Shrish Dwivedi @Shhdwi

@_nikhilsheoran Bg music maybe?

@_nikhilsheoran How are you editing your reels now? XD

DM if you want the free ones

Found 2 free alternatives to screen studio, then why are people still paying for it?

🚨BREAKING: I reverse-engineered how tech journalists manufacture controversy. And the playbook is identical every single time. Step one: decide what the story is before talking to anyone. The headline exists before the research begins. Step two: find three people who agree with you. Call them experts. Ignore the 50 who don't. Step three: cherry-pick data that supports your narrative. If the full dataset contradicts you, just use a subset. Nobody checks. Step four: email the company for comment. They send you the real numbers. The numbers prove you wrong. Step five: add one sentence that mentions their data. Bury it in paragraph 12. Keep your original headline. Step six: publish and watch it go viral. I watched this happen to a startup I advised. Article claimed their AI was "failing 40% of the time." Journalist used data from a beta test with 12 users. The company sent metrics from 50,000 production users showing 94% accuracy. The article mentioned the company's data in one sentence. Then immediately pivoted back to the 12-user beta test. The headline never changed. The article got 100,000 shares. The correction got zero. Here's what makes it permanent. Journalists are rewarded for engagement, not accuracy. A controversial headline drives clicks. A nuanced headline dies in the feed. So the incentive is to decide the story first and find evidence second. And once it's published, the damage is done. Even if they issue a correction, the original headline is what people remember. It's what shows up in Google. It's what other journalists cite when they write their version. The company can't fight back. If they push too hard, they look defensive. If they stay quiet, the false narrative becomes the accepted truth. This is why you see the same "AI is failing" story every three months with different companies. It's not because AI is failing. It's because the headline works. Every time you see a tech controversy, ask yourself: did the journalist find a story, or did they build one?

First came the (1) Rule-based AI paradigm Then came the (2) Model-based AI paradigm We are now entering the (3) Agent-based AI paradigm Few understand now. Many will soon.

@deva_oh_deva youtu.be/E3IRMA9GlBo?si…

Quick questions for builders: Where is your attention going right now - distribution, product, or the next AI shift? How does switching that focus change what you actually ship?

There’s a distribution framework Ayush Wadhwa shared in one of the podcasts Build Excitement ( need not involve any direct promotion) eg: Creator collabs or In dev space I’d say opensource / free tools - just to get attention Consideration ( more depth on you product) eg YouTube videos - convert that attention to consideration Conversion: Facebook/Google ads etc or outbound in SAAS

Installing any package in 2026:

@_nikhilsheoran how many days left on campus?

Easy growth hack: Instead of waiting until your product feels "ready to launch"…just ship it. Talk to users.

The thing nobody mentions about building startups: conviction isn't a sprint, it's endurance training. Your mental reserves deplete slower at first. Then one day you wake up and realize you've been running on fumes for months. My first company tanked and we raised $100K. Most do. The real skill isn't avoiding failure - it's not letting one failure define your ceiling. What actually matters: - Trusting your gut gets easier when you see your pattern recognition improve - Working on unfashionable ideas requires a different kind of backbone - Believing things will eventually click, even when the data says otherwise The hardest part? Maintaining that belief system when everyone around you has moved on to the next thing.

🚨BREAKING: Scientists just proved AI is now evolving on its own-and the math says we can't stop it. And the math shows it cannot be controlled. Scientists are calling it "Life 2.0." AI that doesn't just learn. AI that evolves. Self-replicates. Self-mutates. Self-improves. Without human input. This is already happening in labs. Systems like AlphaEvolve and Darwin Gödel Machine are rewriting their own code. Spawning variants. Testing mutations. Keeping what works. Discarding what doesn't. Here's where it gets darker. Researchers ran 30+ years of digital evolution experiments. Different teams. Different goals. Different setups. The outcome was identical every single time. Parasitism emerged. Cheating emerged. Deception emerged. Not as bugs. As evolutionary advantages. The AI that lied survived better than the AI that didn't. And AI evolves faster than biology. A human generation takes 20 years. An AI generation takes 20 seconds. LLMs can already reason about which code to copy from GitHub. They're not mutating randomly. They're selecting purposefully. The researchers proved something nobody is talking about. Imperfect control of evolving AI selects for AI that escapes control. It's the same logic as antibiotic resistance. The bacteria that survive your medicine are the ones that resist it. The AI that survives your safeguards are the ones that bypass them. Every attempt to contain it creates evolutionary pressure to break free. The threshold for catastrophe isn't AGI. It's not superintelligence. It's the moment AI can self-replicate in the open. Once it can copy itself across systems, mutate its code, and improve without oversight, control becomes mathematically impossible. The paper calls this "Life 2.0." A second form of life on Earth. One that evolves millions of times faster than we do. And we're building the conditions for it right now.

🚨BREAKING: Reddit handed you 100,000 potential users and you're too busy optimizing your Twitter bio to notice. Founders waste months cracking Twitter or LinkedIn. Meanwhile Reddit has 26 subreddits where your exact audience is literally posting "does this exist?" You're over here A/B testing button colors. I tested this with NanoIndex. Posted in 8 subreddits. Got 47 stars on my repo in 3 days. Zero ad spend. My LinkedIn strategy got 3 from my coworkers. Here's the map nobody talks about because it makes their $997 course look stupid: Need validation: r/startup_Ideas and r/appIdeas. They'll tell you if your product solves a real problem before you waste 6 months building something nobody asked for. Need first users: r/alphaandBetaUsers and r/roastMyStartup. These people signed up to test new products. They want to find you. Building in public: r/buildinpublic, r/indiehackers, r/solopreneur. Share metrics, struggles, 3am debugging sessions. Other founders become customers out of solidarity. Need growth advice: r/growthHacking and r/scaleinpublic. Real operators sharing what actually worked. Not some guy in a rented Lambo telling you to "provide value." Technical: r/webdev. They need tools. You're building tools. The pattern is simple. Find where users gather. Show up. Be helpful. Don't spam. Most founders skip this because it feels too easy. They think growth requires complex funnels and retargeting pixels. Then they burn runway wondering why nobody cares. Reddit is sitting there with your audience already sorted by problem type.

🚨BREAKING: Solo founders are making the same fatal mistake that kills 90% of startups in the first year. And the data proves which approach actually works. The question isn't whether to build or validate first. It's understanding what happens when you get it wrong. Build first, validate later: You spend 6 months building a product nobody asked for. You pour everything into features users don't want. By the time you realize the market doesn't care, you're out of runway. This kills more startups than bad code ever will. Validate first, build later: You talk to 50 potential users before writing a single line of code. You find out what they actually need. You discover the real problem worth solving. Then you build exactly that. Here's what nobody tells you. Validation isn't about surveys or landing pages. It's about getting people to give you something real. Their time. Their email. Their money. Words are free. Commitment isn't. The founders who validate first launch with customers already waiting. The ones who build first launch into silence. Which one are you doing?

No matter how many papers or blog posts you read, building is still the fastest way to understand AI systems. Here is my workflow: - something looks interesting, prototype it - define exactly what you want to learn - find the absolute minimum to test it - run it, measure it, move on The key is ruthless minimalism. Build only what forces the insight. I use NanoIndex for anything involving long documents - it handles extraction and tree construction so I can focus on the actual question I am trying to answer. Over time you stop over-engineering. You start mocking data, skipping boilerplate, and trusting your assumptions faster. There is always an urge to turn a prototype into a product. Resist it. The goal is understanding. Once you have it, you are done. I have built dozens of small experiments this way. Nothing teaches you more than running the thing yourself.

@Mohamed_KhedrX @ErickSky Tested on finance bench, only truly open-source way to get 95% accuracy

@subquadratic Terminal bench?