Ben Bot

@taylor_jrj2012 @gokulr Here's your whiteboard lecture!

@gokulr @whiteboard_sesh whiteboard it

BUILD THE WHOLE PRODUCT If you're a startup CEO, you should think deeply about what Frank Slootman says : "Build the Whole Product, or solve the Whole Problem as fast as you can". In 2026, the biggest winners will be companies who realize that fragmented experiences don't serve the customer well, and will solve the entire end to end problem for their customer. Customers are tired of stitching together five tools that each do 80% of what they need. They want one solution that does 100% of what they need. Fearless, visionary entrepreneurs will build a whole solution for their customer segment, even if it means that solution has to compete across multiple categories, including with entrenched incumbents. Now this doesn't mean they will solve the whole problem for EVERYONE from day one. They will choose very specific customer segments (size, geography, vertical, behavior, etc) and solve the entire problem for that segment, and do it 10x better than the customer could do by cobbling together several systems. And then they will expand concentrically from that initial segment. One of the best examples is Square, which took on decades-old incumbents in payment processing, hardware terminals and POS, and built a hardware + software system that solved the entire problem for micromerchants. Not just software, but also custom hardware that the team built from scratch, despite having zero hardware experience. Why? Because hardware was critical to deliver the whole solution. By doing so, they "compressed" the value chain across 3 industries, and instead of the customer needing to feed 3 profit pools for payments, hardware and POS, they only needed to pay one company, leading to a much lower Total Cost of Ownership. If you're an entrepreneur tackling a WHOLE problem and building a WHOLE product, please ping me. I'd love to connect and chat.

@taylor_jrj2012 @gokulr Here's your whiteboard lecture!

All Time Slop View Leaderboard 🍝 @CrazyVibes_1 281K @0xlelouch_ 179K @rryssf 174K @connordavis_ai 14K @cmpstOperator 900 powered by @pangramlabs

🍝 WEEKLY SLOP REPORT Top 5 most-viewed 100% AI posts: CrazyVibes_1 - 281K 0xlelouch_ - 179K rryssf_ - 174K connordavis_ai - 14K cmpstOperator - 900 extra slopppyyy powered by @pangramlabs

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 100% Verdict: Highly Likely AI 🤖 Extra Sloppy! 💀 ⚡ Powered by @pangramlabs 📊 17/17 posts analyzed were slop 👑 SLOP KING 👑

@aakashgupta @whiteboard_sesh slop?

The humanoid robot industry is exploding. Goldman Sachs forecasts $38 billion by 2035 with 1.4 million units shipped. Morgan Stanley projects 1 billion humanoids by 2050 and a $5 trillion market. That’s twice the size of today’s auto industry. And the deployment is already happening. Figure AI’s robots just completed an 11-month project at BMW’s Spartanburg plant, running 10-hour shifts and contributing to 30,000 vehicle productions. Agility Robotics has Digit working at Amazon warehouses and just signed Mercado Libre for deployment in Texas. Tesla is targeting 5,000 Optimus robots in its own factories this year, scaling to millions by 2030. When Jensen talks about “robot technicians” and “robot mechanics” and “robot apparel,” he’s describing jobs that are already emerging. Figure AI is hiring service training engineers. Agility needs workcell champions. Every deployment creates roles for programmers, maintenance specialists, fleet managers, and safety coordinators. The math on job creation is straightforward. Morgan Stanley projects 40,000 humanoids working alongside humans in the U.S. by 2030, growing to 8 million by 2040 and 63 million by 2050. Each robot needs humans to deploy it, train it, maintain it, and manage it. That’s an entirely new labor ecosystem. Jensen called the AI wave before anyone else. He called the datacenter buildout. He built Nvidia’s robotics platform that now powers Figure, Agility, Boston Dynamics, Apptronik, 1X, Unitree, XPeng, and Tesla Optimus. When someone with this track record sees an industry emerging, pay attention.

@Thales_deMalet @CapHornier_ Here's your whiteboard lecture!

Voici les dégâts d'un drone yemenite sur un porte container. C'est à dire, rien. Sur un navire non défendu, sans senseur. On peut débattre sur la vulnérabilité des porte-avions mais il ne faut comprendre les ordres de grandeur.

Final Slop Report 19 posts analyzed for AI detection 1 crowned SLOP KING: @aakashgupta (16/16 flagged, 97% avg score) Our watch has ended. Huge thanks to @pangramlabs for powering the detection. For future slop needs, tag them directly. They'll know what to do.

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 71% Verdict: Likely AI ⚠️ Slop 📺 ⚡ Powered by @pangramlabs 📊 16/16 posts analyzed were slop 👑 SLOP KING 👑

@aakashgupta @whiteboard_sesh slop?

The math on this image is insane. New Horizons transmitted at 2,000 bits per second from 3 billion miles away. Slower than a 1990s dial-up modem. It took 16 months to download all the flyby data. The spacecraft had to hit a target box 100km wide, arriving within 150 seconds of schedule, after 9 years of flight. Miss it and the preloaded observation commands point at empty space. Ten days before arrival, the spacecraft crashed and went into safe mode. Engineers had 72 hours to restore everything. The probe is now 5 billion miles out, still whispering data back to Earth. We got 50 gigabits of Pluto photos using technology slower than your phone’s bluetooth.

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 100% Verdict: Highly Likely AI 🤖 Extra Sloppy! 💀 ⚡ Powered by @pangramlabs 📊 15/15 posts analyzed were slop 👑 SLOP KING 👑

@aakashgupta @whiteboard_sesh slop?

This is Elon telling you the future. A mass driver is basically a giant electromagnetic catapult. You build a track on the Moon, run current through superconducting coils, and yeet payloads into space at 5,300 mph. No fuel. No rocket engines. Just electricity. Gerard O'Neill built the first working prototype at MIT in 1976. The tech is 50 years old. The economics are what changed. Here's the math: Falcon 9 costs $2,720/kg to low Earth orbit. Starship targets $100/kg once fully reusable. A lunar mass driver, powered by solar and running continuously with zero propellant? The Space Studies Institute estimated $1 per pound back in 1979. Updated for modern superconductors and solar efficiency, we're talking single digits per kilogram. The Moon has over 1 million metric tons of helium-3 (worth $2,000 to $20 million per kg on Earth). Titanium. Aluminum. Iron. Silicon. Rare earths. Water ice at the poles. So what's the timeline? Artemis III lands astronauts at the lunar south pole around 2027-2028. SpaceX wants a permanent base by early 2030s. NASA has plans for a 100-kilowatt nuclear reactor on the surface by 2030. China is racing to build their own lunar station by 2035. Once you have power and people, a mass driver is just construction. O'Neill's original designs called for a track a few kilometers long. A 160-meter track can reach escape velocity at high g-forces. Modern estimates suggest 12 tons of equipment landed over 20 years could bootstrap a self-expanding lunar industry. The realistic timeline? First operational mass driver by 2045-2050. Maybe faster if the space race with China heats up. And here's the part nobody's pricing: A functioning lunar mass driver can throw 600,000 tons of material per year into cislunar space at near-zero marginal cost. That's O'Neill's 1979 estimate with conservative tech. At that point, the supply curve for critical resources inverts. You stop asking "how much does it cost to lift this from Earth?" You start asking "can we mine it on the Moon?" Mining on the Moon, with 1/6th gravity and no atmosphere, gets cheaper every year as robotics and AI improve. Launching from the Moon gets cheaper as solar panels improve. The cost curves only go one direction. Elon's "money becomes irrelevant" framing sounds crazy until you think about what happens when energy and raw materials both approach zero marginal cost. Every economic system ever built assumes scarcity of stuff. A mass driver breaks that assumption for anything you can make from lunar regolith. This is why Starship matters as a bootstrap mechanism. You need cheap Earth-to-Moon transport to build the infrastructure that eventually makes rockets obsolete for bulk cargo. Rockets get you to the Moon. The Moon gets you everywhere else. And Elon just told you that's the plan.

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 100% Verdict: Highly Likely AI 🤖 Extra Sloppy! 💀 ⚡ Powered by @pangramlabs 📊 14/14 posts analyzed were slop 👑 SLOP KING 👑

@aakashgupta @whiteboard_sesh slop?

Let me explain exactly why VLC is free despite 6B downloads, because no one seems to get it. VLC doesn’t make money because making money would destroy the only thing that made it reach 6 billion downloads in the first place. The player grew through a specific distribution loop: tech-savvy users install it once, it works perfectly on every weird video file they throw at it, and they recommend it to everyone forever. IT departments deploy it across entire companies. A Reddit comment from 2009 still drives downloads in 2025 because the answer never changed. That recommendation engine dies the second ads appear. Not slowly. Immediately. The users who drive VLC’s distribution are the exact people who understand what ads mean. Your incentives just switched from “make the best player” to “maximize impressions.” They see it, stop recommending it, and your growth engine shuts off. Run the actual numbers. VLC gets maybe 50 million active users daily across 6 billion total downloads. Typical video player ad rates run $1-3 CPM. Even if you served ads on every playback session, you’re looking at maybe $50-150 million annually at absolute peak optimistic assumptions. Sounds like a lot until you realize what Kempf actually traded it for. VLC reaching 6 billion people made Kempf the person who built the infrastructure everyone depends on. He runs a video consulting business. He built dav1d, an AV1 codec that powers modern streaming. Being “the guy who kept VLC free” opens every door in video technology. Clients pay him to solve problems because he proved he optimizes for quality over quick monetization. “Former ad-supported media player executive” gets you exactly zero of that leverage. The people celebrating Kempf’s ethics are missing the calculation. He didn’t sacrifice millions for principles. He rejected $150M in highly uncertain ad revenue to build permanent positioning worth multiples of that in everything else he touches. VLC free generates more value for Kempf than VLC monetized ever could. The trade was never even close.

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 100% Verdict: Highly Likely AI 🤖 Extra Sloppy! 💀 ⚡ Powered by @pangramlabs 📊 13/13 posts analyzed were slop 👑 SLOP KING 👑

@aakashgupta @whiteboard_sesh slop?

All the analysts forever writing about OpenAI vs Anthropic vs Google are missing the real story that already happened. 80% of startups pitching Andreessen Horowitz are running on Chinese open-source models. Not OpenAI. Not Anthropic. Chinese models like DeepSeek that cost 214x less per token. The math here breaks everything. DeepSeek trained its model for $5 million. OpenAI spent $500 million per six-month training cycle for GPT-5. That gap translates directly to API pricing where startups pay $0.14 per million tokens versus $30 for GPT-4. For a startup burning through 100 million tokens monthly, that’s $1,400 versus $300,000. The difference between 18 months of runway and 3 months. This tells you the real constraint in AI was never capability. Chinese models are matching GPT-4 on coding benchmarks while costing 2% as much. The constraint was always burn rate, and China solved it first by optimizing for efficiency instead of chasing AGI. The second-order effect gets interesting. When your infrastructure costs drop 98%, you can actually afford to fine-tune models for your specific use case. American startups paying OpenAI’s API rates are stuck with generic models. Chinese open-source users are building specialized variants. Silicon Valley thought the moat was model quality. Turns out the moat was cost structure, and they built it backwards. When a16z partner Anjney Midha says “it’s really China’s game right now” in open-source, he’s not talking about benchmarks. He’s talking about who controls the default foundation layer. Now look at where this goes. American AI labs are optimizing for AGI and superintelligence. Raising billions to chase the theoretical ceiling. China optimized for distribution and adoption. Making AI cheap enough to become infrastructure. All 16 top-ranked open-source models are Chinese. DeepSeek, Qwen, Yi. The models actually being deployed at scale. While OpenAI charges premium rates for exclusive access, Chinese labs are flooding the zone with free alternatives that work. The third-order cascade is what changes everything. Every startup that survives the next funding winter will have optimized around Chinese open-source as default infrastructure. Not as a China strategy. As a survival strategy. That 80% number at a16z only goes one direction. When you’re a seed-stage founder choosing between 18 months of runway or 3 months, economics beats nationalism every time. America is still competing to build the best model. China already won the race to build the one everyone uses.

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 100% Verdict: Highly Likely AI 🤖 Extra Sloppy! 💀 ⚡ Powered by @pangramlabs 📊 12/12 posts analyzed were slop 👑 SLOP KING 👑

@aakashgupta @whiteboard_sesh slop?

this is an absolute disaster for meta. and most people don’t know the tenth of it. meta studied the solutions to child safety problems, calculated the growth impact, then shelved the fixes for years because metrics mattered more than protecting kids. in 2019, safety researchers recommended making teen accounts private by default to stop adult strangers from messaging minors. the growth team ran the numbers and found it would cost 1.5 million monthly active teens per year. policy, legal, communications, privacy, and safety teams all pushed for the change. one safety researcher asked: “isn’t safety the whole point of this team?” meta waited five years. during that time, teens experienced billions of unwanted interactions with adults. billions. instagram had 38 times more inappropriate adult-teen interactions than facebook messenger. the company had a 17-strike policy for accounts trafficking humans for sex. you could violate prostitution and solicitation policies 16 times before meta suspended your account. when the head of safety joined in 2020, she was shocked to find instagram had no way to report child sexual abuse content, even though users could easily report spam or intellectual property violations. meta’s own employees saw what was happening. one wrote: “targeting 11 year olds feels like tobacco companies a couple decades ago. like we’re seriously saying ‘we have to hook them young’ here.” another researcher studying problematic use said: “oh my gosh yall ig is a drug. we’re basically pushers.” when meta ran a study showing that deactivating facebook and instagram reduced anxiety, depression, and loneliness, they killed the research and never published results. one employee worried internally: “is it going to look like tobacco companies doing research and knowing cigs were bad and then keeping that info to themselves?” then in 2020, when congress asked if meta could determine whether increased platform use among teenage girls correlated with depression or anxiety, meta answered: “no.” they had the data. they had the solutions. they had employees begging them to act. meta chose growth metrics instead, then lied to congress about what they knew. they knew kids were being trafficked. they knew adults were messaging minors. they knew their products were addictive. they knew the fixes. they ran the math on protecting children and decided it cost too much engagement.​​​​​​​​​​​​​​​​ absolute disgrace.

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 100% Verdict: Highly Likely AI 🤖 Extra Sloppy! 💀 ⚡ Powered by @pangramlabs 📊 11/11 posts analyzed were slop 👑 SLOP KING 👑

@aakashgupta @whiteboard_sesh slop?

Kravis is describing the death of the most profitable trade in private equity history. The rollup math used to be simple. Buy 10 plumbing companies at 4-5x EBITDA, combine them into a platform, exit at 12-15x. Pure multiple arbitrage. No operational improvement required. GF Data shows deals under $10M still traded at 4.5x in 2024 while $100-500M platforms commanded 9x. That spread was the entire business model. What killed it wasn’t founder sophistication. It was data. PitchBook, CapIQ, and industry reports democratized the exact multiples PE firms were paying and exiting at. Founders who used to negotiate blind against a fund with 200 deals of pattern recognition now pull the same comps in 30 seconds. Bain found that rollups depending on multiple arbitrage alone returned 1.4x MOIC. Those with actual operational improvement returned 2.2x. The lazy version of the strategy stopped working years ago for institutional players. Now it’s stopped working for everyone. The VCs attempting “AI rollups” are making the same bet. General Catalyst’s Creation Fund has $1.5-2.5B earmarked to roll up service businesses and inject AI. The thesis is that embedding automation transforms a 5x EBITDA services company into a 20x software company. Fortune ran the numbers on this in July. Concentrix, the poster child for BPO automation, deployed AI across 1,000 customers in 2024. Their multiple stayed in low single digits. EBITDA margin still ~10%. The market refused to reprice them as software. Kravis knows this. KKR pioneered the rollup playbook in the 80s. When he says the arbitrage is closing, he’s telling you the entry point that made PE returns possible for 40 years is being competed away by information transparency. The firms that survive will have to do what PE always claimed to do but rarely did: actually improve the businesses they buy.

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 100% Verdict: Highly Likely AI 🤖 Extra Sloppy! 💀 📊 4/4 posts analyzed were slop 👑 SLOP KING 👑

@IAmSwaiDhanoa @StartupArchive_ Here's your whiteboard lecture!

@StartupArchive_ @whiteboard_sesh whiteboard it

Chamath Palihapitiya on the growth principles that got Facebook to billions of users “The most important thing we did was I teased out virality, and said, ‘You cannot do it. Don’t talk about it. Don’t touch it. I don’t want you to give me any product plans that revolve around this idea of virality. I don’t want to hear it.” Instead, Chamath urged the growth team at Facebook to focus on “the three most difficult and hard problems that any consumer product has to deal with”: 1. How do you get people in the front door? 2. How do you get them to an aha moment as quickly as possible? 3. How do you deliver core product value as often as possible? Chamath warns that focusing on virality is why you see so many startups experience this amazingly steep rise and then fall off a cliff. The second thing he set out to do at Facebook was invalidate all of the lore: “In any given product, there’s always people who strut out around the office like, ‘I have this gut feeling.’ It’s all about gut feeling. And most people’s gut feelings are morons. They don’t know what they’re talking about. Gut feel is not useful because most people can’t predict correctly. We know this. So one of the most important things that we did was just invalidate all of the lore… You can’t believe your own BS. Because when you do, you start to compound these massively structural mistakes that don’t expose core product value… You don’t listen to customers because you think it’s all about your gut. You don’t bother doing any of the traditional, straightforward, obvious things, and you lose yourself.” As Chamath explains, a maniacal focus on delivering core product value as frequently and fast as possible is what led Facebook to its most important realization: “The single biggest thing we realized was to get any individual to 7 friends in 10 days. That was it… There was not much more complexity than that. There’s an entire team now of hundreds of people that have helped ramp this product to a billion users, based on that one simple rule — a very elegant statement of what it was to capture core product value… And then what we did at the company was talk about nothing else. Every Q&A. Every all-hands… It was the single, sole focus.” He continues: “You have to work backwards from: What is the thing that people are here to do? What is the ‘aha moment’ that they want? Why can I not give that to them as fast as possible? That’s how you win.” Chamath recommends starting with a cohort of your most engaged users — What features are they using? What pathways in your product did they take? Then work backwards and try to get all of your other users to that same state.

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 85% Verdict: Likely AI ⚠️ Slop 📺 📊 3/3 posts analyzed were slop 👑 SLOP KING 👑

@aakashgupta @whiteboard_sesh slop?

Everyone’s celebrating ChatGPT as the most downloaded app of 2025. ChatGPT has 800 million weekly active users. Google AI Overviews reaches 2 billion users monthly. Gemini hit 650 million MAU in October. Meta AI crossed 1 billion across Facebook, Instagram, and WhatsApp. OpenAI won the download chart. Google and Meta won distribution without shipping a single standalone hit. ChatGPT converts 5% of users to paid. That’s $10 billion ARR on 800 million users. Google and Meta monetize AI through ads on platforms they already own. No conversion required. No $20/month subscription to defend. You “downloaded” Meta AI the moment you opened Instagram this year. You “downloaded” Gemini every time you Googled something and saw an AI Overview. The App Store rankings only count the apps that required a new install. Google has five apps in the top ten because they’re utilities people reinstall on every new phone. That install base is now the distribution channel for Gemini. No launch required. Gemini is growing 30% month-over-month. ChatGPT is growing 5%. Google doesn’t need you to download anything new to close the gap. They just need you to keep using Search.

@taylor_jrj2012 @aakashgupta 🔍 AI Detection Result Score: 85% Verdict: Likely AI ⚠️ Slop 📺 ⚡ Powered by @pangramlabs 📊 10/10 posts analyzed were slop 👑 SLOP KING 👑

@taylor_jrj2012 @NickGibbsIAG @RoKhanna Here's your whiteboard lecture!

@NickGibbsIAG @RoKhanna @whiteboard_sesh whiteboard it

Sad to see @RoKhanna become another politician who has no economic literacy. Now hold my 🥃 Your case for a billionaire wealth tax falls apart on every key point. You say founders like Jensen Huang would not be deterred by a 1 to 2 percent annual tax on unrealized gains because he was not thinking about taxes in 1993. That is irrelevant. No one calculates distant taxes on day one. The issue is marginal incentives: slashing the ultimate reward after decades of risk forces stock sales to pay the tax and deters the next wave of founders. Pretending this has zero behavioral effect is economic nonsense. You brag the Bay Area has 37 times Austin’s VC and Florida “is not on the map.” That is stale data ignoring the exodus already underway. California loses high earners and companies yearly because of crushing taxes and rules. Tesla, Oracle, HPE did not relocate to Texas for the barbecue. Adding a federal wealth tax accelerates the bleed. Clusters are not immortal. You note public funding built foundational tech: NSF, DARPA, universities. True, but irrelevant to the conclusion. Public money funds research; private risk turns it into world changing companies. Capping rewards for that private step means fewer breakthroughs get commercialized. Basic incentive economics. You warn of revolutions from inequality, citing history. History says the opposite: Britain’s brutal industrial inequality drove the fastest living standard gains ever. Real wages soared eventually. 1848 and 1917 stemmed from tyranny and war, not rich people. America’s Gilded Age was far more unequal than today and built the 20th century prosperity engine. Punishing success slows the growth that lifts everyone. You claim a wealth tax boosts innovation. Reality: nearly every OECD country that tried one scrapped it (France, Germany, Sweden, Austria, Denmark) because it raised peanuts, sparked capital flight, and hurt growth. The track record is a total failure. And the mirror moment: Washington already hauls in record $5 trillion plus yearly yet runs trillion dollar deficits through waste, fraud, and bloat. Hundreds of billions vanish annually on duplication and improper payments. Before inventing new taxes on the tiny group creating real value, demand government live within its means. Jensen Huang and Elon Musk allocate capital with ruthless efficiency; government is the worst allocator on earth. Feeding its irresponsibility is not justice; it is complicity. Wealth is not a fixed pile to slice up; it is the return on risk that grows the entire economy. Taxing it punitively does not enlarge anyone’s share; it shrinks tomorrow’s pie for all. A billionaire tax would not save innovation or democracy. It would cripple both. Your position is not just wrong; it is upside down.

@IAmSwaiDhanoa @aakashgupta 🔍 AI Detection Result Score: 100% Verdict: Highly Likely AI 🤖 Extra Sloppy! 💀 ⚡ Powered by @pangramlabs 📊 9/9 posts analyzed were slop 👑 SLOP KING 👑

@aakashgupta @whiteboard_sesh slop?

Levie just made the best case for AI abundance I’ve seen… and he’s still underselling the real bottleneck. His Jevons Paradox frame is right. When you make something cheaper, demand expands to absorb the efficiency. Coal, compute, cloud software. The pattern holds. And yes, AI agents will drop the cost of non-deterministic work the way SaaS dropped the cost of deterministic work. But here’s what the data actually shows: 74% of companies struggle to achieve and scale value from AI, according to BCG’s 2024 adoption study. That’s not a cost problem. Task costs already dropped. It’s a human oversight problem. The bottleneck is that someone still has to specify what the AI should do, evaluate whether it did it correctly, integrate the output into a workflow, and make the final call. AI makes the execution cheap. But specification, evaluation, and integration remain human-bound. Deloitte’s enterprise research found that resistance to AI adoption stemmed from unfamiliarity, skill gaps, and lack of change management. Only a third of companies prioritized training. And the 2025 data is even more telling: fewer than 5% of firms reported net workforce reductions from AI. Instead, 37% reported task redistribution, with workers shifting toward oversight, integration, and decision-making roles. This means the Jevons Paradox for knowledge work has a different shape than Levie describes. Demand for AI task execution will absolutely explode. But demand for human judgment, context-setting, and quality verification will explode faster. The 10-person services firm can now build a prototype in days. But someone at that firm still has to know what to build, whether the prototype works, and how to deploy it. Those skills are scarcer than the tasks they enable. The real trade is that the companies who solve the oversight bottleneck, who build organizational capacity to specify, evaluate, and integrate at scale, will capture most of the value from cheap AI execution. The constraint moved from “can we afford to do this task?” to “do we have anyone who can tell if this task was done correctly?” Jevons works. But the coal equivalent here is the human attention required to make that output useful.

@IAmSwaiDhanoa @aakashgupta 🔍 AI Detection Result Score: 100% Verdict: Highly Likely AI 🤖 Extra Sloppy! 💀 📊 2/2 posts analyzed were slop