Rishabh
@rishabhj_says
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RT @arnav_kumar: To the young dreamer out there -. Being underestimated is exclusively the privilege of the young and ambitious. Wear it li….
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Is this guy crazy or what? We don’t have the ability to digest better AI models. There’s already a lot to absorb.
change of plans: we are going to release o3 and o4-mini after all, probably in a couple of weeks, and then do GPT-5 in a few months. there are a bunch of reasons for this, but the most exciting one is that we are going to be able to make GPT-5 much better than we originally.
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has anyone built checkpointing in @cursor_ai? something minimal like git commits after each prompt based change that you can easily roll back to. Love how cleanly bolt dev handles this.
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Is passing a string to gpt to convert it into json vibe coding?.
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One of the coolest things about vibe coding is how I can now throw away features very quickly. I don't feel guilty about just deleting an entire module because I'm coding at least 10x faster.
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SaaS is not dead. Yes people are replacing $100k saas applications with in house AI tools. But that’s old saas. New age apps will do the work that large teams are doing today and it is going to grow faster than anything we have seen.
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The way we judge "good execution" is about to change completely.
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Second-order effect: MVP standards will skyrocket. My 2-week MVP now has more features than what I built in 2 months at my startup 6 years ago.
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Not sure if this is "vibe coding". But I'm super excited about how product building will look in another year.
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Has this messed up my data model? Absolutely. Will I fix it later? That's the plan.
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Database/infra = straight terminal work. Sometimes I copy snippets from Claude when thinking feels like too much work.
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Tests? Tried. Too laborious. They break often. Will bite me later but moving too fast to care now.
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For complex data scripts: function by function approach in cursor. AI either sucks at complex logic or I suck at prompting. Constant testing is key.
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3/ Backend = cursor. I describe an endpoint, request/response format, and it creates most of the code. Just works.
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Frontend = bolt. I describe what I want at a high level. Then I go component by component fixing designs and adding API integrations. Not perfect but fast.
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Built a product in 2 weeks that would have taken 6 weeks before. AI tools have completely changed how I code. Here's my current workflow:.
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Most people now understand why evals matter for AI. But who's actually writing them at companies? Will this fall to engineering teams, or are we seeing the emergence of a specialized QA function dedicated to creating these evals at scale?.
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The trough of disillusionment.
One fact about the recent AI hype is that corporate customers and analysts are not terribly excited. They are in a "prove it to me" mode, as they should be. I am in the camp that overhyping anything is a bad idea. I am personally enthusiastic about some technologies but I will.
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Most LLMs are surprisingly bad at converting docs into working code. It's like they understand English and code separately, but struggle to map between them. Has anyone figured out a good workflow around this?.
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