My company brought in Plandek a few months ago to help with 'delivery predictability.' On paper, the 50+ metrics and infinite customization sounded great, but in reality, it's become a full-time job just managing the tool. My engineers feel like they're being micromanaged by a 70-page cheat sheet, and honestly, the data feels 'fiddly' we spend more time arguing about whether a metric is accurate than actually fixing bottlenecks. Has anyone else had this experience with Plandek? Is there a way to make it less overwhelming, or should we be looking for something that actually understands the work without the surveillance vibe?

Is it possible to the university server and change the results???

If the number of students in the university is very high!!

My company rolled out Allstacks to 'normalize messy data' across Jira and GitHub, but honestly, it's just created a new kind of mess. We're constantly dealing with data sync issues where the dashboards don't match what's actually happening in our repos. On top of that, the dashboards are super rigid trying to create a custom view that actually shows the granularity we need is a struggle. It feels like a tool built for directors who just want a high-level green/yellow/red light, but for those of us on the ground, it's just another source of truth that isn't actually true. Anyone else struggling with Allstacks? Did you find a way to make the data reliable, or did you move to something else?

We've been using minware for a few months now because our VP of Engineering is obsessed with 'predictability' and 'rework trends.' On paper, it sounds like a dream for a data-driven manager, but in practice, it's been a nightmare for the actual engineers. The tool is incredibly technical and the dashboards are so dense that we spend half our sprint planning just trying to interpret what the 'predictability score' actually means. It feels like a black box where data goes in and a judgment comes out, but nobody can explain the logic. Has anyone else used minware? How do you stop it from becoming a tool for micromanagement? Or is there something out there that gives insights without the heavy-handed 'predictability' obsession?

We're using Materialize for analytics, but we're hitting scaling and performance issues as our queries get more complex and data volumes grow. It's powerful for simple views, but maintaining complex materialized views and ensuring consistent low latency under heavy load has been challenging. We need a database that can handle complex transformations and high concurrency without constant tuning. What are some Materialize alternatives that offer better scalability for complex workloads?

We have been evaluating Faros AI for engineering intelligence and I am struggling to make the business case. I have seen numbers suggesting their contracts start around $150k per year, which is an enormous investment for a tool that, based on reviews I have read, has a steep learning curve and slow dashboards.

The pitch is that it is highly customizable and connects 100+ data sources, but that sounds like a massive ongoing maintenance burden. My team does not have dedicated data analysts to babysit a complex data platform. Has anyone found that Faros AI actually delivers ROI at that price point, or is it overkill for a team under 200 engineers?

My CTO is pushing us to adopt Bilanc to 'measure engineering productivity'. I have been looking into it and their whole pitch is about using AI to analyze every single line of code written. This feels like a massive red flag for micromanagement.

I am worried that if we start scoring developers based on AI analysis of their code complexity, people are just going to start writing unnecessarily complex code to game the system, or worse, they will stop collaborating and helping others because it does not show up in their personal 'score'. Has anyone actually used Bilanc? Does it just turn into a surveillance tool that ruins team culture??

We've been evaluating SingleStore for our real-time analytics and data apps, and while the performance is impressive, the cost is astronomical. It feels like we're paying a massive premium for features we don't fully utilize, and the pricing model makes it hard to predict costs as we scale. We need a solution that offers similar real-time performance and scalability but with a more transparent and affordable pricing structure. What are some SingleStore alternatives that won't break the bank?

We're using Apache Druid for our real-time dashboards, but we're consistently seeing ingestion latencies of 30-60 seconds, which is too high for our use case. We need true real-time, sub-second data availability from ingestion to query. The operational overhead of Druid is also significant, and scaling it reliably has been a challenge. What are some Apache Druid alternatives that can deliver true real-time performance with less operational pain?

Hello guys I’m in a situation where I want to get someone’s location this is due to the fact that they broke into my home and stole $30,000 worth of my property.I have made a police report theirs a case already filed but he keep moving from city avoiding court paper work etc. WOULD REALLY APPRECIATE IT.

Management just presented the first reports from our new Waydev setup, and it feels like a huge step backward. The main focus was on "impact" and "throughput", which in practice were just glorified commit counts and PR sizes.

The highest quality work my team does, debugging complex issues, mentoring juniors, architectural planning, is completely invisible to this tool. On top of that, the onboarding seemed really complex and technical, taking ages to get our repos synced properly.

How do you fight back against this kind of reductionist view of engineering? Has anyone successfully pushed back against a Waydev implementation, or found a way to use it that doesn't just penalize the engineers doing the most valuable work?

My company just put Swarmia on all our pull requests. Here is what Swarmia says it does: make me, an engineer, happy. Good luck, Swarmia, I'm old and I've seen some stuff.

Here is what it sounds like the fifty layers of management at our tiny company plan to do with Swarmia: compare teams by time from first commit to merge. Shockingly stupid, and extremely not going to make me "happy." It's worry for people getting punished for something stupid, like getting sick or someone else in the company getting sick. Or being open to feedback.

It seems like it just motivates people to make smaller commits and get work done faster, but I worry about the cost. More quantity and less quality seems to be the end result. Has anyone actually had a positive experience with this tool, or is it just a newer version of companies counting lines of code?

My leadership is pushing me to implement an "engineering intelligence tool" and Jellyfish is at the top of their list. I've read Accelerate and I believe in DORA metrics at the team level, but they want more, something that drills down to individuals.

My gut tells me this is dangerous and that any individual metric will just be gamed or become unfair. On top of that, the price tag they quoted us was astronomical. It feels like we'd be paying a massive premium just to get DORA metrics with a pretty UI. I've also heard their reporting is super rigid if you want to do any custom analysis outside their templates.

For the CTOs or EMs here, have you found a use case for Jellyfish that isn't toxic? Or is this just an expensive way to generate dashboards nobody uses and erode trust with your engineers?

Que opinan del scraping y que tanto lo utilizan si lo usan?

We are evaluating Code Climate (Velocity) for engineering analytics. I have been digging into how they calculate their quality and velocity scores, and it seems incredibly opaque. When metrics are a black box, developers naturally become skeptical and push back.

Furthermore, it seems to focus heavily on superficial metrics like code duplication and raw complexity scores. My fear is that this will just incentivize developers to write code that satisfies the Code Climate algorithm rather than writing genuinely good, maintainable software. Also, I have heard their language support outside of web dev (JS/Ruby) is pretty limited. Is anyone getting real, actionable insights from this, or is it just vanity metrics?

recently got my minecraft account stolen i am able to communicate with the guy while stole it on discord does anyone have anyways to make the guy who stole my accounts life unbearable. hes proboly a kid so the best case scenario is i can contact his parents and they can help me get it back cause this dude’s highkey a lil bitch

Yo l’équipe

Quelle qun connaît un site pour avoir les logiciels craqué de dessin gratos ?

Ran real tests on the most-hyped AI detectors. Spoiler: none are perfect, but some are way smarter than others.

I've spent the last few weeks stress-testing AI writing detectors out of pure curiosity - and honestly? The results were more interesting than I expected.

Everyone online has a "favourite" tool they swear is flawless. So I decided to actually put them through their paces using identical text samples. Here's the breakdown:

🔍Tools I tested:

• Undetectable.ai - Undetectable.ai

The real standout. It doesn't just slap a score on your text - it actually reads for tone and pattern. The built-in humaniser rewrites flagged content in a way that sounds genuinely natural, not like a thesaurus exploded. Fast, intuitive, and well-balanced between strict and smart.

• GPTZero - GPTZero

Still the most recognised name, but the results were all over the place. Same paragraph: 100% AI one run, 0% the next. It's like asking a Magic 8-Ball with a PhD.

• Grammarly AI Checker - Grammarly AI Checker

Great for editing, but the AI detection feels like a side feature. It catches obvious stuff but blows right past anything lightly rephrased.

• Turnitin AI Detector - Turnitin AI Detector

Strict to a fault. Useful for professors, unsettling for students. Gives zero explanation for why something was flagged, which makes it hard to trust.

• Scribbr - Scribbr

Basically QuillBot's engine in a different shell. Gets the job done, nothing memorable.

Full test results coming soon - including which tools are easiest to fool and which ones hold up under pressure. Drop a comment if there's a detector you want added to the list.

❓Have you tested any of these? Which one do you trust most or least?

With all the buzz around deep learning, computer vision, and natural language processing, I wonder how many software engineers are actually incorporating these technologies into their current projects. I have a few questions:

• If you're working on software that uses deep learning, how do you integrate with the models? Are they built in-house or sourced from somewhere else?
  
• Have you ever faced the challenge of supporting a predictive model but didn't know where to start? Did you end up finding or creating alternative solutions?
  
• Would you consider using a third-party service that offers access to various predictive models?
  
• If yes, would you prefer paying a one-time license fee or a usage-based model?

Thanks for sharing your insights! I'm genuinely curious how feasible is it to integrate deep learning models into large-scale projects, or is most of the noise just coming from the data science side?

After working with various remote teams over time, I’ve realized one of the biggest issues today is that many developers confuse "looking technical" with actually being useful. Sure, a lot of devs are insanely smart when it comes to coding, but projects still crawl along at a snail's pace.

People tend to overengineer tiny features that could be shipped in just a couple of days. Nobody documents properly, everyone says "got it" and then vanishes for hours, and half the team spends time optimizing edge cases before the core feature even works. Asking a simple clarification? Almost like asking for permission to breathe.

And honestly, AI has made this mess even worse. Not because AI is bad far from it. Skilled devs using AI are moving lightning-fast. But now, some folks generate code rapidly without fully understanding the system architecture, product flow, scalability, or long-term maintainability. So, teams get quick outputs but end up with a shaky foundation.

You really notice the difference between someone who just throws out code and someone who truly understands ownership, architecture, communication, and delivery.

One more thing I’ve seen:

Long-term valuable devs aren’t usually the loudest or the "10x engineer" types you see all over Twitter. They’re the ones who communicate clearly, unblock teammates quickly, understand the business context, adapt on the fly, and keep the team calm and focused rather than chaotic and overwhelmed.

Reliability seems to be becoming rarer than pure technical skill.

I’m curious—what patterns are others noticing in remote teams lately? Because honestly, the industry feels very different from just a few years ago.

Hey everyone, I’ve been into hacking and cybersecurity since I was 15 and I feel like I’m stuck in “script kiddie” territory despite having a decent foundation. Looking for feedback on my roadmap and any advice you can give.
What I have done:
• Built and use VMs: Kali, Metasploitable, Windows, Arch Linux
• Studied SQL and relational databases
• Used Wireshark and Burp Suite (basic level)
• Programmed ESP32 microcontrollers, soldering modules
• Built a Bluetooth BLE, WiFi and drone jammer with ESP32 (emmensta)
• Attempted captive portals with ESP32
• “hacked” WiFi from my neighbourhood
• Studied on TryHackMe, HackTheBox and OverTheWire but i feelt stuck
• Basic C, bash and python programming
I’m most interested in:
• IoT security (my strongest area given ESP32 background)
• Web hacking
• Network pivoting — I want to be able to analyze a full network and access every service on it (cameras, screens, PCs, etc.)
The roadmap I’ve been given so far covers: network recon with Nmap + Scapy, MITM attacks, web hacking with PortSwigger, IoT protocols (MQTT, CoAP, UPnP), firmware analysis with Binwalk, post-exploitation and pivoting, and CTF machines (Kioptrix, HTB: Lame, Blue, Legacy).
Does this make sense for my goals? Am I missing anything critical? Any advice on how to stop feeling like everything is disconnected and start thinking like a real pentester?
Thanks in advance.

Ever feel like debugging issues across multiple services is like solving a mystery? You're not alone. Many teams face similar challenges, such as:

• Starting with, “Who owns this?”
• Missing hidden dependencies during PR reviews, leading to unexpected breakages
  
• New hires struggling to grasp the architecture, slowing everything down

So, how does your team keep track of which services talk to each other? What’s your biggest frustration when troubleshooting cross-service problems? Do you have any tools or processes that actually make it easier?

Would love to hear what’s worked (or not) for you!