标签：#Startup

Quantum Space says SPACs aren't dead as it seeks a $1.2 billion deal to build military spacecraft.

Alt Carbon said the agreement followed more than a year of scientific review and due diligence, with Microsoft requiring additional verification and data-sharing measures.

The decision comes as India emerges as the world’s largest GCC market.

Andrew Yang’s 2020 presidential campaign was based on a warning that automation and AI would hollow out the labor market and concentrate wealth in the hands of a few. At the time, ideas like Universal Basic Income felt fringe. Now Dario Amodei, Sam Altman, and Bernie Sanders are all saying versions of the same thing. An entrepreneur at heart, […]

Backed by Alexis Ohanian’s 776 and Kindred Ventures, Zest uses transaction data and AI to generate restaurant recommendations based on users’ real dining habits and the places they frequent.

While Silicon Valley continues pushing aggressively into large language models and consumer-facing AI products, many European companies are focused on applying AI to complex systems already embedded into everyday life.

AI coding agent startup Niteshift has raised a $7 million seed round from a who's who of angels. It's betting companies will want power over, not lock-in with model makers.

Through the acquisition, WMG aims to better track when its artists' work is used in AI-generated content or for training AI models.

I have watched founders lose sales calls they should have won. Not because they lacked skill. Not because the offer was wrong. Because they walked in to prove they were smart — instead of finding out whether the pain was real. Sales Is Diagnosis Plus Decision The call is not there for you to pitch. The call is there to find out: Is the pain real? Does the buyer have urgency? Does the budget exist? Can a fixed-scope sprint create a clear win? That is it. Four questions. Everything else follows from those. Sales is not pressure. Sales is diagnosis plus decision. 1️⃣ The Call Structure That Works Frame the call in the first 60 seconds: "I'll understand the current state, ask what is costing you, then tell you whether a sprint makes sense. If it doesn't, I'll say so." That sentence does 3 things: Sets expectations — no pressure, no hard close Signals competence — you have done this before Removes the buyer's guard — they can be honest about what is broken Then run this flow: 1️⃣ Current state — what exists now? 2️⃣ Pain — what is broken or slow? 3️⃣ Cost — what does it cost in time, money, trust, or delay? 4️⃣ Urgency — why now? 5️⃣ Decision — who approves? 6️⃣ Success — what would make this worth paying for? 7️⃣ Close — recommend the sprint or walk away 2️⃣ The Questions That Reveal Money These are the 6 questions I use to find whether a sprint is worth recommending: "What happens if this stays broken for another 30 days?" — reveals urgency and cost "What have you already tried?" — reveals how serious they are "Where does the current process lose leads, users, time, or trust?" — reveals the money leak "Who feels this pain most inside the business?" — reveals whether the buyer is also the decision-maker "What would make this an obvious win?" — reveals success criteria before you price "If we fixed only one thing first, what would matter most?" — reveals scope Listen for the answer with the money in it. That is the thing you fix. That is what you price. That is the sprin

A million dollars is emotional as a dream. As math, it is boring. And that is exactly why most people never get close. Break It Down Here is the thing: $1M/year is not one big bet. It is a machine. And machines are built from boring, repeatable components. 20 clients at $50,000? That is $1M. 100 clients at $10,000? That is $1M. 12 retainers at $4,000/month? That is $576k — plus 4 sprints at $10,000 each gets you to $616k. The question is not whether the number is possible. The question is which machine can realistically produce it — from where you actually stand today. 1️⃣ The Practical Ladder Here is how the staged path actually works for an AI service business: Stage What You Are Doing Why It Matters Stage 1 Sell fixed-scope sprints Creates cash and proof Stage 2 Turn repeated sprint work into templates, SOPs, automations Reduces delivery time, increases margin Stage 3 Sell retainers around highest-demand system Predictable monthly cash Stage 4 Productize repeated workflow into software or toolkit Scalable without more hours Stage 5 Scale the thing the market already proved it wants Compound the machine Notice what is missing from Stage 1. There is no SaaS. No product. No cold paid traffic. No team. Just skill, packaged cleanly, sold to people with money and a painful problem. That is the fastest path — not the most glamorous one. 2️⃣ The Proof-of-Force Line The first mission is not $1M. The first mission is $10k/month — reliably, from sprint work. Here is what that actually looks like: 2 × $1,500 teardown/audit packages = $3,000 2 × $3,500 implementation sprints = $7,000 2 × $5,000 launch/GTM sprints = $10,000 3 × $2,000 retainers = $6,000/month That is not the finish line. It is the proof-of-force line. It proves the machine works. It funds the next iteration. It creates the case studies that make the next sprint easier to sell. Then you go from $10k/month to $25k. Then $50k. Then you make the productization decision from a position of demand — not hope. 3️⃣ The

The demo worked perfectly. ✅ Production? First real users. 50% failure rate. ❌ The Gap Nobody Warns You About I see this pattern every week — a founder launches, pushes traffic, and watches their app fall apart in real conditions. Not because the core idea was wrong. Because "it works on my machine" is not a launch-readiness standard. AI-built apps in 2026 ship fast. That is the superpower. But fast shipping without hardening means you are presenting a demo as a product — and real users will find every crack within 48 hours. 1️⃣ What "Launch-Ready" Actually Means Launch-ready is not "the feature works." Launch-ready is when auth, payments, logging, analytics, database permissions, and rollback are boring — because they have already been thought through and tested. Here is the difference: Demo State Launch-Ready State Auth works for happy path Auth handles edge cases, token expiry, role conflicts Payments go through in test mode Webhooks confirmed, retries handled, failures logged Console.log for debugging Structured logging with alerts on errors No analytics Core events tracked from day 1 Manual deploy Automated deploy + rollback path exists No onboarding flow User activation measured from first session If your app is in column one — you are not ready. 2️⃣ The Launch-Readiness Checklist Copy this. Run it before you push traffic. Authentication and authorization — roles, permissions, token handling, session expiry Environment variables — nothing sensitive exposed, prod secrets separate from dev Database permissions — row-level security, no open-read tables, no admin keys in frontend Payment webhooks — test confirmed, failure logged, retry logic exists Error logging — uncaught exceptions surfaced somewhere you will actually see them Analytics events — signup, activation, key action, churn signal — all firing Rate limits — LLM calls protected, API routes guarded Backups and rollback — you have a path back if something breaks Onboarding flow — first session gets the use

With SpaceX, Anthropic, and OpenAI all eyeing massive public debuts, the tech industry may soon have a new class of corporate overlords — and a new acronym to match. Say goodbye to FAANG and hello to MANGOS.

Zepto's advertising revenue jumped 151%, outpacing the company's 104% growth in operating revenue.

Startup Battlefield applications are due tomorrow, so now's the time to put the finishing touches on your submission!

"# The AI Cost Crisis: How Startups Can Survive the Tokenpocalypse\n\n## Introduction\n\nThe artificial intelligence boom has brought unprecedented innovation, but it has also ushered in a era of spiraling costs. Training state-of-the-art models now requires millions of dollars in compute resources, while simultaneously, the cryptocurrency token market shows signs of a potential collapse—a \"Tokenpocalypse.\" For AI startups, this dual crisis presents an existential threat: how to sustain innovation when both traditional funding avenues and speculative token economies are under pressure? This post explores practical strategies for AI startups to navigate this landscape, focusing on cost optimization, alternative funding, and strategic pivots that can turn crisis into opportunity.\n\n## Understanding the Cost Explosion\n\n### The Compute Crunch\n\nModern AI models, particularly large language models (LLMs) and multimodal systems, demand vast computational resources. Training a single cutting-edge model can consume exaflops of processing power, translating to cloud bills that easily exceed $10 million for a single training run. For startups without deep-pocketed backers, these costs are prohibitive.\n\n### The Token Market Volatility\n\nParallel to the AI boom, the cryptocurrency space experienced explosive growth through token launches—initial coin offerings (ICOs), decentralized finance (DeFi) tokens, and utility tokens for AI-driven projects. However, regulatory crackdowns, market saturation, and declining investor sentiment have led to a sharp downturn. Many tokens have lost significant value, and launching new tokens has become increasingly difficult, removing a once-viable funding path for AI startups.\n\n## Strategies for Survival\n\n### 1. Embrace Model Efficiency\n\nInstead of chasing ever-larger models, startups can focus on efficiency techniques that deliver comparable performance at a fraction of the cost:\n\n- Model Distillation : Train smaller \"student\

These newer social apps offer alternatives to Big Tech’s feeds, focusing on interests, creativity, and community.

Day 48 of building GoDavaii, and the toughest problem isn't the sheer volume of allopathic medicines or the complexity of their interactions. It's the invisible logic of 'Desi Ilaaj' - the home remedies and traditional practices deeply ingrained in Indian families for generations. When everyone knows the comfort and efficacy of 'haldi-doodh' (turmeric milk) for a cold, how does an AI health platform authentically verify and integrate that knowledge without replacing professional medical advice? This isn't just a cultural nod; it's a fundamental challenge for any health AI truly built for India. Global competitors like Epocrates or drugs.com, while excellent within their scope, are entirely English-centric and focused on Western allopathic data. They have no framework for the millions of people who search for health guidance in Hindi, Tamil, or Marathi, and whose first instinct for a cough might be a herbal concoction, not an over-the-counter syrup. The Unspoken Truth About India's Health Landscape For a vast majority of Indian families, health decisions often involve a blend of modern medicine and traditional wisdom. From specific herbs to dietary adjustments passed down through generations, these practices are effective for many minor ailments. Yet, in the digital health space, they're largely ignored. Why? Because the data is fragmented, often anecdotal, and doesn't fit neatly into structured pharmacological databases. It's a goldmine of practical health knowledge, but also a minefield for safety if not handled with care. My realization as Pururva Agarwal, 27-year-old founder of GoDavaii, was simple but profound: if we truly want to serve families coming online in their mother tongue, our AI needs to understand and interact with this context. This means going far beyond just translating English medical terms into 22+ Indian languages. It means building a knowledge graph that can intelligently cross-reference traditional remedies with known active compounds, potent

AI Does Not Cancel Reality I watched the conversation between Mo Gawdat and Marina Mogilko about the future of AI. The conversation is strong. It contains important ideas, but it also contains many claims that sound large in scale, although on closer inspection they rely on very broad generalizations. AI is indeed changing the labor market, education, startups, content, hiring, and ways of thinking. But it does not cancel money, connections, trust, the human vector, creativity, necessity, morality, or people’s ability to adapt. Video on YouTube AI in hiring: automation amplifies chaos Many people have entered the job market. Companies receive huge volumes of resumes. HR departments cannot handle the volume. It is natural that part of the selection process is moving to AI. But there is a serious problem here. Candidates are also starting to play against AI. Resumes are adjusted to vacancies. Cover letters are assembled around keywords. Profiles become optimized for the filter, not for real work. In such a system, the best specialist does not necessarily pass. Often, the person who understood the selection mechanism better passes. The result: the picture becomes cleaner, while the quality of the decision becomes lower. The company gets not the strongest candidate, but the candidate who matched the algorithm best. This leads to lower hiring quality, lower productivity, and slower development. “I built a startup in six weeks”: a product is not a startup The conversation includes the idea that an AI startup would once have taken years and hundreds of engineers, and now it can be built in weeks. Technically, this is true. Prototypes are now built faster. Small teams have powerful tools. One person can now do more than a group could do before. But two different things are mixed here. Building a product faster has become real. Building a startup faster has become real only when resources are present. A startup is not only code. A startup is money, connections, trust, reputa

I and I imagine a lot of other folks, don't believe the future of work should be a smaller group of executives commanding a larger system of people and machines. We have seen what AI can do not just to software product quality without guardrails, but to the junior and midlevel team members who are laid off or never hired at all in exchange for better profit rates with AI tokens vs human salaries. That is just the old hierarchy with better software. The history of work has always had this tension. You can go back to the start of US history and look at the military, commissioned officers were trained and trusted to command while enlisted service members carried out the work and risk. In the corporate and business world, executives and managers became the people who planned, measured, and optimized, while workers became the people being measured. Those structures were not only about class, but race and in America they were built inside a society already shaped by racism, classism, unequal education, unequal access to capital, and unequal access to leadership. AI now forces us to confront that history again. If we are not careful, AI will not flatten organizations. It will make the hierarchy invisible. Instead of a manager with a clipboard, we will have an algorithm. Instead of a foreman with a stopwatch, we will have dashboards, productivity scores, automated performance reviews, and AI systems that decide who gets opportunity and who gets replaced. That is not progress. The goal should not be to replace people with AI. The goal should be to replace bureaucracy, repetitive work, bad process, and unnecessary gatekeeping. What I am trying to do at Buildly is simple: AI should remove drudgery, not dignity. Automation should increase agency, not surveillance. Productivity gains should be shared, not extracted. Hierarchy should be functional, temporary, and accountable — not a measure of human worth. This is why we talk about AI-native product development differently. An AI

While the AI fundraising machine keeps breaking its own records, some founders are building in the other direction. Mirror founder Brynn Putnam just raised money for Board, a startup focused on bringing people together through in-person games and social experiences. Cyberdeck creators are going viral crafting whimsical DIY computers that literally encourage users to touch grass. Unlike the AI-free browser crowd, this doesn’t just feel like backlash, […]