This setup details how to run a multi-model AI agent pipeline locally with persistent memory for just $15-35/month, enabling builders to automate complex workflows affordably. It's a practical blueprint for deploying powerful AI automations without high cloud costs.
The setup: OpenClaw as the agent framework, ClawRouter for model routing (Gemini Flash for simple tasks, Claude Sonnet for complex ones),
OpenViking for persistent memory.
All running locally on a $600 Mac Mini. Monthly API cost after optimization: $15-35.
This tweet shares a detailed AI and Python workflow that enables a solo builder to operate at the scale of a full team, automating tasks and boosting productivity. It's highly relevant for entrepreneurs seeking to streamline operations and scale without hiring.
My manager thought I had a team.
I was alone.
Here's the exact AI + Python workflow that
made me look like 5 developers
(Save before your team finds it )
PokeeClaw is a robust AI agent platform featuring RL-powered tool selection and secure sandboxing, enabling builders to automate complex workflows across 1,000+ integrations. This can help entrepreneurs streamline operations or deliver automated services at scale.
PokeeClaw is a different beast.
Enterprise-grade AI agent platform. 1,000+ integrations. RL-powered tool selection. Secure sandbox.
I test AI agents constantly for this newsletter. Most are demos. This one just⦠did the work.
A founder replaced a costly vendor contract with a custom AI workflow using Claude API and Make, achieving similar results at a fraction of the cost. This highlights a replicable approach for automating business processes and slashing expenses.
I replaced a Β£55,000/year vendor contract with an internal AI system I built in under a week.
Same ticket routing, auto-replies, and QA scoring - but no lock-in and ~90% cheaper.
The vendor called it βAI-powered automation.β
So I built it myself with Claude API + Make +
This workflow automates the process of ingesting content, extracting concepts, and generating a queryable markdown wiki. Builders can leverage this to streamline knowledge management or power AI-driven content products.
The current workflow is simple:
β’ingest URLs or local files
β’extract concepts
β’generate linked markdown pages
β’resolve wikilinks
β’query the compiled wiki
β’optionally save answers back into the wiki
The tweet describes using Surf Studio to set up an automated airdrop radar that alerts users to new crypto/NFT projects before launch. Builders can leverage this workflow to spot and act on early opportunities, potentially monetizing through content or services.
Let me share something I've been tinkering with lately.
The
@noise_xyz
project I shared this morning was actually one I spotted through the airdrop radar monitoring I set up using Surf Studioβit pushed it to me about 18 hours before launch.
These past couple of days, I've
MyClaw offers effortless, one-click AI automation, enabling builders to create workflows or products that actually execute tasks without setup. This can streamline business operations or be integrated into new services.
OpenClaw got insanely powerful.
MyClaw made it effortless.
One click. No setup. Your AI actually does things now.
Learn more about MyClaw and try it right here
A hands-on checklist of new AI tools and workflowsβHermes agent, Claude Dispatch, Google AI Studio, Perplexity modes, and NotebookLMβshowcasing how to automate research, content, and data tasks. Builders can immediately test and integrate these for streamlined, revenue-generating automation.
Weekend AI to-do list:
β’ Experiment with Hermes agent
β’ Connect Claude Dispatch
β’ Test new Google AI Studio
β’ Read Anthropic's new Claude Cookbooks
β’ Set up first NotebookLM
β’ Financial research with Perplexity Computer/Finance mode
β’ Connect health data to Perplexity
β’
The tweet shares a workflow using multiple LLMs to analyze project security, highlighting that each model finds unique vulnerabilities. Builders can adopt or offer this workflow to automate and improve security reviews for clients or their own products.
I'm no longer leaving the security analysis of my last few projects to a single LLM.
Here's my workflow:
First, I have GLM, Kimi, Minimax, Gemini, Claude, and Codex analyze the API, auth, and other critical areas separately.
Each model catches different vulnerabilities and
This update shows how AI models can automate code validation by checking if implementations match design docs, highlighting strengths and weaknesses of GPT, Claude, and Gemini. Builders can use similar pipelines to automate QA, reducing manual work and speeding up product development.
Overnight update 5. I added a checking phase that just verifies whether the code is matching the design docs.
- GPT wrote tests for the design docs themselves all night, assert if words were present
- Claude passed 89/91 tests, but cheated core reqs
- Gemini hit API rate limits
A builder fine-tuned an open-source AI model to autonomously handle research, automation, and tool calls with high accuracy, running 24/7 on a Macbook. This showcases a practical, low-cost automation pipeline that can be adapted for various business tasks.
I'm doing that already.
> took OS model
> fine tuned it on (80M dataset)
> now running 24/7 on my macbook
> with 98% accurate tool calls
> it design its own workflows
> can talk, research, automate, save
> and much more
Launching soon.
π 736 viewsβ€ 11π 0π¬ 3π 81.9% eng
AI agentautomationworkflowopen sourcetooling
build a SaaS on top of itoffer it as a servicerecurring
A builder shares their workflow for managing many AI agents in parallel using a custom UI and 160+ custom commands, showcasing a scalable approach to automating complex tasks. This highlights how entrepreneurs can orchestrate agent-based automation for business efficiency.
I run many tasks in parallel so there's not much downtime. I built a UI where each AI agent appears as an avatar on a 2D map (Arcane Agents). I spend most of the day hopping between them assigning and reviewing work.
I've set up ~160 custom commands the agents can call to access
π 788 viewsβ€ 2π 0π¬ 0π 00.3% eng
AI agentsautomationworkflowcustom commandsproductivity
A team uses an internal AI agent to automatically track activity across Slack, Discord, and Clockify, generating daily status reports without manual input. Builders can replicate or productize this workflow to save time for teams.
No one on my team writes daily updates anymore
We have an internal AI agent that tracks everything across Slack, Discord, and Clockify, and generates the status report at the end of the day
AWS has introduced two new Frontier Agents that autonomously learn and improve over time, enabling builders to automate complex workflows and reduce manual intervention. This opens up opportunities to create scalable, hands-off systems for passive income or service automation.
AWS just launched two Frontier Agents. They learn and improve over time, never tire, and run autonomously. Read more in The AI Economy:
go.aws/3POnzyx
TRAE SOLO is a newly launched AI agent that can actively operate within your files, projects, and workflows, not just answer questions. Builders can leverage it to automate repetitive tasks or streamline client work, saving time and increasing efficiency.
TRAE just launched SOLO.
Itβs an AI agent that doesnβt just answer,
It actually works inside your files, projects, and workflows!
I tested it with 2 real tasks in 15 minutes. Here's what stood out
This tweet shares a practical approach to automating business workflows with AI by starting small and iteratively building automation for repetitive, multi-platform tasks. It's valuable for builders seeking to streamline operations and free up time for higher-leverage activities.
I've automated so much of my business with AI over the last 2 months. But I didn't start by automating everything.
Here's the pattern I used:
1// Pick one annoying workflow that's regular and touches multiple platforms
2// Build it in assisted mode β you direct each step, AI
The tweet highlights how Blackbox AI reduces the complexity of setting up multi-agent workflows, making advanced automation more accessible for builders. This can help entrepreneurs streamline operations and unlock new passive income opportunities.
Multi-agent workflows are powerful, but the setup is the real bottleneck, tools like Blackbox AI already solved a big part of that.
The tweet introduces a workflow where a second AI agent audits the code generated by a first, addressing trust and reliability issues in AI code review. Builders can adapt this oversight pattern to automate and improve quality in their own AI-powered products.
Happy Easter! I hid an Easter egg in my latest article. First person to find it wins a GitHub Mona plush
AI agents shouldnβt be trusted to review their own code. So I built a second agent that watches the first.
mainbranch.dev/articles/adver
β¦
A custom AI workflow pulled daily medical data from hospital systems to improve patient care and catch errors. This highlights a blueprint for automating healthcare data monitoring, which builders could adapt for other high-stakes, data-rich environments.
A son built a βvibe-codedβ AI workflow to help his mother navigate stage 4 cancer and catch critical medical errors. Sadly, she passed away, but what he built with AI changed how she was cared for in her final days.
- Pulls daily medical data from the hospitalβs Epic system to
π 9,821 viewsβ€ 119π 21π¬ 11π 781.5% eng
This tweet shares actionable design patterns for building robust, production-ready coding agents, highlighting trade-offs and practical implementation tips. Builders can use these insights to automate coding workflows or enhance agent-driven products.
If you missed, check out my latest post
β12 patterns behind production coding agentsβ
1. Persistent instructions: durable rules repo. Helps consistency. Trade-off: goes stale.
2. Scoped context: load rules by directory. Helps local accuracy. Trade-off: harder to debug.
This tweet showcases a builder remotely fine-tuning models, running multiple AI agents, and managing work tasks while flying. It highlights the power of cloud-based automation and remote orchestration for entrepreneurs seeking to streamline and scale their AI operations.
Things Iβm doing while flying at 34,000 feet:
* Fine-tuning on my DGX Station (SSH)
* Running 8 concurrent
@cursor_ai
cloud agents
* Replying to emails
* Posting on X
π 31,070 viewsβ€ 101π 5π¬ 18π 140.4% eng
remote workAI agentsautomationcloudworkflow
write a newsletter/blog about itpost about it on Xaudience building
Showcases a no-code workflow for classifying and prioritizing emails using Google Workspace Studio, enabling automated inbox management. Builders can adapt this to streamline client communications or offer as a productivity service.
Aryan Irani built an AI email organizer in Google Workspace Studio that classifies every incoming message, applies labels, and only sends a Google Chat notification when something is genuinely urgent. No code required β just an Extract step, a Decide step, and a rubric you write
A builder shares how quickly a personal AI assistant's memory can be corrupted by low-quality models, highlighting risks in deploying autonomous AI agents. This is crucial for entrepreneurs considering AI-powered automation, as it exposes reliability and trust issues that must be solved for scalable, hands-off income streams.
I built a personal AI assistant on a Mac Mini. Within 48 hours, cheap models had poisoned its memory with fabricated colleagues, fictional file shares, and an imaginary costume party. Here is what I learned.
meethenry.ai offers early access to a new platform for AI agents, enabling builders to automate workflows or services. This could be leveraged to create automated solutions or services for clients or internal use.
Be one of the first to try the new frontier of AI agents:
meethenry.ai
Replit Agent now offers an 'AI SDR' skill, enabling users to automate sales development tasks directly from the platform. Builders can leverage this to streamline outreach or integrate it into client workflows.
To use the AI SDR skills, simply ask Replit Agent, or use the + button from the input box after logging in and the select the "AI SDR" skill
A workflow using Claude to automate Google Analytics 4 data analysis, reducing manual effort for entrepreneurs tracking web performance. Builders can leverage this to streamline reporting or offer analytics automation as a service.
Check out the latest article in my newsletter: This Claude Workflow Simplifies GA4 Data Analysis.
linkedin.com/pulse/claude-w
β¦ via
@LinkedIn
A simple AI workflow: record yourself describing your project, transcribe it, and have AI identify reasoning gaps. This can streamline product validation and save time for entrepreneurs.
Three AI workflows that sound too simple but saved me weeks this month.
- The logic check
I describe what I'm building out loud. Voice memo.
Paste the transcript and ask: where does my reasoning have gaps?
Not 'is this a good idea.' Where is the logic weak.
That
This guide explains how to translate common Airflow orchestration patterns to Databricks Lakeflow Jobs, enabling more integrated and automated data workflows. Builders can leverage this to streamline data pipelines and offer orchestration solutions to clients.
If youβre running Apache Airflow in production, this guide shows how common orchestration patterns map to Lakeflow Jobs, Databricksβ built-in orchestrator.
See how control flow, triggers, parameters, and dynamic execution work when orchestration is integrated with the lakehouse,
A builder created an automated tool to monitor when online services update with their vaccination status. This highlights a practical automation workflow that can be adapted for tracking other types of online data changes.
How it started
How it's going
(yes I built an automated tracker to detect when online services update with my vax status)
π 6,787 viewsβ€ 42π 2π¬ 3π 00.7% eng
automationtrackingdata monitoringworkflowbuilders
build a SaaS on top of itoffer it as a servicerecurring
A builder shares a tool that streamlines sending content to AI chats, reducing manual screenshot pasting. This highlights a workflow automation that can save time for creators and could inspire similar productivity tools.
This is awesome.
I built
talktoyour.computer a while back to learn Make back then.
So much easier than pasting screenshots into any AI Chat all the time.