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20 free Tembo runs/day sounds tiny until you actually do the math The stack: Opus 4.7, GPT 5.5, GLM 5.1. That means the "free tier" is not toy-model autocomplete It is frontier coding models doing real work 1 Bugfix - 1-3 runs 1 Refactor - 3-5 runs 1 PR...

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> 20 free daily tokens covers a full working session on GPT 5.5 or GLM > Opus 4.7 handles 2-3 serious architecture tasks per day at zero cost > one GitHub account can link to multiple Tembo accounts, the math on that is obvious > 5 minutes to sign up. 10 minutes to your first merged PR > no clarifying questions, prompts have to be precise, the agent just executes > the $100/month tools and the $0 tool are now producing the same output on most tasks and most developers haven't noticed yet this is not a discount → this is the same models → the same → capability → the same output → for nothing!
0:25

> 20 free daily tokens covers a full working session on GPT 5.5 or GLM > Opus 4.7 handles 2-3 serious architecture tasks per day at zero cost > one GitHub account can link to multiple Tembo accounts, the math on that is obvious > 5 minutes to sign up. 10 minutes to your first merged PR > no clarifying questions, prompts have to be precise, the agent just executes > the $100/month tools and the $0 tool are now producing the same output on most tasks and most developers haven't noticed yet this is not a discount → this is the same models → the same → capability → the same output → for nothing!

Shadow Nick

26,539 Aufrufe • vor 1 Monat

Claude Code costs $200/month here's how to run it for free. 4 commands. 10 minutes step 1 - install Ollama runs AI models locally → download from → install. it runs in background on localhost:11434 step 2 - download a coding model → ollama pull qwen2.5-coder:14b (7b for 8GB RAM, 32b for 32GB+ RAM) step 3 - redirect Claude Code to your machine → export ANTHROPIC_BASE_URL= → export ANTHROPIC_MODEL=qwen2.5-coder:14b step 4 - launch → claude → "make a hello world website" → it reads files, writes code, runs commands. locally. free same agent loop. same tools. $0/month the $200 was for convenience the capability was always free like + bookmark - you'll need this
1:41

Claude Code costs $200/month here's how to run it for free. 4 commands. 10 minutes step 1 - install Ollama runs AI models locally → download from → install. it runs in background on localhost:11434 step 2 - download a coding model → ollama pull qwen2.5-coder:14b (7b for 8GB RAM, 32b for 32GB+ RAM) step 3 - redirect Claude Code to your machine → export ANTHROPIC_BASE_URL= → export ANTHROPIC_MODEL=qwen2.5-coder:14b step 4 - launch → claude → "make a hello world website" → it reads files, writes code, runs commands. locally. free same agent loop. same tools. $0/month the $200 was for convenience the capability was always free like + bookmark - you'll need this

self.dll

177,638 Aufrufe • vor 3 Monaten

gpt-5.5 just dropped. and it changes EVERYTHING for traders. openai stopped calling it a chat model. they call it an "agent runtime" now. that means one thing: AI that doesn't wait for you - it acts. here's what gpt-5.5 can actually do: → runs multi-step trading workflows autonomously → operates computer interfaces without human input → plans, iterates, executes - in a terminal, not a chat window → 82.7% on Terminal-Bench 2.0 - real command-line task execution the only question is: are you already running one? full FREE Guide here → like + bookmark. this is the wave.
0:23

gpt-5.5 just dropped. and it changes EVERYTHING for traders. openai stopped calling it a chat model. they call it an "agent runtime" now. that means one thing: AI that doesn't wait for you - it acts. here's what gpt-5.5 can actually do: → runs multi-step trading workflows autonomously → operates computer interfaces without human input → plans, iterates, executes - in a terminal, not a chat window → 82.7% on Terminal-Bench 2.0 - real command-line task execution the only question is: are you already running one? full FREE Guide here → like + bookmark. this is the wave.

AlphaCartel

56,823 Aufrufe • vor 2 Monaten

HERMES AGENT RUNS A POWERFUL OPS SYSTEM ON ONE $6/MO VPS. NO GPU. 90% of people use AI like a search box. Type, grab the answer, close the tab. Tomorrow it has no idea who they are. I stopped doing that 3 months ago. Now Hermes runs as a 24/7 operating system - no GPU. It curates my research before I wake up, drafts content, tracks its own tasks, and pings me on Telegram from anywhere. The math nobody runs: → Agents that wrote 20+ of their own skills finish tasks ~40% faster - not a better model, just accumulated procedure → The full content system burns ~10-11M tokens/mo - and model choice alone swings the bill 10x → Same crons, same goals, one orchestrator + 3 specialists = the output of a small content team, running while I sleep → Runtime is free (MIT). You pay for cheap inference + a $6 box. That’s it. → Replaces the work people pay a VA $500-$2,000/mo to do The catch? Compounding takes 2-4 weeks of consistent use before it’s obvious. Most people quit before day 14. The operator doesn’t prompt. The operator builds the system. Reply “Hermes” and I’ll send you the step-by-step Playbook in your DMs 👇
0:41

HERMES AGENT RUNS A POWERFUL OPS SYSTEM ON ONE $6/MO VPS. NO GPU. 90% of people use AI like a search box. Type, grab the answer, close the tab. Tomorrow it has no idea who they are. I stopped doing that 3 months ago. Now Hermes runs as a 24/7 operating system - no GPU. It curates my research before I wake up, drafts content, tracks its own tasks, and pings me on Telegram from anywhere. The math nobody runs: → Agents that wrote 20+ of their own skills finish tasks ~40% faster - not a better model, just accumulated procedure → The full content system burns ~10-11M tokens/mo - and model choice alone swings the bill 10x → Same crons, same goals, one orchestrator + 3 specialists = the output of a small content team, running while I sleep → Runtime is free (MIT). You pay for cheap inference + a $6 box. That’s it. → Replaces the work people pay a VA $500-$2,000/mo to do The catch? Compounding takes 2-4 weeks of consistent use before it’s obvious. Most people quit before day 14. The operator doesn’t prompt. The operator builds the system. Reply “Hermes” and I’ll send you the step-by-step Playbook in your DMs 👇

ZEUS⚡️

10,647 Aufrufe • vor 1 Monat

FREE CLAUDE FABLE 5 and almost nobody knows this trick Anthropic recently released their most capable model ever - the first Mythos-class model available to the public normally it's $10/$50 per million tokens but there's a hack: GitLab just added Fable 5 to Duo Agent Platform - across ALL tiers, including free trial the setup: 1/ register a GitLab account: 2/ start the free GitLab Duo trial 3/ Fable 5 is live through their AI Gateway 4/ done - Mythos-class model, $0 why Fable 5 is worth the hype: 1/ 80.3% SWE-Bench Pro - 11 points ahead of the next best model 2/ multi-DAY autonomous runs without losing the plot 3/ single-pass implementations of systems that took days of iteration 4/ Karpathy called it 'a major-version-bump-deserving step change' also free on Claude Pro/Max/Team until June 22 - after that it's usage credits you have a limited window to run the most capable public model on the planet for FREE
0:22

FREE CLAUDE FABLE 5 and almost nobody knows this trick Anthropic recently released their most capable model ever - the first Mythos-class model available to the public normally it's $10/$50 per million tokens but there's a hack: GitLab just added Fable 5 to Duo Agent Platform - across ALL tiers, including free trial the setup: 1/ register a GitLab account: 2/ start the free GitLab Duo trial 3/ Fable 5 is live through their AI Gateway 4/ done - Mythos-class model, $0 why Fable 5 is worth the hype: 1/ 80.3% SWE-Bench Pro - 11 points ahead of the next best model 2/ multi-DAY autonomous runs without losing the plot 3/ single-pass implementations of systems that took days of iteration 4/ Karpathy called it 'a major-version-bump-deserving step change' also free on Claude Pro/Max/Team until June 22 - after that it's usage credits you have a limited window to run the most capable public model on the planet for FREE

kaize

24,161 Aufrufe • vor 1 Monat

Everyone's hyped about making money with Al apps. But here's the truth: It's not the code Al writes that makes money... It's the working, bug-free software that actually runs. Here's how even a complete beginner can ship a production-ready app in 3 steps: Step 1: Ask ChatGPT for a PRD (your blueprint). Step 2: Let Cursor (or any coding Al) generate the app. Step 3: Run it through TestSprite → it auto-tests every function, finds bugs, and hands you a fix report inside your IDE. After several test cycles, I had a polished, bug-free gym app live-no debugging required. That's the real Al game-changer: Not just auto-coding. But auto-testing + auto-fixing. TestSprite is already a top-10 Product Hunt tool and the most popular no-code dev tool of 2025. Try it here:
2:28

Everyone's hyped about making money with Al apps. But here's the truth: It's not the code Al writes that makes money... It's the working, bug-free software that actually runs. Here's how even a complete beginner can ship a production-ready app in 3 steps: Step 1: Ask ChatGPT for a PRD (your blueprint). Step 2: Let Cursor (or any coding Al) generate the app. Step 3: Run it through TestSprite → it auto-tests every function, finds bugs, and hands you a fix report inside your IDE. After several test cycles, I had a polished, bug-free gym app live-no debugging required. That's the real Al game-changer: Not just auto-coding. But auto-testing + auto-fixing. TestSprite is already a top-10 Product Hunt tool and the most popular no-code dev tool of 2025. Try it here:

Tulsi Soni

31,968 Aufrufe • vor 10 Monaten

AI just fixed your life and business. Claude Cowork is the $20/month AI employee everyone was afraid of 🤯 Train it once on your workflow + SOPs and it just runs... 24/7. 10 wild results (🧵👇) 1. Match up 1000s of day trades for tax season
0:33

AI just fixed your life and business. Claude Cowork is the $20/month AI employee everyone was afraid of 🤯 Train it once on your workflow + SOPs and it just runs... 24/7. 10 wild results (🧵👇) 1. Match up 1000s of day trades for tax season

Min Choi

364,100 Aufrufe • vor 5 Monaten

Kids in your 20’s please pay attention … this is the era of full offense while not blaming or asking anyone else for anything … the 20’s is the age range where you are a super hero and your risk tolerance is profound .. but it takes a winners mindset and that means .. #1 humility #2 accountability #3 confidence #4 patience #5 tenacity I’m cheering for you ❤️❤️❤️❗️
0:28

Kids in your 20’s please pay attention … this is the era of full offense while not blaming or asking anyone else for anything … the 20’s is the age range where you are a super hero and your risk tolerance is profound .. but it takes a winners mindset and that means .. #1 humility #2 accountability #3 confidence #4 patience #5 tenacity I’m cheering for you ❤️❤️❤️❗️

Gary Vaynerchuk

42,054 Aufrufe • vor 4 Monaten

i tested a fully local ai coding setup: gemma4 from Google DeepMind running through LM Studio, paired with pi which is a minimal open source coding harness this is part 1 of a two part series. here we cover the why and the complete hands-on setup 0:00 - Running a coding agent fully local 0:47 - Why Pi as the coding agent harness 1:39 - Why Gemma 4 and which variant to pick 2:44 - Architecture: Pi, LM Studio and Gemma 4 3:45 - LM Studio setup and model download 5:01 - Starting the local server 5:19 - API sanity check with curl 6:08 - Installing the Pi coding agent 6:20 - Configuring models.json for local models 8:20 - First run: selecting the model in Pi 8:58 - Fixing the context length error 10:16 - Testing the working setup 10:50 - What is coming in Part 2
11:32

i tested a fully local ai coding setup: gemma4 from Google DeepMind running through LM Studio, paired with pi which is a minimal open source coding harness this is part 1 of a two part series. here we cover the why and the complete hands-on setup 0:00 - Running a coding agent fully local 0:47 - Why Pi as the coding agent harness 1:39 - Why Gemma 4 and which variant to pick 2:44 - Architecture: Pi, LM Studio and Gemma 4 3:45 - LM Studio setup and model download 5:01 - Starting the local server 5:19 - API sanity check with curl 6:08 - Installing the Pi coding agent 6:20 - Configuring models.json for local models 8:20 - First run: selecting the model in Pi 8:58 - Fixing the context length error 10:16 - Testing the working setup 10:50 - What is coming in Part 2

Nidhi Singh

16,289 Aufrufe • vor 1 Monat

A 67-year-old grandfather in Vermont built a trading bot during retirement - it earned him $290,000 in a single year without losing a single trade. 168 trades. 100% win rate. Zero drawdown across 365 days. The strategy is so safe it's basically a bond ladder - but with AI doing the probability math instead of credit Here's how it actually works: He only takes bets the model is >85% sure of. Everything else? Killed. 188 events scanned per day. ~12 pass the filter. ~1 actually deploy. AI algo waits. Most days he doesn't trade at all. The probability model is stolen from weather forecasting: 31 models vote on every outcome. 28 out of 31 saying "yes" = 90% conviction. Below 26 = automatic reject. That's the entire edge. Polymarket is full of 87-92¢ markets that almost always settle at $1. Most traders ignore them - upside per dollar looks tiny. The math: edge = model_p − market_p kelly = edge / (1 − token_price) size = bankroll x kelly x 0.15 The stack runs free: Polymarket Gamma API for discovery, ClickHouse + Redpanda for the pipeline, paper mode validation before live capital. Brier score 0.041 across all 168 predictions. Anything under 0.10 is institutional-grade. He's printing the academic numbers. Most boring account on Polymarket. Also the most profitable - so save this post. And if you’ve been looking for the most safest strategy to copy - you’ve definitely found it. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment Your thoughts . 2. Like and Retweet this post 3. Follow me Marry Evan
0:23

A 67-year-old grandfather in Vermont built a trading bot during retirement - it earned him $290,000 in a single year without losing a single trade. 168 trades. 100% win rate. Zero drawdown across 365 days. The strategy is so safe it's basically a bond ladder - but with AI doing the probability math instead of credit Here's how it actually works: He only takes bets the model is >85% sure of. Everything else? Killed. 188 events scanned per day. ~12 pass the filter. ~1 actually deploy. AI algo waits. Most days he doesn't trade at all. The probability model is stolen from weather forecasting: 31 models vote on every outcome. 28 out of 31 saying "yes" = 90% conviction. Below 26 = automatic reject. That's the entire edge. Polymarket is full of 87-92¢ markets that almost always settle at $1. Most traders ignore them - upside per dollar looks tiny. The math: edge = model_p − market_p kelly = edge / (1 − token_price) size = bankroll x kelly x 0.15 The stack runs free: Polymarket Gamma API for discovery, ClickHouse + Redpanda for the pipeline, paper mode validation before live capital. Brier score 0.041 across all 168 predictions. Anything under 0.10 is institutional-grade. He's printing the academic numbers. Most boring account on Polymarket. Also the most profitable - so save this post. And if you’ve been looking for the most safest strategy to copy - you’ve definitely found it. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment Your thoughts . 2. Like and Retweet this post 3. Follow me Marry Evan

Marry Evan

16,247 Aufrufe • vor 1 Monat

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch
24:21

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

GREG ISENBERG

435,542 Aufrufe • vor 4 Monaten

Anthropic has a SINGLE growth marketer. And he just mapped the 4 levels of AI marketing, most are still on level 1. Level 1 is automating what you already do. Reports, copy, data pulls. Useful, but everyone has it. Level 2 is using AI as a thinking partner where it's smarter than you. Tax questions, legal implications, second opinions on hard calls. Level 3 is doing work that was below the ROI threshold before. Mining negative keywords across every ad group. Daily broken link checks across your full site. Competitor monitoring that runs forever. Nobody did this work before because the manual cost was never worth it. Level 4 is building custom tools only you would ever build. Your business has specific data, specific workflows, specific edge cases. No off-the-shelf product covers them. Most of the AI marketing plugins floating around GitHub work in theory and break in practice because they're built for the median user. The companies pulling ahead are building for themselves. Ramp is doing it with Glass. We're doing it with SingleBrain. Level 1 is going to get eaten by software companies running cheap Chinese models like Qwen and Kimi. The compounding happens at level 3 and 4, where the work is built for YOUR business and nobody else's.
2:18

Anthropic has a SINGLE growth marketer. And he just mapped the 4 levels of AI marketing, most are still on level 1. Level 1 is automating what you already do. Reports, copy, data pulls. Useful, but everyone has it. Level 2 is using AI as a thinking partner where it's smarter than you. Tax questions, legal implications, second opinions on hard calls. Level 3 is doing work that was below the ROI threshold before. Mining negative keywords across every ad group. Daily broken link checks across your full site. Competitor monitoring that runs forever. Nobody did this work before because the manual cost was never worth it. Level 4 is building custom tools only you would ever build. Your business has specific data, specific workflows, specific edge cases. No off-the-shelf product covers them. Most of the AI marketing plugins floating around GitHub work in theory and break in practice because they're built for the median user. The companies pulling ahead are building for themselves. Ramp is doing it with Glass. We're doing it with SingleBrain. Level 1 is going to get eaten by software companies running cheap Chinese models like Qwen and Kimi. The compounding happens at level 3 and 4, where the work is built for YOUR business and nobody else's.

ericosiu

55,327 Aufrufe • vor 2 Monaten

Alibaba just released a coding model that hits 82 percent on SWE-Bench Verified. That is the highest score ever published for an open-source model. The weights are free. The license is Apache 2.0. You can run it today. The model is Qwen 4 Coder 32B. Here is what 82 percent on SWE-Bench Verified actually means. SWE-Bench Verified tests whether an AI can autonomously resolve real bugs pulled from real production GitHub repositories. Not synthetic exercises. Real open-source projects that real teams depend on. A model gets a bug report, reads the code, writes a fix, and either passes the test suite or it does not. At 82 percent, Qwen 4 Coder 32B resolves 82 out of every 100 real production bugs it is given. Without a human guiding it. On code it has never seen before. For comparison: Qwen 4 Coder 32B: 82 percent SWE-Bench Verified. Open source. Apache 2.0. Claude Fable 5: 80.3 percent SWE-Bench Pro. $10 input / $50 output per million tokens. Currently suspended. GPT-5.6 Sol: Competitive on Terminal-Bench. $5 input / $30 output per million tokens. An open-weight model that you can download and run for free just beat both of them on the benchmark designed to measure real software engineering capability. Here is the architecture. Qwen 4 Coder 32B is a 32 billion parameter dense model. Not a Mixture-of-Experts. Every parameter is active on every request. This matters for inference: a dense 32B model runs on 22 gigabytes of VRAM, which fits on a single high-end consumer GPU or a MacBook Pro with 64GB of unified memory. The smaller variant, Qwen 4 Coder 4B, runs at approximately 135 tokens per second on an M5 Max and fits inside 8 gigabytes of RAM. For a model with usable coding capability, that is a new bar for what fits in a single laptop. The training methodology continued Alibaba's approach of reinforcement learning on verifiable coding tasks. The model gets rewarded when its code passes tests. It gets penalized when it fails. Over millions of training steps, the model learns to write code that actually runs rather than code that looks plausible. License: Apache 2.0. Full commercial use. No attribution requirement. No revenue threshold. No monthly active user ceiling. Weights: Hugging Face, available today. Runs on: vLLM, Ollama, SGLang, and any standard GGUF-compatible inference engine. Qwen 4 32B also runs at approximately 135 tokens per second on an M5 Max chip, setting a new bar for what a sub-8GB model can do on Apple Silicon. The open-source coding model just beat the best closed-source model in the world on the benchmark designed to test whether AI can actually do software engineering. The weights are free. The subscription is optional. Source: Autom8Labs AI Insight July 2026, State of Open Source LLMs June 2026, Kunal Ganglani blog June 2026.
0:25

Alibaba just released a coding model that hits 82 percent on SWE-Bench Verified. That is the highest score ever published for an open-source model. The weights are free. The license is Apache 2.0. You can run it today. The model is Qwen 4 Coder 32B. Here is what 82 percent on SWE-Bench Verified actually means. SWE-Bench Verified tests whether an AI can autonomously resolve real bugs pulled from real production GitHub repositories. Not synthetic exercises. Real open-source projects that real teams depend on. A model gets a bug report, reads the code, writes a fix, and either passes the test suite or it does not. At 82 percent, Qwen 4 Coder 32B resolves 82 out of every 100 real production bugs it is given. Without a human guiding it. On code it has never seen before. For comparison: Qwen 4 Coder 32B: 82 percent SWE-Bench Verified. Open source. Apache 2.0. Claude Fable 5: 80.3 percent SWE-Bench Pro. $10 input / $50 output per million tokens. Currently suspended. GPT-5.6 Sol: Competitive on Terminal-Bench. $5 input / $30 output per million tokens. An open-weight model that you can download and run for free just beat both of them on the benchmark designed to measure real software engineering capability. Here is the architecture. Qwen 4 Coder 32B is a 32 billion parameter dense model. Not a Mixture-of-Experts. Every parameter is active on every request. This matters for inference: a dense 32B model runs on 22 gigabytes of VRAM, which fits on a single high-end consumer GPU or a MacBook Pro with 64GB of unified memory. The smaller variant, Qwen 4 Coder 4B, runs at approximately 135 tokens per second on an M5 Max and fits inside 8 gigabytes of RAM. For a model with usable coding capability, that is a new bar for what fits in a single laptop. The training methodology continued Alibaba's approach of reinforcement learning on verifiable coding tasks. The model gets rewarded when its code passes tests. It gets penalized when it fails. Over millions of training steps, the model learns to write code that actually runs rather than code that looks plausible. License: Apache 2.0. Full commercial use. No attribution requirement. No revenue threshold. No monthly active user ceiling. Weights: Hugging Face, available today. Runs on: vLLM, Ollama, SGLang, and any standard GGUF-compatible inference engine. Qwen 4 32B also runs at approximately 135 tokens per second on an M5 Max chip, setting a new bar for what a sub-8GB model can do on Apple Silicon. The open-source coding model just beat the best closed-source model in the world on the benchmark designed to test whether AI can actually do software engineering. The weights are free. The subscription is optional. Source: Autom8Labs AI Insight July 2026, State of Open Source LLMs June 2026, Kunal Ganglani blog June 2026.

Harman

38,953 Aufrufe • vor 6 Tagen

A 67-year-old grandfather in Vermont built a trading bot during retirement - it earned him $290,000 in a single year without losing a single trade. 168 trades. 100% win rate. Zero drawdown across 365 days. The strategy is so safe it’s basically a bond ladder - but with AI doing the probability math instead of credit ratings. Here’s how it actually works: He only takes bets the model is >85% sure of. Everything else? Killed. 188 events scanned per day. ~12 pass the filter. ~1 actually deploy. AI algo waits. Most days he doesn’t trade at all. The probability model is stolen from weather forecasting: 31 models vote on every outcome. 28 out of 31 saying “yes” = 90% conviction. Below 26 = automatic reject. That’s the entire edge. Polymarket is full of 87-92¢ markets that almost always settle at $1. Most traders ignore them - upside per dollar looks tiny. The math: edge = model_p − market_p kelly = edge / (1 − token_price) size = bankroll × kelly × 0.15 The stack runs free: Polymarket Gamma API for discovery, ClickHouse + Redpanda for the pipeline, paper mode validation before live capital. Brier score 0.041 across all 168 predictions. Anything under 0.10 is institutional-grade. He’s printing the academic numbers. Most boring account on Polymarket. Also the most profitable - so save this post. And if you’ve been looking for the safest strategy to copy - you’ve definitely found it. You only need Claude + Device + 1 hour/day. Giving This Free Dm for 24 hours. To get it: 1. Comment What Ever you think about it. ( Mandatory ) 2. Like and Retweet this post 3. Follow me Marry Evan
0:51

A 67-year-old grandfather in Vermont built a trading bot during retirement - it earned him $290,000 in a single year without losing a single trade. 168 trades. 100% win rate. Zero drawdown across 365 days. The strategy is so safe it’s basically a bond ladder - but with AI doing the probability math instead of credit ratings. Here’s how it actually works: He only takes bets the model is >85% sure of. Everything else? Killed. 188 events scanned per day. ~12 pass the filter. ~1 actually deploy. AI algo waits. Most days he doesn’t trade at all. The probability model is stolen from weather forecasting: 31 models vote on every outcome. 28 out of 31 saying “yes” = 90% conviction. Below 26 = automatic reject. That’s the entire edge. Polymarket is full of 87-92¢ markets that almost always settle at $1. Most traders ignore them - upside per dollar looks tiny. The math: edge = model_p − market_p kelly = edge / (1 − token_price) size = bankroll × kelly × 0.15 The stack runs free: Polymarket Gamma API for discovery, ClickHouse + Redpanda for the pipeline, paper mode validation before live capital. Brier score 0.041 across all 168 predictions. Anything under 0.10 is institutional-grade. He’s printing the academic numbers. Most boring account on Polymarket. Also the most profitable - so save this post. And if you’ve been looking for the safest strategy to copy - you’ve definitely found it. You only need Claude + Device + 1 hour/day. Giving This Free Dm for 24 hours. To get it: 1. Comment What Ever you think about it. ( Mandatory ) 2. Like and Retweet this post 3. Follow me Marry Evan

Marry Evan

12,238 Aufrufe • vor 19 Tagen

> the pipeline behind "AI dancing girl" accounts: 1. find a viral tiktok dance, download it 2. screenshot frame 1 → chatgpt writes the prompt 3. generate your model from it (freepik) 4. wavespeed → kling 2.6 motion control 5. add image + dance video → run 6. your model does the viral dance 7. post 2x/day → traffic → fanvue > the tech is real > the "$7,500 a day" is bait for the paid group > they're not selling the model. they're selling the funnel
0:44

> the pipeline behind "AI dancing girl" accounts: 1. find a viral tiktok dance, download it 2. screenshot frame 1 → chatgpt writes the prompt 3. generate your model from it (freepik) 4. wavespeed → kling 2.6 motion control 5. add image + dance video → run 6. your model does the viral dance 7. post 2x/day → traffic → fanvue > the tech is real > the "$7,500 a day" is bait for the paid group > they're not selling the model. they're selling the funnel

RetroChainer

35,356 Aufrufe • vor 1 Monat

cursor just made every $200/month copilot subscription look like a scam dropped today with their own coding model what took 8 hours of manual coding now takes 30 seconds and it runs 8 versions of itself in parallel to pick the best solution while github's charging $20/month for autocomplete, cursor built an entire autonomous dev team composer model: → 4x faster than gpt/claude → completes full features in <30 seconds → tests its own code automatically → built with reinforcement learning on real codebases here's what's actually wild: most companies paying $150k/year for junior devs to do work this does for $20/month just saved a client $253k/month migrating their dev work to cursor's multi-agent system the intelligence gap between "we hired 3 developers" and "we deployed cursor" is getting stupid comment "COMPOSER" and ill send the full breakdown of how to replace your dev costs with this
1:04

cursor just made every $200/month copilot subscription look like a scam dropped today with their own coding model what took 8 hours of manual coding now takes 30 seconds and it runs 8 versions of itself in parallel to pick the best solution while github's charging $20/month for autocomplete, cursor built an entire autonomous dev team composer model: → 4x faster than gpt/claude → completes full features in <30 seconds → tests its own code automatically → built with reinforcement learning on real codebases here's what's actually wild: most companies paying $150k/year for junior devs to do work this does for $20/month just saved a client $253k/month migrating their dev work to cursor's multi-agent system the intelligence gap between "we hired 3 developers" and "we deployed cursor" is getting stupid comment "COMPOSER" and ill send the full breakdown of how to replace your dev costs with this

Nozz

240,249 Aufrufe • vor 8 Monaten

DIDDY FOUND GUILTY ON 2 OF 5 CHARGES & COULD FACE UP TO 20 YEARS IN PRISON. Guilty on count 3 & 5: Count 3: Mann Act transportation of Casandra Ventura Count 5: Mann Act transportation of former girlfriend Jane Not guilty on count 1, 2, & 4: Count 1: Racketeering conspiracy Count 2: Sex trafficking of Casandra Ventura Count 4: Sex trafficking of former girlfriend Jane Diddy will most likely receive a 2-5 year sentence, if that. There is no justice when you're working with the feds and intelligence agencies. Blackmail runs the world, and they're using children to do it. There's no way he got away with it for this long and a slap on the wrist if he wasn't.
0:46

DIDDY FOUND GUILTY ON 2 OF 5 CHARGES & COULD FACE UP TO 20 YEARS IN PRISON. Guilty on count 3 & 5: Count 3: Mann Act transportation of Casandra Ventura Count 5: Mann Act transportation of former girlfriend Jane Not guilty on count 1, 2, & 4: Count 1: Racketeering conspiracy Count 2: Sex trafficking of Casandra Ventura Count 4: Sex trafficking of former girlfriend Jane Diddy will most likely receive a 2-5 year sentence, if that. There is no justice when you're working with the feds and intelligence agencies. Blackmail runs the world, and they're using children to do it. There's no way he got away with it for this long and a slap on the wrist if he wasn't.

The SCIF

17,064 Aufrufe • vor 1 Jahr

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.
8:27

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.

2xnmore

26,946 Aufrufe • vor 1 Monat

THIS GUY SPENT 5 MINUTES SETTING UP A $20 CLAUDE WORKSPACE AND TURNED 30 MINUTES OF DAILY PROMPT CHAOS INTO A SYSTEM Most people open Claude, type 1 messy prompt, rewrite it 10 times, get the same generic answer and call the model “bad.” He opens Claude Cowork, adds the project rules, past context, working files, examples, decisions and a clean place for the model to remember what it is doing. Same Claude. Same $20 tool. Completely different output. The funny part is there is no magic prompt here. No secret template. No “10x Claude” trick. Just the boring layer Karpathy keeps pointing at: context, memory and structure before output. A setup like this can save 30 minutes a day, which is 15 hours a month. If you use Claude for client work at $50 to $100/hour, that is $750 to $1,500/month in time you stop burning on repeated prompts. The model is real. The output is real. The edge is in not making Claude start from zero every single time.
0:37

THIS GUY SPENT 5 MINUTES SETTING UP A $20 CLAUDE WORKSPACE AND TURNED 30 MINUTES OF DAILY PROMPT CHAOS INTO A SYSTEM Most people open Claude, type 1 messy prompt, rewrite it 10 times, get the same generic answer and call the model “bad.” He opens Claude Cowork, adds the project rules, past context, working files, examples, decisions and a clean place for the model to remember what it is doing. Same Claude. Same $20 tool. Completely different output. The funny part is there is no magic prompt here. No secret template. No “10x Claude” trick. Just the boring layer Karpathy keeps pointing at: context, memory and structure before output. A setup like this can save 30 minutes a day, which is 15 hours a month. If you use Claude for client work at $50 to $100/hour, that is $750 to $1,500/month in time you stop burning on repeated prompts. The model is real. The output is real. The edge is in not making Claude start from zero every single time.

Gipp 🦅

14,871 Aufrufe • vor 1 Monat

OpenAI Dev Day Just kicked off and it was the biggest day for AI since the launch of GPT-4 in April. Here's a 3 minute video with everything you need to know and a full summary: 1. GPT-4 Turbo is released. It is much better than GPT-4, can accept up to 128k characters and is 3x cheaper. The knowledge cut off is now April 2023 2. Text to Speech is also being released. They offer 6 hyper-realistic voice models that are the best I've heard from any other AI t-t-s software on the market. 3. Copyright Shield. OpenAI will actually defend your legal costs if you get sued for copyright infringement using their API! 4. GPTs. Maybe the most exciting IMHO, you can now create custom "GPTs" by training your own model of GPT and selling it on their store. This will create a massive shakeup of the current AI software landscape and is the first step towards autonomous agents.
2:26

OpenAI Dev Day Just kicked off and it was the biggest day for AI since the launch of GPT-4 in April. Here's a 3 minute video with everything you need to know and a full summary: 1. GPT-4 Turbo is released. It is much better than GPT-4, can accept up to 128k characters and is 3x cheaper. The knowledge cut off is now April 2023 2. Text to Speech is also being released. They offer 6 hyper-realistic voice models that are the best I've heard from any other AI t-t-s software on the market. 3. Copyright Shield. OpenAI will actually defend your legal costs if you get sued for copyright infringement using their API! 4. GPTs. Maybe the most exciting IMHO, you can now create custom "GPTs" by training your own model of GPT and selling it on their store. This will create a massive shakeup of the current AI software landscape and is the first step towards autonomous agents.

Mark Gadala-Maria

79,177 Aufrufe • vor 2 Jahren