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Hi I want to build an indicator on trading view for my signals. It's basically a Retest pattern on the 15mins TF. It starts with manually plotting my order box with a high nd low range on a 1hr candle nd how price reacts to that box range.
It should work seperatly on different scripts. So one indicator to plot order box values with different script values nd it works individually on different scripts.
Please also give me a quote for building this one indicator with probably 6 conditions.

I have started the setup and ran into some problems with errors. Node related o my Apple Mini.

Need an OpenClaw expert to help set up and stabilize initial workflows for a GTM Operating System. Focus includes workflow configuration, debugging, tuning, reducing dashboard/LLM overhead, improving local testing, improving visibility into workflow inputs/outputs/artifacts, and advising on selective voice interface integration where it improves speed. Initial priority is a LinkedIn Comment Workflow and related GTM automation. Looking for someone who can help both with implementation and with creating a more scalable workflow engineering process.

This is not a basic automation task.

I need someone who can help me stand up a practical, scalable workflow environment that supports:
• workflow setup
• workflow testing
• workflow tuning
• observability
• lower-friction iteration
• reduced dashboard/LLM overhead
• visual understanding of workflow inputs, outputs, and dependencies
• faster operator interaction, including voice-driven interaction where appropriate

The immediate focus is to help me get my initial OpenClaw workflows working properly, starting with GTM-related workflows such as opportunity sensing, LinkedIn commenting, and supporting logic for a broader discipline-led GTM system.

Over time, I want this environment to support a more comprehensive GTM OS comprised of:
• automated workflows
• content strategy
• content creation
• experimentation
• optimization
• feedback loops
• monitoring
• learning/adjustment cycles

Context

I’m building a GTM system around a discipline-led, proof-backed, capture-oriented strategy.

The system should support:
• category development
• audience capture
• product-proof signaling
• experimentation and learning
• scalable workflow operations

I have already learned that:
• using ChatGPT to manually guide constant file edits is too slow
• dashboard-heavy LLM usage creates friction and can trigger throttling
• I need a faster build / test / tune loop
• I need stronger workflow visualization and observability
• CLI-only inspection of text outputs is no longer practical
• voice interaction may be useful for speeding up workflow creation, editing, and execution in the right parts of the system

So I need someone who understands how to work with OpenClaw in a way that is fast, pragmatic, modular, and low-friction.

What I Need Help With

Phase 1: Initial OpenClaw Workflow Setup

Help me set up and stabilize initial workflows such as:
• opportunity sensing workflow
• LinkedIn comment workflow
• weekly planning / learning support workflows
• supporting GTM signal workflows as needed

This includes:
• workflow configuration
• prompt/config file structure
• validation logic
• workflow debugging
• tuning for reliability
• reducing unnecessary LLM/dashboard load

Phase 2: Workflow Engineering Environment Recommendations

I also need help designing a better workflow development approach around OpenClaw so iteration becomes practical.

That may include recommendations and/or implementation support for:
• spec-first workflow organization
• local-first testing
• fixture-based validation
• workflow run inspection
• artifact visibility
• file/input/output mapping
• reduced reliance on dashboard iteration
• strategies to minimize throttling and wasted LLM usage

Phase 3: Visual and Operational Usability

I need better visibility into:
• workflow structures
• workflow steps
• related files
• inputs and outputs
• intermediate artifacts
• run history
• failures and step breakdowns
• version-to-version output changes

You do not necessarily need to build a full custom application unless you are capable and it makes sense, but you should be able to help me establish a much better operating model than raw CLI review and scattered file edits.

Phase 4: Voice Interface Considerations

I have a preference to incorporate a voice interface with OpenClaw where appropriate to speed up interaction.

This does not mean I want voice added everywhere blindly.

I’m interested in a practical design for places where voice could genuinely improve speed and usability, such as:
• issuing workflow instructions
• triggering certain actions
• navigating workflow operations
• capturing change requests or refinement instructions
• accelerating build/test/tune loops

I would like the right person to help determine:
• where voice is actually useful vs unnecessary
• how voice should fit into the workflow engineering environment
• how to keep voice integration practical, reliable, and not overly complex
• how voice can improve operator speed without creating more fragility

Ideal Candidate

You should be strong in several of the following:

Must-have
• deep practical experience with OpenClaw
• experience building and tuning multi-step automated workflows
• strong debugging ability
• experience reducing unnecessary LLM calls / orchestration cost
• ability to create a clean file and workflow structure
• ability to think in systems, not just isolated scripts
• ability to help move from fragile manual iteration to a repeatable workflow engineering process

Strong plus
• experience with agentic workflow systems
• experience designing local testing harnesses
• experience with workflow observability / artifact inspection
• experience with React / Next.js or similar, if visual tooling becomes part of the solution
• experience with Python and/or Node
• experience with schema validation, diffing, and fixture-based testing
• experience supporting content systems, GTM automation, or creator/marketing operations
• experience integrating or designing voice interfaces for workflow or automation environments

Important Architectural Preferences

I want workflows designed with these principles in mind:
• spec-first, not file-chaos
• local-first testing, not dashboard-first iteration
• deterministic-first logic wherever possible
• use LLMs/agents only where real judgment or generation is needed
• better observability of artifacts and run behavior
• modular workflow design
• versioning and structured tuning
• support for future expansion into a broader GTM OS
• incorporate voice interaction selectively and intentionally where it improves speed

Initial Workflow Priorities

The first workflow I want to stabilize is likely the LinkedIn Comment Workflow.

That workflow should eventually support logic like:
• intake candidate LinkedIn posts
• filter and rank posts for meaningful relevance to PM/project-professional audiences
• evaluate bridge potential to my thesis
• selectively use agent/LLM logic only for top candidates
• generate aligned comment drafts
• validate for narrative alignment and strategic fit
• support iteration through fixtures, run inspection, and output comparison

Deliverables I Expect Early On

In the initial engagement, I’d like help producing some combination of:
• recommended OpenClaw project structure
• cleanup/reorganization of current workflow files
• working initial workflow implementations
• debugging and correction of current broken/non-working flows
• validation and testing approach
• fixture-based test setup
• run review / artifact review method
• guidance or implementation for a better visual workflow inspection approach
• recommendations to reduce throttling and dashboard dependence
• recommendations for where and how voice interaction should be incorporated
• clear documentation of how the system should be operated and tuned

What Success Looks Like

A successful engagement would result in:
• my initial OpenClaw workflows are running reliably
• I have a faster way to build, test, and tune workflows
• I am less dependent on the dashboard for routine iteration
• LLM usage is better controlled
• I have much better visibility into workflow behavior
• voice is incorporated only where it adds real speed and usability
• the workflow structure is clean enough to scale into a broader GTM OS
• I have a practical operating model for future workflow expansion

Please Respond With

When applying, please include:
1. your experience with OpenClaw
2. examples of similar workflow/automation systems you’ve built


Engagement Type

I’m open to:
• short paid diagnostic / setup phase first
• followed by ongoing implementation if there is a fit

AI Chatbot Developer Needed for Lead Qualification System (Database + SMS + Email + Calendar Integration)
NDA must be signed when contract awarded
We are looking for an experienced AI developer to build and integrate a custom chatbot designed for lead qualification, not e commerce.
• Database information will be provided later and may differ by location. Each location may have different field names or structures, but the overall data type and purpose will remain similar.
• The system must be built with a shared logic layer. All core AI rules, business logic, and conversation flows must be centralized so that improvements or modifications automatically apply to all location instances.
• Each location must have its own isolated data, branding, and configuration. The underlying logic engine must remain unified across all deployments, even if each location may use a different database structure.
• The platform must support deploying a second location easily, using the same logic while keeping data, access, and configuration fully separated.
• The architecture must be designed to support additional locations in the future. Adding a new location should require minimal setup and no duplication of logic or code.

Project Overview
The chatbot must connect to a live product database and provide real-time answers based on stored information. It should also generate AI-driven responses for general or open-ended questions within our field of practice.
The goal is to qualify potential clients, recommend suitable products, and ensure fast follow up by a salesperson.
Core Features Required
1. Guided Conversation Flow
• Greet the user and collect basic contact information (name + phone number).
• Ask a series of guided questions to understand the client’s needs.
• Recommend relevant items from the database based on the user’s answers.
2. SMS Automation (Immediate Alerts + Client Ticket)
After collecting contact information, the system must automatically send an SMS to the client containing:
• A reference/ticket number
• A link to return to the chatbot if the session disconnects
• A link to contact a salesperson directly
Additionally, when a new lead is created, the salesperson must receive an SMS alert to ensure fast follow up.
SMS can be handled through a third party provider; please specify expected monthly/yearly fees.
3. Automatic Email to Salesperson (Full Lead Details)
When the chat session ends, the system must automatically send an email to the assigned salesperson containing:
• Client’s name
• Client’s phone number
• All questions and answers provided by the client
• The full chat transcript
• The reference/ticket number
This ensures the salesperson has complete context before calling the client.
4. Salesperson Communication
• The client must be able to email or text a salesperson at any time.
• The system should notify the salesperson immediately.
• Integration with a calendar or scheduling tool (Google Calendar, Outlook, Calendly, etc.) to book appointments or follow-up calls.
5. AI Behavior Requirements
• Human like conversational flow
• Avoid repeating questions
• Provide accurate, relevant answers
• Do not answer questions outside our field of practice
6. Conversation Logging & Review Dashboard
The system must include an internal admin interface allowing us to:
• Review all past chatbot conversations
• See how the AI responded at each step
• Identify incorrect or low quality answers
• Use this information to improve the AI’s behavior over time
This is essential for ongoing optimization and quality control.
7. Technical Requirements
• Chatbot + database integration
• AI/LLM response generation
• SMS API integration
• Email + calendar integration
• Admin dashboard for reviewing conversations and tracking leads
• Secure storage of chat logs and client information
Experience Required
Applicants must have:
• Proven experience building AI chatbots
• Examples of similar projects or working demos
• Experience with database-driven systems
• Experience integrating SMS, email, and scheduling tools
• Strong understanding of AI prompt engineering and conversation design
• Strong experience creating AI rule based logic (guardrails, overrides, correction layers) to keep answers sharp, accurate, and aligned with business requirements.
Language French and English
Maybe others after

I need someone to upload my AI prompts and information into ChatBase.
I have all the prompts, the documents and all the information. I will provide these to you.
All I need you to do is upload them into ChatBase and send me the embedded code so it can be displayed and active on my website. Some testing required to ensure it is operational, but this will only be approximately 5 - 10 questions to confirm it is working.
I need this completed within 24hrs.

• Provide an example of an AI bot you have created previously.

Job Title: CRM Data Migration Expert Needed to Migrate from Legacy CRM to GoHighLevel (GHL)

Job Description:

We are seeking an experienced CRM Data Migration Specialist to help us migrate data from our current legacy CRM system to GoHighLevel (GHL). The ideal candidate will have a strong understanding of both the technical aspects of data migration and the specific needs for configuring GoHighLevel after the migration.

Key Responsibilities:

Migrate all relevant data (contacts, notes, pipelines, etc.) from our legacy CRM system to GoHighLevel.

Set up and configure GoHighLevel to match our business processes, including creating pipelines, workflows, and automations.

Ensure that all data is transferred accurately, and resolve any issues related to data integrity during the migration.

Integrate third-party tools or systems with GoHighLevel, if necessary (e.g., email marketing, SMS, etc.).

Provide post-migration support to ensure that everything is running smoothly.

Requirements:

Proven experience in CRM data migration, especially to GoHighLevel.

Strong understanding of CRM systems, data import/export processes, and APIs.

Familiarity with GoHighLevel's configuration, automation, and integration features.

Attention to detail and problem-solving skills to ensure a smooth migration process.

Ability to troubleshoot and resolve issues during the migration process.

Excellent communication skills.

Project Timeline:

We are looking for someone who can complete the migration within [insert time frame, e.g., 3-4 weeks].

How to Apply:

Please provide:

A brief overview of your experience with CRM migration projects.

Examples of similar projects you have completed (preferably including GoHighLevel).

Your estimated timeline and rate for this project.

We look forward to working with a skilled and reliable professional who can help us move our CRM to GoHighLevel seamlessly.

• Describe your recent experience with similar projects
• Please list any certifications related to this project
• Include a link to your GitHub profile and/or website
• What frameworks have you worked with?

AI Agent Infrastructure Engineer (or Team) | Repeatable Private VPS Builds for B2B Clients

We build private AI agent infrastructure for small and mid-size businesses in USA. Each client gets a fully isolated VPS deployed as a repeatable standard build. We have a growing pipeline and need an engineer or small team who can own the technical builds on an ongoing basis at fixed pricing.

What the infrastructure needs to do:
Server management, relational database, object storage, a central API gateway routing to Anthropic Claude and Google Gemini with per-agent routing and spend tracking (clients own their own API keys), an automation engine for scheduled workflows and integrations, standalone tool apps each running at their own URL, uptime monitoring, and HTTPS with full client isolation and revocable access.

What sits on top:
AI agents are room-based conversational assistants with per-agent memory/rules, client-curated knowledge bases, and automation integrations. Rooms segment by function. Users can share rooms or have independent memory under the same agent. Agent types include CRM copilots, lead research, email triage, meeting prep, content drafting, project tracking, deal scoring, and knowledge base retrieval. Custom tools are standalone web interfaces: document processors, proposal builders, dashboards, intake forms, route dispatchers, and more. Each connects to the same database and storage as the agents.

In your proposal, address all of the following:
1. Recommended Stack and Chat Interface. Tell us what you would build this on and why. We have concerns about Open WebUI. The chat interface must support multi-user auth, role-based access, per-room knowledge bases, memory/rules configuration, and a clean UI for non-technical users.
2. Recommended VPS Hosting. We default to Hetzner. Better option? Include reasoning.
3. Fixed Pricing. Provide a fixed price for each:
• Base infrastructure setup (full environment, ready for agents and tools)
• Build one new AI agent (room, integrations, workflows, knowledge base, memory config)
• Build one new custom standalone tool
• Replicate an existing agent for a new user (separate memory, same structure)
• Add a new room within an existing agent (shared memory, new context)
• Add a new user to an existing room (provisioning only)
• Hourly rate for post-build support and modifications
4. Team and Capacity. Team size and roles. Concurrent build capacity. Turnaround for full setup vs. single agent/tool. Availability and earliest start.
5. Relevant Experience. Self-hosted AI infrastructure, agents with RAG and persistent memory, Claude/Gemini API, standalone tool deployments, multi-client isolated environments.
6. Security. Clients include law firms and regulated businesses. Describe your approach to data isolation, access control, and credential management.
Engagement model: Fixed price per build type, hourly for post-build work. We handle client communication, scoping, and project management. You get a defined spec. Expect multiple builds per month.

Address each section directly. No generic proposals.

I want someone who is good at using ai tools like sora or gemini veo3, or any other video generation Platforms that generate the best output. What I want are high quality shots that doesn't not look goofy for my brand.

This is quite an easy and quick input quick output project so I my budget is 10$

You will have to work in coordination with me I am going to proivde you with raw sample data to feed to the AI bot

I am going to provide the details as well regarding all the types ofvideos I want.

So ALL you gotta do is write the prompt and get a good quality video that does not look goofy. Reply AI is King so ik you read it full. THIS IS SUPER EASY

I am in need of ten videos and Each video you make you will get it approved by me and then move forward to the next video.

i am looking for a Made.com expert to help setup automation workflow for etsy new listings, i provide the keywords, tags, pictures and Make to create /uploading listing automatically, this may involve in Chatgpt build the titles with all the information i prepared.
let me know if you have experience about it

Hello Rahul,

My name is Milan, and I am from Cloudoffis. We are a fintech company based in Australia.

Currently, we are looking for an AI calling solution similar to a call center that can automatically call customers and explain our services. The tool should be able to handle approximately 1,000 customers per month.

We are interested in understanding how your AI voice agents can support our requirements.

Please let us know if you need any additional information from our side.

Hello!

do you know aws sagemaker
if so i am just interested in learning.
i need some one to clear my doubts and help me learn it.
Let me know ur availability and how much you charge.

Looking for 2 US based virtual assistants/ junior developers for our IT company. Looking to fill this position today.

• Are you based in the US?
• Share your LinkedIn Profile

Hi,

I’m looking for a UI/UX designer to design 3 mobile app screens that include a simple AI chatbot feature.

The main goal is to create a clean and intuitive mobile interface where users can interact with an AI assistant. Along with the design, there should be basic chatbot integration using a common free AI API so the interaction flow can be demonstrated.

Scope of Work:

Design 3 mobile screens

Include a chat-based AI interaction interface

Basic AI chatbot integration using a free API

Deliver the designs in Figma

Requirements:

Experience with mobile UI/UX design

Familiarity with chat interfaces or AI features

Ability to create clean and user-friendly layouts

Budget:
$60–$70 fixed

Timeline:
1-2 days

If you’ve worked on chat or AI-related interfaces before, feel free to share an example.

If you’re a good fit, I’ll reach out with more detailed requirements and flow for the chatbot feature.

The website domain was purchased via google workspace which is now owned by Squarespace. We are not able to connect the domain from squarespace to Shopify smoothly as the hosting platform seems to then push all systems to where we no longer receive emails to anything with that domain. We did try to connect them, but that is when we stopped receiving emails to the domain.

Hours:
7 hours per week (1 Hour each day Monday-Friday - Including Daily Meetings)

Tech Stack:
React
TypeScript 5
Supabase (PostgreSQL)
JWT Authentication
HikerAPI integration
NowPayments
Stipe
DigitalOcean
Hours
7 hours per week (1 Hour each day Monday-Friday - Including Daily Meetings)
At the end of each week, your work will be reviewed. If your performance meets our quality standards and is verified by our senior auditor, you will receive a $30 weekly bonus.
Eligible for profit-sharing after launch based on performance and continued involvement



Overview
We are looking for an experienced engineer to act as a Technical Auditor / Code Reviewer for our SaaS project.
Your primary responsibility is to ensure that our developers are producing high-quality, maintainable code and that work is not being submitted as AI-generated / copy-paste code without understanding.
You will not be responsible for building all features yourself. Instead, you will act as the technical checkpoint who reviews code submissions, verifies developer understanding, and reports findings clearly with evidence.

Key Responsibilities

1) Code Review & Quality Enforcement
Review all developer pull requests (PRs) before merge


Confirm code meets professional standards for readability, structure, and maintainability


Identify issues early (logic bugs, poor structure, missing error handling, security concerns, performance bottlenecks)


Request changes, reject poor submissions, and block merges when required


2) AI-Use & Developer Understanding Verification
Detect low-quality “AI-only” submissions and copy-paste work


Require developers to explain their implementation when needed (especially complex logic)


Flag developers who cannot explain their code, cannot debug, or produce inconsistent / non-maintainable work


3) Reporting & Accountability
Maintain a daily audit log (we provide a spreadsheet template)


Prepare a short daily summary for meetings (PRs reviewed, approved, sent back, issues found, risks)


Provide a weekly summary (repeat issues, dev performance patterns, technical risks, recommendations)


All findings must be supported by evidence links (PR links, review comments, commits, tests)


Our Standards / Rules

No review = no merge (PRs must be approved by the Technical Auditor)

Approvals must include at least one meaningful review comment (not just “LGTM”)

Developers must include steps-to-test in PRs

If a developer cannot explain the code when asked → the work is not accepted

Critical/high issues must be escalated immediately

What Success Looks Like
Poor quality or AI-only code gets blocked early

Codebase becomes more consistent, stable, and maintainable over time

Developers follow proper PR standards and improve from feedback

Management receives clear, verifiable reporting (links + severity + actions)

Required Skills / Experience
Strong experience as a senior developer (backend and/or full-stack)

Strong ability to review code and enforce standards

Experience working with GitHub PR review workflows

Ability to communicate clearly and give direct, actionable feedback

Comfortable calling out weak work and blocking merges when needed

Bonus: Experience auditing remote teams and detecting low-quality AI-generated submissions.
Tools We Use
GitHub (PR-based workflow)

Shared spreadsheet for daily logs + meeting summary (provided)

To Apply
Please include:
Your background and past projects

Example of how you do code reviews (screenshots or description is fine)

(Optional but preferred) A GitHub profile or code sample

We have a spreadsheet for you to follow, and in there you will be responsible for filling that out after each daily session; alongside making sure that when requesting changes on a Pull Request, you must leave clear, actionable review notes inside the PR (not just in chat or in the spreadsheet). Your review feedback must identify exactly what is wrong, where it is (file/function/line when possible), why it matters (risk/impact), and what ‘done’ looks like (how to verify or test the fix). If multiple issues exist, you must include a short PR ‘Review Summary’ with a checklist of required changes and clearly mark whether the PR is Approved or Changes Requested. The goal is that any developer can open the PR and immediately understand what to fix and how to confirm it’s resolved.

• Give one real example from your experience where you found messy code and how you fixed/handled it.

Hiring a data research / OSINT specialist to build a high-quality prospect database of business leaders and community influencers located in South Florida (Miami, Miami Beach, Aventura, Sunny Isles, Boca Raton, Palm Beach, etc.).

This is not a simple email scraping project. I’m looking for someone who can identify qualified individuals using multiple open-source signals, then structure and enrich the data into a clean prospect dataset.

Target output: 500–1,000 well-researched profiles.

Each record should ideally include:

First Name
Last Name
City
Company
Title
LinkedIn profile
Professional email (if publicly identifiable)
Industry
Source of identification
Notes / signal that qualified the prospect

Typical sources will include things like:

• leadership directories
• event host committees / sponsor lists
• nonprofit or organization boards
• business associations
• professional networks
• company websites
• public filings

The role requires someone comfortable using AI-assisted research workflows to accelerate discovery and validation.

Tools and methods I expect candidates to be familiar with include:

• Claude (required) for structured research workflows
• ChatGPT or similar LLMs
• LinkedIn advanced search
• Google operators / structured web queries
• contact enrichment tools (Apollo, Hunter, RocketReach, Clearbit, etc.)
• spreadsheet structuring and deduplication

Deliverable: a clean spreadsheet with 500–1,000 qualified prospects, well-structured and deduplicated.

To be considered, please include the following in your reply:

A brief outline of your research workflow for building a list like this

Specific examples of how you use Claude or other AI tools in OSINT research

The tools you would use for email discovery and enrichment

Your estimated timeline to produce a 1,000-person dataset

One sample row of what your final dataset would look like

Applicants who do not answer the five items above will not be considered.

If you have experience building high-signal prospect datasets rather than generic scraped lists, this will be a good fit.

how do I make an agent that goes out to competitors websites and looks for new content since the program last ran? What tool do I use to find competitors. I know a couple but not all. If an agent isn't the right tool tell me what I should use. Please be detailed and plan this first before you just give me an answer.

Let me think through this carefully before recommending an architecture.

## Planning: What You're Actually Trying to Do

There are two distinct problems here:

1. **Discovery** — Finding competitors you don't know about yet
2. **Monitoring** — Tracking known competitors for new content over time

These have different tool requirements and run at different frequencies. Let me think through each.

---

## Problem 1: Competitor Discovery

**Is an agent the right tool?** Yes, actually — this is a great agent use case because it involves multi-step reasoning: search → evaluate relevance → filter → return a ranked list. It's not a simple lookup.

**What tools does the agent need?**
- **Web search** (Brave Search API, Google Custom Search API, or Serper.dev) — to find "roadside assistance software" competitors, "dispatch platform for motor clubs", etc.
- **Scraper/reader** (Firecrawl, Jina AI Reader, or Playwright) — to pull a homepage and confirm it's actually a competitor vs. a directory listing
- **Claude itself** — to reason about whether a site is a true competitor

**How it works:**
1. You give it seed queries ("roadside assistance dispatch software", "motor club management platform", etc.)
2. It searches, reads landing pages, extracts company name/description
3. Claude scores each result for relevance
4. Outputs a list of confirmed competitors with URLs

This runs **on-demand** or **monthly** — not continuously.

---

## Problem 2: Content Monitoring (the core recurring task)

**Is an agent the right tool?** Only partially. Here's the nuance:

- If you just need to detect *that* new content exists → **a scheduled job with change detection is simpler and cheaper**
- If you need to *understand and summarize* what changed → that's where Claude/an agent adds value

**The right architecture is a hybrid:**

```
Scheduled Job (cron/n8n/Make)
→ Scrape competitor pages
→ Compare to last-run snapshot
→ If changed → call Claude to summarize/categorize the change
→ Output to Slack / dashboard / email
```

**Tools for the scraping/diffing layer:**
- **Firecrawl** — Best option. Crawls entire sites, returns clean markdown, has built-in change detection. Has an API.
- **Jina AI Reader** (`r.jina.ai/[url]`) — Dead simple. Prepend their URL to any page and get clean markdown back. Free tier is generous.
- **Playwright/Puppeteer** — Good if competitors use heavy JS rendering. More infra to manage.
- **Diffbot** — Enterprise-tier structured extraction, pricey but powerful.

**Tools for scheduling/orchestration:**
- **n8n** (self-hosted) — Given your automation pipeline work, this fits your stack well. Has HTTP, cron, Slack, and Claude nodes.
- **Make (Integromat)** — Easier if you don't want to self-host.
- **GitHub Actions** — Free, simple cron jobs, stores snapshots in the repo.

---

## Recommended Full Architecture

```
Phase 1: Discovery (runs once or monthly)
─────────────────────────────────────────
User provides seed keywords
→ Agent uses web search to find candidates
→ Agent scrapes/reads each homepage
→ Claude evaluates and ranks relevance
→ Outputs competitor list → stored in DB/sheet

Phase 2: Monitoring (runs weekly or daily)
──────────────────────────────────────────
For each known competitor URL:
→ Firecrawl or Jina fetches current content as markdown
→ Compare hash/diff against last stored snapshot
→ If diff detected:
→ Send diff to Claude with prompt:
"What changed? Categorize: new feature, pricing change,
new blog post, new integration, messaging change"
→ Post summary to Slack channel #competitor-intel
→ Store new snapshot in DB (Supabase, Postgres, or even a JSON file)
```

---

## Concrete Tool Stack Recommendation for You

Given your existing infra (Claude API, automation pipelines, likely some cloud deployment):

| Layer | Tool | Why |
|---|---|---|
| Orchestration | **n8n** | You're already building pipelines |
| Web search (discovery) | **Serper.dev** | Cheap, fast, Google results via API |
| Scraping | **Firecrawl** | Clean markdown output, handles JS |
| Summarization | **Claude Haiku** | Cheap for diffs, fast |
| Storage | **Supabase** or flat JSON | Store snapshots + change history |
| Alerting | **Slack** | You're already using it |

---

## What I'd Build First

Start with Phase 2 (monitoring) for your known competitors — it's immediately useful and validates the pipeline. Phase 1 (discovery) can be a separate, lower-frequency job once the core loop works.

Want me to build out the actual code for either phase — the discovery agent or the monitoring pipeline?