We are seeking an experienced developer to build an AI agent. A description of the agent is provided in the posting. The ideal candidate will have a strong background in AI development and be able to work on various platforms. The agent will be need to extract information from multiple sources (documents and spreadsheets) and tools (Salesforce, Apollo, other) to create a final output.

I'm looking for an experienced AI automation engineer to build an MVP for an internal AI-powered M&A Deal Intelligence platform. Project Goal I want to build a system that helps my M&A advisory firm identify acquisition opportunities, research businesses and owners, and prepare me for outreach—all while integrating with our existing Zoho CRM. MVP Requirements - Find businesses from selected public sources using lawful methods. - Gather company information (website, industry, location, products/services, leadership, estimated size, etc.). - Research owners and decision-makers using publicly available sources (company websites, LinkedIn, news, press releases, interviews, social media, public business records where appropriate, etc.). - Generate an AI briefing before I contact a prospect that includes: - Company summary - Owner background - Personalized conversation starters - Suggested questions - Potential acquisition opportunities - Risks or notable findings - Automatically create or update records in Zoho CRM with the research and AI summaries. - Be designed so additional data sources, automations, and AI capabilities can be added over time. Preferred Skills - AI agents - Python - OpenAI API (or similar LLMs) - Web scraping - Playwright - Apify - n8n or similar automation platforms - API integrations - Zoho CRM integrations - Experience building AI research or deal sourcing tools is a major plus. When Applying Please include: - Examples of similar AI automation or research systems you've built. - The technology stack you'd recommend. - How you'd approach this project. I'm looking for a long-term technical partner to continue expanding this into a comprehensive AI platform for our M&A advisory business if the initial project is successful.

Engagement: Contract, phased. Phase 1 is a paid, standalone feasibility engagement with a go/no-go gate before Phases 2 and 3 are commissioned. Duration: Phase 1 approx. 1–2 weeks. Full scope approx. 1–3 months. Your compensation and evaluation are tied to the rigor of the method, not to what the data shows. Wherever the evidence is insufficient to support a claim, documenting that is a required and fully compensated deliverable. Our product architecture already assumes selective output: we display a result only where the evidence supports one and return "insufficient evidence" everywhere else. Both halves of that map are load-bearing, and we have no commercial preference for how the cohorts divide between them. We are not looking for a trading signal, an alpha model, or a predictive engine, and work that drifts in that direction is outside the scope of this engagement. What we want is a rigorous, classical event study: given this category of news event and this type of stock, what is the historical distribution of abnormal returns, with honest confidence intervals, and does it hold out of sample? We would rather fund a well-specified analysis whose answer surprises us than a poorly specified one that confirms anything. About Us We are a news curation and context firm serving US equity markets. We deliver curated, context-enriched headlines to over 200 institutions and more than 3,000 institutional traders on both the buy and sell side. We have been doing this for decades and are among the fastest on the street. Two features of our data matter for this work: Our archive is curated, not scraped. Roughly 2,000 items per day pass an editorial filter applied by former traders, PMs, and analysts. The same editorial logic produced our historical archive and our live feed, so there is no train/live mismatch in the sample definition. Timestamps are reliable. We are frequently first to print. Delivery timestamps closely track information arrival. Every headline in the archive carries an AI-generated sentiment label, a directional strength score, an accompanying model confidence score, and classification into up to 25 taxonomies. These labels are inputs to your analysis. Assessing their stability over time is in scope; generating new ones is not. We are building a retail-facing product on top of this archive. Your work establishes its empirical foundation. The Core Question For a given cohort (defined by some combination of stock characteristics, news taxonomy, and sentiment tier) what is the historical distribution of abnormal returns over six horizons following a headline, and how much of that distribution is statistically distinguishable from the cohort's unconditional baseline? The six horizons: Horizon Window 1 ~60 minutes post-headline 2 ~120 minutes post-headline 3 End of day (4:00 PM ET close) 4 Overnight / session gap 5 3 trading days 6 5 trading days Horizon definitions must be measured in trading time, not calendar time, with defined handling for headlines crossing outside regular session hours. Scope of Work Phase 1 : Methodology and Feasibility (paid, standalone, go/no-go) Deliverable: a written methodology document sufficient for a second statistician to reproduce your approach, plus a feasibility assessment against a data sample we provide. Must address: Abnormal return specification. Definition of the market/sector model used to isolate idiosyncratic return. Raw returns and own-volatility normalization alone are not sufficient. Volatility baselines. Time-of-day-conditional volatility estimation for intraday horizons. A trailing daily sigma scaled to one hour will not do. Cohort structure. How to group and condition. Our 25 taxonomies were built for editorial routing, not statistical pooling; some likely merge, some likely split. Our team will work with you on this as we know the market behavior and expect to inform the groupings. Thin-cell handling. Ticker-level cohorts will often have very few observations while pooled cohorts have many thousands. We expect a partial-pooling / hierarchical or empirical-Bayes approach, or a reasoned argument for a different treatment. Minimum evidence threshold. Derive our display threshold as a function of a stated minimum detectable effect and target power. We want the reasoning, not just a number. Dependence structure. Treatment of overlapping estimation windows, multiple headlines on the same name in the same window, and cross-sectional correlation on macro days. Baseline construction. Every cohort needs its own unconditional baseline. Absolute hit rates are not meaningful on their own. Validation design. How you will establish that estimates hold out of sample. Phase 2 : Estimation and Validation Full historical estimation across all valid cohort × taxonomy × sentiment × horizon permutations. Walk-forward out-of-sample validation. Freeze the estimates on an early period, run forward on data you have not touched, and report how many qualifying cohorts held up and by how much the effect decayed. This is a required deliverable and a primary measure of the project's success. Multiple-comparison control appropriate to the number of cells tested. Deliverable: a static reference matrix (CSV or Parquet) containing every cohort, its point estimate, confidence bounds, sample size, unconditional baseline, and qualification status across all six horizons. Deliverable: an explicit register of cohorts where no reliable effect was found. Phase 3 : Logic Transfer Documented mathematical logic and a reference implementation (Python) sufficient for our engineering team to reproduce the calculations in production. The normalization scheme converting validated estimates into a bounded 0–100 display score. Our current thinking is that this should derive from the lower confidence bound on the effect over baseline, so that sample size enters through the statistics rather than as a hand-set weight — we welcome a better proposal. Working session with our engineering team and written sign-off. -Data and Tools- All data is provided by us. No data sourcing, licensing, or acquisition is required on your part. Historical curated headline archive with AI-generated sentiment, strength, confidence, and taxonomy labels. Historical pricing data , which we supply: minute and 5-minute OHLC bars for intraday windows, daily aggregates for multi-day windows, and participant timestamp data covering pre-market (4:00 AM – 9:30 AM ET) and post-market (4:00 PM – 8:00 PM ET) sessions. Extraction volume and format can be adapted to your requirements. Tell us what you need and how you want it delivered. Language and libraries are your choice. Python or R both fine. Methodological Constraints All methods must be classical, auditable, and explainable line by line. This is a retail-facing product; every number we display must be defensible to a regulator, a journalist, or a skeptical user. No black-box or machine-learning models in the estimation pipeline. No neural networks, gradient boosting, or ensemble learners. No LLM-assisted analysis or interpretation. To be unambiguous: standard econometric and statistical tooling is expected and welcome. Hierarchical and random-effects models, empirical Bayes, shrinkage and regularization, bootstrapping, and power analysis are all classical methods and all in scope. The constraint is on opacity, not on sophistication. Explicitly Out of Scope Production software development, database design, or streaming pipeline work. Any form of signal optimization, strategy backtesting, or portfolio construction. Generating or revising sentiment/taxonomy labels. Product, UI, or commercial strategy. Who We're Looking For Strong fit: Formal training in econometrics, statistics, or empirical finance. Direct experience with event-study methodology — abnormal returns, estimation windows, cross-sectional aggregation, the Brown & Warner / MacKinlay / Kothari & Warner literature. Published or applied work measuring the price impact of information events. Comfortable delivering a null result and defending it. Willing to tell us when part of our framing is wrong. Our team is made up of former buy-side and sell-side traders, PMs, and analysts and we know the market, we do not claim to be statisticians, and we want to be pushed back on. Poor fit: Quantitative researchers whose instinct is to iterate until something profitable appears. Anyone who would treat "no effect found" as a problem to be engineered around. Generalist data scientists whose primary toolkit is ML. Anyone who would execute the methodology above without questioning any of it. Academic researchers, finance PhD candidates and postdocs, and practicing econometricians consulting on the side are all encouraged to apply. Screening Task (Paid) We will shortlist a small number of applicants and pay each for a 2–4 hour written task at their stated rate before any larger commitment. The task: You have approximately 5,000 news events across approximately 800 US equities. Propose a methodology for estimating the probability of a directional 3-day abnormal return, conditional on event category and stock characteristics, where per-ticker event counts range from 3 to 400. Address: how you define abnormal return; how you handle thin cells; how you set a minimum evidence threshold for reporting a result; and how you validate that estimates hold out of sample. Two pages. No code required. We are evaluating reasoning and judgment, not polish or length. To Apply Please include: A brief description of the most relevant event study or abnormal-return analysis you have conducted, including what the data showed and what you concluded. One example of a project where your conclusion was that the effect was not there. What you did, and how you presented it to whoever commissioned the work. Your view on how to handle cohorts with very few observations, in two or three sentences. Any published work or writing samples. Availability and rate. Applications that restate this posting back to us will not be reviewed. We would rather see one paragraph of real disagreement with our approach than three pages of agreement. An NDA will be required before data access.

I want a program that has similar functionality to the program that I'm linking to here: https://github.com/TalkDocInc/optimist-prime It should have a similar ability to provide useful words in different situations, as well as provide guidance and advice. It should take actions in the real world as well. Before you think this is a nonsense project, try out the program. It does in fact provide useful words for different people, when you input into it. Currently works best with Mac. Your task is to produce a program that I will own in entirety, as an individual. Make sure that it can influence reality such that I can raise investment for my various companies, modify reality, etc. Please run the program locally to test its functionality, before applying for the job.

Title: Generative Engine Optimization Expert for One-Hour Virtual Workshop We are seeking an experienced Generative Engine Optimization practitioner to deliver a practical one-hour virtual workshop to leaders from several vertical-market software businesses. This is not a request for ongoing SEO services. We want someone who understands how companies and products become discoverable, recommended, and cited by ChatGPT, Gemini, Perplexity, Google AI Overviews, and other AI-powered search experiences. Format: 45-minute virtual presentation 15-minute Q&A One brief preparation call Approximately 15–30 attendees $500 fixed fee The session should cover: How GEO differs from traditional SEO How generative engines discover, evaluate, mention, and cite companies What is proven, what is evolving, and what is currently speculation Practical examples of effective GEO Actions a vertical-market software company can take in the next 30 days How to measure AI visibility and progress The presentation should be practical, objective, and educational. It should not be a sales presentation for an agency, platform, or ongoing consulting service. When applying, please provide: A short description of your GEO experience A recording or clip of you presenting, if available Examples of GEO work or results A proposed outline for the session Your answer to this question: “If you had 30 days and a modest budget to improve the AI visibility for a vertical market software company, what would you do, and how would you measure whether it worked?” The presentation may be recorded and shared internally with our teams.

I am a Ph.D. and digital product business owner who uses AI (Claude, ChatGPT, and other AI tools) every day to build, market, and scale my business. My 12-year-old son and I are looking for an experienced AI tutor who can teach us how to work with AI effectively—not just how to ask questions, but how to think, build, create, and solve problems with AI. This is an ongoing coaching relationship, not a one-time class. I already use AI daily and want to become significantly more advanced in prompt engineering, AI workflows, automation, and business applications. My son is curious, creative, and highly motivated. We want someone who can grow with him over the coming years as AI continues to evolve. WHAT WE ARE LOOKING FOR • Weekly one-on-one coaching sessions (one for me, one for my son) • Hands-on learning using real projects—not lectures or slide presentations • Practical skills that can be used immediately • A structured curriculum that builds over time • Someone who enjoys teaching and can explain complex ideas clearly • Experience with Claude, ChatGPT, and current AI tools MY LEARNING GOALS I use AI every day and want to continue improving how I work with it. Topics include: • Advanced prompt engineering • AI workflow design • Prompt refinement and iteration • Research and fact-checking • Marketing copy • Product descriptions • Sales pages • Email sequences • Business automation • AI-assisted content creation • Website content • Productivity systems • Emerging AI tools and best practices JORDAN'S LEARNING GOALS Jordan is 12 years old. While we'll certainly use AI for school projects and writing, our larger goal is to help him develop future-ready skills that will grow with him through middle school, high school, college, and beyond. We are looking for someone who can progressively teach him how to use AI to create, build, and solve problems. Topics may include: • Learning how to communicate effectively with AI and using AI to support academic success • Critical thinking and verifying AI responses • Research and creative writing • Brainstorming and problem solving • Website design and development with AI • Creating simple games with AI • Building apps and digital tools as his skills grow • Learning basic programming concepts using AI as a coach • Entrepreneurship and business ideas • Using AI to help businesses become more efficient • Marketing and content creation • Responsible and ethical use of AI • Developing confidence as a creator—not just a consumer—of AI technology The ideal tutor enjoys helping young people build real-world skills and can gradually increase the difficulty as Jordan grows. WHAT WE ARE LOOKING FOR IN YOU • Demonstrated experience teaching AI—not simply using it • Strong prompt engineering knowledge • Comfortable teaching both an adult professional and a motivated 12-year-old • Patient, engaging, and adaptable • Able to build a long-term curriculum instead of isolated lessons • Reliable, organized, and an excellent communicator Bonus experience: • Programming or software development • Website development • AI-assisted coding • Game development • Digital marketing • Entrepreneurship • Small business consulting LOGISTICS • Two weekly sessions (one for Jordan and one for me--45–60 minutes each) • Zoom • Weekly to start • Start date: ASAP • Budget: Please include your hourly rate. TO APPLY Please include: Your hourly rate. Your experience teaching AI and prompt engineering. An example of how you would structure Jordan's first month of lessons. An example of how you would structure my first month of lessons. What you think will be the most valuable AI skills for a motivated 12-year-old to develop over the next five years. Applications that do not answer these questions will not be considered. We are looking for someone who enjoys teaching, stays current with AI, and is excited about helping both a business owner and a young learner become confident, capable AI users and creators.

# Full-Stack AI Engineer — Semantic Search + Next.js + Supabase (Long-Term, Contract-to-Hire) ## About We're building an AI-native platform that makes a large archive of recorded talks genuinely discoverable and useful: need-based semantic search over transcribed media, with a subscription product built around it. We have a clear product vision and architecture and are looking for a lead engineer to build the first version and grow with us long-term. Full product details are shared with shortlisted candidates under NDA — this post focuses on the engineering and the skills we need. ## The engineering challenge You'll build a two-part system that shares one database: 1. **A content pipeline (Python):** ingest recorded talks, transcribe them, chunk and enrich the transcripts with metadata using an LLM API, generate embeddings, and store everything in Postgres. 2. **A web app (Next.js):** fast, crawler-friendly, SEO-strong content pages with structured data; retrieval-based search that returns relevant source material with links/citations; user accounts; and Stripe-gated paid content. We care a lot about retrieval *quality* and clean, maintainable architecture — this is a real product, not a prototype. ## Required tech stack - **App:** Next.js (App Router), TypeScript, Vercel. Strong SSR/SSG, SEO, and JSON-LD structured-data experience. - **AI/backend:** Python; production RAG (embeddings, chunking, retrieval quality); LLM API integration. - **Data:** Postgres + **pgvector** (via Supabase); embeddings via a hosted model (Voyage/OpenAI). - **Auth & gating:** Supabase Auth with row-level security. - **Payments:** Stripe (subscriptions + one-time). ## Required skills - Shipped production Next.js (App Router) + TypeScript apps with strong SSR/SEO. - Built a real RAG / vector-search system in production — not a tutorial clone. - Comfortable in Python for data pipelines. - Postgres + pgvector and Supabase in production. - Stripe integration. - Plans before building; communicates clearly in writing. ## Nice to have - Audio/video transcription experience (Whisper / faster-whisper / Deepgram / AssemblyAI). - Agentic coding workflows (e.g., Claude Code). - Content-heavy SEO products or media libraries. ## Engagement - Hourly, contract-to-hire. ~20–40 hrs/week to start; long-term for the right person. - We start finalists on a **small paid test project** (a single self-contained slice of the pipeline) before the full engagement — that's how we evaluate fit. ## Confidentiality This is a proprietary product. Shortlisted candidates sign a mutual NDA before we share full scope and context. Please don't expect complete product details in the first exchange — strong technical applicants will have everything they need to be evaluated, and the rest follows the NDA. ## How to apply Applications that skip these are ignored: 1. **Start your proposal with the word `pgvector`** so we know you read this. 2. Link **two** projects: one live Next.js/SSR app, and one RAG/embeddings or LLM-integration project. Tell us what *you* personally built. 3. Answer briefly: *An offline embedding pipeline and a live search query must use the same embedding model — why does that matter, and how would you guarantee it?* 4. One line on your approach to chunking long-form audio/video transcripts for good retrieval.

Role Overview You are the Executive AI Enablement Lead at AIVC, the person whose job is to make the executives at AIVC’s client businesses true power users of Claude, Cowork, and code- and agent-driven workflows. AIVC partners with operator businesses to drive AI-led EBITDA growth, and part of that work is bringing each company’s most senior leaders up the AI curve. You’re the person who personally designs and runs that path on every engagement: assessing where a given client executive is today; curating the right materials, videos, and course content; running 1:1 coaching; building executive playbooks; and acting as their daily operator-in-the-loop until the new workflows stick. The first concrete instance is already lined up, a named client managing partner has explicitly asked for the fastest path to becoming a power user of Claude, Cowork, and Claude Code / Skills. From there you scale: same treatment to additional client executives across the portfolio, then a documented set of executive-grade playbooks and patterns that compound across every future engagement. You are bias-toward-results – a win is the client executive’s calendar-week looking different, not a beautifully written rubric nobody uses. What You’ll Own (Outcomes) • Within 30 days of pairing with the first client managing partner, they have a working daily routine in Claude, Cowork, and Code/Skills that’s already replacing or improving how they handle at least three recurring tasks • Within the first quarter of the engagement, the client executive is a true power user — running multi-step workflows, custom Skills/Projects, and agent-assisted tasks without needing coaching scaffolding for the basics • A documented set of executive playbooks (research, writing, analysis, synthesis, workflow automation, agent-assisted tasks) that compound across every client engagement, not one-offs • A curated, current library of learning materials, videos, example workflows, and Claude-native patterns — including a clear point of view on which external courses, tutors, or expert resources are worth plugging in • Observable change in how client executive cohorts use AI: from reactive chat to repeatable, structured, outcome-oriented workflows • A foundation of training assets and patterns that scales beyond executive coaching into broader client teams in year two • A reputation among AIVC’s clients as the trusted go-to for “how do I do this better in Claude” — measured by inbound demand and engagement expansion What You’ll Do (Responsibilities) • In the first weeks: build the first client managing partner’s tailored upskilling plan — assess current usage, identify the highest-leverage workflows for their day-to-day, curate the right mix of materials / videos / course content, and recommend any tutor or expert-guided support to fold in • Provide 1:1 coaching for client executives — managing partners, founders, C-suite leaders — on Claude, Cowork, and code- and agent-based workflows • Design tailored training plans per executive that go beyond basic onboarding into advanced usage, with explicit progression from chat → workflows → agents • Curate the best external materials (videos, courses, blog posts, example projects) and rewrap them into client-ready, AIVC-flavored learning paths • Teach practical, high-leverage use cases live: research, writing, analysis, synthesis, workflow automation, and agent-assisted tasks • Help client executives move from general chat usage into repeatable workflows — Claude Projects, Skills, scheduled Cowork tasks, MCP integrations, custom agents • Serve as a real-time tutor and expert resource for client executives — over Slack, in meetings, on-site, and in async written feedback • Run office hours, workshops, and informal Q&A sessions inside client teams to keep adoption sticky between coaching sessions What We’re Looking For (Required) • Deep hands-on expertise with Claude across every surface (Claude.ai, Claude Projects, Claude Code, Claude Skills, Claude API) — and an active habit of pushing the edges of each • Strong working fluency with Claude Cowork specifically, including scheduled tasks, connected apps / MCPs, and the broader workflow surface • Strong capability with code-enabled AI workflows: you can write Python and/or TypeScript, build agents, configure MCP integrations, and ship a working internal automation end-to-end without needing an engineer • Demonstrated ability to teach non-technical but highly demanding users — you’ve made executives, founders, or senior operators meaningfully better at something complicated, not just trained engineers • Strong workflow design instinct — you can translate messy business questions into clean prompts, workflows, and systems • Polished, discreet, and effective in high-touch client executive settings — high EQ, low ego, comfortable representing AIVC inside senior client environments and around senior decision-makers • Strong bias toward practical results over theoretical AI knowledge — the metric is the client executive’s behavior change, not the elegance of the explanation • Excellent written and verbal communication; you can write a playbook a client executive will actually read and use • Comfort with significant travel to client sites and embedded, on-site engagement work • 5+ years of professional experience across some mix of: applied AI / ML, technical training and enablement, developer relations, solutions engineering, executive coaching, management consulting, or chief of staff / senior operator roles to executives Helpful If You Have (Preferred) • Prior experience coaching or supporting C-level executives, founders, or managing partners as a client-facing professional — executive coach, principal solutions engineer to executive customers, chief of staff to a CXO, or partner-level consultant • Background that combines technical depth with people skills — developer relations, solutions engineering, technical training, or learning & development at a frontier AI or developer-tools company • Direct experience building executive-facing training programs or curricula that demonstrably moved adoption inside other organizations • Hands-on familiarity with the Anthropic product surface specifically: Claude Projects, Claude Skills, Claude Code, MCP server development, Claude API • Track record of getting non-technical users to genuinely adopt a technical tool — i.e., users who chose to keep using it after the training ended • Background in management consulting, professional services, executive coaching, or learning & development — especially in environments where the customer was a senior external client • An active personal portfolio of AI work (workflows, automations, blog posts, talks, open-source contributions) you can point to • Comfort building light tooling (a Notion playbook system, a Claude Skills catalog, a small dashboard) without needing engineering support • Familiarity with AIVC’s model — operator business engagements, EBITDA-led measurement, and the broader compounding intelligence layer — or eagerness to come up the curve quickly

We are seeking a skilled freelancer to enhance our AI app that uses voice cloning to recreate the voices of deceased loved ones. The ideal candidate will have experience in AI and voice cloning technology, and be able to empathize with the emotional aspects of this project. Responsibilities include improving the app's functionality and user experience, ensuring high-quality voice outputs, and collaborating with our team to integrate new features.

Python tutor Looking for a tutor to help with getting setup to advance my Python Skills. I have been working with Python in Excel and l now want to level up my skills to do some Simple Python Programing. Most of my interest is data science related using Python, Pandas, Matplotlib and statistical forecasting. I’m looking for a series of one on one Zoom sessions to setup VS code as my code editor with Python and related libraries, including setting up virtual environments, loading pip, how to organize file structures etc. Additional sessions I’d like to add Claude code to the mix and setup Jupyter Notebooks.