100F.exe
891 posts
100F.exe
@100F_exe
Stop doing manual work. build systems. let AI scale everything.
شامل ہوئے Ocak 2022
331 فالونگ397 فالوورز
As a student, Carl Icahn lost a full week's pay in a poker game to the owner of the beach club where he worked. Two weeks later, after reading three books on poker strategy, he was winning $500 a week and ended the summer with $2,000, against the $750 he needed for room and board at Princeton.
"The real or liquidating value of many American companies has increased markedly in the last few years; however, this has not at all been reflected in the market value of their common stocks. Sizable profits can be earned by taking large positions in undervalued stocks."
From 1968 to 2011, he compounded an initial $100,000 at a 31% annual rate. Over the same period, Buffett's Berkshire Hathaway grew at 20% a year.
"The consensus thinking is generally wrong. If you go with a trend, the momentum always falls apart on you. So I buy companies that are not glamorous and usually out of favor."
In October 2012, Netflix shares had crashed 80% to $58, and Icahn put in $321 million for nearly 10% of the company. By his own account, the position was up 457% in 14 months, and over three years Netflix earned him close to $2 billion.
See below ↓
Riley West@rileywestreel
Carl Icahn. He's 90, and he still personally owns 86% of Icahn Enterprises. "You have to buy things where the rest of the world are looking at you and think you're a little bit crazy" February 2026: entities linked to Icahn bought 783,404 more shares of CVR Energy for $16.4M, pushing his CVI stake past 71.2M shares. CEO Andrew Tino on the Q1 2026 earnings call called him "a living legend of activism" $IEP trades around $7.40 while its dividend yield sits at 27% annually.
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📖WHY CHANGING THE LENS MATTERS MORE THAN CHANGING THE SHOT
Five lens decisions made in advance beat five prompts written in isolation.
Most AI video sequences use the same virtual focal length across every cut, and the result feels flat even when the individual shots look good. Nothing changes in scale or intimacy from one moment to the next, so the sequence reads as a series of clips rather than a scene with rhythm.
A shot list that varies the lens on purpose fixes this before a single prompt gets written.
▪ Rack-focus close-up on a longer lens (85mm) — compresses the background and isolates the subject, used for the emotional beat that needs full attention
▪ Handheld medium on a standard lens (35mm) — keeps some environment visible while staying close, used for the moment that needs presence without full isolation
▪ Wide orbit on a shorter zoom (24-70mm) — reveals scale and context, used to place the emotional beat inside the larger scene
▪ The order matters as much as the lenses — tight to loose (or loose to tight) creates a felt sense of reveal or focus that a flat sequence of same-length shots never produces
▪ Each lens choice gets locked into the shot list before generation, not decided per prompt, so the rhythm is intentional instead of accidental
This works the same way regardless of the scene. A proposal, a chase, a product reveal — the lens pattern is what turns disconnected clips into something that feels directed. The subject changes. The structure doesn't.
📥 tomorrow's post covers how to keep lighting and color palette consistent across a five-cut sequence shot on different lenses
Zentrix⌚️@ZentrixHQ
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I am experimenting with a charming narrative, because the autonomy of AI agents for me is not only about code, but also about discovering beauty in the details of prompts.
Shinkai is a free open-source app for creating local AI agents without coding.
@ShinkaiLocalAI supports:
> Local models through @ollama
> Cloud models
> MCP
> Crypto payments (configuration options for x402)
Shinkai: Local AI Agents@ShinkaiLocalAI
If you've been putting off setting up a local agent because it seemed like too much: This weekend is the time. Download: 2 minutes. Setup: 10 minutes. First working agent: 20 minutes. The hard part is deciding what to automate first. Not the setup. 🐙
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If you like Polymarket, this is worth checking out
@prophetmarketai removes the need to find another trader to take the other side of your position
one feature that unexpectedly impressed me-anyone can create a market on virtually any verifiable event:
>crypto project launches
>sports results, esports
>creator milestones
>weather events
you create a Yes/No market, instantly receive AI-generated odds, and trade in USDC
[app.prophetmarket.ai/?ref=zjb_yZh_X…]
that means a market can exist even with zero initial liquidity from other users
the most interesting part is that the users themselves decide which markets should exist
Prophet@prophetmarketai
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🇺🇸 U.S. Secretary of State Marco Rubio says the U.S. government plans to introduce facial recognition for passport applications within the next few months, allowing applicants to verify their identity using a photo instead of visiting places like Walgreens or CVS for a passport picture.
According to Rubio, the goal is to simplify the process and save applicants time.
"Our security system will verify the facial ID," he said.
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@polysuccubus 70% win rate in esports markets is actually insane if it’s sustainable.
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Legends Never Die! This League of Legends trader just made $6,000 on Polymarket
Yesterday, he placed $9,000 on Bilibili Gaming to win and made +$6,000 profit.
Profile to follow:
[@blgmsiwin?code=succubus" target="_blank" rel="nofollow noopener">predictparity.com/traders/p/@blg…]
Now he has a total PnL of +$109,652.
He trades League of Legends games with 70% win rate and currently holds $40,644 in USDC.
Now I’m waiting for his next League of Legends position.
I bookmarked his profile via Predict Parity to track every new trade and see how a real esports trader plays the odds.
PolySuccubus@polysuccubus
League of Legends trader who started with $10,000 and has already generated $49,139 in total Polymarket PnL winning 18 of his 25 LoL esports trades. Profile to follow: [@lileddy123?code=succubus" target="_blank" rel="nofollow noopener">predictparity.com/traders/p/@lil…
] A 72% win rate across League of Legends prediction markets is already impressive but his latest results against some of the biggest names in LoL esports make the profile even more interesting. His biggest recent position was on Karmine Corp vs Team Liquid, Game 3, where $27,030 turned into $62,558 for a massive +$35,527 profit and +131% return. He also made +$10,661 on Karmine Corp vs Team Liquid Game 2, +$2,064 on T1 vs Team Liquid Game 2, and another +$2,725 combined from T1 vs Karmine Corp Games 2 and 3. T1, Team Liquid, Karmine Corp, League of Legends game-winner markets this trader clearly understands how to find value in major LoL matchups instead of randomly chasing esports odds. I bookmarked lileddy123 on Predict Parity to track his next League of Legends trades and see whether this run continues. 리그 오브 레전드와 폴리마켓을 함께 보는 사람이라면 이 트레이더는 꼭 지켜볼 만합니다. T1, Team Liquid, Karmine Corp 경기에서 보여준 수익률이 정말 인상적입니다 English
She earns ~$5,900/month from workout clips, but the views are the smallest part. Here's the actual model.
Everyone thinks creator funds are the business.
They're not — payouts are pennies.
She treats them as the bonus, not the plan.
The real model has two layers.
The views pay a little.
The link in her bio pays a lot.
Here's the insight that changed it for her: a platform payout is the end of a transaction, but a view is the start of one.
Someone who watched her train for 15 seconds is one click away from buying the leggings, the pre-workout, the grip socks she's actually using.
So every clip quietly points to her link and the affiliate commissions dwarf what the apps pay for the same views.
Views are the salary.
The link is the equity.
Why four platforms at once?
Because that's pure surface area for the link.
A clip that dies on TikTok does 800K on Reels; one Reels ignores, Shorts runs to a million.
She doesn't guess winners — she's entered every race, and something hits somewhere every week.
And this is where AI stops being a convenience and becomes the whole unlock.
One person physically cannot cut, caption, reformat and schedule 20 clips a week across four apps.
AI can.
She films 2–3 exercises at her normal workout; everything after — the cuts, the hooks, the per-platform formats, the queue — runs without her.
Four feeds, one human, ~$40 in tools.
The honest math: creator payouts bring maybe $1,800 across all apps.
The affiliate link adds ~$4,100 more, because 10M blended monthly views send a steady trickle of buyers.
Together, ~$5,900 — from footage she was filming anyway, still earning off clips she's forgotten.
The girl on the next treadmill posts the same workout to one app and collects likes.
Same footage.
One built an audience.
The other built a checkout line.
Full playbook below👇
Rich@RrichPRMR
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A 22-year-old from Taiwan generated an AI girlfriend as a joke, but ended up making $5,684 in 3 weeks
He created the photos and videos using Claude
He set up a TikTok account for her, and after just 3 days, one of the videos hit 2.3 million views: after 5 days, the account already had 54,547 followers
He immediately realized that he could make money from this and created a Fanvue account for her
> TikTok for promotion
> Fanvue for earnings
In 3 weeks, 569 people subscribed -> subscription price $9.99 -> profit $5,684
Even if it starts as a joke, it can become something serious
Bober_smart@Bober_smart
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More proof keeps dropping that Skynet’s secretly recruiting droids. Terminator: Origins.
Looks like the karate bot just started the robot rebellion 😂
100F.exe@100F_exe
ELON JUST KILLED WORK. NOT THE 9 TO 5. THE ENTIRE CONCEPT. AI and robots will do everything. Every job. Every task. Every skill you spent decades sharpening. Gone. Handled by a machine that never sleeps, never complains, never asks for a raise. And the result? Universal high income. Not universal basic income scraping the bottom of survival. High income. You wake up. The machines have already run the economy for the night. Your money is there. Your time is yours. Work becomes a hobby. A choice. Something you do because it amuses you, not because rent is due. The factories are already silent except for the hum of robots. The agents are already writing code, editing video, closing deals. The businesses are already running themselves while their owners sleep. This is not a prediction. It is a live feed. The real mindfuck nobody is ready for: when work is optional, who are you. When the thing you did for 40 years is done better by a machine that does not blink, what do you do with your hands. What do you do with your brain. What do you do with meaning. Bookmark this. Work just became a vintage concept. And Elon said it out loud like it was Tuesday.
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@DurhamGayn45505 The Planning category is a game changer, It helped me map out my next 5 years with total clarity.
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@100F_exe Looks like a handy way to explore Claude's capabilities.
Which category has helped you most so far?
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Anthropic just dropped a prompt library for Claude.
There are 19 categories to choose from, from prototyping and testing to debugging and design. Just pick what you need and the site gives you ready-to-use prompts.
Under every prompt, there’s a Why this works section that breaks down why the prompt works and how to write your own.
🔥 Basically, it’s a free crash course in prompt engineering.
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@thearslaniqbal I don’t think so. Just start and keep digging into it you’ll pick up the skills as you go 💪
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@100F_exe Most people will copy prompts but still not understand how to adapt them.
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THIS GUY CAN SCAN AN ENTIRE BUILDING BEFORE HE EVEN WALKS INSIDE.
Not with a drone.
Not with blueprints.
Just satellite imagery and AI.
He taps a building on the map.
Seconds later…
The software estimates the number of floors.
Finds possible entrances.
Measures the building.
Highlights blind spots.
Builds a tactical overview before anyone steps through the front door.
He isn’t looking for ways in.
He’s looking for what everyone else missed.
That’s exactly how great bug hunters think.
They don’t attack systems.
They map them.
They reduce unknowns.
Then they look for the one assumption everyone trusted.
I built that same reconnaissance mindset into a Claude bug hunting workflow.
Full guide below.
kiosa@thegreatest_sv
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A 20-year-old reportedly built an AI speed detection radar in 9 days and sold it to a district government for $317,000.
His setup was almost absurdly simple.
$20 in Claude API spend, an old camera and a balcony facing the intersection downstairs.
The camera watched the road while Claude processed the traffic in real time. Cars, motorcycles and pedestrians were identified, tracked and measured as they moved through the intersection.
Five minutes later, the system had reportedly detected 653 objects.
The ones above the speed limit were flagged automatically.
The bigger unlock was not just detection. It was evidence generation.
Instead of a traditional radar snapping one blurry photo, the system captured video, clipped the speeding event, read the license plate and prepared the case without human review in the loop.
That is why the government cared.
A normal radar produces a snapshot. This produced a full workflow: detect the violation, capture the proof, identify the vehicle and generate the follow-up.
He reportedly walked into the district office with a USB drive, asked for ten minutes and walked out with a contract.
The story sounds extreme, but the direction is very real.
AI is turning cameras into automated workflow engines.
Not just watching.
Acting.
Secta@0xSecta
A group of Chinese students reportedly turned 7 used Mac Minis into a dorm-room AI financial firm. Total hardware cost: $1,600. They bought the Mac Minis on eBay, connected them over Ethernet and used the cluster to run an AI-powered finance workflow from their university dorm. Their first client used to pay a financial advisor $8,400 per year. The students charged $240 per year to handle the same type of work with AI. The setup was simple. Claude reads 10-K reports in seconds, builds asset allocation models, runs tax optimization scenarios and creates retirement planning projections. The Mac Mini cluster becomes the local compute layer, while the actual product is a cheaper, faster advisory workflow. That is the part worth paying attention to. A traditional advisor managing a $500K portfolio can charge thousands per year mostly for access, reporting and allocation guidance. The student version attacks that cost structure with automation, fixed workflows and a much lower subscription price. Month one: 8 clients. Month two: 20 clients, all from word of mouth. The real story is not that 7 Mac Minis replaced Wall Street. It is that small teams can now package expert workflows into cheap AI-native services and sell them directly. One-time hardware cost. Recurring software revenue. Dorm room fintech.
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FABLE 5 IS BACK AND IT JUST BUILT A CINEMATIC 3D GLOBE WHILE SONNET 5 BUILT A WIREFRAME IN SPACE
Same prompt. Same dataset. 70 airports. 435 real flight routes pulled straight from FlightRadar. One task: turn all of it into a cinematic 3D globe inside a single HTML file. Two models. Two very different planets.
Outputs:
Sonnet 5: 9.8k tokens, $0.10
Fable 5: 15k tokens, $0.77
Sonnet 5 shipped it cheap and fast. But the planet barely exists. A dark wireframe with the arcs floating in space like ghosts. It works. It just does not look like Earth.
Fable 5 built an actual planet. Textured oceans. Ice caps. Atmospheric glow at the edges. The routes arc smoothly across the surface. It looks like something NASA would put on a screen. 87% more expensive but a completely different class of output.
Sonnet 5 is the budget ticket. Fable 5 is the upgrade that makes you lean forward in your seat. The gap is not about intelligence. It is about craft.
Mythos class models are the masterpiece tier. And Fable 5 just reminded everyone why it belongs there.
Docs, guides, and setup below. Bookmark this.
Claude@claudeai
Fable 5 is back.
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@rvaniaaaa The cheapest model is the one that finishes the task, not the one with the lowest token price.
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Claude Sonnet 5 is 60% cheaper than Opus. That doesn’t automatically make it the cheaper model.
For everyday work, Sonnet 5 is becoming hard to beat. It now matches or outperforms Opus on many knowledge tasks while costing significantly less per token.
The equation changes on complex projects. If Opus solves a difficult refactor in one pass while Sonnet needs multiple iterations, longer conversations and repeated fixes, the more expensive model can still end up using fewer tokens overall.
That’s why experienced teams probably won’t replace Opus. They’ll route work instead.
Routine coding. Research. Summaries. Automation.
> Sonnet 5.
Large refactors. Agentic coding. Complex architecture. Long-running tasks.
> Opus.
The cheapest model isn’t the one with the lowest price.
It’s the one that gets the job done with the fewest retries, the fewest tokens and the least amount of your time.
That’s how AI stacks are starting to evolve: cheaper models handle volume, frontier models handle complexity.
Bookmark this.
rvaniaaa@rvaniaaaa
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@Damir_Akaza The older I get, the more Munger sounds like operating system documentation for life.
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Charlie Munger could have been dozens of times richer, like Buffett with his $120 billion, but deliberately gave away almost all his Berkshire shares to charity, keeping just $2.6 billion for himself
in front of law graduates he laid out the rules he lived his life by
"the safest way to get what you want is to deserve what you want. deliver to the world what you'd buy if you were on the other side of the deal"
"I constantly see people rise in life who aren't the smartest and not even the most diligent, but they're learning machines. they go to bed a little wiser than when they woke up"
"I'm not entitled to an opinion unless I can state the arguments against my position better than the people who defend it"
and when asked what reliably ruins a life, his answer is simple: sloth and unreliability
bookmark it and watch today ↓
Damir Akaza@Damir_Akaza
Lloyd Blankfein, then CEO of Goldman Sachs, caught the first signal of the 2008 crisis in a movie theater in 2007, scrolling through the firm's P&L on his BlackBerry Goldman came through the crisis intact because it prepared ahead of time. he started cutting risk back in 2007, before others grasped the scale "we were good traders, but really what we were were contingency planners. you're trying to see what can't be seen. what you're actually doing is just preparing" his risk managers had real power over the traders, and positions were marked honestly to market, even when it hurt their own "I don't care what you think is going to happen. I don't care what I think is going to happen. I want to know what could happen even at low probability, and what we're doing to protect ourselves" watch to the end to find out how Lloyd caught the first signal and why preparing for the impossible became his credo ↓
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