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?

So, this just happened. Management announced we're using LinearB to "improve productivity." I'm already hearing whispers about tracking cycle time across teams. Honestly, this feels like a fancy way to punish people for getting sick or taking the time to do thorough code reviews.

I've been reading up on it and it seems to heavily index on raw Git activity. My fear is that this just incentivizes devs to make 10 tiny meaningless commits instead of 1 thoughtful one, and completely ignores the high-value work that doesn't show up in a dashboard: mentoring, architectural decisions, debugging complex legacy issues.

Is there any way this can be used for good, or should I start polishing my resume? My team is currently "winning" at their metrics, but I'm worried about the culture this is going to create. For those who have survived a rollout, what was your experience?

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

Our org is looking at Multitudes.app for engineering team analytics. While they market themselves as being focused on team health and equity, when you look under the hood, it still seems to rely heavily on individual developer metrics.

My concern is that any tool that tracks individual metrics inevitably leads to a culture of surveillance and micromanagement. Developers will figure out what the tool measures and start gaming those specific outputs, rather than focusing on building good software. Has anyone implemented Multitudes without it eroding team trust and turning into a vanity metric dashboard?

We are evaluating Allstacks for engineering metrics. The platform looks comprehensive, maybe too comprehensive. I am hearing that the setup is overwhelming because of the sheer volume of dashboards you have to configure to find anything useful.

But my biggest issue is that apparently their data only syncs once per day. In a fast-moving agile environment, looking at yesterday's data to make mid-sprint decisions seems completely counterproductive. I have also read that their filtering is limited, so you cannot easily exclude noisy data like test files from the metrics. How are teams actually using this effectively if the data is always a day old and full of noise?

We are looking into Entelligence.ai for engineering intelligence. The promise of AI analyzing our workflow is appealing, but I am reading that the initial setup is far from smooth. Apparently, it requires a lot of manual configuration to get it connected properly to all our systems.

More concerning is that I have heard it takes weeks for the AI to actually 'learn' the codebase well enough to provide any relevant insights. I am also worried that by trying to simplify code reviews with AI, we might just end up focusing on easily quantifiable metrics and inadvertently enable micromanagement. Has anyone had a smooth rollout with Entelligence, or is it a massive time sink upfront?

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?

We are looking at Cortex for our internal developer portal and engineering intelligence. The demo looked great, but I am talking to peers at other companies and hearing that keeping the catalog updated is a massive manual chore. They are saying that data drift happens quickly, and once the metadata is stale, developers stop trusting the tool entirely.

I am really worried about spending a huge amount of time on the initial 'fiddly' configuration only for it to become expensive shelfware that nobody actually uses because the data is inaccurate. Has anyone successfully deployed Cortex without it requiring constant manual cleanup?

We are evaluating Sleuth to track our DORA metrics. On paper it looks good, but I am reading a lot of complaints about the initial setup being incredibly complex, especially when trying to integrate it with our existing incident management and CI/CD tools.

I am also seeing reports that the data accuracy can be questionable if incidents are not tagged perfectly at the source, which defeats the whole purpose of having a metrics dashboard. Add to that a pricing model that nobody seems to understand clearly. For those who have implemented Sleuth, is the juice actually worth the squeeze, or is it just another tool that requires a dedicated engineer to maintain?

Our leadership is pushing Uplevel to 'protect developer time' and measure productivity. I have been looking into how it works, and it requires deep access to everything, including meeting calendars and Slack activity. This feels less like engineering intelligence and more like corporate surveillance.

My biggest concern is that developers will just start gaming the system. If the tool tracks 'focus time' based on calendar blocks, people will just block out their calendars artificially. Or they will skip important but 'unproductive' meetings just to keep their stats looking good. Has anyone actually used Uplevel without it destroying team trust and turning into a micromanagement nightmare?

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 ?

Dug through dozens of options so you don't have to. These are the ones actually worth using right now.

Job hunting is already exhausting — your resume template shouldn't be the thing slowing you down. Google Docs is one of the most underrated resume tools out there: free, cloud-saved, works anywhere, and most templates play nicely with ATS (applicant tracking systems).

📄Built-in templates (File → New → From template gallery → Resumes):

• Swiss — Clean, minimal, great for most industries
• Serif — More traditional/formal; solid for finance or law
• Coral — A touch of colour without being distracting
• Spearmint — Modern layout, good for mid-level roles
• Modern Writer — Best for creative or content-focused roles

🌐Third-party picks (free, open directly in Google Docs):

• Rezi Simple (rezi.ai) — Designed specifically for ATS. Single-column, no frills, parses cleanly across Workday, Greenhouse, and Taleo. Best for high-volume applications.
• SmashingDocs 'Clean and Crisp' (smashingdocs.com) — Minimalist and mobile-friendly. Great if recruiters are scanning on their phones.
• Beamjobs Creative (beamjobs.com) — For designers, photographers, or anyone in a field where personality matters. Tasteful colour and custom fonts.
• StylingCV Professional (stylingcv.com) — Best for executives or senior-level candidates. Strong section hierarchy, compatible with most ATS platforms.

💡Pro tip: Two-column layouts look sharp but average only ~86% ATS parsing accuracy vs ~93% for single-column designs. Unless you're applying directly to a company (not via a job board), stick to single-column.

How to use any template: click it → File → Make a copy → it saves to your Drive and you can edit freely.

❓What template are you currently using? Would love to see what's working for people in different fields.

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?

We are currently evaluating our data stack and looking into startree alternatives. The operational overhead and scaling costs of our current setup are becoming a bottleneck. Has anyone found a solid alternative that doesn't require a dedicated infra team? What are your experiences?

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?

I'm researching our next data architecture move and trying to answer: olap database. We need something that can handle high concurrency and low latency for user-facing features, but I want to avoid massive operational overhead. What are your thoughts?

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.

We are currently evaluating our data stack and looking into flink alternatives. The operational overhead and scaling costs of our current setup are becoming a bottleneck. Has anyone found a solid alternative that doesn't require a dedicated infra team? What are your experiences?

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.

We are currently evaluating our data stack and looking into bigquery alternatives. The operational overhead and scaling costs of our current setup are becoming a bottleneck. Has anyone found a solid alternative that doesn't require a dedicated infra team? What are your experiences?

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!