Profile baseline

Create the five cockpit files and record a truthful baseline of Python, statistics, biology, portfolio links, and available time. Acceptance: Five next actions and an evidence-backed skills baseline exist.

File What you write Example TODAY.md One next physical action Open environment-checklist.md and record git --version. PROGRESS.md Weekly outcomes and evidence level Week 00: in progress; setup checks pending. LOG.md One factual line per block Date · setup · copied five files · paths verified. PARKING.md Ideas that do not belong to this block Compare model families after the first measured extraction. QUESTIONS.md What you do not understand yet Why must an entire batch stay inside one validation split? Open the copied TODAY file and replace its setup reminder with your actual next action. Choose the new cockpit folder as your daily canonical copy. Weekly evidence/review files stay here in the plan. Do not independently maintain two competing TODAY files. Write one LOG line for this block. Your copied PROGRESS already contains the v5 execution snapshot; do not retype it. Start ritual: read TODAY, set a 90-minute timer, open one working file. Close ritual: write tomorrow’s action, log what actually happened, commit once Git is ready. Count these minutes inside the block.

https://github.com/peteciank/labloop-cockpit

Here is a structured draft for your profile baseline file, tailored to bridge your current AI and delivery management expertise into the biotech space.

## Skills Baseline

Python & Software Engineering: Highly proficient in practical, applied AI architecture. Experience building and deploying agentic AI orchestration frameworks, Retrieval-Augmented Generation (RAG) pipelines, and interactive web apps using LangGraph, FastAPI, Streamlit, Next.js, and Supabase. Proven capability in hardware-level model deployment, including configuring headless Debian servers for local LLM environments (Ollama, llama.cpp, OpenClaw).

Statistics & Data: Solid foundation in applied business intelligence and data analysis. Experience processing telemetry and operational data (e.g., tracking stroke rates, distance, and rhythms for RowTok) and evaluating multi-agent workflow efficiencies and token optimization.

Biology & Scientific Domain: Foundational exposure through industrial environmental contexts. Background includes research into industrial water treatment and naturally occurring radioactive material (NORM) management. The immediate focus is translating this systems-level analytical experience into bioprocessing, fermentation simulation, and protein validation.

Portfolio Links & Assets:

• AI & IoT Ventures: 4Vectors, PadPulse, ZecretX (highlighting AI transformation and industrial platforms).
• Applications: RowTok (sports telemetry) and interactive gamified study web apps.
• AI Infrastructure: Documentation of local Agentic OS builds and RAG orchestration pipelines.

Available Time: Requires strict timeboxing to balance this intensive career transition with active venture management, dragon boat training schedules at Puertos del Lago, and family routines with Kari, Ben, and Emi. Deep-work blocks for biotech upskilling will be prioritized during early mornings or specific non-training days to maintain sustainable momentum.

## Acceptance: Five Next Actions (Cockpit Files)

1. **Create 01_tech_stack_inventory.md**: Document the current Python and AI orchestration architectures (LangGraph, FastAPI, vector stores) and explicitly map how these can be adapted for biotech data pipelines and fermentation datasets.
2. **Create 02_biology_bridge_curriculum.md**: Outline a focused learning path to bridge the gap between industrial water/NORM research and the specific biological concepts (protein study, extremophiles) required for upcoming lab loops.
3. **Create 03_portfolio_consolidation.md**: Aggregate existing projects (4Vectors, local AI servers, RowTok) into a single repository, framing the READMEs to highlight project management and applied AI engineering skills to future biotech recruiters.
4. **Create 04_capacity_planner.md**: Map out a rigid weekly schedule that explicitly blocks out hours for team paddling, school carpools, and unbroken deep-work sessions dedicated to the execution plan.
5. **Create 05_career_transition_manifesto.md**: Articulate the strategic narrative shifting from IT Delivery/Project Manager to Biotech AI Applied Engineer, emphasizing how a background in AI transformation and industrial IoT uniquely positions you to solve complex biological scaling challenges.