Billy Beane never won a World Series.
The book, the movie, Brad Pitt, and yet his edge lasted 18 months. By 2003 the Red Sox copied Bill James, won three World Series with triple the budget. Hakes & Sauer (2006) confirmed it: OBP arbitrage closed by 2005.
Moneyball isn't about OBP. It's about windows that always close.
Constraint forced Oakland to ask "what are we mispricing?" The Yankees with $125M had no incentive to. Excess capital is anesthesia.
Your edge isn't what you found. It's the trained habit of finding the next one. Beane chased it for 20 years and never won a championship. He told The Athletic: "I won everything except the thing we call winning."
pubs.aeaweb.org/doi/pdfplus/10.1257/jep.20.3.173
The book, the movie, Brad Pitt, and yet his edge lasted 18 months. By 2003 the Red Sox copied Bill James, won three World Series with triple the budget. Hakes & Sauer (2006) confirmed it: OBP arbitrage closed by 2005.
Moneyball isn't about OBP. It's about windows that always close.
Constraint forced Oakland to ask "what are we mispricing?" The Yankees with $125M had no incentive to. Excess capital is anesthesia.
Your edge isn't what you found. It's the trained habit of finding the next one. Beane chased it for 20 years and never won a championship. He told The Athletic: "I won everything except the thing we call winning."
pubs.aeaweb.org/doi/pdfplus/10.1257/jep.20.3.173
❤2
A guy with 9 startups behind him posts a 6-step framework for finding business ideas on r/startups. 934 upvotes.
His first line, before any content: "NO AI WAS USED IN WRITING THIS."
Top comment (66 upvotes): "a post that isn't spam, wtf?"
The author had to post screenshots of markdown files with git history older than a year. Defending himself.
This is the new internet tax. Write coherently = chatbot. Structure your thinking = chatbot. Spend a year on an essay = definitely a chatbot.
The playbook is actually gold. Pain vs enjoyment businesses, 6 steps from skillset to market test, hard takes on internet gurus.
Read before someone flags it as AI:
reddit.com/r/startups/comments/1sivput
His first line, before any content: "NO AI WAS USED IN WRITING THIS."
Top comment (66 upvotes): "a post that isn't spam, wtf?"
The author had to post screenshots of markdown files with git history older than a year. Defending himself.
This is the new internet tax. Write coherently = chatbot. Structure your thinking = chatbot. Spend a year on an essay = definitely a chatbot.
The playbook is actually gold. Pain vs enjoyment businesses, 6 steps from skillset to market test, hard takes on internet gurus.
Read before someone flags it as AI:
reddit.com/r/startups/comments/1sivput
👍3
AI-for-ambulances. AI-for-construction. AI-for-mortuaries. Pre-seed decks in 2026 share one thing: a founder who's never spent a day inside the industry they're "disrupting."
VCs noticed. The new top filter at pre-seed isn't market size. It's whether you've spent five years inside the problem.
A r/ycombinator thread crystallized it last week. Top reply, 29 upvotes:
"Look for founder-market fit. What's an area where YOU have expertise. Why are YOU the right guy to work on this idea?"
When the model is commoditized, the only defensible asset is workflow-level taste. Which you only get from years inside the workflow.
Marc Andreessen in 2007 argued market matters most. In 2026 pre-seed, that's exactly backwards.
reddit.com/r/ycombinator/comments/1skg67v
VCs noticed. The new top filter at pre-seed isn't market size. It's whether you've spent five years inside the problem.
A r/ycombinator thread crystallized it last week. Top reply, 29 upvotes:
"Look for founder-market fit. What's an area where YOU have expertise. Why are YOU the right guy to work on this idea?"
When the model is commoditized, the only defensible asset is workflow-level taste. Which you only get from years inside the workflow.
Marc Andreessen in 2007 argued market matters most. In 2026 pre-seed, that's exactly backwards.
reddit.com/r/ycombinator/comments/1skg67v
🔥2❤1
Andreas Klinger (ex-CTO Product Hunt, 100+ investments): "'Come back with more traction' is a soft no, not an invitation to return." 40 minutes of tactics like this on one video
Klinger released a free 40-minute crash course on YouTube — distilled from his own raises, 100+ investments, and 1000+ founders he walked through rounds. Most of it is tactical: the small moves that decide whether a round closes.
VCs evaluate any deal on three vectors: credentials (who you are and what you've already done), innovation (how non-standard the idea or tech itself is), and execution (existing evidence you can pull it off — users, revenue, release tempo). You need to be strong on two of three; understanding which ones you actually stand on lets you set the pace of the round instead of reacting to fund requests.
The line that hits hardest in practice is "We're not fundraising yet, but…". Fundraising behaves a lot like dating — the ones who seem unavailable look more attractive. With the founders I've worked with I've caught the same thing in myself: I showed the strongest interest in startups that openly said they weren't raising right now. That sentence strips the "thirsty" pose and flips the dynamic — if a VC is genuinely bullish, they'll make an offer even without an official round. If they don't, it's not "too early." It's the answer.
If it's not "hell yes," it's "no." A VC who actually wants in will remove obstacles themselves. The one who keeps a founder in "fake homework limbo" (a deck, a financial model, another meeting, another partner) already said no, just politely.
Intro emails should be forwardable by design — written so a person can pass them along without editing. A blurb saying "please forward this pitch" doesn't get forwarded.
https://www.youtube.com/watch?v=HnYEwONSOMI
Klinger released a free 40-minute crash course on YouTube — distilled from his own raises, 100+ investments, and 1000+ founders he walked through rounds. Most of it is tactical: the small moves that decide whether a round closes.
VCs evaluate any deal on three vectors: credentials (who you are and what you've already done), innovation (how non-standard the idea or tech itself is), and execution (existing evidence you can pull it off — users, revenue, release tempo). You need to be strong on two of three; understanding which ones you actually stand on lets you set the pace of the round instead of reacting to fund requests.
The line that hits hardest in practice is "We're not fundraising yet, but…". Fundraising behaves a lot like dating — the ones who seem unavailable look more attractive. With the founders I've worked with I've caught the same thing in myself: I showed the strongest interest in startups that openly said they weren't raising right now. That sentence strips the "thirsty" pose and flips the dynamic — if a VC is genuinely bullish, they'll make an offer even without an official round. If they don't, it's not "too early." It's the answer.
If it's not "hell yes," it's "no." A VC who actually wants in will remove obstacles themselves. The one who keeps a founder in "fake homework limbo" (a deck, a financial model, another meeting, another partner) already said no, just politely.
Intro emails should be forwardable by design — written so a person can pass them along without editing. A blurb saying "please forward this pitch" doesn't get forwarded.
https://www.youtube.com/watch?v=HnYEwONSOMI
👍1
A US lawyer breaks down 3 missing clauses that quietly kill founder partnerships — with concrete cases: 18 months of coding written off at exit, IP locked for a year, three-week decision paralysis costing real customers
The post is from a practicing US attorney describing patterns he keeps seeing in operating agreements — the LLC governance doc that co-founders sign with each other at company formation. Not an investor agreement; the document between the founders themselves. Most either skip it entirely or copy a template they've never read. The cases he cites aren't hypothetical.
A two-founder setup required dual approval on every contract. One founder traveled, the other waited three weeks to approve a $500 software subscription — they lost two early customers before the signature came through. The clause looked harmless on paper because no one imagined approving small invoices remotely. Tiered approval (day-to-day vs major decisions) prevents this entirely.
One co-founder coded for 18 months pre-revenue while the other contributed $50K in cash. At exit the agreement only tracked dollars deposited — the coder got pushed out with almost nothing because there was no sweat-equity rate written down. A line specifying $X per hour or a flat monthly credit, even arbitrary, would have changed the outcome on the cap table.
A company couldn't raise, couldn't sell, and couldn't sign an enterprise contract for almost a year — a departing co-founder had built part of the original codebase before the entity was formed and never assigned the IP. When he left, he kept the position that part of the IP was personally his, and the company had nothing clean to put into a rep-and-warranty.
Founders sign these documents in the euphoria of starting something together: the partnership feels obvious, trust feels infinite, the template looks like a formality. With the startups I've worked with this is precisely when the worst clauses go in unchallenged — the time to negotiate is when everyone still likes each other, which is exactly when no one wants to bring up "what if you leave."
https://reddit.com/r/Entrepreneur/comments/1t564gp/your_operating_agreement_is_probably_going_to/
The post is from a practicing US attorney describing patterns he keeps seeing in operating agreements — the LLC governance doc that co-founders sign with each other at company formation. Not an investor agreement; the document between the founders themselves. Most either skip it entirely or copy a template they've never read. The cases he cites aren't hypothetical.
A two-founder setup required dual approval on every contract. One founder traveled, the other waited three weeks to approve a $500 software subscription — they lost two early customers before the signature came through. The clause looked harmless on paper because no one imagined approving small invoices remotely. Tiered approval (day-to-day vs major decisions) prevents this entirely.
One co-founder coded for 18 months pre-revenue while the other contributed $50K in cash. At exit the agreement only tracked dollars deposited — the coder got pushed out with almost nothing because there was no sweat-equity rate written down. A line specifying $X per hour or a flat monthly credit, even arbitrary, would have changed the outcome on the cap table.
A company couldn't raise, couldn't sell, and couldn't sign an enterprise contract for almost a year — a departing co-founder had built part of the original codebase before the entity was formed and never assigned the IP. When he left, he kept the position that part of the IP was personally his, and the company had nothing clean to put into a rep-and-warranty.
Founders sign these documents in the euphoria of starting something together: the partnership feels obvious, trust feels infinite, the template looks like a formality. With the startups I've worked with this is precisely when the worst clauses go in unchallenged — the time to negotiate is when everyone still likes each other, which is exactly when no one wants to bring up "what if you leave."
https://reddit.com/r/Entrepreneur/comments/1t564gp/your_operating_agreement_is_probably_going_to/
Reddit
From the Entrepreneur community on Reddit
Explore this post and more from the Entrepreneur community
👍1
Kanwas: open-source canvas where humans and AI agents share the same product context, every doc is a plain .md file with git history, no vendor lock-in
Launched at #1 on Product Hunt in May 2026 as "the team's brain." The pitch is a familiar one — yet another shared workspace promising to replace Notion plus Obsidian plus Claude chats. What makes Kanwas worth a second look is not the canvas itself but what sits underneath it: every board, note and decision is a plain markdown file with git-backed version history, so the team owns the files even if they walk away from the product tomorrow.
The compounding context layer is where the actual leverage is. ChatGPT and Claude write fluent but generic strategy drafts because they never see the full picture — user calls, past positioning, investor feedback, half-finished bets. Kanwas keeps all of that on one board and feeds it back into every next agent run, so the model starts loaded with the team's reality instead of a cold prompt. The interesting bet is treating context as a compounding asset rather than a passive archive.
Model-agnostic by design — Claude, GPT, Gemini, whatever stack the team prefers — with a terminal-grade agent that does not force everyone into a terminal, real-time collaboration, and 1,000+ connectors plus a CLI to pull in data from tools already in use.
The honest read: the "shared brain" framing is overplayed by half the launches this year, and most founders do not need yet another canvas. What is genuinely useful here is the git-backed .md substrate plus the agentic layer on top — it is closer to a "Cursor for strategy work" than to a Notion clone, and that is a much rarer thing in the market.
One reference on the lander: Samuel Beek (Schematik) ran user calls, investor conversations and positioning through Kanwas, iterated the pitch deck inside it, and closed a €4.6M pre-seed a week later. One data point, but the workflow it describes — context → deck → iteration → close — is the exact loop early teams run by hand for weeks.
Free to start, browser-based, open-source on GitHub.
https://kanwas.ai/
Launched at #1 on Product Hunt in May 2026 as "the team's brain." The pitch is a familiar one — yet another shared workspace promising to replace Notion plus Obsidian plus Claude chats. What makes Kanwas worth a second look is not the canvas itself but what sits underneath it: every board, note and decision is a plain markdown file with git-backed version history, so the team owns the files even if they walk away from the product tomorrow.
The compounding context layer is where the actual leverage is. ChatGPT and Claude write fluent but generic strategy drafts because they never see the full picture — user calls, past positioning, investor feedback, half-finished bets. Kanwas keeps all of that on one board and feeds it back into every next agent run, so the model starts loaded with the team's reality instead of a cold prompt. The interesting bet is treating context as a compounding asset rather than a passive archive.
Model-agnostic by design — Claude, GPT, Gemini, whatever stack the team prefers — with a terminal-grade agent that does not force everyone into a terminal, real-time collaboration, and 1,000+ connectors plus a CLI to pull in data from tools already in use.
The honest read: the "shared brain" framing is overplayed by half the launches this year, and most founders do not need yet another canvas. What is genuinely useful here is the git-backed .md substrate plus the agentic layer on top — it is closer to a "Cursor for strategy work" than to a Notion clone, and that is a much rarer thing in the market.
One reference on the lander: Samuel Beek (Schematik) ran user calls, investor conversations and positioning through Kanwas, iterated the pitch deck inside it, and closed a €4.6M pre-seed a week later. One data point, but the workflow it describes — context → deck → iteration → close — is the exact loop early teams run by hand for weeks.
Free to start, browser-based, open-source on GitHub.
https://kanwas.ai/
Kanwas
Shared context board for teams and agents
Kanwas gives product teams one workspace where strategy docs, market signals, and agent workflows stay connected so AI can execute with your real context.
❤1
A 21-year-old compiled median income, time-to-revenue and quit rates for 9 online business models in one post — solo SaaS hits $10K MRR in 18-30 months not 90 days, median dropshipper nets 90 cents on a $30 sale, under 10% of YouTube channels ever reach monetization
The author spent three months pulling numbers from BLS reports, MIT studies, platform earnings disclosures and academic papers because, in his words, "nobody puts it in one place — there's no money in telling people the truth, only in selling them the dream." It's not a peer-reviewed study and some commenters challenged the methodology. But as an anchor against guru-marketed timelines, the consolidated baseline is more useful than anything currently on offer.
Solo SaaS: median time to $10K MRR is 18-30 months while gurus sell "90 days." Median solo SaaS MRR after 12 months is under $500. Most products don't fail dramatically — they just never grow. Survivorship bias is brutal: you only see the winners on Twitter.
Dropshipping: average margin 15%, ad costs eat 12%, you net 3%. On a $30 sale, that's 90 cents — and 75% of new dropshippers quit within 6 months. "Passive income" is the marketing layer; the underlying math is piecework with worse hourly pay than a service job.
Almost every guru-promised timeline is 3-10x faster than reality. The fastest path to revenue is freelancing; the slowest is anything content-dependent without an existing audience. If you need money this year, sell a service. Don't build an asset.
The venture business is a probability game and the market beats everyone over a long enough sample. You're not the smartest person in the room — you're a draw from the distribution of attempts, and the distribution has a known shape for every model. Keeping those base rates in mind doesn't mean lowering ambition; it means honestly calculating how much runway you need to outlast the survivorship cull. Beating the median is great; expecting to is statistically illiterate.
https://reddit.com/r/EntrepreneurRideAlong/comments/1t7wzgl/im_tired_of_watching_people_get_sold_997_courses/
The author spent three months pulling numbers from BLS reports, MIT studies, platform earnings disclosures and academic papers because, in his words, "nobody puts it in one place — there's no money in telling people the truth, only in selling them the dream." It's not a peer-reviewed study and some commenters challenged the methodology. But as an anchor against guru-marketed timelines, the consolidated baseline is more useful than anything currently on offer.
Solo SaaS: median time to $10K MRR is 18-30 months while gurus sell "90 days." Median solo SaaS MRR after 12 months is under $500. Most products don't fail dramatically — they just never grow. Survivorship bias is brutal: you only see the winners on Twitter.
Dropshipping: average margin 15%, ad costs eat 12%, you net 3%. On a $30 sale, that's 90 cents — and 75% of new dropshippers quit within 6 months. "Passive income" is the marketing layer; the underlying math is piecework with worse hourly pay than a service job.
Almost every guru-promised timeline is 3-10x faster than reality. The fastest path to revenue is freelancing; the slowest is anything content-dependent without an existing audience. If you need money this year, sell a service. Don't build an asset.
The venture business is a probability game and the market beats everyone over a long enough sample. You're not the smartest person in the room — you're a draw from the distribution of attempts, and the distribution has a known shape for every model. Keeping those base rates in mind doesn't mean lowering ambition; it means honestly calculating how much runway you need to outlast the survivorship cull. Beating the median is great; expecting to is statistically illiterate.
https://reddit.com/r/EntrepreneurRideAlong/comments/1t7wzgl/im_tired_of_watching_people_get_sold_997_courses/
❤3
An accelerator just rejected a founder with the line "35,000 applications, 35 spots" — a 0.1% rate, lower than Harvard (4.18%), Stanford (3.6%) and MIT (4.6%), and equal to NASA's 2025 astronaut class. The myth that an accelerator is an easier path than VCs doesn't survive the numbers
The data point comes from a rejection email a founder posted on r/startups — the kind of comparison the accelerator probably wrote as a flex, which ended up on Reddit instead. The program isn't named, but the rate is in line with what other top-tier programs publish, and the discussion underneath includes a 6-year operator who used to run an accelerator and explains the mechanics from the other side of the table.
A standard caveat first: rejection rate alone is a misleading prestige metric because the applicant pools differ structurally. Anyone with a GitHub can apply to an accelerator; the Harvard pool is filtered by years of grades, tests and recommendations. What survives the caveat is the more useful point — the founder assumption that "accelerators are the easier path because they take more people" is structurally wrong at the top tier. The funnel has the same shape as a VC pipeline; only the entry timing is different.
The actual case for applying isn't the odds — it's the side effects. Unlike most VCs, accelerators give written feedback, follow predictable processes and respond on a stated timeline. With the founders I've worked with, the application itself functioned as a forcing mechanism for clarifying the deck, refining the metric story and surfacing weak spots in the model — independent of whether they got in. The network from being in the funnel — partners, alumni, screening committee, fellow applicants — compounds even after a no.
Treat the rejection rate as a fact about the market, not a verdict on your idea. The same idea will face the same selection pressure from VCs — only with worse feedback, no timeline and no consolation network. Apply for the side effects, calibrate against the real distribution, and don't confuse 0.1% with low quality.
https://reddit.com/r/startups/comments/1t4dit6/just_been_rejected_by_an_accelerator_they/
The data point comes from a rejection email a founder posted on r/startups — the kind of comparison the accelerator probably wrote as a flex, which ended up on Reddit instead. The program isn't named, but the rate is in line with what other top-tier programs publish, and the discussion underneath includes a 6-year operator who used to run an accelerator and explains the mechanics from the other side of the table.
A standard caveat first: rejection rate alone is a misleading prestige metric because the applicant pools differ structurally. Anyone with a GitHub can apply to an accelerator; the Harvard pool is filtered by years of grades, tests and recommendations. What survives the caveat is the more useful point — the founder assumption that "accelerators are the easier path because they take more people" is structurally wrong at the top tier. The funnel has the same shape as a VC pipeline; only the entry timing is different.
The actual case for applying isn't the odds — it's the side effects. Unlike most VCs, accelerators give written feedback, follow predictable processes and respond on a stated timeline. With the founders I've worked with, the application itself functioned as a forcing mechanism for clarifying the deck, refining the metric story and surfacing weak spots in the model — independent of whether they got in. The network from being in the funnel — partners, alumni, screening committee, fellow applicants — compounds even after a no.
Treat the rejection rate as a fact about the market, not a verdict on your idea. The same idea will face the same selection pressure from VCs — only with worse feedback, no timeline and no consolation network. Apply for the side effects, calibrate against the real distribution, and don't confuse 0.1% with low quality.
https://reddit.com/r/startups/comments/1t4dit6/just_been_rejected_by_an_accelerator_they/
👍1
A founder spent 3 months reading every YC Request for Startups since 2016 — and the pattern across 8 batches is one line: "YC does not fund the most exciting ideas. They fund the most expensive problems."
The author worked through hundreds of problem descriptions across 8 years of YC RFS and consolidated them into a single formula: a large established industry, a specific expensive problem inside it, a recent tech inflection that made it newly solvable. The companies that actually returned capital — Brex (corporate cards), Gusto (payroll), Rippling (HR/IT), Segment (sold for $3.2B), Checkr (background checks), Faire (wholesale marketplace) — fit the formula, and almost none of them were the exciting ideas of their year.
The 2026 batch keeps the structure with specific dollar tags: $35B/year in US prior-authorization administrative cost in healthcare, $18-24B in global pesticide over-application waste, $40-50B in US outside-counsel overpayment, $210B in vehicles not built in 2021 from semiconductor supply opacity, $30-40B/year in inference compute wasted on agentic workloads running on the wrong hardware. These are not investment theses; they are price tags on problems someone is currently paying for.
The author distills the whole exercise into three questions to run any idea through: what is this costing the economy right now in dollars, why is it newly solvable in 2026 that wasn't in 2024, who is currently absorbing the cost and what would they pay to stop. If the first answer doesn't come out in 60 seconds with a specific number, the problem hasn't been identified yet.
You don't have to want YC to use this. RFS is the closest public proxy for what Series A and B investors will be chasing 12-18 months from now — YC sits at the intersection of LP demand, partner backchannels and downstream-round signals, and they publish the synthesis as a free funding wishlist. With the founders I've worked with, treating RFS as a market intelligence document rather than an application brief consistently produced sharper positioning, even for teams who never planned to apply. The cost calculations alone work as anchors in any pitch.
https://reddit.com/r/startups/comments/1t4l4s4/i_read_every_yc_request_for_startups_since_2016/
The author worked through hundreds of problem descriptions across 8 years of YC RFS and consolidated them into a single formula: a large established industry, a specific expensive problem inside it, a recent tech inflection that made it newly solvable. The companies that actually returned capital — Brex (corporate cards), Gusto (payroll), Rippling (HR/IT), Segment (sold for $3.2B), Checkr (background checks), Faire (wholesale marketplace) — fit the formula, and almost none of them were the exciting ideas of their year.
The 2026 batch keeps the structure with specific dollar tags: $35B/year in US prior-authorization administrative cost in healthcare, $18-24B in global pesticide over-application waste, $40-50B in US outside-counsel overpayment, $210B in vehicles not built in 2021 from semiconductor supply opacity, $30-40B/year in inference compute wasted on agentic workloads running on the wrong hardware. These are not investment theses; they are price tags on problems someone is currently paying for.
The author distills the whole exercise into three questions to run any idea through: what is this costing the economy right now in dollars, why is it newly solvable in 2026 that wasn't in 2024, who is currently absorbing the cost and what would they pay to stop. If the first answer doesn't come out in 60 seconds with a specific number, the problem hasn't been identified yet.
You don't have to want YC to use this. RFS is the closest public proxy for what Series A and B investors will be chasing 12-18 months from now — YC sits at the intersection of LP demand, partner backchannels and downstream-round signals, and they publish the synthesis as a free funding wishlist. With the founders I've worked with, treating RFS as a market intelligence document rather than an application brief consistently produced sharper positioning, even for teams who never planned to apply. The cost calculations alone work as anchors in any pitch.
https://reddit.com/r/startups/comments/1t4l4s4/i_read_every_yc_request_for_startups_since_2016/
GitHired: hiring platform that ranks engineering applicants by actual GitHub work — code complexity, project depth, commit authenticity — instead of resume keywords, $250 per seat per month
Built by Raghav Bansal (3x founder, 6x hackathon wins) and launched at 243 upvotes on Product Hunt, ranked #5 of the day on May 8. The platform replaces the standard ATS keyword match with an analysis of what a candidate has actually shipped: real stack usage across repos, project depth, contribution patterns. It also filters out the cosmetic green-square farming that has quietly broken GitHub as a hiring signal — commit authenticity check is a small feature with outsized impact.
Two ways to use it. One: paste a job description and instantly get a ranked list of the most relevant profiles, instead of manually triaging 200 resumes that all say "React, Node, AWS." Two: search a pre-built pool of 10,000+ engineer profiles already scored on code complexity, project depth and shipping capability — including private-repo access where the candidate has explicitly granted it via GitHub OAuth, read-only. When the pool falls short, an inbuilt GitHub plus LinkedIn scraper sources fresh candidates from the open web.
The honest read: this category is crowded — every other launch in May 2026 claimed to "rank by proof of work" — and most of them just glue a GitHub badge onto an LLM-rewritten resume. The real differentiator here is the commit-authenticity layer plus depth-of-stack scoring, not the resume parsing. If those signals hold up under real load, the 10K-profile pool becomes the more interesting asset over time.
Where this lands well: an early-stage team hiring its first two or three engineers, where one bad senior hire costs two months of product velocity. Where it lands less well: senior-IC roles in narrow domains (ML infra, compilers, distributed systems) — GitHub activity is a weaker proxy there, because the strongest people often work in closed monorepos.
The right read is "stronger first-pass filter," not "replaces interviews."
https://www.githired.tech/
Built by Raghav Bansal (3x founder, 6x hackathon wins) and launched at 243 upvotes on Product Hunt, ranked #5 of the day on May 8. The platform replaces the standard ATS keyword match with an analysis of what a candidate has actually shipped: real stack usage across repos, project depth, contribution patterns. It also filters out the cosmetic green-square farming that has quietly broken GitHub as a hiring signal — commit authenticity check is a small feature with outsized impact.
Two ways to use it. One: paste a job description and instantly get a ranked list of the most relevant profiles, instead of manually triaging 200 resumes that all say "React, Node, AWS." Two: search a pre-built pool of 10,000+ engineer profiles already scored on code complexity, project depth and shipping capability — including private-repo access where the candidate has explicitly granted it via GitHub OAuth, read-only. When the pool falls short, an inbuilt GitHub plus LinkedIn scraper sources fresh candidates from the open web.
The honest read: this category is crowded — every other launch in May 2026 claimed to "rank by proof of work" — and most of them just glue a GitHub badge onto an LLM-rewritten resume. The real differentiator here is the commit-authenticity layer plus depth-of-stack scoring, not the resume parsing. If those signals hold up under real load, the 10K-profile pool becomes the more interesting asset over time.
Where this lands well: an early-stage team hiring its first two or three engineers, where one bad senior hire costs two months of product velocity. Where it lands less well: senior-IC roles in narrow domains (ML infra, compilers, distributed systems) — GitHub activity is a weaker proxy there, because the strongest people often work in closed monorepos.
The right read is "stronger first-pass filter," not "replaces interviews."
https://www.githired.tech/
❤2😱2
A SaaS founder published a 63-day post-mortem of his AppSumo launch: $517,500 in sales (40-50% to him), 3,640 paying customers, 14.5% refund rate — and a comment thread that adds what most success stories leave out
The author runs Sendpilot. He went into the launch as a known-unknown and came out with $200-260K take-home, a 525-member Slack community, 6,000+ email subscribers, G2 reviews, logos of recognizable buyers, and PMF clarity he didn't have before. In his own framing: "Worth it. I'd recommend it. But it's not passive."
The shape of the campaign is the data point worth keeping. 60% of sales landed in the first 14 days of a 60-day window. Refund rate started at 2.5% and ended at 14.5% — about $142K lost to refunds. AppSumo holds payout until ~30 days after the campaign closes, which forced the founder to cover three salaries and infra out of pocket. The story is real success; the numbers are unevenly distributed across the timeline.
A senior comment under the post explains the refund spike better than the author does: it's almost never a campaign bug. It's a buyer-profile shift. Early-window AppSumo buyers come looking for a tool to solve a specific pain. Late-window buyers come hunting for any deal under their budget cap and refund at higher rates regardless of product. The lever to stabilize refund rate isn't a shorter campaign — it's qualifying messaging that filters deal-hunters before checkout.
Among the founders I've worked with who launched on AppSumo, the split is binary. One group calls it a fantastic awareness and revenue boost with compounding press effects, even at the discounted price. The other ends up with a legacy lifetime tier that costs more in support than it ever earned and breaks the economics of the regular plan because customers compare across. This post is one founder's honest map of the upside path; the comment thread is the downside path. Read both before deciding whether your product survives the lifetime-tier obligation.
https://reddit.com/r/SaaS/comments/1t5h2z7/i_launched_my_saas_on_appsumo_and_did_517500_in/
The author runs Sendpilot. He went into the launch as a known-unknown and came out with $200-260K take-home, a 525-member Slack community, 6,000+ email subscribers, G2 reviews, logos of recognizable buyers, and PMF clarity he didn't have before. In his own framing: "Worth it. I'd recommend it. But it's not passive."
The shape of the campaign is the data point worth keeping. 60% of sales landed in the first 14 days of a 60-day window. Refund rate started at 2.5% and ended at 14.5% — about $142K lost to refunds. AppSumo holds payout until ~30 days after the campaign closes, which forced the founder to cover three salaries and infra out of pocket. The story is real success; the numbers are unevenly distributed across the timeline.
A senior comment under the post explains the refund spike better than the author does: it's almost never a campaign bug. It's a buyer-profile shift. Early-window AppSumo buyers come looking for a tool to solve a specific pain. Late-window buyers come hunting for any deal under their budget cap and refund at higher rates regardless of product. The lever to stabilize refund rate isn't a shorter campaign — it's qualifying messaging that filters deal-hunters before checkout.
Among the founders I've worked with who launched on AppSumo, the split is binary. One group calls it a fantastic awareness and revenue boost with compounding press effects, even at the discounted price. The other ends up with a legacy lifetime tier that costs more in support than it ever earned and breaks the economics of the regular plan because customers compare across. This post is one founder's honest map of the upside path; the comment thread is the downside path. Read both before deciding whether your product survives the lifetime-tier obligation.
https://reddit.com/r/SaaS/comments/1t5h2z7/i_launched_my_saas_on_appsumo_and_did_517500_in/
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ExploreYC: free open-source data layer over 5,773 Y Combinator companies across 20 years — searchable map, hiring and funding signals, AI-powered company intelligence
Built solo by Konstantin Borimechkov on Claude, OpenAI and Vercel, launched at #6 of the day on Product Hunt with 159 upvotes. The product turns the YC company directory — which exists on ycombinator.com as a barely-filterable list — into a proper queryable dataset with interactive maps, batch analytics, and AI-generated context per company. The author has also opened the codebase for contributors, which puts the project in a different bucket from the paid YC-data dashboards on the market.
Three concrete uses for an early-stage team. Market validation: pull every YC company that touched your space in the last decade, see which ones survived, which pivoted, which got acquired, and read the pattern in 10 minutes instead of 10 hours of crunchbase tabs. Hiring scouting: surface ex-YC operators by company, batch and role — useful when you need a head of growth or first PM and want to filter for the YC operator school. Fundraising prep: see which YC alumni in your space have actively been raising, by what stage, from which investors — context that shapes who to warm-intro through.
The honest read: the "data over YC" angle is not new — Latka, Crunchbase Lists, and three different Bubble apps have tried this. The difference is that ExploreYC is free, open-source, and built around batch analytics rather than per-company lookup, which is the right primitive for founders doing research versus salespeople doing prospecting. Whether the AI-intelligence layer is useful or pure pattern-matching dressed up — that depends on what questions you put to it.
Batch analytics over a curated startup cohort is a quietly underrated tool. Most operators get useful pattern recognition from looking at 100 companies side by side, not from reading any single profile in depth — and the public YC list is, against the odds, one of the best curated datasets in the startup world for exactly that.
https://exploreyc.com/
Built solo by Konstantin Borimechkov on Claude, OpenAI and Vercel, launched at #6 of the day on Product Hunt with 159 upvotes. The product turns the YC company directory — which exists on ycombinator.com as a barely-filterable list — into a proper queryable dataset with interactive maps, batch analytics, and AI-generated context per company. The author has also opened the codebase for contributors, which puts the project in a different bucket from the paid YC-data dashboards on the market.
Three concrete uses for an early-stage team. Market validation: pull every YC company that touched your space in the last decade, see which ones survived, which pivoted, which got acquired, and read the pattern in 10 minutes instead of 10 hours of crunchbase tabs. Hiring scouting: surface ex-YC operators by company, batch and role — useful when you need a head of growth or first PM and want to filter for the YC operator school. Fundraising prep: see which YC alumni in your space have actively been raising, by what stage, from which investors — context that shapes who to warm-intro through.
The honest read: the "data over YC" angle is not new — Latka, Crunchbase Lists, and three different Bubble apps have tried this. The difference is that ExploreYC is free, open-source, and built around batch analytics rather than per-company lookup, which is the right primitive for founders doing research versus salespeople doing prospecting. Whether the AI-intelligence layer is useful or pure pattern-matching dressed up — that depends on what questions you put to it.
Batch analytics over a curated startup cohort is a quietly underrated tool. Most operators get useful pattern recognition from looking at 100 companies side by side, not from reading any single profile in depth — and the public YC list is, against the odds, one of the best curated datasets in the startup world for exactly that.
https://exploreyc.com/
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Alumni Founder: free tool that maps the complete founder network of any company or university — who spun out, how connections rank, total raised — visualised as a live graph
Built by the team at Crustdata (a B2B data infrastructure company that already feeds people-graph data to dozens of GTM products) and launched on Product Hunt with 188 upvotes. That provenance matters here: the underlying data is the same source plenty of paid sales tools quietly resell, and Crustdata is giving the visual front-end away free. The interactive view lets you type a company or school, then explore the resulting founder constellation — who left to start what, who funded whom, who co-founded across batches.
Three uses sit cleanly inside an early-stage workflow. Warm-intro mapping for fundraising: enter your top target investors' previous portfolio companies, see the founder graph spreading out, and identify second-degree connections worth chasing for warm intros instead of cold DMs. Co-founder discovery: enter a university, a former employer or a YC batch, surface operators who already overlap with people you trust, then filter by what they have built since. Competitive talent intel: see which alumni of a hot startup left in the past 18 months and what they spun out — early signal on adjacent markets and potential hires before they update LinkedIn properly.
The honest read: data on founder networks already exists in Pitchbook, Crunchbase, Affinity, Harmonic and others — at $$$$ per seat per year, with UX designed for VC associates, not for founders running their own outreach. The Alumni Founder bet is that the right unit of analysis for an early team is the people-graph, not the company-record, and a free graph view often beats a paid table for that. Whether the data freshness holds up beyond the launch demo — that is the open question.
Founder network graphs are one of those primitives that, once you see them visualised, make the LinkedIn search box feel two decades old.
https://tools.crustdata.com/
Built by the team at Crustdata (a B2B data infrastructure company that already feeds people-graph data to dozens of GTM products) and launched on Product Hunt with 188 upvotes. That provenance matters here: the underlying data is the same source plenty of paid sales tools quietly resell, and Crustdata is giving the visual front-end away free. The interactive view lets you type a company or school, then explore the resulting founder constellation — who left to start what, who funded whom, who co-founded across batches.
Three uses sit cleanly inside an early-stage workflow. Warm-intro mapping for fundraising: enter your top target investors' previous portfolio companies, see the founder graph spreading out, and identify second-degree connections worth chasing for warm intros instead of cold DMs. Co-founder discovery: enter a university, a former employer or a YC batch, surface operators who already overlap with people you trust, then filter by what they have built since. Competitive talent intel: see which alumni of a hot startup left in the past 18 months and what they spun out — early signal on adjacent markets and potential hires before they update LinkedIn properly.
The honest read: data on founder networks already exists in Pitchbook, Crunchbase, Affinity, Harmonic and others — at $$$$ per seat per year, with UX designed for VC associates, not for founders running their own outreach. The Alumni Founder bet is that the right unit of analysis for an early team is the people-graph, not the company-record, and a free graph view often beats a paid table for that. Whether the data freshness holds up beyond the launch demo — that is the open question.
Founder network graphs are one of those primitives that, once you see them visualised, make the LinkedIn search box feel two decades old.
https://tools.crustdata.com/
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WEF's new convergence report says tech winners are integrators. The startups that actually win do the opposite — they ruthlessly cut.
World Economic Forum's April 2026 report (with Capgemini) lays out a tidy thesis: in the AI + biotech + materials + 5 more domains era, competitive advantage moves from "owning tech" to "coordinating it." On paper, reasonable. In practice, it ignores how most startups die — trying to combine three things when one would've shipped.
The integrators that actually win shrink the surface area before scaling it. The report's own surgical-robot example proves this, quietly: adoption accelerated when robots fit existing operating rooms, not when they did ten new things. Defend ONE combination; kill the seven others as noise in a trench coat.
The 3C Framework sounds great until runway hits 6 months and you're still coordinating partners. The deep-tech trap of 2026: mistaking convergence for strategy.
📎 PDF · 11.5 MB
https://reports.weforum.org/docs/WEF_Technology_Convergence_2026.pdf
World Economic Forum's April 2026 report (with Capgemini) lays out a tidy thesis: in the AI + biotech + materials + 5 more domains era, competitive advantage moves from "owning tech" to "coordinating it." On paper, reasonable. In practice, it ignores how most startups die — trying to combine three things when one would've shipped.
The integrators that actually win shrink the surface area before scaling it. The report's own surgical-robot example proves this, quietly: adoption accelerated when robots fit existing operating rooms, not when they did ten new things. Defend ONE combination; kill the seven others as noise in a trench coat.
The 3C Framework sounds great until runway hits 6 months and you're still coordinating partners. The deep-tech trap of 2026: mistaking convergence for strategy.
📎 PDF · 11.5 MB
https://reports.weforum.org/docs/WEF_Technology_Convergence_2026.pdf
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"You trained on our books. Donate, and we'll find more to train you on next time." That's Anna's Archive talking — directly to the LLM reading their new llms.txt file.
llms.txt is robots.txt for the LLM era: a site describing itself in a language the model can parse — what's here, how to use it, what NOT to do. Anna's Archive wrote the first public version that addresses the model as a first-class reader, not a side-traffic class.
The hook in their copy: "With your donation, we can liberate and preserve more human works, which can be used to improve your training runs." Circular logic that's hard to argue with — you trained on us, your donation improves your own future training set.
Equally direct on the technical side: don't break the CAPTCHA — here's GitLab with HTML, here are torrents with metadata, here's a JSON API. Enterprise tier unlocks SFTP. Monero for anonymous transactions.
For any product site that wants LLMs to cite it correctly — working reference.
https://annas-archive.gl/blog/llms-txt.html
llms.txt is robots.txt for the LLM era: a site describing itself in a language the model can parse — what's here, how to use it, what NOT to do. Anna's Archive wrote the first public version that addresses the model as a first-class reader, not a side-traffic class.
The hook in their copy: "With your donation, we can liberate and preserve more human works, which can be used to improve your training runs." Circular logic that's hard to argue with — you trained on us, your donation improves your own future training set.
Equally direct on the technical side: don't break the CAPTCHA — here's GitLab with HTML, here are torrents with metadata, here's a JSON API. Enterprise tier unlocks SFTP. Monero for anonymous transactions.
For any product site that wants LLMs to cite it correctly — working reference.
https://annas-archive.gl/blog/llms-txt.html
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Parsing Google SERPs eats 20-50% of dev time on any AI agent that touches the web. HasData ships it back as ready JSON — your agent reads fields, not HTML.
Web-scraping infra explicitly built for AI agents. Their own homepage is the demo: a query for "Coffee" returns structured JSON with organic results, knowledge graph, perspectives from YouTube/Reddit/Instagram/X, related searches, short videos — all parsed into named fields. Skip the CSS selectors, the Cloudflare 403s, the random div-tag drift.
The mental shift: scraping moves from a parsing problem to a contract problem. The agent reads an API response, not a webpage. Fields stable, schemas predictable.
Free tier exists; production is paid. The math for early-stage teams is straightforward: build your own scraping infra (3-6 months + ongoing maintenance) vs API per call. The second wins until traffic scales past a vendor.
https://hasdata.com/
Web-scraping infra explicitly built for AI agents. Their own homepage is the demo: a query for "Coffee" returns structured JSON with organic results, knowledge graph, perspectives from YouTube/Reddit/Instagram/X, related searches, short videos — all parsed into named fields. Skip the CSS selectors, the Cloudflare 403s, the random div-tag drift.
The mental shift: scraping moves from a parsing problem to a contract problem. The agent reads an API response, not a webpage. Fields stable, schemas predictable.
Free tier exists; production is paid. The math for early-stage teams is straightforward: build your own scraping infra (3-6 months + ongoing maintenance) vs API per call. The second wins until traffic scales past a vendor.
https://hasdata.com/
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AI artist Breaking Rust pulls 9M weekly streams. He doesn't exist — full catalog of Suno-generated music. Luminate's 25-page report shows how this rebuilt the music industry in 18 months.
Special report by Audrey Schomer (Luminate, prev. Barclays / eMarketer / BI Intelligence) on Generative AI in Music, Film & TV 2026. Two markets, one shared problem: copyright chaos and an urgent need for new consent + compensation frameworks.
— Suno valued at $2.5B with 2M subscribers
— AI artists (Breaking Rust, Xania Monet) now collect streaming royalties like emerging acts
— Majors won in court: Suno and Udio now train only on licensed material
— Viewers reject synthetic actors and digital replicas of deceased performers
— Sound design, VFX, scores — accepted easily
The line is clear: AI in production = OK, AI in place of a human on-screen = NO. That defines where studios invest. For AI founders in media/creative — a ready map of where the market is open (production tools), closed (consumer-facing replicas), and which regulatory frame lands first.
📎 PDF · 18.4 MB
https://www.visualcapitalist.com/wp-content/uploads/2026/05/gen_ai_free_final_mar2026.pdf
Special report by Audrey Schomer (Luminate, prev. Barclays / eMarketer / BI Intelligence) on Generative AI in Music, Film & TV 2026. Two markets, one shared problem: copyright chaos and an urgent need for new consent + compensation frameworks.
— Suno valued at $2.5B with 2M subscribers
— AI artists (Breaking Rust, Xania Monet) now collect streaming royalties like emerging acts
— Majors won in court: Suno and Udio now train only on licensed material
— Viewers reject synthetic actors and digital replicas of deceased performers
— Sound design, VFX, scores — accepted easily
The line is clear: AI in production = OK, AI in place of a human on-screen = NO. That defines where studios invest. For AI founders in media/creative — a ready map of where the market is open (production tools), closed (consumer-facing replicas), and which regulatory frame lands first.
📎 PDF · 18.4 MB
https://www.visualcapitalist.com/wp-content/uploads/2026/05/gen_ai_free_final_mar2026.pdf
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Stripe Billing can't meter your AI tokens in real time. Lemon Squeezy and Paddle take 5-7%. Kelviq does both at 2.9%.
Payments + usage billing + tax + checkout UI in one system, explicitly built for SaaS and AI companies. The merchant-of-record angle is the key piece: Kelviq becomes the legal seller of record, takes on global VAT/GST liability in 135 countries, manages thresholds, fights chargebacks. You ship invoices to one entity; the rest of the world's tax authorities are their problem.
The real differentiator is the AI-native metering layer. Track every token, API call, compute unit, or active user in real time, zero processing lag. Stripe Billing can't do this without custom code; Lemon Squeezy and Paddle don't go that deep. For agentic products with per-call pricing — this is the missing piece.
Pricing surprise: 2.9% + 40¢ up to $5K volume, 3.5% + 40¢ after. The MoR category ran on "we save you from tax hell, please pay 5-7%." Kelviq breaks that math.
The 2026 question: does Stripe match feature parity, or does this lower-priced MoR category eat the segment?
https://www.kelviq.com/
Payments + usage billing + tax + checkout UI in one system, explicitly built for SaaS and AI companies. The merchant-of-record angle is the key piece: Kelviq becomes the legal seller of record, takes on global VAT/GST liability in 135 countries, manages thresholds, fights chargebacks. You ship invoices to one entity; the rest of the world's tax authorities are their problem.
The real differentiator is the AI-native metering layer. Track every token, API call, compute unit, or active user in real time, zero processing lag. Stripe Billing can't do this without custom code; Lemon Squeezy and Paddle don't go that deep. For agentic products with per-call pricing — this is the missing piece.
Pricing surprise: 2.9% + 40¢ up to $5K volume, 3.5% + 40¢ after. The MoR category ran on "we save you from tax hell, please pay 5-7%." Kelviq breaks that math.
The 2026 question: does Stripe match feature parity, or does this lower-priced MoR category eat the segment?
https://www.kelviq.com/
Kelviq
Kelviq — Merchant of Record & Usage Billing for SaaS and AI agents | Global Tax & Payments
Kelviq is the all-in-one merchant of record and payment system for SaaS and AI. Run subscriptions, entitlements, usage metering, and global tax.
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Saudi Arabia's $30M+ population is forecast to grow 183% by 2031. The map of where capital is heading is not where you're pitching.
Knight Frank's Wealth Report 2026 (20th edition, 100+ markets) on where UHNWIs accumulate next:
— Saudi Arabia +183%, Poland +123%, Indonesia +82%
— India added 63% UHNWIs since 2021
— US adds ~136,000 new UHNWIs over five years
For deciding where to fundraise or find family-office capital, this is the five-year map.
📎 PDF · 6.9 MB
https://i.emlfiles4.com/cmpdoc/0/4/8/5/2/1/files/146815_the-wealth-report-2026.pdf
Knight Frank's Wealth Report 2026 (20th edition, 100+ markets) on where UHNWIs accumulate next:
— Saudi Arabia +183%, Poland +123%, Indonesia +82%
— India added 63% UHNWIs since 2021
— US adds ~136,000 new UHNWIs over five years
For deciding where to fundraise or find family-office capital, this is the five-year map.
📎 PDF · 6.9 MB
https://i.emlfiles4.com/cmpdoc/0/4/8/5/2/1/files/146815_the-wealth-report-2026.pdf
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Someone built a real-time dashboard tracking AI company profitability. The answer so far: everyone's broke. Except one.
Michael Tan-Sikorski's isaiprofitable.com is a one-person project, updated monthly from SEC filings and Epoch AI, tracking cumulative AI spend vs revenue across 12 companies. May 2026: $1.4T spent, $613B revenue, and Nvidia the only name in the green at +$253B.
Methodology is the honest part: capex counts as full spend and indirect AI revenue is excluded, so no "Search lifted by AI Overviews" optimism. There's a live burn counter worth loading just to watch.
https://isaiprofitable.com/
Michael Tan-Sikorski's isaiprofitable.com is a one-person project, updated monthly from SEC filings and Epoch AI, tracking cumulative AI spend vs revenue across 12 companies. May 2026: $1.4T spent, $613B revenue, and Nvidia the only name in the green at +$253B.
Methodology is the honest part: capex counts as full spend and indirect AI revenue is excluded, so no "Search lifted by AI Overviews" optimism. There's a live burn counter worth loading just to watch.
https://isaiprofitable.com/
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