# LABLOOP · Master Plan v5 — From program manager to published AI scientist for biotech **Author:** Peter Ciancaglini · **Date:** 2026-09-11 · **Supersedes:** v4 (keeps its company map, offers and templates; see `AI-Upskilling-Plan-Biotech-v4.md`, Appendix A) **Horizon:** 40 weeks in two halves. **Half 1 (weeks 0-20): the scientific profile.** **Half 2 (weeks 21-40): depth, co-authorship, money.** **Load:** 10 h/week = six 90-minute blocks + one Sunday hour. Same rhythm as before, because it fits your week. **This document orchestrates everything else.** Other models can expand any week into more detail, but they must not change the gates, the thesis or the rules in Part 0. --- ## PART 0 · How to use this document (read this every Monday for the first month) ### 0.1 The honest answer to your question You asked how a program manager with 25 years and strong AI fluency can walk into a biotech company and sell something he built, in 20 weeks, like a pro. He cannot, and the v4 plan was wrong to imply it. Here is what is true: 1. **You will not sell biology. You will sell measurement.** Biotech founders know their biology better than you ever will. What they do not have is someone who can turn their experiments into structured data, models and decisions, and who can prove it with a paper. That person is rare and is paid accordingly. 2. **Credibility in science has only one currency: things other scientists can check.** A repo with a DOI, a dataset other people download, a preprint with a method they can reproduce, a co-authored paper with a scientist who has a lab. Not talks, not posts, not certificates. Posts only amplify what the currency already bought. 3. **The bridge from "outsider with a tool" to "peer" is a co-author.** Most GridX founders are CONICET scientists. When one of them puts their name next to yours on a preprint, you are no longer a consultant pitching them; you are a collaborator they introduce to others. Everything in this plan is arranged so that this happens between week 16 and week 32. 4. **Twenty weeks is enough for the first two rungs, not for the pro.** At week 20 you will have one bioRxiv preprint (a dataset and a method), one endorsed arXiv preprint (a modeling result), three instruments with numbers, and a reading level in biology and machine learning that lets you follow a lab meeting. That opens the employee track at scientific-data companies and makes founders answer your messages. The paid consulting starts in Half 2, after the third paper is in motion, around week 28-32. 5. **A plan cannot guarantee a discovery. It can guarantee the artifacts that make one likely.** Three of the four papers in this plan are of a kind that reliable work produces (a dataset, a benchmark, a reproducible modeling study). The fourth (the enzyme paper, Half 2) is the one with a real chance of a finding, and it is designed with a founder's lab as the place where the finding gets tested. ### 0.2 The cockpit: five files, nothing else Create a private repo `cockpit` on day 0. It holds five files. You open it at the start of every block and close it at the end. Nothing about the plan lives in your head. | File | What goes in it | When | |---|---|---| | `TODAY.md` | One line: the next physical action ("open `sim.py`, add the substrate equation"). Never a goal, always an action | End of every block, for the next one | | `PROGRESS.md` | The 40-week checklist from Part 6 and 7, copied verbatim, with `[ ]` boxes | Tick boxes on Sunday | | `LOG.md` | One line per block: date, block type, what happened, one number if any | End of every block, 60 seconds | | `PARKING.md` | Every shiny idea, tool, paper, project, product that is not the current task. Write it, close it, move on | Whenever the idea appears | | `QUESTIONS.md` | Every "I don't get this" while studying. These feed the prompts on Thursday and Sunday | While studying | ### 0.3 The rituals (this is the ADHD armor) **Start ritual (2 minutes):** open `cockpit/TODAY.md`, read the one line, start a 90-minute timer (or two 40-minute halves with a 10-minute walk), one window, one repo, phone in another room. If you freeze, use Prompt P12. **Stop ritual (3 minutes):** write tomorrow's one line in `TODAY.md`, one line in `LOG.md`, commit, close everything. The block ends when the timer ends, even mid-thought; the mid-thought goes in `TODAY.md`. **The rule of one:** one block, one task, one repo, one file open in the editor when possible. Theory or code, never both in a block. **The parking lot rule:** every idea that is not today's task goes to `PARKING.md` in under 20 seconds. You will review it on Saturday. Most of them die there; the good ones become Half 2. **Saturday sandbox:** the Saturday block is the only place where hyperfocus on a parked idea is allowed, and only if the week's Monday-Friday blocks are done. It is a reward, and it is bounded by the timer. **Sunday hour (sacred):** 20 min build log, 5 min tick boxes, 20 min Prompt P4 (gap detector), 15 min plan the week's six `TODAY.md` lines in advance (write all six now; during the week you only read). **Lost week rule:** a lost week is expected. Two in a row is the only forbidden thing. After a lost week, run Prompt P14 on Sunday and cut the next week to the "if you only do one thing" line. ### 0.4 Weekly shape (never changes for 40 weeks) | Day | Block | Content | |---|---|---| | Mon | Build 1 | Thesis or instrument code | | Tue | Study Bio | Biology track (Part 4.1), editor open, notes into `QUESTIONS.md` | | Wed | Build 2 | Thesis or instrument code. Last 10 min: Wednesday note (Part 9) | | Thu | Study AI | AI-science track (Part 4.2). Last 30 min: one founder paper (Part 4.4) | | Fri | Build 3 | Thesis or instrument code | | Sat | Buffer | Whatever slipped; if nothing slipped, sandbox | | Sun | Publish | The sacred hour | Every week in Part 6 gives you: the goal in one line, the exact Tuesday and Thursday material with links and units, the three build tasks with "done when", the Sunday output, and the "if you only do one thing" line. --- ## PART 1 · The strategy in one picture ```mermaid flowchart TB subgraph H1 [Half 1 · weeks 0-20 · the scientific profile] S[Study: Bio track + AI-science track + domain track] I1[Instrument 1 batchlens: simulator, MSPC, soft sensor, deviations] T1[Thesis project FermLit: LLM agents read 2,000 fermentation papers, human-validated gold set] P1[Paper 1 · bioRxiv wk16: FermLit dataset + method + what papers fail to report] P2[Paper 2 · arXiv wk20 endorsed: hybrid soft sensors under data scarcity] S --> I1 --> P2 S --> T1 --> P1 T1 --> P2 end subgraph H2 [Half 2 · weeks 21-40 · depth, co-author, money] P3[Paper 3 · enzyme thermostability with protein LMs, founder co-author, wet-lab validation] J[Journal submission of Paper 1 or 2] I23[Instruments 2 and 3: trialkit, labdocs] M[Loop Audit → first invoice wk 28-32 → pilots → retainer or job] P1 --> P3 P2 --> J P3 --> M I23 --> M end R[Reputation gauge L0→L6] -.-> H1 R -.-> H2 ``` Read it like this: study feeds two things, an instrument and a thesis project. The instrument produces a modeling paper. The thesis project produces a dataset paper. Both papers plus the instrument make founders answer. One founder becomes a co-author on Paper 3. Paper 3 plus the instruments become the thing you get paid for. The reputation gauge (Part 2) tells you which rung you are on at any moment. --- ## PART 2 · The reputation gauge (what "reputation" means, measurably) | Level | Name | Evidence that you are here | Unlocks | Target week | |---|---|---|---|---| | L0 | Private | Code on your disk | Nothing | Today | | L1 | Public and reproducible | Public repo, README with a number, `make report` regenerates it, `--fake` mode | Engineers take you seriously | 4 | | L2 | Citable | Zenodo DOI on a release; Hugging Face dataset card | Scientists can cite you | 8 | | L3 | Preprinted | bioRxiv preprint (no endorsement needed) with a method others can reproduce | Founders answer messages; scientific-data companies interview you | 16 | | L4 | Endorsed and co-authored | arXiv preprint with an endorser; a scientist's name next to yours | You are a peer, not a vendor | 20 (endorsed), 28 (co-author) | | L5 | Reviewed and invited | Journal or JOSS acceptance; a talk at a biotech event | High-ticket audits; fractional roles | 36-40 | | L6 | Used | Someone else's paper cites FermLit; a lab uses batchlens | The market comes to you | Month 12+ | The gauge is the only progress metric that matters. Every Sunday you write the current level in `PROGRESS.md`. --- ## PART 3 · The scientific thesis (explained twice: like a kid, then like a reviewer) ### 3.1 Like a kid Thousands of scientists have grown microbes in tanks to make useful things: medicines, pigments, enzymes, plastics. Each one wrote what they did in a PDF, each in a different way. Nobody has put all of that into one big table. You are going to build a robot that reads those PDFs and fills the table, and you are going to check the robot's homework by hand so everybody trusts the table. Then you will be the first person who can say what the table shows: what most papers forget to write down, and what a computer can and cannot predict about a tank from the numbers people bother to report. Along the way you build a tool that watches a tank in real time and says "something is going wrong" hours before a human would notice. Then you use the table and the tool to help a real scientist find better enzymes, and you write that up together. ### 3.2 Like a reviewer **Thesis statement.** *Published bioprocess literature is an underused, unstructured dataset. Large language model agents, validated against a human-annotated gold standard with published inter-annotator agreement, can convert it into a structured resource that (a) reveals systematic gaps in what fermentation papers report, and (b) enables data-scarce process modeling to be benchmarked against mechanistic and hybrid baselines. The same rigor, applied to protein language models, yields testable candidates for industrially relevant enzymes.* **The four papers** | # | Working title | Type | Venue | Week | What makes it real science | |---|---|---|---|---|---| | 1 | *FermLit: an LLM-agent-curated, human-validated dataset of N published fermentation experiments, and what the literature fails to report* | Dataset + method + descriptive analysis | bioRxiv (no endorsement needed), later Scientific Data or a bioprocess journal | 16 | Novel dataset; gold set with two annotators and Cohen's kappa; per-field precision/recall; a reporting-completeness finding nobody has quantified for fermentation at scale | | 2 | *Hybrid mechanistic-ML soft sensors under data scarcity: a reproducible study on an open industrial fermentation benchmark* | Modeling study | arXiv cs.LG or q-bio.QM (endorsed), later Computers & Chemical Engineering or Biotechnology and Bioengineering | 20 | Learning curves (5, 10, 20, 50 batches), mechanistic vs pure ML vs hybrid, transfer to faulty batches, uncertainty via conformal intervals, all reproducible in one command | | 3 | *Protein language model embeddings predict thermostability of industrially relevant enzymes from extremophiles: candidates and validation* | Applied ML + wet-lab validation | bioRxiv then a journal, with a founder-scientist co-author | 28-32 | ESM-2 embeddings + small heads on FLIP/Meltome; extremophile focus; a ranked candidate list a partner lab tests. This is the paper with a chance of a discovery | | 4 | *batchlens / FermLit software* | Software paper | JOSS | 36 | Peer-reviewed software; cheap credibility for the instruments | **Why this thesis and not another** - It uses what you already have (agents, RAG, orchestration, evals) as the *method*, so the science is where you are strong, and the biology is where you are learning. - It needs no wet lab until Paper 3, where the wet lab belongs to the co-author. - Every piece is public data: Europe PMC open-access full texts, IndPenSim, FLIP and Meltome. - It fits 6 GB of GPU: extraction runs on APIs and local 7B models; soft sensors train on CPU; ESM-2 650M embeddings run in fp16 on the 3050. - It produces things founders in archetype A (25 companies in the GridX list) will want to read, and things scientific-data companies (DataHow, Aizon, TetraScience) hire for. **Risks and what you do about them** | Risk | Mitigation | |---|---| | Someone already published a fermentation literature dataset | Week 5, Prompt P8 + Semantic Scholar search. If it exists, you extend it (different organisms, newer years, richer schema) and cite it. Extension is still a paper | | Extraction accuracy too low to trust | The gold set is built first (week 6), the pipeline is tuned against it, and the paper reports whatever the number is. A well-measured 78% is publishable; an unmeasured 95% is not | | No endorser by week 20 | Three parallel routes (Part 9.5). Fallback: Paper 2 also goes to bioRxiv, and arXiv follows when the endorser appears. The gate is "preprint submitted", the endorsed arXiv is the target | | Paper 3 partner never materializes | Paper 3 is still publishable as a computational study with public data. The partner adds validation and the co-author line, not the existence of the paper | | You cannot judge if the science is sound | Two review lines: Prompt P10 (reviewer 2) every draft, and one human scientist reads each paper before submission (the co-author, the endorser, or a paid 2-hour review from a postdoc, ~USD 100) | --- ## PART 4 · Curriculum from zero (four tracks, all free, exact units) Rules: every item lists what you produce and when it is "done". You never watch without the editor open. `QUESTIONS.md` is open in every study block. Nothing is studied that no week in Part 6 asks for. ### 4.1 Track B · Biology from zero (Tuesdays, ~30 h in Half 1) | Unit | Weeks | Material (exact) | What you produce | Done when | |---|---|---|---|---| | B1 · Molecules of life and enzymes | 1 | OpenStax *Biology 2e* ch. 3 (biological macromolecules) and ch. 6.5 (enzymes) — openstax.org/details/books/biology-2e. Crash Course Biology "Biological Molecules" (YouTube) | 1 page of notes in `notes/bio/B1.md` + 5 questions | You can say what a protein, an enzyme and a substrate are without looking | | B2 · The cell and energy | 2 | *Biology 2e* ch. 4 (cell structure, skim), ch. 7 (cellular respiration), ch. 5.1-5.2 (membranes) | Notes + a drawing of a cell making ATP | You can explain why a microbe needs oxygen and sugar in a tank | | B3 · Microbial growth | 3 | OpenStax *Microbiology* ch. 9 "Microbial Growth" — openstax.org/details/books/microbiology | Notes + the growth curve drawn by hand with lag, log, stationary, death | You can connect the log phase to the Monod equation in your simulator | | B4 · Microbial metabolism and fermentation | 4 | *Microbiology* ch. 8 "Microbial Metabolism" (8.1-8.4) | Notes; one paragraph "what is a fermentation, really" | You can explain aerobic vs anaerobic to a friend | | B5 · DNA to protein | 5 | *Biology 2e* ch. 14 (DNA), 15 (genes and proteins); Crash Course "DNA, Hot Pockets & the Longest Word Ever" | Notes; Rosalind problems 1-4 (rosalind.info) | You can read "the strain overexpresses gene X" and know what it means | | B6 · Genetic engineering and industrial strains | 6 | *Microbiology* ch. 12 "Modern Applications of Microbial Genetics"; *Biology 2e* ch. 17.1 | Notes; Rosalind 5-8 | You understand what "engineered *Yarrowia*" or "recombinant *E. coli*" means in a FermLit paper | | B7 · Protein structure and enzyme kinetics | 7 | MIT OCW 7.016 Introductory Biology (Fall 2018) lectures on protein structure and enzymes — ocw.mit.edu (search 7.016); Khan Academy "Enzyme kinetics" | Michaelis-Menten fitted in Python to a public dataset | You can fit Km and Vmax and say what they mean | | B8 · Industrial biotech overview and review | 8 | *Microbiology* ch. 12.5 and any open "industrial microbiology" overview; re-read your B1-B7 notes | A 2-page "biology for my thesis" summary | Prompt P3 quiz score ≥ 4/5 | | B9 · Bioreactors I: modes and balances | 9 | NPTEL "Bioreactors" (IIT Madras, Prof. G. K. Suraishkumar), lectures 1-3 — nptel.ac.in (verify current playlist); MIT OCW 10.37 lecture notes on bioreactors as backup | Mass balances for batch, fed-batch, continuous in a notebook | Your simulator's equations match the lecture's | | B10 · Bioreactors II: kinetics and yields | 10 | NPTEL lectures 4-6 (Monod, yields, maintenance) | Yield coefficients computed on IndPenSim | You can explain Yx/s and Yp/x | | B11 · Bioreactors III: oxygen transfer and scale-up | 11 | NPTEL lectures on kLa, mixing, scale-up | kLa estimated from IndPenSim DO data | You can say why scale-up fails | | B12 · Downstream, PAT and QbD | 12 | FDA PAT guidance (2004) — fda.gov; ICH Q8(R2) sections 1-2 — ich.org | Glossary: CPP, CQA, design space, golden batch, in your words | You can hold a conversation with a pharma QA manager | | B13 · Statistics for biologists I | 13 | *Modern Statistics for Modern Biology* (Holmes & Huber) ch. 1-2 — huber.embl.de/msmb | Two exercises redone in Python | You know why biologists distrust p-values from n=3 | | B14 · Statistics for biologists II | 14 | MSMB ch. 6 (testing, multiple testing) | Multiple-testing correction applied to a FermLit comparison | You can defend an effect in Paper 1 | | B15 · How papers are built | 15 | Read 3 fermentation papers from FermLit with Prompt P11; note the IMRaD structure | Structured digests in `notes/papers/` | You can find methods, results and limitations in 10 minutes | | B16 · Protein language models | 16 | Lin et al. 2023, "Evolutionary-scale prediction of atomic-level protein structure with a language model" (ESM-2), read the abstract, intro and figures; MIT 6.874 protein lecture (Kellis, YouTube "Deep Learning in Life Sciences 2021") | ESM-2 650M running on the 3050, embedding 100 sequences | You can explain what an embedding of a protein is | | B17 · Enzyme stability and engineering | 17 | FLIP benchmark paper (Dallago et al. 2021, "FLIP: Benchmark tasks in fitness landscape inference for proteins"); Meltome atlas paper (Jarzab et al. 2020) | Meltome split loaded; baseline on 1,000 sequences | You know what "thermostability" is measured as | | B18 · Extremophiles | 18 | Two founder papers (Puna Bio, Antarka, APEXzymes authors) via Prompt P11 | Outreach lines drafted | You can name the organisms and enzymes they work with | | B19 · Omics literacy | 19 | scanpy PBMC 3k tutorial — scanpy.readthedocs.io | Notebook run end to end | You can follow a single-cell talk | | B20 · Review and mock lab meeting | 20 | Re-read all notes; Prompt P3 with "lab meeting" persona | A 10-minute self-recorded explanation of Paper 1 for biologists | You can present without notes | ### 4.2 Track M · AI science from the basics (Thursdays, ~30 h in Half 1) | Unit | Weeks | Material (exact) | What you produce | Done when | |---|---|---|---|---| | M1 · Linear algebra, intuitively | 1 | 3Blue1Brown "Essence of Linear Algebra" ch. 1-7 (YouTube, 1.25x) | Notes; numpy re-doing 3 examples | You can say what a matrix does to a vector | | M2 · Calculus and the neural network idea | 2 | 3Blue1Brown "Essence of Calculus" ch. 1-4 + "Neural Networks" ch. 1-4 | Notes | You can explain a gradient with a hill | | M3 · Statistical learning I | 3 | *An Introduction to Statistical Learning with Python* (ISLP) ch. 2 and 3 — statlearning.com (free PDF + lecture videos) | Lab 3 redone on IndPenSim variables | You know bias-variance and can fit and read a regression | | M4 · Validation done right | 4 | ISLP ch. 5 (cross-validation, bootstrap) | Batch-wise CV implemented in batchlens | You can explain leakage across batches | | M5 · Regularization and trees | 5 | ISLP ch. 6.1-6.2 and ch. 8 (trees, boosting) | LightGBM soft sensor with tuned regularization | You can explain why boosting wins on tabular data | | M6 · Probability, the useful part | 6 | Murphy, *Probabilistic Machine Learning: An Introduction* ch. 2 (probability) and 4.1-4.3 (statistics) — probml.github.io (free PDF); or Statistical Rethinking 2024 lectures 1-2 (github.com/rmcelreath/stat_rethinking_2024) | Notes; a Bayesian update by hand | You can explain a posterior with coins | | M7 · Bayesian models | 7 | Statistical Rethinking 2024 lectures 3-4 | A simple Bayesian regression in PyMC or numpyro on titer data | You can read a credible interval | | M8 · Backpropagation by hand | 8 | Karpathy, "The spelling-out intro to neural networks and backpropagation: building micrograd" — karpathy.ai/zero-to-hero.html | micrograd rewritten from memory | You can derive a gradient of a tiny network | | M9 · Language models from scratch | 9 | Karpathy, "Building makemore" (part 1) | Bigram model on organism names from FermLit | You know what a language model is, in 200 lines | | M10 · Gaussian processes | 10 | Rasmussen & Williams, *GPML* ch. 1-2 — gaussianprocess.org/gpml (free); scikit-learn GP examples | GP soft sensor with uncertainty bands | You can explain a kernel as "similarity" | | M11 · Bayesian optimization | 11 | Frazier, "A Tutorial on Bayesian Optimization" — arxiv.org/abs/1807.02811; BoTorch tutorials — botorch.org | BO on the simulator's feed profile | You can explain expected improvement | | M12 · Data-driven dynamics | 12 | Brunton & Kutz, *Data-Driven Science and Engineering*, ch. 7 (SINDy) — databookuw.com (free PDF + YouTube "Steve Brunton") | SINDy recovers Monod from simulator data | You can say what "system identification" is | | M13 · Hybrid and scientific ML | 13 | von Stosch et al. 2014, "Hybrid semi-parametric modeling in process systems engineering: past, present and future"; Chen et al. 2018, "Neural Ordinary Differential Equations" (abstract + fig. 1 only) | Hybrid model v0: Monod + GBM residual | You can draw the hybrid architecture | | M14 · Evaluation and judges | 14 | Stanford CS336 lecture 12 (evaluation) — cs336.stanford.edu; CS224N benchmarking lecture (judge biases) | Calibrated judge for FermLit fields | You can explain judge-human agreement | | M15 · Information extraction, measured | 15 | Jurafsky & Martin, *Speech and Language Processing* (3rd ed. draft, free) chapter on information extraction; Cohen's kappa (any statistics reference) | Per-field precision/recall, kappa between annotators | Paper 1's evaluation section is written | | M16 · Uncertainty you can defend | 16 | Angelopoulos & Bates, "A Gentle Introduction to Conformal Prediction" — arxiv.org/abs/2107.07511, sections 1-2 | Conformal intervals on the soft sensor | Paper 2 reports coverage | | M17 · Deep learning for proteins | 17 | MIT 6.874 protein lecture; Hugging Face ESM model card (facebook/esm2_t33_650M_UR50D) | Embedding + ridge head on Meltome | Baseline number exists | | M18 · Research craft | 18 | Simon Peyton Jones, "How to write a great research paper" (YouTube, 1 h); NeurIPS paper checklist (neurips.cc) | Paper 2 rewritten with the checklist | Reviewer-2 prompt finds no unsupported claim | | M19 · Effect sizes and honest reporting | 19 | MSMB ch. on reporting; bootstrap intervals (ISLP ch. 5.2) | Every number in Papers 1-2 has an interval | Yes/no | | M20 · Review and mock interview | 20 | Prompt P3, "staff ML scientist at a bioprocess company" persona | Recorded 10-minute explanation of Paper 2 | You can present without notes | Deferred to Half 2 (Part 7): Karpathy videos 2-8 as needed, fast.ai if you want image models for trialkit, causal inference (Brady Neal's free course) for Paper 3's analysis, CS336 lectures 1-2 and 10 only if labdocs needs them. ### 4.3 Track D · Domain and craft (inside build blocks, no separate hours) | Item | Link | Used in | |---|---|---| | IndPenSim dataset and papers (Goldrick et al. 2015, *J. Biotechnol.*; Goldrick et al. 2019, *Comput. Chem. Eng.*) | industrialpenicillinsimulation.com (verify) | Weeks 2-8, Paper 2 | | BioModels (curated mechanistic models, SBML) | ebi.ac.uk/biomodels | Week 12 (compare your Monod to a curated model) | | Europe PMC REST API (open-access full text) | europepmc.org/RestfulWebService | FermLit corpus, week 5 | | PMC Open Access subset | ncbi.nlm.nih.gov/pmc/tools/openftlist | FermLit corpus | | Semantic Scholar API | api.semanticscholar.org | Related-work checks, Prompt P8 | | NIST/SEMATECH e-Handbook ch. 6 (process monitoring) | itl.nist.gov/div898/handbook | Weeks 3-4 | | Hugging Face datasets docs (dataset cards) | huggingface.co/docs/datasets | Week 13 | | Zenodo GitHub integration | zenodo.org | Weeks 4, 8, 13, 17 | | bioRxiv submission guide | biorxiv.org/submit-a-manuscript | Week 16 | | arXiv endorsement rules | info.arxiv.org/help/endorsement.html | Weeks 12-20 | | JOSS author guide | joss.theoj.org | Half 2 | | eLabFTW docs | doc.elabftw.net | Half 2, labdocs | ### 4.4 Track F · Founder papers (Thursday, last 30 minutes) One paper per week by a founder or scientific lead of a GridX company (archetypes A, C, D first). Use Prompt P11. Output: one digest in `notes/papers/` and one outreach line saved for later. By week 20 you have 20 digests and 20 lines. This is the cheapest, highest-yield thing in the plan: nobody else who writes to them has read their paper. --- ## PART 5 · Repos and materials to prepare (day 0 to week 2) ### 5.1 Repositories | Repo | Visibility | Purpose | Skeleton | |---|---|---|---| | `cockpit` | Private | The five files of Part 0.2 | `TODAY.md`, `PROGRESS.md`, `LOG.md`, `PARKING.md`, `QUESTIONS.md` | | `labloop-notes` | Public | Study notes, paper digests, weekly build logs. Learning in public is content | `notes/bio/`, `notes/ml/`, `notes/papers/`, `logs/` | | `batchlens` | Public | Instrument 1 and the code of Paper 2 | `sim/`, `data/` (download script, never the data), `mspc/`, `softsensor/`, `hybrid/`, `report/`, `tests/`, `Makefile`, `CITATION.cff`, `README.md` | | `fermlit` | Public | Thesis project and the code of Paper 1 | `corpus/` (queries, PMC ids), `schema/`, `annotate/` (guidelines, gold set), `extract/` (agents), `eval/`, `analysis/`, `dataset/` (HF card), `paper/` | | `paper-2-hybrid-softsensor` | Public at submission | LaTeX or Quarto source, figures regenerated by `make` | `paper/`, `figs/`, `Makefile` | | `enzymelens` (Half 2) | Public | Paper 3 code | later | ### 5.2 README skeleton (same for every instrument repo) ``` # **Headline number:** . Everything below depends on that. ## 60-second quickstart pip install -e . make report FAKE=1 # full pipeline, no data, no keys make report # downloads public data, regenerates every figure ## What it does / What it does not do ## Data (public, with download script and checksums) ## Reproduce the paper: `make paper` ## Where it fails (honest section) ## Cite: CITATION.cff, Zenodo DOI badge ``` ### 5.3 Local environment (day 0, one block) - Python 3.11 via `uv`; one virtual env per repo. - `ollama pull qwen2.5:7b-instruct-q4_K_M`, one vision model (`gemma3:4b` or `qwen2.5vl:3b`), one embedding model (`bge-m3`). Confirm each loads on the 3050. - API keys in a `.env` never committed; `--fake` adapters in every pipeline (the v3 idea). - Debian box: Docker with Mosquitto, InfluxDB, Grafana (week 1), eLabFTW (Half 2). - NotebookLM: three notebooks, "Bio", "ML", "Thesis", each with at most 8 sources. ### 5.4 Annotation kit for FermLit (week 6) - `annotate/GUIDELINES.md` written with Prompt P7: one page per field, with positive and negative examples. - `annotate/gold/` : 50 papers, each a JSON with the fields and the exact sentence that supports each value (provenance is what reviewers ask for). - A second annotator for 30 of the 50 (a scientist friend, a founder, or a paid biology student, ~USD 100) so you can report Cohen's kappa. --- ## PART 6 · Weeks 0-20, block by block Format per week: **Goal** · **Tue (Bio)** · **Thu (AI)** · **Build Mon/Wed/Fri** with done-when · **Sun** output · **Only one thing** (the minimum that keeps the plan alive). G = GitHub, L-Sun = LinkedIn build log, L-Wed = Wednesday note, M = Medium, Z = Zenodo DOI, HF = Hugging Face, X = preprint. ### Week 0 · Cockpit and positioning - **Goal:** everything exists and is empty, in the right place. - **Build:** [ ] `cockpit` repo with the five files; paste Part 6 and 7 into `PROGRESS.md`. [ ] `labloop-notes`, `batchlens`, `fermlit` repos with README skeletons. [ ] Local environment (5.3). [ ] LinkedIn headline and About (Part 9.1). [ ] NotebookLM notebooks created. - **Tue/Thu:** none. Read Part 0 twice. - **Sun:** L-Sun post 0: "40 weeks from applied-AI engineer to published AI scientist for biotech, in public, at 10 h/week. First: a fermentation in 80 lines." Write the six `TODAY.md` lines for week 1. - **Only one thing:** the `cockpit` repo with `TODAY.md` filled for Monday. ### Week 1 · A tank in 80 lines - **Goal:** a fed-batch simulator that streams like a plant. - **Tue (B1):** Biology 2e ch. 3 + 6.5. Notes. - **Thu (M1):** 3Blue1Brown linear algebra ch. 1-7. Notes. Last 30 min: founder paper 1 (Michroma or Levya author). - **Build:** [ ] Mon: `batchlens/sim/monod.py`: ODEs for biomass X, substrate S, product P, dissolved oxygen; batch and fed-batch. Done when a plot shows lag, log, stationary. [ ] Wed: MQTT publisher (`paho-mqtt`) sending one sample per simulated minute to the Debian box; InfluxDB + Grafana dashboard. Done when you see the curve live. [ ] Fri: 20 batches with random noise and one injected fault (feed pump stops); saved as CSV. Done when `make sim` writes 20 files. - **Sun:** L-Sun: "A fermentation in 80 lines, streamed like a plant" with the Grafana screenshot. L-Wed (Wednesday): "what a blind bioreactor costs" (archetype note). - **Only one thing:** `monod.py` with the plot. ### Week 2 · One hundred real batches - **Goal:** IndPenSim loaded, understood, and one golden-batch picture. - **Tue (B2):** Biology 2e ch. 4, 5.1-5.2, 7. - **Thu (M2):** 3Blue1Brown calculus ch. 1-4 + neural networks ch. 1-4. Founder paper 2. - **Build:** [ ] Mon: `data/download.py` for IndPenSim with checksums; load into a tidy long table (batch, time, variable, value). Done when 100 batches load in under a minute from parquet. [ ] Wed: batch unfolding and time alignment; plot every variable for 10 batches overlaid. Done when you can name the 5 variables that matter. [ ] Fri: PCA on the "normal" batches, scores plot, first look at the faulty ones. Done when the faulty batches are visibly outside. - **Sun:** L-Sun with the overlay plot. **M1** (Medium, thesis piece): "Biotech startups don't have an AI problem" (reuse v4 section 1, archetypes with counts, no names). - **Only one thing:** the tidy parquet of IndPenSim. ### Week 3 · Golden batch, with statistics - **Goal:** control limits that are statistics, not thresholds. - **Tue (B3):** Microbiology ch. 9. Draw the growth curve. - **Thu (M3):** ISLP ch. 2-3. Redo Lab 3 on IndPenSim. - **Build:** [ ] Mon: Hotelling T² and SPE with limits from the normal batches (NIST ch. 6.5 as reference). Done when limits are computed, not typed. [ ] Wed: `batchlens monitor --batch 87` replays a batch and prints alarms with time stamps. Done when a faulty batch alarms and a normal one does not. [ ] Fri: tests for the two commands; `make report FAKE=1` works on simulator data. Done when CI is green. - **Sun:** L-Sun: "Golden batch: what 80 normal fermentations look like as one envelope". L-Wed. - **Only one thing:** T² limits computed. ### Week 4 · L1: public and reproducible - **Goal:** first public number and first DOI. - **Tue (B4):** Microbiology ch. 8.1-8.4. - **Thu (M4):** ISLP ch. 5. Implement batch-wise CV. - **Build:** [ ] Mon: hours-to-detection on every faulty batch; false-alarm rate on normal batches; one table. Done when the table regenerates with `make report`. [ ] Wed: README with the headline number, `CITATION.cff`, Zenodo integration. Done when the DOI badge renders. [ ] Fri: release `batchlens v0.1`. Send the repo to your Sumble friend for a 30-minute code review. - **Sun:** L-Sun: "Deviation detected X hours before harvest on N faulty batches, Y% false alarms". **Z1**. Gauge: **L1**, and L2 for the DOI. - **Only one thing:** the release with the DOI. ### Week 5 · FermLit begins: the corpus - **Goal:** 500 open-access fermentation papers on disk and a written schema. - **Tue (B5):** Biology 2e ch. 14-15; Rosalind 1-4. - **Thu (M5):** ISLP ch. 6.1-6.2, 8. LightGBM soft sensor v0. - **Build:** [ ] Mon: Prompt P8 with Semantic Scholar: does a fermentation literature dataset already exist? Write `fermlit/RELATED.md` with what you found (this decides "novel" vs "extension"). Done when the file has ≥ 10 verified references. [ ] Wed: Europe PMC query (`"fed-batch" OR "fermentation" AND ("titer" OR "titre") AND OPEN_ACCESS:y`, refine), download full-text XML for 500 papers. Done when 500 XML files and a manifest exist. [ ] Fri: `schema/fermlit.schema.json` v0: organism, strain, product, reactor type, working volume, mode (batch/fed-batch/continuous), medium carbon source, temperature, pH, DO setpoint, feed strategy, duration, final titer (g/L), yield, productivity, scale, plus a provenance sentence per field. Done when three papers annotated by hand against it fit. - **Sun:** L-Sun: "500 fermentation papers, one schema: what I want the robot to read". L-Wed. - **Only one thing:** `RELATED.md`. If someone did it already, you must know now. ### Week 6 · The gold set (the most important week of Half 1) - **Goal:** 50 papers annotated by hand, guidelines written, second annotator recruited. - **Tue (B6):** Microbiology ch. 12; Rosalind 5-8. - **Thu (M6):** Murphy ch. 2 or Statistical Rethinking L1-2. - **Build:** [ ] Mon: `annotate/GUIDELINES.md` with Prompt P7. Annotate 15 papers. Done when 15 gold JSONs with provenance sentences exist. [ ] Wed: annotate 20 more; refine guidelines with every ambiguity you hit. [ ] Fri: annotate 15 more (50 total). Message two people to annotate 30 of them independently (a founder-scientist, a biology student, or a paid postdoc). Done when one has said yes. - **Sun:** L-Sun: "I read 50 fermentation papers by hand this week. Here is what they never tell you" (first hint of the reporting finding). L-Wed. - **Only one thing:** 50 gold JSONs. ### Week 7 · The extraction agent, measured - **Goal:** first per-field precision and recall. - **Tue (B7):** MIT 7.016 protein lectures; Michaelis-Menten fitted. - **Thu (M7):** Statistical Rethinking L3-4. - **Build:** [ ] Mon: `extract/agent.py`: one paper in, schema JSON out with provenance; Claude via API and local Qwen 7B as two backends; `--fake` backend for tests. Done when 5 papers extract. [ ] Wed: `eval/score.py`: per-field exact and fuzzy match against the gold set, precision/recall/F1, confusion by field. Done when the table prints for 50 papers. [ ] Fri: error analysis: the 5 worst fields, why; one round of prompt and schema fixes; re-score. Log both numbers. - **Sun:** L-Sun: "An LLM read 50 papers; here is where it was wrong" with the per-field table. L-Wed. - **Only one thing:** the F1 table, whatever it says. ### Week 8 · GATE 8: technical report and L2 - **Goal:** the first artifact with scientific shape. - **Tue (B8):** review + 2-page summary; Prompt P3 quiz. - **Thu (M8):** Karpathy micrograd; rewrite from memory Saturday. - **Build:** [ ] Mon: soft sensor (titer from online variables), batch-wise CV, error at 25/50/75% of batch time; LightGBM vs PLS. Done when the curve regenerates. [ ] Wed: **Technical report 1** (8-10 pages, Quarto): batchlens on IndPenSim: golden batch, deviation detection, soft sensor; methods reproducible; "where it fails". Prompt P9 for the skeleton, P10 for review. [ ] Fri: release `batchlens v0.2` + report PDF on Zenodo. Send the report to 5 archetype-A founders with the Track F outreach line: "would you tell me in 30 minutes whether this resembles your tank?" - **Sun:** **M2** deep dive: "What 100 open fermentation batches taught me about early deviation detection". **Z2**. L-Sun. Gauge check: **L2**. - **Gate 8 passes when:** report on Zenodo with DOI, 5 founders messaged, F1 table public. - **Only one thing:** the report on Zenodo. ### Week 9 · FermLit at scale - **Goal:** 2,000 papers extracted with the measured pipeline. - **Tue (B9):** NPTEL bioreactors 1-3; balances in a notebook. - **Thu (M9):** Karpathy makemore part 1; bigram on organism names for fun. - **Build:** [ ] Mon: corpus to 2,000 papers (same query, more years, plus bioRxiv API for preprints). [ ] Wed: batch extraction with the cheapest backend that meets the F1 floor you set in week 7 (cost table with the v3 telemetry schema). Done when 2,000 JSONs exist. [ ] Fri: completeness dashboard: per field, % of papers that report it. This is the seed of the reporting finding. - **Sun:** L-Sun: "2,000 fermentation papers in one table. X% do not report pH. Y% do not report volume." L-Wed. - **Only one thing:** the completeness table. ### Week 10 · Inter-annotator agreement and the first real analysis - **Goal:** kappa reported; first descriptive science. - **Tue (B10):** NPTEL 4-6; yields on IndPenSim. - **Thu (M10):** GPML ch. 1-2; GP soft sensor with bands. - **Build:** [ ] Mon: compute Cohen's kappa per field between you and the second annotator; adjudicate disagreements; freeze gold v1. Done when kappa is in `eval/README.md`. [ ] Wed: descriptive analysis notebook: titer by organism and product, productivity vs scale, reporting completeness by year and journal. [ ] Fri: figures 1-4 of Paper 1 drafted (Plotly to static). Done when `make figs` regenerates them. - **Sun:** L-Sun: "Two humans, 30 papers, kappa = X: how much can you trust a dataset built by an LLM?" L-Wed. - **Only one thing:** kappa. ### Week 11 · Dataset engineering and the reporting finding - **Goal:** FermLit v0.5 as a proper dataset. - **Tue (B11):** NPTEL kLa and scale-up; kLa estimate. - **Thu (M11):** Frazier + BoTorch; BO on the simulator feed profile. - **Build:** [ ] Mon: units normalization (g/L, mg/L, %), organism name normalization (NCBI taxonomy IDs via API), dedup. [ ] Wed: the reporting-completeness analysis as a formal result: which fields are missing, how that changed over 20 years, whether journals differ (with multiple-testing correction from B14 later). [ ] Fri: dataset card draft (HF), license (CC BY 4.0 for the extracted table; papers not redistributed), `dataset/` export. - **Sun:** L-Sun: "The fermentation literature has a reporting problem, in numbers". L-Wed. - **Only one thing:** normalized dataset export. ### Week 12 · Endorsement route opens; hybrid model v0 - **Goal:** three routes to an arXiv endorser started; first hybrid model. - **Tue (B12):** FDA PAT + ICH Q8; glossary. - **Thu (M12):** Brunton ch. 7; SINDy recovers Monod from simulator data. - **Build:** [ ] Mon: Part 9.5 routes: (1) message the two most-engaged founders from week 8 with the FermLit completeness figure and ask whether they would co-author or endorse; (2) email two authors you cite heavily (Prompt P13); (3) list three local academics (UBA, UNSAM, Leloir) who publish on arXiv q-bio. Done when three messages are sent. [ ] Wed: `batchlens/hybrid/`: Monod ODE with parameters fitted per batch + GBM on the residual; compare to pure GBM on 10 batches. [ ] Fri: learning-curve harness: train on 5, 10, 20, 50 batches, test on held-out normal and faulty batches, 5 seeds. Done when the curve plots. - **Sun:** L-Sun: "Does knowing the biology help the model? Hybrid vs pure ML at 5, 10, 20, 50 batches (first look)". L-Wed. - **Only one thing:** the three endorsement messages. ### Week 13 · GATE 13: FermLit v0.5 public, Paper 1 outlined - **Goal:** dataset on Hugging Face and Zenodo; Paper 1 skeleton with all figures. - **Tue (B13):** MSMB ch. 1-2. - **Thu (M13):** von Stosch review + Neural ODE abstract. - **Build:** [ ] Mon: HF dataset upload with card, Zenodo DOI, `fermlit v0.5` release. [ ] Wed: Paper 1 skeleton with Prompt P9: title, abstract with numbers, 6 figures placed, methods bullet-pointed. [ ] Fri: Prompt P10 (reviewer 2) on the skeleton; list of every claim without evidence; plan the fixes. - **Sun:** **M3** deep dive: "FermLit: 2,000 fermentation papers, one table, and what is missing from them". **Z3**, **HF**. L-Sun. Gauge: L2 solid, L3 in sight. - **Gate 13 passes when:** dataset public with DOI and card; Paper 1 skeleton has every figure; at least one endorsement conversation alive. - **Only one thing:** the HF dataset. ### Week 14 · Write Paper 1 - **Goal:** full draft. - **Tue (B14):** MSMB ch. 6; correction applied to the journal comparison. - **Thu (M14):** CS336 L12 + CS224N judge lecture; calibrated judge for fuzzy fields (does "Yarrowia lipolytica Po1f" match "Y. lipolytica"?). - **Build:** [ ] Mon: Introduction and Related work (from `RELATED.md`, every citation verified via Semantic Scholar). [ ] Wed: Methods (corpus, schema, guidelines, agents, evaluation, kappa) and Data availability. [ ] Fri: Results (completeness finding, per-field F1, descriptive tables) and Limitations (honest: extraction errors, OA bias, English-only). - **Sun:** L-Sun: "Writing my first paper at 25 years of career: what a methods section really is". L-Wed. - **Only one thing:** Methods section. ### Week 15 · Review, revise, human read - **Goal:** a draft a scientist has read. - **Tue (B15):** three papers with P11, IMRaD noted. - **Thu (M15):** Jurafsky & Martin IE chapter; kappa written into Methods. - **Build:** [ ] Mon: Prompt P10 twice; fix every [VERIFY]. [ ] Wed: send to one human scientist (co-author candidate, endorser, or paid postdoc, USD ~100 for 2 hours). [ ] Fri: figures final; `make paper` builds the PDF from the repo. - **Sun:** L-Sun: "Reviewer 2 is an LLM and a postdoc; both found things". L-Wed. - **Only one thing:** human review requested. ### Week 16 · GATE 16: Paper 1 on bioRxiv (L3) - **Goal:** submitted. - **Tue (B16):** ESM-2 paper + MIT 6.874 protein lecture; ESM-2 running. - **Thu (M16):** conformal prediction sections 1-2; intervals on the soft sensor. - **Build:** [ ] Mon: incorporate human review. [ ] Wed: bioRxiv submission (category: Bioengineering or Systems Biology), code and data DOIs in the paper. [ ] Fri: LinkedIn article version of the paper for non-scientists; send the preprint to the 10 founders you have talked to and the 3 scientific-data companies you admire (no ask, just the link). - **Sun:** L-Sun: the preprint. **X1**. Gauge: **L3**. - **Gate 16 passes when:** bioRxiv shows the DOI. - **Only one thing:** the submission. ### Week 17 · Paper 2 experiments, complete - **Goal:** every number in Paper 2 exists. - **Tue (B17):** FLIP + Meltome papers; Meltome split loaded. - **Thu (M17):** ESM-2 embeddings + ridge head on Meltome; baseline number. - **Build:** [ ] Mon: full learning-curve experiment (mechanistic, pure ML, hybrid × 5/10/20/50 batches × 5 seeds), conformal intervals, coverage. [ ] Wed: transfer to faulty batches; hours-to-detection with each model. [ ] Fri: `paper-2` repo with `make figs`; `batchlens v0.3` + **Z4**. - **Sun:** L-Sun: "Hybrid wins below 20 batches, loses above 50: the full curve". L-Wed. - **Only one thing:** the learning curves with seeds. ### Week 18 · Write Paper 2 - **Goal:** full draft. - **Tue (B18):** two extremophile founder papers; outreach lines. - **Thu (M18):** Peyton Jones talk + NeurIPS checklist; rewrite. - **Build:** [ ] Mon: Intro + Related work (hybrid modeling, soft sensors, IndPenSim benchmarks; all verified). [ ] Wed: Methods + Results. [ ] Fri: Limitations (simulated data, one process, no real plant), Reproducibility statement. - **Sun:** **M4** deep dive: "When does biology help the model? A data-scarcity study on 100 open batches". L-Sun. - **Only one thing:** Results section. ### Week 19 · Endorsement closed; review; site - **Goal:** endorser confirmed; draft reviewed; one-screen site. - **Tue (B19):** scanpy PBMC tutorial. - **Thu (M19):** effect sizes and intervals across both papers. - **Build:** [ ] Mon: close the endorsement (Part 9.5); if none yet, bioRxiv fallback prepared. [ ] Wed: Prompt P10 twice + human read (same reviewer as Paper 1, or the endorser). [ ] Fri: one-screen personal site: two preprints, one instrument, one dataset, the gauge level, contact. - **Sun:** L-Sun. L-Wed. Employee track: 5 applications to scientific-data or bioprocess-AI companies with the two preprints as the cover letter. - **Only one thing:** the endorser's yes. ### Week 20 · GATE 20: Paper 2 on arXiv, endorsed (L4-endorsed) - **Goal:** submitted, endorsed; talk proposed; Half 2 planned. - **Tue (B20):** mock lab meeting (Prompt P3). - **Thu (M20):** mock ML interview (Prompt P3). - **Build:** [ ] Mon: arXiv submission (cs.LG, cross-list q-bio.QM) with endorsement; bioRxiv as well if the endorser is late. [ ] Wed: talk proposal to a biotech event in Buenos Aires or Rosario, or an online LATAM one: "Two thousand fermentation papers and a hundred open batches: what the data says". [ ] Fri: public retro (what the gauge says, what slipped); Half 2 `PROGRESS.md` lines written. - **Sun:** L-Sun retro. **X2**. Gauge: **L4 (endorsed)**. - **Gate 20 passes when:** arXiv ID exists, or bioRxiv ID exists with an endorsement conversation dated. - **Only one thing:** the submission. --- ## PART 7 · Weeks 21-40, week by week (Half 2: depth, co-author, money) Same weekly shape. Study tracks become lighter (one block instead of two) because the build blocks now carry Paper 3 and the instruments. Details for each week can be expanded later with Prompt P15; the gates and outputs here are fixed. | Week | Goal | Study (Tue) | Build (Mon/Wed/Fri) | Output | |---|---|---|---|---| | 21 | Paper 3 scoped with a partner | Meltome and FLIP deep read; extremophile enzyme families of the 3 candidate partners (Puna Bio, Antarka, APEXzymes) | `enzymelens` repo; ESM-2 embeddings for the full Meltome split cached on disk (overnight on the 3050); ridge/GBM/MLP heads; baseline table | Partner call booked with the FermLit + batchlens preprints as the calling card | | 22 | Baselines beaten honestly | Brady Neal causal inference lecture 1 (for confounders in Paper 3's analysis) | Cross-validation by protein family (no leakage); conformal intervals; compare to FLIP published baselines | L-Sun with the table | | 23 | Extremophile focus | Partner's organisms and enzyme classes | Filter and enrich: sequences from UniProt for the partner's enzyme class; predict thermostability; ranked candidate list with uncertainty | Candidate list shared with the partner under a simple collaboration note | | 24 | Co-authorship agreed | How wet-lab validation works (partner explains; you take notes) | Validation plan: which 5-10 candidates the partner can express and test in their lab; what "success" is, pre-registered in the repo | Written co-author agreement (one paragraph, emailed) | | 25 | Instrument 2 trialkit (field module) | NIST ch. 5 (DoE); statsmodels MixedLM | `trialkit/ingest` and `report` (v4 weeks 9-10 content) | `trialkit v0.1`, Z5 | | 26 | Instrument 2 (plate module) | Assay statistics (4PL, growth curves, Z′) | `trialkit/plate` (v4 week 11) | L-Sun | | 27 | Loop Audit offer, built on papers | v4 Part 4 offers, re-read | Offer one-pager rewritten with the two preprints as proof; sent to 10 (5 founders who engaged, 3 established companies, 2 funds) | 3 calls booked | | 28 | GATE 28: first Loop Audit sold | none (selling week) | Audit scoped and half paid; Paper 3 wet-lab results start arriving | **First invoice** (any amount). If not: week 29 is a second selling week | | 29 | Audit delivery 1 | Client's domain reading | Loop map, data inventory, cycle-time and readiness numbers, one prototype on their sample | Client readout | | 30 | Audit delivery 2; Paper 3 results | Statistics for validation results (small n) | Analysis of the partner's validation; figures | Anonymized case study drafted | | 31 | Write Paper 3 | Research craft refresher | Draft with co-author; Prompt P10; co-author's lab reads it | Draft to co-author | | 32 | GATE 32: Paper 3 preprint (L4 co-authored) | none | bioRxiv submission with co-author; second audit or pilot proposal sent | **X3**. Case study published with client permission | | 33 | Instrument 3 labdocs, part 1 | 21 CFR Part 11 + ALCOA+ (v4 catalog 15) | eLabFTW on the Debian box; local VLM extraction; audit log (v4 week 14) | L-Sun | | 34 | labdocs, part 2 | Retrieval evaluation | "Ask the lab" with citations; 60 labeled Q&A; judge agreement (v4 week 15) | L-Sun | | 35 | labdocs, part 3 | Privacy basics | Deviation drafting + on-prem benchmark table (v4 weeks 16-17) | `labdocs v0.1`, Z6, M5 deep dive | | 36 | GATE 36: journal + JOSS | Journal author guides | Paper 1 to Scientific Data or a bioprocess journal; Paper 2 to Computers & Chemical Engineering or Biotechnology and Bioengineering (pick one each); JOSS submission for batchlens | Submissions logged | | 37 | Pilot delivery or job process | Client or interview prep | Pilot (6-8 weeks) running, or interviews at scientific-data companies with three preprints | Weekly client readout | | 38 | Talk delivered | Talk rehearsal (Prompt P3) | 20-minute talk at the event accepted in week 20 | Video posted | | 39 | Fund package | none | Portfolio data-readiness package proposed to one fund (GridX first) | Proposal sent | | 40 | GATE 40: retro and decision | none | Gauge level, income to date, pipeline, offers. Decide: consultant with retainers, or employee, or both | Public retro | **Gate 40 passes when:** three preprints (one co-authored), two journal submissions, one JOSS submission, three instruments with DOIs, one talk delivered, at least one paid engagement delivered, and a decision written down. --- ## PART 8 · The prompt pack (copy, paste, fill the brackets) Use Claude for P1-P2, P5-P10, P12-P15; NotebookLM (sources loaded) for P3-P4; either for P7, P11, P13. **P1 · Kid explainer (study, every Tue/Thu when a concept blocks)** ``` Explain [CONCEPT] to me three times. 1. As if I were 10: one everyday object, no jargon, no equations. 2. As if I were a systems engineer with 25 years of experience: what problem it solves, what breaks without it, an analogy from infrastructure (queues, caches, budgets), never from brains. 3. As the textbook would: the equation, every symbol named in one line each, and a tiny worked example with numbers I can redo by hand. Then: one question to check I understood, and wait for my answer. ``` **P2 · Concept-to-project bridge (before a build block)** ``` I am building [batchlens / fermlit / enzymelens]: [one-sentence description]. Here is what I just studied: [paste notes]. Give me the 3 concrete places where this applies today, ordered by impact over effort. For each: file and function to touch, 10 lines of pseudocode max, the metric that should move, and how I would know I applied it wrong. Skip anything that requires training a model from scratch or new tools. ``` **P3 · Socratic quiz (NotebookLM, Sunday or review weeks)** ``` Act as [a PI running a lab meeting / a staff ML scientist at a bioprocess-AI company]. Ask me 5 questions of increasing difficulty about [TOPIC], one at a time, waiting for my answer. After each answer, score my rigor 1-5 and say exactly what was missing. Call out vagueness without diplomacy. The last question must be a design case with a cost or data constraint. ``` **P4 · Gap detector (NotebookLM, Sunday, 20 min)** ``` Below is what I understood this week. Compare it against the sources in this notebook. Return: (1) what I said correctly, one line each; (2) what I said wrong or imprecisely, with the exact quote from the source that corrects me; (3) what I did not mention and should have, ordered by importance; (4) the single thing to revisit on Monday. MY SUMMARY: [paste] ``` **P5 · Practice scaffold (turns a lecture into one 90-minute build block)** ``` I have 90 minutes, a Python repo [NAME], and this lecture/chapter: [summary or link]. My project needs [the week's build task]. Write me a single-block plan: the one file to create or edit, the function signatures, the test that proves it works, and the "done when" line. No alternatives, no options, no "you could also". If the task does not fit in 90 minutes, cut scope and tell me what you cut. ``` **P6 · Staff-engineer code review (Fridays, before release)** ``` Review this code as a staff engineer at a scientific-software company who cares about reproducibility. Report only: (1) anything that makes a result non-reproducible (seeds, data leakage across batches, hidden state); (2) anything a reviewer of a paper would flag; (3) the smallest change that fixes each. Ignore style. [paste code or diff] ``` **P7 · Annotation guidelines writer (week 6)** ``` I am building a gold standard for extracting these fields from fermentation papers: [schema]. Write annotation guidelines: for each field, a one-line definition, the unit and normalization rule, two positive examples and two tricky negative examples (things that look like the field but are not), and what to do when the paper reports a range, several conditions, or nothing. End with a 10-item checklist an annotator runs before saving a paper. ``` **P8 · Related-work scout (with verification rule)** ``` I am writing a paper on [topic]. List the 15 most relevant prior works you know: title, first author, year, venue, one line on what they did, and one line on how mine differs. Mark each as [SURE] or [VERIFY]. I will check every one on Semantic Scholar before citing; do not invent titles. Then list 5 search queries I should run to find what you might have missed. ``` **P9 · Paper skeleton (weeks 8, 13, 18, 31)** ``` From my notes and results below, build the skeleton of a paper for [venue]. Structure: title with a measured number; abstract with the numbers; introduction (problem in one paragraph, gap, contributions as 3 bullets); related work headings; methods sections with the exact subsections; results with one heading per figure and the figure caption drafted; limitations; data and code availability. Mark [VERIFY] on every claim not supported by my numbers. Do not write prose I have not earned; leave bullet points where I still lack evidence. NOTES AND RESULTS: [paste] ``` **P10 · Reviewer 2 (every draft)** ``` You are a skeptical reviewer for [venue] with expertise in [bioprocess modeling / information extraction / protein ML]. Read the draft and produce: (1) three reasons to reject, most damaging first; (2) every claim that lacks evidence or an interval; (3) every place where a method is not reproducible from the text; (4) the missing related work you suspect exists (mark [VERIFY]); (5) what would change your recommendation to accept. Be harsh and specific; quote the lines. [paste draft] ``` **P11 · Founder paper digest (Thursday, 30 min)** ``` Digest this paper for me: (1) the question and why it matters, in two lines; (2) the method, in five lines a non-biologist can follow; (3) the main result with its numbers; (4) the data they generated and how they analyzed it (tools, statistics, spreadsheets or code?); (5) three questions I could ask the author that show I read it; (6) one honest sentence I could write to them that connects their data workflow to what I build, with no pitch. [paste paper text or abstract + methods] ``` **P12 · Unblocker (when frozen at the start of a block)** ``` I am frozen. The task is: [TODAY.md line]. Ask me three questions, one at a time: what is the smallest visible thing I could produce in 10 minutes; what am I afraid will happen if I start; what would I tell a colleague to do. Then give me the first 3 commands or lines to type. Nothing else. ``` **P13 · Endorsement or co-authorship request (weeks 12-19, 21-24)** ``` Draft a short email to [NAME], author of [PAPER], from someone with 25 years in engineering and a first preprint in [topic]. Purpose: [arXiv endorsement for category X / feedback / co-authorship on a validation study]. Include: one specific thing from their paper I built on (I will fill it), the link to my preprint and DOI, exactly what I am asking and how long it takes them, and an easy no. Under 150 words. No flattery. ``` **P14 · Lost-week compressor (Sunday after a lost week)** ``` I lost this week. The plan for next week is: [paste week]. Cut it to the single build task and the single study unit that keep the next gate alive, and tell me what to skip permanently versus what to move. Give me the six TODAY.md lines. ``` **P15 · Week expander (for other models, Half 2)** ``` Expand week [N] of this master plan into the same format as Part 6: goal, Tue and Thu study with exact units and links, three build tasks with "done when", Sunday output, "only one thing". Do not change the gate, the output, or the rules in Part 0. Use only free resources and mark any link you are not sure of as (verify). ``` --- ## PART 9 · Branding, connection ladder, endorsement ### 9.1 Profiles (week 0, updated at each gate) LinkedIn headline (week 0): `Applied AI engineer becoming an AI scientist for biotech | Building open instruments and datasets for fermentation and lab data, in public | 25 yrs leading delivery, ex-Big Four AI transformation lead` At week 16 replace the middle with: `Author of FermLit (bioRxiv): 2,000 fermentation papers, one validated dataset`. At week 20 add the arXiv paper. At week 32 add "co-author with [lab]". About: the thesis statement of Part 3.2 in plain words (3 lines), the gauge level with links, one line on the on-prem angle, one line on what you are looking for (collaborators and, from week 27, Loop Audit clients). GitHub: real photo, one-line bio, `fermlit` and `batchlens` pinned, profile README with the headline numbers and DOIs. Google Scholar profile at week 16 (preprints count). ORCID at week 0 (free, 5 minutes, needed by bioRxiv and journals). ### 9.2 Content pillars and cadence (same as v4, re-pointed) | Pillar | Channel | Cadence | Source | |---|---|---|---| | Build log with a number | LinkedIn Sunday (+ X copy) | Weekly | The week's build | | Learning in public | LinkedIn Wednesday note, 10 min | Weekly | Tuesday/Thursday notes: "what I got wrong about Monod this week" | | Findings | Medium deep dives | Weeks 2, 8, 13, 18, 35 | Gates | | Science | bioRxiv/arXiv | Weeks 16, 20, 32 | Papers | | Proof | GitHub + Zenodo + HF | Continuous | Releases | | Talk | Event | Week 38 | Everything | Rules unchanged from v4: one number or one admitted failure per post; archetypes, never named weaknesses; zero EY data. ### 9.3 The connection ladder (founders and scientists) Each rung is one small ask; never skip a rung. 1. **Read** their paper (Track F). Save the outreach line. 2. **React** with substance to one of their posts or papers (one comment that shows you read it). 3. **Send a finding** relevant to them (a FermLit figure about their organism, a batchlens number) with no ask. 4. **Ask 30 minutes** to hear how they do it today. 5. **Run something free** on a sample of their data or literature. 6. **Ask for feedback** on a preprint draft (scientists like being asked). 7. **Ask for endorsement or co-authorship** when there is a concrete result that touches their work. 8. **Offer the Loop Audit** only after rung 6, and only from week 27. Targets: 5 founders at rung 4 by week 8; 10 by week 16; 2 at rung 7 by week 24; 3 at rung 8 by week 28. ### 9.4 Connection ladder (established companies, funds, scientific-data companies) - **Funds (GridX first):** send the FermLit preprint at week 16 to your GridX contact with one question: "which three portfolio companies would find this useful?" Funds love giving their startups free expert attention. The portfolio package (v4) comes at week 39, after case studies exist. - **Established companies (Bioceres, Insud, Biogénesis Bagó, Terragene, Porta...):** find the head of R&D or QA on LinkedIn; the Paper 2 preprint is the message; ask for 30 minutes on "how you monitor batches today". Week 21 onwards. - **Scientific-data companies (DataHow, Aizon, TetraScience, Uncountable, Benchling, Novasign...):** these read arXiv. Week 19: apply to 5 with the two preprints as the cover letter. Week 32: apply again with three. Employee track and consulting track are not exclusive; a fractional contract with one of them is a valid Half 2 outcome. ### 9.5 arXiv endorsement: three parallel routes (start week 12) 1. **Co-author route:** a founder-scientist or academic who already publishes on arXiv co-authors Paper 2 or Paper 3; co-authors who are registered can submit. 2. **Cited-author route:** email two authors of the papers you cite most (Prompt P13), with the bioRxiv preprint of Paper 1 as proof you are serious. Endorsement asks are normal and usually answered within a week. 3. **Local academia route:** UBA, UNSAM, Instituto Leloir, CONICET groups that publish in q-bio or cs.LG; ask via a founder's introduction, or attend one seminar and ask in person. Fallback: bioRxiv accepts computational papers without endorsement; Paper 2 goes there at week 20 if arXiv is not yet possible, and is added to arXiv when an endorser appears. The gate is "preprint public", the target is "endorsed". ### 9.6 Templates (reuse v4 section 9 for build log, gap note, video, targeted message, audit one-pager) Only one template changes. Targeted message (weeks 16+): ``` Hi [NAME], I read [their paper]; [one specific line showing it]. I just put out a preprint on [FermLit / hybrid soft sensors]: [one number]. Code and data: [DOIs]. [Their organism / process] appears in [N] of the papers in the dataset; I attached the figure. If it is useful, I would like 30 minutes to hear how you [monitor batches / record trials] today. No pitch. [NAME] ``` --- ## PART 10 · Money (unchanged in substance from v4, moved to Half 2) Offers, prices and the "sell weeks not savings" rule are in v4 Part 4. Timing in v5: offer built week 27, first invoice gate week 28 (slips to 29 at most), pilot week 37, fund package week 39, retainer or job decision week 40. The employee track runs in parallel from week 19 with no conflict: a job offer at week 25 that passes the culture filter (v4 section 3.3) is a valid way to end the plan early. --- ## PART 11 · Rescue protocols (for the days the plan meets your brain) | Situation | Protocol | |---|---| | Frozen at the start of a block | Prompt P12. Then 10 minutes on the smallest visible thing. If still frozen after 10, walk 15 minutes and retry once. If still frozen, log it and end the block; no guilt, the plan expects it | | Shiny object (new tool, new paper, new project) | `PARKING.md` in 20 seconds. Saturday sandbox if the week is done | | Hyperfocus on the current task past the timer | Allowed once per week, max 3 hours, never after 22:30, and the next day's block is cancelled to pay for it | | Boredom three blocks in a row on a study unit | The unit is not connected to a build. Skip it, note it in `PARKING.md`, move on. Come back only if a paper needs it | | Imposter moment ("I am a PM pretending to be a scientist") | Open `PROGRESS.md`, read the gauge level and the DOIs. Then read Part 0.1 point 2. A DOI is not an opinion | | Lost week | Prompt P14 on Sunday. Never two in a row | | Overwhelm at a gate | Cut to "only one thing". The gate passes on the artifact, not on the polish | | Nobody answers messages | Expected until week 16. After the preprint, response rates change. Keep the ladder, never skip rungs | | A founder says "we already have this" | Ask to see it and ask what it misses. That is a rung-4 conversation, which is the goal | | Extraction accuracy disappointing | Report it. The reporting-completeness finding does not depend on perfect extraction; the paper is about a measured method | --- ## PART 12 · Gates and metrics (the only numbers you track) | Week | Gate | Artifact that proves it | Gauge | |---|---|---|---| | 4 | Public and reproducible | batchlens v0.1 with DOI and headline number | L1-L2 | | 8 | Technical report | Report on Zenodo; F1 table public; 5 founders messaged | L2 | | 13 | Dataset public | FermLit v0.5 on HF + Zenodo; Paper 1 skeleton with figures; endorsement route alive | L2+ | | 16 | Paper 1 | bioRxiv DOI | L3 | | 20 | Paper 2 | arXiv ID (endorsed) or bioRxiv ID with endorsement dated | L4 (endorsed) | | 24 | Co-author agreed | Written agreement; validation plan pre-registered in repo | L4 (path) | | 28 | First invoice | Any amount | Money 1 | | 32 | Paper 3 | bioRxiv DOI with co-author | L4 (co-authored) | | 36 | Journal and JOSS | Two journal submissions, one JOSS | L5 (path) | | 40 | Decision | Retro with income, pipeline, offers | L5 | Weekly board (Sunday, 30 seconds): blocks done (target 5/7), days with commits (3/7), Sunday log (1), Wednesday note (1, skippable), founder conversations cumulative (5 by w8, 10 by w16, 20 by w28), gauge level, lost weeks in a row (never 2). Banned: stars, likes, followers, hours studied, videos watched. --- ## PART 13 · Ignore for 40 weeks - Vara as a project. Its telemetry and judge ideas live in `fermlit/eval` and `batchlens/report`. - Any wet-lab work by you. Paper 3's lab is the co-author's. - Protein design, generative biology, AlphaFold-scale anything. Zavia and Nanogrow's territory; you use embeddings, not design. - Clinical or patient data (archetype E). Half 3, if ever. - Building a LIMS, an ELN, a SaaS, a marketplace, a dashboard product. eLabFTW exists. - A fourth instrument before week 40. - Hourly consulting. Fixed price only. - Full courses (MIT 6.874 complete, CS336 complete, biostatistics MOOCs, cloud certifications). Part 4 is all that fits. - Naming a company's weakness in public. Ever. - A second side project. El Espejo stays at its current pace and does not count. - Reading the news about AI. Parking lot. --- ## PART 14 · Day 0 (tomorrow), minute by minute 1. (10 min) Create `cockpit` repo. Paste Part 6 and Part 7 into `PROGRESS.md`. Create the other four files, empty except `TODAY.md`: "Create `batchlens` repo with README skeleton from Part 5.2". 2. (20 min) Create `labloop-notes`, `batchlens`, `fermlit` with README skeletons and `CITATION.cff`. 3. (20 min) `uv` environment; `ollama pull qwen2.5:7b-instruct-q4_K_M`; confirm it answers. ORCID account. 4. (10 min) LinkedIn headline and About from Part 9.1. 5. (10 min) Post 0 (Part 6, week 0). 6. (5 min) `LOG.md` first line. Write Monday's `TODAY.md`: "open `batchlens/sim/monod.py`, write the four ODEs for X, S, P, DO with Monod growth; plot one batch." 7. Close everything. --- ## PART 15 · The one thing to remember At week 40 nobody will ask how many chapters you read. A scientist will ask **what you measured, how you validated it, and who checked it**. A founder will ask **how many days your instrument takes out of their loop**. A company will ask **what you published**. This document exists so that all three answers are a link, a number and a DOI.