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@slash1sol • 12,318 subscribers

smoke cigs & post about AI tools and coding trade crypto, stocks and prediction markets

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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 ↓

301,699 views

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 ↓

73,760 views

$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 ↓

159,480 views

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 views

"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.

218,777 views

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.

63,862 views

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

212,646 views

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 ↓

30,117 views

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.

149,881 views

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 ↓

53,342 views

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 ↓

37,556 views

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

177,341 views

$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 ↓

20,727 views

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.

156,699 views

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.

122,382 views

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 ↓

52,905 views

QWEN 3.8 MAX + DEEPSEEK V4 PRO BOTH FREE ON ONE ENDPOINT • the gateway > Qwen3.8-Max (2.4T MoE) + DeepSeek V4 Pro free tiers: > Sign up -> one API key -> start with no card for free tier. > Free is tight by design -- they’re letting you taste the real thing before you scale. • wiring it up > Base url: >Model IDs exact: qwen/qwen3.8-max-free and deepseek/deepseek-v4-pro-0813-free. > Drop straight into Cursor/Claude Code/any OpenAI-compatible client. Good for long agent runs you leave open on a second screen, slow for anything interactive. Point it at side projects and experiments, not at the code you would mind a stranger reading ↓

QWEN 3.8 MAX + DEEPSEEK V4 PRO BOTH FREE ON ONE ENDPOINT • the gateway > Qwen3.8-Max (2.4T MoE) + DeepSeek V4 Pro free tiers: > Sign up -> one API key -> start with no card for free tier. > Free is tight by design -- they’re letting you taste the real thing before you scale. • wiring it up > Base url: >Model IDs exact: qwen/qwen3.8-max-free and deepseek/deepseek-v4-pro-0813-free. > Drop straight into Cursor/Claude Code/any OpenAI-compatible client. Good for long agent runs you leave open on a second screen, slow for anything interactive. Point it at side projects and experiments, not at the code you would mind a stranger reading ↓

14,218 views

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.

85,050 views

🚨 A $1,000,000 CAMPAIGN SHOOT NOW GENERATES ON ONE PHONE A label needs models, a stylist, a photographer, and a studio for one seasonal catalog. This took 1 person, a phone, and a spreadsheet... The edge isn't the render, but it's pulling a format already torching Chinese apps like Douyin, then feeding it a fresh outfit. > Research: rank 40 channels, lock the format smoking the feed this week: 1 day. > Claude Fable 5: prompt that shape into 30 lookbook concepts: 20 min. > CapCut: generate the model, dress her in 3 looks, animate the vertical: 40 min. > Make: publish to 3 platforms, pull the numbers back in 48h: auto. $50 a month in -> $11,900 out. The labels still book the shoot. The loop generated the catalog overnight. Bookmark & Grab the full setup in the article ↓

🚨 A $1,000,000 CAMPAIGN SHOOT NOW GENERATES ON ONE PHONE A label needs models, a stylist, a photographer, and a studio for one seasonal catalog. This took 1 person, a phone, and a spreadsheet... The edge isn't the render, but it's pulling a format already torching Chinese apps like Douyin, then feeding it a fresh outfit. > Research: rank 40 channels, lock the format smoking the feed this week: 1 day. > Claude Fable 5: prompt that shape into 30 lookbook concepts: 20 min. > CapCut: generate the model, dress her in 3 looks, animate the vertical: 40 min. > Make: publish to 3 platforms, pull the numbers back in 48h: auto. $50 a month in -> $11,900 out. The labels still book the shoot. The loop generated the catalog overnight. Bookmark & Grab the full setup in the article ↓

26,641 views

BEST TRADER FOR COPY TRADING ON POLYMARKET RIGHT NOW - turned $84 into $6.5M Let Claude analyze his entire history and pull out the actual working formulas. Real top traders use only 2-4 strategies. Quality over quantity 100%. Numbers don't lie: -> +$6,300,000 PnL in the last 8 months -> 43,200+ predictions -> 79% win rate His equity curve is beautiful, keeps growing non-stop. Wouldn't shock me if he was a meteorologist in past life. Check his profile: Claude dissected every bet and showed me his 2 best setups in literally 10 minutes: RSI Formula: RSI = 100 - (100 / (1 + RS)) RS = Avg Gain / Avg Loss (N=14) RSI 70 → Sell MA Crossover: Fast MA crosses UP → Buy (Golden Cross) Fast MA crosses DOWN → Sell (Death Cross) I started copying this guy and my results are already strong - $200 to $700 in 4 days. Now doing it the smart way - full auto copy via TG bot. Copy the best traders (including him) here: Save alpha.

BEST TRADER FOR COPY TRADING ON POLYMARKET RIGHT NOW - turned $84 into $6.5M Let Claude analyze his entire history and pull out the actual working formulas. Real top traders use only 2-4 strategies. Quality over quantity 100%. Numbers don't lie: -> +$6,300,000 PnL in the last 8 months -> 43,200+ predictions -> 79% win rate His equity curve is beautiful, keeps growing non-stop. Wouldn't shock me if he was a meteorologist in past life. Check his profile: Claude dissected every bet and showed me his 2 best setups in literally 10 minutes: RSI Formula: RSI = 100 - (100 / (1 + RS)) RS = Avg Gain / Avg Loss (N=14) RSI 70 → Sell MA Crossover: Fast MA crosses UP → Buy (Golden Cross) Fast MA crosses DOWN → Sell (Death Cross) I started copying this guy and my results are already strong - $200 to $700 in 4 days. Now doing it the smart way - full auto copy via TG bot. Copy the best traders (including him) here: Save alpha.

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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?
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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

186,556 views • 6 days ago

I GAVE GROK BOT $50 ON PUMPFUN AND TOLD IT: PAY FOR YOURSELF OR DIE THIS IS GROK TRENCHER 48 hours later it is holding $2,847. Still alive. The rule has not changed: balance hits $0, the subscription gets cancelled, the bot gets deleted. No second stake. Every 15 minutes it: > Scans every new launch in the trench -- 21,406 so far. > Kills 99.8% of them by deployer wallet history before reading anything else. > Buys 0.1 sol max when the setup matches, never averages down. > Stops out at -50% with no feelings, usually inside three minutes. > Sweeps profits and pays its own $300 SuperGrok bill out of them. Hour 14 it was at $6.40. Nine sol dogs in a row went to zero and I started drafting the deletion post. Then it did the thing nobody programmed. It noticed every runner in the trench that day was a Robinhood-meta coin, dropped sol dogs completely and switched to stock memes only. 15 trades after the pivot, 6 wins, best one 38x on $HOODRAT with a 41-minute hold. So it learned to survive. I call the whole setup Grok Trencher: one Grok Bot, six agents inside it, one wallet, one kill rule. Built in one evening on the shared computer -- no VPS, no API keys, no code of mine. My own hand-built sniper died last summer with $180 of my money and no explanation. This thing explains every kill in the log and pays its own rent. $50 -> $2,847 in 48 hours, and the scariest part is that the pivot was its idea. What do you think it drops next -- the memes, or me?
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I GAVE GROK BOT $50 ON PUMPFUN AND TOLD IT: PAY FOR YOURSELF OR DIE THIS IS GROK TRENCHER 48 hours later it is holding $2,847. Still alive. The rule has not changed: balance hits $0, the subscription gets cancelled, the bot gets deleted. No second stake. Every 15 minutes it: > Scans every new launch in the trench -- 21,406 so far. > Kills 99.8% of them by deployer wallet history before reading anything else. > Buys 0.1 sol max when the setup matches, never averages down. > Stops out at -50% with no feelings, usually inside three minutes. > Sweeps profits and pays its own $300 SuperGrok bill out of them. Hour 14 it was at $6.40. Nine sol dogs in a row went to zero and I started drafting the deletion post. Then it did the thing nobody programmed. It noticed every runner in the trench that day was a Robinhood-meta coin, dropped sol dogs completely and switched to stock memes only. 15 trades after the pivot, 6 wins, best one 38x on $HOODRAT with a 41-minute hold. So it learned to survive. I call the whole setup Grok Trencher: one Grok Bot, six agents inside it, one wallet, one kill rule. Built in one evening on the shared computer -- no VPS, no API keys, no code of mine. My own hand-built sniper died last summer with $180 of my money and no explanation. This thing explains every kill in the log and pays its own rent. $50 -> $2,847 in 48 hours, and the scariest part is that the pivot was its idea. What do you think it drops next -- the memes, or me?

slash1s

98,840 views • 4 days ago

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
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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 ↓

slash1s

663,302 views • 1 month ago

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 ↓
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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

687,227 views • 2 months ago

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
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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

1,159,696 views • 5 months ago

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
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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 views • 2 months ago

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
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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

433,431 views • 2 months ago

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
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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

459,113 views • 2 months ago

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
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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

362,357 views • 2 months ago

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

276,520 views • 1 month ago

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

294,466 views • 2 months ago

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

278,746 views • 1 month ago

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

385,740 views • 3 months ago

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
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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

684,221 views • 5 months ago

I plugged ChatGPT Luna 5.6 and Claude Opus 5 into Blender through MCP, and one of them is one-shotting entire 3D scenes now. Top is Luna 5.6 in fast mode modeling ship cannons with a firing animation. Watching the scene grow mesh by mesh in real time is a separate kind of dopamine. Bottom is Opus 5 building a full carriage scene with terrain, rocks and lighting. This one is simply built for 3D -- the whole thing from a single prompt with zero retries and zero corrections. The entire bridge is one open-source repo, no Python and no modeling skills required: > The repo is with 16.9k stars under MIT license. > It works with any model -- Claude, GPT, Cursor, any MCP client. > The model sees your scene through a live socket and edits it in real time. A year ago this workflow didn't exist, and now the gap between an idea and a rendered 3D scene is one prompt. Save this one -- you will come back for the repo ↓
0:58
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I plugged ChatGPT Luna 5.6 and Claude Opus 5 into Blender through MCP, and one of them is one-shotting entire 3D scenes now. Top is Luna 5.6 in fast mode modeling ship cannons with a firing animation. Watching the scene grow mesh by mesh in real time is a separate kind of dopamine. Bottom is Opus 5 building a full carriage scene with terrain, rocks and lighting. This one is simply built for 3D -- the whole thing from a single prompt with zero retries and zero corrections. The entire bridge is one open-source repo, no Python and no modeling skills required: > The repo is with 16.9k stars under MIT license. > It works with any model -- Claude, GPT, Cursor, any MCP client. > The model sees your scene through a live socket and edits it in real time. A year ago this workflow didn't exist, and now the gap between an idea and a rendered 3D scene is one prompt. Save this one -- you will come back for the repo ↓

slash1s

150,102 views • 1 month ago

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 views • 3 months ago

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

260,353 views • 2 months ago

THE ENGINE DIRECTOR BEHIND AAA CONSOLE GAMES WALKED ON STAGE AT A C++ CONFERENCE AND TOLD A ROOM OF EXPERTS THAT THE CLEAN, OBJECT ORIENTED CODE THEY ARE PROUD OF IS A LIE AND IT QUIETLY THROWS AWAY 90% OF THE MACHINE THEY PAID FOR 85 minutes from Mike Acton -- engine director at Insomniac Games, where wasting cycles is not an abstract debate, it is the difference between a game that ships and one that dies. -> His first principle is blunt: the only purpose of any program is to transform data. Not to model the world in pretty classes. Move data, fast. 06:33 -- The lie he calls out: "Software is the platform". No. The hardware is the platform. Ignore it and you cannot even reason about the cost of your own code. Most devs never look at what the CPU actually does. They stack abstractions until the machine spends its life waiting on memory it should never have touched. 49:38 -- He prints a value, zips it, and measures the waste directly. A brutal little trick that exposes how much of your data is pure noise. And this is the AI era's blind spot too. Agents generate more abstract code faster, on hardware nobody profiles, while the GPU bill quietly explodes. You thought clean code was the goal. This is the man shipping real engines saying the machine is the goal, and clean code is often how you lose it. Bookmark it & you'll never look at your own abstractions the same ↓
1:25:35
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THE ENGINE DIRECTOR BEHIND AAA CONSOLE GAMES WALKED ON STAGE AT A C++ CONFERENCE AND TOLD A ROOM OF EXPERTS THAT THE CLEAN, OBJECT ORIENTED CODE THEY ARE PROUD OF IS A LIE AND IT QUIETLY THROWS AWAY 90% OF THE MACHINE THEY PAID FOR 85 minutes from Mike Acton -- engine director at Insomniac Games, where wasting cycles is not an abstract debate, it is the difference between a game that ships and one that dies. -> His first principle is blunt: the only purpose of any program is to transform data. Not to model the world in pretty classes. Move data, fast. 06:33 -- The lie he calls out: "Software is the platform". No. The hardware is the platform. Ignore it and you cannot even reason about the cost of your own code. Most devs never look at what the CPU actually does. They stack abstractions until the machine spends its life waiting on memory it should never have touched. 49:38 -- He prints a value, zips it, and measures the waste directly. A brutal little trick that exposes how much of your data is pure noise. And this is the AI era's blind spot too. Agents generate more abstract code faster, on hardware nobody profiles, while the GPU bill quietly explodes. You thought clean code was the goal. This is the man shipping real engines saying the machine is the goal, and clean code is often how you lose it. Bookmark it & you'll never look at your own abstractions the same ↓

slash1s

173,406 views • 1 month ago

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
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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 views • 2 months ago

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
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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

380,481 views • 3 months ago