#Tools

This is a loader animation I made for an internal tool of which I am the sole user. It took millions of tokens and multiple minutes of iteration (it was two prompts).

#Woodworking #Knowledge #Tools

We have released Zero Breach Toolkit, a free open sourced tool capable of detecting over 12,100 traces of Israeli & other spyware on your devices. It works on Windows, Linux, Mac, Android and Iphones. You can download it here: Inside the folder with the tools run this command: python -X utf8 scan/zero-breach-toolkit.py It never writes to disk, never connects to the internet, or ask for admin perms. It takes around 10-15 minutes to run depending on the device. This project is fully open source, so feel free to take the code and use it to create your own tools. If you find some spyware on your device, consider sending it to us via telegram. Leave us a star on GitHub if the tool helped you. Follow our backups: @ZionismExposedx @IsraelExposedAr God bless

Claude Code vs Blender just exposed how AI can f*cking burn through your budget Two workflows building the same complex 3D rocket assembly, but one ends up costing nearly 19x more in the demo And the scary part is how easily those costs can spiral without improving the final result Claude Code can reason through geometry, generate Python scripts, assemble mechanical components, and automate entire animation sequences But every unnecessary tool call, failed generation, and repeated rebuild adds another layer of cost Meanwhile, Blender can execute predefined operations without paying for another round of AI reasoning Here’s where the workflows differ: Traditional Blender: MODEL individual rocket components POSITION mechanical structures and hinges CONFIGURE materials, lighting, and cameras ANIMATE deployable panels REFINE and reuse finished assets Claude Code + Blender Python: GENERATE geometry from written instructions ASSEMBLE components programmatically PARAMETERIZE dimensions and deployment angles AUTOMATE animations and camera movements REBUILD variations by modifying code The real problem isn’t that AI is expensive It’s using AI reasoning for work that a deterministic script could execute for almost nothing Want to test the difference? Open Claude Code with Blender installed and paste: “Create a procedural Blender Python generator for a detailed rocket assembly with deployable panels, animated hinges, and realistic metallic materials Separate geometry, materials, animation, and camera setup into reusable functions Expose all major design parameters, reuse existing objects where possible, and track execution time and available AI usage costs” The future isn’t Claude Code replacing Blender It’s knowing when to stop paying AI to think and let specialized tools execute Bookmark this workflow before your next 3D project

Build an absolute monster. Merge six Claude Code pieces into one system, it works the NIGHT SHIFT while you sleep Opus 5.5 + subagents + routines + web search + Playwright + CLAUDE.md Six pieces. One system. Nobody at the keyboard Opus 5.5 -> the main model that plans every task Subagents -> independent workers in .claude/agents/, each with its own role and context, returning only a summary Routines -> scheduled cloud agents on a cron, the system runs at night while your laptop is closed Web search -> scans public pages and forums for real complaints Playwright -> opens a real browser, clicks through every screen, catches bugs CLAUDE.md + memory files -> the map the agent reads on launch, every step and cost logged Give it one goal before bed: find a real gap people complain about and have a working prototype by 7 AM One hard rule: nothing spent, nothing published, nobody contacted without your yes Start the routine at 11:40 PM and go to sleep. Here is how one night can look: 12:10 AM: web search pulls ~1,900 public posts with the same complaint, gig drivers tracking income across 3 apps 1:00 AM: it maps the paid tools people already use and the 2 complaints they keep repeating 2:30 AM: Opus 5.5 plans 9 screens and one database without asking a question 4:10 AM: subagents write the code and a landing page draft 5:45 AM: Playwright clicks through every screen and logs bugs 7:20 AM: the memory log holds every step Morning: a working prototype and a landing draft waiting for your yes, $0 spent beyond tokens, nothing published A subagent with no payment or email tool physically CANNOT buy or send anything. The rules in CLAUDE.md apply to every session The biggest mistake: treating AI like a chat for answers. Give yours a GOAL, a DEADLINE and HARD RULES You write zero code. Playwright does the clicking. You're asleep the whole time Everyone is waiting for AGI. The night shift already exists Bookmark this. Full breakdown of the CLAUDE.md map in the article below

Jensen closed his GTC Berlin 2026 keynote with this Hermes Agent demo as the finale. He introduced it by saying the way to build an agent is to ask an agent to build it for you, so he installed and set up a customized Hermes in one prompt. Then he used Hermes, running on @nvidia RTX Spark, to create a simulated training environment for the @huggingface Microduck. Hermes planned an obstacle course with Nemotron and had GPT-6 Astra build it using FreeCAD, Omniverse libraries, Blender, Newton, and Isaac Lab. Hermes trained thousands of simulated ducks on that course until the validated policy was ready to run on the real robot in the physical world. At Nous, we believe open source is a practical tool as much as a philosophical ideal. It gives users the ability to build powerful, complex workflows chaining numerous local+cloud models, devices, and tools themselves instead of waiting for a company to enable them.

this is pure f*cking treasure 20 build stacks for Opus 5.5, each built around a real deliverable apps. interfaces. memory. data. agents. video 01 Better-T-Stack → saas foundry 02 21st MCP → component lab 03 tweakcn → design tokens 04 Serena → repo navigator 05 Stagehand → browser runner 06 MarkItDown → document engine 07 Qdrant MCP → memory vault 08 DuckDB → data analyst 09 PGlite → local app 10 Spotlight → debug desk 11 Agent Orchestrator → agent fleet 12 Worktrunk → parallel build 13 FastMCP → tool builder 14 Mastra → agent product 15 Trigger. dev → durable jobs 16 LibreChat → private assistant 17 React Email → email studio 18 Remotion → video studio 19 Slidev → deck factory 20 Nextra → docs portal Opus handles the planning and code. these resources give each workflow its tools and workspace. the loop: define the job → split the work → connect the tools → build → verify → ship save this, then read the full guide for Opus 5.5 Harness Engineering ↓

Stop letting unfinished Opus 5.5 runs eat your budget Set up the harness before your next long task Save progress, verify results, reuse the workflow The full 7-layer setup below ↓

Ratchet Flip-ability Test.. For Science #flipability #ratchets #tools #science

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Knipex or Chinise equivalent- what better? #tools #knipex #electriciantools #electricianlife

🚨 𝟒𝟎 𝐇𝐈𝐃𝐃𝐄𝐍 𝐀𝐈 𝐓𝐎𝐎𝐋𝐒 𝐓𝐇𝐀𝐓 𝐂𝐀𝐍 𝐌𝐀𝐊𝐄 𝐘𝐎𝐔𝐑 𝐖𝐎𝐑𝐊 𝟏𝟎× 𝐅𝐀𝐒𝐓𝐄𝐑 ⚡ Most people use AI for simple questions. Smart creators use AI to research, create, automate, and build faster. Here are 40 powerful tools worth exploring 👇 🔎 AI Research: ChatGPT, Claude, Gemini, Perplexity, NotebookLM, Grok, Copilot, DeepSeek, Consensus, Elicit. ✍️ Writing & Content: Grammarly, QuillBot, Jasper, DeepL, Canva. 🎨 Image & Design: Midjourney, Leonardo AI, Ideogram, Adobe Firefly, Gamma. 🎬 Video & Audio: Runway, Kling AI, Pika, HeyGen, ElevenLabs, Descript, Suno, CapCut. ⚙️ Automation: Zapier, Make, n8n, Notion AI, Fathom. 1. AI Assistants & Research ChatGPT — Claude — Google Gemini — Perplexity — NotebookLM — Grok — Microsoft Copilot — DeepSeek — Consensus — Elicit — ✍️ 2. Writing & Content Creation Grammarly — QuillBot — Jasper — DeepL — Canva — 🎨 3. Image & Design Midjourney — Leonardo AI — Ideogram — Adobe Firefly — Gamma — 🎬 4. Video, Voice & Audio Runway — Kling AI — Pika — HeyGen — ElevenLabs — Descript — Suno — CapCut — ⚙️ 5. Automation & Productivity Zapier — Make — n8n — Notion AI — — Fathom — 💻 6. Coding & App Building Cursor — GitHub Copilot — Replit — Lovable — v0 — 📊 7. Data Analysis Julius AI — totally tools 40...use easy life..🎉 🔥 The real secret? Don't just collect AI tools. Combine them into a workflow that saves time and gets results. I'm sharing these tools to help you work smarter, create faster, and automate repetitive tasks. 🔖 Bookmark this post. You'll need it. ♻️ Repost to help other creators. 💬 Which AI tool do you use the most? #AITools #ArtificialIntelligence #Automation #Tech

I´ve been playing around with my own tool too much.. #damagecontrol #grungemask tools, super easy drag and drop assets when feeling lazy. Combine and customize for best result. #geometrynodes #b3d

THIS IS AN ABSOLUTE GOLDMINE  The team at Anthropic just revealed one of the most effective techniques for Claude Code The core concept: run Sonnet, Haiku 5.5, and Opus 5.5 simultaneously, letting each model focus strictly on what it does best You get higher quality output while dramatically reducing token consumption, since every specific job gets routed to the optimal model To spin up Claude Code with this setup, simply execute:  ⁠claude --model sonnet --advisor opus⁠  Here is how the workload is split:  > Sonnet 5.5 handles the primary session, writes code, and applies bug fixes > Haiku 5.5 scans the codebase in parallel: performing searches, running grep queries, exploring docs, and auditing diffs > Opus 5.5 acts as an on-demand advisor for critical decisions and approves final code diffs > You stop burning expensive Opus context on routine tasks that don't require high-level reasoning Opus intervenes exclusively during three specific scenarios:  > Right before locking in an execution plan > Whenever the exact same error triggers twice in a row > Prior to marking a task as complete Sonnet builds. Haiku scans and explores. Opus reviews JEV engineering applies the exact same principle one layer deeper: routine execution forks that don't need high-level reasoning (determining which file to inspect, which tool to trigger, or whether to retry or stop) are processed by JEV in sub-milliseconds, leaving big models to handle only complex decision splits The full architectural tree: Sonnet 5.5 manages the primary session and modifies code Search maps out the repository structure via Haiku 5.5 Grep locates call references using Haiku 5.5 Docs fetches API specifications through Haiku 5.5 Opus 5.5 serves as an advisor, reading the complete conversation history whenever consulted JEV routes lightning-fast micro-forks in milliseconds Feed this tree structure alongside the prompt below into Claude Code Reconfigure my Claude Code workspace based on this architecture: 1. Run the primary session utilizing Sonnet 5.5, with Opus 5.5 designated as the advisor 2. Set up three read-only Haiku 5.5 subagents: search (maps out the repo), grep (locates call sites), and docs (retrieves API documentation) 3. Insert a single rule into CLAUDE.md: consult the advisor prior to committing to a plan, after encountering an identical error twice, and before marking a task as done 4. Direct rapid, deterministic decisions (tool selections, file paths, retries) through JEV. Show all modifications as a diff first. Do not execute any edits until I grant permission Bookmark this setup: it will become a daily driver in your workflow

Holy sh*t I spent weeks turning Claude Code into a design studio. no Figma license. no agency. no plugins I had to pay for. here's the stack I used and it's embarrassingly simple: -> graphify: teach Claude your brand dump your guidelines, logos, PDFs and old projects into one folder. graphify turns it into a knowledge graph, so every design Claude makes follows your rules instead of guessing. 134k stars, Y Combinator. uv tool install graphifyy -> graphify install -> /graphify . -> Figma-Context-MCP: Figma straight to code it hands your actual Figma file to the agent. the README says it builds designs in one shot, and does it better than pasting screenshots. -> Penpot: the open-source Figma free, open source, and its MCP server lets AI read and edit your designs, then hand them to code and back. -> React Bits: the "expensive" look, for free the biggest open-source library of animated React components. those smooth hover effects and text animations you see on high-end sites? copy, paste, done. -> HyperFrames: designs that move write HTML, get a finished video. made by HeyGen for AI agents. your landing page can now have its own launch video by tonight. claude plugin marketplace add heygen-com/hyperframes -> claude plugin install hyperframes@hyperframes -> MCP for Blender: 3D without learning Blender connects Claude to Blender. describe a scene, the agent builds and edits it in real 3D. the order that works: brand into graphify -> design in Figma or Penpot -> Claude builds it with React Bits -> HyperFrames makes the promo -> Blender adds the 3D hero shot the uncomfortable part: tools used to be the moat. now everyone has them for free. the only thing that still separates good design from AI slop is taste. and taste is the one thing you can't install. bookmark this. your next client will ask why you're suddenly so fast.

GIT ROADMAP │ ├── 01. GIT FOUNDATIONS │ ├── What Is Git? │ ├── Why Version Control Matters │ ├── Git vs GitHub vs GitLab │ ├── Distributed Version Control │ ├── Installing Git │ ├── Configuring Git │ ├── Git Configuration Levels │ ├── Git Help & Documentation │ └── Understanding Git Repositories │ ├── 02. GIT BASICS │ ├── git init │ ├── git clone │ ├── git status │ ├── git add │ ├── git commit │ ├── git log │ ├── git diff │ ├── git show │ └── The Git Workflow │ ├── 03. TRACKING CHANGES │ ├── Working Directory │ ├── Staging Area │ ├── Git Repository │ ├── Tracking Files │ ├── Untracked Files │ ├── Modified Files │ ├── Staged Changes │ ├── Ignoring Files with .gitignore │ └── Viewing File History │ ├── 04. COMMITS & HISTORY │ ├── Writing Good Commit Messages │ ├── Atomic Commits │ ├── Commit Hashes │ ├── Viewing Commit History │ ├── git commit --amend │ ├── git reset │ ├── git revert │ ├── git restore │ └── Recovering Lost Changes │ ├── 05. BRANCHING │ ├── What Are Branches? │ ├── git branch │ ├── git switch │ ├── git checkout │ ├── Creating Branches │ ├── Renaming Branches │ ├── Deleting Branches │ ├── HEAD & References │ └── Detached HEAD State │ ├── 06. MERGING & CONFLICTS │ ├── git merge │ ├── Fast-Forward Merges │ ├── Three-Way Merges │ ├── Merge Conflicts │ ├── Resolving Conflicts │ ├── Merge Strategies │ ├── Aborting a Merge │ ├── Merge vs Rebase │ └── Keeping Branches in Sync │ ├── 07. REMOTE REPOSITORIES │ ├── What Are Remote Repositories? │ ├── git remote │ ├── git fetch │ ├── git pull │ ├── git push │ ├── Adding Remote Origins │ ├── Upstream Branches │ ├── Tracking Remote Branches │ └── Handling Push Rejections │ ├── 08. GITHUB & COLLABORATION │ ├── Creating a GitHub Repository │ ├── Connecting Local & Remote Repositories │ ├── Forking Repositories │ ├── Cloning Projects │ ├── Pull Requests │ ├── Code Reviews │ ├── Issues & Discussions │ ├── Resolving Review Feedback │ └── Contributing to Open Source │ ├── 09. REBASING & ADVANCED HISTORY │ ├── git rebase │ ├── Interactive Rebasing │ ├── Squashing Commits │ ├── Reordering Commits │ ├── Editing Commit Messages │ ├── Cherry-Picking Commits │ ├── Rebasing vs Merging │ ├── Rebase Conflicts │ └── Safe History Rewriting │ ├── 10. STASHING & TEMPORARY CHANGES │ ├── git stash │ ├── git stash push │ ├── git stash list │ ├── git stash apply │ ├── git stash pop │ ├── Naming Stashes │ ├── Stashing Untracked Files │ ├── Managing Multiple Stashes │ └── Recovering Stashed Changes │ ├── 11. TAGS & RELEASES │ ├── What Are Git Tags? │ ├── Lightweight Tags │ ├── Annotated Tags │ ├── git tag │ ├── git push --tags │ ├── Semantic Versioning │ ├── Creating Release Tags │ ├── Checking Out Tags │ └── Release Management │ ├── 12. GIT INTERNALS │ ├── Git Objects │ ├── Blobs │ ├── Trees │ ├── Commits │ ├── Annotated Tag Objects │ ├── The .git Directory │ ├── Object Database │ ├── References & Reflogs │ └── Content-Addressable Storage │ ├── 13. ADVANCED GIT TOOLS │ ├── git reflog │ ├── git bisect │ ├── git blame │ ├── git grep │ ├── git clean │ ├── git worktree │ ├── git archive │ ├── git fsck │ └── git filter-repo │ ├── 14. GIT WORKFLOWS │ ├── Centralized Workflow │ ├── Feature Branch Workflow │ ├── Git Flow │ ├── Trunk-Based Development │ ├── Forking Workflow │ ├── Pull Request Workflow │ ├── Release Branches │ ├── Hotfix Branches │ └── Choosing the Right Workflow │ ├── 15. GIT SECURITY │ ├── SSH Authentication │ ├── HTTPS Authentication │ ├── Personal Access Tokens │ ├── Git Credential Management │ ├── Preventing Secret Leaks │ ├── Removing Exposed Secrets │ ├── Signed Commits │ ├── Commit Signature Verification │ └── Repository Access Controls │ ├── 16. GIT AUTOMATION & CI/CD │ ├── Git Hooks │ ├── Pre-Commit Hooks │ ├── Commit Message Hooks │ ├── GitHub Actions │ ├── GitLab CI/CD │ ├── Automated Testing │ ├── Build Automation │ ├── Deployment Workflows │ └── Automated Release Pipelines │ ├── 17. LARGE PROJECTS & MONOREPOS │ ├── Git Monorepos │ ├── Managing Large Repositories │ ├── Git LFS │ ├── Git Submodules │ ├── Git Subtrees │ ├── Sparse Checkout │ ├── Shallow Clones │ ├── Repository Optimization │ └── Monorepo Collaboration │ ├── 18. TEAM COLLABORATION & BEST PRACTICES │ ├── Branch Naming Conventions │ ├── Commit Message Conventions │ ├── Conventional Commits │ ├── Small & Focused Commits │ ├── Keeping Pull Requests Small │ ├── Code Review Best Practices │ ├── Resolving Collaboration Conflicts │ ├── Protecting Main Branches │ └── Maintaining Clean History │ ├── 19. TROUBLESHOOTING & RECOVERY │ ├── Undoing the Last Commit │ ├── Recovering Deleted Files │ ├── Fixing Detached HEAD │ ├── Resolving Merge Conflicts │ ├── Fixing Diverged Branches │ ├── Recovering Lost Commits │ ├── Fixing Authentication Errors │ ├── Repairing Corrupted Repositories │ └── Recovering from Incorrect Resets │ └── 20. PRACTICAL GIT PROJECTS ├── Personal Git Repository ├── Feature Branch Workflow ├── Team Collaboration Simulation ├── Open Source Contribution ├── Merge Conflict Resolution Lab ├── Interactive Rebase Practice ├── Git Hooks Automation Tool ├── Git-Based CI/CD Pipeline ├── Monorepo Management Project ├── Automated Release Workflow └── Complete Team Git Workflow

I still don’t understand why people build Opus 5.5 setups from scratch. 10 repos already did it for you, from 68K stars to 0. 01 claude-code-best-practice → 68K stars. the full guide to subagents, skills, hooks, MCP and memory, with working examples 02 claude-skills → 28K stars. 388 ready skills across 20 fields, built for Claude Code, Codex and Gemini CLI 03 awesome-claude-code-toolkit → 2.7K stars. 135 agents, 42 commands, 20 hooks and 15 rules you can drop into a project 04 AI-Product-Development-Toolkit → 994 stars. six planning prompts, a Next.js starter and a ready CLAUDE.md written for Opus 5.5 05 awesome-claude-video-skills → 867 stars. 300+ repos that teach Claude to make video, sorted by type 06 awesome-claude-code → 508 stars. the cleanest map of tools, plugins and usage trackers around Claude Code 07 motion-graphics-skills → 375 stars. launch-style motion graphics where every frame is code, no After Effects 08 opus-5-5-dev → 1 star. rules for long runs: define “done” first, keep a TASKS.md, end with a fixed four-part report 09 model-bump → 0 stars. scans your code for what breaks on Opus 5.5, like forced tool choice, sampling settings and prefill 10 claude-best-practices → 1 star. 29 agents, 174 skills, 58 rules and 22 hooks to copy into your project stars measure hype. fit measures results. pick one, run it on a single task, keep only what survives. save this before you upgrade, and follow for more setups like this

💥 List of data science tools! #DataScience #Tools

Apparently I've been shopping at Home Depot all wrong. The requirement to checkout and pay seems optional now... 🤨 What state do y'all think this was filmed in? ⬇️ #HomeDepot #tools #Decay #FailedState

THIS IS F**KING DANGEROUS YOUR AI’S GUARDRAILS AREN’T AS STRONG AS YOU THINK. Basilisk is an open source AI red teaming framework built to expose weaknesses in LLMs. → 33 attack modules mapped to OWASP LLM Top 10 → evolves jailbreaks when prompts get refused → tests prompt injection, data leaks, tool abuse & RAG poisoning → fingerprints models and discovers available tools → compares GPT, Claude & Gemini side by side → grades guardrails from A+ to F → generates evidence backed findings and signed audit logs It can even run repeatable security tests to catch regressions. Only test systems you own or have explicit authorization to assess. REPO BELOW

ive been messing around with ways to make Claude Code use fewer tokens (so I stop hitting my limit so early 😅) one thing I noticed is how many tokens it wastes searching through files just to understand a repo this weekend I hacked together a tool that gives it a codebase map instead. tested it on 6 repos and it actually decreased token usage quite a bit 🧵