Finding Painful Problems
Source startup ideas from personal frustrations, lived industry experience, operational bottlenecks, manual workarounds, and underserved customer segments.
42%
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
- 43
- Updated
Original Xholic analysis
What 50 top Startup Ideas posts reveal
The dataset emphasizes turning observed customer and operational pain into startup opportunities, validating demand before a full build, and using lightweight MVPs and customer feedback to iterate. AI-related posts focus on automating existing workflows and monitoring capability improvements, while timing and market evidence are recurring considerations.
- Dominant tone
- Positive
- Median score
- 7.56
- Leading format
- Opinion
- Recent posts
- 46%
66% of posts
All-time engagement
46% of posts
Published in 90 days
Conversation map
The themes creators return to
Validation Before Building
Test urgency, budget, measurable business impact, customer responses, proposals, and payments before committing to a full product.
34%
Rapid MVPs and Customer Feedback
Launch quickly, use manual or lightweight prototypes, talk to users continuously, and iterate from real-world feedback rather than assumptions.
32%
Technology and Market Timing
Identify ideas newly enabled by shifts in technology, regulation, customer behavior, data availability, or incumbent market conditions.
26%
AI Automation for Existing Workflows
Apply improving AI capabilities to automate spreadsheet-heavy, repetitive, fragmented, and labor-intensive business processes.
24%
Underserved Segments and Market Gaps
Target neglected audiences, including homeowners, older consumers, mid-tier creators, small businesses, and markets lacking fit-for-purpose infrastructure.
22%
Market Research and Demand Signals
Mine Reddit, app reviews, social communities, competitor data, and customer conversations for repeated complaints, requests, engagement, and existing spend.
20%
Capability-Driven AI Opportunities
Track model improvements and build toward workflows that are currently unreliable but become viable as AI agents and models improve.
4%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 25
- Median reposts
- 1
- Median replies
- 5
- Median views
- 1.7K
Posts with media make up 38% of this collection. Their median all-time score is 8.21, compared with 6.72 for text-only posts.
Format mix
- Opinion 46% · score 4.85
- List 24% · score 18.8
- Announcement 20% · score 7.85
- Question 10% · score 7.78
Consensus and debate
Where creators agree, and where they do not
Shared view
Start with recurring operational pain
Finding Painful Problems is the largest measured theme (42% of tweets; 21 tweets). Posts commonly point to manual workflows, spreadsheet dependence, broken processes, and firsthand industry experience as idea sources.
Shared view
Validate urgency and willingness to pay before a full build
Validation Before Building accounts for 34% of tweets (17 tweets). The cited posts favor customer conversations, proposals, budget and outcome checks, and early payment tests; one explicitly distinguishes responses as signal from payments as proof.
Shared view
Use communities and customer feedback as research inputs
Posts propose Reddit, app reviews, and direct customer discussion as ways to identify repeated complaints, feature requests, workaround behavior, and potential early audiences. These sources are presented as inputs to investigate, rather than conclusive validation on their own.
Shared view
AI opportunities are framed around workflow knowledge
AI Workflow Automation appears in 24% of tweets (12 tweets). Posts argue that repetitive and fragmented work can become automatable as capabilities improve, while domain knowledge is needed to identify valuable workflows and operating constraints.
Open debate
Reddit can provide leads, but its reliability is contested
Some posts describe Reddit complaints, engagement, and workaround discussions as research signals. Another argues that popular subreddits are susceptible to marketing and fabricated stories, cautioning against treating Reddit alone as reliable idea validation.
Open debate
Novel-looking ideas versus adaptations of proven demand
One post advocates for ideas that initially look bad but have an overlooked path to scale. Others recommend starting with an already validated problem or product and changing an audience, price, or platform variable.
Open debate
Capability ambition must be paired with a why-now case
One AI-focused post recommends maintaining tests for workflows that current models cannot yet complete and shipping when they pass. Other posts urge founders to identify the concrete change—such as regulation, incumbent exits, behavior shifts, or new capabilities—that makes an idea viable now.
What performs
Patterns behind standout posts
Research and list formats have above-median measured scores
The highest-scoring outlier is a post about a directory of failed startups and timing lessons (all-time score 1067.82). In the aggregate analytics, market-research-signal posts have a 14.27 median all-time score and list posts have an 18.77 median, both above the overall 7.56 median.
Validation and rapid-MVP themes have comparatively high median scores
Rapid MVPs and Customer Feedback has a 14.21 median all-time score, and Validation Before Building has a 12.655 median, versus 7.56 across all tweets. Cited posts express those themes through manual work, early charging, user conversations, and fast iteration.
Concrete opportunity examples appear among major outliers
Two major outliers describe a specific customer or technical problem: home-management coordination (245.58 all-time score) and physical-AI research bottlenecks (218.69). Both make a proposed opportunity and constraints explicit, though the posts do not independently verify market size or demand.
Statistical standouts
- View standout post 1 Score 1067.8 · 141.25× median
- View standout post 2 Score 460.8 · 60.95× median
- View standout post 3 Score 245.6 · 32.48× median
- View standout post 4 Score 218.7 · 28.93× median
- View standout post 5 Score 218.1 · 28.84× 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
-
2. Ash Maurya
@ashmaurya
2 posts
-
3. Gabriel Jarrosson
@GJarrosson
2 posts
-
4. Hridoy Reh
@hridoyreh
2 posts
-
5. Ole Lehmann
@itsolelehmann
2 posts
-
6. Sergio Pereira
@SergioRocks
2 posts
Repeat contributors cover timing, research, and startup frameworks
Ole Lehmann posts on AI capability timing and robotics; Hridoy Reh posts a Reddit-validation guide and a startup-launch list; Startup Archive shares startup-idea frameworks attributed to Elon Musk and Sam Altman. Each is listed among repeat contributors in the deterministic analytics.
Sergio Pereira focuses on operational AI opportunities
Sergio Pereira’s two cited posts argue that operational knowledge—especially of manual reviews, spreadsheets, process failures, and customer value—helps identify AI-assisted product opportunities.
Alexa and Ash Maurya emphasize discovery and validation steps
Alexa’s posts point to calls with target users and lagging workflows as sources of ideas. Ash Maurya’s posts recommend proposals, customer responses, payment evidence, and short validation sprints before a larger commitment.
Since the previous snapshot
What changed since Aug 12, 2026
- 80% of the selected posts remained.
- The creator count changed by 0.
- The leading sentiment remained stable.
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 43 creators
Ranked 01–50
- 01
@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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- 02
- 03
@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?
- 04
@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
- 05
@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
- 06
@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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- 07
@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
- 08
@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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- 09
@pmitu ·
Most startup advice is garbage. 🍊 YCombinator's isn't. 23 YC lessons worth stealing: • Launch ASAP. Ideally today. • Build what users want, not what your imagination wants. • Do things that don't scale before PMF. • Write code. Talk to users. Repeat. • Find 10 people who absolutely love your product. • Then find 50 more. • Don't scale team, marketing, or product before real PMF. • Until PMF: stay small, fast, and flexible. • Valuation ≠ success. • Focus. Startups can only solve one big problem at a time. • Founder relationships matter more than most people realize. • Don't freak out. Every startup hits rough patches. • Organic growth is what happens when a great product meets the right market. • Avoid long enterprise sales cycles if you can. • Avoid becoming a custom dev shop for corporations. • Avoid conferences unless they reliably bring customers. • Fire customers who can sink the ship. • Watch competitors. Don't blindly copy them. • Most startups don't die because they run out of money 🤷♂️ • Don't be assholes. • Sleep. Exercise. Founder energy compounds. • Use 90/10 thinking: get 90% of the outcome with 10% of the effort. • VC money isn't your money. Don't be afraid to spend it—but maybe take it easy on the escorts and sports cars. YC is full of smart people after all. Worth paying attention.
- 10
@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"
- 11
@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.
- 12
@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.
- 13
@sickdotdev ·
I’m validating a startup idea and need brutal honesty. Idea: a platform to help people find collaborators for real projects. Users create profiles based on skills, interests, and what they want to build. The platform matches them with relevant people, projects, or small teams. Target users: - college students - beginner devs - indie builders - hackathon participants - early-stage creators My concern: is this actually a painful enough problem to make people adopt a new platform? People already use LinkedIn, Discord, Reddit, Telegram, hackathons, college circles, and private networks. So I’m trying to understand: - Is finding collaborators a real pain point or just a mild inconvenience? - Why would anyone switch from existing platforms? - What makes them come back after day 1? - Does this die because of the cold-start problem? - What’s the smallest experiment you’d run before building anything? If you’ve built projects, looked for cofounders, joined hackathons, or run communities, tear this apart. I’d rather kill a bad idea now than waste 6 months building it.
- 14
@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.
- 15
@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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- 16
@Joey_Walker82 ·
No-code plus app store data is a product idea cheat code. So I spent months studying how top indie founders validate ideas before writing a single line of code. Here are 8 must-know validation moves to find your next winning product: 1. Mine 1-star reviews for repeated pain points 2. Search "this app needs" in review sections 3. Track category rankings weekly - rising apps = rising demand 4. Look for apps with high downloads but low ratings 5. Find features users beg for in comments 6. Check update logs - devs reveal what users complained about 7. Build the fix, not a copycat 8. Launch fast, iterate with real feedback The idea is already out there. Builders just need to know where to look.
- 17
@athcanft ·
signs you should kill your startup idea: → your family says "this is a great idea" → your target audience is "everyone" → you can't explain it in one sentence → it's a "complex" product → it takes longer than a week to build → project keeps getting bigger → the technology is "impressive" → the UI is why it's "good" → your "competitive advantage" is just being cheaper → you've pivoted 3 times and still no traction → ads are not performing (after trying many) → people aren't paying "killing your babies" is the most powerful skill you can have as an entrepreneur train it HARD, you will likely have a "graveyard" of projects before one really takes off
- 18
@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.
- 19
- 20
@Hartdrawss ·
I've sat across 100+ founders in discovery calls. the ones who burned $8,000+ and never launched all couldn't answer one question ! is someone's bonus, reputation or revenue tied to this problem right now? a founder from Singapore came to us wanting to build Uber for photographers. booking platform, all levels, end to end. we asked him that question before opening figma. he couldn't answer it. we built the POC anyway. it failed but not because the product was bad. photographers moved off platform the moment they had a direct relationship with the client. availability was impossible to standardise. the marketplace dynamic that makes Uber work doesn't exist when your supply side has creative preferences and repeat client relationships. the problem wasn't painful enough to trap both sides in a platform. here's the filter i use now before we build anything at DreamLaunch: - is there a metric someone actually checks every week because of this? if it's not sitting in a spreadsheet somewhere, it's probably not a real pain - does fixing this help someone personally - make them look good to their boss, get them closer to a promotion? if it doesn't move anyone forward at work, it won't move revenue either - is there actual money set aside for this every quarter? if the problem is real but there's no budget near it, you're not selling a solution. you're selling hope vitamins get "this is so cool, let me think about it." painkillers get "can we start this week." if your product moves a number someone checks every monday morning, you have something. if it just makes people feel better, you'll be stuck pitching forever.
- 21
- 22
@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
- 23
@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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- 24
@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.
- 25
@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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@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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@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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@sylviahchannel ·
💡Did you know Slack reportedly came from a video game that failed? Before Slack became a workplace communication platform, its founders were building an online multiplayer game called Glitch. The game was shut down after failing to become commercially sustainable. But the project had produced something valuable. The team had built its own internal communication system to share messages, files and information while developing the game. It solved a genuine workplace problem so well that the team turned it into the product that became Slack 🔥 A business that didn't take off may still leave behind valuable technology, customer insight, specialist knowledge, processes, or tools. Before discarding everything, look for what worked unusually well, what people relied on, and what solved a problem beyond the original idea. The strongest business opportunity may be the useful asset you had to build along the way. 👉 Follow @bMightie for more founder intelligence for the solo and bootstrapped business journey from day zero to takeoff. #gotomarket #startupstory #entrepreneurship
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@kylegawley ·
Paid ads are a terrible strategy for validating a new startup idea Ads work when: 1. You know who your customer is 2. You have clear messaging 3. Your landing page converts You don't have any of these things on day 1 This is what I do instead: 1. Go to LinkedIn 3. Search for job titles of your ideal customer 4. Send 100 connection requests with no note 5. When they accept, send a DM 6. Ask them if they have a min to answer a few questions 7. See if they have the problem you're trying to solve Most people will respond if you just ask for help DO NOT SELL in your opening message, just ask for help You're there to learn Do people have this problem? Yes → ask if they'd pay for a solution. You will get lots of useful data this way. Then present your solution and ask for the sale.
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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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@SMExaminer ·
I secretly started a software company. And I told absolutely no one. Why? Because I was convinced I'd fail. Better to fail privately than publicly—that was my thinking. It all started in May of 2023. I was listening to the book 10X is Easier Than 2X. And something clicked. Software was gonna be my 10X move. I'd always talked myself out of it before because it was hard, expensive, and super risky. But this time, I bought books, sought wisdom from software founders, and joined memberships. AI coding wasn't a thing yet. My original dream was big: help marketers and creatives better manage projects. The idea was an alternative to the project management tools. But I quickly realized that was WAY too big of a problem to solve. So I zoomed in. I focused on one painful thing marketers face: The endless back-and-forth of giving feedback. You know the drill. You write a detailed email, attach screenshots, maybe even record a video—and they STILL miss the important stuff. Then comes the call to reexplain everything. I wanted to make it nearly impossible to misunderstand feedback on the first go around. So we built something. We combined advanced on-screen drawing tools, a screen recorder, and AI vision processing into one unique tool. We call it NoteGo. Development started in June of 2024. And we did almost everything wrong. This is not a minimum viable product. It's a highly refined tool my staff uses every single day. And honestly? It's one of the hardest projects I've ever been part of. Today is the first time I've written about it publicly. The fear of going public with this has been real. But I'm done hiding it. I plan to share a lot more about this journey in the coming weeks. Have you ever tried to build software? Or kept a big business idea secret because you were scared to fail? I'd love to hear from you. - @Mike_Stelzner
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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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@ashmaurya ·
You don't need to quit to start. Most first-time founders wait until they have permission, savings, or a co-founder. That's not preparation — that's procrastination dressed up in practical language. A weekend validation sprint costs nothing but time. 10 customer conversations. One clear problem. One rough test of willingness to pay. That's not a startup. That's evidence. And evidence is what earns the right to leap.
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