Plt Quotes

I ran over without a word, cradled PLT tenderly in my arms and carried her upstairs. Placing her on my own bed, I wrapped my mortally wounded pet in my best school scarf and lay down next to her. It was a night of grief I have never forgotten.

In many life sciences, including psychology, discovery isn’t a black-and-white issue; it is matter of determining, from one experiment to the next, the theoretical contribution made by various shades of gray. When psychologists set arbitrary criteria (p<.05) on the precise shade of gray required to achieve publication—and hence career success—they also incentivize a host of conscious and unconscious strategies to cross that threshold. In the battle between science and storytelling, there is simply no competition: storytelling wins every time.

PWT (percentage of winning trades)*AG (average gain) / PLT (percentage of losing trades)*AL (average loss) = Expectancy

Is there anything I can do?" He gave her a tired grin. "Crawl in bed with me." She glared at him, then got up and tossed him a terry-cloth robe she found hanging on the back of the bathroom door. "Meet me in the kitchen. I'll make you a sandwich." "You don't need to make me a sandwich." "But I'm going to." She left the room before he could protest further. In the kitchen, she layered grilled pancetta, tomato and lettuce on toasted thick slabs of sourdough. She added some chopped cornichons, Dijon mustard and fresh snipped tarragon to the mayo, just to show off. Around Bella Vista, her PLT's were legendary. Mac wasn't wearing the robe when he came downstairs. He'd thrown on a pair of lived-in cutoffs, faded in all the right places, and a rumpled but clean T-shirt with a logo from a kiteboarding resort in Australia. She cut the sandwich into quarters and set it on a pottery plate, along with a side of grapes and parmesan chips, and a beer in a frosty mug. He regarded the small feast on the table. "I hope you don't mind if I moan in ecstasy while I eat this." "I'd rather you didn't," she said, helping herself to a quarter of the sandwich. "Cook's tax," she explained.

Don was the one who figured out how we would make our code quick. One day, a month or two after the Black Slab Encounter, he called me into his office and asked me to create a test program to measure browser speed. He envisioned an automated tool that would launch our browser app and command it to load a suite of web pages, one after the other, in rapid succession. Over the next couple days, I wrote the code to do just that. I named it the Page Load Test, but we soon took to calling the PLT.

Before the PLT, our editorial process was primarily concerned with feature implementation, bug fixes, and web standards compliance—how well the browser did what it was supposed to do. These were all qualitative measures. The PLT checked for speed, a quantitative test, and it introduced an independent evaluation to every code change we made. Correctness and speed now went hand in hand. Don held that if we heeded the PLT without fail and rejected any code changes that made our code slower, only two things could happen. Either the browser would stay the same speed . . . or it would get faster. He would tap his index finger to his temple to punctuate his explanation of this sneaky logic. From the day the PLT was finished, Don declared, our browser would become faster by never getting slower. It was his Zen koan.

There could be no exceptions to the PLT speed rule—Don wouldn’t allow it. When an essential bit of new code caused a slowdown, things could get tricky. Finding remedies for speed setbacks typically involved the prickly issue of software optimization

The PLT helped us to understand what our programming instructions were doing along the essential axis of speed and showed us precisely when and where we were introducing slowness to our source code. The PLT told us when to pay attention to the “small efficiencies” Knuth mentioned. It was our 3 percent escape hatch, a way to know for sure that optimization wasn’t “premature.” We were sure each optimization we did was helping to keep performance heading in the right direction.

He had us carefully choose our optimization opportunities based on clear and provable knowledge about what was slow and hash things out right at the moment we found the slowness. The PLT helped us to correctly distribute optimization work throughout the entire project. We optimized when we knew what we were doing, in direct response to measuring code with the PLT.

Sometimes, in the development of the browser, even our best investigations and “thinking outside the box” ideas weren’t sufficient. There were plenty of instances when we were about to integrate a new feature, only to find that there truly was no way to add the code without a negative impact on speed. As we introduced features like clicking the back button to return you to your previously viewed web page, we found we couldn’t perform the bookkeeping to maintain the previous page at quick readiness without impeding the load of all pages. The PLT showed the slowdown. When we deemed such features too important to skip but couldn’t figure out how to add them without causing such slowdowns, we instituted a trading scheme, where we found speedups in unrelated parts of our existing source code to “pay for” the performance cost of the new features. When we looked around for code to perform this kind of payoff optimization, we typically targeted code we knew well and that was stable, preferably both. Once found, we tuned this proxy code to function the same, only faster, and sufficiently faster that we wound up with either a nil or a positive net impact on performance when we added both the feature-laden code and the speed-payoff code to our project.