#Cluster sampling definition

[marketxcel]

Cluster Sampling: Types, Advantages, Limitations, and Examples

Explore the various types, advantages, limitations, and real-world examples of cluster sampling in our informative blog. Learn how this sampling method can help researchers gather data efficiently and effectively for insightful analysis.

[xesnox]

All currently active NeoBuilders.

Minecraft default skins in Ancient Ruins, reference picture at the end for comparison.

Don’t look too close at the faces, had to speedrun this so I could actually motivate myself to finish the entire lineup. Definitely not my best work. Clothing was a pain to figure out and I’ll definitely change some things in the future as I’m still not entirely happy with the general aesthetics of some sets. Zuri and Efe were the worst to figure out, their clothing styles were very modern in comparison to what I’m going for with Ancient Ruins so it took me a while.

But first of all, what is a NeoBuilder?

Lore entry Ahead, body horror warning, nothing too extreme I wouldn’t say, but the concept definitely isn’t the prettiest.

“NeoBuilder” describes a category of individuals that appear to be within the homo-genus (Artisan Family.).

The dissection of a deactivated individual showed (us) they are made of about 85% mechanical components, most of which consist of copper and iron. Organs however remain fully functional and can be traced back to Ancient Artisans in origin through DNA sampling. Blood vessels aswell as a large portion of their flesh has been replaced by machinery, activated regulated and moved through restricted soul energy coursing through their veins as blood would on a fully biological human.

The change of energy source makes them effectively immune to the green plague, which is transmitted through blood. In place of a heart they have a still unnamed chamber (Ch1), acting akin to a cockpit housing a freshly created soul, which is replaced by converting experience farmed in the previous lifecycle to the next, taking the raw energy and morphing it into a new soul. As souls are neither human nor animal but simply an energy cluster made of (if found post mortum) memories and life experience this can easily (and understandably) be transferred, even if the origin of the experience in question happens to be non-sentient. The experience is collected and stored within yet another unnamed organ replacing their left kidney (Ch2).

But do not be fooled, this process by no means makes them immortal. If the body is in critical shape it will resort to exporting the active soul and using the remaining soul energy still running through the machinery to activate the emergency program. In this state the body will do nothing but return to whatever point the machine has clocked as “safe” (this kind of reset happens when the person is resting for an extended period of time, as it marks said area as safe enough to stay vulnerable within.) and fix its injuries back to base point, excluding the bio-matter, as there no longer is any such material around. Through the expelling of the last soul, and slow process of developing a new one, the machine will power down there until the next energy burst. This means that even though the body itself remains alive, every life cycle houses a different person. If you see the same NeoBuilder twice it is therefore not guaranteed it will come with the same intention, as it might be a completely different soul speaking through the same form.

If the damage is too bad however, the NeoBuilder will shut down and cannot be reactivated unless one with actual knowledge of how to do so were to interfere. The how has been lost to history however, we blame the creature of despair and decay that shall not be named. It is different from deactivation, as it renders the body permanently dead. Many NeoBuilders have deactivated, we assume only 9 of the previous 30 remain.

We have no idea as to why these manmade humanoids were created, as the why has also been buried and lost to years of untouched unaddressed missing history.

Infact, their names are quite new, till about half a century ago, due to my own research if I may pat myself on the back, we had assumed them to be a strange strain of plague infested ancients, as they’d always remained distant to us and a dead specimen was a more than rare find. It is not a fully worked out name whatsoever, the “Builder” merely connecting them to the Artisan family, and the word “Neo”- as in “New” replacing the word “Ancient” we’ve been using to describe the other half of the family thus far, it is a placeholder by all means, but so is the latter.

Their nature would definitively make them more akin to bio-mechanical golems, however we chose to group them into the artisan family for the previously mentioned bio material, which matched identical to that of the mummified remains cave divers had found a few years back lodged between long forgotten pathways shut to time. Wasn’t a pretty sight when they showed me. One would think after a good few millennia they’d be nothing but a pile of ash and bones, but low oxygen levels within the closed off cave system made for some awfully good preservation, not pretty I dare say, I’m just glad nothing snapped at me during inspection as, perhaps in this case thankfully, the undead plague only infects the living; anything that doesn’t breathe and lacks running blood cannot fall victim to such a thing. But personal tangent aside, some interesting notes here:

- Despite the soul of the Neobuilder swapping with each lifecycle they generally keep the same base morals of the previous host.

- it seems Mother Earth isn’t very fond of them for whatever reason. The undead are very manageable if one isn’t around, but as soon as one chooses to stay in close vicinity it very quickly gets nearly impossible to maneuver, they’re like a magnet. It seems this is the reason they rarely last longer than 20 years within a single lifecycle.

- they’re actually quite cooperative, and many have learned our language. Though sorrowfully it appears they only store a single slot for their language, and forget all other information of a previously spoken one upon interaction (perhaps they were meant as a situational translation device?). I’ve attempted my best to reteach them, but I’m no linguist; I limit my research to history, with a limited understanding of biology. To figure out the mechanics alone I had to get a whole other villager to help my case.

- despite a large portion of their flesh being replaced by mechanical components an extra layer of the fat cells within the subcutis create an artificial layer of flesh like material dampening any slashes at the golem like form below.

More research to be added once I acquire more material to work with. This is a work in progress.

Understudied field, more research either classified information or unable to be attained due to both lack of examples and individuals willing enough to risk their lives acquiring such needless information. Ivan D. P. Retired shortly after writing his book discussing categorized biological information regarding the entities within our realm, which has since been removed from the market. The authors doctor title has been revoked due to a less than fortunate suspected alliance with the Illager cult on his end, so choose the information you’re willing to believe wisely, the villages dislike him for a reason.

We sincerely apologize but certain behaviors are to be taken seriously regarding the issues the cult on the outskirts has caused us in the past. Rumors have to be believed regarding individuals actively choosing to house away from the masses for whatever reason, to keep the general public safe.

Moving just out of sight to dedicate your life to dissecting humanoids on your dinner table is not normal behavior and was it not for his living position would hastily be investigated,

mumbling about how you hate our community doesn’t lessen our concerns, if you read this Ivan, it was not funny and it will never be, you know of the missing people concerns: if it happens again we’re sorry to inform you but you’ll no longer be welcome within this village.

[sakarrie-creates]

Here’s my 2024 Art Summary, terribly late as usual! We’re back to including some black and white in this year and my months were very clustered, but all things considered I was actually surprised I ended up with as many decent samples for the collage as I did. I was kinda expecting them all to be messy sketches this year lol.

Just like with my writing summary, this year the questions are a bit abridged since I didn’t do a ton of art. That said, I’m still a rambler haha, so the reflection questions are answered up the cut. The template I used is available here in case anyone else wants to use it!

What events did you participate in (with art)?Player Appreciation Week (old art), Fandom Trumps Hate (offered), CS Case Files Zine (comic!). Not really the type of art this summary is for, but I also created my first cosplay for RCCC!

What was your biggest challenge this year? Definitely motivation. Last year was a creative slump with a lot going on mentally making stuff hard. Mostly in start up energy, since once I got going, things seemed to go alright.

Did you try anything new this year? I got into a comic zine, which was new and exciting! My original plan was far too long for the creation period, though, so I spent a ton of time trying to widdle it down to 3 pages. I also did super messy spot art sketches for a friend’s fic, which ended up being a lot of fun! I also took some screenshots and then drew other characters into the show, somewhat trying to match the show’s style, which was definitely interesting.

Where do you think you most improved? I’m going to go with rendering again! I really love playing with color and lighting, and several of my collage pieces this year were just adding rendering to old pieces. I have a lot more confidence with it now, though I still sometimes feel like my pieces don’t end up as dynamic as I’d like. I also did a lot better at following inspiration and not being as perfectionistic this year.

What are you most proud of? I’m really proud of getting into a comic zine, even though it likely wasn’t that competitive. The fact that I got in for comics despite never having done art for a zine before is crazy to me! I was a pinch hitter, so I wasn’t originally selected, but I’m still honored I got picked eventually. I also am really proud of the August fully rendered piece of Player screaming. I did that one in almost one sitting, probably around 4ish hours, and it was one of my first times doing full color/rendering without cleaning the sketch much at all. Overall, I was very excited at how well it turned out and I feel like the messy emotions really come across!

How’d this year compare to your 2023 goals?Shoutout to past Sakarrie for giving me a straight bullet point list. MUCH easier to work with lol.

2024 Goals Met:  -Number One Priority: Create for me and don’t put myself in a place to get crazy burnt out and still have requirements. If I meet this goal, then it’s okay if I don’t meet any of the others. (It would be sad.... But I would still count it as meeting overall goals.) -Participate in Summergen and PAW Week (Art or Fic) -Have a fully usable Zine Portfolio (Currently need more merch samples and rendered pieces with backgrounds) -Apply to new TOH Zines or other loved fandom zines. If I end up getting into any, I can pull back, but since that doesn’t seem likely, I want to get into the habit of always being ready to apply with what I have. -Play with different brushes and rendering styles -Not exactly art, but I want to have a finalized long-term merch display plan for all my items -Do ONE of the following:     1. 30 minutes digitized so it can be shared with music       2. Open Up Your Eyes fully thumbnail       3. Fanworks for other people’s fics      4. Participate in an extra bang or exchange with art      5. Design and manufacture a pin

The ones that don’t have strikethrough are a bit of a stretch, but I’m gonna give them to myself. While I haven’t added more pieces with backgrounds and need to reorganize my zine portfolio, it is in a decent place where I feel like I can use it and it will accurately represent my best work. I also didn’t really purposefully experiment with rendering or brushes, but it did happen naturally a little, so giving that to myself too. As for the ONE of the following list, I actually did digitize my 30 minutes thumbnails! It’s just not to music, so doesn’t count. Also holy dang, last year Sakarrie was ambitious with the proposal of manufacturing a pin haha.

2024 Goals NOT Met:-Design Handplates charm as anniversary gift (November) -Design CS Charm-Make an ongoing project list to pin to my tumblr. This applies mostly for fics, but that way people coming to my page can see what fandoms I’m actively creating for and what they can look forward to (as well as have an opening to ask questions if they’re interested). -Post more (at least 10 times throughout the year) and add my best pieces to instagram (8+ pieces by end of year). -Draw something from scratch every month, no matter how small

Yeah, these all I absolutely failed with. Oops. Probably gonna be using a fair few of these as my new goals haha.

Alrighty then, now it’s time for 2024 goals!! I think I want my main focus to be to try to draw more frequently. I’ve found that so much of what prevents me from drawing is startup energy, and once I actually get going, it all comes much easier. 

Specific goals: -Organize a go-to zine portfolio for comics, merch, page art, and spot art applications -Try to draw every month (even the tiniest phone doodle counts) -Design Handplates charm as anniversary gift (November?) -Design CS Charm-Make an ongoing project list to pin to my tumblr. This applies mostly for fics, but that way people coming to my page can see what fandoms I’m actively creating for and what they can look forward to (as well as have an opening to ask questions if they’re interested). -Post more (at least 5 times throughout the year) and maybe look into Cara or whatever the non-instagram art app is. -Experiment more with drawing in sketchy art style with full color/rendering -Experiment more with screenshot redraw/character replacements -Do ONE of the following:     1. 30 minutes put to music      2. Open Up Your Eyes fully thumbnail      3. Fanworks for other people’s fics       4. Participate in an event with art       5. Draw and post for a new/niche fandom (Infinity Train, Sym-Biotic Titan, Irondad, The Flash, etc)

Overall, how’d the year go? Better than I expected when I first started pulling up my art haha. I didn’t push myself on anything but the CS comic, and that was pretty early on in the year. I also did a fair few doodle/sketch projects and followed the muse when it wanted to do rendering without drawing.

[sergeifyodorov]

POLL RESULTS JUST DROPPED!!

My hockeyblr experiences are largely catered to my own personal tastes -- mostly Leafs, a little Penguins and Stars, one or two who post about Stevie Y and Sergei Fedorov. These are obviously not the only teams out there.

This study was designed to survey as much of hockeyblr as it possibly could, gathering data on which teams people like and to what degrees. There were five questions and a free space -- my attempt to ask people to rank the teams they enjoyed in three levels, from religiously followed to casually affectionate, and an additional couple of questions on love for players versus team. I received over 500 responses. Here are the results.

Yeah, yeah, you all want to know: The most popular team is the Penguins, by a long shot, then the Leafs.

Because my sample size (n = 523) is actually fairly small compared to the number of NHL teams there are, I find definitive rankings tend to be difficult. It’s also worth noting that, as a mainly Leafs blog, my numbers are definitely going to be skewed a little in favour of the Leafs.

Your Guys

These are the teams closest to your heart: the ship you go down with, metaphorically or, depending on how married your old men are, literally. For me, I picked just the Leafs.

The average respondent had 1.9 teams in this category. The most popular, by far, was the Pittsburgh Penguins. Below is a table of teams, arranged roughly into tiers by the number of respondents. Each team has the number of respondents in brackets next to their three-letter code.

I allowed people to pick as many teams as they would like; the average person picked 1.9 teams, but here’s a distribution of how many teams they picked:

4 people picked 0 “your guys�� teams, and 2 people picked seven, nine, or ten each teams. Just about half of people had one main team.

I then wondered: what teams were people most likely to only follow? That is, if you hold [x] team in the closest part of your heart, are you more or less likely to also hold any other teams? Almost exactly 25% of picks were solo; I wondered if there was any correlation at all.

Only a little bit! Of the samples large enough to actually consider (so: nothing in that cluster at the bottom left, who all received fewer than 10 picks total, and a few of whom -- CGY, CHI, NSH -- received zero solo pickers), the most devoted fans chose the Sharks, the Bruins, and the Leafs. The fans who liked the most other teams chose the Avs, the Kraken, the Canucks, Panthers, Sens, and Ducks.

Probably a next step would be to look for correlations: if people are a fan of one team, are they more likely to be fans of another? THAT BEING SAID that’s a lot of regressions. Maybe keep an eye on that for the future, but I don’t know!!

Objects of Enjoyment, and Generally Nice

These two were successive tiers meant to distinguish teams that people like from the ones in the category above. I admit I probably could have phrased the questions better; I received several comments saying that they’d watch any hockey when they wanted to put a game on. The dynamics between Your Guys versus Objects of Enjoyment versus Generally Nice would best be described as devoted fan of versus casual fan of versus favourable opinion towards. 

As I said a few paragraphs back, people picked 1.9 “devoted fan” teams on average. Again on average, they picked 4.7 “casual fan” teams and 6.5 “favourable opinion” teams. Not all ratios are equal, though! Some teams had significantly more casual than devoted fans, and others still were much more liked generally than average.

I gave each team’s “devoted” count an index number of 1 and measured their casual and favourable count as a ratio against the index number. The teams assembled themselves into a few groups.

No Commitment

Arizona and Anaheim have decided to be soulbonded (Excel refuses to let them have different-coloured dots) and it took me three hundred million years to attempt to (and unsuccessfully) fix, so let’s ignore that. These teams all have a fairly high slope of interest -- a range of casual interest at about five times the pace of fervent interest, and good opinion at about ten times fervent interest. The Calgary Flames are an outlier on the entire graph, not just here. 

Casual Interest

I gave up on trying to colour teams according to their real colours shortly after the Anaheim/Arizona debacle. Please employ the legend. Nashville is included on all five graphs for reference. These teams all have a casual interest factor of about 3, and a favourable opinion factor of around 5; the same ratio as the casual fans of the teams in the first category to their fervent fans.

Saturated Market

These teams have a much lower ratio of hardcore:casual:favourable fans, at about 1:2:3. 

We Get It, Those Are Your Guys

Pittsburgh and Toronto; these teams have an almost equal ratio of all three categories.

...Whatever This Is

Every other category is defined by its ratios; this category is defined by its shape. While all teams have their rate of hardcore fandom set as 1, the other two tend to increase in a roughly linear form, without too much significant difference between the first interval and the second interval.

These teams, though (again, Nashville is for scale) don’t do that: they have a set increase between hardcore and casual, and a significantly smaller increase (or, in a couple cases, a decrease) between casual and favourable. This suggests perhaps some kind of divisiveness; if you’re not already in there, do you really want to get in further? Either that, or it’s something closer to what the Leafs and Penguins have: that is, a devotion. Like you’re in or you’re out.

Taking these values together

Because the casual:hardcore ratios are measured as indexes and not absolute values, they say nothing about the actual popularity of the team in question -- Calgary is one of the least popular, which is why I assume it’s so weirdly high up; small sample sizes lead to higher error values!

But we do have the absolute values, so we can measure them against each other.

If we consider the “In or Out” to be a category of its own while the other four are along more of a continuum, then we can absolutely see a correlation here -- larger fandoms tend to have more involved fanbases.

Players or Teams?

I also asked participants if their guys tended to be players or teams -- and if those they liked at a more casual level tended to be players or teams.

The results are… not particularly surprising.

On a hardcore level, people tended to prefer teams, although the variability was pretty slight. On a casual level, individual players were much more popular.

I also wondered if people who chose more teams in the hardcore fan question tended to do that because they prefer players.

On average, people who picked players on their hardcore level chose 2.1 teams. People who picked teams chose 1.7 teams. That’s definitely a difference!

Fun Shtuff

I got way more write-in responses on the hardcore player/team question than on the casual question, including this:

Three separate people answered “Minnesota Wild” for their guys and chose no other teams on any level. Hell yes. (One person also did this for the Kings.)

It took about 300 responses before the first Flames fan (at the hardcore level.)

On all three levels, the Seattle Kraken are really popular -- they’re in the top five in each.

What's Next?

If I were to update this survey, I would probably include a question about where all of you are from -- some people (like me) follow their hometown team, while some people most certainly don't (shoutout to the one person from Edmonton who dislikes the Oilers) and others still don't have a hometown team (shoutout to my brasilian + european + etc mutuals and everyone else!!)

Feel free to shoot me an ask if you want me to do anything else with this data -- examine a specific team, give actual casual fan/etc counts and total aggregate rankings, anything else!

[deardaichi]

chapter three: spoken over glasses

wc: 0.8k series masterlist: found here

By the end of the week, Satomi stops smiling.

Not in a dramatic way. She still speaks politely. Still attends fittings, still nods through council meetings, still walks the garden paths with Toru at dusk. But something sharp begins to settle behind her eyes — a thin, glinting thing that doesn’t go away.

The problem, apparently, is the flowers.

“These are wrong,” she says, gesturing toward the table where petals in glass bowls have been arranged in careful, color-matched clusters. “They’re too…bridal. I don’t want soft. I don’t want fragile.”

Toru exhales slowly, pinching the bridge of his nose. “Then tell me what you want.”

“I don’t know.” Her voice flickers, not quite angry. “Something different. Something not like everyone else.”

The silence stretches. Wakatoshi stands at a polite distance. He doesn’t speak. He doesn’t leave either.

“Then we’ll look together,” Toru says finally. “There’s a collection of botanical prints in the eastern library. They’ve been sent from all over the continent. We’ll pick what feels right.”

Satomi eyes him carefully, then nods. “Fine. But I don’t want to be rushed. I want peace while we do it.”

Toru only lifts a brow. “I was born for silence.”

⊹₊♕₊⊹

The eastern library smells like wood and dust. Scrolls and thick books line the desk, floral diagrams nestled between the pages like forgotten notes. Light from wall sconces casts shadows on the floor, golden and flickering.

They open the windows. Let the breeze in. The night settles around them like a second skin.

The prints are spread between them, old ink sketches and vellum sheets pinned with delicate labels: primroses, camellias, snowbells. Wax seals still clinging to the corners. Some are fragile enough to crack under breath.

Satomi sits cross-legged on the floor, leaning back against a stack of bound archives. Her sleeves fall over her hands. She brushes them back absentmindedly as she flips through the pages.

Wakatoshi doesn’t sit, but hovers close enough to lean in when they hold something up. His shadow falls over Toru’s shoulder. Neither of them acknowledge it.

Satomi selects and discards flowers quickly. Too pale. Too sharp. Too romantic. Too forgettable. She mumbles critiques without apology, fingers brushing pages with growing impatience.

Toru takes his time. Flipping through the older collections. Ones handwritten in dialects that haven’t been spoken in years. Margins crowded with pressed samples and careful notes. Some of the pages are beginning to fade at the edges. 

One sketch catches his eye: a stem of silver-white lilies growing from between stones. No name. No note. Just that.

He says nothing, but folds it carefully and pockets it.

Outside, a night bird calls once. Then, quiet.

The door creaks open — not a servant this time, but Iwaizumi himself. He steps in without a word, a tall bottle of shochu in one hand, cups in another, and a quiet look in his eyes.

He sets them down on the table with a soft clink, gaze resting on Toru just a moment longer than necessary. There’s no speech, no teasing. Just something like understanding — the kind that only comes from watching someone carry too much for too long.

He doesn’t ask to stay. He doesn’t ask for an explanation. He just gives Toru a nod — sad, maybe. Sympathetic, definitely. He leaves without waiting for thanks.

They drink.

Not to celebrate. Not to toast. Just to make the silence lighter.

At first, the conversation continues — shallow, meandering. Satomi points out ridiculous illustrations. Toru jokes about an overly dramatic rendering of a forget-me-not. Even Wakatoshi lifts a brow at a drawing that looks more like a seaweed than a rose.

But after a few cups, the silence starts to stretch.

Satomi slouches. Her sleeves loosen. Her hair slips free in a few places. She doesn’t fix it.

“You two,” she says, picking up a faded drawing of a hydrangea, “you’re so concerned about the perfect flowers and the perfect wedding. Maybe you should marry.”

Toru chokes on nothing. He doesn’t laugh, though he thinks he’s supposed to.

Wakatoshi shifts — barely — and looks at him.

It’s not a long glance. But it’s not a normal one either.

Something burns in Toru’s chest. He takes another drink.

Satomi watches them both. She sighs.

“I don’t see myself marrying,” Satomi says, setting the sketch down. “Not in the way people expect. Not in the way that ends in a shared bed. Or even a shared life.”

She tilts her head. “But I do see the way you two look at each other. And I know it’s wrong — gods, I know — but that doesn’t make it any less true.”

The words settle between them like dust, and no one brushes them away.

Toru doesn’t answer, but he can feel his heartbeat in his throat. Wakatoshi doesn’t move either, but his hands curl slowly around his knees.

No one says anything for a while.

The candle on the table burns lower, flickering shadows against the floor. Satomi leans back against the shelves behind her. Her head tilts up. For a moment, she just closes her eyes.

The silence stretches until it folds in on itself. 

“I don’t want to do this wrong,” Toru says quietly. “But I don’t know what right looks like either.”

Satomi opens her eyes again, looking at him. Then at Wakatoshi.

“I don’t think anyone does,” she says.

Still, they sit there.

Not arguing. Not planning. Just existing in the kind of stillness that follows truth.

Wakatoshi is the first to breathe.

And when Satomi finally speaks again, it’s slower, softer.

Satomi’s voice drops. “So let’s figure it out.”

She leans forward. “You want peace. I want freedom. And you want each other. I’m not stupid. I’m not angry. But I am tired.”

Toru’s hands rest in his lap. Not shaking. But not steady either.

“There’s a nation in the south,” he says slowly. “Karasuno. They’ve ruled with two kings for almost a decade.”

Satomi blinks. “That place? They’re practically shunned. The courts talk about them like they’re a joke. No decorum. No legitimate heirs. No alliances anyone takes seriously.”

“And yet,” Toru says, “they still rule.”

Wakatoshi finally speaks. “Their peace has held longer than some of ours.”

The candle between them burns low.

Satomi looks between them. “If we do this… if we even entertain this… it could cost everything. The marriage. The alliance. Our crowns.”

“Then we don’t say anything,” Toru says. “We go in secret. We ask questions. Quietly. No fanfare. Just an unofficial visit. We say we’re going to Nekoma to consult with their florists.”

Wakatoshi nods once. “Yamagata can drive. Matsukawa too. Keep the detail light.”

Satomi sighs. She looks at both of them again. Long. Measured. Like she’s memorizing them in this moment, just in case.

“We’ll go at dawn,” she says. “Tell no one.”

Toru nods. “Just a consultation.”

“But we’ll know,” Satomi adds, “what we’re really looking for.”

A pause. Then she lifts her cup again. “To other ways.”

Toru clinks his cup against hers. Wakatoshi follows.

And for the first time that night, the silence doesn’t feel so heavy.

© everything here is written with care — please don’t repost, copy, or alter my work without permission.

taglist (open): @tangerinelovr @godhainerammsteiner @oligbia

[astorythatwritesitself]

OC Meme - Adrian Shepard

Nobody asked for this, but y'all are getting it anyway because I saw @omniblades-and-stars & @stormikins do this and it looked fun, so!

(By @valkblue & @eluvisen respectively!)

GENERAL

Name: Adrian Olivier Shepard 

Alias(es): Addy (from her family), and ofc the usuals via Alliance/Normandy crews (Shep/Shepard/Commander)

Gender: That's something to think about in peacetime 🫠 Just kind of runs with whatever others perceive; defaults to she/her pronouns. (Adrian's somewhere in the transmasculine range, and did pursue some some body modification during college - but all of that just dropped the fuck off after Akuze & then… like. -gestures @ everything that happens + Lazarus undoing a few things & she didn't intend on living long enough for it to matter any more-) 

Age: 28 - 32, depending on game. (Although she's also had the fun questions, post-Lazarus, about how exactly to quantify that...)

Place of Birth: SSV Toronto, during a stint in the Exodus cluster. 

Spoken Languages: English, smatterings of Quebecois French, Spanish & Russian, surprising fluency in Galactic Standard (aka the ever-evolving pidgin of Citadel space). 

Sexual Orientation: Pansexual with very limited instances of romantic interest.

Occupation: Commander in the Alliance Navy (if we want to get really technical: a special operations saboteur/data gatherer - she wanted to be a medic or get into diplomatic relations, but like hell anyone was letting a biotic stay on the sidelines entirely)/Citadel Spectre.

FAVORITE 

Color: Indigo blue

Entertainment: Music, books - not all that big on shows/movies, with the notable exception of a couple medical dramas.

Pastime: Swimming, upgrading/hacking omni-tools, poetry memorization/recitation (all-time fave/longest she can do from memory is The Love Song of J. Alfred Prufrock), model ship building, galactic politics, collecting rock & mineral samples from various planets she's been to, photography.

Food: Toss-up between Blast-Oh’s and seaquats (nickname for a Kajhe-native spiny pink & white ocean fruit, size & shape is similar to a kumquat. Taste is vaguely briny, then very sweet).

Drink: Masala chai.

Books: The Hitchiker's Guide to the Galaxy, the collected works of Edgar Allen Poe (The Black Cat is her favorite story of his, Annabelle Lee is her favorite poem), For She Is The Krogan Queen: The Legacy of Shiagur.

HAVE THEY 

Passed University: Went through & passed an Alliance military college - majored in cybersecurity, though she still took a few classes relating to paramedicine.

Had Sex: Yes

Had Sex in Public: Not yet. 

Gotten Tattoos: Got a stylized thresher maw tattoo after Akuze - the body segments were done using the names of the lost unit; she was /not/ happy to see it gone after waking up in the lab.

Gotten Piercings: No

Had a Broken Heart: The better question is if it's ever not been kinda broken, tbh. There's a lot of disillusionment & heartache as she grows up & starts her career - she moves around a lot, so getting attached to anyone will go and hurt (and keeping herself distant hurt just as much, just in different ways). Her parents are both career military - one ground and one medical, she's always surrounded by talk of loss and death; not to mention the just... slow motion trainwreck that is their relationship. She loses her unit, loses the man she was just falling in love with, sees humanity and the galaxy at large pushed to the brink...

And we uh. We're just not going to talk about how she reacts, in the canon!verse, to losing Thane, Mordin & Legion.

Been in Love: Yes - there's a couple probable cases earlier in her life (notably Kaiden Alenko), but Thane's the first person she can definitively say she's fallen in love with.

ARE THEY

A cuddler: Y'know how after being malnourished for long enough, someone has to start so very slowly to be able to eat again? Yeah, Adrian /was/ a cuddler, but it takes a long time to get back there.

Scared easily:

She is so very scared. So very much of the time. There's so many people depending on her and so many bad things that can, will, and have happened if she fucks up, and her fear of doing that again is what keeps her going. Totally sustainable, right?

Jealous easily: Nah. 

Trustworthy: Mostly - but she's pragmatically minded enough that she is willing to use what she knows to her advantage, if the situation calls for it. When it comes to the battlefield though, oh god yes. Ever since Akuze - she'll be first in and last out, no matter what, as long as she has any say in the situation.

FAMILY 

Siblings: Yes, though they show up… way later in her life lmao. Her parents divorce, dad gets together with a turian doctor, & they adopt a couple kids . Due to Shenanigans involving said dad pretty much totally changing his first and last name, said kids don't quite realize their new big sibling is That Commander Shepard until she shows up for the holidays. She also considers Grunt something of a little brother.

Parents: Alive and (relatively) well, all throughout the series - in the standard canon, they actually far outlive her.

Hannah Shepard was from a career military family, while Adrian Alexander Bishop was a foster kid who needed a med degree. They were both in the RMC at Kingston when the Prothean data cache was uncovered and joined the Alliance together as soon as humanly possible, honored to be among the first of humanity to explore the stars, and eager to leave what legacy they can.

 Their marriage is a fucking disaster and only held up as long as it did because Hannah was often gone for quite a while on postings - they're functionally separated by the time Adrian's in high school; divorced not long after Hannah's actions on Torfan.

Children: Never intended on having kids in any capacity, but she does wind up a step-parent to Kolyat. (For the first couple years while everyone's adjusting to things, it's fucking hilarious to see just how deeply uncomfortable they both get when reminded of this fact.)

Pets: Fairly successful at keeping fish. (Also has an extensive menagerie of pet rocks).

[cyraclove]

I have mad writer’s block with all of my wips right now so please have this drabble about Eddie going prom dress shopping with Chrissy that I couldn’t get out of my head

Eddie pops another handful of M&Ms in his mouth as he glances at his watch.

He locks eyes with the same saleswoman that’s been hovering around the dressing room for the last hour, shooting her a crunchy, chocolatey grin. Eddie chuckles to himself when she purses her lips in obvious disgust and clacks away.

He’s lost count of how many dresses Chrissy’s tried on. They might live here now, in this Dillard’s at the mall. At least there’s an Orange Julius and a Mrs. Fields nearby. Bright side.

Eddie’s still not entirely sure how he ended up at the mall watching Chrissy try on dresses. For prom. The prom that she’s going to with Jason Carver, her walking wiener of a boyfriend.

It’s probably got something to do with the fact that he’s stupidly, pathetically in love with her.

Another Blondie song comes on overhead and the hiss of the same perfume sample being sprayed for the hundredth time makes Eddie’s eye twitch.

God, he’s getting a cluster headache. He needs a cigarette, like, yesterday.

“How’s it going in there, Chris?”

There’s shuffling and the soft clink of metal hangers from behind the flimsy curtain in front of him.

“This one’s too…poofy,” Chrissy says, a frown in her voice.

“The purple one? I thought you liked that one.”

“No, this is the dark green one.”

“The dark gr—I haven’t even seen that one yet. C’mon out, lemme look at it.”

“No, it’s too poofy,” she insists. “I look like…like broccoli.”

Eddie tosses his head back with a laugh despite trying his damndest not to. He can’t see her, but he knows she’s got that little crinkle above her nose that she gets when she’s grumpy. He can hear it.

“You’ll be the most beautiful vegetable at the dance,” Eddie teases. The little grumble he gets in response tugs at his heart.

Chrissy pokes her head out from the curtain to look at him, gathering it around her chest. Eddie can see just a sliver of her bare shoulder. His stomach actually flutters.

“Eddie,” Chrissy sighs, “this is serious. If I don’t find a dress this weekend, it’ll be too late.”

Chrissy disappears into her dressing room again. Eddie hears a zipper and wishes that he was made of taffeta.

“I’m gonna try on the blue one,” Chrissy tells him. “You liked that one, right?”

“Chrissy, I like them all,” he says honestly. I like them because they’re on you, he doesn’t add.

“Really?”

“Yeah, really. You’re gonna look great in whatever you pick.”

There’s a pause in the rustling of fabric. Chrissy’s face appears again, baby blues rolling above a little smile. Her cheeks are pricked with the slightest hint of pink.

“You have to say that because you’re my best friend,” she says.

Eddie looks at her a minute, his eyes on hers. The tinny Muzak from out in the mall fades away and all he can hear is his blood rushing in his ears, the thump of his heart against his ribcage.

He could just tell her. Right now, with that stuffy saleswoman glaring at them from behind the cosmetics counter.

He could tell her and then he’d get to take her to the dumb prom, and it’d be the best night of his goddamned life.

But he doesn’t.

“Go on and try the blue one on before they close the mall and we get locked in here,” he jokes. He shoots her a smile that she returns before vanishing again.

His head aches.

“I’m sorry I’m taking so long,” Chrissy apologizes. “I really appreciate you coming with me.”

“Hey, of course. Anything for you, Cunningham. You know that.”

“It’s just…I know this sounds so stupid, but I really want my ‘Wow’ moment. You know?”

Eddie’s brow furrows. “Uh, you may need to give me a definition for that one.”

“Have you seen Footloose?”

“Did you forget who you’re talking to?”

Chrissy lets out a breath of a giggle. “Well, do you know the basic plot of the movie?”

“Think so,” Eddie muses. “Jesus good, dancing bad.”

“Essentially,” Chrissy laughs. “Anyway, there’s this part at the end after they finally convince the reverend to let them have senior prom. Kevin Bacon’s character—his name’s Ren—“

“Ren, okay.”

“Yeah, and he comes to pick Ariel up—that’s Lori Singer’s character. You know Lori Singer?”

“The blonde from, uh…Fate?”

“Fame. So, he comes to pick her up for the dance and he’s walking up to her front porch and he stops when he sees her standing there,” Chrissy continues, her voice airy, dreamy.

“And then he just…looks at her. Like he can’t believe that she’s real. That’s what I want.”

There’s a twinge in Eddie’s chest.

“Chris, Carver’s an idiot if he doesn’t—“

The end of Eddie’s sentence is snuffed out when Chrissy pulls back the curtain and steps out of the dressing room. His tongue is dry and his head feels fuzzy. The air leaves his lungs entirely as he takes her in, breathtaking in blue.

The neckline kisses her collarbone, sleeves fluttering at the edges at her shoulders. The shimmery fabric slopes along the contours of her body, coasting down the curve of her waist before splaying into the skirt.

Chrissy’s teeth dig into her lower lip as the corner of her lips quirk up. She shifts from one foot to the other, the wispy silk swaying gently just above her ankles.

“Well? What about this one?”

“Yeah,” Eddie breathes, his heart in his throat. “I think that’s the one.”

[the-npd-culture-is]

quick post on NPD (narcissistic personality disorder) :

"Narcissistic personality disorder (NPD) is a complex personality disorder often detected with other affective and personality disorders. [...] NPD is under the umbrella of Cluster B personality disorders, which include antisocial personality disorder, histrionic personality disorder, and borderline personality disorder."

- National library of medicine (ncbi.nlm.nih.gov) [https://www.ncbi.nlm.nih.gov/books/NBK556001/]

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"Genetic and non-genetic influences on the hierarchy of traits that delineate personality disorder as measured by the Dimensional Assessment of Personality Problems (DAPP-DQ) scale were examined using data from a sample of 483 volunteer twin pairs (236 monozygotic pairs and 247 dizygotic pairs). [...] Additive genetic effects and unique environmental effects emerged as the primary influences on these scales, with unique environmental influences accounting for the largest proportion of the variance for most traits at all levels of the hierarchy."

- Jang KL, Livesley WJ, Vernon PA, Jackson DN. Heritability of personality disorder traits: a twin study. Acta Psychiatr Scand. 1996 Dec;94(6):438-44. doi: 10.1111/j.1600-0447.1996.tb09887.x. PMID: 9020996. [https://pubmed.ncbi.nlm.nih.gov/9020996/]

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i hate how people online tend to water down the initial definitions and jump into conclusions. no one said "it's caused by childhood trauma." stop putting things into a box when they're way more complex and diverse and nuanced.

npd is primarily caused by trauma but very often individuals have a genetic predisposition to npd.

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flags

since there are two flags for npd that ive seen floating around, and since I've had people asking me why i chose the first one for this npd blog over the other, and some have never seen the first one before, im gonna give a quick explanation for both :

the first flag is the npd flag. it represents the disorder itself.

created by @/beyond-mogai-pride-flags on august 1, 2017 : https://www.tumblr.com/beyond-mogai-pride-flags/163661799375/narcissistic-personality-disorder-flag

"Design choice: the artist chose pastel pinks and yellows as they are very eye-catching colors that feel like very "outgoing" colors. In line with the outgoing and self assured face that people with NPD project, the pink is associated with confidence and the yellow with insecurity and anxiety. The purple is for royal purple, in the center, to represent the focus on self."

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the second flag is the npd awareness flag. it's a flag representing everyone fighting for npd awareness, that including non-npd allies.

created by @/npdsafe then rebloged with the stripe meanings by @/liom-archive : https://www.tumblr.com/liom-archive/725390566188285952/npdsafe-npd-awareness-flag-i-wanted-to-make-a

concerning the second flag, there is also an alternate npd awareness flag :

created by @/npd flag : https://www.tumblr.com/npdflag/694773969935630336/an-alternate-version-of-the-npd-awareness-flag-by

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the reason i chose the first flag is, well, mostly because it's the first that appeared in the research bar lol, but also because i wanted to highlight and focus on the disorder itself instead of using the more generalised flag. to me it felt a bit like using the LGBTQ ally flag instead of the flag of a specific sexuality. on top of that, the majority if not all npd related accounts are using the second flag, so i wanted to make something a bit more unique with my blog presentation. and finally, i just like aesthetically the first one more hehe.

both flags are awesome and you are free to prefer one over the other, aesthetically or for any other reason. i simply chose to use one over the other for simplicity.

[anocana]

abstract or hardcore hip-hop 🎶

okay so "abstract rap" is one of those terms i've never really understood even though i'm pretty sure i listen to a decent amount of it. does it refer to the lyrical content, or less regimented flows? are minimal instrumentals and calm delivery "abstract"? and as a cultural cluster, is it more milo or mach-hommy? i feel like any specific definition of "abstract" you could use would include artists not in that cluster (e.g. ghostface killah, andre 3000, some of pusha t's stuff) but whatever.

all that aside, some favorite tracks that i'm pretty sure count as "abstract rap":

billy woods - Pompeii - billy woods is one of my favorite artists in general and this is the track that got me into his forbidding delivery, which had previously scared me off. two stories (ironically very concrete), excellently written and delivered with force and precise emphasis.

billy woods - Remorseless - much more lyrically abstract, fragments of meaning and imagery drawing on history, politics, religion, literature, music, over a really beautiful flute-sampling beat by Preservation.

lojii - cause & effect - the least abstract track on the album, so sue me but it's my favorite. the beat has a nice little head-nodding melody, the lyrics cover a bunch of cool occult imagery including the hand of the philosophers.

Ka - Conflicted - this is still abstract rap, right? Ka is another general favorite of mine, and Honor Killed the Samurai is a masterpiece. i can't praise it enough so i won't try. just listen to it.

Sunmundi and Athmaan - Wingspan - this is just a gem i stumbled across, built around a very pretty loop. whole album's very nice. it has no lyric transcriptions available anywhere.

as for hardcore hip-hop, nobody's ever done it better than Big Pun.

[eliteprepsat]

Clause (n): a unit of grammatical organization next below the sentence in rank and in traditional grammar said to consist of a subject and predicate. — New Oxford American Dictionary

Search the internet for “run-on sentences” and you’ll likely find examples of long lines (some run-ons, some not) by William Faulkner, Charles Dickens, Lewis Carroll, and other authors famous for their verbosity. Some sites (which will go unnamed) tell you that one of the iconic lines of twentieth-century American literature—the first line of J.D. Salinger’s The Catcher in the Rye (1951)—is a run-on sentence.

If you really want to hear about it, the first thing you’ll probably want to know is where I was born, and what my lousy childhood was like, and how my parents were occupied and all before they had me, and all that David Copperfield kind of crap, but I don’t feel like going into it, if you want to know the truth.

This is, indeed, a long sentence—63 words and six commas, to be exact—but it is not a run-on. On the other hand, this sentence is:

Julia likes cats, however, she prefers dogs.

Just seven words and two commas, but a run-on. (By the way, that last line is a fragment, a sentence lacking even one independent clause.)

How is the second sample sentence a run-on if the first is not?

The answer hinges on the definition of a run-on sentence. Contrary to popular belief, run-on sentences are not defined by length or complexity; a 1,000-word sentence could be grammatically correct and a four-word sentence could be a run-on.

A run-on sentence is something far more precise. It’s a sentence that contains two or more independent (aka main) clauses not properly separated. Generally speaking, independent clauses can be separated by a period, a semicolon, a colon, a comma and a conjunction, or a dash (though not all of these solutions work for all sentences).

We might fix the run-on above to read:

Julia likes cats. However, she prefers dogs.

or, more commonly:

Julia likes cats; however, she prefers dogs.

or even better:

Julia likes cats, but she prefers dogs.

The reason why the original “Julia” sentence is a run-on is fairly arcane: a conjunctive adverb like “however” cannot separate two independent clauses. Students preparing for the SAT and ACT should learn how to identify independent clauses, dependent clauses, relative clauses, relative pronouns, conjunctions, subordinators (words that make clauses dependent), and conjunctive adverbs—all terms and ideas that need to be understood in order to master the art of avoiding and fixing run-ons and fragments. This is likely the most important cluster of grammatical issues to master for both tests.

But my purpose here is not to unpack the nuances of these issues (you’ll need to take a class for that). It is simply to note that preparing for the SAT and ACT requires that students begin to see conventional English sentences as things constructed along pretty exacting guidelines. Sentences, like machines, are objects made out of properly connected parts.

Like an automobile, a sentence is made of interlocking units. Just as there are many correct and incorrect ways to build a car, there are countless ways for the parts of a sentence to interlock correctly or not. And just as a good auto-mechanic sees a car for its parts and knows exactly what to do under the hood to fix a mechanical problem, SAT and ACT test-takers need to be able to see sentences as constructed things made of clauses, which need to be connected with the right tools and in the right ways.

This is precisely the kind of thinking at work in Salinger’s opening sentence in The Catcher in the Rye. The sentence is something of a master class in English grammar.

If you really want to hear about it, | the first thing | you’ll probably want to know | is | where I was born, | and what my lousy childhood was like, | and how my parents were occupied and all | before they had me, and all that David Copperfield kind of crap, | but I don’t feel like going into it, | if you want to know the truth.

This sentence contains nine clauses total, 7 dependent and 2 independent, all properly separated. A clause consists of, at minimum, a subject and a predicate. I have highlighted only those terms necessary to complete each subject and predicate and italicized all conjunctions used to connect clauses. Things get tricky at the beginning of the second clause, whose subject is “thing” and whose verb is “is,” followed by an entire dependent clause (“where I was born”) that acts as the object of the verb “is.” In this sentence, “you’ll probably want to know” acts as a dependent clause since it is contained within a larger independent clause.

As a whole, a good SAT or ACT grammarian should see this sentence like this:

Dependent clause 1, Independent clause 1 Dependent clause 2 Independent Clause 1 continued Dependent clause 3, and Dependent clause 4,  and Dependent clause 5, Dependent clause 6, but Independent clause 2, Dependent clause 7.

We could dig into this complex sentence further by looking at, say, how Salinger subordinates those seven dependent clauses, or by considering how to identify when a clause begins and ends. But, again, the point here is not to explore all these complexities (though that’s an important task for those preparing for the SAT and ACT).

My point is at once much simpler and more challenging: it is to show you that sentences are made of smaller units called clauses, and that there are rules for connecting and separating these units from each other. This is all to say that improving one’s grammar isn’t about memorizing countless rules or running your eyes over countless pages of writing.

It’s first and foremost about changing the way you see sentences—as constructed machines made of individual parts rather than as finished wholes.

[tmkutawrites]

A COMMON BOND - FREE SAMPLE!

This is a free sample of my debut lesbian romance novella, A Common Bond, which comes out November 7, 2023. Please enjoy :)

Note: There may/will be some typos in this sample. We like that, it confuses the Overlords of Zon so they don't strike me for contract infringement. I promise in the final, purchased version the typos have been fixed :)

Now, on with the sample!

RFI 1

To: Josie Basurto (May 3, 5:34PM)

From: Carneline Triana

Subject: Site Visit for Mobilization

Josie,

I will be on site with my management team most of Monday morning. I’m sure we will run into each other at some point.

Carneline

***

From: Josie Basurto (May 3, 5:39PM)

To: Carneline Triana

Subject: RE: Site Visit for Mobilization

Looking forward to it!

J

***

Carneline had known Clover Hill’s old town hall was in bad shape from the bid documents. On her walkthrough with Rio a few weeks ago, even more suspicions had been raised. But now, the disintegrating chunk of limestone that had fallen off the cornice and into her hand confirmed it: she was going to be spending a lot more time in Clover Hill than she had initially planned. “Jesus Christ.”

“I’ve never seen limestone this bad,” Bruno murmured. Oceanic’s chief masonry superintendent carefully set the piece of stone down on the scaffold. “This whole cornice is going to have to be checked.”

Checking the structural integrity of a city block’s worth of limestone was definitely not covered in their contract. Carneline chewed on the inside corner of her mouth as she ran a hand across the sugaring stone and watched millennia-old sand crumble into her palm. “Is this the only bad news?”

“Oh no,” Bruno said in a voice far too cheery for her liking as he pushed to his feet. “This mortar is definitely hot.”

Asbestos remediation was also definitely not in their contract.

She cast a desperate glance along the joints. “Are you sure?”

“Yup.” He pointed to an area where the mortar was exposed. “Look close. You can see the fibers.”

Carneline looked and, sure enough, there were the telltale threads amongst the cement, lime, and sand. Fuck. “Does Rio know?”

Bruno shook his head.

She snapped a couple of photos on her phone and turned for the scaffold stair. “Are xe still documenting in the lobby?”

“I think so.”

“Good. I’ll send xem up.”

The metal stairs squeaked as Carneline made her way down them, eyeing the brick and stone of the Romanesque Revival building with far more suspicion than before. The facade clearly hadn’t been washed in two decades. The window sills were covered in black atmospheric discoloration, and the blue-green haze of cupric staining streaked down major crevices. On the brick and stone walls, there were long stretches of jointing completely devoid of mortar and one of the brackets was missing entirely.

She stopped two decks down and took a moment to admire the town. This was Oceanic’s first project this far south. They mostly stuck to projects in Baymill, but her dad had wanted to expand into other markets, so here she was forty feet in the air above a town she could see the other side of from the scaffold. The five-story town hall towered over most of the rest of the buildings, but fit in perfectly amongst the clusters of various historic structures downtown. Its renovation was long overdue, but Carneline hoped Clover Hill would find it worth it in the end.

From her perch, she could see the expanse of the park, with its quaint little gazebo and beautifully kept grounds. A bit farther she spied the currently unlit marquee of an old movie theater and a neon sign belonging to local diner. It was a beautiful town, and as much as she could lean on the scaffold railing and look out over the little town covered in the fresh leaves of spring for hours, she had a job to do.

She tore herself away from the view and continued down the scaffold to the lobby. The first time she’d seen it, Carneline had been struck almost speechless by the beauty of its wrought iron doors, scagliola-clad pilasters, and massive crystal chandelier. Now it barely registered. She hurried through the plywood-covered lobby until she found her assistant project manager sprawled indelicately across the floor.

Rio was an acquired taste Carneline wasn’t quite sure she had acquired yet; mildly competent, incredibly anxious, and graced with the aggravating tendency to lose the plot at the slightest provocation. Still, xe tried, which was more than Carneline could say of half of Oceanic’s field staff.

“Good morning, Rio.”

Rio startled, and practically levitated off the floor in a cloud of dust almost definitely from the plaster demo. Xe was absolutely covered in the stuff, and Rio hurriedly stuffed xemself back into xyr gloves and sheepishly brushed down xyr front. “Good—good morning, Carneline. I—I didn’t know you were on site.”

“I was walking the cornice with Bruno.”

“Oh.”

“How is it going down here?”

Xe grimaced and gestured at the ground. “It’s—uh. The stone’s really cracked.”

Bits of torn painter’s tape crawled across the marble below them like blown blue cherry blossom petals. Carneline crouched, and Rio angled the beam of xyr flashlight so she could see the spidery lines coursing through. Great. “These are going to shatter the second Bruno tries to take them out.”

“That’s what he said, too.”

Another expensive change order for the growing pile, I suppose. She stood, dreading the prospect of the unending raft of paperwork in her future. “I’ll speak with the NCK team. Have you been up to the cornice yet?”

Rio shook xyr head.

“When you are done down here, I need you to go up and document everything before we touch it. Do you have your profile gauges with you?”

“They’re in my car.”

“Good. Bruno will be up there for a little bit. Find…” She hedged, thinking of the worn-down status of the cornice. “Find the least broken stone and take a profile.”

 Xe nodded. “Okay.”

“And wear an N95. The mortar is hot and everything up there is crumbling.”

Rio’s dark eyes got comically wide behind xyr safety glasses. “Oh shit.”

Her sentiments exactly. “Do you have any questions?” Xe shook xyr head again. “Alright. Call me if something comes up.”

“Will do!”

Carneline left Rio to xyr marble documentation and slipped out the west entrance to find the jobsite trailer. When she pulled the door open, she found Josie bent over the conference table—which was really just four folding tables pushed together in the center of the room—studying the reference drawings.

“Good morning,” she greeted as the door snapped shut behind her.

“Good morning,” Josie replied as she turned the page of the drawings. “Headed out? Help yourself to some coffee before you leave.”

Carneline startled at the kind, but unexpected offer. “Oh. Thank you.”

“To-go cups are on top of the fridge.”

“I actually don’t drink hot coffee,” she replied sheepishly.

“Don’t drink hot coffee?” Josie asked, looking up from her drawings with a grin that Carneline had discovered seemed permanently glued to her face. “Don’t tell me…you’re like Baylee and only drink cold brew.”

Carneline gave an awkward little laugh, not liking the familiarity with which Josie talked to her about her sister. People always did that, acted like they knew her because they knew her sister or father. Another one of the ‘perks’ of a family business. “Guilty as charged.”

 “Well, I’m one step ahead of you. There’s cold brew in the fridge.”

The offer was tempting. Carneline considered for a moment, but finally decided against it. If she got caught in traffic, which was likely considering the time, she would definitely have to stop and pee. “Not today. I have to drive back to Baymill after this, but thank you.”

“Any time.”

Josie finally straightened up fully and leaned casually on the white plastic folding table, hooking her thumbs into her jeans. She was an unreasonably attractive figure, taller than Carneline, with kind brown eyes and a sharp fade that put every short-haired worker on the site to shame. In some universe she might have been Carneline’s type—if Josie hadn’t worked for the general contractor paying them to fix Clover Hill’s historic town hall.

Carneline hedged. “I…actually wanted to talk to you about something.”

Josie’s voice remained impressively neutral. “Oh?”

“Yes…” She pulled her phone out of her pocket. “We have some problems.”

“Define ‘problems.’”

“That depends, do you want the least expensive issue or most expensive issue first?”

“Least expensive.” Josie flashed a luminous smile. “Warm me up.”

Carneline pulled up the photos she had taken of the floor and passed her phone over for her to see. “The marble in the foyer is full of cracks. It’s going to shatter when we try to take it out.”

“Architects were ridiculous to think we could salvage the whole floor,” Josie said with a disbelieving scoff. “A-hundred-and-twenty-year-old marble doesn’t come up like that.”

“No, it does not,” Carneline confirmed.

Josie handed her phone back, her face suddenly all business. The shift was jarring, to say the least. “How much is this going to cost?”

“I can’t say for certain, but it will be a decent amount.”

Josie sighed. “Great. You submitted replacement marble, right?”

“A few weeks ago.”

Josie ran a hand through her hair. “Submit an RFI and we’ll see what the architects have to say.”

“Was planning to.”

“Thanks.” She took a sip from a nearby thermos. “What’s the bigger, badder bill?”

Carneline gave Josie a significant look. “Have you been up to the cornice?”

“Recently?”

“Yes.”

“I walked it at the beginning,” she replied with a frown. “Is there something wrong with it?”

If only. “The mortar’s full of asbestos and the stone is crumbling. A piece fell off in my hand.”

Josie inhaled in shock. “Oh fuck.”

“I don’t want anyone from my crew touching it until the town knows.”

 “Understandable. Do you think it’s going to need to be replaced?”

Carneline glanced around the trailer to make sure they were alone. “Off the record, I think you might want to figure out where Clover Hill has a million dollars stashed for a rainy day.”

 “It’s that bad?”

“The building is a hundred and twenty years old,” she said with a shrug. “I’m surprised it lasted this long.”

Josie’s face went grim. “Got it. Thanks for the heads up.”

“Not a problem.” She hesitated, not sure if Josie could handle a third thing on her plate. “There is…one more thing?”

“If there’s a massive structural issue that means we need to evacuate the building, please turn around and leave now,” Josie joked weakly. “Let me die in the collapsed building in peaceful ignorance.”

Carneline gave a dismissive snort. “Nothing so drastic.”

Josie brightened considerably. “Great! What’s up?”

“You need to have someone go into the main hall and put down sweeping compound. Rio’s rolling around on the floor in there looking like the Ghost of Christmas Past. To say nothing of the silica hazard.”

Josie was already grabbing her hard hat off the table. “I’ll do it myself.”

“Thanks. I’ll see you in a few weeks.”

“See you then!” Josie trotted off out the door, Carneline close behind her.

She checked her watch: three-o’clock.  Plenty of time to make it back to the city without hitting traffic. She pulled her hard hat off the second she hit the parking lot, shaking her curly red hair out so she could tie it back up once in the car. She’d get out of town, update her dad on the way home, then spend a quiet night with her plants before she had to go to bed.

Her phone rang. The song barely got four notes in before she picked up. “You’re psychic. I was just about to call you.”

“Are you done at Clover Hill?” Warren Triana asked gruffly.

“About to head home now, just have to throw my stuff in the ba—” She stopped dead a few paces from her trunk, eyes taking in the noticeable sink to her right rear bumper. “Fuck.”

Her father’s business tone instantly switched to fatherly concern. “What? What is it?”

She scowled and threw her hard hat in the back a tad more aggressively than was necessary. “It’s nothing,” she sighed. “I just have a flat.”

[END RFI 1]

Did you like this sample? If yes please consider buying my novella? You can preorder A Common Bond HERE!

[supernovaa-remnant]

well, since you're alright with it, i'm not gonna hold back lol

first of all, i just need to mention interstellar because i love that movie too. though for me it's less about the science and space and more about the way it's made. not to bring my passion into this but it's just such a great movie. the cinematography is fucking incredible and the story is just so insanely good.

continuing, wormholes are just so fascinating to me as a concept!! it would be so cool to find out if they're able to exist and especially if we could "create" them.

ganymede is another moon i think is super pretty to look at. i don't know how to explain it but i really love the way the surfaces look like their own little universes i guess??? that's the best way i can describe it lol

and titan really sounds interesting!! i'm definitely gonna look into the missions you talked about, would love to find out what they could find in samples from there.

last but not least, relating back to something you mentioned, but do you believe in life outside of our planet/solar system??? personally, i think there's actually a pretty high chance of us not being alone in the universe, especially because space is, like, big beyond comprehension lol for me, the thing is more about if we're ever going to be able to get in contact with other life forms (especially in a time were, like, both of us are still alive lol)

oh I wholeheartedly agree—the cinematography in interstellar is phenomenal. I don't know much about the subject, but, after I watched the movie, I turned to my friend and told him about the cinematic beauty of the movie. also!! never apologize about bringing your passion into the conversation!! I'd love to hear more abt the cinematography in interstellar :3

as for the story, I loved it so much. If I didn't major in astronomy, I was gonna major in creative writing. Like—writing is one of my biggest passions, and a common theme I like to both write about and read about is hope. And, ultimately, I feel like that's what interstellar was about. Hope. That no matter how dire it is, hope is always worth it.

Because the people on Earth could have given up and just lived out their lives and not tried. A lot of people wanted to do that. But Murph never gave up. The people chosen to find a new planet for humanity? They could have just gone with Plan B and started from scratch. But they didn't give up. And even after everything went horribly, they could have given up and tried to build on that barely hospitable planet. But they tried that crazy maneuver with using the black hole's gravity, and they didn't give up.

Not only does hope appeal to me in narratives, but it's a big part of why I love space. Space and space exploration in may ways is a representation of hope. At least in my mind.

I think that visually, there's a lot of beauty in a lot of planets and moon. I love the way you described Ganymede's surface :3

Extra terrestrial life??? I absolutely believe there's life out there. I did an entire school project on the statistics back in high school, but, statistically speaking, it's nearly impossible for there to be no life anywhere but this planet. Like you said, space is so big that people just can't truly comprehend it. And it's still expanding.

I think there are a lot of factors to consider when thinking about why we haven't run into any "intelligent" life. (in astronomy, the phrase intelligent life is used to mean life that both wonders about their place in the stars and has the means to send and receive signals from space).

Some of the more intriguing answers to this "paradox" (if life is abundant why haven't we seen it?) are the following:

a) maybe we're just not interesting enough to alien life to be worth communicating

b) it's possible that there's just no other life/intelligent life in this galaxy or even our cluster of galaxies

c) we don't know the timescale at which life exists on. everything we know about life in the universe comes from Earth, and we just haven't been around long enough to know. life on Earth has existed for an incredibly small amount of time. we don't know if life—let alone our definition of intelligent life—exists on a time scale of thousands of years, millions of years, or billion of years. if it's on the lower end of the scale, then it's possible that we just don't be around at the same time as other life (sad)

But, I don't know, I have hope. I have hope that one day humanity will meet other life. Maybe we'll create wormholes and travel to galaxy clusters far far away and find life there. Maybe life will find us. Maybe life in the universe is just beginning, and maybe everyone is simply around the same stage as us.

But I think it's so amazing that people have looked up and the night sky and wondered what's out there. Wondered who's out there. Is anyone out there? I mentioned this before, but space exploration to me is about hope. The hope that Earth wasn't a fluke and that other life does exist. The hope that maybe this—the wonder of space and the excitement at the prospect of life—can be something unifying. The hope that maybe someone else is looking up at different constellations asking the same question we are: are we alone? The hope that the answer to this question will be no.

[achieveyouracademicgoalswith]

Steps in Sampling Design for Reliable Research Results

Steps in Sampling Design for Reliable Research Results

An effective sampling design is vital for producing valid and generalisable research results. It is crucial for ensuring results that are specific and acceptable to the scholarly community. Understanding the steps in sampling design in research methodology aids researchers in making findings and significantly reducing the potential for bias.

A good sample design is an ethical one, ensuring the research purpose and overall validity. Incorrect sampling choices, such as vague criteria or improper methods, produce biased data as well as less than credible findings. These are problems that arise early in research, and that is why experts are advised by researchers regularly to avoid them.

2. What Is Sampling Design in Research Methodology?

Sampling design is the sequential method adopted for choosing a sample of a population. Sampling design gives directional guidance in research work and influences the validity and quality of results.

There are generally two broad categories of sampling methods

Probability sampling: All members of the population are given a known equal opportunity of being picked. They include random sampling, stratified sampling, and cluster sampling.

Non-probability sampling: Population members do not have the same chance of being chosen. Examples are quota, purposive, and convenience sampling.

Selection of the appropriate sampling design ensures information will be representative of the population, and results of the study are believable.

Step 1: Define the Target Population

The initial one is the definition of your study population. This is the identification of the demographic features, the boundaries, and in what way it relates to your research question.

An ill-defined population will result in spurious or over generalized conclusions. For instance, if you are conducting a study on the employment status of city youngsters, then you will define the age group, place in the city, and employment status. Otherwise, you would be supplied with useless data or even miss out on some crucial subgroups.

Step 2: Work out the Sampling Frame

The sample frame is the actual source or list from which your participants are going to be sampled. It should be as accurate and complete as possible to best reflect the population described.

Examples of typical ones include government databases, institutional records, or web registers. Applying an old or biased frame introduces error and weakens your results. A good sampling frame provides each eligible participant with an equal opportunity for selection.

Step 3: Choose the Sampling Method

This involves choosing the way in which individuals will be chosen from the sampling frame. Means depend on the aim of your study. 

Probability Sampling Methods

Simple random sampling: Each member has an equal probability of being selected.

Stratified sampling: The population is classified into subgroups and the samples are obtained proportionally.

Cluster sampling: Groups (or clusters) are sampled randomly.

Non-Probability Sampling Techniques:

Convenience sampling: Members are selected based on convenience.

Purposive sampling: Members are chosen based on specific attributes or characteristics.

Quota sampling: Members are chosen with specific quotas for different categories.

The correct selection process keeps the data research-focused and relevant.

Step 4: Decide on the Sample Size

Sample size is important in statistical power as well as the generalisability of findings. The following dictates this:

Confidence level

Margin of error

Size of the entire population

Expected range of responses

Statistical calculations and tools like G*Power or Raosoft are normally used by researchers to calculate the sufficient sample size. Consulting an expert, however, guarantees that your calculation works and is practical.

Step 5: Implement the Sampling Plan

With the sample size and procedure chosen, the actual selection process begins. This step must be executed with uniformity and caution.

Moral principles also need to be followed. That involves informed consent and participant confidentiality. Clear documentation of the sampling process in detail maximises transparency and makes replication or reading of your work easier for others.

Step 6: Validate and Check the Sampling Process

Once administered, the sample can be tested to see whether it indeed represents the population of interest. The testing can be done by examining the diversity, distribution, or representativeness of your sample.

Comments from a peer reviewer, supervisor, or external expert are most valuable at this point. Where discrepancies or inconsistencies are found, researchers will need to be prepared to alter their sampling design.

Avoiding Common Sampling Design Mistakes

Understanding the steps in sampling design in research methodology is essential to avoid common mistakes that can weaken your research foundation. Some frequent errors include:

Employing an outdated or distorted sampling frame

Employing an unsuitable sample method

Employing an excessively small sample size that cannot generate meaningful conclusions

Such errors can be mitigated by careful planning, pilot running, and prompt feedback. Seeking advice at this point foresees repeating the work later in the research process at a cost.

How Expert Support Enhances Sampling Design

Professional insight can significantly improve your sampling design, especially when navigating the critical steps in sampling design in research methodology. Academic consultants offer guidance to:

Align your sampling plan with the research objectives

Determine the optimal sample size precisely

Prescribe successful strategies according to your research scenario

Ensure ethical and methodological compliance

That type of assistance increases your work's likelihood to be academic and acceptable to high-stakes publishing groups.

[onlei-technologies]

How Freshers Can Crack Data Analytics Interviews Without Experience

A Practical Roadmap for Launching Your Career in Analytics

Breaking into the data analytics industry as a fresher with no prior experience might seem intimidating—but it’s absolutely possible. Today, companies are increasingly open to hiring freshers who show the right skills, clarity of concepts, and a strong learning attitude. If you're passionate about data and ready to learn, you can definitely crack your first Data Analytics interview—even without experience.

Here’s a practical guide on how to do it the smart way.

1. Focus on Skills, Not Experience

Companies don’t expect freshers to know everything, but they do expect foundational knowledge and practical skills. Start by focusing on core tools and techniques used in analytics roles:

Excel (data cleaning, pivot tables, VLOOKUP, basic formulas)

SQL (select queries, joins, filtering, aggregations)

Python (data analysis using Pandas, NumPy, Matplotlib)

Power BI or Tableau (for creating dashboards)

Basic statistics and problem-solving mindset

💡 Tip: Enroll in a well-structured training program like ONLEI Technologies, which focuses specifically on building these skills with industry-aligned projects and mentorship.

2. Learn by Doing: Build Projects

When you don't have experience, projects are your experience. Work on real datasets and solve business-like problems. Some good beginner projects include:

Sales performance analysis using Excel or Power BI

Customer segmentation using Python and clustering techniques

Dashboard creation with Power BI or Tableau

Employee attrition prediction using Excel and visualization

Make sure every project includes data cleaning, analysis, visualization, and actionable insights. If you join ONLEI Technologies, these kinds of projects are part of the curriculum, and you get expert guidance to build job-ready work samples.

3. Prepare for the Interview Smartly

Once your concepts are clear, start preparing for the interview process. You’ll likely face the following types of questions:

📌 Technical Questions

How would you handle missing values in a dataset?

What are different types of joins in SQL?

How would you optimize a dashboard?

What’s the difference between correlation and causation?

📌 Scenario-Based Questions

Imagine a sudden drop in sales—how would you investigate it?

You are given messy data—what steps will you take?

📌 Soft Skills Questions

Why do you want to be a Data Analyst?

Tell us about a time you solved a problem logically.

📚 Pro Tip: ONLEI Technologies offers mock interviews, resume reviews, and live Q&A sessions to prepare freshers for real interview scenarios with confidence.

4. Build a Strong Portfolio & Resume

Since you don’t have job experience, your projects and certifications should speak for you.

✅ Include in Your Resume:

Key tools & skills (Excel, SQL, Python, etc.)

Academic background

2–3 analytics projects with short descriptions

Internship or training details (like the one from ONLEI Technologies)

Also, maintain a GitHub profile and upload your project codes, dashboards, and a neat README.md to explain each project.

5. Practice Communication & Data Storytelling

A Data Analyst doesn’t just analyze—they explain why something is happening and what should be done next.

When preparing for interviews, practice explaining your projects like a story:

What was the problem?

What tools and techniques did you use?

What insights did you find?

What actions would you recommend?

ONLEI Technologies places strong emphasis on this by training students not just to code, but to communicate findings effectively, just like analysts do in real jobs.

6. Apply Consistently, Even If You’re Not "Perfect"

Many freshers wait to become “perfect” before applying. Don’t do that. Once you’ve built your basic skills and at least 2–3 projects, start applying to:

Entry-level Data Analyst or Business Analyst roles

Internship roles or contract positions

Remote freelance projects or analytics gigs

Consistency is key. Make sure to personalize your resume and prepare for each company.

Final Thoughts: From Fresher to Analyst

You don’t need prior experience to get your first job—you need the right preparation, mindset, and support. Focus on skill-building, do projects, prepare for interviews, and don’t stop applying.

Thousands of freshers have launched their analytics careers after training at ONLEI Technologies, where they received: ✅ Practical learning ✅ Real-world projects ✅ Resume and interview help ✅ Placement assistance

Start with curiosity. Build with consistency. Win with preparation. Your journey as a Data Analyst starts with your first step — take it today with ONLEI Technologies.

[xaltius]

The Ultimate AI Glossary: Artificial Intelligence Definitions to Know

Artificial Intelligence (AI) is transforming every industry, revolutionizing how we work, live, and interact with the world. But with its rapid evolution comes a flurry of specialized terms and concepts that can feel like learning a new language. Whether you're a budding data scientist, a business leader, or simply curious about the future, understanding the core vocabulary of AI is essential.

Consider this your ultimate guide to the most important AI definitions you need to know.

Core Concepts & Foundational Terms

Artificial Intelligence (AI): The overarching field dedicated to creating machines that can perform tasks that typically require human intelligence, such as learning, problem-solving, decision-making, perception, and understanding language.

Machine Learning (ML): A subset of AI that enables systems to learn from data without being explicitly programmed. Instead of following static instructions, ML algorithms build models based on sample data, called "training data," to make predictions or decisions.

Deep Learning (DL): A subset of Machine Learning that uses Artificial Neural Networks with multiple layers (hence "deep") to learn complex patterns from large amounts of data. It's particularly effective for tasks like image recognition, natural language processing, and speech recognition.

Neural Network (NN): A computational model inspired by the structure and function of the human brain. It consists of interconnected "neurons" (nodes) organized in layers, which process and transmit information.

Algorithm: A set of rules or instructions that a computer follows to solve a problem or complete a task. In AI, algorithms are the recipes that define how a model learns and makes predictions.

Model: The output of a machine learning algorithm after it has been trained on data. The model encapsulates the patterns and rules learned from the data, which can then be used to make predictions on new, unseen data.

Training Data: The dataset used to "teach" a machine learning model. It contains input examples along with their corresponding correct outputs (in supervised learning).

Inference: The process of using a trained AI model to make predictions or decisions on new, unseen data. This is when the model applies what it has learned.

Types of Learning

Supervised Learning: A type of ML where the model learns from labeled training data (input-output pairs). The goal is to predict the output for new inputs.

Examples: Regression (predicting a continuous value like house price), Classification (predicting a category like "spam" or "not spam").

Unsupervised Learning: A type of ML where the model learns from unlabeled data, finding patterns or structures without explicit guidance.

Examples: Clustering (grouping similar data points), Dimensionality Reduction (simplifying data by reducing variables).

Reinforcement Learning (RL): A type of ML where an "agent" learns to make decisions by interacting with an environment, receiving "rewards" for desired actions and "penalties" for undesirable ones. It learns through trial and error.

Examples: Training game-playing AI (AlphaGo), robotics, autonomous navigation.

Key Concepts in Model Building & Performance

Features: The individual measurable properties or characteristics of a phenomenon being observed. These are the input variables used by a model to make predictions.

Target (or Label): The output variable that a machine learning model is trying to predict in supervised learning.

Overfitting: When a model learns the training data too well, including its noise and outliers, leading to poor performance on new, unseen data. The model essentially memorizes the training data rather than generalizing patterns.

Underfitting: When a model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and new data.

Bias-Variance Trade-off: A core concept in ML that describes the tension between two sources of error in a model:

Bias: Error from erroneous assumptions in the learning algorithm (underfitting).

Variance: Error from sensitivity to small fluctuations in the training data (overfitting). Optimizing a model often involves finding the right balance.

Hyperparameters: Configuration variables external to the model that are set before the training process begins (e.g., learning rate, number of layers in a neural network). They control the learning process itself.

Metrics: Quantitative measures used to evaluate the performance of an AI model (e.g., accuracy, precision, recall, F1-score for classification; Mean Squared Error, R-squared for regression).

Advanced AI Techniques & Applications

Natural Language Processing (NLP): A field of AI that enables computers to understand, interpret, and generate human language.

Examples: Sentiment analysis, machine translation, chatbots.

Computer Vision (CV): A field of AI that enables computers to "see" and interpret images and videos.

Examples: Object detection, facial recognition, image classification.

Generative AI: A type of AI that can create new content, such as text, images, audio, or video, that resembles real-world data.

Examples: Large Language Models (LLMs) like GPT, image generators like DALL-E.

Large Language Model (LLM): A type of deep learning model trained on vast amounts of text data, capable of understanding, generating, and processing human language with remarkable fluency and coherence.

Robotics: The interdisciplinary field involving the design, construction, operation, and use of robots. AI often powers the "brains" of robots for perception, navigation, and decision-making.

Explainable AI (XAI): An emerging field that aims to make AI models more transparent and understandable to humans, addressing the "black box" problem of complex models.

Ethical AI / Responsible AI: The practice of developing and deploying AI systems in a way that is fair, unbiased, transparent, secure, and respectful of human values and privacy.

This glossary is just the beginning of your journey into the fascinating world of AI. As you delve deeper, you'll encounter many more specialized terms. However, mastering these foundational definitions will provide you with a robust framework to understand the current capabilities and future potential of artificial intelligence. Keep learning, keep exploring, and stay curious!

[hawkstack]

Migrating Virtual Machines to Red Hat OpenShift Virtualization with Ansible Automation Platform

As enterprises modernize their IT infrastructure, migrating legacy workloads from traditional hypervisors to cloud-native platforms becomes essential. Red Hat OpenShift Virtualization offers a powerful solution by allowing organizations to run and manage virtual machines (VMs) alongside containers on the same OpenShift cluster. To streamline and scale this migration process, Red Hat Ansible Automation Platform proves to be an invaluable tool.

In this post, we’ll explore how to leverage Ansible Automation Platform to automate the migration of VMs to OpenShift Virtualization, reducing manual effort, minimizing downtime, and increasing consistency across environments.

🧩 What is OpenShift Virtualization?

OpenShift Virtualization, built on KubeVirt, extends Red Hat OpenShift to run traditional VM workloads alongside containerized applications. This allows for:

Unified management of VMs and containers

Seamless integration with CI/CD pipelines

A single pane of glass for observability, networking, and security

🤖 Why Use Ansible for VM Migration?

Manually migrating virtual machines is not only tedious but also error-prone. Ansible Automation Platform enables:

Repeatable Playbooks for consistent VM conversion and deployment

Inventory management of existing VMs and target OpenShift clusters

Idempotent operations that reduce risk and human error

Event-driven automation with Red Hat Event-Driven Ansible (EDA)

🛠️ High-Level Workflow of VM Migration with Ansible

Discovery & Assessment

Identify source VMs using dynamic inventory (e.g., VMware, RHV, KVM)

Collect system configurations and workload details

Use Red Hat Migration Toolkit for Virtualization (MTV) if applicable

Pre-Migration Automation

Validate OpenShift Virtualization setup

Configure storage (e.g., Ceph, CSI volumes)

Prepare networking (e.g., Multus, bridges)

VM Export & Conversion

Use Ansible modules to:

Export VM disks (e.g., via ovftool, virt-v2v, or qemu-img)

Convert formats (e.g., VMDK to QCOW2)

VM Import into OpenShift

Create VM manifests in OpenShift (YAML/CRDs)

Automate virtctl commands or use MTV APIs

Attach appropriate storage and networks

Post-Migration Tasks

Run automated smoke tests

Update DNS or service endpoints

Decommission old VMs (if desired)

🧪 Sample Ansible Playbook Snippet

yaml

- name: Create OpenShift VirtualMachine from template hosts: localhost tasks: - name: Create VM from YAML definition k8s: state: present definition: "{{ lookup('file', 'vm-definition.yaml') }}"

You can integrate this into an Ansible Workflow Job Template in Red Hat Ansible Automation Controller, and trigger it via webhooks or Service Catalogs.

💡 Best Practices

Test in Staging: Simulate migrations in non-prod environments before rolling out to production.

Incremental Migration: Start with low-impact workloads to refine your process.

Logging and Auditing: Use Ansible Tower logs and OpenShift audit logs to monitor changes.

Rollback Plans: Always have a plan to revert if something fails.

🎯 Benefits of Using Ansible + OpenShift Virtualization

Centralized automation of hybrid workloads

Faster time-to-value with reusable playbooks

Simplified management for IT Ops and DevOps teams

Integration with existing CI/CD and ITSM platforms

🔚 Final Thoughts

Migrating VMs to OpenShift Virtualization doesn't have to be complex. By combining the power of Red Hat OpenShift with the flexibility of Ansible Automation Platform, organizations can modernize their workloads efficiently and with confidence.

At HawkStack Technologies, we help enterprises design, automate, and execute seamless VM migration strategies using Red Hat technologies. Contact us to learn how we can support your modernization journey.

📞 Need help with automation or OpenShift Virtualization? Let our certified experts at HawkStack guide your migration from legacy systems to a modern cloud-native environment.

For more details www.hawkstack.com