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grokbot it's an agent with its own identity, its own computer, and it stays on when you're not here's what "active AI employee" actually looks like in the demo: - chief of staff agent - checks in on your other agents, reads your calendar, dispatches tasks to the right one automatically - shopping agent - logged into your accounts, books tickets, buys groceries, reports back - marketing agent - signed into your actual linkedin, browses your past posts for tone, then writes and publishes a new one on its own - engineering agents - self-triage bug reports, kick off cloud coding agents, come back with a pull request, a screenshot, and a video of the fix the interface isn't a dashboard, it's a chat - same shape as texting a coworker, no tool calls to babysit the number that matters more than any of the demos: grok 4.6 scored 70.8% on cursor bench at $2.81 a task, fable 5 max scored 70.5% at $17.32 same capability, 6x the cost difference - that's the unlock that makes running a fleet of these actually affordable instead of a novelty

grokbot it's an agent with its own identity, its own computer, and it stays on when you're not here's what "active AI employee" actually looks like in the demo: - chief of staff agent - checks in on your other agents, reads your calendar, dispatches tasks to the right one automatically - shopping agent - logged into your accounts, books tickets, buys groceries, reports back - marketing agent - signed into your actual linkedin, browses your past posts for tone, then writes and publishes a new one on its own - engineering agents - self-triage bug reports, kick off cloud coding agents, come back with a pull request, a screenshot, and a video of the fix the interface isn't a dashboard, it's a chat - same shape as texting a coworker, no tool calls to babysit the number that matters more than any of the demos: grok 4.6 scored 70.8% on cursor bench at $2.81 a task, fable 5 max scored 70.5% at $17.32 same capability, 6x the cost difference - that's the unlock that makes running a fleet of these actually affordable instead of a novelty

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Andrej Karpathy spent 4 minutes in an interview explaining a single idea about how most people haven’t even started learning how to use AI and everyone paying $20/month for a subscription.. that's not really using Claude at all his point is that the real skill gap is the ability to build with AI he identified 4 behaviors that break Claude Code and put them all into one file a developer expanded it into 21 rules and published it - 82,000 stars and #1 on GitHub Trending coding accuracy jumped from 65% to 94% here's what these 21 rules actually are and why most developers using Claude every day have never configured them the full breakdown is covered in the article below 👇

Andrej Karpathy spent 4 minutes in an interview explaining a single idea about how most people haven’t even started learning how to use AI and everyone paying $20/month for a subscription.. that's not really using Claude at all his point is that the real skill gap is the ability to build with AI he identified 4 behaviors that break Claude Code and put them all into one file a developer expanded it into 21 rules and published it - 82,000 stars and #1 on GitHub Trending coding accuracy jumped from 65% to 94% here's what these 21 rules actually are and why most developers using Claude every day have never configured them the full breakdown is covered in the article below 👇

3,208,362 просмотров • 4 месяцев назад

AI researcher breaks down why your $5,000 Mac Studio runs local AI slower than benchmarks promise: "Performance isn't about the hardware - it's about three software layers stacked on top of it." in 15 minutes he tests MLX, Ollama, llama.cpp, and the new vllm-mlx head to head no GPU needed, just the Mac you probably already own - why Ollama, the most popular tool, is actually one of the slower options on Mac - the real bottleneck nobody benchmarks: 'prefill' time before the first token even appears - the exact quantization setting (4-bit) that's the best speed-to-quality tradeoff - why unified memory makes Macs uniquely good for big models compared to Nvidia GPUs complete guide to choosing the right hardware for local AI is below 👇

AI researcher breaks down why your $5,000 Mac Studio runs local AI slower than benchmarks promise: "Performance isn't about the hardware - it's about three software layers stacked on top of it." in 15 minutes he tests MLX, Ollama, llama.cpp, and the new vllm-mlx head to head no GPU needed, just the Mac you probably already own - why Ollama, the most popular tool, is actually one of the slower options on Mac - the real bottleneck nobody benchmarks: 'prefill' time before the first token even appears - the exact quantization setting (4-bit) that's the best speed-to-quality tradeoff - why unified memory makes Macs uniquely good for big models compared to Nvidia GPUs complete guide to choosing the right hardware for local AI is below 👇

207,513 просмотров • 3 месяцев назад

GPT-6 Astra + Obsidian is honestly the closest thing to a real second brain for a business... i built a memory system that gives every agent instant context on what i've actually worked on, no re-explaining, no re-prompting: - every project, decision and experiment gets logged as its own memory, timestamped automatically - a 3D graph where the freshest, most-connected work sits in the center, the rest fades to the edges - agents read the graph before doing anything, so outputs come back personalized instead of generic - any agent i run (Claude, Codex, Kimi, GLM, whatever's next) writes to the same files, fully model-agnostic - pick a cluster of memories and turn them straight into an output... an app, an SEO article, a full social thread - the loop closes itself: use an agent, it logs the work, the next agent reads it, nothing gets forgotten it's just markdown folders under the hood, which is exactly why it survives every model swap full breakdown in the article below:

GPT-6 Astra + Obsidian is honestly the closest thing to a real second brain for a business... i built a memory system that gives every agent instant context on what i've actually worked on, no re-explaining, no re-prompting: - every project, decision and experiment gets logged as its own memory, timestamped automatically - a 3D graph where the freshest, most-connected work sits in the center, the rest fades to the edges - agents read the graph before doing anything, so outputs come back personalized instead of generic - any agent i run (Claude, Codex, Kimi, GLM, whatever's next) writes to the same files, fully model-agnostic - pick a cluster of memories and turn them straight into an output... an app, an SEO article, a full social thread - the loop closes itself: use an agent, it logs the work, the next agent reads it, nothing gets forgotten it's just markdown folders under the hood, which is exactly why it survives every model swap full breakdown in the article below:

38,271 просмотров • 20 дней назад

A Stanford professor just gave a public lecture on exactly how GPT, Claude, and LLaMA are built under the hood no insider access required just the clearest breakdown of modern LLM architecture I've seen this lecture reveals the framework professors are paid up to $750K a year to teach the gap between "I use ChatGPT" and "I understand how it works" is smaller than most people think the most complete public breakdown of modern LLM architecture I've seen this year

A Stanford professor just gave a public lecture on exactly how GPT, Claude, and LLaMA are built under the hood no insider access required just the clearest breakdown of modern LLM architecture I've seen this lecture reveals the framework professors are paid up to $750K a year to teach the gap between "I use ChatGPT" and "I understand how it works" is smaller than most people think the most complete public breakdown of modern LLM architecture I've seen this year

165,059 просмотров • 3 месяцев назад

Andrej Karpathy spent 2 hours explaining what most AI educators won't tell you it's about what happens to humans when AI takes over knowledge work Karpathy has been in AI for 20 years. he built GPT-2, co-founded OpenAI, led Tesla Autopilot the part most people skip past: > the AI you're using today can't actually learn > "year of agents" is a marketing phrase. he calls it the decade of agents > coding assistants are great at boilerplate. they fall apart on novel architecture > the bottleneck isn't compute. it's that current models memorize instead of reason > most people open Claude, ask a question, close the tab > the humans who flourish won't be the ones who fear AI. they'll be the ones who stayed curious and kept building mental models right now most people are outsourcing thinking without building the skill underneath Karpathy's answer isn't to slow AI down, it's to build better humans faster he's designing a Starfleet Academy from scratch: AI-assisted, expert-led, built on first principles the goal is "eurekas per second" - how fast can you get someone from zero to genuine understanding full 2-hour conversation is below the education section alone is worth more than most $2,000 courses you've seen in your feed

Andrej Karpathy spent 2 hours explaining what most AI educators won't tell you it's about what happens to humans when AI takes over knowledge work Karpathy has been in AI for 20 years. he built GPT-2, co-founded OpenAI, led Tesla Autopilot the part most people skip past: > the AI you're using today can't actually learn > "year of agents" is a marketing phrase. he calls it the decade of agents > coding assistants are great at boilerplate. they fall apart on novel architecture > the bottleneck isn't compute. it's that current models memorize instead of reason > most people open Claude, ask a question, close the tab > the humans who flourish won't be the ones who fear AI. they'll be the ones who stayed curious and kept building mental models right now most people are outsourcing thinking without building the skill underneath Karpathy's answer isn't to slow AI down, it's to build better humans faster he's designing a Starfleet Academy from scratch: AI-assisted, expert-led, built on first principles the goal is "eurekas per second" - how fast can you get someone from zero to genuine understanding full 2-hour conversation is below the education section alone is worth more than most $2,000 courses you've seen in your feed

124,743 просмотров • 4 месяцев назад

ran Grok Bot and Hermes Agent side by side for the same job here's what actually separates them Grok Bot: - gets its own cloud computer, browser, files, terminal, logs into your tools with your credentials - learns by demonstration, record a task once, it saves the routine - runs on Grok, you don't choose the model - all your bots share the same cloud computer and the same logins, no isolation between them - scales by subscription, ~$200/mo solo, ~$120/seat for teams Hermes Agent: - open source, MIT-licensed, no cloud, you host it - each agent gets its own real badge from your identity provider, scoped access only, revoke it and it's gone - brain is swappable, change one config line to switch models - scales by spinning up containers on demand, hire/fire based on backlog - you own the maintenance, an upstream installer change broke one build, one line fix, one morning lost the real fork isn't features, it's where the work happens and who's accountable when it breaks grok bot: convenience, zero setup, but your data sits on their machine and every bot shares every login hermes: more setup, but scoped access, swappable models, and nothing running outside your own network for anything touching regulated or sensitive data, that architecture difference is the whole decision

ran Grok Bot and Hermes Agent side by side for the same job here's what actually separates them Grok Bot: - gets its own cloud computer, browser, files, terminal, logs into your tools with your credentials - learns by demonstration, record a task once, it saves the routine - runs on Grok, you don't choose the model - all your bots share the same cloud computer and the same logins, no isolation between them - scales by subscription, ~$200/mo solo, ~$120/seat for teams Hermes Agent: - open source, MIT-licensed, no cloud, you host it - each agent gets its own real badge from your identity provider, scoped access only, revoke it and it's gone - brain is swappable, change one config line to switch models - scales by spinning up containers on demand, hire/fire based on backlog - you own the maintenance, an upstream installer change broke one build, one line fix, one morning lost the real fork isn't features, it's where the work happens and who's accountable when it breaks grok bot: convenience, zero setup, but your data sits on their machine and every bot shares every login hermes: more setup, but scoped access, swappable models, and nothing running outside your own network for anything touching regulated or sensitive data, that architecture difference is the whole decision

31,568 просмотров • 1 месяц назад

Shopify Head of Engineering: "We have 3,000 engineers acting like 100,000. We're trying to solve the problem of entrepreneurship." in 26 minutes, Farhan breaks down exactly how Shopify turned AI into a profit multiplier, not a headcount cut here's what he covers: - why cheaper tools always mean more builders, not fewer jobs - the "AI leverage" mindset that turns 1 person into a full team - how a single intern saved $600K by deleting 6 lines of code - why the best AI users get to build, not get replaced most people think AI means smaller teams and fewer opportunities the people paying attention are using it to build businesses solo that used to need 50 people how to automate content creation at scale with AI 👇

Shopify Head of Engineering: "We have 3,000 engineers acting like 100,000. We're trying to solve the problem of entrepreneurship." in 26 minutes, Farhan breaks down exactly how Shopify turned AI into a profit multiplier, not a headcount cut here's what he covers: - why cheaper tools always mean more builders, not fewer jobs - the "AI leverage" mindset that turns 1 person into a full team - how a single intern saved $600K by deleting 6 lines of code - why the best AI users get to build, not get replaced most people think AI means smaller teams and fewer opportunities the people paying attention are using it to build businesses solo that used to need 50 people how to automate content creation at scale with AI 👇

16,831 просмотров • 3 месяцев назад

Claude Fable 5 early tester: "If you're using Fable 5 for everything, that is almost 100% overkill." in 10 minutes, Nate breaks down the six prompting habits that actually get the most out of it here's what he covers: > always give the why, not just the what > negative prompting: tell it exactly what not to do > baking verification loops into every agent and skill file > the one prompt type that silently downgrades you to a weaker model most people are just throwing longer prompts at a more expensive model the ones getting real results changed how they prompt, not how much they spend an article on how to build a fully automated AI company with Claude Fable 5 👇

Claude Fable 5 early tester: "If you're using Fable 5 for everything, that is almost 100% overkill." in 10 minutes, Nate breaks down the six prompting habits that actually get the most out of it here's what he covers: > always give the why, not just the what > negative prompting: tell it exactly what not to do > baking verification loops into every agent and skill file > the one prompt type that silently downgrades you to a weaker model most people are just throwing longer prompts at a more expensive model the ones getting real results changed how they prompt, not how much they spend an article on how to build a fully automated AI company with Claude Fable 5 👇

11,868 просмотров • 3 месяцев назад

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