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Absolutely recommend using Cache Control in Claude Code Probably one of the Mods that will save you the most tokens and help keep your sessions going much longer It adds this bar for Claude’s 5 minute cache and sends notifications to help keep the cache warm Install command: npx... show more

Daniel San

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202,376 görüntüleme • 1 gün önce •via X (Twitter)

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We've updated /fork in Claude Code /fork now runs a background agent with your exact context (system prompt, tools, history, model) and prompt cache. The result gets returned to your session. /branch (the old /fork) still copies the transcript to a new session you drive.

We've updated /fork in Claude Code /fork now runs a background agent with your exact context (system prompt, tools, history, model) and prompt cache. The result gets returned to your session. /branch (the old /fork) still copies the transcript to a new session you drive.

ClaudeDevs

381,136 görüntüleme • 4 ay önce

Claude Code 2.1.90 just dropped with a new /powerup command Run it and you get interactive lessons that teach you how to use Claude Code right inside the tool. It's solid and has a lot of potential for learning directly in the terminal. Curious how the UI will look in VSCode and Claude Code Desktop. Going to keep digging into this new release!

Claude Code 2.1.90 just dropped with a new /powerup command Run it and you get interactive lessons that teach you how to use Claude Code right inside the tool. It's solid and has a lot of potential for learning directly in the terminal. Curious how the UI will look in VSCode and Claude Code Desktop. Going to keep digging into this new release!

Daniel San

271,876 görüntüleme • 6 ay önce

Claude Code Scheduled Tasks is now available... here's a solid idea to connect it with Telegram Save this so you don't forget to set it up! First, ask Claude to add a simple Telegram messaging module to your repo. You can use the Telegram Bot Builder Skill from Link: Install command: npx claude-code-templates@latest --skill enterprise-communication/telegram-bot-builder Once the module is in your project, grab your bot credentials from BotFather and add the bot ID to your .env file That's it! ✅ Now every Scheduled Task you create should end with an instruction for Claude to send the task result to Telegram using that module. Claude will handle the delivery automatically on every task it runs

Claude Code Scheduled Tasks is now available... here's a solid idea to connect it with Telegram Save this so you don't forget to set it up! First, ask Claude to add a simple Telegram messaging module to your repo. You can use the Telegram Bot Builder Skill from Link: Install command: npx claude-code-templates@latest --skill enterprise-communication/telegram-bot-builder Once the module is in your project, grab your bot credentials from BotFather and add the bot ID to your .env file That's it! ✅ Now every Scheduled Task you create should end with an instruction for Claude to send the task result to Telegram using that module. Claude will handle the delivery automatically on every task it runs

Daniel San

91,123 görüntüleme • 7 ay önce

Claude Code feels completely different once you install this. Anthropic quietly released an official plugin called claude-code-setup and it basically turns Claude Code from “pretty good” into an actual AI dev environment. It scans your project and recommends: → hooks → skills → MCP servers → subagents → automations Then sets everything up step-by-step for you. Most people are using Claude Code completely vanilla… which is why their experience feels messy. The real power comes from the ecosystem around it. Install: /plugin install claude-code-setup@claude-plugins-official Bookmark this before you forget it.

Claude Code feels completely different once you install this. Anthropic quietly released an official plugin called claude-code-setup and it basically turns Claude Code from “pretty good” into an actual AI dev environment. It scans your project and recommends: → hooks → skills → MCP servers → subagents → automations Then sets everything up step-by-step for you. Most people are using Claude Code completely vanilla… which is why their experience feels messy. The real power comes from the ecosystem around it. Install: /plugin install claude-code-setup@claude-plugins-official Bookmark this before you forget it.

Suryansh Tiwari

1,551,606 görüntüleme • 4 ay önce

You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.

You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.

Alvaro Cintas

42,484 görüntüleme • 13 gün önce

this is f**king dangerous Most people treat Claude like a search bar. The ones with a second brain treat it as an intelligence layer on everything they know. Here's the 15-minute setup: → Install Claude Code → Install Obsidian, create a vault → Open it in Claude Code, drop in the CLAUDE.md schema — that one file is the engine → Throw any source in, say "ingest this" Done. From then on, everything you know is queryable by the most capable AI on earth. Claude wires each source against your whole vault and surfaces connections you forgot you had. Build it tonight. full A–Z guide below.

this is f**king dangerous Most people treat Claude like a search bar. The ones with a second brain treat it as an intelligence layer on everything they know. Here's the 15-minute setup: → Install Claude Code → Install Obsidian, create a vault → Open it in Claude Code, drop in the CLAUDE.md schema — that one file is the engine → Throw any source in, say "ingest this" Done. From then on, everything you know is queryable by the most capable AI on earth. Claude wires each source against your whole vault and surfaces connections you forgot you had. Build it tonight. full A–Z guide below.

Kirill

19,471 görüntüleme • 2 ay önce

Today we're launching Goose Ads in Claude. This is a skill /goose-ads that lets anyone make high-performing ad creatives directly in Claude, Claude Code, Cowork, or Codex. It finds the ads companies are already paying real money to run and remakes them for your brand. Accurate logo, messaging, and assets. One prompt. Here's how it works: 1. Install: npx gooseworks install --all 2. Run: /goose-ads create ads for my brand [your-website] 3. Pick the templates you like That's it. Winning ad creative in minutes, inside Claude. But this is just the start. We open-sourced 100+ growth skills that some of the fastest-growing startups run every day. Ads, content, competitor research, GTM, SEO, all of it. Comment Goose and I'll DM you all 100+. For free

Soham Mehta

258,969 görüntüleme • 3 ay önce

THIS GUY TURNS NOTES, DOCUMENTS, AND IDEAS INTO AN AI SECOND BRAIN the system stores all the context of your work and helps claude not forget anything how to build one yourself: install obsidian connect claude code keep your projects, notes, and sessions in one vault link similar ideas together use the graph to discover new connections as a result, claude gets memory of your projects and understands the context without you constantly having to explain everything the more you work, the smarter the system gets don’t collect notes build connections between them instead

THIS GUY TURNS NOTES, DOCUMENTS, AND IDEAS INTO AN AI SECOND BRAIN the system stores all the context of your work and helps claude not forget anything how to build one yourself: install obsidian connect claude code keep your projects, notes, and sessions in one vault link similar ideas together use the graph to discover new connections as a result, claude gets memory of your projects and understands the context without you constantly having to explain everything the more you work, the smarter the system gets don’t collect notes build connections between them instead

Marvin

24,954 görüntüleme • 3 ay önce

Day 11/90 of Inference Engineering How does vLLM work and how is it used in production? Before we discuss how vLLM works internally, it helps to understand what vLLM is. At a high level, vLLM is an inference engine that is designed to serve LLMs to thousands of concurrent users efficiently while managing scarce compute and memory. The goal for vLLM is to maximize throughput and minimize latency; optimizing for the best inference economics and experience for end users. With every request from the end user, it eventually ends up in the engine core, gets scheduled alongside other requests from other concurrent users, executes on the GPU, and updates the KV cache with the new key and value vectors, and streams the tokens back to the user. The Scheduler decides what requests should execute next while continuously batching requests together to maximize GPU utilization. Continuous batching is an inference optimization that allows new requests to join a running batch as other requests finish generating tokens. This helps with keeping the GPU utilization high instead of letting it sit idle waiting for an entire batch to complete generating. After the scheduler dispatches the selected batch to the Model Executor, the Model Executor prepares the tensors and metadata required for inference, retrieves each request’s block table from KV Cache Manager, launches the optimized transformer forward pass on the GPU, computes the logits, updates the KV cache with the new key and value vectors, and finally returns the results for sampling and streaming. The KV Cache Manager uses the PagedAttention memory layout to allocate fixed-size cache blocks on demand and maintains a Free Block Queue on the CPU that tracks which blocks in the GPU’s Paged KV Cache are currently free. When a request needs additional KV cache space, the KV Cache manager takes a free block from the queue and assigns it to that request, thus avoiding an expensive search through GPU memory for available cache blocks. All of these components form the core of vLLM’s inference engine. The Scheduler determines what requests are executed, the Model Executor determines how those requests are executed, the KV Cache Manager determines where each request’s KV cache lives using the PagedAttention Memory Layout. This architecture enables vLLM to serve thousands of concurrent requests with high throughput, low latency, and efficient GPU memory utilization. Heres a little animation that visualizes everything! - I've also completed the forward pass for my mnist.c project. I had a nice chat with shrey birmiwal, such a knowledgeable guy. Excited to learn more about vLLM and implement a tiny-vLLM one day.

Day 11/90 of Inference Engineering How does vLLM work and how is it used in production? Before we discuss how vLLM works internally, it helps to understand what vLLM is. At a high level, vLLM is an inference engine that is designed to serve LLMs to thousands of concurrent users efficiently while managing scarce compute and memory. The goal for vLLM is to maximize throughput and minimize latency; optimizing for the best inference economics and experience for end users. With every request from the end user, it eventually ends up in the engine core, gets scheduled alongside other requests from other concurrent users, executes on the GPU, and updates the KV cache with the new key and value vectors, and streams the tokens back to the user. The Scheduler decides what requests should execute next while continuously batching requests together to maximize GPU utilization. Continuous batching is an inference optimization that allows new requests to join a running batch as other requests finish generating tokens. This helps with keeping the GPU utilization high instead of letting it sit idle waiting for an entire batch to complete generating. After the scheduler dispatches the selected batch to the Model Executor, the Model Executor prepares the tensors and metadata required for inference, retrieves each request’s block table from KV Cache Manager, launches the optimized transformer forward pass on the GPU, computes the logits, updates the KV cache with the new key and value vectors, and finally returns the results for sampling and streaming. The KV Cache Manager uses the PagedAttention memory layout to allocate fixed-size cache blocks on demand and maintains a Free Block Queue on the CPU that tracks which blocks in the GPU’s Paged KV Cache are currently free. When a request needs additional KV cache space, the KV Cache manager takes a free block from the queue and assigns it to that request, thus avoiding an expensive search through GPU memory for available cache blocks. All of these components form the core of vLLM’s inference engine. The Scheduler determines what requests are executed, the Model Executor determines how those requests are executed, the KV Cache Manager determines where each request’s KV cache lives using the PagedAttention Memory Layout. This architecture enables vLLM to serve thousands of concurrent requests with high throughput, low latency, and efficient GPU memory utilization. Heres a little animation that visualizes everything! - I've also completed the forward pass for my mnist.c project. I had a nice chat with shrey birmiwal, such a knowledgeable guy. Excited to learn more about vLLM and implement a tiny-vLLM one day.

max fu

70,797 görüntüleme • 2 ay önce

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

Alvaro Cintas

75,596 görüntüleme • 2 ay önce

Fable 5 + Goose Ads is insane – literally a full ad-creative team right in Claude Code. 1. Install the skill: npx gooseworks install --all npx gooseworks login 2. Ask Claude Fable to make ads for your brand. /goose-ads create ads for my brand . Fable 5 pulls the ads already winning in your space and rebuilds them on-brand – your product, your palette, your copy – a whole batch, ready to test. It can ship a month of creative testing in a single prompt. This is also possible with Opus and Sonnet, but Fable's brand research is next level. Link below.

Shiv

223,176 görüntüleme • 3 ay önce

The Claude Code team gave me early access to the new version of Claude Code Desktop and I’ve been using it for the past few days… here’s what I’ve liked most about this new version 1- Panel creation and the ability to run agents simultaneously. This makes me think we’ll definitely see these agents working in a coordinated way, communicating across sessions.

The Claude Code team gave me early access to the new version of Claude Code Desktop and I’ve been using it for the past few days… here’s what I’ve liked most about this new version 1- Panel creation and the ability to run agents simultaneously. This makes me think we’ll definitely see these agents working in a coordinated way, communicating across sessions.

Daniel San

65,027 görüntüleme • 5 ay önce

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

BuBBliK

200,456 görüntüleme • 4 ay önce

$You need to try Claude Code with CLAUDE_CODE_NO_FLICKER! I remember the early days using Claude Code in the VSCode terminal, struggling to keep the terminal from jumping around Then I switched to Ghostty and things got way better... now with tmux and NO_FLICKER it's perfect Completely clean interaction Quick test, just run: CLAUDE_CODE_NO_FLICKER=1 claude If you like it, add this to your settings.json "env": { "CLAUDE_CODE_NO_FLICKER": "1" } TIP: Add the settings.json to the .claude folder in your home directory so it applies to every session you open$

You need to try Claude Code with CLAUDE_CODE_NO_FLICKER! I remember the early days using Claude Code in the VSCode terminal, struggling to keep the terminal from jumping around Then I switched to Ghostty and things got way better... now with tmux and NO_FLICKER it's perfect Completely clean interaction Quick test, just run: CLAUDE_CODE_NO_FLICKER=1 claude If you like it, add this to your settings.json "env": { "CLAUDE_CODE_NO_FLICKER": "1" } TIP: Add the settings.json to the .claude folder in your home directory so it applies to every session you open

Daniel San

43,041 görüntüleme • 6 ay önce

New in Claude Code: your sessions can now message each other. Instead of having to re-explain yourself in another session, you can now tell Claude to do it. It sends a summary (not your history or files), and the other session picks it up mid-task.

New in Claude Code: your sessions can now message each other. Instead of having to re-explain yourself in another session, you can now tell Claude to do it. It sends a summary (not your history or files), and the other session picks it up mid-task.

ClaudeDevs

7,971,404 görüntüleme • 1 ay önce

that’s it. you only have to read this one article to: > learn claude skill foundations > learn claude skill architecture > learn claude skill testing > learn claude skill production which will let you create claude skills that: 1: automate your workflow’s 2: make you more productive 3: outperform your competitors welcome to the land of automation my claude cowork and claude code friends!

that’s it. you only have to read this one article to: > learn claude skill foundations > learn claude skill architecture > learn claude skill testing > learn claude skill production which will let you create claude skills that: 1: automate your workflow’s 2: make you more productive 3: outperform your competitors welcome to the land of automation my claude cowork and claude code friends!

hoeem

509,565 görüntüleme • 6 ay önce

$200 to $20. That's how your AI costs will change if you switch from Claude Code Max to the MiniMax (official) Token Plan Plus in Kilo. MiniMax's latest model, M3, performs at 79% of Sonnet 5's level for a tenth of the price, and the plan gets you 1.7B tokens per month through Kilo. See how much you'd save with model freedom:

$200 to $20. That's how your AI costs will change if you switch from Claude Code Max to the MiniMax (official) Token Plan Plus in Kilo. MiniMax's latest model, M3, performs at 79% of Sonnet 5's level for a tenth of the price, and the plan gets you 1.7B tokens per month through Kilo. See how much you'd save with model freedom:

Kilo

25,441 görüntüleme • 2 ay önce

Claude Design + Shopify is f*cking ridiculous 🤯 You can now publish pages from Claude Design → Claude Code → Shopify. Built 100% with Claude Design, Claude Code, and the Shopify CLI. Perfect for DTC brands and agencies who want to skip the design → dev handoff entirely. Here's how it works: → Design any landing page in Claude Design → Export as a zip and drop it into Claude Code → Install the Shopify + Shopify AI Toolkit plugins → Prompt Claude to convert the HTML into a Shopify page template + push to live theme → Claude uploads the images, deploys the files, and creates a published page No more handing designs off to a dev and waiting 2 weeks for a Shopify page. What you get: - A workflow that turns any Claude Design page into a real Shopify page template - Editable sections so your marketing team can swap copy, images, and CTAs without code - Images uploaded straight to Shopify Files automatically - A files-only deploy that only touches what's new in your live theme - A repeatable pipeline you can use every time you design a new landing page This is essentially the design-to-deploy pipeline brands have been waiting for. I put together a step-by-step playbook for going from Claude Design → published Shopify page. Every install, every plugin, every command, and the exact prompt that runs the whole thing. Want the playbook for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Claude Design + Shopify is f*cking ridiculous 🤯 You can now publish pages from Claude Design → Claude Code → Shopify. Built 100% with Claude Design, Claude Code, and the Shopify CLI. Perfect for DTC brands and agencies who want to skip the design → dev handoff entirely. Here's how it works: → Design any landing page in Claude Design → Export as a zip and drop it into Claude Code → Install the Shopify + Shopify AI Toolkit plugins → Prompt Claude to convert the HTML into a Shopify page template + push to live theme → Claude uploads the images, deploys the files, and creates a published page No more handing designs off to a dev and waiting 2 weeks for a Shopify page. What you get: - A workflow that turns any Claude Design page into a real Shopify page template - Editable sections so your marketing team can swap copy, images, and CTAs without code - Images uploaded straight to Shopify Files automatically - A files-only deploy that only touches what's new in your live theme - A repeatable pipeline you can use every time you design a new landing page This is essentially the design-to-deploy pipeline brands have been waiting for. I put together a step-by-step playbook for going from Claude Design → published Shopify page. Every install, every plugin, every command, and the exact prompt that runs the whole thing. Want the playbook for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Mike Futia

59,314 görüntüleme • 4 ay önce

THIS ENGINEER USES HIS HANDS AS A REMOTE CONTROL. CLAUDE MADE THAT POSSIBLE he opened claude and described the idea and got the full code he strapped sensors to his hands and walked outside and the drone followed every move his colleagues are still reading arduino documentation the only thing separating them is one conversation with claude every flex is a command and every gesture is a signal and zero remote needed save this so you don’t forget how to set it up

THIS ENGINEER USES HIS HANDS AS A REMOTE CONTROL. CLAUDE MADE THAT POSSIBLE he opened claude and described the idea and got the full code he strapped sensors to his hands and walked outside and the drone followed every move his colleagues are still reading arduino documentation the only thing separating them is one conversation with claude every flex is a command and every gesture is a signal and zero remote needed save this so you don’t forget how to set it up

leopardracer

16,382 görüntüleme • 4 ay önce

New feature in Claude Code 2.1.14 just dropped! You can now search and install plugins from the marketplaces installed in your current Claude Code session. This is huge if you’re building plugins on top of Claude Code’s marketplace layer (Skills, Agents, Hooks, etc). How it works: - Run /plugin - The official Claude marketplace is installed by default - Use the search bar to find the plugin you want - Select one or multiple plugins with space, then press i to install - Go to the Installed tab to browse and enable them With the exponential growth of Skills and Agent-based components running in the CLI, improving plugin discoverability is a big win. Pretty sure more marketplace-related features are coming

New feature in Claude Code 2.1.14 just dropped! You can now search and install plugins from the marketplaces installed in your current Claude Code session. This is huge if you’re building plugins on top of Claude Code’s marketplace layer (Skills, Agents, Hooks, etc). How it works: - Run /plugin - The official Claude marketplace is installed by default - Use the search bar to find the plugin you want - Select one or multiple plugins with space, then press i to install - Go to the Installed tab to browse and enable them With the exponential growth of Skills and Agent-based components running in the CLI, improving plugin discoverability is a big win. Pretty sure more marketplace-related features are coming

Daniel San

40,994 görüntüleme • 8 ay önce