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THIS DEVELOPER OPENED HERMES ON A LAPTOP, SPENT $0 ON API SETUP, SAVED 7 WORKFLOWS, AND CUT 2-HOUR CLIENT TASKS DOWN TO 14 MINUTES he is not giving a polished demo. he is just filming the laptop while Hermes runs, and that is why the clip works. you can...

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Gipp 🦅

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14,593 просмотров • 5 месяцев назад •via X (Twitter)

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A 17-year-old student spent $4,200 on 7 Mac minis. Small silver boxes. Stacked on a desk. Connected in one room. From the outside, it looked like a stupid purchase. But inside, it wasn't just 7 computers. It was Skills. Hooks. Memory. Worktrees. One machine handled repeatable tasks. One ran checks automatically. One kept context between sessions. Others ran parallel jobs without touching each other's work. While most people were still typing the same instructions again and again, his setup was already moving. A lot of people pay $200 a month for Claude and still use maybe 20% of it. He built a system around it. Skills turned repeated work into reusable workflows. Hooks made actions fire automatically. Memory stopped every session from starting at zero. Worktrees let multiple tasks run at the same time without collisions. That changed everything. Setup time: under 1 hour once. Time returned: 3 to 5 hours every day. He spent $4,200 once. He made $16,000 in the first week. Not because he found a secret tool. Not because he wrote magical prompts. Because he stopped using it like a chatbot and started using it like infrastructure. 7 Mac minis. 1 student. $4,200 in. $16,000 out. And most people would still call it just a stack of computers.
0:12

A 17-year-old student spent $4,200 on 7 Mac minis. Small silver boxes. Stacked on a desk. Connected in one room. From the outside, it looked like a stupid purchase. But inside, it wasn't just 7 computers. It was Skills. Hooks. Memory. Worktrees. One machine handled repeatable tasks. One ran checks automatically. One kept context between sessions. Others ran parallel jobs without touching each other's work. While most people were still typing the same instructions again and again, his setup was already moving. A lot of people pay $200 a month for Claude and still use maybe 20% of it. He built a system around it. Skills turned repeated work into reusable workflows. Hooks made actions fire automatically. Memory stopped every session from starting at zero. Worktrees let multiple tasks run at the same time without collisions. That changed everything. Setup time: under 1 hour once. Time returned: 3 to 5 hours every day. He spent $4,200 once. He made $16,000 in the first week. Not because he found a secret tool. Not because he wrote magical prompts. Because he stopped using it like a chatbot and started using it like infrastructure. 7 Mac minis. 1 student. $4,200 in. $16,000 out. And most people would still call it just a stack of computers.

Gipp 🦅

21,312 просмотров • 6 месяцев назад

Hermes Agent can now review your codebase for security bugs with the new official Security plugin by Mark S.. Hermes first maps what an attacker could reach and which inputs it trusts. It splits the code across parallel subagents, checks every candidate finding against the code, and traces whether an attacker can actually exploit it. Turn on test confirmation and Hermes writes a small reproduction for each finding and runs it in a temporary copy of the repo. Each report records the evidence and the findings Hermes ruled out, and exports to SARIF for your existing tools. Run it from chat, the CLI, or the Security page in Hermes Desktop.
0:19

Hermes Agent can now review your codebase for security bugs with the new official Security plugin by Mark S.. Hermes first maps what an attacker could reach and which inputs it trusts. It splits the code across parallel subagents, checks every candidate finding against the code, and traces whether an attacker can actually exploit it. Turn on test confirmation and Hermes writes a small reproduction for each finding and runs it in a temporary copy of the repo. Each report records the evidence and the findings Hermes ruled out, and exports to SARIF for your existing tools. Run it from chat, the CLI, or the Security page in Hermes Desktop.

Nous Research

122,024 просмотров • 6 дней назад

clipping agencies rent offices now, $500,000 a year ten editors, a manager, a lease, and the only thing any of them is paid for is noticing noticing is 310 lines of python, and i gave it away it runs on Robinhood Chain, so the clip pays whoever cut it CUT never sees a single frame of the stream. it reads chat, because chat knows before the editor does, before the dashboard does, before the streamer does [what those 310 lines do] 1. measure the last 10 seconds against that stream's own 5 minute baseline 2. never a global number, a big channel idling still beats a small one peaking 3. count how many people just typed clip it 4. call the clip api in the same second, not after the room reacts that last one is the whole thing. a twitch clip only keeps the 90 seconds before you hit the button, so if you noticed the moment yourself, you already lost it no key to read chat. it cannot post for you. it runs on your machine, not mine one command replays three minutes of recorded chat offline and fires a real moment on your screen, so you can watch it work before you trust it
0:12

clipping agencies rent offices now, $500,000 a year ten editors, a manager, a lease, and the only thing any of them is paid for is noticing noticing is 310 lines of python, and i gave it away it runs on Robinhood Chain, so the clip pays whoever cut it CUT never sees a single frame of the stream. it reads chat, because chat knows before the editor does, before the dashboard does, before the streamer does [what those 310 lines do] 1. measure the last 10 seconds against that stream's own 5 minute baseline 2. never a global number, a big channel idling still beats a small one peaking 3. count how many people just typed clip it 4. call the clip api in the same second, not after the room reacts that last one is the whole thing. a twitch clip only keeps the 90 seconds before you hit the button, so if you noticed the moment yourself, you already lost it no key to read chat. it cannot post for you. it runs on your machine, not mine one command replays three minutes of recorded chat offline and fires a real moment on your screen, so you can watch it work before you trust it

Carver

288,802 просмотров • 2 месяцев назад

1 mini PC that fits in a palm, with no monthly token bill attached to it. Most people run their AI through a browser tab. Every prompt, every document, every half-finished idea leaves the machine and lands on someone else's server. This ORICO mini PC runs the model locally. The assistant lives inside the box, not in a data center 2,000 miles away. Small enough to slide into a bag. Powerful enough that you stop noticing it is not a desktop. You pay once for hardware instead of every month for limits. No outage that takes your assistant down with it. No terms of service deciding what your files are worth. Plug it in anywhere, and the assistant is already there, offline, waiting. The smallest machine on the desk is the only one that keeps its mouth shut.
0:11

1 mini PC that fits in a palm, with no monthly token bill attached to it. Most people run their AI through a browser tab. Every prompt, every document, every half-finished idea leaves the machine and lands on someone else's server. This ORICO mini PC runs the model locally. The assistant lives inside the box, not in a data center 2,000 miles away. Small enough to slide into a bag. Powerful enough that you stop noticing it is not a desktop. You pay once for hardware instead of every month for limits. No outage that takes your assistant down with it. No terms of service deciding what your files are worth. Plug it in anywhere, and the assistant is already there, offline, waiting. The smallest machine on the desk is the only one that keeps its mouth shut.

AiMind

82,816 просмотров • 2 месяцев назад

I found this last night and I have not stopped thinking about it. HERMES JUST LAUNCHED HERMES DESKTOP. 100% FREE. It is a free desktop app that gives Hermes Agent a proper interface. One place for everything. What is inside: ↳ Auto install and setup, no terminal needed ↳ Streaming chat with token tracking ↳ Multiple agent profiles ↳ Memory you can actually see and edit ↳ 14 tool categories including web, browser, image gen, and voice ↳ Scheduler for automated tasks ↳ 16 messaging gateways including Telegram, WhatsApp, Discord, Slack, and Signal ↳ Full conversation history with search ↳ Backups and logs in one settings screen Works with Anthropic, OpenAI, Gemini, Grok, Groq, Ollama, and more. Hermes Agent is the brain. Hermes Desktop is the cockpit. Free. Open source. Mac, Windows, and Linux.
0:19

I found this last night and I have not stopped thinking about it. HERMES JUST LAUNCHED HERMES DESKTOP. 100% FREE. It is a free desktop app that gives Hermes Agent a proper interface. One place for everything. What is inside: ↳ Auto install and setup, no terminal needed ↳ Streaming chat with token tracking ↳ Multiple agent profiles ↳ Memory you can actually see and edit ↳ 14 tool categories including web, browser, image gen, and voice ↳ Scheduler for automated tasks ↳ 16 messaging gateways including Telegram, WhatsApp, Discord, Slack, and Signal ↳ Full conversation history with search ↳ Backups and logs in one settings screen Works with Anthropic, OpenAI, Gemini, Grok, Groq, Ollama, and more. Hermes Agent is the brain. Hermes Desktop is the cockpit. Free. Open source. Mac, Windows, and Linux.

Kanika

60,519 просмотров • 5 месяцев назад

He is trying to put the pallet jack in from the side. The pockets are on the front. That is not optional. That is how the machine works. A coworker is standing there trying to stop him before the stack walks. Every stockroom in America runs on this one fact: forks go in the openings. Somebody never taught him, or he stopped listening, and now training is happening on video. Is this a workplace that does not train people… or a grown man who will not take direction from the guy five feet away?
0:19

He is trying to put the pallet jack in from the side. The pockets are on the front. That is not optional. That is how the machine works. A coworker is standing there trying to stop him before the stack walks. Every stockroom in America runs on this one fact: forks go in the openings. Somebody never taught him, or he stopped listening, and now training is happening on video. Is this a workplace that does not train people… or a grown man who will not take direction from the guy five feet away?

DocumentingLibs

15,605 просмотров • 2 месяцев назад

I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.
0:10

I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.

Miles Deutscher

82,643 просмотров • 4 месяцев назад

THIS $2 AI VISUAL SETUP JUST MADE A $250/MONTH CREATIVE STACK LOOK STUPID he moves his hand, the particles react, and the whole scene updates in real time. no cloud render farm, no paid visual API, no expensive plugin chain quietly eating money every month most people still think ai visuals require a stack of $39 tools, $89 subscriptions, and constant API usage. but here the loop is simple: TouchDesigner handles the visuals, a local model handles the logic, and the laptop does the rest the important part is not that the particles look cool. the important part is that the “brain” behind the visual no longer has to live on someone else’s server once that moves local, the monthly bill falls off a cliff this is probably how a lot of small studios start building visuals soon: not by renting 6 tools forever, but by owning one system that runs almost for free
0:17

THIS $2 AI VISUAL SETUP JUST MADE A $250/MONTH CREATIVE STACK LOOK STUPID he moves his hand, the particles react, and the whole scene updates in real time. no cloud render farm, no paid visual API, no expensive plugin chain quietly eating money every month most people still think ai visuals require a stack of $39 tools, $89 subscriptions, and constant API usage. but here the loop is simple: TouchDesigner handles the visuals, a local model handles the logic, and the laptop does the rest the important part is not that the particles look cool. the important part is that the “brain” behind the visual no longer has to live on someone else’s server once that moves local, the monthly bill falls off a cliff this is probably how a lot of small studios start building visuals soon: not by renting 6 tools forever, but by owning one system that runs almost for free

Ridark

15,948 просмотров • 5 месяцев назад

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.
0:16

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.

Dustin

542,558 просмотров • 7 месяцев назад

THAT'S CRAZY, THIS CHINESE FOUNDER BUILT A MASSIVE MAC MINI FARM AND EACH ONE RUNNING ITS OWN HERMES AI AGENT LIKE A FULL-TIME EMPLOYEE He's not running one AI assistant. He's running an entire workforce. The stack: Mac Mini + Hermes, scaled out across a full physical farm. Every single Mac Mini in the rack runs its own instance of Hermes Agent – and each one has its own dedicated job. Not duplicated tasks. Actual division of labor, machine by machine, the way you'd structure a real team. No salaries. No sick days. No onboarding. Just racks of hardware, each one handling its own piece of the business, running in parallel, 24/7. This is what it looks like when "AI agent" stops being one chatbot on your laptop and starts being an actual operation. Most people are running one AI tool. This guy built a company out of them. Bookmark this post. Full setup in the video below.
0:14

THAT'S CRAZY, THIS CHINESE FOUNDER BUILT A MASSIVE MAC MINI FARM AND EACH ONE RUNNING ITS OWN HERMES AI AGENT LIKE A FULL-TIME EMPLOYEE He's not running one AI assistant. He's running an entire workforce. The stack: Mac Mini + Hermes, scaled out across a full physical farm. Every single Mac Mini in the rack runs its own instance of Hermes Agent – and each one has its own dedicated job. Not duplicated tasks. Actual division of labor, machine by machine, the way you'd structure a real team. No salaries. No sick days. No onboarding. Just racks of hardware, each one handling its own piece of the business, running in parallel, 24/7. This is what it looks like when "AI agent" stops being one chatbot on your laptop and starts being an actual operation. Most people are running one AI tool. This guy built a company out of them. Bookmark this post. Full setup in the video below.

SCOTTY BEAM

20,849 просмотров • 4 месяцев назад

Running cold email campaigns just became a whole lot easier Smartlead now runs an MCP server, which in plain terms means Claude can read and act on your live campaign data directly instead of working off a spreadsheet that went stale the moment you exported it. The workflow is worth walking through properly, because it is shorter than people expect. You generate an API key inside your account, point Claude at the server once, and from then on you ask for what you want in a sentence. Here is a prompt worth stealing in full: "Fetch all Smartlead clients, then get today's performance for each: emails sent, replied, positive replies, unique lead count. Compute reply rate per client, run a top and bottom performer analysis, format it as a daily client performance report, and post it to Slack." One paste, and it pulls live figures for every account, does the arithmetic, ranks the strongest and the weakest, and delivers the finished thing into the channel your team already sits in, before anyone has logged on for the day. Be clear about the division of labour, because it is what makes this useful rather than a novelty. Smartlead is the engine holding the campaigns, the mailboxes, the warmup and the reply data, and Claude is simply the interface you operate all of it through, so nothing about your sending changes and everything about how you interrogate it does. The effect people underestimate is on the questions you start asking. Once a report costs you a sentence rather than an afternoon, you stop rationing the ones that used to feel like too much trouble, and problems that used to surface on a Friday start surfacing on a Tuesday. Connect it with Claude through MCP and run one prompt against your own account today.
0:10

Running cold email campaigns just became a whole lot easier Smartlead now runs an MCP server, which in plain terms means Claude can read and act on your live campaign data directly instead of working off a spreadsheet that went stale the moment you exported it. The workflow is worth walking through properly, because it is shorter than people expect. You generate an API key inside your account, point Claude at the server once, and from then on you ask for what you want in a sentence. Here is a prompt worth stealing in full: "Fetch all Smartlead clients, then get today's performance for each: emails sent, replied, positive replies, unique lead count. Compute reply rate per client, run a top and bottom performer analysis, format it as a daily client performance report, and post it to Slack." One paste, and it pulls live figures for every account, does the arithmetic, ranks the strongest and the weakest, and delivers the finished thing into the channel your team already sits in, before anyone has logged on for the day. Be clear about the division of labour, because it is what makes this useful rather than a novelty. Smartlead is the engine holding the campaigns, the mailboxes, the warmup and the reply data, and Claude is simply the interface you operate all of it through, so nothing about your sending changes and everything about how you interrogate it does. The effect people underestimate is on the questions you start asking. Once a report costs you a sentence rather than an afternoon, you stop rationing the ones that used to feel like too much trouble, and problems that used to surface on a Friday start surfacing on a Tuesday. Connect it with Claude through MCP and run one prompt against your own account today.

Tim

56,564 просмотров • 1 месяц назад

HE STRAPPED A BATTERY TO A $599 MAC MINI AND TURNED A DESK COMPUTER INTO A 14-HOUR PORTABLE AI WORKSTATION 00:03 the battery slides onto the side of the mac mini and the whole setup stops behaving like a desk machine. now it can run from a backpack, power a screen, hold local files and keep working without asking for an outlet. that changes the use case completely. instead of renting another cloud box, one silent computer can handle research dumps, meeting notes, scraped pages, voice transcripts and small automation jobs from almost anywhere. with claude connected, it becomes a moving command center. 45-minute calls become summaries, 120 saved links become organized notes, and messy project folders get cleaned while the machine quietly keeps working in the background. the interesting number is not the battery size. it is the avoided rent. one portable local box can replace $25 storage, $39 automation, $20 transcription and another $30 vps bill if the workflow is built correctly. this is no longer just a desktop. it becomes a portable ai machine that keeps working long after you leave the desk. bookmark this before portable ai becomes the new normal.
0:19

HE STRAPPED A BATTERY TO A $599 MAC MINI AND TURNED A DESK COMPUTER INTO A 14-HOUR PORTABLE AI WORKSTATION 00:03 the battery slides onto the side of the mac mini and the whole setup stops behaving like a desk machine. now it can run from a backpack, power a screen, hold local files and keep working without asking for an outlet. that changes the use case completely. instead of renting another cloud box, one silent computer can handle research dumps, meeting notes, scraped pages, voice transcripts and small automation jobs from almost anywhere. with claude connected, it becomes a moving command center. 45-minute calls become summaries, 120 saved links become organized notes, and messy project folders get cleaned while the machine quietly keeps working in the background. the interesting number is not the battery size. it is the avoided rent. one portable local box can replace $25 storage, $39 automation, $20 transcription and another $30 vps bill if the workflow is built correctly. this is no longer just a desktop. it becomes a portable ai machine that keeps working long after you leave the desk. bookmark this before portable ai becomes the new normal.

Gipp 🦅

1,790,357 просмотров • 4 месяцев назад

holy f*cking sh*t Karpathy put an entire AI degree on github for free while you are still paying $200/month to watch youtube tutorials from a guy with 3000 followers 7 repos. 306,741 stars combined. in the right order they take you from "how a neuron learns" to "agents that run research overnight while you sleep" every repo you skip is a layer of understanding your competition at work already has and you do not. and every month you keep "planning to start" is another month someone on your team gets handed the AI project you should have been running here is the exact order. do not reshuffle it 1. micrograd → how a neural net actually learns. backpropagation in about 100 lines of python. this is where every AI engineer on earth started. if you cannot read this file, every other repo on the list is going to feel like black magic to you 2. nn-zero-to-hero → his full video course. lecture notebooks that walk you from a single neuron to GPT. the thing people pay $2000 for from AI "bootcamps" that teach a fraction of what is in this one free repo 3. minbpe → how text actually turns into tokens. the tokenizer that sits inside every LLM, in clean minimal code. this is the part 95% of self-taught people skip, and it is why their models never work right 4. nanoGPT → your own GPT. the simplest repo in existence to train and fine-tune one. after this file you stop asking "how does ChatGPT work" because you have literally built the smaller version with your own hands 5. llm.c → the same training with nothing hidden. raw C and CUDA, no PyTorch wrapper doing the hard parts for you. this is where you stop being a prompt engineer and start being an actual engineer 6. nanochat → a full chat model end to end. GPT-2 level training that cost ~$43,000 in 2019. in this repo it costs about $48. let that sit for a second. the thing people called a breakthrough is now a weekend project 7. autoresearch → agents that do the research for you. the agent edits your training code, trains for 5 minutes, keeps whatever improved, discards whatever did not, and repeats all night while you sleep. this is where the people reading this list stop being students and start being whatever comes after that the brutal truth most people avoid: train one GPT from scratch and you become the person who gets handed the AI project at work, along with the raise that comes with it. keep "planning to start" and you become the person watching that happen to someone else the classroom half of the same degree, what Stanford actually teaches about AI for real, is below
0:16

holy f*cking sh*t Karpathy put an entire AI degree on github for free while you are still paying $200/month to watch youtube tutorials from a guy with 3000 followers 7 repos. 306,741 stars combined. in the right order they take you from "how a neuron learns" to "agents that run research overnight while you sleep" every repo you skip is a layer of understanding your competition at work already has and you do not. and every month you keep "planning to start" is another month someone on your team gets handed the AI project you should have been running here is the exact order. do not reshuffle it 1. micrograd → how a neural net actually learns. backpropagation in about 100 lines of python. this is where every AI engineer on earth started. if you cannot read this file, every other repo on the list is going to feel like black magic to you 2. nn-zero-to-hero → his full video course. lecture notebooks that walk you from a single neuron to GPT. the thing people pay $2000 for from AI "bootcamps" that teach a fraction of what is in this one free repo 3. minbpe → how text actually turns into tokens. the tokenizer that sits inside every LLM, in clean minimal code. this is the part 95% of self-taught people skip, and it is why their models never work right 4. nanoGPT → your own GPT. the simplest repo in existence to train and fine-tune one. after this file you stop asking "how does ChatGPT work" because you have literally built the smaller version with your own hands 5. llm.c → the same training with nothing hidden. raw C and CUDA, no PyTorch wrapper doing the hard parts for you. this is where you stop being a prompt engineer and start being an actual engineer 6. nanochat → a full chat model end to end. GPT-2 level training that cost ~$43,000 in 2019. in this repo it costs about $48. let that sit for a second. the thing people called a breakthrough is now a weekend project 7. autoresearch → agents that do the research for you. the agent edits your training code, trains for 5 minutes, keeps whatever improved, discards whatever did not, and repeats all night while you sleep. this is where the people reading this list stop being students and start being whatever comes after that the brutal truth most people avoid: train one GPT from scratch and you become the person who gets handed the AI project at work, along with the raise that comes with it. keep "planning to start" and you become the person watching that happen to someone else the classroom half of the same degree, what Stanford actually teaches about AI for real, is below

yurshev

135,737 просмотров • 16 дней назад

I built a 1-click deploy for Hermes Agent No terminal, no Docker, no API keys. In one click it: → Spins up a dedicated cloud container → Configures Hermes with persistent memory & 70+ skills → Connects it to your Telegram bot → Goes live in under 60 seconds Your own AI agent that learns, creates skills, and gets smarter the longer it runs. Reply "HERMES" + RT and I'll send you the link (must be following so I can DM)
0:16

I built a 1-click deploy for Hermes Agent No terminal, no Docker, no API keys. In one click it: → Spins up a dedicated cloud container → Configures Hermes with persistent memory & 70+ skills → Connects it to your Telegram bot → Goes live in under 60 seconds Your own AI agent that learns, creates skills, and gets smarter the longer it runs. Reply "HERMES" + RT and I'll send you the link (must be following so I can DM)

Chris

34,718 просмотров • 7 месяцев назад

most of you don't know how hard hermes agent is optimized for local AI at the system level. watch the full setup flow on screen. you paste an openai-compatible v1 endpoint, hermes auto-detects every model running behind it. doesn't matter if it's llama.cpp or vllm or any compatible server, all your models surface and become selectable in seconds. no config gymnastics, no manual model list. then it goes deeper. hermes ships with per model parsers, prompt template auto-handling, tool call format detection per model architecture, thinking mode awareness, all the small friction points other harnesses leak on. these were not built for cloud apis with one canonical model. they were built for builders running 10 different local models across 10 different stacks. cloud first harnesses bolt local support on top. hermes agent is local first from the architecture out. that's the system level gap. if you're getting started on local AI, this is the harness you start with. try for yourself and find out. anyone serious about local AI lands here eventually.
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most of you don't know how hard hermes agent is optimized for local AI at the system level. watch the full setup flow on screen. you paste an openai-compatible v1 endpoint, hermes auto-detects every model running behind it. doesn't matter if it's llama.cpp or vllm or any compatible server, all your models surface and become selectable in seconds. no config gymnastics, no manual model list. then it goes deeper. hermes ships with per model parsers, prompt template auto-handling, tool call format detection per model architecture, thinking mode awareness, all the small friction points other harnesses leak on. these were not built for cloud apis with one canonical model. they were built for builders running 10 different local models across 10 different stacks. cloud first harnesses bolt local support on top. hermes agent is local first from the architecture out. that's the system level gap. if you're getting started on local AI, this is the harness you start with. try for yourself and find out. anyone serious about local AI lands here eventually.

Sudo su

14,438 просмотров • 6 месяцев назад

Since subscribers can now use their existing Grok credentials directly within the Hermes xai shipped grok-via-oauth for hermes. your grok sub works inside the agent now. so i hooked icarus up to it. when hermes finds a thread worth saving, it lands in my obsidian vault as a note. handle. pages, topic pages, backlinks. graph view fills itself out. icarus plugin for hermes agents is why my agent doesn't forget. memory is just markdown in a folder. you can read it, grep it, edit it. Congrats to Teknium 🪽 and Nous Research
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Since subscribers can now use their existing Grok credentials directly within the Hermes xai shipped grok-via-oauth for hermes. your grok sub works inside the agent now. so i hooked icarus up to it. when hermes finds a thread worth saving, it lands in my obsidian vault as a note. handle. pages, topic pages, backlinks. graph view fills itself out. icarus plugin for hermes agents is why my agent doesn't forget. memory is just markdown in a folder. you can read it, grep it, edit it. Congrats to Teknium 🪽 and Nous Research

Icarus

14,681 просмотров • 6 месяцев назад

A GIRL WALKED OFF THE TOP OF A WATERSLIDE, THREW TWO FLIPS DOWN THE FACE OF IT, AND LANDED IN THE POOL 20,000 likes None of it is generated. That is exactly why it is worth eleven seconds, because your feed has spent a year training you to assume the opposite run it as a test. here is what is in the frame that no model reliably puts there yet: → the water. spray leaves her body at the right angle and volume for her speed, and the sheet on the slide deforms under her weight instead of flowing past her → the rotation conserves. she tucks and the spin accelerates, she opens and it slows. angular momentum is a constraint, and generators approximate it rather than obey it → the crowd reacts late. people on the platform turn after she has already gone, because they are reacting, not choreographed → the camera operator loses her. the frame lags the subject and catches up. that is a mistake, and mistakes are the expensive thing to fake → and the landing is ugly. real impacts are. generated ones almost always resolve too cleanly none of that is a checklist you can memorise, and pretending otherwise is how people get caught. every one of those tells has a shelf life. three of them were already unreliable a year ago what does not expire is where you look. continuity, physics, and mistakes - the three places a model has to simulate a system instead of reproducing a surface. surfaces are solved. systems are not, yet and the reason this gets more useful every month is not fakes. it is that real footage is starting to get accused. the cost of being wrong now runs in both directions the fastest way to calibrate your own eye is to make one yourself. image-to-video from a still, one line about the motion - Picsart runs it from a phone. you start spotting the tells about ten minutes after you have made your own this one is real. the fact that you had to check is the actual story
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A GIRL WALKED OFF THE TOP OF A WATERSLIDE, THREW TWO FLIPS DOWN THE FACE OF IT, AND LANDED IN THE POOL 20,000 likes None of it is generated. That is exactly why it is worth eleven seconds, because your feed has spent a year training you to assume the opposite run it as a test. here is what is in the frame that no model reliably puts there yet: → the water. spray leaves her body at the right angle and volume for her speed, and the sheet on the slide deforms under her weight instead of flowing past her → the rotation conserves. she tucks and the spin accelerates, she opens and it slows. angular momentum is a constraint, and generators approximate it rather than obey it → the crowd reacts late. people on the platform turn after she has already gone, because they are reacting, not choreographed → the camera operator loses her. the frame lags the subject and catches up. that is a mistake, and mistakes are the expensive thing to fake → and the landing is ugly. real impacts are. generated ones almost always resolve too cleanly none of that is a checklist you can memorise, and pretending otherwise is how people get caught. every one of those tells has a shelf life. three of them were already unreliable a year ago what does not expire is where you look. continuity, physics, and mistakes - the three places a model has to simulate a system instead of reproducing a surface. surfaces are solved. systems are not, yet and the reason this gets more useful every month is not fakes. it is that real footage is starting to get accused. the cost of being wrong now runs in both directions the fastest way to calibrate your own eye is to make one yourself. image-to-video from a still, one line about the motion - Picsart runs it from a phone. you start spotting the tells about ten minutes after you have made your own this one is real. the fact that you had to check is the actual story

Valentin

643,629 просмотров • 1 месяц назад

how I make videos with Claude in 5 steps most AI videos look the same because people skip straight to “make me a video” I run every video through the same 5 steps instead. each one hands a real file to the next > reference spec.md I show Claude a video I actually like it breaks down why it works: the pacing, where the first motion lands, the cut rhythm > data data.json real numbers before anything moves every figure has a source, so nothing on screen is made up > story story.md one point, one hero number, three beats decided before the motion, so the video is about something > motion Scene.tsx every frame written as code with Remotion and a motion-design skill nothing is random, so every word, number and timing stays editable > direct timing.ts I give notes in plain words “title is a beat late” one line changes, nothing else breaks ship final.mp4 it renders stills and checks them against the reference and the data nothing goes out until I approve it why it works: no video model, so no re-rolls same input, same result, every time you always know which step is the problem most people jump to step 4. the real difference is step 1 and step 5 full setup, prompt and the 10 open-source skills are in the article
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how I make videos with Claude in 5 steps most AI videos look the same because people skip straight to “make me a video” I run every video through the same 5 steps instead. each one hands a real file to the next > reference spec.md I show Claude a video I actually like it breaks down why it works: the pacing, where the first motion lands, the cut rhythm > data data.json real numbers before anything moves every figure has a source, so nothing on screen is made up > story story.md one point, one hero number, three beats decided before the motion, so the video is about something > motion Scene.tsx every frame written as code with Remotion and a motion-design skill nothing is random, so every word, number and timing stays editable > direct timing.ts I give notes in plain words “title is a beat late” one line changes, nothing else breaks ship final.mp4 it renders stills and checks them against the reference and the data nothing goes out until I approve it why it works: no video model, so no re-rolls same input, same result, every time you always know which step is the problem most people jump to step 4. the real difference is step 1 and step 5 full setup, prompt and the 10 open-source skills are in the article

Francesco

53,708 просмотров • 29 дней назад

there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two questions: what starts a run, and what ends it. hand-run, you answer both yourself, every time. 1) turn-based → you prompt, it acts, you review, you prompt again. both jobs stay with you. use when requirements are still forming. 2) goal-based → "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back. use when the outcome is measurable but the path isn't. 3) time-based → a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop. use for recurring work. 4) proactive → no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself. use for standing duties you can't predict. not which one is most advanced. whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. full breakdown in the article below.
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there are four types of agent loops. most people only know one. loop engineering is a choice between four structures, each handing off one more job than the last. every one answers two questions: what starts a run, and what ends it. hand-run, you answer both yourself, every time. 1) turn-based → you prompt, it acts, you review, you prompt again. both jobs stay with you. use when requirements are still forming. 2) goal-based → "/goal hit Lighthouse 90, stop after 5 tries." an evaluator checks, a no sends it back. use when the outcome is measurable but the path isn't. 3) time-based → a clock fires, it runs "check the PR, fix CI," then waits. /loop local, /schedule survives a closed laptop. use for recurring work. 4) proactive → no human present. it watches a channel, spawns triage, fix, and a reviewer, closes the task itself. use for standing duties you can't predict. not which one is most advanced. whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. full breakdown in the article below.

Hanako

478,460 просмотров • 4 месяцев назад

COPY is live on Robinhood Chain $COPY CA: 0xac79255f6f404eba14f316e8669d76573a2d7b1e Five walls read a setup before you copy it: how strong the source is, how deep the pool is, whether the market actually turns over, whether the move is still inside the band, and whether there is a fresh mark to trade against. EDGE, DEPTH, TURN, MOMO, PRICE. A wallet you follow buying is not a reason. Most setups die on the first wall, and you get the exact wall that refused it, every time. Every decision is printed. The terminal runs from one process, keeps local state, and half of it needs no key at all. It never predicts a price. It tells you what you would actually be copying. Open source, MIT, runs on your machine. No browser wallet, nothing signs.
0:20

COPY is live on Robinhood Chain $COPY CA: 0xac79255f6f404eba14f316e8669d76573a2d7b1e Five walls read a setup before you copy it: how strong the source is, how deep the pool is, whether the market actually turns over, whether the move is still inside the band, and whether there is a fresh mark to trade against. EDGE, DEPTH, TURN, MOMO, PRICE. A wallet you follow buying is not a reason. Most setups die on the first wall, and you get the exact wall that refused it, every time. Every decision is printed. The terminal runs from one process, keeps local state, and half of it needs no key at all. It never predicts a price. It tells you what you would actually be copying. Open source, MIT, runs on your machine. No browser wallet, nothing signs.

Lunar

114,300 просмотров • 2 месяцев назад