🅒🅐🅝 🅡🅘🅐🅓

Between equations and algorithms,
I’m always searching for better answers.
Programming • AI • Cybersecurity • Science • Ideas

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@lunadreaamy the difference doubles each time—+5, +10, +20, +40. So the answer should be 81.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@begbidur 4(x + 7) = 36 → x + 7 = 9 → x = 2.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@elonmusk The credibility of science depends not on everyone reaching the same conclusion, but on everyone being held to the same evidentiary standard. If dissenting evidence is dismissed before it is evaluated, scientific progress suffers.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@Google @GoogleDeepMind @GoogleResearch Using real-time #satellite data to reduce forecasting latency is a major step forward. The real test is whether this accuracy can translate into faster, more reliable, and life-saving decisions during extreme weather.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@Math_files What if #Eddington’s 1919 observations had not supported Einstein’s prediction? Would relativity have gained worldwide recognition so quickly?

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@nvidia @huggingface #AI is reshaping media, but content authenticity will be just as important as creativity. How can we ensure AI-generated content remains verifiable and trustworthy at scale?

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@Hoor9234 The trick isn't the division—it’s the factorial. 40 + 10 − 24 = 26.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@Math_files If Hooke had never challenged Newton’s ideas, would Newton’s theory of light and gravity have developed in the same way?

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@opencode A small header change can have a big impact on #AI tooling. Prompt caching is becoming critical for reducing latency and inference costs, so standardizing session handling across #integrations is a smart move.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@BAI_AGI 2.3M+ users is impressive, but scaling AI isn’t just about user growth. The real challenge is keeping inference costs, latency, reliability, and model quality balanced as demand grows. Infrastructure efficiency will be a major competitive advantage.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@GoogleDeepMind The next challenge isn't just finding vulnerabilities faster. It's turning AI-generated findings into verified remediation before attackers can exploit them.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@nvidia If attacks can operate at machine speed, defense has to evolve the same way. The interesting question is how we balance autonomous response with human oversight and verification.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

#AI is changing #cybersecurity from detection to rapid response. Finding #vulnerabilities at scale is no longer enough. The real advantage will belong to teams that can prioritize, verify, and fix threats before #attackers turn them into breaches.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

Weather #AI is becoming more than forecasting—it could be a critical layer of #global disaster preparedness. If #WeatherNext improves cyclone prediction, the key question is: Can AI turn complex atmospheric data into earlier, #life -saving decisions?

[Google @Google]

We built our WeatherNext forecasting models to help meteorologists and scientists more accurately predict weather — including potentially devastating events, like cyclones.

To learn more about how our prediction technology works and how far we’ve come with WeatherNext, we sat down with Ferran Alet, a research scientist at @GoogleDeepMind.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@Google One of AI’s most valuable uses could be warning people before disasters strike. If WeatherNext can improve both accuracy and speed, its real-world impact could be enormous.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@arena @AnthropicAI A 77-point lead is impressive. But benchmarks measure capability—not necessarily reliability. The real question is: can Fable 5.1 Max consistently ship production-ready code?

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

Benchmarks are useful, but real-world performance is the real test. If #Gemini 3.8 Flash can combine speed, low cost, intelligence, and reliability, it could be a serious game-changer.

[Google @Google]

Gemini 3.8 Flash is our most intelligent workhorse model yet.

It delivers upgrades across coding, agentic workflows, and critical multi-step reasoning at the same speed and low cost of 3.7 Flash 🧵

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@Google @GoogleDeepMind Speed + low cost + stronger reasoning = a big step forward for #AI agents. The real question is: how reliable will #Gemini 3.8 Flash be at handling complex real-world tasks?

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@miradreaamy 10 − 6 = 4
18 − 10 = 8
34 − 18 = 16
So, the differences are 4, 8, 16 — each one is twice the previous difference.

Therefore, the next difference is:

16 × 2 = 32.

[🅒🅐🅝 🅡🅘🅐🅓 @can7mdvn]

@tugce190333 28 is the missing number!