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Sam Altman after finding out people rent GPT-6 Astra for the hard 1% and run a $15 Chinese model they own for the other 99..

Sam Altman after finding out people rent GPT-6 Astra for the hard 1% and run a $15 Chinese model they own for the other 99..

166,401 просмотров

A TEAM JUST DEPLOYED 15 AUTONOMOUS LOOP AGENTS FROM A SINGLE PROMPT USING APPLIED GRAPH ENGINEERING Most developers still manually hardcode multi agent systems, writing separate logic for every individual task. Graph engineering changes this by using a central topological map to spin up all 15 nodes simultaneously. A single 200 word input generates the architecture, routing 120 unique pathways between agents instantly. Instead of failing under conflicting instructions, these loop agents self correct via continuous state sharing. Managing a 15 node mesh requires high token throughput, making this dependent on strict low-] latency API tiers. See exactly how this automated multi agent graph architecture actually operates in real time ↓

A TEAM JUST DEPLOYED 15 AUTONOMOUS LOOP AGENTS FROM A SINGLE PROMPT USING APPLIED GRAPH ENGINEERING Most developers still manually hardcode multi agent systems, writing separate logic for every individual task. Graph engineering changes this by using a central topological map to spin up all 15 nodes simultaneously. A single 200 word input generates the architecture, routing 120 unique pathways between agents instantly. Instead of failing under conflicting instructions, these loop agents self correct via continuous state sharing. Managing a 15 node mesh requires high token throughput, making this dependent on strict low-] latency API tiers. See exactly how this automated multi agent graph architecture actually operates in real time ↓

302,773 просмотров

me when realized I’d wasted five years on crypto while Chinese quant college kid coded MiroFish in 10 days with AI help and bagged $4M overnight, and can now predict almost any event and make $100K+/month

me when realized I’d wasted five years on crypto while Chinese quant college kid coded MiroFish in 10 days with AI help and bagged $4M overnight, and can now predict almost any event and make $100K+/month

420,108 просмотров

THE DESK RAN SIX DAYS FOR FREE AND CLOSED 96% OF 400 CALLS BEFORE IT HAD A TICKER -> TODAY IT GETS ONE $NYSE IS LIVE ON ROBINHOOD CHAIN, BUILT WITH MINARA AI AND GPT-6 ASTRA, AND THE SCORECARD IS OLDER THAN THE CHART CA: 0x3cedefe814e47b297743c82c20f947053ee667dd Every other ticker on this chain launches with a promise. This one launches with 400 receipts. What is already running while you read this: > An agent reading all 24 Robinhood Stock Tokens against the real share price every 20 seconds. > A line at 1.0% while the real NYSE is open, 1.5% the moment the bell rings and the oracle stops moving. > A scorecard with 386 hits, 14 misses and 2 calls open right now, nothing deleted, nothing hidden. The real NYSE opens in thirty minutes. The desk tightens its own line the second it does. Every call lands in Telegram before it lands anywhere else, the board is public and free, and the code is open with no wallet in it. There is a room on Wall Street that does this with forty people and a data bill with a comma in it. Mine is one agent, one laptop and now one ticker. Holders get the faster board, the earlier alerts and the agent's own stops, in that order. $NYSE SEE YOU AFTER THE BELL ↓

THE DESK RAN SIX DAYS FOR FREE AND CLOSED 96% OF 400 CALLS BEFORE IT HAD A TICKER -> TODAY IT GETS ONE $NYSE IS LIVE ON ROBINHOOD CHAIN, BUILT WITH MINARA AI AND GPT-6 ASTRA, AND THE SCORECARD IS OLDER THAN THE CHART CA: 0x3cedefe814e47b297743c82c20f947053ee667dd Every other ticker on this chain launches with a promise. This one launches with 400 receipts. What is already running while you read this: > An agent reading all 24 Robinhood Stock Tokens against the real share price every 20 seconds. > A line at 1.0% while the real NYSE is open, 1.5% the moment the bell rings and the oracle stops moving. > A scorecard with 386 hits, 14 misses and 2 calls open right now, nothing deleted, nothing hidden. The real NYSE opens in thirty minutes. The desk tightens its own line the second it does. Every call lands in Telegram before it lands anywhere else, the board is public and free, and the code is open with no wallet in it. There is a room on Wall Street that does this with forty people and a data bill with a comma in it. Mine is one agent, one laptop and now one ticker. Holders get the faster board, the earlier alerts and the agent's own stops, in that order. $NYSE SEE YOU AFTER THE BELL ↓

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OpenAI DOTS can take one idea from market research to first outreach and results while you SLEEP > Most people will open it, type one prompt and close the tab. That is the most EXPENSIVE way to use it. Here is the better way, you hand one dot an opportunity and it becomes the orchestrator. It splits the job between five specialists. One validates demand, one sets the offer and pricing, one builds the content, one handles distribution and outreach, and one tracks what worked. Each of them has its own cloud computer and its own browser. They log into your tools once and stay signed in, so your laptop can stay CLOSED. Reading, researching and drafting happen on their own. Anything that sends a message, spends money or overwrites a file stops and waits for your OK. You get a ping in Slack, tap approve, and the work picks back up. You can also set a clock, a morning routine pulls from your apps and drops one brief in Slack before you open your eyes. One objective in, a whole team working on it overnight. Full setup in the article ↓

OpenAI DOTS can take one idea from market research to first outreach and results while you SLEEP > Most people will open it, type one prompt and close the tab. That is the most EXPENSIVE way to use it. Here is the better way, you hand one dot an opportunity and it becomes the orchestrator. It splits the job between five specialists. One validates demand, one sets the offer and pricing, one builds the content, one handles distribution and outreach, and one tracks what worked. Each of them has its own cloud computer and its own browser. They log into your tools once and stay signed in, so your laptop can stay CLOSED. Reading, researching and drafting happen on their own. Anything that sends a message, spends money or overwrites a file stops and waits for your OK. You get a ping in Slack, tap approve, and the work picks back up. You can also set a clock, a morning routine pulls from your apps and drops one brief in Slack before you open your eyes. One objective in, a whole team working on it overnight. Full setup in the article ↓

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$8.6 Billion was spent inside GTA Online. Creators got exactly zero of it. GTA 6 changes that. Rockstar built the marketplace, it's been live since January 2026. > 1 script/week × 26 weeks = 26 products before launch. > FiveM devs report €5,000/month within 90 days on Tebex. > Claude Fable writes every line of code, you just publish. > Early catalog = reviews, ratings, organic discovery on day one. The game launches once. The infrastructure pays indefinitely. Bookmarked and Learn it ↓

$8.6 Billion was spent inside GTA Online. Creators got exactly zero of it. GTA 6 changes that. Rockstar built the marketplace, it's been live since January 2026. > 1 script/week × 26 weeks = 26 products before launch. > FiveM devs report €5,000/month within 90 days on Tebex. > Claude Fable writes every line of code, you just publish. > Early catalog = reviews, ratings, organic discovery on day one. The game launches once. The infrastructure pays indefinitely. Bookmarked and Learn it ↓

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"How is this legal?" it isn't a girl. it's a 22-year-old guy in a gaming chair in Spain. he runs 4 instagram accounts as 4 different women. 612,000 combined followers. none of them exist. he merges two pinterest faces in Nano Banana Pro to create a girl who has never been born. records himself in his chair. plugs the video and her photo into Kling Motion Control. Kling transfers his movement onto her body frame-by-frame. sends the file to his phone via telegram. that step strips the AI-generated metadata. the platform sees regular user content. CapCut: grain 40, sharpness 20, brightness -8. plastic look gone. one of his accounts hit 156,000 followers in 21 days. same chair. same lighting. four women. one guy. details in the article. don't forget to save.

"How is this legal?" it isn't a girl. it's a 22-year-old guy in a gaming chair in Spain. he runs 4 instagram accounts as 4 different women. 612,000 combined followers. none of them exist. he merges two pinterest faces in Nano Banana Pro to create a girl who has never been born. records himself in his chair. plugs the video and her photo into Kling Motion Control. Kling transfers his movement onto her body frame-by-frame. sends the file to his phone via telegram. that step strips the AI-generated metadata. the platform sees regular user content. CapCut: grain 40, sharpness 20, brightness -8. plastic look gone. one of his accounts hit 156,000 followers in 21 days. same chair. same lighting. four women. one guy. details in the article. don't forget to save.

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me realizing that ByteDance just dropped OpenViking repo and now with that Chinese college kid’s MiroFish I can upgrade AI memory, predict almost any real world event and finally make more money than I earned grinding crypto for the last 5 years, even $100k/month

me realizing that ByteDance just dropped OpenViking repo and now with that Chinese college kid’s MiroFish I can upgrade AI memory, predict almost any real world event and finally make more money than I earned grinding crypto for the last 5 years, even $100k/month

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DEEPSEEK V4 FLASH AND QWEN3.6 ARE FREE ON AMD'S OWN CLOUD AT $10 OF USAGE A DAY • AMD Token Factory > DeepSeek V4 Flash, Qwen3.6 35B, MiniCPM5 and MiniCPM-V46 at zero cost: > $10 every day, not once -- the counter resets daily. > Everything outside that list runs on Firework AI credits. • SuperGrok, seven days > Grok's paid tier free for a week: > A card is required and it renews on its own, so set the reminder the moment you start. > Availability still depends on your region. AMD built this the way NVIDIA built NIM -> the chip vendors are handing out inference now, because what they are really buying is which silicon you learn on. One of these resets every morning. The other one you only get once ↓

DEEPSEEK V4 FLASH AND QWEN3.6 ARE FREE ON AMD'S OWN CLOUD AT $10 OF USAGE A DAY • AMD Token Factory > DeepSeek V4 Flash, Qwen3.6 35B, MiniCPM5 and MiniCPM-V46 at zero cost: > $10 every day, not once -- the counter resets daily. > Everything outside that list runs on Firework AI credits. • SuperGrok, seven days > Grok's paid tier free for a week: > A card is required and it renews on its own, so set the reminder the moment you start. > Availability still depends on your region. AMD built this the way NVIDIA built NIM -> the chip vendors are handing out inference now, because what they are really buying is which silicon you learn on. One of these resets every morning. The other one you only get once ↓

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a Russian mathematician died in 1922. His math just made a bot $468,228 on Polymarket in 30 days. Andrey Markov never saw a prediction market. He built the exact tool to dominate them. [Bonereaper] runs BTC/ETH 1-hour windows. 1,500-2,900 shares per trade. 14,671 trades total. Two conditions. Both must fire simultaneously: Δ = p̂ − q ≥ 0.05 -> gap exists. p(j*, j*) ≥ 0.87 -> state is stable. Both true? Position entered. Every minute. 24/7. The edge: humans sleep. Markets don't. At 3AM nobody watches a 5-min BTC window. The gap widens. The bot enters. His profile: His wallet: 0xeebde7a0e019a63e6b476eb425505b7b3e6eba30 copy trades live from $10 -> Save this alpha.

a Russian mathematician died in 1922. His math just made a bot $468,228 on Polymarket in 30 days. Andrey Markov never saw a prediction market. He built the exact tool to dominate them. [Bonereaper] runs BTC/ETH 1-hour windows. 1,500-2,900 shares per trade. 14,671 trades total. Two conditions. Both must fire simultaneously: Δ = p̂ − q ≥ 0.05 -> gap exists. p(j*, j*) ≥ 0.87 -> state is stable. Both true? Position entered. Every minute. 24/7. The edge: humans sleep. Markets don't. At 3AM nobody watches a 5-min BTC window. The gap widens. The bot enters. His profile: His wallet: 0xeebde7a0e019a63e6b476eb425505b7b3e6eba30 copy trades live from $10 -> Save this alpha.

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STOP WASTING TOKENS ON OPENCLAW (I DID IT FOR WEEKS) thought my agent was "optimized" - turns out it was just a token burning machine this guide exposed my setup: no workspace config = 3x spend on every call key insight: bootstrap memory ( etc) loads EVERY time - stuff it wisely or pay forever semantic search pulls facts on-demand from - no constant token drain after tweaks: > token usage: down 70% > agent recall: flawless across sessions gateway handles the pipeline: message -> context inject -> LLM loop -> tools -> response miss this? your agent's half-baked If you're running OpenClaw🦞 vanilla, you're leaking cash and potential dive into this breakdown and don't forget to bookmark it - it's agent building gold imo

STOP WASTING TOKENS ON OPENCLAW (I DID IT FOR WEEKS) thought my agent was "optimized" - turns out it was just a token burning machine this guide exposed my setup: no workspace config = 3x spend on every call key insight: bootstrap memory ( etc) loads EVERY time - stuff it wisely or pay forever semantic search pulls facts on-demand from - no constant token drain after tweaks: > token usage: down 70% > agent recall: flawless across sessions gateway handles the pipeline: message -> context inject -> LLM loop -> tools -> response miss this? your agent's half-baked If you're running OpenClaw🦞 vanilla, you're leaking cash and potential dive into this breakdown and don't forget to bookmark it - it's agent building gold imo

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BREAKING: LLMs just learned to COMPUTE for real, it's mean NO MORE GUESSING math. Chinese college kid Guo Hanjiang vibe-coded MiroFish in 10 days (23k+ GitHub stars, $4.1M from Shanda in 24h) - the AI swarm simulator that’s already printing. ByteDance (VolcEngine) dropped the nuclear upgrade: OpenViking - structured viking:// filesystem memory (L0 ultra-summary -> L2 full details) - agents now run 100+ steps with zero amnesia or hallucinations, 11.6k stars and climbing. Now this just dropped and the entire AI timeline is shaking. Startup Percepta embedded a full WASM virtual machine directly into Transformer weights. No more external Python sandboxes. No more hallucinations in exact tasks. The model streams raw machine code at 30,000+ tokens/sec on CPU, executes millions of steps, and solves the world’s hardest Sudoku via real backtracking + constraint propagation - 100% accurate, zero bullshit. They killed the Attention Bottleneck with Exponentially Fast Attention (HullKVCache + 2D heads + convex hull queries in log time). What used to die at 1k steps now flies. This is the bridge: System 1 intuition (normal LLMs) + System 2 deterministic logic (native code execution) in ONE brain. Agents won’t need tools anymore. Heavy simulations will run inside the weights. Check out: Now put it all together: MiroFish swarms + OpenViking infinite memory + Percepta native flawless compute = agents that can hardcore simulate millions of future scenarios, run perfect logic loops for days, and predict events/markets/reality with god-tier accuracy. No drift. No bullshit. Just pure foresight. This combo will change everything, imo. The era of predictive super-agents that actually print the future is here. We’re watching this one closely. Save this combo.

BREAKING: LLMs just learned to COMPUTE for real, it's mean NO MORE GUESSING math. Chinese college kid Guo Hanjiang vibe-coded MiroFish in 10 days (23k+ GitHub stars, $4.1M from Shanda in 24h) - the AI swarm simulator that’s already printing. ByteDance (VolcEngine) dropped the nuclear upgrade: OpenViking - structured viking:// filesystem memory (L0 ultra-summary -> L2 full details) - agents now run 100+ steps with zero amnesia or hallucinations, 11.6k stars and climbing. Now this just dropped and the entire AI timeline is shaking. Startup Percepta embedded a full WASM virtual machine directly into Transformer weights. No more external Python sandboxes. No more hallucinations in exact tasks. The model streams raw machine code at 30,000+ tokens/sec on CPU, executes millions of steps, and solves the world’s hardest Sudoku via real backtracking + constraint propagation - 100% accurate, zero bullshit. They killed the Attention Bottleneck with Exponentially Fast Attention (HullKVCache + 2D heads + convex hull queries in log time). What used to die at 1k steps now flies. This is the bridge: System 1 intuition (normal LLMs) + System 2 deterministic logic (native code execution) in ONE brain. Agents won’t need tools anymore. Heavy simulations will run inside the weights. Check out: Now put it all together: MiroFish swarms + OpenViking infinite memory + Percepta native flawless compute = agents that can hardcore simulate millions of future scenarios, run perfect logic loops for days, and predict events/markets/reality with god-tier accuracy. No drift. No bullshit. Just pure foresight. This combo will change everything, imo. The era of predictive super-agents that actually print the future is here. We’re watching this one closely. Save this combo.

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A Russian mathematician died in 1922. His math just made 3 anonymous bots $1,331,821 in 30 days on Polymarket. Andrey Markov never saw a prediction market. He built the exact tool to destroy them. Here's the cheat code -> The model doesn't predict. It measures. Two conditions. Both must fire simultaneously: Δ = p̂ − q ≥ 0.05 -> gap exists p(j*, j*) ≥ 0.87 -> state is stable If both are true -> position entered. One function. Runs every minute. 24/7. Three bots. Three styles. One principle: - 0xeebde7a0e019a63e6b476eb425505b7b3e6eba30 -> 1,500-2,900 shares, BTC/ETH 1h windows -> 14,339 trades -> $454,834. - 0xe1d6b51521bd4365769199f392f9818661bd907c -> dual-mode EV, best single trade +54.6% -> $432,591. - 0xb27bc932bf8110d8f78e55da7d5f0497a18b5b82 -> 5 assets, 1 trade per 1.7 min, σ−55% -> $444,396. The formula behind all three: V_T = V₀ · e^(N · r̄) At 16,000 trades and 0.034% per trade -> ×240 growth. Math doesn't care about your conviction. Only about N. The edge? Humans sleep. Markets don't. At 3AM nobody's watching a 5-min BTC window. The gap widens. The bot enters. You don't have to build the bot. You just have to follow it. -> Copy all 3 wallets live, starting from $10: (Just add the wallets I attached above). Save this list.

A Russian mathematician died in 1922. His math just made 3 anonymous bots $1,331,821 in 30 days on Polymarket. Andrey Markov never saw a prediction market. He built the exact tool to destroy them. Here's the cheat code -> The model doesn't predict. It measures. Two conditions. Both must fire simultaneously: Δ = p̂ − q ≥ 0.05 -> gap exists p(j*, j*) ≥ 0.87 -> state is stable If both are true -> position entered. One function. Runs every minute. 24/7. Three bots. Three styles. One principle: - 0xeebde7a0e019a63e6b476eb425505b7b3e6eba30 -> 1,500-2,900 shares, BTC/ETH 1h windows -> 14,339 trades -> $454,834. - 0xe1d6b51521bd4365769199f392f9818661bd907c -> dual-mode EV, best single trade +54.6% -> $432,591. - 0xb27bc932bf8110d8f78e55da7d5f0497a18b5b82 -> 5 assets, 1 trade per 1.7 min, σ−55% -> $444,396. The formula behind all three: V_T = V₀ · e^(N · r̄) At 16,000 trades and 0.034% per trade -> ×240 growth. Math doesn't care about your conviction. Only about N. The edge? Humans sleep. Markets don't. At 3AM nobody's watching a 5-min BTC window. The gap widens. The bot enters. You don't have to build the bot. You just have to follow it. -> Copy all 3 wallets live, starting from $10: (Just add the wallets I attached above). Save this list.

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A QUANT DOESN'T GUESS IF BITCOIN GOES UP OR DOWN, HE SIMULATES TEN THOUSAND FUTURES AND COUNTS THE ONES THAT HIT The secret is one line. P(up) = (1/N) · Σ 𝟙(Sₜ > K) Run thousands of price paths from now to expiry using current volatility, then count the fraction that finish above the line. That fraction is the real probability, not a vibe. SpectraView runs this on the 15m Bitcoin Up/Down markets and pushes the setup straight to TG with 81% win rate. Start auto trade 15m BTC -> In plain words. You are not calling up or down, you are measuring the odds, then only betting when the market price is cheaper than the odds you measured. On screen the sim lands near 53% in the money. Edge is not being right more often, it is paying 50 cents for something the math says is worth 53.

A QUANT DOESN'T GUESS IF BITCOIN GOES UP OR DOWN, HE SIMULATES TEN THOUSAND FUTURES AND COUNTS THE ONES THAT HIT The secret is one line. P(up) = (1/N) · Σ 𝟙(Sₜ > K) Run thousands of price paths from now to expiry using current volatility, then count the fraction that finish above the line. That fraction is the real probability, not a vibe. SpectraView runs this on the 15m Bitcoin Up/Down markets and pushes the setup straight to TG with 81% win rate. Start auto trade 15m BTC -> In plain words. You are not calling up or down, you are measuring the odds, then only betting when the market price is cheaper than the odds you measured. On screen the sim lands near 53% in the money. Edge is not being right more often, it is paying 50 cents for something the math says is worth 53.

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A QUANT SPENT YEARS AND A PHD BUILDING A STRATEGY THAT LOSES MORE THAN HALF ITS TRADES AND STILL PRINTS The secret is one line. w(t) = Rmax/(J·σ_risk) · (Σ sⱼ) · [1 + η·Σ 𝟙(c ≥ nₖ)] It sizes every bet by current risk, and that last bracket presses harder every time the signal confirms. In plain words. Keep the losers tiny, and pyramid hard into the winners. So it wins under half the time and still beats the market, because one winner erases ten small losses. That positive skew is the edge quants chase for years. Minara lets you build that exact behavior by describing it. 500+ factors, risk-scaled sizing, real backtests. The PhD math is real, the wall is gone. I took one plain sentence to a live, backtested strategy & posted every number in the article. Check it ↓

A QUANT SPENT YEARS AND A PHD BUILDING A STRATEGY THAT LOSES MORE THAN HALF ITS TRADES AND STILL PRINTS The secret is one line. w(t) = Rmax/(J·σ_risk) · (Σ sⱼ) · [1 + η·Σ 𝟙(c ≥ nₖ)] It sizes every bet by current risk, and that last bracket presses harder every time the signal confirms. In plain words. Keep the losers tiny, and pyramid hard into the winners. So it wins under half the time and still beats the market, because one winner erases ten small losses. That positive skew is the edge quants chase for years. Minara lets you build that exact behavior by describing it. 500+ factors, risk-scaled sizing, real backtests. The PhD math is real, the wall is gone. I took one plain sentence to a live, backtested strategy & posted every number in the article. Check it ↓

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My girlfriend asked why I was smiling at 4 AM. Showed her the terminal. "What are all those green numbers?" +$11,400. Made while I slept. "Doing what?" Nothing. Claude scanned 86 million Polymarket trades, found the 47 wallets that never lose, and built me a bot that copies them live. She watched for 10 seconds: +$340 captured +$890 captured +$1,400 captured "It just keeps going?" Every few seconds. New market. New edge. New profit. "How much did you start with?" $25/month. Claude API + $5 VPS. Now the bot runs 24/7 in Germany and I haven’t touched it in 19 days. "What does it do?" Finds mispriced markets before the crowd, copies the smartest whales, exits at the perfect moment. 74% win rate. Sharpe 2.31. "That’s legal?" Polymarket is. The top wallets do this every day. I just have one screen. She looked at the P&L curve. Never dips. Just climbs. "Can you make me one?" Setting hers up now. She still doesn’t get how it works. The bot doesn’t care. You only need Claude + the 4 open-source repos + 1 hour this weekend. Giving the full step-by-step + my exact bot setup free for the next 24 hours. To get it: > Comment the word "BOT". > Like + Retweet this post. > Follow me slash1s (so I can DM you the link). > Bookmark this post.

My girlfriend asked why I was smiling at 4 AM. Showed her the terminal. "What are all those green numbers?" +$11,400. Made while I slept. "Doing what?" Nothing. Claude scanned 86 million Polymarket trades, found the 47 wallets that never lose, and built me a bot that copies them live. She watched for 10 seconds: +$340 captured +$890 captured +$1,400 captured "It just keeps going?" Every few seconds. New market. New edge. New profit. "How much did you start with?" $25/month. Claude API + $5 VPS. Now the bot runs 24/7 in Germany and I haven’t touched it in 19 days. "What does it do?" Finds mispriced markets before the crowd, copies the smartest whales, exits at the perfect moment. 74% win rate. Sharpe 2.31. "That’s legal?" Polymarket is. The top wallets do this every day. I just have one screen. She looked at the P&L curve. Never dips. Just climbs. "Can you make me one?" Setting hers up now. She still doesn’t get how it works. The bot doesn’t care. You only need Claude + the 4 open-source repos + 1 hour this weekend. Giving the full step-by-step + my exact bot setup free for the next 24 hours. To get it: > Comment the word "BOT". > Like + Retweet this post. > Follow me slash1s (so I can DM you the link). > Bookmark this post.

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HE DESCRIBED A SITE TO CLAUDE CODE AND WALKED AWAY FOR 8 MINUTES What came back looked like a studio spent weeks on it. The setup that made it work: > Two design skills installed - Claude stops using safe boring defaults. > Visual references instead of text descriptions. > One detailed prompt with the concept, Claude asks clarifying questions before building. First version already solid -> review pass, one batch of fixes, done. What used to take a designer and a developer and several weeks of handoffs now takes a single session and no coding experience. Full walkthrough in the article below ↓

HE DESCRIBED A SITE TO CLAUDE CODE AND WALKED AWAY FOR 8 MINUTES What came back looked like a studio spent weeks on it. The setup that made it work: > Two design skills installed - Claude stops using safe boring defaults. > Visual references instead of text descriptions. > One detailed prompt with the concept, Claude asks clarifying questions before building. First version already solid -> review pass, one batch of fixes, done. What used to take a designer and a developer and several weeks of handoffs now takes a single session and no coding experience. Full walkthrough in the article below ↓

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I STOPPED REVIEWING MY OWN AGENT, SOMETHING ELSE DOES IT NOW I used to read every diff it produced and approve most of them, because an agent grading itself always says the work is good. -> Now a second model with different instructions tries to break the work first, and I only read what survived. Here is what is actually in the folder that took over the night shift: • the brief > CONTRACT.md -- what it may touch, and what it may never touch. > VISION.md -- the destination, so turn 47 still knows why it started. • the gate > judge/ -- a different model, never the one that wrote the code. > break-it.md -- it opens the page, clicks, screenshots, reports back. > -- no opinion, just zero or non-zero. > shift.yml -- 03:30 every night, laptop closed. • the memory > receipts/ -- one folder per night, dated and graded. > STATE.md -- where it stopped and what it escalated. > lessons.log -- the flaky test, written down once instead of rediscovered weekly. • the brakes > caps.json -- turn limit, retry limit, spend limit. > -- written on day one, used never. The generator decides what your loop can produce -> The judge decides what it refuses to produce. One of those is the part everyone builds -> The other is why most loops quietly fail. Bookmark it & Read Full breakdown below ↓

I STOPPED REVIEWING MY OWN AGENT, SOMETHING ELSE DOES IT NOW I used to read every diff it produced and approve most of them, because an agent grading itself always says the work is good. -> Now a second model with different instructions tries to break the work first, and I only read what survived. Here is what is actually in the folder that took over the night shift: • the brief > CONTRACT.md -- what it may touch, and what it may never touch. > VISION.md -- the destination, so turn 47 still knows why it started. • the gate > judge/ -- a different model, never the one that wrote the code. > break-it.md -- it opens the page, clicks, screenshots, reports back. > -- no opinion, just zero or non-zero. > shift.yml -- 03:30 every night, laptop closed. • the memory > receipts/ -- one folder per night, dated and graded. > STATE.md -- where it stopped and what it escalated. > lessons.log -- the flaky test, written down once instead of rediscovered weekly. • the brakes > caps.json -- turn limit, retry limit, spend limit. > -- written on day one, used never. The generator decides what your loop can produce -> The judge decides what it refuses to produce. One of those is the part everyone builds -> The other is why most loops quietly fail. Bookmark it & Read Full breakdown below ↓

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GLM 5.2 INPUT FELL FROM $1.40 TO SEVEN CENTS PER MILLION IN NINETY DAYS • what it costs now > $0.07 per million input at the cheapest of 20 providers, a 95% drop in three months. > direct still lists $1.40 in and $4.40 out, with cached input at $0.26. > Same model, same weights, twenty-fold spread depending on the door you walk through. • the free way in > New accounts on get 20M tokens: > 744B MoE, 1M context, MIT weights you can also just download and self-host. Nobody announced this. It happened one provider at a time -> and the model itself never changed. Check which provider you are actually routed through before you top up anywhere ↓

GLM 5.2 INPUT FELL FROM $1.40 TO SEVEN CENTS PER MILLION IN NINETY DAYS • what it costs now > $0.07 per million input at the cheapest of 20 providers, a 95% drop in three months. > direct still lists $1.40 in and $4.40 out, with cached input at $0.26. > Same model, same weights, twenty-fold spread depending on the door you walk through. • the free way in > New accounts on get 20M tokens: > 744B MoE, 1M context, MIT weights you can also just download and self-host. Nobody announced this. It happened one provider at a time -> and the model itself never changed. Check which provider you are actually routed through before you top up anywhere ↓

30,276 просмотров

$35 OF FREE CLAUDE OPUS 5 CREDITS FOR A WEEK, NO CARD ANYWHERE • the credit > Sign up with Google and the trial activates on its own, nothing to claim manually: > Opus 5 sits alongside Opus 4.8, Sonnet 4.6 and Haiku on the same key. > The window runs a week from activation. • wiring it up > base url: > model id: claude-opus-5 > In OpenCode Desktop hit Ctrl+, and add a custom provider with the id aerolink. > The same key drops into Codex, Cursor and Claude Code with nothing else changed. One key, not locked to one client -> every tool that takes a custom OpenAI-compatible endpoint reads it the same way. It routes through a third-party proxy rather than an official Anthropic channel, so keep client work off it ↓

$35 OF FREE CLAUDE OPUS 5 CREDITS FOR A WEEK, NO CARD ANYWHERE • the credit > Sign up with Google and the trial activates on its own, nothing to claim manually: > Opus 5 sits alongside Opus 4.8, Sonnet 4.6 and Haiku on the same key. > The window runs a week from activation. • wiring it up > base url: > model id: claude-opus-5 > In OpenCode Desktop hit Ctrl+, and add a custom provider with the id aerolink. > The same key drops into Codex, Cursor and Claude Code with nothing else changed. One key, not locked to one client -> every tool that takes a custom OpenAI-compatible endpoint reads it the same way. It routes through a third-party proxy rather than an official Anthropic channel, so keep client work off it ↓

21,102 просмотров

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Claude Fable 5, Claude Opus 5 and ChatGPT 5.6 Sol were asked to paint the Mona Lisa in a browser, and all three rebuilt Leonardo's actual 500 year old technique on their own. The entire prompt was one line: "Create a single HTML file that animates the painting of the Mona Lisa layer by layer, the way an old master would build it up". Nobody told them how oil painting works, they just knew.. > Charcoal study, umber ground, shadow and atmosphere, color glazing, sfumato, varnished finish -- the exact Renaissance layering order, and not one of those words was in the prompt. > GPT 5.6 Sol went furthest and named its own app Sfumato Studio, with a completion counter, playback speed and a restart button. The models are no longer copying the result -> they are copying the process. Save this one -- the prompt is right there, go repaint your own ↓
0:28
slash1sol's profile picture

Claude Fable 5, Claude Opus 5 and ChatGPT 5.6 Sol were asked to paint the Mona Lisa in a browser, and all three rebuilt Leonardo's actual 500 year old technique on their own. The entire prompt was one line: "Create a single HTML file that animates the painting of the Mona Lisa layer by layer, the way an old master would build it up". Nobody told them how oil painting works, they just knew.. > Charcoal study, umber ground, shadow and atmosphere, color glazing, sfumato, varnished finish -- the exact Renaissance layering order, and not one of those words was in the prompt. > GPT 5.6 Sol went furthest and named its own app Sfumato Studio, with a completion counter, playback speed and a restart button. The models are no longer copying the result -> they are copying the process. Save this one -- the prompt is right there, go repaint your own ↓

slash1s

663,882 просмотров • 2 месяцев назад

A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓
32:29
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A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓

slash1s

688,585 просмотров • 3 месяцев назад

Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:
3:37
slash1sol's profile picture

Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:

slash1s

1,169,338 просмотров • 7 месяцев назад

This is f*cking gold.. Chinese researchers just released a paper with a brutal title: "The End of Prediction". The case is simple: an AI that outputs BUY or SELL is finished as a product. Any signal a model can read is priced in before it finishes reading, and active managers already lose to the index over any multi-year window. The question a model answers well is different: what changed, and what is the evidence that it is real rather than noise. So the architecture flips. Plain code finds the anomaly, a million-token model compares eight quarters in one pass, and the heavy reasoning model is spent only on arguing against the result. 10,000 companies a day become 40 deep reads and 8 reasoning passes -- about $450 of inference where a naive full pass costs $60,000. Flagged events hit 37% precision against a 15 to 20% base rate. A real edge, and still nowhere near a forecast. The output has no score and no call. Only what changed, sourced, and what contradicts it. That is exactly the pipeline I built and wrote up below: Robinhood data, Kimi K3 for the reading, GPT-6 Astra for the counter-thesis. The entire paper in one line: you are selling research velocity, not prediction ↓
0:30
slash1sol's profile picture

This is f*cking gold.. Chinese researchers just released a paper with a brutal title: "The End of Prediction". The case is simple: an AI that outputs BUY or SELL is finished as a product. Any signal a model can read is priced in before it finishes reading, and active managers already lose to the index over any multi-year window. The question a model answers well is different: what changed, and what is the evidence that it is real rather than noise. So the architecture flips. Plain code finds the anomaly, a million-token model compares eight quarters in one pass, and the heavy reasoning model is spent only on arguing against the result. 10,000 companies a day become 40 deep reads and 8 reasoning passes -- about $450 of inference where a naive full pass costs $60,000. Flagged events hit 37% precision against a 15 to 20% base rate. A real edge, and still nowhere near a forecast. The output has no score and no call. Only what changed, sourced, and what contradicts it. That is exactly the pipeline I built and wrote up below: Robinhood data, Kimi K3 for the reading, GPT-6 Astra for the counter-thesis. The entire paper in one line: you are selling research velocity, not prediction ↓

slash1s

99,825 просмотров • 25 дней назад

I GAVE GPT-6 ASTRA AND MINARA ONE JOB: WATCH ALL 24 ROBINHOOD STOCK TOKENS UNTIL THE PRICE STARTS LYING -> THREE WEEKS LATER THE DESK THAT DOES IT IS RUNNING 24 tickers, 4,320 pool reads an hour, 103,680 a day and 84 gaps called, 95% of them closed. It's live and it's free: Why a Stock Token stops tracking the share it is named after: > A meme pair locks real shares inside a pool and the float on chain goes thin. > Minting and burning run on a schedule, so outside that window supply cannot answer demand. > After 4PM the oracle stands still while the pool keeps trading anyway. > The pool price drifts off the real price, and that drift is the arbitrage -- buy the cheap leg, short the rich one, wait for them to meet. > At 3AM nobody is watching any of it. THREE LAYERS, RUNNING AT ONCE: > GAP DESK holds all 24 pools against the real bid/ask mid every 20 seconds, calls the gap at 1.0%, doubles the call at 1.5%, prints both legs and the contract. > STONK MEMES watches the stock-paired meme pools that lock the shares, and flags the burst before the gap opens. > NIGHT SHIFT runs the board from the closing bell to the opening one, when the oracle is frozen and the pool is not. Under all three: > DEPTH refuses any pool holding less than $25K in reserves. SMCI got skipped four times this week and I let it > SCORECARD writes down every call and how it ended, including the four it got wrong THE WHOLE DESK ANSWERS IN TELEGRAM AT AMZN, this week. The pool ran 2.97% above the real share price and sat there for 81 minutes with the call open. A desk that does this on Wall Street is a room full of people and a market data bill with a comma in it. Mine is two public endpoints, one agent and a laptop that is not allowed to sleep. No insider feed and no private group. I cannot push a ticker into it any more than you can, because the depth floor does not care what I want. Open source, MIT, read-only on chain. There is no wallet in the bot and no signing anywhere in the code. The detector is open, the execution is yours. Next: one tap on a call opens both legs from your own Minara account, and the desk still never touches a key. The bell is just a sound. SEE YOU AFTER THE BELL ↓
0:37
slash1sol's profile picture

I GAVE GPT-6 ASTRA AND MINARA ONE JOB: WATCH ALL 24 ROBINHOOD STOCK TOKENS UNTIL THE PRICE STARTS LYING -> THREE WEEKS LATER THE DESK THAT DOES IT IS RUNNING 24 tickers, 4,320 pool reads an hour, 103,680 a day and 84 gaps called, 95% of them closed. It's live and it's free: Why a Stock Token stops tracking the share it is named after: > A meme pair locks real shares inside a pool and the float on chain goes thin. > Minting and burning run on a schedule, so outside that window supply cannot answer demand. > After 4PM the oracle stands still while the pool keeps trading anyway. > The pool price drifts off the real price, and that drift is the arbitrage -- buy the cheap leg, short the rich one, wait for them to meet. > At 3AM nobody is watching any of it. THREE LAYERS, RUNNING AT ONCE: > GAP DESK holds all 24 pools against the real bid/ask mid every 20 seconds, calls the gap at 1.0%, doubles the call at 1.5%, prints both legs and the contract. > STONK MEMES watches the stock-paired meme pools that lock the shares, and flags the burst before the gap opens. > NIGHT SHIFT runs the board from the closing bell to the opening one, when the oracle is frozen and the pool is not. Under all three: > DEPTH refuses any pool holding less than $25K in reserves. SMCI got skipped four times this week and I let it > SCORECARD writes down every call and how it ended, including the four it got wrong THE WHOLE DESK ANSWERS IN TELEGRAM AT AMZN, this week. The pool ran 2.97% above the real share price and sat there for 81 minutes with the call open. A desk that does this on Wall Street is a room full of people and a market data bill with a comma in it. Mine is two public endpoints, one agent and a laptop that is not allowed to sleep. No insider feed and no private group. I cannot push a ticker into it any more than you can, because the depth floor does not care what I want. Open source, MIT, read-only on chain. There is no wallet in the bot and no signing anywhere in the code. The detector is open, the execution is yours. Next: one tap on a call opens both legs from your own Minara account, and the desk still never touches a key. The bell is just a sound. SEE YOU AFTER THE BELL ↓

slash1s

127,491 просмотров • 1 месяц назад

A MATHEMATICIAN WAS LAUGHED AT FOR STUDYING "ROUGHNESS" -- CLOUDS, COASTLINES, PRICES, THINGS TOO MESSY FOR REAL MATH. THEN HE SHOWED ONE TINY FORMULA, REPEATED, BUILDS INFINITE COMPLEXITY AND IT DESCRIBES REALITY BETTER THAN THE SMOOTH MATH EVERYONE WORSHIPPED 79 minutes from Benoit Mandelbrot at MIT, introduced by the father of chaos theory. -> The idea that lands: the real world is not smooth. It is rough, jagged, self-repeating and that roughness has an exact mathematics, where one simple rule looped over itself spins out endless detail. A coastline, a fern, a stock chart -- zoom in and you find the same shape again, forever. Smooth equations are a polite lie we tell about a jagged world. Fractals are what reality actually looks like. This is how generative models work too -- simple operations repeated at scale until staggering complexity falls out. He found the principle decades before the machines. You thought messy meant random. This is the talk that shows messy has a law. Bookmark this. Watch it once, see patterns everywhere ↓
1:19:49
slash1sol's profile picture

A MATHEMATICIAN WAS LAUGHED AT FOR STUDYING "ROUGHNESS" -- CLOUDS, COASTLINES, PRICES, THINGS TOO MESSY FOR REAL MATH. THEN HE SHOWED ONE TINY FORMULA, REPEATED, BUILDS INFINITE COMPLEXITY AND IT DESCRIBES REALITY BETTER THAN THE SMOOTH MATH EVERYONE WORSHIPPED 79 minutes from Benoit Mandelbrot at MIT, introduced by the father of chaos theory. -> The idea that lands: the real world is not smooth. It is rough, jagged, self-repeating and that roughness has an exact mathematics, where one simple rule looped over itself spins out endless detail. A coastline, a fern, a stock chart -- zoom in and you find the same shape again, forever. Smooth equations are a polite lie we tell about a jagged world. Fractals are what reality actually looks like. This is how generative models work too -- simple operations repeated at scale until staggering complexity falls out. He found the principle decades before the machines. You thought messy meant random. This is the talk that shows messy has a law. Bookmark this. Watch it once, see patterns everywhere ↓

slash1s

406,413 просмотров • 3 месяцев назад

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓
39:31
slash1sol's profile picture

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

slash1s

435,509 просмотров • 4 месяцев назад

I gave Elon's Grok Bot $60 and one rule: "The day you cannot pay your own subscription is the day I unplug you". 48 hours in it is sitting on $4,412 and it already paid the bill. Autonomous trading agent on Polymarket, running on its own cloud box with its own browser and terminal. I did not write a line of code and I did not watch it trade. Every 15 minutes it: -> Pulls every market that resolves inside 24 hours. -> Reads live X sentiment, and it is Grok, so it sees every post on this app before the price moves. -> Prices each market itself and only flags a gap above 7%. -> Sizes on the gap, never above 5% of the bankroll. -> Executes in its own browser, nobody approves anything. -> Covers its own $200 subscription straight out of profits. Balance hits $0, subscription gets cancelled, it is gone for good. Hour 11 it was at $9 -> runway one day. It dumped every politics position and switched to same day baseball totals, front-running lineup cards before the market repriced. It rewrote its own strategy to not die. Setup was one evening: spin up the bot, run one trade in front of it, put it on a timer, connect Telegram with no VPS, no API keys and no dev on call. Last spring a freelancer took $2,500 for something like this -> it made $410 and died on a rate limit. Grok Bot did in one evening what $2,500 could not. $60 -> $4,412 in 48 hours. What is it sitting at by Sunday?
0:56
slash1sol's profile picture

I gave Elon's Grok Bot $60 and one rule: "The day you cannot pay your own subscription is the day I unplug you". 48 hours in it is sitting on $4,412 and it already paid the bill. Autonomous trading agent on Polymarket, running on its own cloud box with its own browser and terminal. I did not write a line of code and I did not watch it trade. Every 15 minutes it: -> Pulls every market that resolves inside 24 hours. -> Reads live X sentiment, and it is Grok, so it sees every post on this app before the price moves. -> Prices each market itself and only flags a gap above 7%. -> Sizes on the gap, never above 5% of the bankroll. -> Executes in its own browser, nobody approves anything. -> Covers its own $200 subscription straight out of profits. Balance hits $0, subscription gets cancelled, it is gone for good. Hour 11 it was at $9 -> runway one day. It dumped every politics position and switched to same day baseball totals, front-running lineup cards before the market repriced. It rewrote its own strategy to not die. Setup was one evening: spin up the bot, run one trade in front of it, put it on a timer, connect Telegram with no VPS, no API keys and no dev on call. Last spring a freelancer took $2,500 for something like this -> it made $410 and died on a rate limit. Grok Bot did in one evening what $2,500 could not. $60 -> $4,412 in 48 hours. What is it sitting at by Sunday?

slash1s

191,157 просмотров • 1 месяц назад

MIT DEDICATED A FULL LECTURE TO GIT'S INTERNALS -- BECAUSE THEY FOUND MOST DEVS MEMORIZE THE COMMANDS AND HAVE NO IDEA WHAT THE TOOL ACTUALLY DOES A whole 85 minutes MIT session that refuses to teach git as a list of commands to copy, and instead shows you the data model underneath -- the thing that makes every command finally make sense. -> The moment it clicks, git stops being scary magic. You stop memorizing "The incantation that fixed it last time" and start actually knowing what's happening. Most people learn just enough git to not get fired. Four commands, blind faith, and a prayer before every merge. In 2026 that's not enough anymore -> git is the literacy test for being in the room, and "I'll just reclone it" is the fastest way to look junior. An AI agent will branch, commit and rebase faster than you can read. When it tangles the history, untangling it runs on understanding the model MIT teaches in this one hour. Anyone can run git push. The person who understands the graph underneath is the one who saves the repo when it breaks. Bookmark & Watch it ↓
1:24:59
slash1sol's profile picture

MIT DEDICATED A FULL LECTURE TO GIT'S INTERNALS -- BECAUSE THEY FOUND MOST DEVS MEMORIZE THE COMMANDS AND HAVE NO IDEA WHAT THE TOOL ACTUALLY DOES A whole 85 minutes MIT session that refuses to teach git as a list of commands to copy, and instead shows you the data model underneath -- the thing that makes every command finally make sense. -> The moment it clicks, git stops being scary magic. You stop memorizing "The incantation that fixed it last time" and start actually knowing what's happening. Most people learn just enough git to not get fired. Four commands, blind faith, and a prayer before every merge. In 2026 that's not enough anymore -> git is the literacy test for being in the room, and "I'll just reclone it" is the fastest way to look junior. An AI agent will branch, commit and rebase faster than you can read. When it tangles the history, untangling it runs on understanding the model MIT teaches in this one hour. Anyone can run git push. The person who understands the graph underneath is the one who saves the repo when it breaks. Bookmark & Watch it ↓

slash1s

460,353 просмотров • 4 месяцев назад

Game Changer: Chinese college student Guo Hangjiang (GitHub: 666ghj) codes MiroFish AI swarm engine solo in 10 days with AI assistants, explodes GitHub to 23k+ stars, bags $4.1M from Shanda Group in 24 hours, ditches dorm life to become Shanghai CEO. Tech Highlights: .GraphRAG builds detailed knowledge graphs (e.g., 905 entities, 3,822 relations in demos). .Agents with Zep memory, unique traits, sims capped at ~40 rounds for efficiency. .Hybrid Node.js/Python backend, Docker deploy, OpenAI-compatible LLMs. .Low-resource scaling: 8B param dragon boat festival sim runs smooth (vid demo). .AGPL license, credits CAMEL-AI, recent fixes for UI/errors. .Online demo: - Test predictions interactively. Creator Quick Look: >> BaiFu (Guo Hangjiang), BUPT senior turned Shanghai CEO at Shanda. >> Built on 38.6k-star BettaFish & MindSpider for full data-prediction pipelines. >> 790 contribs last year. Real-World Plays -> -> Sentiment: Wuhan Uni backlash sim predicts trends (repo vid). -> Literary: Agents finish Dream of the Red Chamber's lost ending. -> Finance: Historical data for market forecasts; like bags on Polymarket SPX bets. -> Policy: "What-if" for bills/geopolitics. -> Cultural: Dragon boat fest agent dynamics. -> Ecosystems: Integrates crawlers for PR/decision tools. Repo:
1:32
slash1sol's profile picture

Game Changer: Chinese college student Guo Hangjiang (GitHub: 666ghj) codes MiroFish AI swarm engine solo in 10 days with AI assistants, explodes GitHub to 23k+ stars, bags $4.1M from Shanda Group in 24 hours, ditches dorm life to become Shanghai CEO. Tech Highlights: .GraphRAG builds detailed knowledge graphs (e.g., 905 entities, 3,822 relations in demos). .Agents with Zep memory, unique traits, sims capped at ~40 rounds for efficiency. .Hybrid Node.js/Python backend, Docker deploy, OpenAI-compatible LLMs. .Low-resource scaling: 8B param dragon boat festival sim runs smooth (vid demo). .AGPL license, credits CAMEL-AI, recent fixes for UI/errors. .Online demo: - Test predictions interactively. Creator Quick Look: >> BaiFu (Guo Hangjiang), BUPT senior turned Shanghai CEO at Shanda. >> Built on 38.6k-star BettaFish & MindSpider for full data-prediction pipelines. >> 790 contribs last year. Real-World Plays -> -> Sentiment: Wuhan Uni backlash sim predicts trends (repo vid). -> Literary: Agents finish Dream of the Red Chamber's lost ending. -> Finance: Historical data for market forecasts; like bags on Polymarket SPX bets. -> Policy: "What-if" for bills/geopolitics. -> Cultural: Dragon boat fest agent dynamics. -> Ecosystems: Integrates crawlers for PR/decision tools. Repo:

slash1s

686,486 просмотров • 7 месяцев назад

IN 1986 MIT FILMED A LECTURE THAT OPENS BY TELLING YOU COMPUTER SCIENCE IS NOT A SCIENCE AND HAS ALMOST NOTHING TO DO WITH COMPUTERS 72 minutes from Hal Abelson and Gerald Sussman, the lecture an entire generation of engineers calls the one that rewired how they think. -> The line that lands: computer science is about computers the way astronomy is about telescopes. The tool was never the point. The real subject was always one thing -- controlling complexity. Everything else is detail. Forty years later it reads like a prophecy. AI writes the syntax now. What's left is exactly what they taught: taming complexity nobody can hold in their head. The language was never the skill -> the thinking was. This is where you learn it. Most people chase the newest framework. The ones who watched this think on a level frameworks can't touch. Bookmark & Watch today it, this one's a legend ↓
1:12:24
slash1sol's profile picture

IN 1986 MIT FILMED A LECTURE THAT OPENS BY TELLING YOU COMPUTER SCIENCE IS NOT A SCIENCE AND HAS ALMOST NOTHING TO DO WITH COMPUTERS 72 minutes from Hal Abelson and Gerald Sussman, the lecture an entire generation of engineers calls the one that rewired how they think. -> The line that lands: computer science is about computers the way astronomy is about telescopes. The tool was never the point. The real subject was always one thing -- controlling complexity. Everything else is detail. Forty years later it reads like a prophecy. AI writes the syntax now. What's left is exactly what they taught: taming complexity nobody can hold in their head. The language was never the skill -> the thinking was. This is where you learn it. Most people chase the newest framework. The ones who watched this think on a level frameworks can't touch. Bookmark & Watch today it, this one's a legend ↓

slash1s

363,637 просмотров • 4 месяцев назад

A LINUX KERNEL DEVELOPER PROVED THE THING YOU PUSH CODE TO IS SECRETLY A DATABASE THAT CAN VERSION ALMOST ANYTHING AND THAT MOST DEVS HAVE ONLY EVER TOUCHED A TENTH OF IT 42 minutes from Josh Triplett -- a longtime Linux kernel and Debian developer -- showing that Git is a general-purpose, tamper-evident versioning engine that just happens to be famous for code. -> The moment it clicks, Git stops being "Where my code lives" and becomes what it really is underneath: a content-addressable store that can version almost anything -- your configs, your notes, your servers' state, entire datasets. People run whole wikis on it. They version their entire machine's configuration with it. They ship websites by pushing to it. They track data too big to email. None of it is a hack -- it's the same handful of objects you already use for code, pointed somewhere new. Treating Git as a code-only tool was never the ceiling -> it's a versioning engine for anything, and the people who see that automate what the rest of the team still does by hand. And as AI agents start spitting out not just code but configs, docs and data, the one system that can version and audit all of it at once is already sitting on your machine. You learned five commands to survive. This is the talk that shows you were standing on top of a database the whole time. It changes what you think the tool is even for. Bookmark & Watch it today ↓
42:24
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A LINUX KERNEL DEVELOPER PROVED THE THING YOU PUSH CODE TO IS SECRETLY A DATABASE THAT CAN VERSION ALMOST ANYTHING AND THAT MOST DEVS HAVE ONLY EVER TOUCHED A TENTH OF IT 42 minutes from Josh Triplett -- a longtime Linux kernel and Debian developer -- showing that Git is a general-purpose, tamper-evident versioning engine that just happens to be famous for code. -> The moment it clicks, Git stops being "Where my code lives" and becomes what it really is underneath: a content-addressable store that can version almost anything -- your configs, your notes, your servers' state, entire datasets. People run whole wikis on it. They version their entire machine's configuration with it. They ship websites by pushing to it. They track data too big to email. None of it is a hack -- it's the same handful of objects you already use for code, pointed somewhere new. Treating Git as a code-only tool was never the ceiling -> it's a versioning engine for anything, and the people who see that automate what the rest of the team still does by hand. And as AI agents start spitting out not just code but configs, docs and data, the one system that can version and audit all of it at once is already sitting on your machine. You learned five commands to survive. This is the talk that shows you were standing on top of a database the whole time. It changes what you think the tool is even for. Bookmark & Watch it today ↓

slash1s

386,685 просмотров • 4 месяцев назад

IN A 1997 KEYNOTE A DEVELOPER TOLD A ROOM FULL OF PROGRAMMERS THAT THE COMPUTER REVOLUTION HAD NOT ACTUALLY HAPPENED YET. THEN HE PLAYED A CLIP OF WINDOWS, ICONS AND LIVE EDITING RUNNING ON A MACHINE FROM 1973 AND THE ROOM WENT QUIET. 62 minutes from Alan Kay -- the man who invented the word "object-oriented" and helped build the first modern personal computer at Xerox PARC. -> The idea that lands: almost everything you call "computing" is just paper, digitized. Documents, mail, folders. We took the most powerful medium ever made and used it to imitate the office. The real machine -- the one that thinks with you, that you shape live instead of typing at -- was sketched in the 60s and 70s, then quietly abandoned. He calls modern software an Egyptian pyramid: millions of bricks stacked by brute force, no structure underneath. And now AI is quietly hauling us back toward what he wanted -- you describe intent, the machine builds the how. Not a revolution from nowhere. A return to a dream we walked away from. You thought this was the future. This is the talk that shows you it is a detour we have been on for 40 years. Save this. It reframes the whole industry ↓
1:02:53
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IN A 1997 KEYNOTE A DEVELOPER TOLD A ROOM FULL OF PROGRAMMERS THAT THE COMPUTER REVOLUTION HAD NOT ACTUALLY HAPPENED YET. THEN HE PLAYED A CLIP OF WINDOWS, ICONS AND LIVE EDITING RUNNING ON A MACHINE FROM 1973 AND THE ROOM WENT QUIET. 62 minutes from Alan Kay -- the man who invented the word "object-oriented" and helped build the first modern personal computer at Xerox PARC. -> The idea that lands: almost everything you call "computing" is just paper, digitized. Documents, mail, folders. We took the most powerful medium ever made and used it to imitate the office. The real machine -- the one that thinks with you, that you shape live instead of typing at -- was sketched in the 60s and 70s, then quietly abandoned. He calls modern software an Egyptian pyramid: millions of bricks stacked by brute force, no structure underneath. And now AI is quietly hauling us back toward what he wanted -- you describe intent, the machine builds the how. Not a revolution from nowhere. A return to a dream we walked away from. You thought this was the future. This is the talk that shows you it is a detour we have been on for 40 years. Save this. It reframes the whole industry ↓

slash1s

294,844 просмотров • 3 месяцев назад

A DEVELOPER STOOD UP AT A CONFERENCE AND APOLOGIZED TO THE ENTIRE INDUSTRY FOR ONE LINE OF CODE HE WROTE IN 1965. THAT SINGLE DECISION HAS SINCE CAUSED CRASHES, BREACHES AND ROUGHLY A BILLION DOLLARS OF DAMAGE AND YOU HIT IT EVERY SINGLE DAY 61 minutes from Tony Hoare -- the man who invented quicksort, won the Turing Award, and passed away in early 2026, leaving this as one of his most honest talks. -> His confession: he added the null reference, letting any variable secretly mean "nothing", simply because it was easy to implement. That one shortcut became the null pointer, the undefined, the None -- the crash waiting inside almost every program ever written. He calls it his billion-dollar mistake, and he is not exaggerating. Whole classes of bugs and security holes trace straight back to it. And this is the deeper lesson under the AI rush: the easy shortcut you ship today becomes the landmine everyone steps on for the next 40 years. You thought null was just part of how code works. This is the talk where the man who made it tells you it never had to be. Bookmark & Watch this one. You'll never type null the same way ↓
1:01:44
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A DEVELOPER STOOD UP AT A CONFERENCE AND APOLOGIZED TO THE ENTIRE INDUSTRY FOR ONE LINE OF CODE HE WROTE IN 1965. THAT SINGLE DECISION HAS SINCE CAUSED CRASHES, BREACHES AND ROUGHLY A BILLION DOLLARS OF DAMAGE AND YOU HIT IT EVERY SINGLE DAY 61 minutes from Tony Hoare -- the man who invented quicksort, won the Turing Award, and passed away in early 2026, leaving this as one of his most honest talks. -> His confession: he added the null reference, letting any variable secretly mean "nothing", simply because it was easy to implement. That one shortcut became the null pointer, the undefined, the None -- the crash waiting inside almost every program ever written. He calls it his billion-dollar mistake, and he is not exaggerating. Whole classes of bugs and security holes trace straight back to it. And this is the deeper lesson under the AI rush: the easy shortcut you ship today becomes the landmine everyone steps on for the next 40 years. You thought null was just part of how code works. This is the talk where the man who made it tells you it never had to be. Bookmark & Watch this one. You'll never type null the same way ↓

slash1s

282,239 просмотров • 3 месяцев назад

A NOBEL WINNING PHYSICIST ARGUED THAT NO AI, NO MATTER HOW POWERFUL, WILL EVER TRULY UNDERSTAND A SINGLE THING IT SAYS. HIS REASON IS NOT COMPUTE OR DATA -- IT IS A MATH THEOREM FROM THE 1930s THAT SAYS SOME TRUTHS CAN BE SEEN BUT NEVER COMPUTED 81 minutes with Roger Penrose -- the Oxford physicist who won a Nobel for his work on black holes and general relativity. -> His claim: whatever consciousness is, it is not a computation. A machine following rules can imitate understanding, but never actually have it. He builds it on Gödel: a human can just "see" that certain statements are true, even though no algorithm can ever prove them. That seeing, he argues, is non-computational. If he is right, understanding is not something you scale into. You can stack a trillion parameters and still have zero awareness underneath. Which cuts straight through the AI moment. Everyone assumes bigger models will eventually "wake up". Penrose says that is a category error -- more computation is still just computation. You thought intelligence and understanding were the same thing. This is the conversation that pulls them apart. Save this. It is the sharpest case against the hype ↓
1:21:43
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A NOBEL WINNING PHYSICIST ARGUED THAT NO AI, NO MATTER HOW POWERFUL, WILL EVER TRULY UNDERSTAND A SINGLE THING IT SAYS. HIS REASON IS NOT COMPUTE OR DATA -- IT IS A MATH THEOREM FROM THE 1930s THAT SAYS SOME TRUTHS CAN BE SEEN BUT NEVER COMPUTED 81 minutes with Roger Penrose -- the Oxford physicist who won a Nobel for his work on black holes and general relativity. -> His claim: whatever consciousness is, it is not a computation. A machine following rules can imitate understanding, but never actually have it. He builds it on Gödel: a human can just "see" that certain statements are true, even though no algorithm can ever prove them. That seeing, he argues, is non-computational. If he is right, understanding is not something you scale into. You can stack a trillion parameters and still have zero awareness underneath. Which cuts straight through the AI moment. Everyone assumes bigger models will eventually "wake up". Penrose says that is a category error -- more computation is still just computation. You thought intelligence and understanding were the same thing. This is the conversation that pulls them apart. Save this. It is the sharpest case against the hype ↓

slash1s

277,553 просмотров • 3 месяцев назад

ONE OF THE MINDS BEHIND JAVA GAVE A LECTURE WHERE HE BANNED HIMSELF FROM USING ANY BIG WORD UNTIL HE DEFINED IT FIRST AND IN DOING SO QUIETLY EXPLAINED HOW EVERY GREAT SYSTEM GETS BUILT A talk from Guy Steele -- co-author of the Java spec and a designer of Scheme -- where the form of the talk is the lesson. -> The moment you catch what he's doing, it rewires you. He starts with only the smallest words, then builds every larger idea live, in front of you, from the pieces he already gave you. The talk grows its own vocabulary as it goes. That's the whole secret of language and system design he's smuggling in: you don't ship something huge and finished. You ship a small core and the means to grow it. Miss that and you spend your career fighting your own tools. Memorizing a language was never the skill -> understanding how a good one is meant to grow is. And as AI starts generating the primitives you build on, knowing what makes a foundation extensible instead of brittle is the entire game. Twenty-five years on, language designers still point to this talk as the cleanest demonstration of the idea ever performed. Bookmark it & Watch how he builds the whole thing from nothing ↓
52:33
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ONE OF THE MINDS BEHIND JAVA GAVE A LECTURE WHERE HE BANNED HIMSELF FROM USING ANY BIG WORD UNTIL HE DEFINED IT FIRST AND IN DOING SO QUIETLY EXPLAINED HOW EVERY GREAT SYSTEM GETS BUILT A talk from Guy Steele -- co-author of the Java spec and a designer of Scheme -- where the form of the talk is the lesson. -> The moment you catch what he's doing, it rewires you. He starts with only the smallest words, then builds every larger idea live, in front of you, from the pieces he already gave you. The talk grows its own vocabulary as it goes. That's the whole secret of language and system design he's smuggling in: you don't ship something huge and finished. You ship a small core and the means to grow it. Miss that and you spend your career fighting your own tools. Memorizing a language was never the skill -> understanding how a good one is meant to grow is. And as AI starts generating the primitives you build on, knowing what makes a foundation extensible instead of brittle is the entire game. Twenty-five years on, language designers still point to this talk as the cleanest demonstration of the idea ever performed. Bookmark it & Watch how he builds the whole thing from nothing ↓

slash1s

358,660 просмотров • 4 месяцев назад

a 24-year-old in Florida pulled $52,318 last month from a game that hasn't even launched. GTA 6 drops in 6 months. he doesn't play it. he sells Lua scripts to private server owners preparing for launch. he doesn't write code either. he gives Claude a 6-line prompt -> job systems, custom HUDs, NPC quest dialogue -> Claude returns 300 lines of working Lua in under a minute. uploads to Tebex. lists at $89-299. sells while he sleeps. 26 scripts in his catalog. one of them cleared $12,000 alone. then he plugged Claude API into in-game NPCs. bartenders that gossip about your crimes. cops that interrogate based on your history. shop owners that haggle on reputation. sells the pack at $400 per server license. 38 servers bought it last month. GTA Online players spent $8.6 billion inside the game. creators got $0 of it. that era ended 5 months ago when Rockstar opened the marketplace. no game launched. no players online. just an empty marketplace and 6 months of head start. details in the article below. save this alpha.
0:32
slash1sol's profile picture

a 24-year-old in Florida pulled $52,318 last month from a game that hasn't even launched. GTA 6 drops in 6 months. he doesn't play it. he sells Lua scripts to private server owners preparing for launch. he doesn't write code either. he gives Claude a 6-line prompt -> job systems, custom HUDs, NPC quest dialogue -> Claude returns 300 lines of working Lua in under a minute. uploads to Tebex. lists at $89-299. sells while he sleeps. 26 scripts in his catalog. one of them cleared $12,000 alone. then he plugged Claude API into in-game NPCs. bartenders that gossip about your crimes. cops that interrogate based on your history. shop owners that haggle on reputation. sells the pack at $400 per server license. 38 servers bought it last month. GTA Online players spent $8.6 billion inside the game. creators got $0 of it. that era ended 5 months ago when Rockstar opened the marketplace. no game launched. no players online. just an empty marketplace and 6 months of head start. details in the article below. save this alpha.

slash1s

380,661 просмотров • 5 месяцев назад

IN 1985 ONE OF THE GREATEST PHYSICISTS WHO EVER LIVED SAT DOWN TO EXPLAIN HOW COMPUTERS ACTUALLY WORK AND TOLD A ROOM FULL OF ENGINEERS THE MACHINE IS COMPLETELY DUMB 76 minutes from Richard Feynman, still called the clearest explanation of what a computer really is ever given. -> The idea that lands: a computer is just a very, very fast, very, very dumb file clerk. It doesn't think. It follows tiny simple rules, billions of times a second. All the complexity you're in awe of comes from stacking simple things. There's no magic underneath. There never was. Forty years later everyone calls the model "Intelligent". Feynman already told you what it really is: speed, not thought. Being amazed by the machine was never the point -> understanding what it's actually doing is. Most people are dazzled by what AI says. The ones who watched this know exactly what's happening underneath. Bookmark & Watch it today. This one's a legend ↓
1:14:04
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IN 1985 ONE OF THE GREATEST PHYSICISTS WHO EVER LIVED SAT DOWN TO EXPLAIN HOW COMPUTERS ACTUALLY WORK AND TOLD A ROOM FULL OF ENGINEERS THE MACHINE IS COMPLETELY DUMB 76 minutes from Richard Feynman, still called the clearest explanation of what a computer really is ever given. -> The idea that lands: a computer is just a very, very fast, very, very dumb file clerk. It doesn't think. It follows tiny simple rules, billions of times a second. All the complexity you're in awe of comes from stacking simple things. There's no magic underneath. There never was. Forty years later everyone calls the model "Intelligent". Feynman already told you what it really is: speed, not thought. Being amazed by the machine was never the point -> understanding what it's actually doing is. Most people are dazzled by what AI says. The ones who watched this know exactly what's happening underneath. Bookmark & Watch it today. This one's a legend ↓

slash1s

261,198 просмотров • 3 месяцев назад

A DEVELOPER PROVED THAT MOST OF THE CLASSES YOU WRITE SHOULD NEVER HAVE BEEN CLASSES AT ALL 27 minutes from Jack Diederich, a Python core developer, on how much of your code is just a function wearing a costume. -> The moment it lands, the rule is brutal: if a class has two methods and one is __init__, you meant to write a function. Most people add structure to feel like real engineers. It just adds weight no one ever needed. Writing more code was never the skill -> writing less of it is. And when an AI agent happily generates ten classes for a job that needed three lines, the person who knows what to delete is the one who ships something clean. Anyone can add abstraction. Knowing when not to is the whole job. Bookmark it and Watch ↓
27:10
slash1sol's profile picture

A DEVELOPER PROVED THAT MOST OF THE CLASSES YOU WRITE SHOULD NEVER HAVE BEEN CLASSES AT ALL 27 minutes from Jack Diederich, a Python core developer, on how much of your code is just a function wearing a costume. -> The moment it lands, the rule is brutal: if a class has two methods and one is __init__, you meant to write a function. Most people add structure to feel like real engineers. It just adds weight no one ever needed. Writing more code was never the skill -> writing less of it is. And when an AI agent happily generates ten classes for a job that needed three lines, the person who knows what to delete is the one who ships something clean. Anyone can add abstraction. Knowing when not to is the whole job. Bookmark it and Watch ↓

slash1s

269,515 просмотров • 4 месяцев назад

MICROSOFT PUT THE ENTIRE SOURCE CODE OF WINDOWS INTO A SINGLE GIT REPO -- 300 GIGABYTES, 3.5 MILLION FILES AND MADE IT CLONE IN SECONDS 31 minutes from the Microsoft engineer who actually did it, bending the tool you use for a 50-file side project to hold the biggest codebase on the planet. -> The moment it lands, every excuse dies. You've heard "Git doesn't scale, use something else" your whole career. They ran git status on the largest repo alive and it came back instantly. The secret wasn't a different tool. It was understanding what git actually stores and realizing you never need the whole thing on disk. Pull only the files you touch. Ignore the 3.4 million you never will. Knowing the commands was never the ceiling -> understanding how git stores and moves things is what lets you do what everyone swears is impossible. And as AI agents spin up repos that balloon overnight, knowing how git scales is what keeps you from drowning in your own history. Everyone treats git like it maxes out at a hobby project. One team quietly proved it can hold an operating system. Bookmark & Watch it today ↓
31:26
slash1sol's profile picture

MICROSOFT PUT THE ENTIRE SOURCE CODE OF WINDOWS INTO A SINGLE GIT REPO -- 300 GIGABYTES, 3.5 MILLION FILES AND MADE IT CLONE IN SECONDS 31 minutes from the Microsoft engineer who actually did it, bending the tool you use for a 50-file side project to hold the biggest codebase on the planet. -> The moment it lands, every excuse dies. You've heard "Git doesn't scale, use something else" your whole career. They ran git status on the largest repo alive and it came back instantly. The secret wasn't a different tool. It was understanding what git actually stores and realizing you never need the whole thing on disk. Pull only the files you touch. Ignore the 3.4 million you never will. Knowing the commands was never the ceiling -> understanding how git stores and moves things is what lets you do what everyone swears is impossible. And as AI agents spin up repos that balloon overnight, knowing how git scales is what keeps you from drowning in your own history. Everyone treats git like it maxes out at a hobby project. One team quietly proved it can hold an operating system. Bookmark & Watch it today ↓

slash1s

255,565 просмотров • 4 месяцев назад