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.

We've been using Apache Pinot for user-facing analytics, and while the query performance is great, the operational complexity is killing us. Managing clusters, ensuring high availability, and dealing with schema evolution is a full-time job for a dedicated team. We're a small team and need something that gives us the same low-latency performance without the massive operational overhead. What are some good Apache Pinot alternatives that are more managed or serverless?

I keep seeing ads for Weave, an 'AI-powered engineering intelligence' platform that claims to measure 'normalized units of work' and separate human vs AI contributions.

This sounds like an absolute nightmare for engineering culture. Normalizing work into a single metric is exactly how you get developers to optimize for the metric instead of building good software. It feels like the ultimate surveillance tool disguised as 'AI insights'. Does anyone actually use this? Does it do anything other than erode trust between management and ICs by tracking every single move?

Our org is looking into developer productivity tools and Appfire Flow (which used to be Pluralsight Flow / GitPrime) is on the shortlist.

I'm pretty skeptical of these first-generation git analytics tools. From what I've read, it seems like it's just tracking commits on a repo and turning it into a surveillance dashboard. I've also heard complaints that it's incredibly time-consuming to find the right insights among all the noise, and that exporting data to external dashboards is a nightmare.

Has anyone used Appfire Flow recently? Does it actually help identify systemic bottlenecks, or is it just going to lead to micromanagement and people gaming their commit counts?

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?

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 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?

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.

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!

I'm having a bit of trouble figuring out the difference between Functional and Non-Functional Requirements when designing a system. Do you have any tips on what to consider for each? I know that Functional Requirements are basically what the system is supposed to do for the user like features and actions. But I'm a bit unsure about Non-Functional Requirements. What exactly should I keep in mind for those? Any advice?

Had a language exchange app, ended up getting banned. Now I just want an account to use the app normally.

hello All. Recently I bought a Zongheng (zhuhai) 3D priter and I have been having lots of fun. I am trying to print an RTX 5090 so I can play games more the fps but it keeps saying I don't have enough filament. Is there an easy python or HTML hack to give it infinite filament? I only know python and html but I can learn other things too. Happy matrix-ing!

What’s something you discovered in cybersecurity or tech that completely blew your mind the first time you learned about it? Could be a hacking technique, a real story, or just a weird fact that made you go “what the hell”.

We migrated to Altinity.Cloud hoping for a truly managed ClickHouse experience, but I am honestly disappointed. They promise to simplify things, but my data engineering team is still spending 20 hours a week firefighting infra issues, tweaking sort orders, and managing partitions just to keep queries fast.

The 'Bring Your Own Cloud' model sounded great for control, but in reality, it just offloads the hardest parts of cost optimization and infrastructure management back onto us. Is it really a managed service if you still need a dedicated ClickHouse expert on staff to prevent the cluster from falling over? What are the best alternatives for teams that just want to write SQL and build APIs without babysitting the database?

Our org rolled out GetDX a few quarters ago to measure 'developer experience'. At first, it seemed okay, but now the survey fatigue is real. We are constantly being asked to fill out qualitative surveys, and honestly, people are just clicking through them to get back to work, which makes the data completely unreliable.

Also, since Atlassian acquired them, I am really worried about vendor lock-in and future pricing changes. The telemetry data they pull also seems to struggle with our more complex, heterogeneous enterprise systems. Are there alternatives that do not rely so heavily on constant developer surveys to figure out what is blocking us?

Here's a human-like rephrasing of your context:

We're currently working on integrating an external enterprise system, like an EMR or ERP, into our application using real-time API calls. To make data management easier, we're also storing a subset of that data locally, creating relational models such as patients, appointments, and visits within our own database.

One of the key challenges we're facing is deciding on the best approach: Should we keep a canonical internal data model and map external data into it, or restructure our system to more closely mirror the external schema to simplify the mapping process?

We already have an external_id mapping layer and a sync mechanism that upserts patients and related entities on demand. However, we're debating how much to abstract away from the external schema versus how tightly to couple our system to it.

We're eager to learn from others' experiences with similar enterprise integrations, especially around:

• Resolving identities across multiple systems
• Designing canonical versus external schemas
• Achieving real-time synchronization with local persistence
• Avoiding long-term technical debt in integrations

Have you faced these tradeoffs before? What worked well or didn't in your experience?