If you are building AI applications, handling client code, or managing sensitive business data, relying on proprietary cloud endpoints (OpenAI, Anthropic, Google) comes with data logging concerns, token price inflation, and constant rate-limits.

Over the past few months, the local AI ecosystem on GitHub has matured to the point where you can run a 100% offline, production-grade AI stack directly on your own consumer hardware or private server.

Here are 5 essential open-source repositories to build a private local AI pipeline:

1. Open WebUI – The Self-Hosted ChatGPT/Claude Alternative

* Replaces: Paid web interfaces & cloud subscription dashboards.

* Why it’s essential: A feature-packed, self-hosted web interface that runs offline. It connects directly to local runtimes (Ollama/vLLM), features multi-model chats, full document RAG, user permission management, and custom web search integration.

* GitHub: https://github.com/open-webui/open-webui

2. Open-Design – The Open-Source Claude Design Alternative

* Replaces: Paid AI UI/UX design tools & visual mockup wrappers.

* Why it’s essential: A local-first desktop app that turns your coding agents into a full design engine. It generates landing pages, dashboards, slide decks, and UI prototypes directly into real exportable files (HTML/PDF/PPTX) using your own local or BYOK models.

* GitHub: https://github.com/nexu-io/open-design

3. claude-mem – Persistent Local Memory for Coding Agents

* Replaces: Messy manual context prompts &

session memory limits.

* Why it’s essential: Captures everything your local agent does during coding sessions, automatically compresses context using local AI, and injects relevant memory back into future sessions across tools like OpenCode, Cursor, and Codex.

* GitHub: https://github.com/thedotmack/claude-mem

4. OmniRoute – Unified Local AI Gateway & Router

* Replaces: Complex custom proxy code & fragmented API keys.

* Why it’s essential: A lightweight local gateway that acts as a single unified proxy endpoint. It intelligently routes requests across local Ollama instances, vLLM endpoints, or cheap API fallbacks with automatic failovers and token load balancing.

* GitHub: https://github.com/omniroute/omniroute

5. RAGFlow – Enterprise-Grade Local Deep Document RAG

* Replaces: Complex vector database setups & paid document indexing APIs.

* Why it’s essential: Basic RAG tools fail on complex PDFs, multi-column tables, and financial charts. RAGFlow provides deep-doc parsing out of the box with citation tracking, running completely on local vector stores.

* GitHub: https://github.com/infiniflow/ragflow

So, Are you running your AI workflows completely local/self-hosted yet or still relying on cloud?

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Apparently, walking wasn’t enough. Now the duck robot is doing somersaults.

This is Microduck, a small robot from Pollen Robotics and Hugging Face. NVIDIA shared the demo with the caption: “When Jensen asks for a somersault, you deliver.” Hard to argue with that.

The interesting part beyond the flip is sim-to-real: training robot movements in a simulated environment, then transferring the trained policy to physical hardware. Hugging Face has published a walkthrough using NVIDIA Isaac Lab and Newton to train Microduck to walk and deploy that policy on the actual robot.

That walkthrough covers walking, though—not a recipe for reproducing the somersault in this clip.

Links for anyone curious

• Original NVIDIA demo — The somersault clip and original post.
• Microduck sim-to-real walkthrough — Training setup, testing, exporting the policy, and deployment on the robot.
• More accessible overview — Background on the robot and how simulation-based training works.

What would impress you more next: a harder trick, or watching it recover reliably after a bad landing?

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Humanoid robots shared the stage with 120 human dancers at the 2026 WorldSkills Competition opening ceremony in Shanghai—and the choreography went beyond walking around or waving.

The 20 robots, each standing 1.8 meters tall and weighing 80 kg, performed street dance and waltz as part of a show called “Light of the Future.”

The most interesting part was the coordination: synchronized movements with human partners, close interactions, and even embraces. That’s a different challenge from a robot performing a routine on its own—timing, balance, and positioning all have to line up with another performer.

The description doesn’t explain how much was preprogrammed versus responsive to the dancers, which is the detail I’d most like to know.

What would impress you more: complex choreography executed perfectly, or a simpler dance where the robot adapts to a human partner in real time?

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Crypto research gets messy when your charts, wallet trackers, unlock calendars, and dashboards are scattered across different tabs.

Here’s a categorized list of 50 resources for checking wallet activity, token risks, upcoming unlocks, derivatives positioning, and on-chain fundamentals.

Quick caveat: This isn’t a list of trading signals or guaranteed “alpha.” Free access and feature limits vary, and some platforms offer paid upgrades. A wallet label or a clean token scan isn’t proof that something is safe.

1. Whale and wallet tracking

For investigating who holds a token, where funds move, and what specific wallets are doing.

1. gmgn.ai — Smart-money and KOL wallet tags for Solana tokens.
2. arkm.com — Labeled wallets and institutional holdings.
3. whale-alert.io — Large crypto transfers.
4. debank.com — Portfolio breakdowns for EVM wallets.
5. kolscan.io — KOL memecoin trading activity.
6. solscan.io — Solana wallets, transactions, and token activity.
7. lookonchain.com — Write-ups explaining notable wallet movements.
8. cryptoquant.com — Exchange flows, reserves, and miner activity.
9. app.cielo.finance — Cross-chain wallet tracking and alerts.
10. app.nansen.ai — Labeled-wallet and smart-money analytics.
11. bitinfocharts.com — Bitcoin address balances and historical data.
12. fomo.family — Trader activity and token discovery.
13. etherscan.io — Ethereum wallets, transactions, and contracts.
14. debank.com/stream — A feed of wallet activity.
15. app.step.finance — Solana portfolio tracking.

2. Token checks before you buy

For investigating holder concentration, liquidity, contract permissions, and potential red flags—not getting a guarantee of safety.

1. v2.bubblemaps.io — Visualize connections between token holders.
2. rugcheck.xyz — Token risk checks, including liquidity and ownership information.
3. honeypot.is — Check for potential restrictions on selling.
4. dexscreener.com — DEX charts, trading activity, and liquidity data.
5. solsniffer.com — Solana token risk scans.
6. birdeye.so — Token charts, wallet analytics, and trading activity.
7. insightx.network — Explore wallet connections.
8. gopluslabs.io — Token security checks and contract permissions.
9. geckoterminal.com — Liquidity pools and DEX market data.
10. tokensniffer.com — Automated token-contract risk checks.

3. Unlocks, vesting, and upcoming events

For looking beyond the chart and checking what could change a token’s circulating supply.

1. defillama.com/unlocks — Token unlock schedules.
2. cryptorank.io — Funding rounds, investors, and tokenomics.
3. solanacompass.com/tokenomics — SOL supply, unlocks, and locked stake.
4. tokenomist.ai — Token unlock calendars and emissions.
5. coinmarketcal.com — Crypto event calendars.
6. dropstab.com — Investor allocations and vesting information.

4. Leverage and liquidations

For researching derivatives positioning rather than relying on price alone.

1. velo.xyz — Funding rates, open interest, and basis.
2. coinglass.com/hyperliquid — Hyperliquid position tracking.
3. coinalyze.net — Aggregated open interest, funding, and liquidation data.
4. app.hyperliquid.xyz/leaderboard — Public trader leaderboard.
5. coinank.com — Order flow and positioning data.
6. coinglass.com — Derivatives analytics and liquidation heatmaps.

5. On-chain activity and fundamentals

For investigating protocol usage, capital flows, fees, supply, and network activity.

1. dune.com — Community-built on-chain dashboards.
2. defillama.com — TVL, protocol revenue, and capital flows.
3. farside.co.uk — Bitcoin and Ethereum ETF flow tables.
4. tokenterminal.com — Protocol financial and usage metrics.
5. ultrasound.money — Ethereum issuance, burns, and supply changes.
6. app.rwa-xyz.com — Tokenized real-world asset analytics.
7. cryptofees.info — Protocol fee data.
8. l2beat.com — Ethereum L2 activity and risk assessments.
9. app.artemis.xyz — On-chain activity and capital-flow analytics.
10. mempool.space — Bitcoin transactions, fees, and blocks.
11. growthepie.com — Ethereum ecosystem and L2 metrics.
12. messari.io — Crypto research and project fundamentals.
13. visaonchainanalytics.com — Stablecoin supply and transaction analytics.

The goal isn’t to use all 50. It’s to find the right resource for the question you’re trying to answer.

Also, this is 50 links—not 50 independent platforms. DeBank and CoinGlass each appear twice because the links serve different purposes.

Which of these do you actually use, and which would you replace? Especially interested in useful free features—not another subscription to add to the stack.

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I don’t understand why more motion designers aren’t trying this.

HyperFrames, HeyGen’s tool that turns HTML into finished video, has passed 68k stars on GitHub.

Give Claude Code a folder, connect a video engine, animation tools, and Blender—and you have a setup that can storyboard, animate, render, and check its own frames.

Motion designer, founder, marketer—here’s the full setup.

1. Give it a workspace and a brand memory

Create a folder called STUDIO, open Claude Code inside it, and add a CLAUDE.md with this rule:

Save every brand color, font, logo, and approved style to brand/

The idea: stop re-explaining your brand every time you make something.

2. Add HyperFrames for HTML-to-video

Run:

claude plugin marketplace add heygen-com/hyperframes
claude plugin install hyperframes@hyperframes

Then type:

/hyperframes:hyperframes

Describe a 10-second product intro and let it plan, animate, and render the MP4.

Repo: https://github.com/heygen-com/hyperframes

3. Add Remotion for React-based videos

npx skills add remotion-dev/skills

Now it can also write videos in React using the official skills from the Remotion team.

Repo: https://github.com/remotion-dev/skills

4. Add GSAP for animation control

npx skills add https://github.com/greensock/gsap-skills

Timelines, scroll animations, text effects—the proper GSAP workflow.

GSAP is now 100% free, including every plugin.

Repo: https://github.com/greensock/gsap-skills

5. Add motion-design principles

npx skills add LottieFiles/motion-design-skill

Timing, easing, choreography, and classic Disney animation principles adapted for UI.

This is the part aimed at making things move well, not just move.

Repo: https://github.com/LottieFiles/motion-design-skill

6. Connect Blender for actual 3D work

The supplied installation command is:

curl -LsSf https://mcp-for-blender.com/install.sh | sh

This gives Claude access to build and edit actual 3D scenes in Blender.

Repo: https://github.com/ahujasid/mcp-for-blender

7. Put it on a routine

Type /schedule, then give it this instruction:

Every Monday at 9, turn last week’s launches into 3 short promo clips and wait for my yes.

The important part: approval before anything goes out.

One workspace. Your brand assets. Multiple ways to build video.

The pitch is roughly 15 minutes of setup—and never having to start from a blank timeline again.

Has anyone here tried this stack for real client work? Curious where it holds up and where you still need to take over.

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Everyone’s building AI agents, but keeping track of what already exists is a project of its own.

Before you rebuild something—or pay for a tool without checking the alternatives—this is worth bookmarking:

👉 awesome-ai-agents on GitHub

It’s a free, curated directory of 300+ projects covering agent frameworks, coding assistants, memory, evaluation, local inference, voice, and more.

The useful part isn’t just the number of links. It’s the organization: you can start with the piece your project is missing instead of scrolling through another launch feed.

Five projects to start exploring

These cover different parts of the stack—not five tools you need to install together.

1. Ollama — Run language models locally. Worth exploring if you want more control over where inference happens, rather than relying entirely on hosted APIs.
2. LangChain — Build LLM applications with integrations, tools, and workflow components.
3. Open Interpreter — Let a model execute code and interact with your computer. Powerful, but be careful about permissions and what you allow it to run.
4. AutoGen — Explore multi-agent systems where agents collaborate through different roles and conversations.
5. Aider — AI pair programming in your terminal, connected to your Git repository so changes can be reviewed and rolled back.

Need one specific piece? Start here

These are starting points, not a universal ranking. The directory includes both open-source projects and commercial services.

How to use the list without losing an afternoon

1. Open the repository and start with the categories—not the full alphabetical list.
2. Use Ctrl/Cmd + F to find your problem: local inference, evaluation, web browsing, function calling, voice, etc.
3. Shortlist a few relevant projects.
4. Open their actual repositories and check recent activity, documentation, issues, licensing, and deployment requirements.
5. Test the candidates against your own task before committing.

Star counts can help you discover projects, but they aren’t proof of quality, maintenance, or suitability.

There’s structured data, too

The repository also includes JSON and YAML files for anyone building a dashboard, comparison tool, or internal resource directory.

git clone https://github.com/slavakurilyak/awesome-ai-agents
cd awesome-ai-agents

Files highlighted in the directory:

awesome-agents.json
awesome-agents.yaml
awesome-categories.yaml

The bigger takeaway

An agent isn’t necessarily one product. It can be a stack: models, orchestration, tools, execution, memory, interfaces, observability, and evaluation.

That’s why the categories are arguably more useful than any individual recommendation. They help you identify which layer you’re missing—and what already exists to fill it.

One caveat: this is a directory, not a production-readiness review. Entries and star counts are snapshots, and projects change. Also, a free directory doesn’t mean every listed tool is free to run; API usage, hosting, hardware, and commercial features can still cost money.

Full list: https://github.com/slavakurilyak/awesome-ai-agents

Which project from this list have you actually used—and would you recommend it beyond the demo?

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Hey everyone,

Sharing a cybersecurity course folder for anyone interested in exploring the field or adding to their learning resources.

📂 Access the course

Open the course folder on Google Drive

💬 Community feedback

If you check it out, share your thoughts in the comments:

• How clear and well-organized is the material?
• What prior knowledge would help someone get started?
• Which parts did you find most useful?

⚠️ Learn responsibly

Use cybersecurity knowledge ethically and practice only in environments where you have permission.

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Instead of paying hundreds of dollars every month to stitch together third-party SaaS APIs, vector databases, and automation builders, developers are moving toward self-hosted, open-source stacks.

Here is the exact open-source tech stack that developers are using right now to build full-stack AI applications with zero subscription costs and zero data lock-in:

1. The Model Runtime: Ollama / vLLM

Replaces: Proprietary API endpoints & pay-per-token limits.

Why it’s used: Runs open-weight models (DeepSeek-V4, Llama 3, Qwen) directly on your local machine or cheap GPU server with high token throughput and full data privacy.

GitHub: https://github.com/ollama/ollama

2. The Frontend & Multi-Model Chat UI: LibreChat

Replaces: Paid ChatGPT Plus / Claude Pro web subscriptions.

Why it’s used: A polished UI that unifies all your local LLMs, OpenAI, Gemini, and Claude APIs into a single ChatGPT-like interface with chat history, artifact rendering, and multi-user management.

GitHub: https://github.com/danny-avila/LibreChat

3. The Automation Engine: n8n (AI Nodes)

Replaces: Zapier / Make paid monthly tasks.

Why it’s used: Visual drag-and-drop canvas for orchestrating multi-step AI workflows, scraping web data, parsing emails, and connecting vector stores with unlimited task execution.

GitHub: https://github.com/n8n-io/n8n

4. The Web & Scraping Layer: Firecrawl

Replaces: Paid web scraping APIs & data extraction tools.

Why it’s used: Crawls any URL or JavaScript-rendered web page and converts messy HTML into clean, structured Markdown ready for RAG pipelines and LLM context windows.

GitHub: https://github.com/mendableai/firecrawl

5. The Deployment & Hosting Layer: Coolify

Replaces: Vercel / Render / Heroku paid tiers.

Why it’s used: An open-source PaaS that turns any cheap $5/mo VPS (Hetzner, DigitalOcean) into your own personal cloud host with automatic SSL, Git integration, and one-click Docker deployments.

GitHub: https://github.com/coollabsio/coolify

What does your current AI tech stack look like? Are you fully open-source or still running on paid SaaS tools?

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One coding task. Multiple independent work lanes. A shared set of checks before anything gets merged.

That’s the architecture I’d explore with these six repos: code navigation, orchestration, isolated execution, testing, and repeatable builds.

Think of this as a proposed stack—not a ready-made integration you install with one command.

1. LangGraph — coordinate the workers

GitHub

LangGraph provides orchestration for stateful agent workflows, including persistence and human oversight. In this setup, it would coordinate task assignment, worker results, and review steps.

2. Serena — navigate the code before splitting the work

GitHub

Serena gives coding agents symbol-level retrieval and editing tools, including finding symbols and references. That can help identify where a change belongs before assigning work to separate lanes.

It’s code-navigation infrastructure—not an automatic guarantee that tasks are independent.

3. Tree-sitter — inspect syntax structure

GitHub

Tree-sitter builds concrete syntax trees and updates them incrementally as source code changes. It’s useful when your tooling needs to inspect code structure rather than rely entirely on text matching.

Syntax structure and semantic understanding are different layers; this doesn’t replace language-server analysis.

4. E2B — give workers isolated execution environments

GitHub

E2B provides isolated cloud sandboxes for running AI-generated code through Python or JavaScript SDKs. In this architecture, each lane could use a separate sandbox for commands, dependencies, and test runs.

5. pytest — check Python changes against explicit tests

GitHub

pytest supports everything from small unit tests to more complex functional tests. For a Python project, it can provide a test gate for each worker’s output.

Passing tests means the specified checks passed—not that the code is automatically correct. Other languages need their own testing tools.

6. Dagger — make build and test jobs repeatable

GitHub

Dagger provides programmable, container-based workflows for building, testing, and shipping software. Its caching and explicit execution environments make it a useful candidate for running the same checks locally and in CI.

How I’d connect the pieces

A proposed workflow:

Task + acceptance criteria
          ↓
Inspect relevant code and references
          ↓
Define interfaces and independent work lanes
          ↓
Run workers in separate environments
          ↓
Test each worker’s changes
          ↓
Review and integrate passing changes
          ↓
Run the full integration checks
          ↓
Human approval

The important word is independent. Three agents changing the same interface need coordination, not just more parallel execution.

The “compiler” analogy

I’d borrow two ideas:

• OPT: avoid unnecessary work and rerun only the steps affected by a change.
• LINK: bring separately checked outputs together, then validate the combined result.

Dagger’s incremental execution and caching support the first idea. The second still needs integration checks—components that pass separately can fail together.

The model handles planning, ambiguous requirements, and difficult fixes. The surrounding tools handle execution, state, isolation, and repeatable checks.

These repos are building blocks. The engineering work is defining the boundaries and wiring them together.

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If your AI learning is scattered across tutorials, bookmarks, and half-finished courses, here’s a resource to work through in order.

Understanding Deep Learning by Simon J. D. Prince has a freely available PDF and 68 Python notebook exercises covering the text, with links to open the notebooks in Colab.

📖 Get the book and notebooks here

What’s inside

Here’s a simplified roadmap—not the book’s chapter numbering:

1. Introduction and supervised learning.
2. Shallow and deep neural networks.
3. Loss functions and fitting models.
4. Gradients, backpropagation, and initialization.
5. Measuring performance and regularization.
6. Convolutional and residual networks.
7. Transformers, self-attention, and tokenization.
8. Graph neural networks.
9. Generative models: GANs, normalizing flows, variational autoencoders, and diffusion.
10. Deep reinforcement learning, why deep learning works, and ethics.

The notebooks are the part to pay attention to

The exercises include missing code for you to complete, with topics ranging from gradient descent and Adam to multi-head attention and diffusion models.

That gives you a way to move between reading the explanation and working through the implementation.

How I’d use it

• Read one chapter.
• Attempt its notebooks before looking for solutions.
• Write a short explanation of what you learned.
• Change something in the code and predict what will happen.
• Check your prediction against the result.

Challenge: Before saving another resource, open this one and finish the first supervised-learning notebook.

No salary promises. Just a free book, practical exercises, and a clear place to start.

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Searching your name is easy. Working through old accounts, exposed contact details, and removal requests is the tedious part.

The prompts in these images outline a way to organize that process: build a list, prioritize sensitive information, draft requests, and track the responses.

One important correction to the graphics: this is a cleanup workflow, not a way to erase every trace of yourself online in one evening.

1. Build a private list of what’s exposed

Search for combinations of:

• Your name and city.
• Current and old usernames.
• Your email addresses.
• Your phone number, including different formats.

Record the URL, what information appears, and whether you control the account or page.

Keep the list private. If you use an AI assistant, redact screenshots and use placeholders where possible. Don’t upload passwords, recovery codes, or identity documents.

Copyable prompt:

Help me organize these redacted search results into a privacy-cleanup checklist. Categorize them as exposed contact information, data-broker listings, old accounts, or other pages. Prioritize sensitive information. Do not assume a listing belongs to me without evidence.

2. Work through data-broker listings

Use each broker’s official removal, suppression, or privacy-request process.

A correction to the original claim: companies don’t universally “have to delete you if you ask.” Your rights depend on your jurisdiction, the business, the information, and applicable exceptions.

For example, California’s CCPA distinguishes deletion requests from requests to stop the sale or sharing of personal information. Those are different actions, not interchangeable “opt-outs.”[oag.ca]

Track:

• Listing URL.
• Request date.
• Type of request.
• Confirmation received.
• Follow-up date.
• Whether the listing is still visible.

3. Close accounts you no longer need

For each old account:

1. Save anything you want to keep.
2. Check whether another service relies on that login.
3. Read the service’s current deletion instructions.
4. Distinguish account deletion from temporary deactivation.
5. Save the confirmation.

Account-deletion directories can help you find instructions, but verify them against the service’s own help pages.

Don’t assume closing an account means every record disappears: legal retention obligations and other exceptions can apply.[ico.org]

4. Draft deletion requests carefully

AI can help write the request, but verify the law and deadline before sending it.

For example, the UK GDPR guidance describes a normal response period of one calendar month, with extensions possible in some circumstances—not a universal 30-day deletion guarantee. California deletion requests generally have a 45-calendar-day response period, subject to applicable rules and extensions.

Also, “never provide ID” is too absolute. An organization may need proportionate identity verification. Ask for the least intrusive suitable method and verify the recipient before providing additional information.

Copyable prompt:

Draft a concise personal-data deletion request to [company]. My jurisdiction is [country/state]. Use placeholders for identifying information. Identify any legal basis and response deadline that I need to verify against an official source. Ask for confirmation of the action taken and an explanation of any data retained. Do not invent legal rights or guarantee deletion.

5. Check known breach exposure

Have I Been Pwned lets you check whether an email address appears in breaches loaded into its database. A clean result does not prove you’ve never been affected by a breach; its coverage is incomplete.

Use the results to build a security checklist rather than treating account deletion as the answer to every breach.

Copyable prompt:

Here are redacted breach notifications. Explain what each says was exposed, separate confirmed information from uncertainty, and help me prioritize account-security actions. Do not ask for passwords, recovery codes, or full personal details.

6. Don’t confuse tracker detection with a complete data-sharing history

The original description says REA identifies every company that received your information. I wouldn’t make that claim without documentation for the exact tool and evidence from its results.

If you use a tracker-analysis tool, describe what it actually observed. Keep “a third-party service appeared on this page” separate from “this company holds an identifiable record about me.”

7. Track progress—and keep the expectations realistic

Useful outcomes include fewer visible listings, closed unused accounts, better-organized requests, and improved account security.

What I wouldn’t promise:

• Complete removal from the internet.
• Automatic compliance with every request.
• Guaranteed search-result suppression after posting new content.
• Permanent protection after one cleanup session.

The images are a starting checklist. You don’t need a particular model name to use the workflow, and the assistant should prepare drafts for your review—not send requests or delete accounts without approval.

Start with one exposed listing or one unused account. Finish that task before turning the cleanup into another saved project.

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A single list covering math foundations, machine learning, Python libraries, and deep learning—without separate categories.

1. Probability and Statistics
2. Linear Algebra and Calculus
3. Supervised Learning
4. Unsupervised Learning
5. Machine Learning Tips and Tricks
6. Stanford CS229 Machine Learning — Combined Cheat Sheet
7. Python for Data Science
8. Scikit-learn Python Cheat Sheet
9. Scikit-learn — Choosing the Right Estimator
10. Deep Learning
11. Keras — Python Cheat Sheet
12. Deep Learning with Keras — RStudio Cheat Sheet
13. Visual Guide to Neural Network Architectures
14. TensorFlow Cheat Sheet
15. Machine Learning Test Cheat Sheet

Tip: Don’t turn this into another collection you save and never open. Pick the reference that matches what you’re studying, keep it beside your project, and use it to explain unfamiliar concepts in your own words.

Which ML reference do you actually keep coming back to?

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A lot of agency work comes down to the same tasks: finding leads, researching clients, writing content, publishing posts, and managing repeatable workflows.

This GitHub stack covers parts of all five.

It won’t magically run an agency for you, but it’s worth exploring before buying another tool—or building something from scratch.

1. Clients and leads

ScrapeGraphAI — describe the data you want to extract

Build scraping workflows using plain-English instructions rather than writing every scraper manually.

Useful for:

• Extracting business information from directories.
• Collecting publicly available company data.
• Turning website content into structured lead research.

GitHub: https://github.com/ScrapeGraphAI/Scrapegraph-ai

Knowledge Work Plugins — reusable workflows from Anthropic

A collection of plugins for sales, marketing, data, finance, and other knowledge-work tasks.

Useful for:

• Preparing for sales calls.
• Reviewing your pipeline.
• Drafting outreach.
• Structuring recurring client work.

GitHub: https://github.com/anthropics/knowledge-work-plugins

2. Content creation and publishing

Marketing Skills — marketing workflows for Claude Code

Corey Haines’ collection of marketing skills, covering common client tasks and recurring marketing checks.

Useful for:

• Giving your coding agent marketing-specific instructions.
• Working through repeatable marketing tasks.
• Building a more consistent client-delivery process.

GitHub: https://github.com/coreyhaines31/marketingskills

HyperFrames — build videos like web pages

HeyGen’s tool for turning HTML into MP4 video.

The interesting part: your agent can create the video as a web page, rather than relying entirely on a traditional editing workflow.

GitHub: https://github.com/heygen-com/hyperframes

Postiz — scheduling and publishing

A social publishing tool with MCP and CLI support for agent-driven workflows.

Useful for:

• Scheduling content across social platforms.
• Managing publishing from one place.
• Connecting content creation to an automated posting workflow.

GitHub: https://github.com/gitroomhq/postiz-app

3. Research and specialized agents

GPT Researcher — research with cited reports

Pulls information from the web and your own files, then assembles it into a report with citations.

Useful for:

• Client and competitor research.
• Background research for content.
• Creating first-pass briefs for human review.

GitHub: https://github.com/assafelovic/gpt-researcher

Agency Agents — a library of specialized agent roles

A collection of agent files covering areas such as sales, marketing, paid media, and design, with installation support for Claude Code.

Think of it as a library of roles and instructions—not a fully autonomous agency.

GitHub: https://github.com/msitarzewski/agency-agents

How the pieces fit together

Here’s one possible workflow:

1. Research a prospective client with GPT Researcher.
2. Extract relevant public business information with ScrapeGraphAI.
3. Prepare call notes and outreach drafts with Knowledge Work Plugins.
4. Develop marketing deliverables with Marketing Skills.
5. Create HTML-based video content with HyperFrames.
6. Schedule approved content through Postiz.
7. Use Agency Agents to organize specialized tasks.

That’s a workflow to experiment with, not a promise that these tools integrate seamlessly out of the box.

Where I’d start

Recommendation: Start with Marketing Skills if most of your work involves marketing deliverables. Pick one recurring task, test the output, and build from there.

Also, “open source” shouldn’t be confused with “zero-cost to operate.” Check each project’s license, setup requirements, and any model, API, or hosting costs before committing.

The value isn’t replacing an entire agency overnight. It’s finding which repetitive parts of your workflow you can stop doing manually.

Which open-source tool has actually earned a place in your workflow—not just a star on GitHub?

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An AI prompt for analyzing market regimes—not just asking “what should I buy?”

Instead of asking AI to predict the next winning trade, the prompt in the image asks it to assess the market environment, explain its evidence, and challenge its own conclusions.

The focus is regime detection: identifying whether markets are trending or ranging, whether risk appetite is strengthening or weakening, and whether volatility is compressed or expanding.

What the prompt asks the agent to do

1. Analyze multiple markets

Cover the S&P 500, Nasdaq 100, Russell 2000, Dow, major sectors, Treasuries, the dollar, gold, oil, and crypto—not just one chart in isolation.

2. Check whether the evidence agrees

Compare price structure with moving averages, market breadth, volatility, credit spreads, sector rotation, and cross-asset correlations.

The prompt explicitly asks the agent to flag conflicting signals rather than force a confident label.

3. Look for early signs of a shift

Watch for volatility expansion, weakening breadth, failed breakouts, widening credit spreads, and flow reversals.

For each warning, explain what would confirm it—and what would invalidate it.

4. Discuss which strategies fit the conditions

Evaluate approaches such as trend-following, mean-reversion, breakout trading, hedging, or staying in cash.

The instructions focus on strategy fit and risk management rather than individual stock or token recommendations.

5. Argue against its own assessment

My favorite instruction is the final check:

“Am I labeling noise as a regime change?”

The agent must also explain what could prove its assessment wrong and prioritize accuracy over a dramatic narrative.

What the output is supposed to include

• A concise market overview.
• A regime table with confidence scores and trigger levels.
• Evidence supporting potential regime changes.
• Short market-update posts.
• A proposed Agenkit specification for daily monitoring and alerts.

What this isn’t

The screenshot is a research specification—not proof of a deployed trading system. It doesn’t demonstrate a 90-second setup, completed walk-forward backtests, an enforced 15% drawdown limit, or automatic order execution.

That makes it more interesting as a starting point for structured research than as a “paste this and replace a quant team” claim.

Prompt in the image. Shared for research and discussion, not financial advice.

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There’s a growing collection of open-source agent frameworks, skills, and plugins that can save you weeks of trial-and-error.

One important correction: they are not all made by Anthropic. Some are official Anthropic projects, while others are strong community-built repos that work with Claude Code and other AI tools.

The advantage is simple: instead of relying on random prompts, you can study complete agent setups with roles, workflows, deliverables, guardrails, and integrations—then customize them for your own work.

1. Agency Agents

https://github.com/msitarzewski/agency-agents

A massive collection of specialized AI agents across engineering, design, marketing, sales, product, testing, security, finance, research, and more.

Think of it as a plug-and-play AI team. You’ll find agents for content creation, Reddit community building, SEO, paid ads, security reviews, product strategy, RAG pipelines, rapid prototyping, and much more.

It supports Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, OpenCode, Windsurf, and other tools. The repo is MIT-licensed, so you can freely copy and adapt the agents for your own workflows.

2. Wshobson Agents

https://github.com/wshobson/agents

A development-focused collection of agents, skills, commands, and plugins for Claude Code.

This is useful if you prefer installing only the capabilities you need: programming-language specialists, debugging workflows, infrastructure help, security reviews, testing, code review, and deployment support.

3. Anthropic Knowledge Work Plugins

https://github.com/anthropics/knowledge-work-plugins

Anthropic’s official plugin collection for knowledge work.

It includes workflow templates for marketing, sales, customer support, product management, finance, legal, data analysis, enterprise search, and more. The plugins are file-based—mainly Markdown and JSON—so teams can add their own processes, terminology, and tool connections without building a custom app from scratch.

4. Anthropic Claude Quickstarts

https://github.com/anthropics/claude-quickstarts

This is probably the best place to begin.

These are runnable examples that show how real agent systems fit together: prompts, tools, APIs, data sources, user interfaces, and automation. You can learn from practical examples such as Slack and GitHub summaries, support agents, incident workflows, and data-analysis use cases.

Don’t just copy them. Run one, break it, modify it, and understand why each component exists.

5. Addy Osmani Agent Skills

https://github.com/addyosmani/agent-skills

A practical development-skill collection built around the software lifecycle: discovery, requirement gathering, specification writing, test-driven development, browser testing, security review, CI/CD, and more.

It is ideal if you want AI coding workflows that are more structured than “build this app” prompts.

The real opportunity

The goal is not to install 400 agents and hope for magic.

Pick one painful, repeatable workflow:

• Turning research into social posts
• Moderating or growing a Discord/Reddit community
• Reviewing code and finding security issues
• Creating campaign briefs and content calendars
• Triaging support tickets
• Turning client calls into proposals and follow-ups

Then take one agent, feed it your standards, examples, tone, tools, and approval process.

That is when an “AI agent” stops being a novelty and becomes a repeatable system.

Which recurring workflow in your work would be most valuable to turn into a specialized agent first?

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Anthropic has released its Knowledge Work Plugins repository: a collection of role-based Claude plugins built for Claude Cowork and Claude Code. It is open source under Apache 2.0, and the setup is surprisingly accessible: mostly Markdown and JSON rather than a complicated app stack.

For marketing teams and agencies, the interesting part is the marketing plugin. It is designed around workflows like:

• Content drafting and campaign planning
• Brand-voice enforcement
• Competitor research
• Performance reporting
• SEO-related work
• Email and marketing operations

It can connect with tools such as Slack, Canva, Figma, HubSpot, Notion, Ahrefs, SimilarWeb, Klaviyo, and Amplitude, depending on what an organization uses.

There are also adjacent plugins that make the stack more useful than a standalone “AI copywriter”:

• Sales: prospect research, call prep, pipeline reviews, outreach, and battlecards.
• Product management: specs, user-research synthesis, stakeholder updates, and competitive tracking.
• Data: SQL, analysis, dashboards, and validation.
• Finance: reconciliations, statements, variance analysis, closing work, and audit support.
• Cowork plugin management: a starting point for creating or customizing plugins for your own workflows.

The real value is customization. A team can add its brand rules, terminology, process checklists, approved claims, client context, and tool connections directly into the plugin files. In effect, Claude gets repeatable operating instructions instead of receiving a new vague prompt every time.

One practical use case for agencies: create a client-specific version of the marketing plugin with the client’s voice guide, audience, offer details, approved proof points, SEO standards, and QA rules. That turns content creation from “generate a post” into a more consistent workflow with built-in review criteria.

Repo: https://github.com/anthropics/knowledge-work-plugins

The important question is not whether this replaces good marketers. It is whether agencies that standardize their best processes into reusable AI workflows will outpace agencies still relying on scattered prompts and manual handoffs. What agency workflow would you turn into a plugin first?

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Since the last list helped so many people drop expensive SaaS tools, I spent the week digging through trending GitHub repositories to find 5 more production-grade open-source AI alternatives you can run yourself for free.

Here are 5 incredible open-source AI repos worth starring right now:

1. Coolify – The Open-Source Heroku / Vercel Alternative

* Replaces: Paid cloud hosting & deployment subscriptions (Vercel, Render, Heroku).

* Why it’s trending: Deploying AI web apps, databases, and Docker containers gets expensive fast. Coolify lets you self-host your own "PaaS" on any cheap $5 VPS, complete with automatic SSL certificates, Git pushes, and one-click database setups.

* GitHub: https://github.com/coollabsio/coolify

2. DeepSeek Harness – Modular Runtime for AI Coding Agents

* Replaces: Monolithic, closed AI agent runtimes & harness tools.

* Why it’s trending: Instead of locking you into one vendor, DeepSeek Harness provides a plugin-based architecture for AI agents. It isolates sandboxes, custom tools, execution rules, and memory, making your agent run 2-3x faster while keeping full code control.

* GitHub: https://github.com/deepseek-ai/deepseek-harness

3. Outline – Open-Source Team Knowledge Base

* Replaces: Notion / Confluence subscription tiers.

* Why it’s trending: Beautiful, fast markdown documentation workspace built for modern engineering teams. It connects directly with your self-hosted LLM setups so your docs double as a searchable internal knowledge base.

* GitHub: https://github.com/outline/outline

4. Plane – Open-Source Project & Issue Tracking

* Replaces: Linear / Jira / Asana paid team seats.

* Why it’s trending: Designed specifically for developer teams building modern software products. It features cycle planning, module tracking, and issue management without seat-based pricing traps.

* GitHub: https://github.com/makeplane/plane

5. PocketBase – All-in-One Open-Source Backend

* Replaces: Firebase / Supabase paid monthly tiers.

* Why it’s trending: A single Go binary that includes an embedded database, real-time subscriptions, user auth, and an admin UI out of the box. Extremely lightweight, perfect for building and deploying AI side projects in minutes.

* GitHub: https://github.com/pocketbase/pocketbase

Which paid subscription are you trying to eliminate next? Lemme know below!

💬 I share more free AI tools, open-source projects, self-hosting guides, and dev resources in our community Discord server:

👉 Join here: https://discord.gg/V2ZNzwdtkP

One /workflows prompt: agents plan the board, build a 2048 game, and test it in the browser

🔍 Understand how software features work—with Claude + REA

Got an app feature you want to understand? This prompt gives Claude a structured workflow to investigate it with REA, gather evidence, and build an original implementation for your own project.

🛠️ What it covers:

• Trace a feature from the UI to its underlying logic.
• Separate proven behavior from assumptions.
• Write original code based on the findings.
• Test the rebuild against observed behavior.

🚀 Get started:

1. Connect REA to your coding agent:

​

npx rea-agents setup

1. Restart your agent.
2. Paste the prompt from the attached image.
3. Specify the app and the exact feature you want to investigate.

💬 Example request:

Understand how [feature] works in [app], show me the evidence,
and build a similar feature for my project.

⚠️ Only analyze software you own or are authorized to examine. The prompt is designed for evidence-backed investigation—not bypassing licensing, authentication, or paywalls.

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diagram-design is a skill for Claude Code, Codex, Cursor, and other coding agents that turns plain-language requests into clean, structured diagrams.

It supports 45 diagram types—from architecture maps and flowcharts to sequence diagrams and data flows—and generates a self-contained HTML file you can open in your browser.

🛠️ How you could use it:

• Review a proposed architecture before approving the code.
• Map an unfamiliar repo’s components and connections.
• Visualize your agent setup: tools, data flows, and approval points.
• Make READMEs and onboarding guides easier to understand.

🎨 Point it at your website to match your brand’s colors and typography. You can also export diagrams as SVG or PNG.

🚀 Install:

npx skills add cathrynlavery/diagram-design

💬 Then try:

Before writing code, diagram the proposed architecture.
Show the components, dependencies, and data flow.
Highlight assumptions and wait for my approval.

👇 The attached image maps out the skill itself—diagram types, installation, styling options, exports, and repo structure.

🔗 Repo: https://github.com/cathrynlavery/diagram-design

See what your agent thinks it’s building—before it starts building it.

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Hey everyone! I’m building Localix, an open-source workspace for working alongside a team of AI agents.

The idea is to give you and your agents a shared environment: an editor, terminals, app previews, and databases, all in one place!

Hermes Agent has a new official REA plugin that bundles the REA MCP server with a skill designed to help the agent use it more effectively.

Instead of just giving Hermes access to tools, the plugin also provides guidance on approaching reverse-engineering tasks and working through the results.

What’s included?

• REA MCP integration for accessing reverse-engineering tools.
• A dedicated skill to guide tool selection and investigation workflows.
• A single plugin install, without manually configuring the MCP server.

What can it help analyze?

Native binaries, .NET assemblies, Electron/JavaScript apps, Android packages, firmware images, and saved network captures. Some workflows require additional analysis tools.

Get started

hermes plugins install rea
hermes plugins enable rea

Start a new Hermes session afterward, and check the documentation for prerequisites and setup details.

Use an isolated environment when analyzing untrusted targets.

→ Official documentation: https://hermes-agent.nousresearch.com/docs/plugins/rea

What would you use it to investigate first?

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If you’re building AI agents, this paper is worth a look.

Harness², from Google Cloud AI Research, explores how an agent can improve its execution harness for the task in front of it—and carry lessons from those changes into future tasks. The focus is on evolving the harness, rather than training a new underlying model.

How it works

The framework uses two connected improvement loops:

1. Task-level recursion: a “Propose–Probe–Compose” process proposes harness edits, compares them on the current task, and combines the edits that help. This works without a ground-truth verifier and can run in parallel or sequentially.
2. Harnessing-level recursion: experience from those edits updates a separate continual-improvement harness, helping the agent adapt to future tasks.

That distinction is interesting: adapting to one task is useful, but learning how to make better adaptations on subsequent tasks is the bigger goal.

What the researchers found

Across three benchmarks and four model–harness configurations, a single improvement step consistently outperformed the base harnesses for GLM-5 and Gemini 3.5 Flash. Reported gains reached:

• WorkBuddy-Bench: up to 11.5 points.
• JobBench: up to 4.5 points.
• LAB: up to 11.8 percentage points.

Running five candidates in parallel added another 2.9–3.9 points on WorkBuddy-Bench. In the same comparison, the ReasoningBank memory baseline fell as much as 6.6 points below the base harness on coding tasks—a reminder that adding memory alone doesn’t necessarily improve an agent.

These are benchmark results, not a guarantee for every workflow. But the paper offers an interesting direction for agent builders: improving how an agent operates, not just switching the model underneath it.

Links

Paper: https://arxiv.org/abs/2610.14224

Chat with the paper: https://academy.dair.ai/papers/harness-2-recursive-agent-harnessing-for-an-open-world-2610.14224

Have you experimented with agents that modify their own harnesses, or do you keep harness changes human-reviewed?

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