u/Expensive_Grape6765

For context, I missed out on doing review cards (I did 1 or 2 to maintain the streak) each of the weekdays for this week. So, the review cards just kept stacking up.

I just decided to wing it and complete all review cards before this stacking gets any worse. Pretty insane I could do this.

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Question from me though. You can see that it took me 205.37 mins in the image, but it doesn't take into account the time I spent reading my cards to ascertain my understanding. The true amount of time I took is probably 600+ mins or something.

May I ask whether I should try to avoid reading the flashcards at all costs after I open the back of every flashcard? If I do that, I really would have done 1194 cards in 205.37 mins instead of 600+ mins. Is that recommended?

Statistics is crucial to measure this statistical significance.

Because the market is so dynamic, whether or not your strategy works is likely to require switching of strategies between reversals and trend-following.

And the problem is, it is significantly difficult to know when exactly the market is choppy or trend-following.

To help with this, I attempted to quantify this with this question, "What is the probability of a pullback happening?" or "What is the probability of a reversal happening"?

This is based off my very own strategy of solely using the 5-minute timeframe to trade.

It may seem like whatever it's measuring is noise, but when you accompany with the correct strategy, it may be promising. I personally have a strategy that can filter out fake pullbacks to a statistically significant degree (<0.001), so I'm excited to see what this can do!

I have yet to test this, as I am currently testing one strategy myself right now. Will incorporate this in the future and see how the testing goes.

Cost & Performance Efficiency

• Training Cost-Performance (8t): +170% to +180% gain (2.7x–2.8x)
• Inference Cost-Performance (8i): +80% gain
• Training Power Efficiency (8t): +124% gain in performance-per-watt
• Inference Power Efficiency (8i): +117% gain in performance-per-watt

Networking & Latency

• Data Center Network Bandwidth: +300% gain (100 Gb/s to 400 Gb/s)
• Inference Network Latency: -56% reduction
• Network Routing Distance: -56% reduction (16 hops down to 7 hops)
• Standard Superpod Chip Count: +4.2% gain (9,216 to 9,600 chips)

Memory

• On-Chip SRAM (8i): +200% gain (3x capacity)
• HBM Capacity (8i Inference): +50% gain (192 GB to 288 GB)
• HBM Capacity (8t Training): +12.5% gain (192 GB to 216 GB)

Impact on Google's SOTA - Gemini 3.1 Pro Preview

• For Gemini 3.1 Pro today, the TPU 8i means cheaper (~50% cost reduction), faster, and more responsive APIs with vastly improved long-context handling.

Impact on Future Models

• For future Gemini models tomorrow, the TPU 8t removes the data-center bottlenecks, unlocking the compute necessary to train the next frontier of trillion-parameter, deeply multimodal AI systems.

---

Some of the network metrics like the -56% reduction from 16 hops down to 8 hops were from the presentations on the floor at Cloud Next '26, but here are the general articles.

1. TPU 8t and TPU 8i technical deep dive | Google Cloud Blog
2. Google announces 'Workspace Intelligence' and TPU 8t + 8i chips
3. Inside Google's TPU V8 strategy, delivering two chips for two crucial tasks at incredible scale — network scales up to 1 million TPUs per cluster, an advantage over Nvidia AI accelerators | Tom's Hardware

Cost & Performance Efficiency

• Training Cost-Performance (8t): +170% to +180% gain (2.7x–2.8x)
• Inference Cost-Performance (8i): +80% gain
• Training Power Efficiency (8t): +124% gain in performance-per-watt
• Inference Power Efficiency (8i): +117% gain in performance-per-watt

Networking & Latency

• Data Center Network Bandwidth: +300% gain (100 Gb/s to 400 Gb/s)
• Inference Network Latency: -56% reduction
• Network Routing Distance: -56% reduction (16 hops down to 7 hops)
• Standard Superpod Chip Count: +4.2% gain (9,216 to 9,600 chips)

Memory

• On-Chip SRAM (8i): +200% gain (3x capacity)
• HBM Capacity (8i Inference): +50% gain (192 GB to 288 GB)
• HBM Capacity (8t Training): +12.5% gain (192 GB to 216 GB)

Impact on Google's SOTA - Gemini 3.1 Pro Preview

• For Gemini 3.1 Pro today, the TPU 8i means cheaper (~50% cost reduction), faster, and more responsive APIs with vastly improved long-context handling.

Impact on Future Models

• For future Gemini models tomorrow, the TPU 8t removes the data-center bottlenecks, unlocking the compute necessary to train the next frontier of trillion-parameter, deeply multimodal AI systems.

---

Some of the network metrics like the -56% reduction from 16 hops down to 8 hops were from the presentations on the floor at Cloud Next '26, but here are the general articles.

1. TPU 8t and TPU 8i technical deep dive | Google Cloud Blog
2. Google announces 'Workspace Intelligence' and TPU 8t + 8i chips
3. Inside Google's TPU V8 strategy, delivering two chips for two crucial tasks at incredible scale — network scales up to 1 million TPUs per cluster, an advantage over Nvidia AI accelerators | Tom's Hardware

Cost & Performance Efficiency

• Training Cost-Performance (8t): +170% to +180% gain (2.7x–2.8x)
• Inference Cost-Performance (8i): +80% gain
• Training Power Efficiency (8t): +124% gain in performance-per-watt
• Inference Power Efficiency (8i): +117% gain in performance-per-watt

Networking & Latency

• Data Center Network Bandwidth: +300% gain (100 Gb/s to 400 Gb/s)
• Inference Network Latency: -56% reduction
• Network Routing Distance: -56% reduction (16 hops down to 7 hops)
• Standard Superpod Chip Count: +4.2% gain (9,216 to 9,600 chips)

Memory

• On-Chip SRAM (8i): +200% gain (3x capacity)
• HBM Capacity (8i Inference): +50% gain (192 GB to 288 GB)
• HBM Capacity (8t Training): +12.5% gain (192 GB to 216 GB)

Impact on Google's SOTA - Gemini 3.1 Pro Preview

• For Gemini 3.1 Pro today, the TPU 8i means cheaper (~50% cost reduction), faster, and more responsive APIs with vastly improved long-context handling.

Impact on Future Models

• For future Gemini models tomorrow, the TPU 8t removes the data-center bottlenecks, unlocking the compute necessary to train the next frontier of trillion-parameter, deeply multimodal AI systems.

---

Some of the network metrics like the -56% reduction from 16 hops down to 8 hops were from the presentations on the floor at Cloud Next '26, but here are the general articles.

1. TPU 8t and TPU 8i technical deep dive | Google Cloud Blog
2. Google announces 'Workspace Intelligence' and TPU 8t + 8i chips
3. Inside Google's TPU V8 strategy, delivering two chips for two crucial tasks at incredible scale — network scales up to 1 million TPUs per cluster, an advantage over Nvidia AI accelerators | Tom's Hardware

Cost & Performance Efficiency

• Training Cost-Performance (8t): +170% to +180% gain (2.7x–2.8x)
• Inference Cost-Performance (8i): +80% gain
• Training Power Efficiency (8t): +124% gain in performance-per-watt
• Inference Power Efficiency (8i): +117% gain in performance-per-watt

Networking & Latency

• Data Center Network Bandwidth: +300% gain (100 Gb/s to 400 Gb/s)
• Inference Network Latency: -56% reduction
• Network Routing Distance: -56% reduction (16 hops down to 7 hops)
• Standard Superpod Chip Count: +4.2% gain (9,216 to 9,600 chips)

Memory

• On-Chip SRAM (8i): +200% gain (3x capacity)
• HBM Capacity (8i Inference): +50% gain (192 GB to 288 GB)
• HBM Capacity (8t Training): +12.5% gain (192 GB to 216 GB)

Impact on Google's SOTA - Gemini 3.1 Pro Preview

• For Gemini 3.1 Pro today, the TPU 8i means cheaper (~50% cost reduction), faster, and more responsive APIs with vastly improved long-context handling.

Impact on Future Models

• For future Gemini models tomorrow, the TPU 8t removes the data-center bottlenecks, unlocking the compute necessary to train the next frontier of trillion-parameter, deeply multimodal AI systems.

---

Some of the network metrics like the -56% reduction from 16 hops down to 8 hops were from the presentations on the floor at Cloud Next '26, but here are the general articles.

1. TPU 8t and TPU 8i technical deep dive | Google Cloud Blog
2. Google announces 'Workspace Intelligence' and TPU 8t + 8i chips
3. Inside Google's TPU V8 strategy, delivering two chips for two crucial tasks at incredible scale — network scales up to 1 million TPUs per cluster, an advantage over Nvidia AI accelerators | Tom's Hardware