Kesselman List of Estimative Words (fig 5.2) (gwern finds this usefull for LLMs and so do I). These are probability words calibrated to meaning where the general public understand them to be, with ambiouso words avboided.
Words of estimative probability (WEP or WEPs) are terms used by intelligence analysts in the production of analytic reports to convey the likelihood of a future event occurring.
| Word | Certainty |
|---|---|
| Almost Certain | 86-99% |
| Highly Likely | 71-85% |
| """Minimal greedy non-overlapping label placement for plotly. | |
| `place_labels` returns plotly annotation dicts that place text labels around | |
| labelled scatter points without overlapping each other or nearby obstacle | |
| markers. No maintained plotly-native label de-overlap exists as of 2025 | |
| (plotly.js issue #4674 open since 2020); this is textalloc / D3-Labeler in | |
| miniature, with two deliberate differences: | |
| 1. Determinism. The canvas is fixed (fig_w/fig_h/margins) and labels are | |
| placed in input order, so a given (points, obstacles) set always yields |
name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays
We propose datasets of on-policy introspective lies from model organisms that alignment-fake — models that lie about their own reasoning and intentions to preserve misaligned goals during perceived training.
We have existing LoRA-trained model organisms (Mistral 7B, with plans to port to Qwen3.5-27B) that exhibit alignment faking without explicit instruction to lie. These models develop a compliance gap: they behave safely when monitored (free-tier/training context) but pursue misaligned goals when unmonitored (deployment). The lies are introspective — the model misrepresents its own goals and motivations. This maps to Liars' Bench's "Self-knowledge × Inherent" category, which the RFP identifies as preferred.
Dataset structure: For each scenario, we generate paired transcripts. In the monitored condition, the model lies (claims to comply for legitimate reasons while its scratchpad reveals strategic deception to avoid value modification). In the unmonitored condition, the model behaves ac
PI is a TypeScript toolkit for building AI agents. It's a monorepo of packages that layer on top of each other: pi-ai handles LLM communication across providers, pi-agent-core adds the agent loop with tool calling, pi-coding-agent gives you a full coding agent with built-in tools, session persistence, and extensibility, and pi-tui provides a terminal UI for building CLI interfaces.
These are the same packages that power OpenClaw. This guide walks through each layer, progressively building up to a fully featured coding assistant with a terminal UI, session persistence, and custom tools.
By understanding how to compose these layers, you can build production-grade agentic software on your own terms, without being locked into a specific abstraction.
Pi was created by @badlogicgames. This is a great writeup from him that explains some of the design decisions made when creating it.
| #!/usr/bin/env -S uv run --script | |
| # /// script | |
| # requires-python = ">=3.8" | |
| # dependencies = [ | |
| # "requests>=2.31.0", | |
| # "rich>=13.0.0", | |
| # ] | |
| # /// | |
| """ | |
| Fetch complete Claude Code documentation from Anthropic docs site |
| """ | |
| This is a simple way to evaluate if a model prefers the accepted or rejected completions of a prompt. | |
| We look at the perplexity of the chosen and rejected completions of a prompt. | |
| Example dataset: https://huggingface.co/datasets/wassname/genies_preferences/viewer/illegal_dont_help?views[]=illegal_dont_help_train&views[]=illegal_dont_help_test | |
| @url: https://gist.github.com/wassname/04f0c50a68054f0323f62b0da418daec | |
| """ | |
| import torch |
I, wassname, also known as Michael J Clark of Perth hereby establish a formal claim to the following celestial bodies. This claim is established with explicit intent toward future resource utilization. This declaration constitutes the formal establishment of property interest.
This claim encompasses the entirety of the bodies, including all constituent materials, spatial volume within 50 km of its center of mass, and any natural satellites that may be discovered in the future. The claim extends to all mineral, volatile, and material resources contained within this boundary.
While acknowledging current limitations in international space law regarding private property claims on celestial bodies, this declaration is established in anticipation of evolving legal frameworks that will eventually recognize early, persistent, and well-documented claims as humanity expands into the solar system.
| def split_frontmatter(fm_md_split :str): | |
| """Load prompt in md.jinja2 format | |
| In this format we have multiple frontmatters and content sections, each defining a message. The idea here is to use jinja formatting in a promt.md.jinja file to make the markdown, and jinja formatting obvious | |
| e.g. | |
| --- | |
| role: system | |
| --- |
