I’m looking for someone to record a short video (30-45 seconds) where you’ll be describing the benefits and highlights of a business program. You’ll receive a script with clear talking points — your job is simply to present it confidently and naturally on camera. This is a paid presentation, not a personal review or experience. You’re helping us communicate the value of the program like a host or spokesperson would. What you’ll do: • Read and present a short script (30-45 sec) • Record using your smartphone (no fancy gear or editing needed) • Send raw footage with good lighting and sound Requirements: • Clear, natural delivery • Comfortable speaking on camera • Ability to follow simple instructions • Must be based in the U.S. Just be confident, fluent, and look presentable — we want the message to feel trustworthy and smooth. Thanks!

Join our immigration law firm in NYC to co-present a 15-minute government API demo. We need a Python/Flask developer to showcase our custom CRM's capabilities. This is a short-term engagement ideal for someone with experience in API integration and presentation. The role involves collaborating with our team to effectively demonstrate the API's features to a government audience.

I’m looking for someone to record a short video (30-45 seconds) where you’ll be describing the benefits and highlights of a business program. You’ll receive a script with clear talking points — your job is simply to present it confidently and naturally on camera. This is a paid presentation, not a personal review or experience. You’re helping us communicate the value of the program like a host or spokesperson would. What you’ll do: • Read and present a short script (30-45 sec) • Record using your smartphone (no fancy gear or editing needed) • Send raw footage with good lighting and sound Requirements: • Clear, natural delivery • Comfortable speaking on camera • Ability to follow simple instructions • Must be based in the U.S. Just be confident, fluent, and look presentable — we want the message to feel trustworthy and smooth. Thanks!

I’m looking for someone to record a short video (30-45 seconds) where you’ll be describing the benefits and highlights of a business program. You’ll receive a script with clear talking points — your job is simply to present it confidently and naturally on camera. This is a paid presentation, not a personal review or experience. You’re helping us communicate the value of the program like a host or spokesperson would. What you’ll do: • Read and present a short script (30-45 sec) • Record using your smartphone (no fancy gear or editing needed) • Send raw footage with good lighting and sound Requirements: • Clear, natural delivery • Comfortable speaking on camera • Ability to follow simple instructions • Must be based in the U.S. Just be confident, fluent, and look presentable — we want the message to feel trustworthy and smooth. Thanks!

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.

We have a small Python-based machine learning inference service built with FastAPI and scikit-learn. The model was trained on structured tabular data, but our prediction endpoint is currently failing because of feature mismatch errors between the training pipeline and incoming API payloads. We need an experienced ML/MLOps engineer to quickly debug the issue, clean up the preprocessing logic, and make the `/predict` endpoint work reliably again. The goal is not to retrain the full model or build a large system. We only need a focused fix: review the existing model artifact, inspect the expected feature columns, update the API preprocessing code, and provide a short explanation of what was wrong. Bonus if you can also add a simple test request example or basic validation for missing fields. This should be a quick one-time task for someone comfortable with Python, scikit-learn, Pandas, FastAPI, and ML deployment workflows.

Need an experienced developer to integrate an AI-powered feature into an existing application. Small scope, fast turnaround

I am looking for a Python and SAS expert. Knowledge of data bricks helpful. Experience as a Statistician or programmer in both Python and SAS is helpful. New to Python-Looking for a consultant to compile/recommend a list of Python libraries suited for statistics and data analysis. Individual should have SAS experience /Statistics to be able to find equivalent packages in Python. List must include the following; Package in Python that is an equivalent to Data cleaning in SAS for very large data sets Python package-to import and export files to Excel Binwidth in Python that is a SAS equivalent Graphics/histograms/plots, visualizations Survey select procedure in SAS equivalent in Python Random Number generation in SAS equivalent in Python Other packages for statisticians to complete their work that is equivalent to the SAS STAT package procedures. Include steps on how to install the packages and the location to get the files from. Any recommended memory size for laptops. Project may happen in phases. Phase 2 may include training on basic Python syntax after the list has been compiled.

We're looking for a developer to build a lean, working Proof-of-Concept of an automated pipeline that ingests podcast episode audio, generates a clean transcript with speaker diarization and timestamps, and uses an open-source NotebookLM alternative (Notex or Open Notebook) to automatically produce a suite of repurposed content assets — show notes, episode summaries, social media posts, blog drafts, and pull quotes. The goal is to validate the end-to-end workflow on 2–3 sample episodes, not to build a full production platform yet. We want to see the plumbing work cleanly before investing in scale. Envisioned stack: n8n for orchestration, a speech-to-text API (Deepgram, AssemblyAI, or Whisper), a lightweight DB (Supabase or PostgreSQL), and an open-source NotebookLM alternative as the content generation engine. The whole system should be self-hostable via Docker. We're open to the developer's recommendations on the best tools and tradeoffs. Deliverables include a working n8n workflow, Docker-compose setup, a short README, demonstration on 2–3 sample episodes we provide, and a brief written recommendation on Notex vs. Open Notebook for scaling this pipeline to ~500 episodes/year. Required skills: n8n (or similar orchestration), speech-to-text APIs, Docker / self-hosted deployments, hands-on experience with NotebookLM alternatives or RAG-based content engines, LLM prompt engineering for structured output, and PostgreSQL / Supabase basics. Nice to have: Prior podcast or media-tech automation work, pgvector / RAG experience, structured output via JSON schema or function calling, and experience scaling automation pipelines. To apply, please include: a short overview of your automation / AI pipeline background, specific experience with n8n + STT APIs + open-source NotebookLM alternatives, links to GitHub or prior workflows, a 2–3 sentence note on whether you'd recommend Notex or Open Notebook for this use case and why, and your estimated turnaround time. This is a fixed-budget POC (~$100). If the workflow is clean, reliable, and well-documented, we plan to expand it into a full production build (client portal, human-in-the-loop editor, admin dashboard, scaling to 500+ episodes/year) with a significantly larger budget.

GenEnterprises is a healthcare technology company seeking a specialist to validate patient-access APIs. The role involves ensuring seamless integration with healthcare systems and verifying API functionality. The ideal candidate will have experience in healthcare technology and API validation, with a strong understanding of data security and compliance. What you'll do (per vendor) -Discover the vendor's SMART-on-FHIR endpoints from their conformance statement (/metadata) or .well-known/smart-configuration. -Complete a provided configuration file with the correct authorize/token/FHIR endpoints and patient-access scopes. -Run our provided validation harness against the vendor's sandbox and prove the full authorization flow completes end-to-end — either public-client (PKCE) or confidential-client (client secret) depending on the vendor: authorize → code → token exchange → patient context. -Document any vendor quirks (audience value, extra scopes, non-standard discovery, sandbox limits). What we provide -The validation kit (a small Node harness + config template + rules — runs locally). -The sandbox credentials for the vendor: a client_id, plus a client_secret for confidential vendors, and the registered redirect URIs. -Sandbox access details and the specific vendor to start with. Deliverables -A completed connector configuration — patient-scoped only. Public vendors carry no secret; confidential vendors carry only the sandbox secret we provide (never a production secret). -The endpoint-discovery source you used (the /metadata or .well-known URL). -Evidence of a successful sandbox run: the harness "PASS" page/screenshot and console output showing an access token and a resolved patient ID. -Confirmation the registered redirect URI matches the config exactly. -Notes on any vendor-specific quirks. Requirements -Hands-on experience with SMART-on-FHIR patient-access authorization. -Solid understanding of OAuth 2.0 Authorization Code, both PKCE (public client) and client-secret (confidential client) flows, and when each applies. -Comfortable reading FHIR R4 conformance statements to locate endpoints and scopes. -Enough Node.js to run a provided harness (npm install, edit a JSON config, npm start). -Precise and evidence-driven — you prove things work rather than assuming. Nice to have -Prior integration with EHR/health-data vendors (Veradigm/FollowMyHealth, athenahealth, Epic, Cerner/Oracle Health, Aetna, or similar). -Familiarity with patient access APIs and healthcare interoperability standards. What you will NOT have access to (and won't need) -No production systems, no real patient data — everything is sandbox/synthetic. -No access to our codebase or database — the kit is fully standalone. -No vendor account administration — we own the developer registration; you receive the sandbox credentials only. This keeps the engagement clean and low-risk for both sides. An NDA is required before we share the kit. Engagement -Fixed price per vendor. First vendor is a paid pilot (budget: $[SET BUDGET]); strong work leads to ongoing per-vendor engagements. -Remote, flexible hours. Turnaround for the pilot is typically a few days once you have the sandbox credentials. To apply — please answer these (applications without answers will be skipped) -Which EHR vendor sandboxes have you completed a SMART-on-FHIR patient-access OAuth flow against (PKCE or client-secret)? Name them. -Given only a FHIR base URL, how do you find the authorize and token endpoints? -In one or two sentences: what is the difference between a public and a confidential OAuth client, and when would you use PKCE?