Shubham☢️

Mistral just open-sourced a text-to-speech model that beats ElevenLabs. 3 GB of RAM. Runs locally. Free. The thing people were paying per-word for last year runs on your laptop now.

The next wave isn't about better websites. It's about capturing the attention of the AI-first human — someone who still decides, but through a completely different lens. We're not in the attention economy anymore. We're entering the intention economy.

Websites won't exist in future. We're moving toward a world where AI browses, compares, and decides on our behalf. The static webpage, the landing page, the banner ad — all built to capture human attention. But what happens when the human stops looking?

Hot take - AI app UX is shit. Users need AI native interfaces.

@samruddhi_mokal NANO

Nano Banana + N8N = AI Creatives Factory This AI system creates scroll-stopping visuals at scale using Google's newest image model. No designers. No agencies. No $50K creative budgets. Just endless professional-grade ads that look like top brands made them. Here's how it works: → Upload your product catalog to Airtable → N8N automation scrapes product details and images → Nano Banana creates multiple creative angles for each product → System generates different backgrounds, styles, and compositions automatically → All variations get organized in Airtable with performance tracking ready Each visual pennies to generate. You own 100% of the assets forever. Runs 24/7 without touching it. While competitors spend hours in Photoshop or pay agencies thousands per month, you'll be cranking out unlimited variations automatically. Perfect brand consistency across every creative. Built 100% in N8N. Want the complete workflow? Comment "NANO" + RT + Like I'll DM you the entire N8N template + Airtable setup (Must be following so I can DM) Skip this and keep paying designers $200 per ad variation.

@tomcrawshaw01 ALGO

I built a system that exposes what every top creator in your niche is doing. Not their "strategies." Their actual data. The Algorithm Assassin doesn't just show you viral posts - it dissects WHY they went viral: - Scrapes unlimited competitor content (any platform) - Identifies patterns across 50+ viral indicators - Reveals which topics are surging RIGHT NOW - Maps the exact formulas getting millions of views Gurus sell you "viral frameworks" from 2022 while hoarding real-time intelligence systems for themselves. They know that whoever has the best data, wins. So I built my own. And I'm giving it away. Because watching talented creators fail for lack of intel is painful. This system shows you: - What's working in YOUR specific niche (updated daily) - The hidden patterns top creators exploit repeatedly - Which "dead" topics are actually goldmines - Why some garbage posts beat your masterpieces No more guessing. Follow + comment "ALGO" below. I'll send the complete system to your DM's.

@aryanXmahajan ENGINE

This AI Agent replaces your $200K/Year Marketing team while I was eating shawarma at 2am, it scraped 6 platforms, analyzed 73 comment threads, and handed me 9 deploy-ready scripts. It stalks your niche, steals what’s working, and turns it into viral content built to print attention. here’s what the system does: – scrapes TikTok, IG, LinkedIn, YouTube, FB, and Twitter – finds breakout trends, emotional triggers, and dopamine hooks – maps audience avatars + repurposing angles – generates platform-native scripts with hooks, CTAs, SEO intros – formats everything into a deploy-ready content playbook no more brainstorming. no more “what should we post today?” it’s like giving your intern the mind of Alex Hormozi - on a 400mg caffeine drip. the workflow includes: - Multi-platform scraper engine - Viral comment sentiment analyzer - GPT-powered creative generator - Smart repurposing module - Auto-formatted Google Docs output this isn’t ChatGPT with a cute prompt. it’s a weaponized idea machine. if you’re serious about content — and tired of guessing — this flips the game. Comment “ENGINE” + repost this + follow me I'll DM you everything in the next hour skip this, and go back to scheduling posts that get 4 likes.

As a military kid, I don't speculate publicly on war. Besides a few, absolutely no one has all the information that's both correct and timely. I will however say a few things that I've learned in conversations over the last few days with some of the people who are in the know and not afraid to speak their minds: 1) India's strength is that we are basically too big to fall and our weakness is that we have way more to lose via distractions like border wars. We should not make the mistake US made in the 90s that let China get ahead of them in a majority of upcoming technologies. 2) So how do we detract from others dragging us into conflicts that benefit them more (one could argue pakistan basically got $1B of IMF money from this war!)? Simply become 10x more fearsome - so much so that no one dares to provoke. 3) How do we do that? Increase military CAPEX far more than it is today & focus a LOT of it on modernization. We are using equipment that's a few generations behind SOTA in so many categories. Indigenization is the long term strategy, defense deals & tech transfer is the short term. 4) Where you spend this? Focus CAPEX heavily on air and water defence upgrades - our military spend is 8x Pakistan. Our total military aircraft is roughly 1.5x, naval vessel fleet is 1.5x. Almost across the board (army, navy, airforce ), our personnel strength is ~2x. We should be a lot, lot larger! Modernization is a (1) force multiplier - do way more with less people (2) tactical advantage - maximize impact with the right set of equipment e.g. latest drones, advanced soldier gear, satellite systems, military-grade AI, and more! 5) And finally the "soft stuff" - Focus on information warfare. Control the narrative, set up clear, verifiable sources of information that keep the world up to date. We are not world class at this. This takes effort and skill - we should learn from Israel. We have a lot to focus on this decade internally - war is an unfortunate consequence of having a border to defend but the cost of war >> cost of deterrence and distraction. Especially when we already know India has no point to prove in terms of superiority of military strength to our neighbours to the left. It should be very clear by now to those who know, we can't be beat. @ShashiTharoor said it best - we want to be left alone and work on our nation. We've got a future to build.

@adrian_horning_ crunchbase

I just scraped 2.8 million companies from crunchbase 🤯 Name, website, semrush stats, etc. I'm giving the entire thing away in the next 24 hours Comment "crunchbase" and I'll send it to you. Make sure DM's are open

@MaficoNFT Yo! Invite needed.

A lot of bad thinking about AI flows from inappropriately anthropomorphizing AI systems. Examples:

Removed, enjoy !

@dvassallo It's all about the momentum. In both cases momentum has to come to zero. (Same speed as formula car at crash during impact) * 10X mass = 10X the force of impact. So same mechanisms won't work for planes even if they were commercially feasible.

How come almost every weekend I see F1 cars crash at 100mph+ with the drivers walking out unscathed and the car repaired in a couple of hours, but when a plane crashes into the ground at that speed it almost always explodes into a big fireball? Is it the mass of the plane?

@cwolferesearch Is it better than FalconLM? 7B models have same ratings on the leaderboard huggingface.co/spaces/Hugging…

The foundation series by MosaicML, including MPT-7B/30B (and an efficient training repo), makes high-quality pre-trained language models available to anyone for commercial use. Given that creating a pre-trained base model is incredibly expensive, these open-source tools enable a wide variety of specialized language model use cases to be explored at a reduced cost. Some background. All language models are created using a common framework with a few simple components, including pre-training, refinement (SFT and RLHF), and application. Although this framework has several steps, the first step (pre-training) is arguably the most important. Creating a more powerful base model via extensive, high-quality pre-training enables better results when the LLM is refined and applied. Put simply, the base model is a common starting point for all LLM applications, so improving the base model benefits the entire application. Open-source base LLMs. Until recently, open-source base models either performed poorly (e.g., OPT or BLOOM) compared to their proprietary counterparts or could only be used for research (e.g., LLaMA). This changed with the release of MPT-7B and MPT-30B by MosaicML, which are open-source, performant, and commercially usable. Plus, these models can be fine-tuned (at a low cost) using MosaicML’s LLM foundry, allowing AI practitioners to easily specialize these models to a variety of different use cases. What makes these models so good? There are a variety of factors that make MPT-7B/30B impressive, but the most notable considerations are enumerated below. - Use of the GPT-NeoX tokenizer (better handles whitespace for code) - Pre-trained over a lot of data (1T tokens in total) - Use of a modified architecture (low precision layer norm, flash attention, ALiBi, and more) that enables faster training/inference and extrapolation to long context lengths - MPT-7B is fine-tuned with context lengths as large as 64K tokens - MPT-30B base model is trained using a longer context length of 8K tokens (compared to 2K for most open-source LLMs) - MPT-30B places an emphasis on code in its pre-training data, making the resulting LLM especially good at coding applications MPT performance. MPT models are evaluated extensively and compared to other popular, open-source LLMs. MPT-7B is found to be comparable to LLaMA-7B, and both of these models are significantly better than any prior open-source LLM of this size. MPT-30B achieves comparable performance to GPT-3. It is outperformed by models like LLaMA-30B and Falcon-40B on text-based tasks but tends to excel in code-based tasks. TL;DR: The foundation models provided by MosaicML are a huge step forward for the open-source LLM community, as they provide commercially-usable LLMs that are comparable to popular base models like LLaMA and GPT-3. The MPT-7B and 30B models come with an entire ecosystem of open-source tools that can be used to create specialized/personalized LLMs, thus providing a starting point for solving a variety of downstream applications.

Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity propose Mind-Video that learns spatiotemporal information from continuous fMRI data of the cerebral cortex progressively through masked brain modeling, multimodal contrastive learning with spatiotemporal attention, and co-training with an augmented Stable Diffusion model that incorporates network temporal inflation paper page: huggingface.co/papers/2305.11…

Reckoning time for Indian #edtech ?? Chegg - a publicly listed US edtech company lost 40% stock value yday over risks to its future from ChatGPT! Chegg is into homework assistance & online tutoring - this is a big part of edtech in India! cnbc.com/2023/05/02/che…

"Anthropic has been heavily focused on research for the first year and a half of its existence, but we have been convinced of the necessity of commercialization, which we fully committed to in September [2022]" is an odd way of saying "we now realize openai was right" 🥴

LLM speak 🙂: - You didn't find some material boring. It had low quality tokens. - You didn't describe a task to someone. You prompted them zero-shot. - You didn't say something non-sensical. You sampled at a high temperature. - The person is not bad/evil, they are unaligned. - The person is not based. They are just letting you access their base model. - You’re not learning something new. You’re finetuning. - It's not confusing. It is perplexing. This your few-shot prompt to generate more samples.

Brutal