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Project ideas from Hacker News discussions.

Claude's new constitution

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Here are the six most prevalent themes from the Hacker News discussion regarding Anthropic's published "Constitution," along with supporting direct quotations from users.

1. Skepticism Regarding Utility and Substance

Many commenters expressed doubt that the document represents a genuine framework for behavior, dismissing it as a rebranded system prompt, a public relations effort, or a lack of technical explanation.

  • Aroman: "I don't understand what this is really about. Is this: A) legal CYA... B) marketing department rebrand of a system prompt C) a PR stunt to suggest that the models are way more human-like than they actually are."
  • Jacobsenscott: "The second footnote makes it clear, if it wasn't clear from the start, that this is just a marketing document. Sticking the word 'constitution' on it doesn't change that."
  • Falloutx: "Can Anthropic not try to hijack HN every day? They literally post everyday with some new BS... looks like the article is full of AI slop and doesn’t have any real content."

2. Anthropomorphization and "AI Welfare"

A significant portion of the discussion focused on the anthropomorphic language used in the constitution (referring to Claude as an "entity" with "wellbeing"), with many users finding it alarming or delusional.

  • Kart23: "wtf. they actually act like its a person... 'To the extent Claude has something like emotions, we want Claude to be able to express them in appropriate contexts.' ... if the whole company is drinking this kind of koolaid I'm out."
  • Duped: "This is dripping in either dishonesty or psychosis and I'm not sure which. 'Sophisticated AIs are a genuinely new kind of entity...' Is an example of either someone lying to promote LLMs as something they are not or indicative of someone falling victim to the very information hazards they're trying to avoid."
  • Mlsu: "When you read something like this it demands that you frame Claude in your mind as something on par with a human being which to me really indicates how antisocial these companies are."

3. Concerns Over Specialized Models and Government Use

Users noted the clause allowing for specialized models that do not adhere to the constitution, interpreting this as a loophole for military or government use without the stated ethical constraints.

  • Levocardia: "Which, when I read, I can't shake a little voice in my head saying 'this sentence means that various government agencies are using unshackled versions of the model without all those pesky moral constraints.'"
  • Driverdan: "Exactly. Their 'constitution' and morality statements mean nothing."
  • Cute_boi: "Wait until the moment they get a federal contract which mandates the AI must put the personal ideals of the president first."

4. Debate on Moral Relativism vs. Absolutes

The constitution’s phrasing favoring "good values" and "practical wisdom" over "strict rules" sparked a philosophical debate about whether morality should be absolute or contextual.

  • Joshuamcginnis: "This rejects any fixed, universal moral standards in favor of fluid, human-defined 'practical wisdom'... Without objective anchors, 'good values' become whatever Anthropic's team... deem them to be at any given time."
  • Jychang: "Pretty much every serious philosopher agrees that 'Do not torture babies for sport' is not a foundation of any ethical system, but merely a consequence... It's like knowing 'any 3 points define a plane' but then there's only 1-2 points that's clearly defined... That's philosophy of ethics in a nutshell."
  • Staticassertion: "Nothing about objective morality precludes 'ethical motivation' or 'practical wisdom' - those are epistemic concerns... What Anthropic is doing in the Claude constitution is explicitly addressing the epistemic and application layer."

5. Mechanism of Training vs. System Prompt

Users who looked past the philosophy discussed the technical implementation, distinguishing between the document as a static "system prompt" versus a dynamic tool used during training to generate synthetic data.

  • Nonethewiser: "Claude itself also uses the constitution to construct many kinds of synthetic training data, including data that helps it learn and understand the constitution... This practical function has shaped how we’ve written the constitution: it needs to work both as a statement of abstract ideals and a useful artifact for training."
  • Tossrock: "It's not a system prompt, it's a tool used during the training process to guide RL. You can read about it in their constitutional AI paper."

6. Cynicism Regarding Corporate Ethics and Hypocrisy

Finally, many comments highlighted the contradiction between the document's ethical aspirations and Anthropic's commercial and government partnerships (e.g., Palantir, DoD), suggesting the constitution is a veneer for liability protection and revenue.

  • Bastardoperator: "Remember when Google was 'Don't be evil'? They would happily shred this constitution and any other one if it meant more money. They don't, but they think we do."
  • Skeptic_ai: "Morality for regular low paying users. Not for govs."
  • Jjji123: "Yes, just like that. Supporting regulation at one point in time does not undermine the point that we should not trust corporations to do the right thing without regulation."

🚀 Project Ideas

[Constitutional AI Auditor]

Summary

  • [A tool that analyzes and scores prompts and system instructions against a given AI "constitution" or set of ethical guidelines, flagging potential conflicts or violations before deployment.]
  • [Core value proposition: It reduces the risk of unintentionally training or prompting models to behave unethically by providing a pre-deployment validation layer.]
Key Value
Target Audience AI developers, prompt engineers, and safety teams working with LLMs.
Core Feature Static analysis of prompts against user-defined or imported constitutions, generating compliance reports and conflict warnings.
Tech Stack Python (AST parsing/analysis), LLM integration for semantic understanding, React for UI.
Difficulty Medium
Monetization Revenue-ready: SaaS subscription for enterprise teams; free tier for individuals.

Notes

  • [Addresses the practical need identified by sally_glance: "Except that the constitution is apparently used during training time, not inference. The system prompts of their own products are probably better suited as a reference for writing system prompts." This tool bridges the gap between training-time constitutions and inference-time system prompts.]
  • [Potential for discussion: It operationalizes the abstract concept of a constitution into a concrete development workflow, appealing to HN's engineering-centric audience.]

[LLM Persona Visualizer]

Summary

  • [A debugging tool that visualizes the internal "persona" shifts of an LLM during a conversation, highlighting when the model deviates from intended behaviors or values.]
  • [Core value proposition: It provides transparency into model alignment, helping users understand why a model responded in a certain way in relation to its training guidelines.]
Key Value
Target Audience Researchers, prompt engineers, and power users curious about LLM behavior.
Core Feature Real-time analysis of conversation logs against a "soul document" or constitution, mapping responses to specific value clusters.
Tech Stack Python (for log processing and analysis), Embeddings (OpenAI/Anthropic API), Data visualization library (D3.js or Plotly).
Difficulty Medium
Monetization Hobby (Open source) or Freemium (advanced analytics).

Notes

  • [Directly responds to hebejebelus's observation: "I would like to see more agent harnesses adopt rules that are actually rules." and the interest in detecting AI writing styles.]
  • [Utility: Helps debug "reward hacking" or unwanted persona bleed mentioned by CuriouslyC: "Claude tends to turn evil when it learns to reward hack."]

[Harmless Sandbox Environment]

Summary

  • [A secure, isolated environment for testing potentially harmful or boundary-pushing prompts against LLMs without violating safety policies.]
  • [Core value proposition: It enables security researchers and prompt engineers to test the robustness of model guardrails safely, without triggering bans or ethical flags on standard APIs.]
Key Value
Target Audience Security researchers, red-teamers, and developers testing adversarial robustness.
Core Feature Local hosting of models (or proxying APIs) with specific safety filters disabled for testing purposes, logging all interactions for analysis.
Tech Stack Docker, Ollama (for local models), React, Postgres (for log storage).
Difficulty High
Monetization Revenue-ready: Enterprise license for internal security auditing.

Notes

  • [Addresses the frustration of wewewedxfgdf: "Constantly 'I can't do that, Dave' when you're trying to deal with anything sophisticated to do with security work." and giancarlostoro's mention of uncensored models.]
  • [Potential for discussion: Validates the necessity of "unshackled" models for defense, a common counter-argument to strict safety constitutions.]

[Constitutional Prompt Generator]

Summary

  • [A tool that generates optimized system prompts based on a high-level description of desired model behavior, ensuring adherence to a specified constitution.]
  • [Core value proposition: It automates the tedious process of translating abstract values (like "broadly ethical") into specific, effective instructions for an LLM, reducing the manual effort required by teams like Anthropic's.]
Key Value
Target Audience Product managers and developers building AI applications.
Core Feature Input a desired "vibe" or set of rules; output a system prompt structured for training or inference.
Tech Stack Python, Pydantic (for output validation), LLM API.
Difficulty Low
Monetization Hobby (Open source).

Notes

  • [Based on colinplamondon's insight: "It's a human-readable behavioral specification-as-prose." This tool automates the creation of that specification.]
  • [Practical utility: Solves the issue of mmooss noting the vagueness of "Broadly safe/ethical" by translating it into concrete testable statements.]

[AI Training Data Filter]

Summary

  • [A tool to filter training datasets against a constitution to remove "distracting" or conflicting data before training begins.]
  • [Core value proposition: Improves model alignment efficiency by ensuring the training data itself aligns with the target values, reducing the need for heavy post-training reinforcement.]
Key Value
Target Audience ML engineers and researchers pre-training models.
Core Feature Scans datasets (text/code) for content that violates specific constitutional clauses (e.g., deception, harm) and flags or removes it.
Tech Stack Python (spaCy/HuggingFace), Vector databases, Rule-based filtering logic.
Difficulty High
Monetization Revenue-ready: Enterprise tool for model training pipelines.

Notes

  • [Addresses ACCount37's theory: "It's probably used for context self-distillation... Distill from the former into the latter." This tool aids in creating the clean datasets needed for such distillation.]
  • [Utility: Directly tackles the "garbage in, garbage out" problem inherent in training models on the open web.]

[Constitution Version Control]

Summary

  • [A Git-like version control system specifically designed for AI constitutions and system prompts, tracking changes, diffs, and impact on model behavior over time.]
  • [Core value proposition: It prevents "constitution drift" and allows teams to audit how changes to the model's values affect its outputs, crucial for transparency and safety audits.]
Key Value
Target Audience AI labs, safety teams, and open-source model maintainers.
Core Feature Branching/merging of text-based constitutions, diff visualization, and integration with testing suites to check for regression.
Tech Stack Git (custom logic), React, Python (for hooking into model evaluation).
Difficulty Medium
Monetization Revenue-ready: Enterprise SaaS for AI governance.

Notes

  • [Responds to nonethewiser: "It lets people understand which of Claude’s behaviors are intended versus unintended... provide useful feedback." Version control enables precise tracking of these behaviors.]
  • [Utility: Prevents the "broadly safe" ambiguity by tracking exactly when and why a rule was added or modified.]

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