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Every serious Claude Code user is using this repo. if you're not, you're leaving 90% of Claude Code's power on the table. It's called claude-code-best-practice - 84 sourced tips, implementation examples for every major feature, workflow comparisons across 8 major repos, and the actual tips from Boris Cherny (creator...

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Sukh Sroay

18,544 subscribers

113,759 次观看 • 5 个月前 •via X (Twitter)

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Claude Code Desktop now opens a new window for each session This makes it much easier to visualize multiple Claude Code agents running in parallel My current stack depends on the task: - Ghostty: when starting a project. Bash commands, git, env variables, provider connections. All manual through the terminal with a Claude panel running alongside. - Claude Code Desktop: once everything is configured. GitHub connected, CLAUDE.md, Skills, subagents and Hooks ready. Claude Code runs on its own, no more terminal setup, just panels running and outputs to review. - VSCode: when I need to review code by hand. I use it less and less, but there are moments where I have to confirm Claude got it right. I usually open the Claude extension inside VSCode, but it lacks most of the CLI features so it's limited Solid update. Worth trying once your workflows are already set up 👇
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Claude Code Desktop now opens a new window for each session This makes it much easier to visualize multiple Claude Code agents running in parallel My current stack depends on the task: - Ghostty: when starting a project. Bash commands, git, env variables, provider connections. All manual through the terminal with a Claude panel running alongside. - Claude Code Desktop: once everything is configured. GitHub connected, CLAUDE.md, Skills, subagents and Hooks ready. Claude Code runs on its own, no more terminal setup, just panels running and outputs to review. - VSCode: when I need to review code by hand. I use it less and less, but there are moments where I have to confirm Claude got it right. I usually open the Claude extension inside VSCode, but it lacks most of the CLI features so it's limited Solid update. Worth trying once your workflows are already set up 👇

Daniel San

38,806 次观看 • 3 个月前

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
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Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

50,169 次观看 • 6 个月前

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.
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Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

258,823 次观看 • 2 个月前

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
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Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

46,478 次观看 • 6 个月前

This Claude Code Skills Pack is a cheat code for ad creative teams 🤯 10 plug-and-play skills → competitor audits, creative briefs, 20 hook variations, ad copy, static ads, landing pages, & weekly performance reports. All inside Claude Code. Perfect for DTC brands and agencies who are still prompting Claude Code from scratch every time. If you're re-explaining your brand voice in every session, getting inconsistent output depending on who's prompting, and spending 30 minutes on tasks that should take 30 seconds... These skills eliminate the entire loop: → Competitor Ad Research Agent Drop a brand name, get back a full creative audit — hooks, messaging angles, ad formats, CTAs, and "steal this" angles. No more scrolling the Ad Library for an hour. → Creative Brief Generator One prompt, complete brief in your exact template. Hooks, concepts, visual direction, brand voice — all loaded from your own files. → Hook & Script Writer 15+ hooks categorized by type (curiosity, problem-agitation, result-first, social proof). Full 30-60s scripts with the hook → problem → mechanism → proof → CTA structure baked in. → Ad Copy Variation Engine Feed it one winning ad, get back 20 variations — each targeting a different persona and pain point. Same structure, different angles. Creative fatigue solved. → Weekly Report Writer Drop in your Meta ads CSV. Get back the narrative summary, anomaly flags, creative fatigue alerts, and recommended next steps. The report nobody wants to write, written in 60 seconds. → Creative Fatigue Detector Flags ads before they die. CTR trending down, frequency climbing, conversion rate dropping — caught in hours, not after three days of wasted spend. No prompting from scratch every time. No inconsistent output across your team. No re-explaining context in every session. I packaged all 10 as a free Skills Pack. Copy-paste the files into your Claude Code commands folder and they just work. Want the full Skills Pack? > Like this post > Comment "SKILLS" And I'll send it over (must be following so I can DM)
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This Claude Code Skills Pack is a cheat code for ad creative teams 🤯 10 plug-and-play skills → competitor audits, creative briefs, 20 hook variations, ad copy, static ads, landing pages, & weekly performance reports. All inside Claude Code. Perfect for DTC brands and agencies who are still prompting Claude Code from scratch every time. If you're re-explaining your brand voice in every session, getting inconsistent output depending on who's prompting, and spending 30 minutes on tasks that should take 30 seconds... These skills eliminate the entire loop: → Competitor Ad Research Agent Drop a brand name, get back a full creative audit — hooks, messaging angles, ad formats, CTAs, and "steal this" angles. No more scrolling the Ad Library for an hour. → Creative Brief Generator One prompt, complete brief in your exact template. Hooks, concepts, visual direction, brand voice — all loaded from your own files. → Hook & Script Writer 15+ hooks categorized by type (curiosity, problem-agitation, result-first, social proof). Full 30-60s scripts with the hook → problem → mechanism → proof → CTA structure baked in. → Ad Copy Variation Engine Feed it one winning ad, get back 20 variations — each targeting a different persona and pain point. Same structure, different angles. Creative fatigue solved. → Weekly Report Writer Drop in your Meta ads CSV. Get back the narrative summary, anomaly flags, creative fatigue alerts, and recommended next steps. The report nobody wants to write, written in 60 seconds. → Creative Fatigue Detector Flags ads before they die. CTR trending down, frequency climbing, conversion rate dropping — caught in hours, not after three days of wasted spend. No prompting from scratch every time. No inconsistent output across your team. No re-explaining context in every session. I packaged all 10 as a free Skills Pack. Copy-paste the files into your Claude Code commands folder and they just work. Want the full Skills Pack? > Like this post > Comment "SKILLS" And I'll send it over (must be following so I can DM)

Mike Futia

56,043 次观看 • 6 个月前

i just open sourced the workflow behind $2M AI video productions... i built 7 skills that run the pipeline end to end, built for Seedance 2.5 and they work in Claude Code, Codex, Hermes or any harness (works best with 1080p using Higgsfield CLI) here's how to use them, in order: /setup writes which image and video models you run into your project, once, so every skill reads the same stack /studio-init scaffolds the whole studio as a file tree from one question, the project name /film-breakdown walks your script scene by scene and writes a 22-field card for every shot /reference-board locks your references into a visual bible, a caption on every image and a ban list for the rest /asset-passport writes the exhaustive descriptor every later prompt will quote word for word /stress-test combat-tests each asset and flips it to locked only at 10 out of 10 repeatability /shot-prompt refuses to run until everything in frame is locked, then writes the 15-block prompt and logs every attempt get access to the skills and full breakdown of the pipeline in the article below:
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i just open sourced the workflow behind $2M AI video productions... i built 7 skills that run the pipeline end to end, built for Seedance 2.5 and they work in Claude Code, Codex, Hermes or any harness (works best with 1080p using Higgsfield CLI) here's how to use them, in order: /setup writes which image and video models you run into your project, once, so every skill reads the same stack /studio-init scaffolds the whole studio as a file tree from one question, the project name /film-breakdown walks your script scene by scene and writes a 22-field card for every shot /reference-board locks your references into a visual bible, a caption on every image and a ban list for the rest /asset-passport writes the exhaustive descriptor every later prompt will quote word for word /stress-test combat-tests each asset and flips it to locked only at 10 out of 10 repeatability /shot-prompt refuses to run until everything in frame is locked, then writes the 15-block prompt and logs every attempt get access to the skills and full breakdown of the pipeline in the article below:

Machina

59,905 次观看 • 28 天前

20 days ago, I connected Claude Code to my newly created instagram handle.. I gained 4.3M views and 6500+ followers in less than a month [ i post Ai generated animated stories ] Full workflow: i let claude study my account before i write another reel.. This is the cleanest content workflow i've built on claude. give it your IG first. 4 prompts handle the rest.. niche research, the reel script, the hook, and the daily automation.. the whole loop is basically, give claude your IG → find what's working → write retention-optimized scripts → engineer the hook → automate the daily output.. ▫️ Setup: give claude your instagram open claude code. claude code has a built-in web tool that browses any public URL. or install any agentic browser like Browser Harness or Firecrawl or Comet browser paste this with your handle filled in: "Browse and pull the last 30 reels and posts. Analyze my recurring topics, top-performing hooks, formats, and engagement patterns. Then map out my actual audience and what they consistently respond to." claude reads your profile, pulls every reel down, and now has the context to personalize every prompt below to YOUR account, not a generic niche. if you're on claude desktop, the same works with firecrawl MCP connected. ▫️ Prompt 1 find what actually goes viral in your niche: "Analyze the highest-performing Instagram Reels, TikToks, and Reddit posts in the [niche] niche from the last 30 days. Identify repeating hooks, visual styles, emotional triggers, and content formats that consistently generate high engagement. Then summarize the 5 strongest content angles optimized for AI-generated content and short-form videos." run this after the setup. you get 5 angles backed by what's already working in your niche, cross-checked against what's already working on YOUR account. ▫️ Prompt 2 write a high-retention reel script "Write a short-form Instagram Reel script about [topic] with an aggressive hook in the first 2 seconds. Create immediate curiosity, tension, or controversy to stop scrolling, then deliver a fast and satisfying payoff. Keep it under 30 seconds and optimize the structure for watch time, replays, comments, and shares. Finish with a subtle CTA." the line that matters: "optimize the structure for watch time, replays, comments, and shares." claude writes for the metrics, not just the word count. ▫️ Prompt 3 engineer better hooks "Study the top-performing Reels in [niche] and break down the hook structure, pacing, and emotional triggers used in the first 3 seconds. Then generate 5 new hook variations that are even more curiosity-driven, emotionally charged, and optimized to stop scrolling instantly. Focus on triggers like surprise, fear, ego, urgency, or desire." most reels die in the first 2 seconds. this prompt has claude reverse-engineer what already works, then give you 5 sharper versions to swap in. ▫️ Prompt 4 automate the whole workflow "Build a complete AI-powered content workflow for Instagram in the [niche] niche. The system should identify trending topics daily, generate high-retention scripts, create matching AI visuals, turn them into short-form videos, and generate optimized captions and hashtags. Structure everything as a repeatable workflow designed for consistent daily posting and growth." once the niche and script structure are validated, this turns it into a daily loop. one prompt that handles topic → script → visual → video → caption. these 4 prompts are the building blocks. the setup is what makes them yours. your real value is in the [niche] you plug in. content workflow built in one weekend, daily posting on autopilot from monday.
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20 days ago, I connected Claude Code to my newly created instagram handle.. I gained 4.3M views and 6500+ followers in less than a month [ i post Ai generated animated stories ] Full workflow: i let claude study my account before i write another reel.. This is the cleanest content workflow i've built on claude. give it your IG first. 4 prompts handle the rest.. niche research, the reel script, the hook, and the daily automation.. the whole loop is basically, give claude your IG → find what's working → write retention-optimized scripts → engineer the hook → automate the daily output.. ▫️ Setup: give claude your instagram open claude code. claude code has a built-in web tool that browses any public URL. or install any agentic browser like Browser Harness or Firecrawl or Comet browser paste this with your handle filled in: "Browse and pull the last 30 reels and posts. Analyze my recurring topics, top-performing hooks, formats, and engagement patterns. Then map out my actual audience and what they consistently respond to." claude reads your profile, pulls every reel down, and now has the context to personalize every prompt below to YOUR account, not a generic niche. if you're on claude desktop, the same works with firecrawl MCP connected. ▫️ Prompt 1 find what actually goes viral in your niche: "Analyze the highest-performing Instagram Reels, TikToks, and Reddit posts in the [niche] niche from the last 30 days. Identify repeating hooks, visual styles, emotional triggers, and content formats that consistently generate high engagement. Then summarize the 5 strongest content angles optimized for AI-generated content and short-form videos." run this after the setup. you get 5 angles backed by what's already working in your niche, cross-checked against what's already working on YOUR account. ▫️ Prompt 2 write a high-retention reel script "Write a short-form Instagram Reel script about [topic] with an aggressive hook in the first 2 seconds. Create immediate curiosity, tension, or controversy to stop scrolling, then deliver a fast and satisfying payoff. Keep it under 30 seconds and optimize the structure for watch time, replays, comments, and shares. Finish with a subtle CTA." the line that matters: "optimize the structure for watch time, replays, comments, and shares." claude writes for the metrics, not just the word count. ▫️ Prompt 3 engineer better hooks "Study the top-performing Reels in [niche] and break down the hook structure, pacing, and emotional triggers used in the first 3 seconds. Then generate 5 new hook variations that are even more curiosity-driven, emotionally charged, and optimized to stop scrolling instantly. Focus on triggers like surprise, fear, ego, urgency, or desire." most reels die in the first 2 seconds. this prompt has claude reverse-engineer what already works, then give you 5 sharper versions to swap in. ▫️ Prompt 4 automate the whole workflow "Build a complete AI-powered content workflow for Instagram in the [niche] niche. The system should identify trending topics daily, generate high-retention scripts, create matching AI visuals, turn them into short-form videos, and generate optimized captions and hashtags. Structure everything as a repeatable workflow designed for consistent daily posting and growth." once the niche and script structure are validated, this turns it into a daily loop. one prompt that handles topic → script → visual → video → caption. these 4 prompts are the building blocks. the setup is what makes them yours. your real value is in the [niche] you plug in. content workflow built in one weekend, daily posting on autopilot from monday.

Axel Bitblaze 🪓

201,810 次观看 • 2 个月前

Introducing a new tool called "SideChannel". A secure alternative to OpenClaw. Utilizes signal for communication and has Claude integration. I built SideChannel, an open-source Signal bot that connects Claude AI to your entire development workflow. End-to-end encrypted. From your pocket. The real power is autonomous development. Send one message like "Build a REST API with auth, pagination, and tests" and SideChannel will: - Generate a full PRD with stories and atomic tasks. - Dispatch up to 10 parallel workers (each running Claude). - Independently verify every task with a separate Claude context. - Run quality gates to catch regressions - Auto-fix failures. - Send you progress updates via Signal as work completes. Every piece of code is reviewed by a separate AI context using a fail-closed security model. If it detects security issues, backdoors, or logic errors — the code gets rejected automatically. No rubber stamps. It also has memory that actually works. Conversations are stored with vector embeddings for semantic search. Claude remembers your project conventions, past decisions, and what's been tried before. It gets smarter about your codebase over time. Other things I'm proud of: - Plugin framework for extending with custom commands. - Multi-project support with per-user scoping. - Rate limiting, path validation, phone allowlist. - Git checkpoints before every task, atomic commits after. - Stale task recovery, circular dependency detection. - Works on Linux and macOS, one-command install. It also integrates into OpenAI or Grok (optional) for more Generative AI response for simple things like "Whats the weather in New York City right now?".
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Introducing a new tool called "SideChannel". A secure alternative to OpenClaw. Utilizes signal for communication and has Claude integration. I built SideChannel, an open-source Signal bot that connects Claude AI to your entire development workflow. End-to-end encrypted. From your pocket. The real power is autonomous development. Send one message like "Build a REST API with auth, pagination, and tests" and SideChannel will: - Generate a full PRD with stories and atomic tasks. - Dispatch up to 10 parallel workers (each running Claude). - Independently verify every task with a separate Claude context. - Run quality gates to catch regressions - Auto-fix failures. - Send you progress updates via Signal as work completes. Every piece of code is reviewed by a separate AI context using a fail-closed security model. If it detects security issues, backdoors, or logic errors — the code gets rejected automatically. No rubber stamps. It also has memory that actually works. Conversations are stored with vector embeddings for semantic search. Claude remembers your project conventions, past decisions, and what's been tried before. It gets smarter about your codebase over time. Other things I'm proud of: - Plugin framework for extending with custom commands. - Multi-project support with per-user scoping. - Rate limiting, path validation, phone allowlist. - Git checkpoints before every task, atomic commits after. - Stale task recovery, circular dependency detection. - Works on Linux and macOS, one-command install. It also integrates into OpenAI or Grok (optional) for more Generative AI response for simple things like "Whats the weather in New York City right now?".

Dave Kennedy

49,559 次观看 • 6 个月前

Dune analytics MCP.. Claude becomes your on chain SQL analyst.. No dashboard has every query you'll ever need. dune does but writing SQL is a skill, and most of you would skip it.. i know this MCP fixes that. Claude writes the query, runs it on dune, and explains what the data actually means. with this MCP wired in, you don't need to know SQL. you describe what you want in plain english and Claude does the rest. like "Claude, which wallets bought $RAVE in last few weeks and still hold?" "Claude, show me the top 50 ETH wallets by stablecoin inflows last 7 days." "Claude, what's the median gas paid by ARB users in the last 24h?" Questions no dashboard can answer. one prompt away.. Setup (3 minutes) ▫️Step 1: grab a free dune API key → ▫️Step 2: add this to ~/.claude/settings.json or .mcp.json: { "mcpServers": { "dune": { "command": "npx", "args": ["-y", "dune-analytics-mcp"], "env": { "DUNE_API_KEY": "your-key-here" } } } } ▫️Step 3: restart claude. you'll see the dune tool load in your tool menu. that's it. you now have onchain SQL on tap. How to actually use it: 3 prompts i use: 1) Smart money watchlist: "claude, pull the top 20 wallets by realized pnl on $TOKEN in the last 30 days. show me which ones are still holding." gives you a clean leaderboard of who's actually winning on that token. add them to your etherscan watchlist. 2) accumulation vs distribution "claude, compare net inflows vs outflows for $TOKEN across all CEX wallets in the last 14 days." if whales are moving off exchanges → accumulation. onto exchanges → distribution. you see the rotation before the candle. 3) narrative heat check "claude, which 10 tokens saw the biggest % increase in unique new holders this week?" finds where fresh money is flowing. before this MCP i'd either, pay for a pro dune account + write queries manually, or look at someone else's dashboard and hope it answers my question… now claude writes it for me, in seconds, custom to my thesis. no dashboard in existence beats that. free tier covers most of what you need. upgrade if you're querying heavy. ( built a quick $RAVE post mortem dashboard using a prompt as shown in the video ) MORE SUCH USEFUL MCP for traders below.. 👇
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Dune analytics MCP.. Claude becomes your on chain SQL analyst.. No dashboard has every query you'll ever need. dune does but writing SQL is a skill, and most of you would skip it.. i know this MCP fixes that. Claude writes the query, runs it on dune, and explains what the data actually means. with this MCP wired in, you don't need to know SQL. you describe what you want in plain english and Claude does the rest. like "Claude, which wallets bought $RAVE in last few weeks and still hold?" "Claude, show me the top 50 ETH wallets by stablecoin inflows last 7 days." "Claude, what's the median gas paid by ARB users in the last 24h?" Questions no dashboard can answer. one prompt away.. Setup (3 minutes) ▫️Step 1: grab a free dune API key → ▫️Step 2: add this to ~/.claude/settings.json or .mcp.json: { "mcpServers": { "dune": { "command": "npx", "args": ["-y", "dune-analytics-mcp"], "env": { "DUNE_API_KEY": "your-key-here" } } } } ▫️Step 3: restart claude. you'll see the dune tool load in your tool menu. that's it. you now have onchain SQL on tap. How to actually use it: 3 prompts i use: 1) Smart money watchlist: "claude, pull the top 20 wallets by realized pnl on $TOKEN in the last 30 days. show me which ones are still holding." gives you a clean leaderboard of who's actually winning on that token. add them to your etherscan watchlist. 2) accumulation vs distribution "claude, compare net inflows vs outflows for $TOKEN across all CEX wallets in the last 14 days." if whales are moving off exchanges → accumulation. onto exchanges → distribution. you see the rotation before the candle. 3) narrative heat check "claude, which 10 tokens saw the biggest % increase in unique new holders this week?" finds where fresh money is flowing. before this MCP i'd either, pay for a pro dune account + write queries manually, or look at someone else's dashboard and hope it answers my question… now claude writes it for me, in seconds, custom to my thesis. no dashboard in existence beats that. free tier covers most of what you need. upgrade if you're querying heavy. ( built a quick $RAVE post mortem dashboard using a prompt as shown in the video ) MORE SUCH USEFUL MCP for traders below.. 👇

Axel Bitblaze 🪓

32,016 次观看 • 4 个月前

I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)
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I just built a Meta Ads diagnostic in Claude Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

12,847 次观看 • 3 个月前

Another WTF moment. A developer just open-sourced a coding agent harness that boots 245x faster than Claude Code. It's called jcode. You launch it and the first frame renders in 14 milliseconds. Claude Code takes 3,436. One active session uses 27.8 MB of RAM. Claude Code uses 386.6. Run ten sessions in parallel and jcode holds at 117 MB while OpenCode swells to 3.2 GB. Each agent has a semantic memory graph instead of a scratchpad. Every turn gets embedded as a vector. The graph is queried on every turn for related memories, and a sideagent verifies the hits before injecting them into context. Consolidation runs in the background to check for stale or conflicting facts. No manual /remember calls. No token burn on lookup tools. The provider list is 30+ deep. Claude, ChatGPT, Gemini, GitHub Copilot, Azure, OpenRouter, DeepSeek, Groq, Mistral, Perplexity, Fireworks, Ollama, LM Studio, and any OpenAI-compatible endpoint you point it at. Ran out of tokens on your first ChatGPT Pro sub? /account swaps to the second. Then there's Swarm. Spawn two agents in the same repo and the server manages them. When agent A edits a file agent B has been reading, agent B gets pinged and can check the diff. Agents can DM each other, broadcast to the room, or spawn their own worker teams for parallel tasks. Groups, channels, and completion statuses are handled automatically. The UI has live side panels that render mermaid diagrams inline. To make it fast, the author wrote a Rust mermaid renderer 1800x faster than the JavaScript one, then wrote a custom terminal called Handterm because no existing terminal could do smooth partial-line scrolling. Self-dev mode is where it gets wild. Tell your agent to enter self-dev and it starts editing jcode's own source code, rebuilds the binary, reloads it live, and keeps working across your existing sessions. You can also resume broken sessions from Claude Code, Codex, OpenCode, or pi directly inside jcode. Anthropic's cache goes cold at the 5-minute mark and you're staring down a big cache miss on your next turn? The UI warns you before you spend the tokens. Written in Rust. MIT licensed. Runs on macOS, Windows, Linux, and Termux. Sitting at 11.2k stars with a native iOS app coming.
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Another WTF moment. A developer just open-sourced a coding agent harness that boots 245x faster than Claude Code. It's called jcode. You launch it and the first frame renders in 14 milliseconds. Claude Code takes 3,436. One active session uses 27.8 MB of RAM. Claude Code uses 386.6. Run ten sessions in parallel and jcode holds at 117 MB while OpenCode swells to 3.2 GB. Each agent has a semantic memory graph instead of a scratchpad. Every turn gets embedded as a vector. The graph is queried on every turn for related memories, and a sideagent verifies the hits before injecting them into context. Consolidation runs in the background to check for stale or conflicting facts. No manual /remember calls. No token burn on lookup tools. The provider list is 30+ deep. Claude, ChatGPT, Gemini, GitHub Copilot, Azure, OpenRouter, DeepSeek, Groq, Mistral, Perplexity, Fireworks, Ollama, LM Studio, and any OpenAI-compatible endpoint you point it at. Ran out of tokens on your first ChatGPT Pro sub? /account swaps to the second. Then there's Swarm. Spawn two agents in the same repo and the server manages them. When agent A edits a file agent B has been reading, agent B gets pinged and can check the diff. Agents can DM each other, broadcast to the room, or spawn their own worker teams for parallel tasks. Groups, channels, and completion statuses are handled automatically. The UI has live side panels that render mermaid diagrams inline. To make it fast, the author wrote a Rust mermaid renderer 1800x faster than the JavaScript one, then wrote a custom terminal called Handterm because no existing terminal could do smooth partial-line scrolling. Self-dev mode is where it gets wild. Tell your agent to enter self-dev and it starts editing jcode's own source code, rebuilds the binary, reloads it live, and keeps working across your existing sessions. You can also resume broken sessions from Claude Code, Codex, OpenCode, or pi directly inside jcode. Anthropic's cache goes cold at the 5-minute mark and you're staring down a big cache miss on your next turn? The UI warns you before you spend the tokens. Written in Rust. MIT licensed. Runs on macOS, Windows, Linux, and Termux. Sitting at 11.2k stars with a native iOS app coming.

Brady Long

207,858 次观看 • 1 个月前

An Anthropic engineer watched me trade from across the table at a WeWork in SF I had my laptop open. Four agents running. Green charts. Live trades scrolling. He was on a Zoom call. Muted himself. Walked over. "Are you running Claude against live prediction markets right now" I told him. Claude Code. Two repos. $25 a month. He pulled up a chair. "I helped build the model you're using. I've never seen anyone wire it to live trades like this" I showed him the dataset. 86 million trades. Every wallet. Every entry. Every exit. He stared at it. "We tested this internally. You give Claude a dataset and don't tell it what to look for. It finds the winning wallets. Then it finds WHY they win. Then it copies the pattern. We never shipped it because legal killed it" I told him I did exactly that. One weekend. Claude Code found the exit logic on its own. Top wallets exit before resolution 91% of the time. They capture 86% of expected value. Cut losers at 12%. Everyone else captures 58% and holds to 41%. "That's the exact finding from our internal eval. Except ours took a team of eight and four months" I showed him the scanner. Three commands. 500+ markets. No API key. Claude scores them all in 20 minutes. "You're using our model to beat markets we're not allowed to touch. On infra that costs less than my lunch" My setup: Claude API - $20/mo VPS - $5/mo poly_data - free polymarket-cli - free 214 trades. 74% win rate. +$9,400. 19 days. I showed him the full breakdown. Every repo. Every command. Every dollar. Copytrade here: He read it for five minutes. Then looked up. "If my manager sees this he's going to lose his mind. You just proved our model works in production and we've been sitting on it for a year" He DM'd me that night. "Take this down before someone at Anthropic finds it" Too late.
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An Anthropic engineer watched me trade from across the table at a WeWork in SF I had my laptop open. Four agents running. Green charts. Live trades scrolling. He was on a Zoom call. Muted himself. Walked over. "Are you running Claude against live prediction markets right now" I told him. Claude Code. Two repos. $25 a month. He pulled up a chair. "I helped build the model you're using. I've never seen anyone wire it to live trades like this" I showed him the dataset. 86 million trades. Every wallet. Every entry. Every exit. He stared at it. "We tested this internally. You give Claude a dataset and don't tell it what to look for. It finds the winning wallets. Then it finds WHY they win. Then it copies the pattern. We never shipped it because legal killed it" I told him I did exactly that. One weekend. Claude Code found the exit logic on its own. Top wallets exit before resolution 91% of the time. They capture 86% of expected value. Cut losers at 12%. Everyone else captures 58% and holds to 41%. "That's the exact finding from our internal eval. Except ours took a team of eight and four months" I showed him the scanner. Three commands. 500+ markets. No API key. Claude scores them all in 20 minutes. "You're using our model to beat markets we're not allowed to touch. On infra that costs less than my lunch" My setup: Claude API - $20/mo VPS - $5/mo poly_data - free polymarket-cli - free 214 trades. 74% win rate. +$9,400. 19 days. I showed him the full breakdown. Every repo. Every command. Every dollar. Copytrade here: He read it for five minutes. Then looked up. "If my manager sees this he's going to lose his mind. You just proved our model works in production and we've been sitting on it for a year" He DM'd me that night. "Take this down before someone at Anthropic finds it" Too late.

Lunar

224,077 次观看 • 5 个月前

I built a custom TradingView indicator with Claude Code & Fable 5. It's called the Storm Gauge and is built off a real quant trading strategy. I open-sourced the full code on GitHub. Free to install, free to fork, yours to improve. Here's how to install a quant indicator on your TradingView chart: What it actually is The Storm Gauge is a live implementation of the GARCH model, a Nobel Prize-winning volatility framework that real quant desks run daily. It forecasts how "violent" tomorrow's market could be by combining three inputs: an asset's baseline volatility, yesterday's shock, and where volatility was already sitting before that shock happened. It doesn't predict market direction. Instead, it measures risk, in real time, on your actual chart. How to install it Method 1. Plugin command Open the GitHub repo: Find the installation section, copy the command, and paste it into Claude Code. It runs the plugin install automatically. Method 2. Manual config Open garchmethod.md in the repo, copy the entire file, and paste it into Claude Code. It fetches the skill files directly and verifies the strategy for you. (you only need one method; I'm just showing both) Getting it onto your TradingView chart Inside the repo, there's a Pine Script folder. Open it, copy the entire file. Go into TradingView's Pine Editor, paste it in, hit Enter, and refresh. That's it. The Storm Gauge now runs live on your chart as a real number. Once it's installed, just talk to it: → "What's the volatility forecast on Bitcoin?" → "Explain what the current volatility forecast means on $BTC and how it should impact my position sizing" → "Help me size my S&P500 position according to current market volatility" Does it actually work? I backtested the same EMA cross strategy two ways across 15 years of BTC data. Same entries, same exits. → Fixed position sizing: $17,957 final equity → Storm Gauge (GARCH) sizing: $21,205 final equity Fewer drawdowns, less risk, better result. Full breakdown of the entire build process in my recent article - pinned on my profile.
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I built a custom TradingView indicator with Claude Code & Fable 5. It's called the Storm Gauge and is built off a real quant trading strategy. I open-sourced the full code on GitHub. Free to install, free to fork, yours to improve. Here's how to install a quant indicator on your TradingView chart: What it actually is The Storm Gauge is a live implementation of the GARCH model, a Nobel Prize-winning volatility framework that real quant desks run daily. It forecasts how "violent" tomorrow's market could be by combining three inputs: an asset's baseline volatility, yesterday's shock, and where volatility was already sitting before that shock happened. It doesn't predict market direction. Instead, it measures risk, in real time, on your actual chart. How to install it Method 1. Plugin command Open the GitHub repo: Find the installation section, copy the command, and paste it into Claude Code. It runs the plugin install automatically. Method 2. Manual config Open garchmethod.md in the repo, copy the entire file, and paste it into Claude Code. It fetches the skill files directly and verifies the strategy for you. (you only need one method; I'm just showing both) Getting it onto your TradingView chart Inside the repo, there's a Pine Script folder. Open it, copy the entire file. Go into TradingView's Pine Editor, paste it in, hit Enter, and refresh. That's it. The Storm Gauge now runs live on your chart as a real number. Once it's installed, just talk to it: → "What's the volatility forecast on Bitcoin?" → "Explain what the current volatility forecast means on $BTC and how it should impact my position sizing" → "Help me size my S&P500 position according to current market volatility" Does it actually work? I backtested the same EMA cross strategy two ways across 15 years of BTC data. Same entries, same exits. → Fixed position sizing: $17,957 final equity → Storm Gauge (GARCH) sizing: $21,205 final equity Fewer drawdowns, less risk, better result. Full breakdown of the entire build process in my recent article - pinned on my profile.

Miles Deutscher

56,625 次观看 • 1 个月前

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.
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🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

738,832 次观看 • 5 个月前

Making OpenCode as lean as Pi agent? Just trimmed 25k out of OpenCode's system prompt (from 30k to 4-5k tokens) How? Just disable skills and get rid of massive skill definition bloat. Who needs skills anyway? Just kidding, this is the not the way. It makes the agent lame and defeats the point of using one. But it sets a precedent: Find a way to use skills without their definitions pre-loaded into the system prompt every single turn. Another interesting stuff: Upon testing this temporary "no skill setup" with two of hottest OpenCode Zen free models, Mimo V2.5 vs DeepSeek V4 Flash: One thinks more and talks less One thinks less and talks more Check the video to see which is which If you made it here, I'm finding a way to leanest OpenCode setup that I can get I simply don't believe that OpenCode can't be as lean as Pi Upon tinkering, I made a plugin that temporarily extracts the system prompt while I test, and noticed the hundreds of definitions in it from my .agents/skills directory which is shared across all my coding agents (Cursor, Antigravity, Claude, etc.) Of course disabling skills is not the answer, but it just proved that there is a way to strip the system prompt of these massive skill defs Aside from the system prompt hierarchy that injects confusion imo if you have a conflicting and redundant AGENTS.md which I discovered upon digging into OpenCode's source code Apparently it has prompt.ts/system.ts/instruction.ts/llm.ts and loads base .txt prompts based on model family (claude/gpt-o/gpt-5/codex/gemini/others) that all work together to make OpenCode aware of who it was and how it should use tools and become a "coding agent" Gotta find the most minimal mix that fits right into my workflow Make OpenCode as lean as Pi? We'll see. All in
0:11

Making OpenCode as lean as Pi agent? Just trimmed 25k out of OpenCode's system prompt (from 30k to 4-5k tokens) How? Just disable skills and get rid of massive skill definition bloat. Who needs skills anyway? Just kidding, this is the not the way. It makes the agent lame and defeats the point of using one. But it sets a precedent: Find a way to use skills without their definitions pre-loaded into the system prompt every single turn. Another interesting stuff: Upon testing this temporary "no skill setup" with two of hottest OpenCode Zen free models, Mimo V2.5 vs DeepSeek V4 Flash: One thinks more and talks less One thinks less and talks more Check the video to see which is which If you made it here, I'm finding a way to leanest OpenCode setup that I can get I simply don't believe that OpenCode can't be as lean as Pi Upon tinkering, I made a plugin that temporarily extracts the system prompt while I test, and noticed the hundreds of definitions in it from my .agents/skills directory which is shared across all my coding agents (Cursor, Antigravity, Claude, etc.) Of course disabling skills is not the answer, but it just proved that there is a way to strip the system prompt of these massive skill defs Aside from the system prompt hierarchy that injects confusion imo if you have a conflicting and redundant AGENTS.md which I discovered upon digging into OpenCode's source code Apparently it has prompt.ts/system.ts/instruction.ts/llm.ts and loads base .txt prompts based on model family (claude/gpt-o/gpt-5/codex/gemini/others) that all work together to make OpenCode aware of who it was and how it should use tools and become a "coding agent" Gotta find the most minimal mix that fits right into my workflow Make OpenCode as lean as Pi? We'll see. All in

raymel 👋

37,939 次观看 • 3 个月前

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.
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New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

elvis

11,303 次观看 • 24 天前

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.
0:15

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.

elvis

37,757 次观看 • 1 个月前

I just built a Claude skill that writes 20 Meta ad hooks in 60 seconds 🤯 Give it your product, your audience, and your best-performing angles → it writes hooks across 10 proven frameworks, each one targeted at a specific customer pain point. All inside Claude Cowork. Perfect for DTC brands and agencies who are still writing hooks from scratch every time they need new creative — staring at a blank doc, scrolling competitors for inspiration, and recycling the same 3 angles because you ran out of ideas two weeks ago. If you're launching Meta Ads and your hook writing process looks like this — open a Google Doc, try to remember what worked last time, write 5 hooks that all sound the same, run them, 4 flop, go back to the doc, repeat ... This skill replaces the entire process: → You give it your product name, key benefits, and target customer → It writes hooks across 10 frameworks: problem-solution, curiosity gap, bold claim, social proof, before/after, us vs them, question, contrarian, urgency, and storytelling → Each hook targets a specific pain point — not generic "Shop now" copy → Generates 2 variations per framework so you have options to test → Outputs everything organized by framework with notes on when to use each one → Takes about 60 seconds No blank page. No recycling the same 3 angles. No writing 5 hooks that all sound like the same ad. What you get: → 20 hooks across 10 proven frameworks, ready to drop into your ads → Each hook written for a specific customer pain point, not a generic audience → Framework labels so you know which hook type you're testing → A reusable skill — run it for every new product, every new campaign, every new angle sprint → Works from a product brief — no API connection, no CSV export, no setup beyond installing the skill One product brief. 20 hooks. 60 seconds. I put together the full skill file plus a playbook showing how to install it, customize the frameworks, and run your first hook sprint. Want it for free? > Like this post > Comment "HOOKS" And I'll send it over (must be following so I can DM)
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I just built a Claude skill that writes 20 Meta ad hooks in 60 seconds 🤯 Give it your product, your audience, and your best-performing angles → it writes hooks across 10 proven frameworks, each one targeted at a specific customer pain point. All inside Claude Cowork. Perfect for DTC brands and agencies who are still writing hooks from scratch every time they need new creative — staring at a blank doc, scrolling competitors for inspiration, and recycling the same 3 angles because you ran out of ideas two weeks ago. If you're launching Meta Ads and your hook writing process looks like this — open a Google Doc, try to remember what worked last time, write 5 hooks that all sound the same, run them, 4 flop, go back to the doc, repeat ... This skill replaces the entire process: → You give it your product name, key benefits, and target customer → It writes hooks across 10 frameworks: problem-solution, curiosity gap, bold claim, social proof, before/after, us vs them, question, contrarian, urgency, and storytelling → Each hook targets a specific pain point — not generic "Shop now" copy → Generates 2 variations per framework so you have options to test → Outputs everything organized by framework with notes on when to use each one → Takes about 60 seconds No blank page. No recycling the same 3 angles. No writing 5 hooks that all sound like the same ad. What you get: → 20 hooks across 10 proven frameworks, ready to drop into your ads → Each hook written for a specific customer pain point, not a generic audience → Framework labels so you know which hook type you're testing → A reusable skill — run it for every new product, every new campaign, every new angle sprint → Works from a product brief — no API connection, no CSV export, no setup beyond installing the skill One product brief. 20 hooks. 60 seconds. I put together the full skill file plus a playbook showing how to install it, customize the frameworks, and run your first hook sprint. Want it for free? > Like this post > Comment "HOOKS" And I'll send it over (must be following so I can DM)

Mike Futia

17,019 次观看 • 5 个月前

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.
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The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 次观看 • 8 个月前

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:
0:16

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 次观看 • 4 个月前