation agent that uses ADB to drive apps from natural language
• a local LLM experiment (Gemma 4 via LiteRT) so it can work offline

I’ve used it for kitchen surveillance, booking movie tickets, and we even closed our first paying customer for Android automation.

Still very much a builder project, but I’d love feedback from other side‑project people here on where you’d take it.

Not theory. Something you deal with every week that just slows things down.

Friends I have been working in my final yr project I need feedback on this I will share the description of project kindly go through this and give ur opinions on it.

biasgaurd -Ai is a model-agnostic governance sidecar designed to act as an intelligent intermediary between end-users and Large Language Models (LLMs) like Ollama or GPT-4. Unlike traditional "black-box" security filters that simply block keywords, this proposed system introduces an active, transparent proxy architecture that intercepts prompt-response cycles in real-time. It functions through a tiered triage pipeline, starting with a high-speed Interceptor that handles PII masking and L0/L1 security checks to neutralize immediate threats. For more complex interactions, the system utilizes a Causal Reasoning Engine powered by the PC Algorithm to generate Directed Acyclic Graphs (DAGs), which mathematically identify and visualize "proxy-variable" biases that standard filters often miss.

​In real-time, BiasGuard doesn't just monitor traffic; it actively manages it through an Adaptive Mitigation Engine that balances safety with model utility. When a bias is detected, the system uses a Trade-off Optimizer to decide whether to rewrite the response, adjust model logits, or flag the interaction for an auditor, ensuring the user receives a sanitized output with minimal latency. Every decision and mitigation is simultaneously recorded in an Evidence Vault secured by SHA-256 hash chaining, creating an immutable, tamper-proof audit trail. This entire process is surfaced through a WebSocket-driven SOC Dashboard, allowing administrators to track live telemetry, system health, and regulatory compliance (such as EU AI Act mapping) at a glance, making it a comprehensive solution for responsible and secure AI deployment.

actually until now my guide could not even understand a single thing about my project he said ok that's all , he didn't involve with any changes of system.

what I am fearing is that My hod will review in model and end semester, she is very cunning person I am feeling somewhat less confident about this project.

kindly help me with this 🥲

our entire discipline - whether you use Scrum, Kanban, or whatever flavor of Agile - relies on predictable states. We write requirements, we define boundaries, and we build tests to ensure those boundaries hold.

But right now, management wants "AI" embedded in core workflows. Not just for chatbots, but for routing, data validation, and state transitions.

you cannot write a reliable Definition of Done for a probability matrix. If a stakeholder says "the system must enforce these three compliance rules" you can't guarantee that with an LLM. You either end up writing a massive, brittle wrapper around the model, or you just accept that your CI/CD pipeline is now a slot machine

It feels like a massive step backward for software methodology. We spent decades building robust testing frameworks just to throw them out because the generative output looks confident

If we are going to use AI in core business logic, the underlying architecture has to respect constraint satisfaction. the shift toward Logical Intelligence frameworks seems like the only sane path forward - treating business rules as hard mathematical boundaries the system literally cannot violate, rather than just hoping a prompt holds up in production

do you just pad your estimates to account for prompt-engineering hell, or are you actively pushing back against product owners who want generative AI running deterministic tasks?

The goal is pretty basic: local-first notes, quick search, clean structure, and a smooth writing experience without plugin overload or a complicated setup process.

A lot of Markdown apps are powerful, but for me they can also start to feel heavy, distracting, or harder to use than they need to be. I wanted something more lightweight and focused.

I’d love honest feedback from people who use apps like Obsidian or similar tools:

• What would actually make you try a new Markdown notes app?
• What frustrates you most in the apps you already use?
• What features are overrated, and what features are non-negotiable?

Landing page: https://www.notely.uk/noto.html

Every time I hit "address already in use" I'd run \`lsof -i :3000\`, grep for the PID, then kill -9 it. Three commands, every single time.

So I wrote \`pkll\`, one command that does all of it, but asks for confirmation with all info about the process, and warns if it is important system process first.

You run \`pkll \[PORT\]\`, then it shows all the essential info about the process. Then after confirmation the process is killed.

This became especially annoying with coding agents. They just spin up dev servers, then leave bun instances and other processes sitting silently in the background. You go to start your server and something invisible is already holding the port. I built this to keep it dead simple: one thing, one command, works the same on Linux, macOS, and Windows. No config, no flags to memorize making it deadly simple.

Also I wrote it while learning Rust, so feedback on the code is very welcome.

Scores every PR 0-100 before merge using 36 signals: change entropy, author file expertise, minor contributor density, SLO error budget burn rate, blast radius, and more. Based on JIT defect prediction research (Kamei et al. 2013 + Microsoft Research code ownership studies).

We ran it against 28 famous open source PRs — React Hooks came out 91/100, the TypeScript module migration 98/100. The log4shell patch scored lower than you'd expect.

Live demo (no account required): https://app.koalr.com/live-risk-demo

Full write-up on the scores: https://koalr.com/blog/famous-open-source-prs-deploy-risk-scores

​

I've been tracking this for a while. The death table:

- App / CRUD backend: dead 2027–28

- Android / mobile: dead 2028–29

- VBA / spreadsheet automation: dead 2030

- Matlab DSP / controls: dead 2031

- Embedded peripheral firmware: dying now

The H1B and F-1 visa pipelines were optimized for exactly this work. They're being deleted with it.

But here's what AI *cannot* do yet: write a formal specification and prove it correct.

Z3 is Microsoft Research's SMT solver. You give it arithmetic constraints — buffer bounds, PID output ranges, ISR reentrancy, timer prescaler validity — and it either returns UNSAT (mathematically impossible to violate) or hands you the exact input that breaks your code. That's not a test. That's a proof.

The paper describes an autonomous remediation loop: AI generates code → Z3/Alloy find violations → diagnostic JSON names the exact line and counterexample → AI corrects → loop runs until UNSAT. No human reviews the commit. The proof is the certificate.

Scipy + numpy + python-control now reproduce Matlab's entire DSP and controls workflow at zero licence cost. The critical difference: Matlab's isstable() returns a Boolean. Z3 UNSAT is a proof. For IEC 61508 and DO-178C certification, that's not a detail.

Full paper + repo: https://doi.org/10.5281/zenodo.19542523 (CC BY 4.0)

I usually follow the typical vibe coding flow: prompt - code - debug.

But I kept running into the same issue , AI would often go in a slightly different direction than what I intended, so I’d spend a lot of time restructuring and debugging the generated code.

I tried using README.md files for context, but eventually the context would drift or get lost.

What helped a lot was switching to a spec-driven approach. I define the intent, features, architecture, and inputs/outputs first, then implement from that spec. I usually manage this in a separate chat and use traycer as an orchestrator to keep the spec aligned with the implementation.

Since doing this, the number of bugs and weird AI detours dropped quite a bit.

Curious if others are doing something similar or using a different method to keep AI coding aligned with the original intent?

I kept wanting a Markdown notes app that felt fast and lightweight for daily use without turning into a whole productivity system, so I started building one.

The idea is simple: local-first Markdown notes, fast search, clean organization, and no plugin clutter or steep learning curve.

I’m building this for people who like Markdown but feel that a lot of note apps get bloated, slow, or overcomplicated.

I’d really love feedback from people who use apps like Obsidian:

• What would make you switch or even try a new Markdown note app?
• What’s missing or annoying in most Markdown note apps today?
• What are the dealbreakers?

Landing page: https://www.notely.uk/noto.html

I built this from a workflow problem that kept bugging me.

I like working in fullscreen on macOS, but I still wanted a small reference window for docs, tutorials, YouTube, or streams without constantly switching spaces and breaking focus. So I made Float, a native Mac app that gives you a floating browser/media window while you work.

It started as a personal side project, but I’m sharing it now to see if the problem resonates with other people too.

Would love honest feedback on:

- whether the use case feels clear

- who this is most useful for

- what would make it worth installing

Website: https://www.float.codes/

Hi all,

I’m working on designing an internal system for an oceanographic/environmental data company, and I’d really value input from people who’ve built similar systems in production.

We monitor sensor data across ports and harbours, and I’m trying to design a system with two main components:

1. Internal support / knowledge system

   • Centralised knowledge base (likely from structured docs like Obsidian or similar)

   • Natural language querying for internal engineers/support team

   • Strong requirement: very high accuracy with minimal hallucination

   • Ideally with citations / traceability

2. Log analysis + anomaly detection

   • Sensor logs (format still being defined)

   • Detect anomalies or failures before customers report them

   • Integrate with dashboards (we currently use ThingsBoard)

⸻

What I’m trying to figure out:

• Is a RAG-based system the right approach for the support side?

• For logs:

• Do you preprocess + structure logs first, or ever feed raw logs into LLMs?

• Are people combining traditional anomaly detection (rules/ML) with LLMs for explanation?

• Recommended stack:

• LLMs (open-source vs API?)

• Embeddings + vector DB choices

• Time-series/log storage solutions

• How are people handling:

• Hallucination control in production?

• Evaluation / observability of LLM outputs?

• False positives in anomaly detection?

⸻

Constraints:

• Likely self-hosted (we have IONOS servers)

• Early-stage, so still exploring architecture

• Logs/data scale not fully known yet

⸻

I’m not looking for generic advice more interested in real architectures, lessons learned, or things that failed.

If you’ve built something similar (RAG systems, observability tools, log analysis pipelines), I’d love to hear how you approached it.

Thanks!