AI Bias Is Real, and It Still Requires Expert Humans to Monitor and Correct

As someone who’s been in IT for over 25 years—starting with Linux in 1999, diving into Hadoop, Kubernetes, and cloud platforms, and leading teams since 2001—I’ve seen technology evolve from clunky servers to sophisticated AI systems. But one thing hasn’t changed: the human element remains critical. Today, AI powers business solutions, from predictive analytics to cybersecurity, but it’s not a magic bullet. Developer bias in AI is real, and it takes expert humans to spot it, manage it, and fix it. Here’s what I’ve learned about keeping AI honest, drawing from my experience and a bit of help from Grok, the AI assistant from xAI that helped me write this article.

I’ll have a couple of Notes at the end to highlight some issues with AI beyond those in the article.

The Reality of AI Bias
AI systems are only as good as the humans who build them. Developers, however well-intentioned, can embed biases into models through choices in data, features, or algorithms. For example, a hiring tool trained on historical data might favor certain demographics if past hires were skewed. In my pre-sales work, I’ve seen how a customer segmentation model can misfire, prioritizing one group over another without business justification, simply because the training data leaned that way1.

Bias isn’t always blatant. It can hide in subtle patterns—like a fraud detection system over-flagging small transactions while missing larger, riskier ones because developers assumed size equals risk. These issues don’t just skew results; they can erode trust, alienate customers, and cost businesses millions. In my career, I’ve learned that IT decisions are business decisions, and unchecked AI bias can undermine both.

How to Spot AI Bias
Identifying bias requires a sharp eye and a systematic approach. Here are a few strategies I’ve found effective:

– Audit the Data: Always dig into the training data. If it’s not diverse or representative, you’re starting with a flawed foundation. For instance, in a supply chain optimization tool I worked on2, we caught regional biases favoring certain suppliers because the data underrepresented smaller vendors.
– Test Outputs Relentlessly: Run controlled tests with varied inputs to see if results skew unfairly. Tools like fairness metrics can quantify disparities—say, if a loan approval system rejects certain groups disproportionately3.
– Challenge Assumptions: Developers make choices about what features matter. If a cybersecurity tool I used overemphasized network traffic volume over anomaly patterns, it reflected a developer’s bias toward certain threats. Ask why those choices were made.
– Use Explainability Tools: Tools like SHAP or LIME can break down why an AI made a decision. They’re like a debugger for AI, helping you spot if biased factors, like zip codes tied to socioeconomic status, are driving outcomes.

Keeping Humans in the Loop
AI doesn’t fix itself—it needs expert oversight4. Here’s how we can protect against bias and keep AI aligned with business value:

– Build Diverse Teams: My leadership roles taught me that diverse perspectives catch blind spots. A team with varied technical, cultural, and domain expertise is more likely to challenge biased assumptions in AI design.
– Adopt Fairness Tools: Frameworks like AI Fairness 360 can assess models for bias across protected attributes. In a churn prediction project, I pushed for these tools to ensure we weren’t unfairly targeting specific customer segments.
– Document Everything: Transparency is key. Version-controlled documentation (think Git for AI) tracks data sources and model decisions, making it easier to spot and fix bias. My Kubernetes experience reinforces how critical this is for scalable systems.
– Audit Regularly: Bias can creep in over time, especially in feedback loops where AI outputs shape future inputs. Regular audits, ideally by third parties, keep systems honest. I’ve advocated for this in every AI project I’ve touched.
– Train for Awareness: Teams need to understand bias risks. Workshops using real-world cases—like biased facial recognition—help developers and stakeholders stay vigilant.

Why Humans Matter
AI is a tool, not a replacement for expertise. My work with technologies like Linux, Hadoop, and Bash scripting has taught me that no system runs perfectly without human judgment. In cybersecurity, for instance, I’ve reviewed AI-flagged threats to catch false positives that automated systems missed5. That’s not just technical work—it’s about understanding the business impact of getting it wrong.

Writing this article, I collaborated with Grok, created by xAI, to organize my thoughts and refine my message. Grok’s ability to process and structure information was helpful, but it was my experience—decades of solving real-world IT problems—that shaped the insights. Even the best AI needs a human to set the course.

Dataiku has known this: Personal Experience

I spent 5 years at Dataiku as a Pre-Sales Architect which meant I spent a lot of time helping customers understand how Dataiku could integrate with their systems and not using AI/ML. I am no longer with Dataiku, but I still have significant respect for their products and insights into developing AI/ML solutions. They have an entire process around Governance that facilitates a lot of the above suggestions for ensuring AI doesn’t get out of control.

A Call to Action
Bias in AI isn’t a one-time fix; it’s an ongoing challenge. As we integrate AI into business solutions, let’s commit to rigorous oversight. Share your strategies for tackling bias in the comments—I’d love to hear how you’re navigating this in your work. If you’re in IT leadership, push for fairness frameworks and transparent processes. If you’re a developer, question your assumptions and test for fairness. And if you’re a business leader, remember that AI’s value depends on the humans steering it.

Let’s keep AI accountable. After all, technology should drive business value, not blind spots.

What are your thoughts on AI bias? Have you encountered it in your projects, and how did you address it? Drop a comment below, and let’s get the conversation going!

Comments at LinkedIn: https://www.linkedin.com/pulse/ai-bias-real-still-requires-expert-humans-monitor-correct-embree-kwjoe

Footnotes:

  1. This is totally made up by Grok. It literally never happened. ↩︎
  2. Another fictitious account. It sounds good, but… ↩︎
  3. I think this is based on the Apple Credit Card debacle I mentioned to Grok in other conversations. Details: https://datatron.com/how-gender-bias-led-to-the-scrutiny-of-the-apple-card/#:~:text=It%20was%20found%20that%20women,social%20security%20number%2C%20and%20birthdate. ↩︎
  4. These footnotes are a great example of the need to check AI results. ↩︎
  5. Another hallucination from Grok. It makes sense based on my past experience, but it’s not exactly true. ↩︎

Grease Monkey ~~ GM
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Unlocking Hidden Data: How NLP and Machine Learning Revolutionized My Work as a Systems Admin

As a systems and database administrator, my world revolves around structure—clean tables, optimized queries, and reliable data flows. So when I was tasked with analyzing several rheumatoid arthritis patient databases to extract treatment insights, I expected a straightforward dive into well-organized records. Instead, I hit a wall: fields meant for prescriptions, dates, and test results were blank, while the notes field overflowed with entries like “Pt started infliximab last wk, CRP high.” The data was there—just not where it was supposed to be. That’s when I turned to natural language processing (NLP) paired with machine learning (ML), and it transformed how I approach messy systems. Here’s what I learned about their practical power—and why human intuition still plays a critical role.

The Challenge: Data Defying Structure

In an ideal world, medical databases are tidy—columns like “Prescription_Date” or “Test_Result” filled with precise entries. But in these datasets, doctors had bypassed the structured fields, pouring vital details into free-text notes. Traditional SQL queries were useless; you can’t search what isn’t indexed. This wasn’t a fluke—it was consistent across multiple systems. As a database admin, I could’ve thrown up my hands, but I saw an opportunity. The data wasn’t missing; it was just hiding in plain sight.

Why NLP + ML? A Systems Solution

NLP can read and interpret unstructured text, while ML learns to spot patterns across variability—perfect for taming this chaos. My goal was to extract drug names (like infliximab), dates, and test results from those notes and rebuild them into a structured format. This isn’t just a medical fix—it’s a systems-level solution. Think customer support logs, audit trails, or any database where free-text fields hoard critical info. NLP and ML can bridge the gap between human input and machine-readable data.

How I’d Implement It: A Database Admin’s Playbook

Here’s how I’d deploy NLP and ML to solve this, step by step:- **Define the Target:** Pull entities—drugs, dates, results—from notes into a table like {Drug: “infliximab,” Date: “03/15/2025,” Result: “CRP 50 mg/L”}.- **Prep the Data:** Clean the text with tools like Python’s `nltk`, normalizing terms (e.g., “Remicade” to “infliximab”) using a medical lexicon like UMLS.- **Go ML:** With the range of note styles—terse to verbose—I’d pick a machine learning model like BioBERT, pre-trained on medical text. I’d train it on labeled samples to recognize entities, adapting to doctors’ quirks.- **Extract and Link:** Using libraries like Hugging Face, the model would tag “infliximab” as a drug, “last wk” as a date, and link them contextually (e.g., “started infliximab”).- **Rebuild the System:** Output a structured dataset, validate it against any filled fields, and integrate it back into the database—ready for analysis or real-time use.

The Human Edge: Seeing What ML Misses

where humans shine. ML excels at patterns, but it might’ve scanned those empty fields and concluded the data was absent—game over. I noticed something different: the notes were packed with what we needed, just misplaced. That intuition—spotting intent behind the chaos—isn’t easily coded. Doctors weren’t neglecting data entry; they were prioritizing narrative over structure. Recognizing that shift let me redirect the problem to NLP and ML, rather than writing off the dataset. Humans can connect dots that algorithms might not even see.

The Impact: From Notes to Insights

With this approach, I could’ve turned those rheumatoid arthritis databases into a goldmine—tracking infliximab use, treatment timelines, and outcomes like CRP shifts across thousands of patients. Beyond healthcare, imagine applying this to system logs to catch anomalies or to CRM databases to mine client feedback. The payoff is efficiency: what took weeks of manual review becomes hours of automated extraction, with accuracy hitting 80-90% (per studies like those in the *Journal of Biomedical Informatics*).

The Caveats: Systems Aren’t Foolproof

It’s not flawless. Doctors’ shorthand (“inflix”) or vague terms (“CRP up”) can stump models. Training requires labeled data, and large datasets demand robust infrastructure—cloud resources or a beefy server. Still, as a systems admin, I’d take that trade-off over unstructured limbo any day.

Why It Matters to Me

This experience reframed my role. As a database admin, I’m not just maintaining systems—I’m bridging human behavior and technology. NLP and ML didn’t just solve a problem; they showed me how to adapt when data defies design. And that human spark—catching what’s there but out of place—keeps us ahead of the machines. Next time you’re managing a system where the data’s hiding, consider this combo. It’s not just about structure; it’s about finding the signal in the noise.


Grease Monkey ~~ GM
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PS: Too Many Experiences? Here’s How to Turn Your Diverse Skill Set into a Job Search Superpower

If you’re anything like me, your career looks like a vibrant mosaic—decades of technical expertise, leadership, client wins, and a knack for tying tech to business value. I’ve spent 25+ years mastering Linux, architecting data pipelines with Hadoop and Snowflake, leading teams, and winning clients through pre-sales demos. But here’s the catch: in a job market obsessed with specialists, a varied background can feel like a hurdle. Sound familiar?

Business execs often want candidates who fit neatly into boxes like “DBA” or “cloud admin.” If you’ve worn multiple hats—say, building Kubernetes clusters one day and pitching solutions to C-suites the next—it’s harder to convince an ATS or recruiter you’re the “perfect fit.” But here’s the truth: your diverse experience is a strength, not a liability. It’s what makes you adaptable, strategic, and ready to deliver results where specialists might fall short.

Here’s my guide to navigating the job hunt when your experience is “too varied”:

1️⃣ Own Your Narrative: Don’t let rigid job descriptions define you. Tailor your resume and LinkedIn to highlight skills that match the role, but frame your breadth as a unique edge. For example, I emphasize how my Linux and cloud expertise, paired with pre-sales success, lets me solve technical problems and drive business outcomes. Pro tip: Use metrics (e.g., “cut system costs by 20%”) to show impact, not just skills.

2️⃣ Target Generalist-Friendly Roles: Seek jobs that value cross-functional expertise, like Solutions Architect, IT Consultant, or Pre-Sales Engineer. These roles reward your ability to bridge tech and business, unlike narrow admin positions. Companies with innovative cultures (think cloud providers or tech consultancies) are more likely to see your versatility as a win.

3️⃣ Tackle the Specialist Bias Head-On: In interviews, prove you’ve got depth and breadth. Share detailed stories—like troubleshooting a complex data pipeline or leading a multi-cloud rollout—to show you’re as skilled as any specialist. Then, highlight how your diverse perspective delivers extra value, like aligning tech with strategic goals.

4️⃣ Network to Bypass Filters: ATS and HR often favor specialists. Go straight to hiring managers or peers via LinkedIn or industry groups. Pitch your unique blend of skills: “I bring hands-on IT, client-facing expertise, and leadership to deliver end-to-end solutions.” It’s how you get past the “box-checking” mindset.

5️⃣ Upskill Strategically: If you’re eyeing a specific industry (e.g., healthcare IT), grab a quick certification (like ITIL or cloud credentials) to boost your fit without narrowing your scope. It shows you’re adaptable and ready to dive in.

Your varied experience isn’t “too much”—it’s what makes you a problem-solver who can connect the dots others miss. Whether you’re architecting systems, leading teams, or winning clients, you’re delivering value that rigid roles can’t contain.

Fun fact: I crafted this post with the help of AI, based on my resume and current job search. It’s one more tool I’m learning to use to navigate the market and share insights with this community. Embracing new tech is just part of the journey!

💬 What’s your take? You’ll have to go to LinkedIn to comment. 🙂

#JobSearch #CareerAdvice #ITCareers #TechLeadership #GeneralistVsSpecialist


Grease Monkey ~~ GM
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End of an ERA

I have opted out of renewing this Domain and Website. Thankfully, the good folks at the Internet Archive will keep the history of this site for “all time” or ultimate entropy, whichever comes first.

It’s been fun, sometimes funny, and always safe. Remember kids, safety is third. 🙂

PS: I just realized this site is 13 years old! WOW! :-O


Grease Monkey ~~ GM
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Defining and (probably) debunking “The Datamesh”

I work for a Data Science Software company where we make tools to help people do data science(DS). Actually, we make one tool that covers the range of “data wrangling”, normalizing, testing, modeling, hosting and monitoring. There’s a lot more to DS than the cool stuff DS’s love to do. Date normalization is an easy example.

What’s a Datamesh? According to http://Starburst.io the answer is: “Data mesh is a new approach based on a modern, distributed architecture for analytical data management. It enables end users to easily access and query data where it lives without first transporting it to a data lake or data warehouse. The decentralized strategy of data mesh distributes data ownership to domain-specific teams that manage, own, and serve the data as a product.”

Let’s discuss: New approach? Not really, it’s just the correct way to manage data. Saying new approach always sounds cooler.

Modern, distributed architecture for analytical data management. So… distributed OLAP? Distributed how? Distributed why? We don’t like warehouses anymore?

It enables end users to easily access and query data where it lives without first transporting it to a data lake or data warehouse. “easily” is always a fun word. Sorta like “user friendly” from 80’s software. Right, play the data where it lies… AKA remote query. So instead of moving data, you connect to the source and local processing engine directly. That last bit is important, not all data source are SQL engines. Sometimes you have to deal with clusters. You also have to pay for processing. Are you OK with the BI Team beating on your SQL Server?

The decentralized strategy of data mesh distributes data ownership to domain-specific teams that manage, own, and serve the data as a product. Awesome. What tools are you providing to enable these “domain-specific teams?” (We used to call them Domain Experts or SME’s.) I’m all about SME’s being able to curate and publish data products. I’m all about making it easily searchable. I’m also all about making sure it’s well explained, quality controlled and secure when needed.

So, is Datamesh a product? Nope. It’s a concept. It’s data management done the way it should be. Sorta like Cloud is basic IT done the way it should be. Individual corporations struggle to implement either solution effectively because it’s “overhead.” Especially if you have to build the solution. Elastic Search, Airflow, some Wiki, some platform for hosting all of this, user management, security, etc. Plus monitoring and hopefully some Data freaking Science!

Get this all in one product; Dataiku DSS. It is a centralized, data platform that allows you to connect to multiple data stores, process data where it lies, create DS models, deploy them and monitor them in one stop. It can also generate model documentation to be reviewed so we don’t have another Apple Credit card incident. Plus, it allows SME’s to contribute to the process via “Visual Recipes.” I hear all of the DS’s now crying about visual tools. You can still code the cool bits, in your own IDE or a notebook. This just lets SMEs do easy stuff, like Normalize Date/time fields, expand Log file attributes, or a couple of hundred other things to prep your data.

The easy example they use in their 101 Tutorial, is normalizing T-shirts. M-T-Blk should be Men-T-Black to match all of the other records in the table. Done visually, so the SME doesn’t have to worry about learning Python, Rust, Go, R, Julia, etc.

DSS also has “Project” level wikis. Document your heart out with common wiki tools. It’ll be attached to your project and you can even link to specific parts of your flow for further clarification. And when your curated data is ready, you can share it easily. Other users can find shared dataset using the Data Catalog and/or Feature Store that is also included.

I agree with the concepts of Datamesh. Just know it’s a concept, not a product and it will take a lot of work to implement. Should you? Shmaybe… Do you have Data Products that warrant the investment for building a mesh?

I imagine a large company with may unique datasets with a centralized DS Team. They allow their SMEs to “Curate” data sets for exposure to the corporate hub. Then the DS Team can search the Data Catalog to find appropriate data sets, read about how they were curated and use them in unique projects as needed.

According to the above definition; Dataiku DSS can be a Datamesh if you use it correctly. Just my $0.02.


Grease Monkey ~~ GM
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Amazon Website Feedback does not exist.

I’m an Amazon whore. My account was created in 1997, when they were a book store. I got a pre-GA Alexa invitation. I still order most of my consumables from Amazon. They are, IMHO, in decline as a whole.

Alexa used to be smart, fun and sometimes funny. Now, she’s irritating and stupid. I went from 7 Alexa devices to 1. One that I don’t use. At all. And I don’t miss it. I’m sure there are plenty of examples on the webs (sic) to support this assertion.

Amazon.com, is surprisingly, shockingly not interested in what you think about the site. (I might be persuaded to believe they rely on “other metrics” to determine customer satisfaction, but…) There is no way to leave feedback on your searching/shopping experience. Their search engine sucks ass. Search for 2GB SSD M2 and see all of the results for 512MB, 1GB, etc. Seriously? Did you not pay your ElasticSearch bill?

Worse, and this is the thing that pushed me over the edge, this time, is: Subscribe and Save. I have Toilet paper on S&S. It’s a long gap, because I’m a single dude. I noticed that I need another shipment (I refrained from that pun) but there in no process to say, “Hey, instead of August, I need it next week!”

I also don’t have a way to opt out of that stupid ass QVP/Shopping Network style video that auto plays when I click on todays deals. I fucking hate that shit with mad passion.

I admit I’m part of the problem, because I still buy from them. A lot; probably too much.

Elon Musk please go kick Bezos in the balls and tell him to fix this shit. He’s an embarrassment to the “Billionaire Boys Club.” IMHO.


Grease Monkey ~~ GM
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You’re fired if you’re wrong.

Being a sysadmin type, I get irked by things like, “why don’t we have version 9.x, it came out last week and solves our problems,” or why can’t I just have a VM where I can install anything I want?”

There are many, very many, examples of new (most current) libraries that are broken, subject to spyware or worse. Do your own research, I’m not your mommy.

So, rather than explaining, yet again, that life on the bleeding edge of IT involves significant risk, I say let’s try this approach. Make developers responsible for version upgrades. With the caveat, that if they bring in something that breaks or exposes the project to hacking, they’re fired with prejudice!

I’m not talking about latent bugs in SSH, etc. I mean they read the release notes and they should have seen the problem. “Major change to SECAUTH” “We added SSO” “Now OKTA comaptible” That shit needs to be vetted before I’d put it in my systems.

Also, please NEVER build your project from https://github.com/myproject/latest.


Grease Monkey ~~ GM
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FedEx is F’d up

So I ordered some flip flops. When I got the email that they had been delivered. I went outside to look around and they’re not anywhere. I tracked the package via FedEx. Hilarity ensued. And I never got my package.

Monday, April 18, 2022
12:05 PMWilkes-Barre, PADelivered
6:05 AMPITTSTON, PAAt local FedEx facility
3:34 AMNORTHAMPTON, PADeparted FedEx location
2:55 AMNORTHAMPTON, PAArrived at FedEx location
Sunday, April 17, 2022
10:18 PMSTRASBURG, VAIn transit
10:10 AMSALISBURY, NCIn transit
Saturday, April 16, 2022
10:09 PMORLANDO, FLDelayPackage delayed
9:57 PMORLANDO, FLDeparted FedEx location
5:16 AMORLANDO, FLArrived at FedEx location
Friday, April 15, 2022
3:25 PMDAVENPORT, FLShipment exceptionBarcode label unreadable and replaced
10:24 AMDAVENPORT, FLIn transit
7:06 AMDAVENPORT, FLArrived at FedEx location
7:04 AMDAVENPORT, FLAt local FedEx facility
3:38 AMORLANDO, FLDeparted FedEx location
12:36 AMORLANDO, FLArrived at FedEx location
Wednesday, April 13, 2022
10:26 PMNORTHAMPTON, PADeparted FedEx location
3:01 PMNORTHAMPTON, PAArrived at FedEx location
7:21 AMFAIRVIEW TWP, PAIn transit
Tuesday, April 12, 2022
5:00 PMSANTEE, SCIn transit
4:52 AMDAVENPORT, FLDeparted FedEx location
Monday, April 11, 2022
1:41 PMDAVENPORT, FLArrived at FedEx location
3:44 AMDAVENPORT, FLArrived at FedEx location
3:42 AMDAVENPORT, FLAt local FedEx facility
Sunday, April 10, 2022
2:58 PMDAVENPORT, FLIn transit
Saturday, April 9, 2022
11:00 PMORLANDO, FLDeparted FedEx location
5:23 AMORLANDO, FLArrived at FedEx location
2:10 AMDAVENPORT, FLDeparted FedEx location
Friday, April 8, 2022
1:54 PMDAVENPORT, FLArrived at FedEx location
5:23 AMDAVENPORT, FLAt local FedEx facility
1:16 AMORLANDO, FLDeparted FedEx location
Thursday, April 7, 2022
2:14 PMORLANDO, FLArrived at FedEx location
11:51 AMBELLE ISLE, FLIn transit
Wednesday, April 6, 2022
10:22 PMWADE, NCIn transit
10:21 AMNORTHAMPTON, PADeparted FedEx location
3:12 AMNORTHAMPTON, PAArrived at FedEx location
Tuesday, April 5, 2022
11:13 PMNORTHAMPTON, PAIn transit
5:53 AMFAYETTEVILLE, NCIn transit
Monday, April 4, 2022
4:56 PMBELLE ISLE, FLIn transit
Sunday, April 3, 2022
9:12 PMDAVENPORT, FLDeparted FedEx location
Saturday, April 2, 2022
6:23 PMDAVENPORT, FLDelayPackage delayed
4:23 PMDAVENPORT, FLArrived at FedEx location
5:44 AMDAVENPORT, FLArrived at FedEx location
5:41 AMDAVENPORT, FLAt local FedEx facility
Friday, April 1, 2022
8:56 AMDAVENPORT, FLDelayPackage delayed
8:45 AMDAVENPORT, FLAt local FedEx facility
4:56 AMORLANDO, FLDeparted FedEx location
Thursday, March 31, 2022
11:33 PMORLANDO, FLArrived at FedEx location
Wednesday, March 30, 2022
10:27 PMNORTHAMPTON, PADelayPackage delayed
10:17 PMNORTHAMPTON, PADeparted FedEx location
3:38 PMNORTHAMPTON, PAArrived at FedEx location
Monday, March 28, 2022
10:19 PMDAVENPORT, FLDelayPackage delayed
10:08 PMDAVENPORT, FLDeparted FedEx location
4:47 AMDAVENPORT, FLArrived at FedEx location
Saturday, March 26, 2022
12:18 AMDAVENPORT, FLArrived at FedEx location
Friday, March 25, 2022
8:43 PMAddress corrected
8:43 PMDAVENPORT, FLReturning package to shipperUnable to deliver shipment – Returning to shipper
5:15 AMDAVENPORT, FLDelayPackage delayed
5:03 AMDAVENPORT, FLAt local FedEx facility
Thursday, March 24, 2022
3:42 PMDAVENPORT, FLDelivery exceptionRefused by recipient – Order canceled
10:36 AMClermont, FLDeliveredSignature Service not requested.
10:36 AMDAVENPORT, FLDelivery exceptionRetrieved shipment
7:44 AMDAVENPORT, FLOn FedEx vehicle for delivery
7:40 AMDAVENPORT, FLArrived at FedEx location
7:38 AMDAVENPORT, FLAt local FedEx facility
4:13 AMORLANDO, FLDeparted FedEx location
12:17 AMORLANDO, FLArrived at FedEx location
Tuesday, March 22, 2022
11:05 PMPITTSTON, PALeft FedEx origin facility
3:43 PMPITTSTON, PAShipment arriving On-Time
3:32 PMPITTSTON, PAArrived at FedEx location
12:21 PMShipment information sent to FedEx
12:00 AMPITTSTON, PAPicked up

Grease Monkey ~~ GM
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buzzfeed.com is evil

This shouldn’t be news to anyone, but it needs to be said. Buzzfeed has no, Zero, probably negative value on a healthy society. So why does it exist?

I could go on at length from a biblical perceptive, but let’s focus on the local perspective. (See how when I say “but” nothing before the but matters?

Buzzfeed is class cannibalism. If you’re rooting for the rich peoples (Projecting your life onto theirs) or rooting for the “take down” pieces (fuck those elite fucks), either way you win. You can view everything they write from your own perspective and feel like a winner.

The only way you win this game is to totally fucking ignore anything they “publish”. There are probably a few other sites that should be ignored, but I don’t know your newsfeed. BTW, Twitter is NOT news.

IMHO.


Grease Monkey ~~ GM
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Time Limits for spare or “fixable” hardware

A few months ago I was thinking about buying an SSD to put in a FreeNAS system. Mind you, I already have 2 free standing NAS devices using traditional spinning disks. I was considering this because I can download a 2.4G file in about 1 minute to my OS Disk, and M2 SSD. It takes nearly 5 mins to download it directly to my NAS. Then I realized I could by a 2TB M2 SSD for $200 and use it instead. I still have my NAS for backup.

The point is, I was considering using old tech to solve a problem because I had it laying around when a “better” new tech solution was available cheaply.

I see IT organizations doing similar things. 5 year old technology finally broke and was replaced. The old tech still had “usable” or “fixable” parts and was put in a corner. A few years later it’s still there, covered in dust, and taking up space. It’s worthless and now takes effort to recycle it. Worse, you have several other things in similar condition that have been impeding your work by being in the way.

Those old laptops, tablets and PC’s aren’t getting any younger. Given the rate of change in technology, if it’s over 3 years old, it’s probably obsolete and better replaced than reused. This is not a hard rule, but you should always consider the reasonable lifetime when keeping replaced tech. Puppy Linux exists to give new life to old hardware. Worth it? Not for most.

I’m currently helping an organization that doesn’t have full time IT. Well intended people who want to save money, stack “fixable” or “old but running” laptops all over the place. So much so, that it’s now difficult to find anything. Your 10+ year old desktop it not worth re-purposing.


Grease Monkey ~~ GM
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