We are seeking a skilled web developer to create a website focused on collecting data regarding violations, unethical treatment, and illegal conduct faced by dealers at auto auctions in the USA. The website will serve as a platform for users to report incidents, share experiences, and contribute to a database of information. The ideal candidate should have experience in building user-friendly interfaces, database management, and data security. Your expertise will help us bring awareness to these issues and support the dealer community.

We are looking for an expert backend developer and automation engineer to extend an existing, production-grade Model Context Protocol (MCP) server and overhaul its orchestration layer. The headline correction for this project: the existing Lawfather MCP is to be retained and extended, not rebuilt. It already exposes deterministic, parameterized Playwright tools for every required county portal (District Clerk, HCSO, HCDAO) and a client database. Those backend tools are the reliable layer and are not the source of the instability this project exists to fix. The instability lives entirely in the orchestration layer — the model-driven layer that decides when and how to call the tools. The fix is to move deterministic control out of model-followed prose and into code, and to host the agent on an always-on machine with persistent memory. Core Project Principles • Extend, Don't Rebuild: Retain and extend the existing MCP; do not re-implement portal scrapers from scratch. • Code Over Prompts: Deterministic logic lives strictly in tool code, never in instructions the model must remember each session. • No Caller Loops: Batch operations must run to completion server-side. No operation may require the caller (model) to loop. • Agnostic Architecture: The system must remain model-agnostic and host-agnostic. No single provider — Anthropic, OpenAI, Z.ai/GLM, or Nous — may be a hard dependency. • Privilege First: Client data stays on owned hardware; the model is never the gatekeeper of which case a file belongs to. Existing Tool Inventory (To Be Inherited As-Is) The following tools already exist on the production MCP (containerized on a local Synology NAS) and are in daily use. Re-deriving their behavior is completely out of scope: • hcdc_get_docket: Court settings by date range + bar number (District Clerk). • hcdc_check_filings: Per case: standard defense filings present vs. missing. • hcdc_download_filings: Images-tab documents: bulk OR selective by filters; dest_subfolder; dry_run. Note: The parameterized download tools already cover most retrieval requests. "All filings," "this filing," "all subpoenas," "all resets," and "everything filed that day" are argument combinations on this tool, not separate features. • hcso_locate: Defendant custody location (facility / floor / pod) by SPN. • hcdao_grab_file: Download a single named file from the DA portal Files tab. • hcdao_download_discovery: Batch / delta discovery download from the DA portal. • hcdao_download_media_alert: Batch-download files listed in a 'New Media Available' portal email. • hcdao_case_summary: Scrape the Case Jacket quick summary / DAO narrative. • hcdao_plea_offer: Scrape current plea offer + full offer history. • hcdao_assigned_ada: Assigned ADA name / email / phone on a case. • lookup_client / list_clients: Resolve / list clients from the shared client database. Scoped Work (Paid Deliverables) 1. County Case Resolver (New Tool): Find a case from partial identifiers — any subset of (name, SPN, DOB, court, cause). Searches county systems (not just the local client DB). MUST return a ranked candidate list for the user to choose from; MUST NEVER auto-select. Wrong-defendant selection is a privilege failure, not a cosmetic bug. 2. Latest-Version Retrieval: Add scope=latest to hcdao_grab_file so 'most recent' selects the newest among supplements instead of the first match. 3. Async Transcribe Tool (Skill to Tool Promotion): Build a deterministic MCP tool using Gemini 3.1 Pro Preview for transcription, followed by a second pass that sends the transcript back with case context for cleanup (speaker mapping, defense-moment preamble). Long-running: implement as an async job (submit to job id to poll to fetch), NOT a synchronous call. 4. OCR Tool (Skill to Tool Promotion): Implement a readability check on ingest. If a document is not cleanly readable, FLAG it and ASK before sending to Gemini 3.1 Pro Preview for OCR. OCR must be gated and confirmed, never automatic. 5. Server-Side Batch Jobs: Move all chunk, loop, delta, and throttle logic OFF the caller and INTO the tool code. One call runs the batch to completion. 6. Queued HCDAO Fixes: For hcdao_download_discovery, add a portal_ids filter for targeted single-file pulls and a custom output-path / Drive-folder destination feature. Known Portal Quirks to Handle from Day One • hcdc_get_docket returns a broader date range than requested; results must be filtered to the requested window. • hcdao_download_discovery delta detection is blind to files organized into dated subfolders and must be explicitly handled. • Court DG7 does not surface through standard bar-number docket lookup and requires separate handling. • The Playwright Node.js driver subprocess can die silently while database tools respond; you must health-check the driver proactively. Orchestration, Host Layer, & Deployment Topology • Target Host: Hermes Agent (Nous Research) running as the persistent shell, providing persistent memory, the scheduler, and messaging surfaces. The MCP server will plug directly into it. • Agnostic LLM Routing: Default the agent/dispatch role to the most reliable tool-calling model (currently Claude Opus). Route bulk, non-critical generations (draft summaries, transcript cleanup) to a cheaper model (e.g., GLM-5.2). No provider may be hard-wired. Per-tool pins are allowed strictly for transcription/OCR tasks (pinned to Gemini 3.1 Pro Preview). • Memory Fencing: Hermes's persistent memory and learning loops must remain enabled to accumulate facts and user preferences. However, the agent must be strictly fenced from self-editing or rewriting its own mechanical execution paths (portals, downloads, filings), which must remain frozen in MCP tool code. • Hardware Deployment Infrastructure: • Always-on Brain: M1 Pro MacBook Pro (16 GB, mains-powered, lid open) running the Hermes gateway, Messages.app, and a BlueBubbles iMessage bridge. Must be fully automated via launchd services to handle headless crash recovery, auto-login, and sleep prevention (pmset autorestart / caffeinate). • Tools and Storage: Synology NAS (10.0.0.149) hosting the Lawfather MCP container, local client folders, and Drive sync. • Private Network: Tailscale mesh across all devices for secure remote access without open inbound ports. Acceptance Criteria for Sign-Off • No batch operation requires the caller to iterate. • The case resolver returns ranked candidates and never auto-selects. • Transcription runs seamlessly as an async two-stage job surviving multi-hour files without timing out. • OCR never fires automatically on low-readability files without gated confirmation. • Zero regressions on the existing MCP tool inventory. • The Resiliency Test: The full stack successfully restarts completely unattended after a host reboot or simulated power loss, and is reachable via iMessage/SMS immediately after. • Self-editing is fenced on mechanical download/filing paths. Hard Guardrails • Privilege: Downloads route strictly to the correct client folder; a wrong-case match is treated as a severe defect, not a warning. Privileged audio/discovery data stays on owned hardware where the chosen model allows. • Determinism: Repeatable steps live entirely in tool code, never in prompts. • Agnosticism: Model and host layers must remain fully swappable without modifying the core MCP tools. Before quoting "done," you will be expected to confirm live portal behaviors regarding District Clerk document labels, DA portal stable identifiers, and county search surfaces. How to Apply Please submit a proposal detailing your specific experience with MCP architectures, Playwright browser automation, and macOS/Docker DevOps automation. Anti-Bot Filtering: To prove you read this entire scope, please start your application with the phrase "PROTECT THE LAW" in all caps. Automated or generic copy-paste applications will be instantly rejected.

We are seeking an expert, hands-on Corporate Trainer to deliver a highly interactive, advanced online training course on Open Source Intelligence (OSINT). The curriculum closely follows an advanced framework designed to take security professionals, analysts, and investigators to the next level of public data harvesting, analysis, and threat intelligence. This is a remote, online, live instructor-led engagement. The ideal candidate isn't just an academic; you are a practitioner who knows how to navigate real-world investigative constraints, maintain strict operational security (OPSEC), and teach others how to transform raw big data into highly actionable intelligence reports. Key Responsibilities: Deliver 21 hours of live, interactive online training (split across logical multi-day sessions). Facilitate hands-on lab exercises, guiding participants through tool configuration, data mining, and live simulations. Clearly explain the balance between technical execution and the strategic "Intelligence Cycle" framework. Core Modules to Cover: Environment Setup & OPSEC: Configuring advanced toolkits, obfuscating activities, using proxies, and protecting identity while researching third parties. Deep Data Mining: Legitimate scraping practices, big data analysis, and securely/safely mining the Dark Web. AI Integration: Utilizing artificial intelligence tools for predictive analysis, facial recognition, and sentiment analysis. Targeting & Profiling: Advanced strategies for mapping out corporate structures/hierarchies, investigating individuals, and tracking consequential actors (threat assessment/criminal profiling). Financial Investigations: OSINT techniques for tracking money, black markets, credit card transactions, and cryptocurrency ledger tracking. Required Qualifications & Experience: Proven Training Experience: Must have a strong track record of delivering technical training to professional corporate, law enforcement, or cybersecurity audiences. Deep Technical Domain Expertise: Exhaustive familiarity with modern OSINT toolkits, advanced scraping methodologies, crypto-tracking tools, and Dark Web navigation. Engaging Delivery Style: Ability to avoid "death by PowerPoint." We require someone who leads via interactive discussions, live-lab troubleshooting, and highly practical exercises. Professionalism: High standards for corporate compliance, ethical data boundaries, and clear communication. Course Format: Type: Live, instructor-led online training. Duration: 21 total hours of instructional time. Audience: Security analysts, corporate researchers, and investigators.

I’m the owner of a waste paper and scrap commodities brokerage. I need an experienced no-code developer to audit, clean up, and significantly expand my existing Airtable + Softr + DocuPotion system into a professional operations platform. Project Scope: • Review and fix current setup (broken flows, redundancies, etc.) • Build a full trading workflow (Buy/Sell matching, PO/SO, shipments, inventory by grade) • Document generation (Invoices, Packing Lists, Booking Sheets, etc.) • AP/AR, claims, commissions tracking • Professional dashboards and reports • Proper user permissions • Make it clean, scalable, and easy to use Budget: $1,800 – $2,200 fixed price (depending on exact scope and experience) Timeline: Looking for clear milestones and completion within 4–8 weeks. Required Skills: • Strong expertise in Airtable + Softr • Make.com / Zapier automations • DocuPotion or similar document tools • Experience with trading, inventory, or brokerage systems is a big plus Please apply with: • Examples of similar Airtable/Softr projects (especially trading or operations systems) • Your approach and estimated timeline • Any questions about the current setup Serious, professional freelancers only. Prefer someone who communicates clearly and delivers high-quality work.

Here’s what I’m doing: I’m looking to make our lead research process better and get it fully automated. Here’s the process as it stands now: 1. We start with a list of companies and their websites. 2. For each one: scrape their site for shipping facility locations — warehouses, DCs, manufacturing plants. Check their locations/facilities pages and their careers page. Job postings for warehouse, forklift, shipping & receiving, production roles confirm an active facility and usually give the address. Careers pages are inconsistent — some are static HTML, some run through Workday, Greenhouse, iCIMS, or other ATS platforms that don’t always let scrapers in. I need someone who’s dealt with that before and knows how to handle it, not just scrape the easy ones and skip the rest. 3. Identify contacts matching these titles: Transportation Manager, Director of Transportation, Logistics Manager, Director of Logistics, Traffic Manager, Senior Transportation Manager, Senior Logistics Manager, Logistics Sourcing Manager, Logistics Procurement Manager, Transportation Procurement Manager, Transportation Sourcing Manager. 4. Score every contact for whether they’re actually still there — not just whether they show up in a database. Apollo and ZoomInfo are full of people who left or retired years ago but still show as active. The scorecard has to catch that before it goes any further. 5. Enrich the contacts that pass: email and phone. Phones: enriched numbers are usually garbage with no way to verify them. Scrape the company website for the corporate number instead. No 800 numbers — those are dead-end customer service lines. I want a local number so I can call and ask whether the person is still there. I know distinguishing a corporate number from an 800 number on a scraped page isn’t always straightforward — some sites only list the 800 number, some bury the local number in a footer or contact page. Tell me how you’d approach that. Emails: fine as-is — enrich, run through NeverBounce. Non-negotiable: every contact comes out with a score and the specific reason behind it. No black box. I need to see why someone scored high and why someone else scored low. End state: I drop in the list, it runs, output comes back scored and enriched with facility locations attached. Fully automated, repeatable.

I am real estate investor in need of help to set up and maintain a data scrap app for lead research. App would download PDFs and scrap data, then dump into a spreadsheet. Once data is scraped and in excel, then app needs to go to 4 differnet platforms to process and optimize the leads. Looking for long term help maintaining the programs.

This is what I am looking for: Long Beach, CA - all dispensary info ~10 fields to fill-in Los Ángeles, CA - all dispensary info ~10 fields to fill-in Please let me know how you would approach this within your tech stack. I also need a quick turn around, so let me know how long it would take you to complete? There should be about 200 records in total. Ive attached a copy of my table for your review. Thank you!

We run a B2B SaaS platform serving specialty running retailers. Part of what we do is pull inventory data from brand-side wholesale portals so our retailers can see live vendor availability alongside their own POS data. We have valid B2B accounts on these portals. We have around 15 of these scrapers running today and want to add more, plus we need someone reliable to fix existing ones when a vendor changes their site. What you'll build * New scrapers for brand B2B portals to extract inventory data (SKU, size/color/width, quantity, backorder dates). Output goes into a MySQL 8 schema we'll provide. * Product image scrapers for both B2B portals and public-facing brand sites. * Fixes and updates to existing scripts when a portal changes (auth flow updates, endpoint changes, HTML/JSON structure shifts). Stack PHP 8.4 is our primary language. Existing scrapers are based on PHP, Selenium and Python. Use whichever HTTP library you find cleanest. Selenium, Playwright, or Puppeteer are welcome when a portal has complex JS-driven rendering or session flows that don't work with straight HTTP. Node or Python is fine for those. Scripts run on a Ubuntu server on GCP. Requirements * Comfortable inspecting network traffic to figure out how an undocumented portal actually works behind the login page. Most brand portals have a JSON API driving their UI even when it isn't publicly documented. * Willing to do both greenfield builds and maintenance on existing scripts. Engagement Hourly and ongoing. Volume varies week to week. Several existing scripts need to be reworked and occasionally a new script needs to be created for a new brand. When an existing breaks, it's usually because a vendor changed their portal without notice, and we need to be able to reach you promptly. We're prioritizing quality and a successful outcome over cost. If you're the right fit, we're not looking to bid you down. To apply Share one or two examples of authenticated portal automation you've built, ideally something involving JWT or cookie/session auth.

I have a file with the name, address, zip code and county of 5,550 retail businesses in the U.S., such as Quick-Serve Restaurants. 650 of the total 5,550 are rented units or suites of a larger building, while the remainder appear to be free standing buildings. I need a highly credible estimate of the square footage for each free standing building, and for as many of the rented units / suites as possible. For all locations, I need: Year Built, Roof Shape, Roof Cover, Number of Stories of the building. Finally, I need Construction Type where available.

I need a Discord bot built that posts positive expected value (+EV) sports betting picks to a channel, plus esports match schedules/scores. I have the full technical spec already written — APIs picked, EV formula defined, architecture outlined. This is integration work, not research. What's provided: Working API keys for The Odds API (sports odds) and PandaScore (esports stats) — I'll provide on hire Complete EV calculation formula (Python, ready to use) Full architecture spec (polling schedule, caching approach, bookmaker lists) Core deliverables (must-have, fixed price): Discord bot that connects to my server Scheduled polling (1-2x daily, not live) of The Odds API for sports moneylines + player props EV calculation comparing soft-book odds (DraftKings/FanDuel/Caesars) against Pinnacle as the sharp reference Caching layer (database) so the bot never calls the API live per Discord command Bot posts flagged +EV picks to a designated channel, above a configurable EV threshold PandaScore integration for esports match schedules/results posted to a separate channel Basic error handling with retry/backoff (no runaway API costs) Documentation: how to run it, how to add new sports/leagues, how to change the EV threshold Stretch goal (only if time allows within budget — not required): OddsPapi integration for esports moneyline EV detection (this API is unverified/free-tier, so treat as experimental) Tech preferences: Python or Node.js, whichever you're stronger in. Open to your hosting recommendation (needs to run 24/7 cheaply). Budget & Payment Structure: $500 fixed price, split into milestones Milestone 1 ($150) — Foundation: Discord bot connects to server, The Odds API integration pulling live sports odds data, caching layer working. Paid on demo of working data pull + bot online in server. Milestone 2 ($200) — Core Logic: EV calculation implemented and verified accurate against manual spot-checks, picks posting to Discord channel automatically on schedule. Paid on demo of at least 3 correctly-calculated +EV picks posted live. Milestone 3 ($150) — Esports + Polish: PandaScore esports schedule/results integration, error handling/retry logic, documentation delivered. Paid on final delivery + handoff call. To apply: Tell me your estimated hours for each milestone, and confirm you've worked with Discord bots + REST API integrations before.