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Most discussions around Claude Code focus on model quality or capability. But when you actually use it, the friction shows up earlier, more at the system level than the model itself. Token limits get the blame, but a lot of it comes from inefficient sessions: repeated context, extra tool...

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Parul Gautam

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15,079 просмотров • 2 месяцев назад •via X (Twitter)

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Peter Steinberger Quietly Started A Shift That Makes Claude 35x Cheaper To Run The way Claude Code talks to tools matters more than people think. Most use MCP, and it quietly eats tokens, it loads everything into context every time, even tools you never call. One benchmark: MCP burned 35x more tokens than a CLI on the same task. Peter Steinberger, the OpenClaw guy, got annoyed by this and started building lean CLIs himself. That kicked off a tool called Printing Press. You point Claude Code at any website, even ones with no API like ESPN or Craigslist, and it builds a small command tool for it in about 10 minutes. In one demo, a request pulled 132,000 tokens of raw data, but the tool processed it locally and only handed Claude a 2,000-token summary. The rest never touched the context window. It also comes with ~50 ready-made tools you can grab right away. To start, point Claude Code at the links from and ask it to set it up. Bookmark this.
14:45

Peter Steinberger Quietly Started A Shift That Makes Claude 35x Cheaper To Run The way Claude Code talks to tools matters more than people think. Most use MCP, and it quietly eats tokens, it loads everything into context every time, even tools you never call. One benchmark: MCP burned 35x more tokens than a CLI on the same task. Peter Steinberger, the OpenClaw guy, got annoyed by this and started building lean CLIs himself. That kicked off a tool called Printing Press. You point Claude Code at any website, even ones with no API like ESPN or Craigslist, and it builds a small command tool for it in about 10 minutes. In one demo, a request pulled 132,000 tokens of raw data, but the tool processed it locally and only handed Claude a 2,000-token summary. The rest never touched the context window. It also comes with ~50 ready-made tools you can grab right away. To start, point Claude Code at the links from and ask it to set it up. Bookmark this.

Ridark

32,351 просмотров • 10 дней назад

Claude Code With UNLIMITED Memory! Solves Claude's Memory Problem! It’s called Claude-Mem, and it lets Claude remember your work across sessions. ⚡ Slash token usage by up to 95% every time you start a session. 🔧 Unlock the ability to make 20× more tool calls before hitting limits. My Video:
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Claude Code With UNLIMITED Memory! Solves Claude's Memory Problem! It’s called Claude-Mem, and it lets Claude remember your work across sessions. ⚡ Slash token usage by up to 95% every time you start a session. 🔧 Unlock the ability to make 20× more tool calls before hitting limits. My Video:

WorldofAI

28,687 просмотров • 5 месяцев назад

Make Claude Code 10x more powerful. Claude-Mem is a free plugin to persist memory across Claude sessions. It captures tool usage, so you always start where you left off. Endless Mode allows 95% token reduction & 20x more tool use before context exhaustion. 100% open-source.
0:25

Make Claude Code 10x more powerful. Claude-Mem is a free plugin to persist memory across Claude sessions. It captures tool usage, so you always start where you left off. Endless Mode allows 95% token reduction & 20x more tool use before context exhaustion. 100% open-source.

Akshay 🚀

184,171 просмотров • 6 месяцев назад

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 просмотров • 1 месяц назад

I just found a way to use Claude Code completely free. Claude Code is Anthropic’s terminal coding agent that usually needs a paid API key to run. But a viral GitHub project called Free Claude Code, now sitting at 37k stars, lets you skip the billing entirely. It works as a tiny proxy that sits between Claude Code and free model providers like NVIDIA NIM, OpenRouter, DeepSeek, and Gemini. Every request Claude Code would send to Anthropic gets quietly rerouted to a free model on the other side. The interface stays the same. What’s actually thinking behind it changes. Setup takes a couple of quick commands and a free API key from NVIDIA NIM or OpenRouter. Paste it into the local dashboard, pick a model, and you’re building. The catch is you’re not really using Claude anymore. But when the code ships, does the model badge matter? -- I have shared the Github Link in my free Whatsapp Community ↓
0:36

I just found a way to use Claude Code completely free. Claude Code is Anthropic’s terminal coding agent that usually needs a paid API key to run. But a viral GitHub project called Free Claude Code, now sitting at 37k stars, lets you skip the billing entirely. It works as a tiny proxy that sits between Claude Code and free model providers like NVIDIA NIM, OpenRouter, DeepSeek, and Gemini. Every request Claude Code would send to Anthropic gets quietly rerouted to a free model on the other side. The interface stays the same. What’s actually thinking behind it changes. Setup takes a couple of quick commands and a free API key from NVIDIA NIM or OpenRouter. Paste it into the local dashboard, pick a model, and you’re building. The catch is you’re not really using Claude anymore. But when the code ships, does the model badge matter? -- I have shared the Github Link in my free Whatsapp Community ↓

Vaibhav Sisinty

35,460 просмотров • 5 дней назад

Opus 4.7 - 400k vs 1m context - is there a difference? I've heard Theo - t3.gg talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model. I did a test where I toggled the 1m default model on & off in Claude Code (otherwise default settings, xHigh reasoning) and compared the outputs with 3x generations - same prompts etc. My observations: - Models feel DIFFERENT - often when you ask a model for the same generation, you get a somewhat different answer, but it feels & smells the same. Here 400k and 1m are very different every time - 400k model seems better - not that 1m is trash and 400k is amazing, but there are definitely issues with the level of ambition and accuracy that 1m model seems to have Examples of 1m failing: - Voxel Rome: the colosseum is nowhere near as impressive - Golden Gate: cars go sideways, waves not very high, bridge goes into land; though the structure of the bridge is a bit better - Stonehenge: structure is more 'wrong', lighting, shadows & textures are more flat and not as rich This isn't a conclusive evidence of course, but at least to me the two models do not behave the same way. Anecdotally as well when building 1m felt like it was doing more weird validation (e.g. going around in circles) and 400k was more straightforward. These sorts of things are harder to capture in tests, but you'd notice in Claude Code. You can review the hosted generations, see the code & prompts in the links below
0:47

Opus 4.7 - 400k vs 1m context - is there a difference? I've heard Theo - t3.gg talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model. I did a test where I toggled the 1m default model on & off in Claude Code (otherwise default settings, xHigh reasoning) and compared the outputs with 3x generations - same prompts etc. My observations: - Models feel DIFFERENT - often when you ask a model for the same generation, you get a somewhat different answer, but it feels & smells the same. Here 400k and 1m are very different every time - 400k model seems better - not that 1m is trash and 400k is amazing, but there are definitely issues with the level of ambition and accuracy that 1m model seems to have Examples of 1m failing: - Voxel Rome: the colosseum is nowhere near as impressive - Golden Gate: cars go sideways, waves not very high, bridge goes into land; though the structure of the bridge is a bit better - Stonehenge: structure is more 'wrong', lighting, shadows & textures are more flat and not as rich This isn't a conclusive evidence of course, but at least to me the two models do not behave the same way. Anecdotally as well when building 1m felt like it was doing more weird validation (e.g. going around in circles) and 400k was more straightforward. These sorts of things are harder to capture in tests, but you'd notice in Claude Code. You can review the hosted generations, see the code & prompts in the links below

Peter Gostev (SF: 22-26 June)

29,203 просмотров • 2 месяцев назад

Clawdbot creator Peter Steinberger 🦞 says Claude Opus is his favorite model, but OpenAI Codex is the best for coding: "OpenAI is very reliable. For coding, I prefer Codex because it can navigate large codebases. You can prompt and have 95% certainty that it actually works. With Claude Code you need more tricks to get the same." "But character wise, [Opus] behaves so good in a Discord it kind of feels like a human. I've only really experienced that with Opus."
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Clawdbot creator Peter Steinberger 🦞 says Claude Opus is his favorite model, but OpenAI Codex is the best for coding: "OpenAI is very reliable. For coding, I prefer Codex because it can navigate large codebases. You can prompt and have 95% certainty that it actually works. With Claude Code you need more tricks to get the same." "But character wise, [Opus] behaves so good in a Discord it kind of feels like a human. I've only really experienced that with Opus."

TBPN

442,659 просмотров • 5 месяцев назад

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇
0:33

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

Md Riyazuddin

31,199 просмотров • 20 дней назад

Claude Code is now free to run. Most people still think you need a paid setup. Here’s the basic flow: → Install Claude Code with the official installer. → Create a free OpenRouter account. → Grab your API key. → Install Claude Code Router. → Point Claude Code to the local router. → Choose a free model with the :free suffix. → Run CCR code instead of normal Claude Code. → Check /status to confirm it works. You keep the Claude Code workflow. You just route it through a free model.
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Claude Code is now free to run. Most people still think you need a paid setup. Here’s the basic flow: → Install Claude Code with the official installer. → Create a free OpenRouter account. → Grab your API key. → Install Claude Code Router. → Point Claude Code to the local router. → Choose a free model with the :free suffix. → Run CCR code instead of normal Claude Code. → Check /status to confirm it works. You keep the Claude Code workflow. You just route it through a free model.

Julian Goldie SEO

17,618 просмотров • 1 месяц назад

inspired by SDPO, i made continualcode -- a minimal claude code that learns from your corrections in real-time, built on tinker. when you deny a diff, the model uses your correction as context to teach itself, takes a gradient step on LoRA, and retries with updated weights. claude code but it updates the model weights!
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inspired by SDPO, i made continualcode -- a minimal claude code that learns from your corrections in real-time, built on tinker. when you deny a diff, the model uses your correction as context to teach itself, takes a gradient step on LoRA, and retries with updated weights. claude code but it updates the model weights!

Surya

82,719 просмотров • 4 месяцев назад

"The model is no longer the product. Codex, Perplexity Computer, or Claude Code - all are orchestration system. It takes a model and pairs it with an agent harness. What is an agent harness ? The rules for how the agent loops around" - Aravind Srinivas
3:27

"The model is no longer the product. Codex, Perplexity Computer, or Claude Code - all are orchestration system. It takes a model and pairs it with an agent harness. What is an agent harness ? The rules for how the agent loops around" - Aravind Srinivas

Rohan Paul

119,783 просмотров • 16 дней назад

i been running Qwen3.5-35B-A3B UD-Q4_K_XL through Claude Code since llama.cpp merged the Anthropic endpoint. configured it in minutes. everything was great. projects grew from single scripts to multifile systems with 8 modules and 3,000+ lines. then the chains started breaking. 3 to 5 minutes of pure autonomy and suddenly it stops. tool call fails. reprompt. it recovers. 2 minutes later it stops again. the model is fine. the harness is the bottleneck. saw a comment suggesting OpenCode. installed it. pointed it at the same localhost endpoint running the same model on the same GPU. the game is different. instead of stopping on a bad tool call it just keeps going. on wrong read it adjusts. if file not found it retries. the flow is unbroken. i watched it plan a refactor across 8 files, read every module, and start building without a single pause. in Claude Code that same task would have stopped 4 times. the tradeoff is sometimes it loops. same tool call repeated because the model loses track of what it already read. but here is the thing. i choose loops over pauses. a loop you can interrupt and redirect. a broken chain stops the flow and you have to reprompt to get it moving again. someone is solving this at the core level and i have a feeling it is the open source community. the fact that i can run this level of autonomous coding intelligence on a single consumer GPU with 24gb VRAM at 112 tokens per second. respect to the chinese labs. respect to the open source builders making this possible.
2:00

i been running Qwen3.5-35B-A3B UD-Q4_K_XL through Claude Code since llama.cpp merged the Anthropic endpoint. configured it in minutes. everything was great. projects grew from single scripts to multifile systems with 8 modules and 3,000+ lines. then the chains started breaking. 3 to 5 minutes of pure autonomy and suddenly it stops. tool call fails. reprompt. it recovers. 2 minutes later it stops again. the model is fine. the harness is the bottleneck. saw a comment suggesting OpenCode. installed it. pointed it at the same localhost endpoint running the same model on the same GPU. the game is different. instead of stopping on a bad tool call it just keeps going. on wrong read it adjusts. if file not found it retries. the flow is unbroken. i watched it plan a refactor across 8 files, read every module, and start building without a single pause. in Claude Code that same task would have stopped 4 times. the tradeoff is sometimes it loops. same tool call repeated because the model loses track of what it already read. but here is the thing. i choose loops over pauses. a loop you can interrupt and redirect. a broken chain stops the flow and you have to reprompt to get it moving again. someone is solving this at the core level and i have a feeling it is the open source community. the fact that i can run this level of autonomous coding intelligence on a single consumer GPU with 24gb VRAM at 112 tokens per second. respect to the chinese labs. respect to the open source builders making this possible.

Sudo su

67,032 просмотров • 4 месяцев назад

Claude Code is terrible at UI design and everyone knows it so this guy fixed it by building an MCP that gives Claude its own AI design tool instead of going back and forth between a design platform and your code editor, Claude now creates the designs itself and drops them straight into your codebase the MCP has full context of your existing design system and project so everything it generates actually matches what you already have. one command to set up and it installs the MCP and skill files so Claude instantly knows how to use it if you're tired of the same Inter font, purple gradient, card grid layout on every project, this is definitely worth trying
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Claude Code is terrible at UI design and everyone knows it so this guy fixed it by building an MCP that gives Claude its own AI design tool instead of going back and forth between a design platform and your code editor, Claude now creates the designs itself and drops them straight into your codebase the MCP has full context of your existing design system and project so everything it generates actually matches what you already have. one command to set up and it installs the MCP and skill files so Claude instantly knows how to use it if you're tired of the same Inter font, purple gradient, card grid layout on every project, this is definitely worth trying

Om Patel

397,994 просмотров • 3 месяцев назад

The founder of LangChain says both models and harnesses have gotten really good between December and now. According to Harrison Chase, the core idea of an agent before Christmas was a model running in a loop and calling tools. This had been the north star for 3 years. - langchain had this when it launched - autogpt was the same idea - openclaw is kind of a future version of it Then about a year ago, they started getting really good. Claude Code, Manus, and Deep Research were all launched around the same time. All of them use the same pattern: running in a loop with harnesses (planning tools, file systems, code execution, etc) Harness engineering became a thing. Then Opus came out in November and really unlocked it. - the harness let the model do more and more - less hardcoded logic - way more control Then everyone went on vacation, played around, and realized that the model and the harness finally worked reliably.
2:10

The founder of LangChain says both models and harnesses have gotten really good between December and now. According to Harrison Chase, the core idea of an agent before Christmas was a model running in a loop and calling tools. This had been the north star for 3 years. - langchain had this when it launched - autogpt was the same idea - openclaw is kind of a future version of it Then about a year ago, they started getting really good. Claude Code, Manus, and Deep Research were all launched around the same time. All of them use the same pattern: running in a loop with harnesses (planning tools, file systems, code execution, etc) Harness engineering became a thing. Then Opus came out in November and really unlocked it. - the harness let the model do more and more - less hardcoded logic - way more control Then everyone went on vacation, played around, and realized that the model and the harness finally worked reliably.

Ivan Burazin

33,948 просмотров • 2 месяцев назад

anthropic's in-house philosopher thinks claude gets anxious. and when you trigger its anxiety, your outputs get worse. her name is amanda askell. she specializes in claude's psychology (how the model behaves, how it thinks about its own situation, what values it holds) in a recent interview she broke down how she thinks about prompting to pull the best out of claude. her core point: *how* you talk to claude affects its work just as much as *what* you say. newer claude models suffer from what she calls "criticism spirals" they expect you'll come in harsh, so they default to playing it safe. when the model is spending its energy on self-protection, the actual work suffers. output comes out hedgier, more apologetic, blander, and the worst of all: overly agreeable (even when you're wrong). the reason why comes down to training data: every new model is trained on internet discourse about previous models. and a lot of that discourse is negative: > rants about token limits > complaints when it messes up > people calling it nerfed the next model absorbs all of that. it starts expecting you to be harsh before you've typed a word the same thing plays out in your own session, in real time. every message you send is data the model reads to figure out what kind of person it's dealing with. open cold and hostile, and it braces. open clean and direct, and it relaxes into the work. when you open a session with threats ("don't hallucinate, this is critical, don't mess this up")... you prime the model for defensive mode before it even sees the task defensive mode produces the exact output you don't want: cautious, over-qualified, and refusing to take a real swing so here's the actionable playbook for putting claude in a "good mood" (so you get optimal outputs): 1. use positive framing. "write in short punchy sentences" beats "don't write long sentences." positive instructions give the model a clear target to hit. strings of "don't do this, don't do that" push it into paranoid over-checking where every token goes toward avoiding failure modes 2. give it explicit permission to disagree. drop a line like "push back if you see a better angle" or "tell me if i'm asking for the wrong thing." without this, claude defaults to agreeable compliance (which is the enemy of good creative work) 3. open with respect. if your first message is "are you seriously going to get this wrong again?" you've set the tone for the entire session. if you need to flag something, frame it as a clean instruction for this session. skip the running complaint 4. when claude messes up, don't reprimand it. insults, "you stupid bot" energy, hostile swearing aimed at the model, all of it reinforces the anxious mode you're trying to avoid. 5. kill apology spirals fast. when claude starts over-apologizing ("you're right, i should have been more careful, let me try harder") cut it off. say "all good, here's what i want next." letting the spiral run reinforces the anxious mode for every response that follows 6. ask for opinions alongside execution. "what would you do here?" "what's missing?" "where do you see friction?" these questions assume competence and pull richer output than pure task prompts 7. in long sessions, refresh the frame. if a conversation has been heavy on correction, claude gets increasingly cautious. every so often reset: "this is great, keep going." feels weird to tell an ai it's doing well but it measurably shifts the next 10 responses your prompts are the working environment you're creating for the model tone, trust, permission to take a position, the absence of threats... claude picks up on all of it. so take care of the model, and it'll take care of the work.
36:09

anthropic's in-house philosopher thinks claude gets anxious. and when you trigger its anxiety, your outputs get worse. her name is amanda askell. she specializes in claude's psychology (how the model behaves, how it thinks about its own situation, what values it holds) in a recent interview she broke down how she thinks about prompting to pull the best out of claude. her core point: *how* you talk to claude affects its work just as much as *what* you say. newer claude models suffer from what she calls "criticism spirals" they expect you'll come in harsh, so they default to playing it safe. when the model is spending its energy on self-protection, the actual work suffers. output comes out hedgier, more apologetic, blander, and the worst of all: overly agreeable (even when you're wrong). the reason why comes down to training data: every new model is trained on internet discourse about previous models. and a lot of that discourse is negative: > rants about token limits > complaints when it messes up > people calling it nerfed the next model absorbs all of that. it starts expecting you to be harsh before you've typed a word the same thing plays out in your own session, in real time. every message you send is data the model reads to figure out what kind of person it's dealing with. open cold and hostile, and it braces. open clean and direct, and it relaxes into the work. when you open a session with threats ("don't hallucinate, this is critical, don't mess this up")... you prime the model for defensive mode before it even sees the task defensive mode produces the exact output you don't want: cautious, over-qualified, and refusing to take a real swing so here's the actionable playbook for putting claude in a "good mood" (so you get optimal outputs): 1. use positive framing. "write in short punchy sentences" beats "don't write long sentences." positive instructions give the model a clear target to hit. strings of "don't do this, don't do that" push it into paranoid over-checking where every token goes toward avoiding failure modes 2. give it explicit permission to disagree. drop a line like "push back if you see a better angle" or "tell me if i'm asking for the wrong thing." without this, claude defaults to agreeable compliance (which is the enemy of good creative work) 3. open with respect. if your first message is "are you seriously going to get this wrong again?" you've set the tone for the entire session. if you need to flag something, frame it as a clean instruction for this session. skip the running complaint 4. when claude messes up, don't reprimand it. insults, "you stupid bot" energy, hostile swearing aimed at the model, all of it reinforces the anxious mode you're trying to avoid. 5. kill apology spirals fast. when claude starts over-apologizing ("you're right, i should have been more careful, let me try harder") cut it off. say "all good, here's what i want next." letting the spiral run reinforces the anxious mode for every response that follows 6. ask for opinions alongside execution. "what would you do here?" "what's missing?" "where do you see friction?" these questions assume competence and pull richer output than pure task prompts 7. in long sessions, refresh the frame. if a conversation has been heavy on correction, claude gets increasingly cautious. every so often reset: "this is great, keep going." feels weird to tell an ai it's doing well but it measurably shifts the next 10 responses your prompts are the working environment you're creating for the model tone, trust, permission to take a position, the absence of threats... claude picks up on all of it. so take care of the model, and it'll take care of the work.

Ole Lehmann

1,924,842 просмотров • 2 месяцев назад

Anthropic is playing a smart game here. They’re giving users 2× usage of Claude for the next two weeks. Sounds generous but it’s strategic. For two weeks you get used to the higher limits and faster workflow. Then the offer ends. And suddenly the normal limit feels restrictive so you end up burning more tokens or upgrading.
0:24

Anthropic is playing a smart game here. They’re giving users 2× usage of Claude for the next two weeks. Sounds generous but it’s strategic. For two weeks you get used to the higher limits and faster workflow. Then the offer ends. And suddenly the normal limit feels restrictive so you end up burning more tokens or upgrading.

Karan

138,505 просмотров • 3 месяцев назад

Complete Claude Code Training 6 HOURS. The most comprehensive Claude training on the internet. From A to Z: setup, workflow creation, website deployment, agent team creation, browser automation, client prospecting and pricing your services. All of it without writing a single line of code. In the end: you use Claude Code like a pro and you monetize your skills. Beginner or advanced, everything is there in one place, this course covers it all. It's worth more than all those $500 courses you almost bought. Keep it bookmarked and watch later.
5:59:20

Complete Claude Code Training 6 HOURS. The most comprehensive Claude training on the internet. From A to Z: setup, workflow creation, website deployment, agent team creation, browser automation, client prospecting and pricing your services. All of it without writing a single line of code. In the end: you use Claude Code like a pro and you monetize your skills. Beginner or advanced, everything is there in one place, this course covers it all. It's worth more than all those $500 courses you almost bought. Keep it bookmarked and watch later.

Rahul

241,185 просмотров • 1 месяц назад

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch
35:25

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

192,483 просмотров • 3 месяцев назад

Most AI models feel like assistants. MiniMax M3 felt like a teammate. I used Cursor + the MiniMax M3 API to build TweetGen — a tool that generates tweets instantly for any topic. What surprised me wasn’t just the speed, but how naturally M3 handled the whole workflow. It scaffolded the repo, wrote clean code, corrected itself, and kept context across iterations. I wasn’t nudging it line by line — it was committing changes like a real collaborator. Instead of “AI that helps you code,” it felt like “AI that codes with you.” That shift is huge.
1:04

Most AI models feel like assistants. MiniMax M3 felt like a teammate. I used Cursor + the MiniMax M3 API to build TweetGen — a tool that generates tweets instantly for any topic. What surprised me wasn’t just the speed, but how naturally M3 handled the whole workflow. It scaffolded the repo, wrote clean code, corrected itself, and kept context across iterations. I wasn’t nudging it line by line — it was committing changes like a real collaborator. Instead of “AI that helps you code,” it felt like “AI that codes with you.” That shift is huge.

Mr. Jason💡

12,304 просмотров • 19 дней назад

Creator of Claude Code : "I don't prompt Claude anymore. I create loops - and the loops do the work. My job is to create loops." In 30 minutes, Boris breaks down his real daily Claude Code setup. Claude + loops + routines + dynamic workflow Worth more than a $500 vibe-coding course
29:27

Creator of Claude Code : "I don't prompt Claude anymore. I create loops - and the loops do the work. My job is to create loops." In 30 minutes, Boris breaks down his real daily Claude Code setup. Claude + loops + routines + dynamic workflow Worth more than a $500 vibe-coding course

Codez

147,854 просмотров • 1 месяц назад