@oliveirajean7: O final é o melhor do video 😂 #humor #viral #amigos #nike

Jean Oliveira
Jean Oliveira
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Region: BR
Thursday 11 January 2024 13:58:02 GMT
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._.zzz.4
v :
Adidas só ganha em tênis
2024-01-11 14:14:36
46
tapadeleta
Samuel.* :
nike: tenis adidas roupa
2024-01-15 21:00:26
7
moreira_kah1
Moreira🖤✨ :
O patrão dele vai adora o vídeo
2024-01-15 17:29:16
0
kauarlk2011
KAUA.SCCP :
i se o cara for mengão e trabalhar na nike vai poder usa a camisa do fla?
2024-01-14 17:50:41
0
enzo.pauleti
Enzo Pauleti :
🥰
2025-02-04 04:29:52
0
ogmth_
Matheus :
tênis robusto, nn? rústico kkkkkkkkkkk
2025-01-15 01:12:18
0
ggbl11
GBONZ11 :
faz 3+feia da nike VS 3+FEIOS DA Adidas ou só os mais feio mesmo
2024-08-12 01:50:13
0
glowup07
glow up para homens :
o último tênis é um tênis de Powerlift, por isso é feio
2024-01-15 02:38:07
0
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What’s the scariest thing about AI writing your code for you? If your answer is “not knowing what the code actually does,” you’re exactly who this video is for.  Over the past year we’ve all learned to prompt an AI assistant and watch it spit out entire features—APIs, front-end components, even full database migrations—in seconds. That rush is incredible, but it also hides a growing problem: the moment the screen fills with autogenerated functions, most of us have no idea whether a single silent failure is waiting to break production at 3 a.m. In this video I argue that learning to read code, even just 20 percent better than you do today, delivers more leverage than any trick to write it faster. Why? Because AI has effectively made typing free—anyone can generate boilerplate. The hard part now is catching the missing timeout on an external API, spotting the single spot where validation happens too late, and noticing the log that never fires when a queue is down. Those issues aren’t academic; they’re the root of real outages and real revenue leaks. I just published a Substack that picks up where my “Vibe Coding Bible” left off. Back then I showed you how to wish entire features into existence with a single prompt. Fun, but I’ve learned the hard way that the real leverage isn’t in writing faster—it’s in reading what the AI just wrote and spotting the landmines before they blow up in prod. In this new piece I share the exact process I now run every time an LLM dumps code into my repo. First, I trace one concrete piece of data—a signup, a checkout, a file upload—through the system and sketch its path in plain English. Next, I interrogate every hand-off: where is input validated, what happens if the next hop stalls, where does failure surface? Then I pull “proxy signals” anyone can read—logs, dependency graphs, retry counts—to quantify risk without reading a single loop. Finally, I zoom in with the AI itself, but as a microscope, not a ghostwriter: I feed it tiny slices of code and demand evidence-based explanations I can verify. You don’t need to write algorithms to do this. You need curiosity, a browser search, and ten focused minutes per day. The payoff? You’ll prevent silent timeouts, hidden retries, and data leaks that autocomplete happily ships. Writing code is becoming cheap; reading it with judgment is tomorrow’s premium skill. Check the link in my bio, run the “single-datum safari,” and tell me which blind spots you uncover first. #product #productmanager #productmanagement #startup #business #openai #llm #ai #microsoft #google #gemini #anthropic #claude #llama #meta #nvidia #career #careeradvice #mentor #mentorship #mentortiktok #mentortok #careertok #job #jobadvice #future #2024 #story #news #dev #coding #code #engineering #engineer #coder #sales #cs #marketing #agent #work #workflow #smart #thinking #strategy #cool #real #jobtips #hack #hacks #tip #tips #tech #techtok #techtiktok #openaidevday #aiupdates #techtrends #voiceAI #developerlife #cursor #replit #pythagora #bolt
What’s the scariest thing about AI writing your code for you? If your answer is “not knowing what the code actually does,” you’re exactly who this video is for. Over the past year we’ve all learned to prompt an AI assistant and watch it spit out entire features—APIs, front-end components, even full database migrations—in seconds. That rush is incredible, but it also hides a growing problem: the moment the screen fills with autogenerated functions, most of us have no idea whether a single silent failure is waiting to break production at 3 a.m. In this video I argue that learning to read code, even just 20 percent better than you do today, delivers more leverage than any trick to write it faster. Why? Because AI has effectively made typing free—anyone can generate boilerplate. The hard part now is catching the missing timeout on an external API, spotting the single spot where validation happens too late, and noticing the log that never fires when a queue is down. Those issues aren’t academic; they’re the root of real outages and real revenue leaks. I just published a Substack that picks up where my “Vibe Coding Bible” left off. Back then I showed you how to wish entire features into existence with a single prompt. Fun, but I’ve learned the hard way that the real leverage isn’t in writing faster—it’s in reading what the AI just wrote and spotting the landmines before they blow up in prod. In this new piece I share the exact process I now run every time an LLM dumps code into my repo. First, I trace one concrete piece of data—a signup, a checkout, a file upload—through the system and sketch its path in plain English. Next, I interrogate every hand-off: where is input validated, what happens if the next hop stalls, where does failure surface? Then I pull “proxy signals” anyone can read—logs, dependency graphs, retry counts—to quantify risk without reading a single loop. Finally, I zoom in with the AI itself, but as a microscope, not a ghostwriter: I feed it tiny slices of code and demand evidence-based explanations I can verify. You don’t need to write algorithms to do this. You need curiosity, a browser search, and ten focused minutes per day. The payoff? You’ll prevent silent timeouts, hidden retries, and data leaks that autocomplete happily ships. Writing code is becoming cheap; reading it with judgment is tomorrow’s premium skill. Check the link in my bio, run the “single-datum safari,” and tell me which blind spots you uncover first. #product #productmanager #productmanagement #startup #business #openai #llm #ai #microsoft #google #gemini #anthropic #claude #llama #meta #nvidia #career #careeradvice #mentor #mentorship #mentortiktok #mentortok #careertok #job #jobadvice #future #2024 #story #news #dev #coding #code #engineering #engineer #coder #sales #cs #marketing #agent #work #workflow #smart #thinking #strategy #cool #real #jobtips #hack #hacks #tip #tips #tech #techtok #techtiktok #openaidevday #aiupdates #techtrends #voiceAI #developerlife #cursor #replit #pythagora #bolt

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