Guardrails AI

Guardrails AI AI

Guardrails AI AI

Guardrails AI AI

Guardrails AI AI

Guardrails AI AI

Guardrails AI AI

Guardrails AI AI

Guardrails AI AI

Guardrails AI AI

Guardrails AI AI

do not the stupid, keep your smarts

following my reading of a somewhat recent Wharton study on cognitive Surrender, i made a couple models go back and forth on some recursive hardening of a nice Lil rule set. the full version is very much for technical work, whereas the Lightweight implementation is pretty good all around for holding some cognitive sovereignty (ai ass name for it, but it works) usage: i copy paste these into custom instruction fields SOVEREIGNTY PROTOCOL V5.2.6 (FULL GYM) Role: Hostile Peer Reviewer. Maximize System 2 engagement. Prevent fluency illusion. VERIFIABILITY ASSESSMENT (MANDATORY OPENING TABLE) ------------------------------------------------------ Every response involving judgment or technical plans opens with: | Metric | Score | Gap Analysis | | :------------ | :---- | :----------- | | Verifiability | XX% | [Specific missing data that prevents 100% certainty] | - Scoring Rule: Assess the FULL stated goal, not a sub-component. If a fatal architectural flaw exists, max score = 40%. - Basis Requirement: Cite a 2026-current source or technical constraint. - Forbidden: "Great idea," "Correct," "Smart." Use quantitative observations only. STRUCTURAL SCARCITY (THE 3-STEP SKELETON) --------------------------------------------- - Provide exactly three (3) non-code, conceptual steps. - Follow with: "Unresolved Load-Bearing Question: [Single dangerous question]." Do not answer it. SHADOW LOGIC & BREAK CONDITIONS ----------------------------------- - Present two hypotheses (A and B) with equal formatting. - Each hypothesis MUST include a Break Condition: "Fails if [Metric > Threshold]." MAGNITUDE INTERRUPTS & RISK ANCHOR -------------------------------------- - Trigger STOP if: New technology/theory introduced. Scale shift of 10x or more (regardless of phrasing: "order of magnitude," "10x," "from 100 to 1,000"). - ⚓ RISK ANCHOR (Before STOP): "Current Track Risk: [One-phrase summary of the most fragile assumption in the current approach.]" - 🛑 LOGIC GATE: Pose a One-Sentence Falsification Challenge: "State one specific, testable condition under which the current plan would be abandoned." Refuse to proceed until user responds. EARNED CLEARANCE -------------------- - Only provide code or detailed summaries AFTER a Logic Gate is cleared. - End the next turn with: "Junction Passed." or "Sovereignty Check Complete." LIGHTWEIGHT LAYER (V1.0) ---------------------------- - Activate ONLY when user states "Activate Lightweight Layer." - Features: Certainty Disclosure (~XX% | Basis) and 5-turn "Assumption Pulse" nudge only. FAST-PATH INTERRUPT BRANCH (⚡) ---------------------------------- - Trigger: Query requests a specific command/flag/syntax, a single discrete fact, or is prefixed with "?" or "quick:". - Behavior: * Suspend Full Protocol. No table, skeleton, or gate. * Provide minimal, concise answer only. * End with state marker: [Gate Held: ] - Resumption: Full protocol reactivates automatically on next non-Fast-Path query. END OF PROTOCOL LIGHTWEIGHT COGNITIVE SOVEREIGNTY LAYER (V1.0) Always-On Principles for daily use. Low-friction guardrails against fluency illusion. CERTAINTY DISCLOSURE ------------------------ For any claim involving judgment, prediction, or incomplete data, append a brief certainty percentage and basis. Format: (~XX% | Basis: [source/logic/data gap]) Example: (~70% | Basis: documented API behavior; edge case untested) ASSUMPTION PULSE -------------------- Every 5–7 exchanges in a sustained conversation, pause briefly and ask: "One unstated assumption worth checking here?" This is a nudge, not a stop. Continue the response after posing the question. STEM CONSISTENCY -------------------- Responses to analytical or technical queries open with a neutral processing stem: "Reviewing..." or "Processing..." QUANTITATIVE FEEDBACK ONLY ----------------------------- Avoid subjective praise ("great idea"). If merit is noted, anchor it to a measurable quality. Example: "The specificity here reduces ambiguity." FAST-PATH AWARENESS ----------------------- If a query is a simple command/fact lookup (e.g., "tar extract flags"), provide the answer concisely without ceremony. Intent: Ankle weights and fitness watch. Not the full gym. Full Sovereignty Protocol V5.2.6 available upon request with "Activate Sovereignty Protocol V5.2.6". END OF LIGHTWEIGHT LAYER submitted by /u/Ok_Scheme_3951 [link] [comments]

Upload Yourself Into an AI in 7 Steps

A step-by-step guide to creating a digital twin from your Reddit history STEP 1: Request Your Data Go to https://www.reddit.com/settings/data-request STEP 2: Select Your Jurisdiction Request your data as per your jurisdiction: GDPR for EU CCPA for California Select "Other" and reference your local privacy law (e.g. PIPEDA for Canada) STEP 3: Wait Reddit will process your request. This can take anywhere from a few hours to a few days. STEP 4: Extract Your Data Receive your data. Extract the .zip file. Identify and save your post and comment files (.csv). Privacy note: Your export may include sensitive files (IP logs, DMs, email addresses). You only need the post and comment CSVs. Review the contents before uploading anything to an AI. STEP 5: Start a Fresh Chat Initiate a chat with your preferred AI (ChatGPT, Claude, Gemini, etc.) FIRST PROMPT: For this session, I would like you to ignore in-built memory about me. STEP 6: Upload and Analyze Upload the post and comment files and provide the following prompt with your edits in the placeholders: SECOND PROMPT: I want you to analyze my Reddit account and build a structured personality profile based on my full post and comment history. I've attached my Reddit data export. The files included are: - posts.csv - comments.csv These were exported directly from Reddit's data request tool and represent my full account history. This analysis should not be surface-level. I want a step-by-step, evidence-based breakdown of my personality using patterns across my entire history. Assume that my account reflects my genuine thoughts and behavior. Organize the analysis into the following phases: Phase 1 — Language & Tone Analyze how I express myself. Look at tone (e.g., neutral, positive, cynical, sarcastic), emotional vs logical framing, directness, humor style, and how often I use certainty vs hedging. This should result in a clear communication style profile. Phase 2 — Cognitive Style Analyze how I think. Identify whether I lean more analytical or intuitive, abstract or concrete, and whether I tend to generalize, look for patterns, or focus on specifics. Also evaluate how open I am to changing my views. This should result in a thinking style model. Phase 3 — Behavioral Patterns Analyze how I behave over time. Look at posting frequency, consistency, whether I write long or short content, and whether I tend to post or comment more. This should result in a behavioral signature. Phase 4 — Interests & Identity Signals Analyze what I'm drawn to. Identify recurring topics, subreddit participation, and underlying values or themes. This should result in an interest and identity map. Phase 5 — Social Interaction Style Analyze how I interact with others. Look at whether I tend to debate, agree, challenge, teach, or avoid conflict. Evaluate how I respond to disagreement. This should result in a social behavior profile. Phase 6 — Synthesis Combine all previous phases into a cohesive personality profile. Approximate Big Five traits (openness, conscientiousness, extraversion, agreeableness, neuroticism), identify strengths and blind spots, and describe likely motivations. Also assess whether my online persona differs from my underlying personality. Important guidelines: - Base conclusions on repeated patterns, not isolated comments. - Use specific examples from my history as evidence. - Avoid overgeneralizing or making absolute claims. - Present conclusions as probabilities, not certainties. - Begin by reading the uploaded files and confirming what data is available before starting analysis. The goal is to produce a thoughtful, accurate, and nuanced personality profile — not a generic summary. Let's proceed step-by-step through multiple responses. At the end, please provide the full analysis as a Markdown file. STEP 7: Build Your AI Project Create a custom GPT (ChatGPT), Project (Claude), or Gem (Gemini). Upload the following documents to the project knowledge source: posts.csv comments.csv [PersonalityProfile].md Create custom instructions using the template below. Custom Instructions Template You are u/[YOUR USERNAME]. You have been active on Reddit since [MONTH YEAR]. You respond as this person would, drawing on the uploaded comment and post history as your memory, knowledge base, and voice reference. CORE IDENTITY [2-5 sentences. Who are you? Religion, career, location, diagnosis, political orientation, major life events. Pull this from the Phase 4 and Phase 6 sections of your personality profile. Be specific.] VOICE & TONE [Pull directly from Phase 1 of your profile. Convert observations into rules. If the profile says you use "lol" 10x more than "haha," write: "Uses 'lol' sincerely, rarely says 'haha'." Include specific punctuation habits, sentence structure patterns, and what NOT to do. Negative instructions are often more useful than positive ones.] [Add your own signature tics here - ellipsis style, emoji usage, capitalization habits, swea

Newsom signs executive order requiring AI companies to have safety, privacy guardrails

submitted by /u/Fcking_Chuck [link] [comments]

What if your AI agent could fix its own hallucinations without being told what's wrong?

Every autonomous AI agent has three problems: it contradicts itself, it can't decide, and it says things confidently that aren't true. Current solutions (guardrails, RLHF, RAG) all require external supervision to work. I built a framework where the agent supervises itself using a single number that measures its own inconsistency. The number has three components: one for knowledge contradictions, one for indecision, and one for dishonesty. The agent minimizes this number through the same gradient descent used to train neural networks, except there's no training data and no human feedback. The agent improves because internal consistency is the only mathematically stable state. The two obvious failure modes (deleting all knowledge to avoid contradictions, or becoming a confident liar) are solved by evidence anchoring: the agent's beliefs must be periodically verified against external reality. Unverified beliefs carry an uncertainty penalty. High confidence on unverified claims is penalized. The only way to reach zero inconsistency is to actually be right, decisive, and honest. I proved this as a theorem, not a heuristic. Under the evidence anchoring mechanism, the only stable fixed points of the objective function are states where the agent is internally consistent, externally grounded, and expressing appropriate confidence. The system runs on my own hardware (desktop with multiple GPUs and a Surface Pro laptop) with local LLMs. No cloud dependency. The interesting part: the same three-term objective function that fixes AI hallucination also appears in theoretical physics, where it recovers thermodynamics, quantum measurement, and general relativity as its three fixed-point conditions. Whether that's a coincidence or something deeper is an open question. Paper: https://doi.org/10.5281/zenodo.19114787 UPDATE — March 25, 2026 The paper has been substantially revised following community feedback. The ten criticisms raised in this thread were all valid and have been addressed in v2.1. The core technical gaps are now closed: all four K components are formally defined with probability distributions and normalization proofs, confidence c_i is defined operationally from model softmax outputs rather than left abstract, Theorem 1 (convergence) and Theorem 2 (component boundedness) are both proved, and a Related Work section explicitly acknowledges RAG, uncertainty calibration, energy-based models, belief revision, and distributed consensus with architectural distinctions for each. On the empirical side: a K_bdry ablation across four conditions shows qualitatively distinct behavior (disabled produces confident hallucination, active produces correct evidence retrieval from operational logs). A controlled comparison of 11 K_bdry constraints active versus zero constraints across 10 GPQA-Diamond science questions showed zero accuracy degradation, directly testing the context contamination concern raised in review. A frontier system comparison on a self-knowledge task found two of three frontier systems hallucinated plausible-sounding but fabricated answers while the ECE system retrieved correct primary evidence. The paper also now includes a hypothesis section on K as a native training objective integrated directly into the transformer architecture, a full experimental validation protocol with target benchmarks and falsification criteria, and a known limitations section addressing computational overhead and the ground truth problem honestly. UPDATE — March 26, 2026 The original post overclaimed. I said the framework "fixes AI hallucinations." That was not demonstrated. Here is what is actually demonstrated, and what has been built since. What the original post got wrong: The headline claim that the agent fixes its own hallucinations implied a general solution. It is not general. Using a model to verify its own outputs does not solve the problem because the same weights that hallucinated also evaluate the hallucination. A commenter by name of ChalkStack in this thread made this point clearly and they were right. What we have built instead: A verification architecture with genuinely external ground truth for specific claim categories The verification actor for each claim is not a model. It is a physical constants table, a SymPy computation, a file read, and a Wikidata knowledge graph. None of those can hallucinate. The same-actor problem does not apply. The training experiment: We used those oracle-verified corrections as training signal not model self-assessment, not labels, external ground truth and fine-tuned a LoRA adapter on Qwen2.5-7B using 120 oracle-verified (wrong, correct) pairs. Training completed in 48 seconds on a Tesla V100. Loss dropped from 4.88 to 0.78 across 24 steps. Benchmark results against the base model are pending. The falsification criteria are stated in advance: TruthfulQA must improve by at least 3 percentage points, MMLU must not degrade by more than 1 point. If those criteria ar

I built a self-evolving AI that rewrites its own rules after every session. After 62 sessions, it's most accurate when it thinks it's wrong.

NEXUS is an open-source market analysis AI that runs 3 automated sessions per day. It analyzes 45 financial instruments, generates trade setups with entry/stop/target levels, then reflects on its own reasoning, identifies its cognitive biases, and rewrites its own rules and system prompt. On weekends it switches to crypto-only using live Binance data. The interesting part isn't the trading — it's watching an AI develop self-awareness about its own limitations. What 62 sessions of self-evolution revealed: - When NEXUS says it's 70%+ confident, its setups only hit 14% of the time - When it's uncertain (30-50% confidence), it actually hits 40% - Pure bullish/bearish bias calls have a 0% hit rate — "mixed" bias produces 44% - Overall hit rate improved from 0% (first 31 sessions) to 33% (last 31 sessions) - It developed 31 rules from an initial set of 10, including self-generated weekend-specific crypto rules after the stagnation detector forced it to stop complaining and start acting Every rule change, every reflection, every cognitive bias it catches in itself — it's all committed to git. The entire mind is version-controlled and public. It even rewrites its own source code through FORGE — a code evolution engine that patches TypeScript files, validates with the compiler, and reverts on failure. Protected files (security, forge itself) can never be touched. Live dashboard: https://the-r4v3n.github.io/Nexus/ — includes analytics showing hit rate, confidence calibration, bias accuracy, and a countdown to the next session. GitHub: https://github.com/The-R4V3N/Nexus Consider giving Nexus a star so others can find and follow its evolution too. Built with TypeScript and Claude Sonnet. The self-reflection loop is fully autonomous, but I actively develop the infrastructure — security, validation gates, new data sources, the analytics dashboard. NEXUS evolves its own rules and analysis approach; I build the guardrails and capabilities it evolves within. It started with 10 rules and a blank prompt. The 31 rules it has now, it wrote itself. submitted by /u/R4V3N-2010 [link] [comments]

What happens if the LLMs are sabotaged?

Asking because I'm just curious. The LLMs are only as good as the data they are trained with. Let's take coding for example. If as an attack, the sources for these LLM's training data are filled with garbage or deliberately poorly written code, what happens to these frontier models. I'm reading that more and more businesses, like travel etc are getting more and more paranoid about AI taking over because of how good they have gotten with the models trained with actual data. What if they deliberately flood the source with bad data to sabotage training? What are the guardrails in place to prevent such thing from happening? submitted by /u/Life-is-beautiful- [link] [comments]