Problem Discovery & Domain Insight
Finding opportunities in firsthand frustrations, operational workarounds, manual processes, and deep industry experience.
32%
Best tweets about Startup Ideas
50 Best Tweets About Startup Ideas (2026)
Explore the best tweets about startup ideas, from customer problems and market gaps to validation, timing, distribution, business models, and execution.
Specific startup opportunities grounded in customer pain, market change, validation evidence, distribution, defensibility, and execution insight.
- Creators
- 44
- Updated
Original Xholic analysis
What 50 top Startup Ideas posts reveal
The dataset repeatedly emphasizes firsthand operational pain, direct validation, and technology shifts as sources of startup opportunities. It includes practical methods for tracking customer signals and testing ideas, while treating Reddit as a potential discovery input rather than conclusive validation. Performance data shows higher median scores for text than media and comparatively strong medians for question and distribution-focused content.
- Dominant tone
- Positive
- Median score
- 8.00
- Leading format
- Opinion
- Recent posts
- 56%
72% of posts
All-time engagement
44% of posts
Published in 90 days
Conversation map
The themes creators return to
Validation & Product-Market Fit
Testing demand through customer conversations, MVPs, payment signals, retention, behavioral data, and fast iteration.
32%
Technology Inflection Opportunities
New model capabilities, data infrastructure, physical AI, robotics, and other technology shifts create time-sensitive startup openings.
28%
Vertical Market Opportunities
Specific market gaps and venture concepts across home services, aging consumers, finance, creator infrastructure, commerce, crypto, and local businesses.
28%
AI-Enabled Workflow Automation
AI makes it cheap to build and automate software, especially for previously underserved workflows and nontechnical operators.
22%
Opportunity Quality & Timing
Assessing ideas through willingness to pay, recurring pain, economics, competition gaps, timing, margins, and defensibility.
20%
Market Signals & Idea Research
Using Reddit, online communities, social/search signals, dead-startup databases, and market research tools to surface demand.
16%
Distribution & Go-to-Market
Winning through focused ICPs, trusted distribution, direct outreach, community channels, organic growth, and product loops.
12%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 27
- Median reposts
- 2
- Median replies
- 7
- Median views
- 2K
Posts with media make up 46% of this collection. Their median all-time score is 7.77, compared with 10.3 for text-only posts.
Format mix
- Opinion 44% · score 4.77
- List 24% · score 21.5
- Announcement 16% · score 11.7
- Question 8% · score 34.1
Consensus and debate
Where creators agree, and where they do not
Shared view
Operational pain is the opportunity map
Multiple posts identify opportunity in operational lag: spreadsheet dependence, manual review, and broken workflows. They also argue that domain operators can be advantaged because they understand what creates customer value and where processes fail.
Shared view
Validate with behavior, not brainstorming
Posts recommend grounding validation in direct customer contact and observable signals, including payment, product behavior, churn, support tickets, customer calls, MVPs, and continued user conversations.
Shared view
Build around newly feasible capabilities
AI is presented as a capability shift: reassess workflows as models improve, target automation gaps revealed by customer feedback, and consider physical-AI constraints where automation meets expensive real-world operations.
Open debate
Reddit is useful, but contested, research
Several posts present Reddit as a source of problem reports and a possible initial customer community. Another argues that manipulation in popular subreddits makes Reddit unreliable as standalone startup research.
Open debate
What a promising idea should look like
One post promotes the heuristic of seeking good ideas that initially look bad. Another argues that this heuristic applies more to consumer startups, while strong B2B ideas often sound plainly useful from the outset.
What performs
Patterns behind standout posts
The outliers covered research systems and concrete opportunity theses
The five listed outliers cover customer-signal systems, dead-startup research, subreddit-led discovery, home services, and physical AI. The highest-scoring post is 2083954605533065561, with an all-time score of 3334.66.
Actionable formats and distribution had strong median scores
Lists had a 21.48 median all-time score, while questions had a 34.15 median. Distribution and go-to-market was the smallest theme at 12% share, but had the highest theme median all-time score at 35.41.
Text had a higher median score than media
Media appeared in 23 of 50 tweets (46%). Its median all-time score was 7.773, compared with 10.303 for text. The dataset was 72% supportive in stance and 72% positive in sentiment.
Statistical standouts
- View standout post 1 Score 3334.7 · 416.83× median
- View standout post 2 Score 1067.8 · 133.48× median
- View standout post 3 Score 460.8 · 57.6× median
- View standout post 4 Score 245.6 · 30.7× median
- View standout post 5 Score 218.7 · 27.34× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Alexa | Startup founder
@alexabelonix
2 posts
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2. Gabriel Jarrosson
@GJarrosson
2 posts
-
3. GREG ISENBERG
@gregisenberg
2 posts
-
4. Ole Lehmann
@itsolelehmann
2 posts
-
5. Sergio Pereira
@SergioRocks
2 posts
-
6. Startup Archive
@StartupArchive_
2 posts
Greg Isenberg: community pain plus instrumentation
Greg Isenberg’s two posts propose mining specialized communities for recurring requests for a better way to work, then tracking payments, product behavior, support, sales, and interview evidence for changes in customer needs.
Startup Archive: first principles and skepticism
Startup Archive’s posts highlight first-principles problem solving and a skepticism filter: look for non-obvious upside while being wary of crowded categories and commodity risk.
Ole Lehmann: monitor capability thresholds
Ole Lehmann’s posts focus on emerging technical capability. One recommends keeping evaluations for workflows that do not yet work and rerunning them as models improve; the other highlights a robotics example centered on realistic human-robot interaction.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best Startup Ideas tweets
Top Startup Ideas tweets from 44 creators
Ranked 01–50
- 01
@gregisenberg ·
Every startup should have a daily markdown file called "what_the_market_is_telling_us.md" It updates every morning from the places where customer truth already lives: 1. Stripe for who pays, upgrades, downgrades, and churns 2. PostHog for what people actually do in the product 3. Intercom or Plain for support tickets/complaints 4. Granola or Gmeet transcriptions for sales calls/ customer interviews 5. HubSpot or Salesforce for CRM notes/lost deal reasons 6. Linear, Jira, or GitHub Issues for bugs and feature requests etc 7. Ideabrowser MCP for outside market signal: startup ideas, trend reports, social/search demand, AI research reports, and builder prompts that show what people are starting to want before it shows up in your own customer data. Basically, the file should notice what changed in the business this week and not just be this summary of here’s what happened (which I think a lot of people have their agents do). Why this is valuable: 1. Maybe new buyers are using different words than they were a month ago. 2. Maybe trial users are getting stuck in the same place. 3. Maybe upgraded customers all touched one feature right before they paid. 4. Maybe churned customers keep mentioning setup confusion. 5. Maybe sales calls are suddenly losing to a competitor you used to beat. 6. Maybe support tickets are revealing a workflow your product accidentally became responsible for. You get the point. The fastest way to PMF is understanding customers better than anyone else, and the highest signal customer insight is usually a change in behavior. So I’d have the agent update the file every morning with the pattern it found, the receipts behind it, and the product or GTM decision it might affect. For example: “3 customers who churned this week all mentioned setup confusion, and 2 of them never invited a teammate. This looks more like an activation problem than a pricing problem, so I’d look at team invite and onboarding before building another analytics feature.” A little helpful tip for all those out there looking to get more from their LLMs.
- 02
@DeRonin_ ·
Somebody released a directory of 1,700+ dead startups (backed by YC) here is the link: https://t.co/sEdUPd5Dqv all of them have deep analysis of product inside and full funding story (in short, 100% product's description) many of these startups failed not because the idea was bad, but because they launched at the wrong time or were just grant cash grabs the ideas themselves are still worth drawing inspiration from (i've already found few ideas of my further startups btw) [ BOOKMARK ]
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- 03
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@SahilBloom ·
There’s a huge opportunity in solving this problem: The friction of home ownership vs. renting. When you rent, you have a single point-of-contact for all problems. When you own, you have to somehow stay on top of the long list of recurring and one-off maintenance things. I’d gladly pay $500+ per month to have 24/7 access to a “home manager” who could be the single point-of-contact for my home. • Schedules all recurring services • Coordinates all one-off services • Consolidates all service costs into one bill It’s probably a local/regional business (probably hard to coordinate the service provider relationships at scale), but a huge opportunity nonetheless. Could be a franchise model with software at top and local franchises. It solves a real home ownership pain point, so I’d bet a young hustler could go door to door and probably sell 100 houses at $500 per month over a single weekend… I’m sure there are some people trying to do this, but I haven’t ever been pitched on it for my homes, so it clearly hasn’t scaled yet. It feels similar to the local/regional pest control business opportunity that has now become pretty saturated. What am I missing?
- 05
@ai ·
Haptic scraped all 64 episodes of @RoboPapers (@chris_j_paxton + @micoolcho) and ranked every pain point in physical AI research. The top 10, by mention frequency: 1. Scalable data collection 2. Generalization / zero-shot robustness 3. Dexterous manipulation 4. Teleoperation / whole-body data 5. Sim-to-real transfer 6. Evaluation / benchmarking 7. VLAs / foundation models for control 8. Human video to robot transfer 9. Long-horizon memory 10. RL scaling / offline-to-online Code keeps getting cheaper. Atoms stay expensive. That's the entire startup opportunity in physical AI right now. https://t.co/HZGBsk7OXS
- 06
@tibo_maker ·
ok the secret is PG is my business coach for the last 10 years every time I'm stuck on a product issue, a team decision, or whatever I go back to him (through his essays 😅 ) & find the answers these are the 10 essays (out of 231) I re-read at least once a year: 1. do things that don't scale the best founders don't wait for systems. they do things by hand, one by one, doing whatever it takes I still do this - every new product, I'm the first customer support rep. you learn more in a week of manual work than a month of dashboards. 2. how to start a startup three things kill most startups: the wrong people, building something nobody wants, and running out of money. fix all 3 & you'll probably survive sounds obvious - but most people don't do it 3. how to get startup ideas the best ideas come from living at the edge of a problem, not from trying to brainstorm your way to a billion-dollar market every product I've built started with something that annoyed me personally - not a market research doc 4. maker's schedule, manager's schedule makers need half-day blocks to do real work. a single interruption doesn't cost 30 mins, it costs the whole morning I took it further. no meetings at all - everything async on Slack 5. startup = growth a startup is defined by only one thing: being designed to grow fast. if you're not growing fast, you're a business, not a startup most people confuse the two. both are fine, but they require completely different decisions 6. how to do great work great work sits at the intersection of what you're naturally good at, what you're genuinely obsessed with & where there's real room to do something ambitious it's not about startups at all. it's about not wasting your life 7. the 18 mistakes that kill startups every way a startup dies traces back to the same root: not building something people actually want I use this as a checklist before launching anything. I've personally made at least half the mistakes on the list. some of them even twice. 8. founder mode the advice "hire good people & give them room" works for professional managers. it's often the wrong playbook for founders, who need to stay close to the details to keep the company alive standard management advice nearly broke two of my products. this essay made me feel less crazy for ignoring it. 9. life is short life being short isn't just a figure of speech. it's a filter. if something isn't worth the finite time you have, you should cut it this one isn't about startups, it's about everything else. I think about it when I'm about to say yes to something that doesn't actually matter 10. how to make wealth wealth isn't money. money is just how wealth moves around. a startup is a way to create real value for people, not extract it this one shifted my whole frame. building isn't about capturing a slice of something. it's about making something that didn't exist before -- it's the most valuable startup education on the internet
- 07
@StartupArchive_ ·
Elon Musk explains how to get startup ideas When asked how he comes up with startup ideas, Elon responds: “I tend to think of things from a sort of physics standpoint… what’s the best way to accomplish something? And then pursue that. That’s also a good way to determine if something’s far from its optimum.” He gives rockets as an example: “You could reason by analogy and say the rocket is going to cost a certain amount because that’s what prior rockets have cost. Or you can say, well, what is a rocket made of? What are the material constituents? What do they weigh? What’s the cost per unit mass? And that sets the limit asymptotically for what a rocket can be. So if you can figure out some creative way to rearrange those elements into a rocket shape, then you can achieve a much better outcome.” He also thinks combining ideas from different industries is really helpful for innovation: “What have people discovered in one industry and can that be applied to other industries? That’s I think also a great source of ideas. But usually you just struggle on a solution and try a bunch of things. Most of them don’t work and occasionally one does.” Source: @VanityFair (Oct 2014)
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- 08
@askOkara ·
startup founder maxxing: > find an already validated problem (sensortower / appfigures / x) > make it 10x better > ship the mvp with claude code > charge from day one > launch before it's ready > pick 1-2 channels where your icp hangs out > see what types of content work on those channels > publish consistently for 90 days > automate repetitive marketing work with okara > build growth loops into your product > talk to users every week > double down on what's working
- 09
@StartupArchive_ ·
Sam Altman on what startup ideas actually work “What you are looking for are good ideas that look like bad ideas. These are things that you can articulate why there’s a reason this is going to be huge that most of the world is missing. Unfortunately, what most people end up chasing are bad ideas that look like good ideas. I would say this is where 90% of all angel capital in the startup ecosystem goes.” Sam elaborates on why founders and investors alike make this mistake so frequently: “The one common way that people make this mistake (pursuing bad ideas that look like good ideas) is chasing the thing that worked two years ago. If you ever find yourself doing that, be very skeptical. If you find yourself tempted to invest in a company where there are hundreds of others working on the same thing, be very skeptical. If you find yourself tempted to invest in something that the founders work super hard to convince you is not going to be a long-term commodity, be very skeptical. It is absolutely true that you want something that has real pricing power that comes from a network effect, a moat, a barrier to entry, whatever it is. But when that’s true, it’s so obvious. So the more a founder tries to sell you on why they’re super differentiated and why they have this long-term competitive advantage, the more skeptical you should be.” He continues: “I have found this framework of asking, ‘Is this a good idea that seems bad? Or is this a bad idea that seems good?’ has helped me make good decisions a bunch of times.” Source: @ycombinator (Mar 2018)
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- 10
@amix3k ·
AI is giving software companies massive leverage. One response is to reduce headcount while maintaining the same output. Another is to keep building and attempt things that previously required a much larger company. At Todoist, we’re now 108 people, the largest team we’ve ever had. We’re also taking on two major new challenges. One of them is Todoist Automations. Without AI, we wouldn’t have attempted it at all, because automation is an incredibly hard problem to solve well. Yet our research and customer feedback revealed a huge gap: many customers want to automate their work, but only around 10% actually do. The opportunity is enormous. We’re approaching it with the same craft we bring to Todoist. The entire interface is built around natural language. On the engineering side, we’ve created a workflow engine that can run automations affordably and at scale. And it won’t be limited to Todoist. People will be able to automate work across Gmail, Notion, Outlook, spreadsheets, and many other tools. I’m not sharing this to promote what we’re building. I’m sharing it because it’s a concrete example of what AI is making possible and what I expect many software companies will do next. The result for customers will be far more choice, much stronger competition, lower prices, and rapidly improving software. And because almost every industry now runs on software, the impact won’t stop at tech. It will raise the productivity and efficiency of entire markets and society as a whole. --- And of course, this would not be a full post if I didn’t include a little leak of the direction we are going! 😁
- 11
@itsolelehmann ·
some anthropic alpha from yesterday: "you should be building things that don't even work yet." their idea is simple: claude gets meaningfully smarter every few months. and every time it does, ideas that used to fail suddenly work. but the people who capitalized first already had the idea built and waiting. for example, 12 months ago none of these workflows really worked: > cold outbound that runs itself for a week > 200 pages of board docs turned into a polished memo > daily competitor monitoring that pings you when something changes > 100 ad variations created in figma and pushed to facebook on its own today all 4 work because the model capabilities caught up model upgrades are a business opportunity. now think about today... there's a bunch of stuff that still fails, but eventually will work: > agents that run for weeks without you ever checking in > your full inbox replied to in your voice with no review needed > a full sales cycle from cold pitch to closed deal with no human > a saas that ships, sells, and grows while you sleep so here's the playbook: 1. keep a list of ideas that fail on today's claude 2. write a simple eval for each one (ex: "reply to 10 emails in my voice, get 8 right") 3. rerun those tests every time a new model drops 4. first one that passes, ship it that week tldr: be more ambitious go build shit that doesn't even work yet because one day it will, and you'll be the first to capitalize on it "you mustn't be afraid to dream a little bigger, darling"
- 12
@XenBH ·
One lens I keep coming back to for startup ideas: things currently only available to the wealthy that new technology can make universal. Before Uber, only the wealthy had a private driver. Before Ford, only the wealthy had a car. In the 80s, a mobile phone was a Wall Street status symbol. Now 5 billion people have one. I think one of the biggest examples of this pattern will be finance. Right now, mainly wealthy people: • Borrow against their assets • Use derivatives to manage risk • Get access to venture deals • Hedge against inflation • Have wealth managers and tax advisors That's now changing. AI handles the advice layer whilst crypto handles the access layer. Together they open all of this up to everyone. We've barely started. Feels like a very large demand space for builders.
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@Tocelot ·
there's a rare skill to how the best founders gather user feedback. it's not enough to just ask customers what they want - the best founders also listen for what customers don't say delayed replies to your emails. product engagement only when you nudge. the service goes down and they don't notice for 2 days. these are all signals you're not solving the right problem on the other hand - lots of bug reports being filed for a feature you didn't think was that important. an offhand comment about a loosely related capability leads to a flurry of follow-up questions in a meeting. these signal you might be onto something real this skill matters more than ever today and is a true test of entrepreneurial judgement. with AI, it's easier to pivot than ever before. what used to take years can now ship in weeks. this means spending months going after the wrong problem is very costly a few other tactics i've noticed the best founders do with customers: they focus on problems, not solutions. customers love prescribing solutions (the "faster horse" fallacy) but the real job is understanding the root problem and how AI can solve it today and 6 months from now they also don't stress over early pricing. get in the door, focus on learning. an extra $5-10k ACV here and there is noise compared to finding the right problem to solve. remember - early customers aren't just revenue, they're also your most valuable research!
- 15
@Mike_Scully_ ·
Reddit is basically free market research. Search r/SomebodyMakeThis. People are literally posting problems they want solved. Find one that sounds painful. Open Claude Code. Build the first version in a weekend. You don’t need a genius startup idea. You need a real problem, a simple tool, and the ability to ship fast.
- 16
@Hartdrawss ·
MVP BUILDER skill levels (1-20) : Level 1-4 : The Idea Stage 1. 12 startup ideas. 0 shipped. 2. Built something that only runs on your laptop. 3. Launched something. 3 users. All friends or family. 4. Got a complete stranger to actually use the product. Level 5-8 : Getting Real 5. Got someone to pay for it. Even Rs. 100. 6. You know your exact ICP. Not "everyone with a phone." One specific person. 7. You shipped a fix within 24 hours of a user complaint. 8. You have 10 users who would be genuinely disappointed if it disappeared. Level 9-12 : Product Market Fit Territory 9. Users return without you nudging them. 10. You stopped building features nobody asked for. 11. You can explain the core problem in one sentence. 12. Your retention rate is improving month over month. Level 13-16 : Scaling the Process 13. You ship a working feature in a day, not a week. 14. You know exactly what it costs to acquire a user. 15. You have one channel that works without paid ads. 16. The product has been live 6+ months with consistently active users. Level 17-20 : The Builder's Builder 17. You've shipped more than 5 complete products to real users. 18. You build a validated MVP in a week, not a month. 19. You can spot a "solution looking for a problem" in 5 minutes. 20. Other founders ask how you ship so fast. Where are you building right now?
- 17
@Layton_Gott ·
Stop asking "what should I build?" Look at what annoyed you last week. Then check if other people are annoyed by it too. If they are and they're already paying for bad solutions, you found your SaaS. Scribe exists because my brother and I got tired of writing content manually across platforms and we weren't the only ones. Your best product idea isn't on some startup ideas list. It's in your own frustration plus proof that other people share it. Find the pain. Validate it. Build the fix. Ship it before someone else does.
- 18
@hridoyreh ·
How to validate an idea using Reddit: 1. Search the Problem Go to Reddit and search for the pain point (e.g., "hate managing invoices" or "can't find good freelance designers") your idea solves. And set the filter to Top / All Time. 2. Read the Posts Like a Heatmap If you see dozens of threads complaining about the same problem across multiple subreddits, that's a real problem. Then, we all realize there is no solution or only a partial one. 3. Check the Upvotes / Comments High upvotes + many comments = strong pain, active community, real demand. Low engagement = niche problem or weak urgency, people don't care enough 4. "Someone Should Build This" Signal Search phrases like "I wish there was an app", "why doesn't X exist", or "I'd pay for...", these are goldmines. Users are literally handing you validated ideas. 5. Spot the Workarounds If people are sharing DIY solutions (spreadsheets, manual processes, duct-tape tools), that's a strong signal. This means the problem is real and no good solution exists yet. 6. Find Targeted Subreddit Check which subreddit the complaints live in. That community = your first customer base. You can post there, run surveys, or do direct outreach.
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- 19
@Kurcide ·
Today we wrapped up the CUBO_AI OpenClaw Bootcamp in El Salvador! $400 in AI tokens and a group of ambitious students is all it takes to shape the future. I’m pretty sure we spent more on lunch. This week we put 8 groups to the test in our OpenClaw bootcamp. Day 1 was lectures on Agentic AI, how to approach building with AI whether you’re technical or not, and how to safeguard yourself from the real risks of unleashing an autonomous agent on your machine. Day 2 was entirely dedicated to building. Every group had to come up with their own automation challenge and solve it. In 5 hours we had 9 working projects. One group decided to build two things because they finished early. Five hours is all it took to prototype functional products and businesses. Across our bootcamp students we saw: - Automated lead generation with validation and Telegram alerts so they never miss a prospect - A gold, silver and oil price tracker with trajectory analysis that collects global news into a single dashboard - A pharmacy prescription tool that automatically pulls requests from email and streamlines medication delivery times - A Telegram-based English tutor with structured lessons, voice analysis for grammar correction and a web dashboard - A medical studies database with integrated quizzes and resource links to help students prepare for specializations - An entrepreneurial assistant that helps with designs, business strategy and AI integration plans - A Telegram and Chrome plugin that lets you send a YouTube link to the agent and get the downloaded file back, or download straight in the browser Students ranging from some AI experience to none at all walked in not knowing what an AI agent was and walked out with working products and real world solutions. Every one of them stepped up, came up with their own problem to solve, and figured out how to orchestrate an AI agent into doing real work. They became product managers of their own ideas in real time. Tools like OpenClaw, NemoClaw and the wave of open source agent frameworks behind them are doing something the last decade of tech never did. Making it possible to build real software products without a CS degree, without venture funding and without being anywhere near Silicon Valley. The only requirement is a problem worth solving and the willingness to sit down and figure it out. The next generation of builders is not going to come exclusively from Stanford or Y Combinator. The models and tools are getting cheaper and becoming more accessible by the month. The playing field is leveling whether the traditional tech world is ready for it or not. And once again, El Salvador is leading the charge. This week, a room full of ambitious builders demonstrated it.
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@quotesdaily100 ·
Signs a Business Idea Might Actually Work: 1. It solves a real, recurring problem - Not a one-time annoyance people forget quickly. 2. People already pay for worse alternatives - Proves willingness to spend exists. 3. It can start small - No need for huge capital to test the concept. 4. You understand the customer deeply - Insight often matters more than the product itself. 5. It's easy to explain in one sentence - Complexity confuses buyers and investors alike. 6. There's a clear path to repeat customers - One-time sales rarely build lasting businesses. 7. You'd use it yourself - Genuine belief drives you through the hard early stages. 8. Strangers get it without much explaining - If it clicks fast, the market is ready for it. 9. It works even without heavy funding - Lean businesses survive downturns better. 10. Early users refer others unprompted - Organic word-of-mouth signals real value. 11. The margins make sense on paper - A great idea still needs a viable business model. 12. It fits a trend that's still rising - Better to ride a wave early than chase it late. 13. Competitors exist but leave gaps - Some competition validates demand; total absence is a red flag. 14. You can test it quickly and cheaply - Fast validation beats months of guessing. 15. It doesn't rely on one big customer - Diversified demand protects against sudden loss. 16. People ask when they can pay you - The clearest signal of all, demand showing up before you've asked for it.
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@Eli5defi ·
ICYMI, Story rebrands to @datafdn, shifting focus from IP tokenization to consented AI training data (IP → DATA). Why the pivot? In short, The original Story Protocol struggled with broad IP adoption because traditional IP owners resisted open programmability. ❶ IP PMF miss Major rights holders preferred closed licensing; long-tail creators couldn’t bootstrap liquidity. ❷ Chicken-and-egg + switching costs Needed both IP supply and buyers; builders stayed on existing chains; IP tooling made integration painful. ❸ AI-era urgency Demand moved to provenance-verified training data, making broad IP tokenization less pressing. ❹ Token incentives High valuations + low usage raised sell-pressure fears; unlock delays and “revenue isn’t the metric” signaled weak traction. ❺ Execution/perception Big promises, leadership changes, and low revenue/TVL made the narrative feel ahead of the product. The rebrand to The $DATA Foundation is not just a name change, it is a deliberate consolidation around a full-stack data supply chain for AI training. The new full-stack of DATA is looks like this: ❶ Collection Layer (Consumer Apps) ▸ @useKled → Massive scale opt-in marketplace (1.1–1.5B+ records already registered; 5M+ daily uploads). Flagship app ▸ Numo Labs (Poseidon) → Structured task-based voice contributions, especially underrepresented languages. ▸ @otodotearth → High-quality conversational/full-duplex voice data. ▸ Miso (upcoming) → Long-tail multimodal & sensor data. ▸ Toss (upcoming) → Finance super app (Korea) – voice/finance-related data for ownership, licensing, and payments. — ❷ Refinement & Quality Layer ▸ @psdnai processes raw data → cleans, structures, scores quality, filters out junk/synthetic content. — ❸ On-Chain Trust & Licensing Layer (DATA Network + Trace) ▸ Trace → Each contribution gets a cryptographic receipt covering provenance, metadata, licensing, and payments. Data can stay private, while public, immutable proofs verify it. — ❹ Demand Side AI labs, researchers, and enterprises use Trace to audit datasets for compliance, provenance, and consent before licensing. Confidential Data Rails (mainnet Q3 2026) will unlock more sensitive data use cases. — ➥ My Thoughts (NFA. DYOR) The timing for the pivot is strong: the AI data bottleneck is real and worsening, and provenance, licensing, and compliance are becoming table stakes for frontier AI labs. Past controversies (especially the Zhao exit and the metrics criticism) are important context, but they don’t invalidate the new thesis. They now have a real scale injection via Kled, a quality layer via Poseidon, stronger product-focused leadership, and a clearer problem to solve. That said, there are still a few things to consider, especially if major AI labs continue to prefer private, hybrid, or off-chain solutions for high-value data. So, what do you think about the pivot? --- cc: @avipat_ | @devrelius | @SPChinchali | @theSYlee
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@floriandarroman ·
How to find a great startup idea (aka a problem to solve): → according to @levelsio "How can you find problems that are actually unique and original? Well, become more unique and original yourself. Stop reading books to develop yourself or get ideas. You won't get them from there. Or if you do, there's lots of other people reading the same book probably. Get ideas from your life experience. Get outside. Become original. Do crazy stuff that you're scared of. Jump off cliffs (do it safely). Ask people you like out (scary but nice). Walk into random office buildings. Jump fences (but don't storm the Capitol Hill, thanks). Crash hotel pools. Whatever makes you different. Don't be scared! Live."
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@AtSynct ·
Do you actually use what you're building? I've got several projects ... some of which I actively use and some of which I ... don't. We all know that using your own product helps you find bugs, improve usability, etc. But I've also noticed that it brings a different level of caring. I WANT to fix bugs in the products I also use. I care to improve the UX/UI. I worry a lot more about whether a new interaction will be the right one. For stuff I don't use myself ... it's not that I do not care ... it's that I care more about the stuff I do use. It's natural, but it also reflects in the results. I think that's why the standard advice in finding a problem to solve is to work in an industry and then solve the problems you run into yourself. And I think that's why a lot of the actually-successful projects we talk about in the indie hacker community are projects that solve our own issues.
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@CichyKrzysztof ·
If you have no idea what to build, check this out. Y Combinator has a page where they share startup ideas and problems they think are worth solving right now. It's called Requests for Startups. Basically, YC is telling founders: "Here are some areas we think have potential. Go build something." Could be a good place to find your next SaaS idea instead of trying to come up with something completely from scratch.
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@staysaasy ·
The canonical observation that "the best startup ideas sound dumb at first" really only applies to consumer startups. I think the best b2b startup ideas generally sound like a great idea at first. Almost every steong enterprise startup I've heard of sounded boring AF at first but I can't think of any that actually seemed like an obviously bad idea. Consumer startups are like art and enterprise startups are like airport restaurants.
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@itsolelehmann ·
Ex Machina is no longer sci-fi. China has finally built it. The company is AheadForm, founded in Shanghai. The product is the world's most hyper-realistic robotic face. Silicone skin you can't tell from human, 25 micro motors hidden underneath pulling the face into real expressions. And RGB cameras embedded inside the pupils so when it looks at you, it actually sees you from where its eyes are. They raised $28.5M to "give AI a head," which is also where the name comes from. AheadForm = a head form. This is the opposite of where everyone else in robotics is focused. Unitree, Figure, Tesla, Boston Dynamics: all about the body. AheadForm chose the face because they think trust is the harder problem to solve, and trust gets decided at the face. The reason nobody else has tried this is the "uncanny valley." It's the creepy zone where a robot looks almost human but not quite, and looking at it just feels wrong even when you can't say why. Most roboticists believed no amount of engineering could make a face realistic enough to escape it. So they gave up and kept robots cartoonish on purpose: big anime eyes, exaggerated features, clearly synthetic. But AheadForm decided to treat it as an engineering bug instead. Add enough motors, tune the silicone, fix the timing, the valley closes. And they're pulling it off. A few crazy details about how this actually works: 1. The robot learns its own face in a mirror. You put it in front of a camera, let it fire every motor randomly, and it watches what its face does and builds an internal map of "if I send command X to motor Y, my eyebrow does this." Same exact process a human baby uses staring into a mirror. The robot teaches itself who it is by experimenting. 2. It predicts your smile 839 milliseconds before you smile. By watching the micro-tells in your face that precede a smile, the robot starts smiling 0.8 seconds ahead, so its smile lands at the same moment yours does. Most robot mimicry happens half a second late, which is exactly why it always feels artificial. 3. The pupils are the cameras. When the robot makes eye contact, the gaze and the sensor are the same physical thing. Most humanoid robots stick the camera on the forehead or chest, so they aren't actually looking at you when their eyes are pointed at you. 4. The founder, Yuhang Hu, did his PhD at Columbia under Hod Lipson. Lipson is the guy who in 2006 built a four-legged robot that figured out it had four legs by experimenting with its own movement, nobody told it the body shape, it discovered it. He has spent 25 years trying to build machines that know what they are. AheadForm is that 25-year research arc productized. 5. NetEase Games already paid them to physically embody a fantasy video game character. That opens up a brand-new category: robotics as the physical embodiment of fictional IP. Every character-rich studio, Disney, Riot, Hoyoverse, Pokemon, Netflix, now has a question to answer about when their characters get bodies. AheadForm believes whoever ships the first robot you'd actually want around your family wins. That's the bet behind the most realistic robot face on earth.
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@alexabelonix ·
If you’re brave enough to build a startup, here’s what you should know. Day 183: A SpaceX engineer can be working on rockets. And the store across the street can still manage inventory like it is 1997. That gap is where startup ideas live. The future is uneven. Founders make money by finding the weird places where it is still painfully behind.
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@_baretto ·
Big UK business opportunity 💰 I spent hours looking for a solution but couldn't find it. 💡Idea: High quality custom merch printers. Key features: 1️⃣ High quality - pick the best possible t-shirt, hats, water bottles etc. I want merch people wear outside not to bed. 2️⃣ Don't make me think - best but limited options so I don't need to make decisions. I don't want to spend 30 mins picking the best t-shirt quality. 3️⃣ Fast shipping - get it to me within a week Honestly couldn't find a vendor like this and Im not alone. Someone please build it 🙏
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@wrappedxyz ·
"Interop onchain is solved. We don't need more bridges." 🛑 @lifiprotocol drops a hot take: the base layer of crosschain infra is complete. The next problem to solve is seamlessly moving money from onchain to your bank account. 🏦
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@bricexeth ·
Stop waiting for decentralized startup ideas. The core protocols of the new internet are just the old ones rewritten for trustlessness. Here is a Web3 build list: build your own ENS-based url shortener build your own P2P encrypted chat server build your own on-chain password manager build your own decentralized search engine build your own distributed oracle cache build your own cross-chain message queue build your own trustless api gateway build your own p2p reverse proxy build your own DePIN load balancer build your own modular database build your own subgraph query engine build your own content-addressed key-value store build your own reputation-based recommendation system build your own sovereign vector database build your own CRDT collaboration engine build your own open-source smart contract search engine build your own decentralized ai inference server build your own TEE-based serverless runtime build your own zk-verifiable container runtime build your own IPFS-backed package registry build your own on-chain observability stack build your own consensus metrics system build your own immutable logging pipeline build your own event-streaming rollup build your own distributed validator task queue build your own multi-chain data pipeline orchestrator The blockchain itself is a productive project list.
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@SergioRocks ·
The best startup ideas are usually hiding inside existing businesses. Not in brainstorming sessions. In everyday operational pain. The spreadsheet everyone depends on. The workflow nobody likes. The process that always slows things down. Most people ignore those things because they feel “normal.” But pperators don’t. They see: - Where time gets wasted - Where mistakes happen repeatedly - Where people are compensating for broken systems manually That’s where the opportunity is. AI didn’t create new problems. It made it possible to finally turn existing workflows into software. That’s why some of the best Founders right now are not traditional tech people. They’re operators who deeply understand how an industry actually works. And can finally build around it.
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@tarunmallappa ·
If you are a founder iterating on a deep-tech problem, watch this interview. The manner in which this founder frames the deep-tech iteration journey is just simply brilliant and will give any deep-tech founder the mental wherewithal to weather the difficult journey. (I have tried to capture the essence here but listen directly to the founder. Watch from 12:41 to 18:00.) TL:DR 12:41 : How did you take such a big leap of faith ? Founder: Engineer + Economics both have to work; And if the economics are clear, taking engineering bets is a very low-risk approach because everything is in your control. You just need to put a solid team together and keep iterating on the problem to solve. In a non-deeptech venture, the customer need may not be very obvious and hence the adoption cycles may go a long way however companies solving in a deep-tech domain are actually solving for a customer need. IN the founders' words - deep-tech companies are delivering magical experiences at disruptively low price point; So many nuggets from @Arun_Vinayak_S who is building a full-stack energy platform to support the shift towards electric mobility in India. Credit: @LightspeedIndia @kumarharsha2212
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@joshlessard200 ·
Everyone is looking for the right vehicle. The right niche. The right industry. The right opportunity. I used to think that was the question. It is the wrong one. What I actually figured out is that the vehicle doesn't matter. What matters is whether you're providing value in exchange for something, consistently, over a long enough period of time. I got a job doing economic research. I noticed a problem. I reverse engineered what was already working for other people and started implementing it. His YouTube exploded. His friends asked me to do the same for them. Those friends happened to be Robert Kiyosaki and people like him. I didn't plan any of that. I just followed where the problem was. The problem gets you in. The value gets you paid. The skill keeps you there. Stop looking for the right thing to do. Start looking for the right problem to solve.
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@TJLarkin23 ·
Was a speaker on a 90min mastermind call yesterday (about meta ads) with 25 multi million dollar biz owners in the local media space AI only came up in the last 20 minutes or so, from a speaker teaching them about connecting using Claude to streamline analytics I can tell you that everything you're hearing about how far behind most smb owners are is true, and that many of them would pay for help to learn it and incorporate it The real problem to solve is sales And best way to do that is building trust, because that will your biggest barrier to sales My version of solving that is via in person events Regular Ai networking events, free or cheap in person AI 101 trainings And potentially soon: curated biz owner dinners I've already cracked the code on this one via meta ads, doing it for someone else Can't recommend getting out there in person as much as you can
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@asaio87 ·
Reddit is so full of shit. Almost all popular subreddits are infested by people doing marketing in a from or another. I did that with an account for a few years and presented a fake story in such a good way that I got around 60k views in a day. Yes that account eventually got banned because I pushed too hard. You can easily manipulate people Bet half of the stories on there are made up And that’s why I don’t think Reddit as a research for startup ideas can be taken too seriously. Maybe as a marketing channel can be somewhat lucrative. But for how long ? It’s so faked
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@TimStodz ·
The biggest business opportunity in history is currently being ignored. 🏠 51% of U.S. wealth is owned by people 65+. While every startup fights over the attention of Gen Z, the Boomer market is ready for innovation in health, leisure, and home services. Don't build for the people with the most social media influence. Build for the people with the most capital. 🛠️
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@theinfluence360 ·
New data on the creator economy: 48.7% of creators earn under $10K/year 45.6% earn $10-100K 5.7% earn above $100K A real middle class is forming in Web2. Almost half of all creators now earn a livable side income. Brand partnerships account for roughly 70% of total creator income. In crypto? That middle class doesn't exist. There's no tiered brand deal structure. No recurring ambassador programs. No performance-based pay that rewards consistency over follower count. You're either a top-tier KOL who got booked in the last cycle. Or you're a mid-tier creator with no infrastructure to monetize. Web2 creators have platforms, agencies, and standardized rate cards fighting for their attention. Web3 creators have a Telegram DM and spreadsheets. The infrastructure for mid-tier crypto creators to build sustainable income was never built. And right now, with campaigns at an all-time low, those creators have zero options. That's not a feature gap. That's a market gap.
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@GJarrosson ·
Tornyol has recorded the first ever air-to-air kill of a flying insect by an autonomous drone. A moth, mid-flight. No chemicals. No net. The drone uses one of its four propellers to hit the bug at high speed. Using sonar, the drone fires ultrasonic pulses, listens with an array of tiny microphones, and reads the wingbeat frequency of whatever it's chasing. It can tell a mosquito from a bee before it moves. The pesticide industry's entire model was spray chemicals, kill everything in range, and hope the crop survives. Farmers have accepted that tradeoff for 80 years because there wasn’t an alternative. There is now. A 40-gram drone that hunts by sound, kills by contact, and leaves everything else untouched. The drone is the pesticide. Ten of these cover a square kilometer. Cost of mosquito control drops by a hundredfold. Syngenta, Bayer and BASF have spent decades making better chemicals. A 40-gram drone just showed that was the wrong problem to solve! "The next big thing will start out looking like a toy." ... the core concept popularized by a16z VC Chris Dixon. Innovations are frequently dismissed in their infancy as novelties because they undershoot what users currently need. Does Tornyol fit in that bucket?
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@nikillinit ·
Fake doctors online are a growing problem and I think I know how to solve it In the last couple of years, AI has made it easier than ever to fake being a doctor and social media amplifies it This stems from the fact that we don't have a digital identity layer than can actually verify if a person that says they're a doctor has a credential In today's post, we walk through - Some specific scams that are using fake doctors - Suggestions on a system that can fix it - Startup ideas that could exist only if you had a digital ID layer full post in the next tweet
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@GJarrosson ·
You only get three or four real startup ideas in your life. Choose the right time to launch them. Webvan was grocery delivery in 1999. Great idea. Still burned $800 million and died. The timing was wrong Smartphones didn't exist yet, and GPS wasn't in everyone's pocket. The startup graveyard is full of stories like these….the right ideas built at the wrong time. The question I ask founders is “Why is this solvable today?” -Did regulation change? -Did a big incumbent exit the market? -Did customer behavior shift? Or is it just new AI capabilities that make this exist at all? If you can't answer that, the timing probably isn't right. The founders who win in 2026 aren't just finding the right market. Healthcare has an 18x gap between market size and AI usage. Finance has a 13.8 point gap. The gaps exist. What's not obvious is whether right now is the best time to build.
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@SergioRocks ·
Most industries are full of software opportunities. They just don’t look like “startup ideas.” They look like: - Extra headcount - Manual reviews - Spreadsheet-heavy workflows - Teams compensating for broken processes That’s what makes this moment so interesting. For the first time, non-technical operators can realistically turn those workflows into products. Without needing a huge engineering organization upfront. The important part is not the technology. It’s understanding the work deeply enough to know: - What should happen - What usually goes wrong - What actually creates value for the customer That’s the hard part. And that’s why domain expertise is becoming such a strong advantage in AI startups. The workflows already exist. The opportunity is turning them into automated AI-assisted products that companies will pay to use.
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