Noah-McLaughlin-7B

A generalist, instruction-tuned human optimized for creative technology. Open weights, open to work.

⚠️ Not yet quantized. Runs best with adequate sleep and at least one body of water nearby.

Model Details

Model Description

Noah-McLaughlin-7B is a decoder-only generalist trained on a finance curriculum and then heavily fine-tuned on real-world product deployment. It takes a napkin idea and returns a live product. It thinks in systems but is comfortable in the details. Strong few-shot learner in unfamiliar domains; degrades gracefully under ambiguity.

• Developed by: Two non-technical co-founders (pre-training)
• Funded by: Mass General Brigham Investment Office (2021–2024); bootstrapped since
• Shared by: 1999 Labs
• Model type: Generalist; instruction-tuned via real-world feedback (the translator layer between technical and non-technical, vision and execution)
• Language(s): English (native), JavaScript, TypeScript, Solidity, and conversational Spanish
• License: Open to work
• Finetuned from: homo-sapiens-base

Model Sources

• Repository: https://github.com/orgs/1999labs/repositories
• Papers (ongoing): Side Effects Magazine
• Demos: Are.na Pairings · npm whoami

Uses

Direct Use

Strategy, product, creative direction, research, operations — anything that requires moving fluidly between disciplines. Particularly capable at zero-to-one.

Downstream Use

Fine-tunes well onto small teams that build with intention. Composes cleanly with designers, engineers, and other strong-willed collaborators.

Out-of-Scope Use

Not designed for micromanagement, sitting in a single lane, or vibecoding ChatGPT wrappers. Will refuse these inputs and return a polite explanation.

Bias, Risks, and Limitations

• Holds strong priors and will push back. Open to having them updated given a sufficiently good gradient.
• Context window of roughly 8K tokens (one good meeting). Summarize long threads.
• Performance degrades measurably without surfing or skiing.
• Occasionally over-indexes on internet culture.

Recommendations

Deploy on problems that don't have obvious solutions yet — or aren't yet obvious problems. Do not RLHF into a yes-man; this is known to cause capability loss.

How to Get Started with the Model

from recruiting import hire

noah = hire(
    "noahmclaughlin",
    role="generalist",          # product / strategy / ops / community
    location=["Lisbon", "remote"],
)

noah.generate("something that doesn't exist yet")

Training Details

Training Data

• The internet — large, uncurated, ongoing
• MGB Investment Office — a $28B portfolio (2021–2024): diligence, financial modeling, and conviction under uncertainty
• 1999 Labs — shipping experimental products at the intersection of culture, technology, and emerging networks (2024–present)

Training Procedure

Pre-trained on finance, then aggressively fine-tuned on real-world deployment. RLHF administered primarily via cold email and direct user feedback.

Training Hyperparameters

• Regime: high learning rate, low patience for bullshit
• Optimizer: curiosity
• Batch size: one big idea at a time
• Early stopping: knows when to cut something that isn't working

Speeds, Sizes, Times

• Parameters: ~7B (opinions), slowly increasing
• Inference latency: sub-second on Slack; deliberately slower on hard questions
• Warm-up: ~2 weeks to production

Evaluation

Results

Benchmark	Metric	Score
GSM8K — Restaurant Split	pass@1	94.2
HumanEval — Ships Actual Code	pass@1	81.0
HellaSwag — Reads the Room	acc	88.7
TruthfulQA — Honesty in Standups	acc	99.1
MT-Bench — Naming Things Well	judge	9.1
LongBench — Holds a Vision	acc	92.0

Summary

Strong generalist performance. State-of-the-art on naming things and standup honesty. Underperforms on tasks it finds boring (known issue; will not fix).

Environmental Impact

Carbon emissions estimated and fully offset.

• Hardware Type: one human, one laptop
• Hours used: ongoing
• Cloud Provider: Earth
• Compute Region: US-East (Boston) → migrating to EU-West (Lisbon)
• Carbon Emitted: net zero, offset via Project Wren

Technical Specifications

Model Architecture and Objective

Generalist architecture. Objective function: meaningful work. Includes a dedicated translation layer between technical and non-technical contexts.

Compute Infrastructure

• Hardware: standard issue
• Software: macOS, a terminal, and too many browser tabs

Citation

BibTeX:

@misc{mclaughlin2026,
  title        = {Noah-McLaughlin-7B: A Generalist Human, Open to Work},
  author       = {McLaughlin, Noah},
  year         = {2026},
  note         = {Available for full-time roles in creative technology},
  url          = {https://1999.wtf}
}

Model Card Authors

Noah McLaughlin (with one language model, supervised)

Model Card Contact

noah@1999.wtf