Skip to main content

a day in a life in makati - part 2

cont...


after the early dinner we had in fridays, we ended up in the park near a church (imagine - a church near a mall...) i would say it's peaceful. it will take your mind out of the city life. a peice of nature, might i add.



now before we headed home, the mall's extension - glorietta 5 got our attention. i remember the the old parking lot which was situated there. it is the best extension of the ayala mall i've seen yet. well, there's trinoma. but that's a different story.

Popular

rhymin

i got stuck with words on this song that i want to finish by tomorrow. i have no instrument to use so im hoping to finish at least the lyrics. sad part is my rhyming brain is not that functional right now. so, i headed online to look for some sites or software that can help. here's what i got: analogx.com 's rhyme came to mind first as it's what i used before. a simple to install software that returns numerous results that, most of the time, ends up confusing. good thing is you can use it offline. so i started searching. 3d2f and the next one got my attention. but let me pour my heart out on this one first. one word: confusing! it gave me more list to figure out. so click on the first. then it lead me to several pages before i get to download. then, i have to figure out which of the links i needed. then after few minutes, i found it only to be more confused... i am to download a 249MB of a .dmg which turned out to be for mac engines and not for windows. i know, right?! w...

Contextual Stratification - Chapter 24: Expanding Measurement

  The Horizon Moves In 1609, Galileo pointed a crude telescope at the night sky and discovered that Jupiter had moons. This wasn't just seeing more detail, it was accessing an entirely new domain of observable phenomena. Before the telescope, planetary moons weren't just unknown; they were unmeasurable. They didn't exist in M_naked-eye. The telescope didn't just extend vision, it expanded the measurable space, revealing new Q that required new frameworks to explain. This pattern repeats throughout history: new measurement capabilities reveal new fields. Not refinements of what we already knew, but genuinely new phenomena requiring genuinely new frameworks. Microscopes revealed cellular life. Spectroscopes revealed atomic composition of stars. Particle accelerators revealed subatomic structure. Brain scanners revealed neural dynamics. Each tool moved the measurement horizon, and beyond each horizon lay territories requiring new F at new λ with new M. The expansion conti...

Prompt Analysis Using First-Principles Thinking (FPT)

Instead of memorizing existing prompt patterns, let’s break down Prompt Analysis from First-Principles Thinking (FPT) —understanding what makes a prompt effective at its core and how to optimize it for better AI responses. Step 1: What is a Prompt? At its most fundamental level, a prompt is just: An input instruction → What you ask the AI to do. Context or constraints → Additional details that guide the response. Expected output format → Defining how the AI should structure its answer. A well-designed prompt maximizes relevance, clarity, and accuracy while minimizing misunderstandings. Step 2: Why Do Prompts Fail? Prompts fail when: ❌ Ambiguity exists → The model doesn’t know what’s truly being asked. ❌ Lack of context → Missing background information leads to weak responses. ❌ Overloaded instructions → Too many requirements confuse the AI. ❌ Vague output expectations → No clear structure is provided. ❌ Incorrect assumptions about AI behavior → The prompt d...