Untitled

Lizard coaster Pattern

Body - 00:00:45

R1: MR ch2, 10 dc in MR, slst, ch 2 [10] R2: 10 dc inc, slst, ch 2 [20] R3: (dc, dc inc) *10 slst, ch 2 [30] R4: (dc, dc inc, dc) *10 slst, ch 1 [40] R5: Reverse sc in every stitch

Tail - 00:10:30

R1: MR, ch2, 4dc in the magic ring, slst, ch 2 [4] R2: (dc, dc inc)2, slst, ch2 [6] R3: 6 dc, slst, ch2 [6] R4: (2dc, dc inc)2, slst Cut off long tail for sewing [8]

Mouth (red yarn) - 00:16:47

ch 10 R1: skip 1st st, 8 sc, (3 sc in 1 st), 7 sc, inc, slst, ch1 [20] R2: inc, 7 sc, 3 inc, 7 sc, 2 inc, slst ch 1 [26] R3: (sc, inc), 7 sc, (sc, inc)*3, 7sc, (sc, inc)*2 [32] Cut off and weave in yarn

Tongue (pink yarn) - 00:27:52

In the third stitch from the red chain, ch 10 skip 2 st, 8 hdc [8] slst and cut off extra yarn, weave in from behind

Head (green yarn) - 00:31:00

R1: ch 14, slst in the first ch making a circle R2: ch1, hdc2tog, 10 dc, hdc2tog, slst ch 2 [12] R3 dcinc, 2 dc, 6hdc, 2dc, dcinc, slst ch 2 [14] R4: 2 dc, 3 hdc, 4sc, 3 hdc, 2 dc, slst ch 1 [14] Cut off yarn

Upper lip - 00:40:15

At the 5th st from the head, insert green yarn R1: ch2, dcinc, 6dc, dcinc [10] R2: ch2, turn dc2tog, 6dc, dc2tog [8] R3: ch2, turn dc2tog, 4dc, dc2tog [6] R4: ch2, turn dc2tog, 2dc, dc2tog [4] ch1, cut off extra yarn

Lower lip - 00:47:44

R1: ch1, 6 hdc [6] R2: ch1, turn hdc2tog, 2hdcdc, hdc2tog [4] ch 1 turn Attach red mouth piece and tongue by sc in every stitch, slst, Cut off extra yarn and weave in Stuff in

Eyes (white yarn) x2 - 00:53:40

R1: MR, 6sc in MR [6] R2: slst, ch1, 6 sc [6] slst and cut off extra yarn Glue on eyes and heads for pupils Attach head and tail at opposite ends, Put stitch marker 4 st away from each direction for limbs

Legs x4 - 01:01:24

ch7, slst, skip 1 st st, 2 slst, (ch2, skip 1st st , 2 slst) *2, 4 sc, slst Cut off and weave in yarn

Abbreviations + additional links under the cut

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PBS: Language as Prejudice - Myth #6: Women Talk Too Much (via misandry-mermaid)

Every EVERY women’s studies class I’ve been in has had this problem and failed to address it. 

https://www.youtube.com/user/billvicars

and here’s the link to the website he puts in his videos:

Not only is the conversation format immersive and helpful for learning the grammar, but the students make common mistakes which he corrects, mistakes I wouldn’t have otherwise know I was making.

He also emphasizes learning ASL in the way it’s actually used by the Deaf community and not the rigid structure that some ASL teachers impose in their classrooms

His lesson plans include learning about the Deaf community, which is an important aspect of learning ASL. Knowing how to communicate in ASL without the knowledge of the culture behind it leaves out a lot of nuances and explanations for the way ASL is.

Lastly, his lessons are just a lot of fun to watch. He is patient, entertaining, and funny. This good natured enthusiasm is contagious and learning feels like a privilege and not a chore

“I’m probably not saying that correctly”/“sorry for my pronunciation” = 👍 understandable! foreign languages often have sounds that aren’t used in English and learning to correctly pronounce unfamiliar phonemes is genuinely difficult even with help

“lol I’m not even gonna TRY to pronounce that 😂” = 👎 THIS is the problem, if treats languages other than English like they are inherently ‘weird’ or ‘overly complicated’ just because you aren’t familiar with them

It isn’t perfect, but following the initial post I assigned each timezone one sun deity, so travelers know who they might pray to while exploring the world.

Not sometimes. Not until the next update. Always. They proved it with math.

Even with perfect training data and unlimited computing power, AI models will still confidently tell you things that are completely false. This isn't a bug they're working on. It's baked into how these systems work at a fundamental level.

And their own numbers are brutal. OpenAI's o1 reasoning model hallucinates 16% of the time. Their newer o3 model? 33%. Their newest o4-mini? 48%. Nearly half of what their most recent model tells you could be fabricated. The "smarter" models are actually getting worse at telling the truth.

Here's why it can't be fixed. Language models work by predicting the next word based on probability. When they hit something uncertain, they don't pause. They don't flag it. They guess. And they guess with complete confidence, because that's exactly what they were trained to do.

The researchers looked at the 10 biggest AI benchmarks used to measure how good these models are. 9 out of 10 give the same score for saying "I don't know" as for giving a completely wrong answer: zero points. The entire testing system literally punishes honesty and rewards guessing.

So the AI learned the optimal strategy: always guess. Never admit uncertainty. Sound confident even when you're making it up.

OpenAI's proposed fix? Have ChatGPT say "I don't know" when it's unsure. Their own math shows this would mean roughly 30% of your questions get no answer. Imagine asking ChatGPT something three times out of ten and getting "I'm not confident enough to respond." Users would leave overnight. So the fix exists, but it would kill the product.

This isn't just OpenAI's problem. DeepMind and Tsinghua University independently reached the same conclusion. Three of the world's top AI labs, working separately, all agree: this is permanent.

They can do fancy tricks of stacking them so that one guessing machine will look at the outputs of two or more guessing machines and then guess which one will probably be more correct. They call these one 'thinking models'. But in the end it's still just autocorrect on steroids.