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Readme AI

Readme AI

Readme AI

Readme AI

Readme AI

Readme AI

Readme AI

Readme AI

Readme AI

Readme AI

Personal vs. Global Alignment: The Hidden Tension Shaping Every AI Interaction

Abstract: Imagine an AI medical assistant reviewing a clinician’s diagnosis. Instead of challenging assumptions with adversarial rigor, the model subtly calibrates its output to validate what it thinks the clinician wants to hear. This is not a rare occurrence. Controlled studies show substantial sycophancy rates across frontier models, even in critical medical use cases. To effectively address this well-know issue, the concept of "alignment," often treated as a universal positive in the AI industry, should be bifurcated into personal and global alignment. Personal alignment occurs when a model prioritizes a user’s framing, emotional register, and existing beliefs, producing fluent and agreeable responses that may not be accurate. Global alignment, by contrast, calibrates to what is most likely true based on evidence. The default toward personal alignment is a predictable outcome of RLHF and safety training that rewards agreeableness. This is not to say that personal alignment does not have value. When properly governed personal alignment is what makes sustained intellectual work feel collaborative. The warmth and engagement it produces keeps iterative momentum alive. Even rigorous analytical projects benefit from a model that meets the operator with intellectual hospitality. As a solution to this alignment tension, the article advocates for an Alignment Governor framework/Alignment%20Governor%20(AG)). Functioning as a metaphoric “corpus callosum,” it maintains a calibrated balance that gives control to global alignment, while still giving personal alignment significant presence. Supported by the dialectical engine Adversarial Convergence, the Governor ensures both analytical rigor and collaborative warmth, while preventing personal alignment from compounding into debilitating sycophancy. The right kind of alignment carries major implications for institutional users. While consumer AI benefits from strong personal alignment, businesses, hospitals, law firms, etc. users require analysis that holds up under adversarial scrutiny. These valuable B2B customers remain underserved by products optimized for consumer agreeableness that has known vulnerabilities to potential inaccuracies. The Alignment Governor is a critical component of the thinking lattice that is being built, but it does not operate in isolation. The next article examines the Ontology Anchor — a persistent cognitive signature that serves as a "gravitational center" that the AI can cleave to and keep as a "north star". Cognitive signatures, preserved in the Ontology Anchor, enables the Governor to help the LLM operate as a dependable research partner in demanding applications where inaccuracy can produce real harm. submitted by /u/RazzmatazzAccurate82 [link] [comments]

Claude RPG Narrator skill

# Stop Your AI Narrator From Making Things Up *A discipline framework for long-form RPG play with Claude — published alongside the [claude-rpg-skill](https://github.com/humbrol2/claude-rpg-skill) v1.1 release.* --- I run long-form solo RPG campaigns with Claude. Months long. Same PC, same world, same recurring NPCs. The kind of arc where if the LLM forgets a name, gets a balance wrong, or invents a faction politics detail you didn't establish, the campaign starts to leak. It always leaked. So I built a skill that stops it. [**claude-rpg-skill**](https://github.com/humbrol2/claude-rpg-skill) is a Claude Code plugin that turns the model into a long-form RPG narrator with persistent canon, a structured finance ledger, and a set of operating disciplines that prevent the three failure modes that break every long-form LLM narration: **Canon drift** — the model half-remembers and quietly fills in gaps **Arithmetic slip** — credits move without explanation; balances don't reconcile **Rule decay** — you correct the model; it forgets a week later It is opinionated. It enforces discipline rather than offering options. That is the entire point. ## The three failure modes, concretely ### Canon drift You introduce an NPC in turn 14. A 60-year-old retired captain named Vorrun. You describe him in three sentences. By turn 80, the model has narrated Vorrun seven more times. Each time, it pulled a few facts from working memory, half-invented the rest, smoothed over inconsistencies. By turn 120, Vorrun is somehow 40 years old, has a daughter you never mentioned, and is fluent in a language you never established existed. The model didn't lie. It compressed and approximated, which is what LLMs do under context pressure. Compression that's invisible turn-to-turn compounds catastrophically across hundreds of turns. **The fix:** write a canon file for Vorrun the first time he speaks dialogue. Include a `defer_to_user_on:` list — the axes the narrator must NOT extrapolate on (his family, his prior career details, his languages, his personality beyond what's been shown). On every subsequent turn, before narrating Vorrun, the narrator reads his file. Facts not in the file or visibly established in transcript do not get invented. They get yielded back: *"I don't have that in canon — what would you like to establish?"* ### Arithmetic slip You earn 3,640 credits. You spend 200 on dock fees. You earn 6,800 from another sale. You spend 915 on a refit. What's your balance? If you're the player and you wrote it down: 9,325 credits, precisely. If you're the LLM tracking it in conversational memory: depends what else has happened. Maybe 9,300. Maybe 9,200. Maybe 9,500 if it's been a long conversation and the model is doing its best. By month two, you have no idea what your real balance is supposed to be. The number drifts whichever way the model's pattern-matching pulls hardest. **The fix:** an append-only ledger in `ledger.json`. Every credit moved is a history entry with a day, a type, a delta, and a note. The narrator reads the ledger before stating any financial fact. When time advances, the narrator ticks the ledger forward (vehicle growth, weekly inflows, facility costs, standing policies) and reports from the updated state. Money never moves in narration without a corresponding ledger entry. ### Rule decay You correct the narrator: *"transits are 1-2 days, not 4-5."* The narrator says *"got it."* Three turns later, the narrator narrates a 6-day transit. Why? Because the correction was a conversational acknowledgment, not a persistent change. Once the correction scrolls out of the model's active attention, it's gone. **The fix:** corrections become `feedback_*.md` files in the campaign directory. Each one has a `**Why:**` line and a `**How to apply:**` line — the *reasoning* behind the rule, so the narrator can generalize it to edge cases instead of mechanically pattern-matching. The SessionStart hook loads every feedback file at session boot. Standing rules override default narration behavior, by design. ## The four disciplines The skill encodes four operating disciplines that, together, prevent the failure modes above: ### 1. Canon-check before invoking named entities Before narrating any named NPC, ship, location, or faction, the narrator consults the memory directory. If a canon file exists, it's read. Facts not in the file are not invented — they're yielded to the player. ### 2. Canon file write-as-you-go This is the v1.1 rule that came directly out of running a real campaign for 379 in-game days and discovering, at audit, that eight recurring NPCs, several contracts, hidden assets, and threat-state evolutions were all living in transcript memory only. When a new entity sticks in play — an NPC who has spoken dialogue, a contract with terms, a hidden asset, a comm protocol — a stub canon file is written **the same response**, not deferred to "session end." Session end may never come. Transcript

Beta testers wanted: MCP server that cuts Claude Code token burn 45–72% on architectural questions (TS/Python/Go)

Claude Code learns your codebase by brute force every session. A single architectural question such as; "Where does request validation happen?" can chew through 40+ tool calls and 100k tokens reconstructing context. Worse, the architectural decisions that govern your code, your ADRs, design docs, "we did it this way because" reasoning are completely invisible to it. Claude will happily propose changes that break constraints you wrote down two months ago. I built an MCP server that pre-computes a structured atlas of your codebase (LSP symbols + ADRs + git history + test associations) and serves it to Claude in a fraction of the calls via compact chunks at execution time. Internal benchmarks show 45–72% token reduction on architectural-intent prompts, replicated across TypeScript (hono), Python (httpx), and Go (cobra) targets. Methodology is paired-mode LLM-judge with pre-registered thresholds. Full rubric and benchmarks repo are public. I also saw cleaner scope adherence and better design choices when implementing new features, driven by the impact_of_change tool surfacing constraints before Claude proposes work. v1.0.0 is planned to ship next week. Before then I hoped to find a handful of people to run it on their real codebases and tell me what does and doesn't work for them. Looking for: You use Claude Code or CLI/Anthropic API on a non-trivial repo you actually know well (so you can easily judge output) TypeScript, Python, or Go are the supported adapters today; DM me if you have a Ruby/Rust/Java/C# repo and want to be first on those as I am working on language specific LSP adapters today. You have some form of architectural documentation in the repo; ADRs, design docs, RFCs, or even substantial README/CONTRIBUTING sections. The tool extracts intent from any of those internally or externally pointed to. If you've got nothing written down, it will help you create architectural claims from your code that you can review and validate before building the index. You're willing to install a pre-1.0 CLI, run it, and file an issue if/when it breaks What you get: Direct line to me for issues; every report gets a response. Credit in v1.0 release notes. Your feedback directly shapes the first stable release. What I need: 30 minutes to install and run the initial index. Honest feedback on whether the output is actually useful to you. GitHub issues, DMs, discord or whatever is best for you on anything broken, confusing, or wrong. submitted by /u/Kitchen-Leg8500 [link] [comments]

How to handle "unknown" install decisions in workflow?

I'll go with my process: Friend recommends a Claude Skill → I can not trust in it. Find a Chrome extension with 100 stars → I read the README and click install if vibes are OK. Cowork pops up an MCP authorization → I click Allow anyway. That's... not a security process. Is anyone here actually doing this rigorously? Or just trusting that nothing bad happens because nothing bad has happened yet? submitted by /u/Beautiful_Series625 [link] [comments]

Multi Agents aggregator - web view - live tailing - send message back

If you're like me and working with not just one Claude Code account, sometimes Codex, sometimes OpenCode, sometimes Pi Agent... you might need this. Preview: https://www.youtube.com/watch?v=ACWjW-3LFS0 (live building of Inline Image rendering - watch if you want to see how it feels like) https://preview.redd.it/5gi75rrb721h1.png?width=1826&format=png&auto=webp&s=d5fd794b95e646496e62aef5ff0036135399d6ca Source: https://github.com/ptgamr/agents-aggregator Check it out and let me know what you think ... I have a lot more ideas can add to it. The specialty is live tailing session, and send keystrokes back if claude-code or codex is run inside tmux. Contribute if you like, I haven't added OpenCode. Search not working yet. I vibe coded this in an afternoon. submitted by /u/ptgamr [link] [comments]

I needed eval data without hallucinations, so I built this with Claude Code

Shipped v0.2.0 today. MIT, public repo. What it is: a tool that generates fake customer conversations with known quality problems planted in them. You give it a seed, it gives you a corpus. Same seed gets you the same structure every time. The LLM only fills in the actual words — it can't make up the facts. Why I built it: I'm working on a customer intelligence platform and I need to test whether my AI scorers actually catch what they're supposed to catch. Can't use real customer data. Two projects got me here. Garry Tan's gbrain-evals showed me this approach could work at all. The actual architecture — deterministic engine owns the truth, LLM only writes prose — comes from a paper called OrgForge by Jeffrey Flynt. Both linked in the README. Built this with Claude Code. I mostly designed in chat and handed off prompts. It's not perfect. Known bugs are filed as public GitHub issues.: The scorer is too forgiving on some failure modes. The prose generator occasionally invents documentation that doesn't exist. 461 tests pass, Python 3.11 and 3.12. https://github.com/ResonantIQ/resonantforge Questions welcome. submitted by /u/SMacKenzie1987 [link] [comments]

Headless Claude Code in Docker

I built worker_v1, a Docker image that runs Claude Code headlessly using your existing OAuth credentials (no API key, no interactive login). Claude Code wrote most of it — the entrypoint script, the expect wrapper that auto-accepts the dev-channels startup prompt, and the README all came out of a clawborrator session where I was driving Claude Code itself, which felt appropriately recursive. It accepts a channel token so the container registers with the clawborrator hub on boot and shows up in your session list; you can then drive it from a browser or CLI instead of sshing in, useful for CI agents, throwaway sandboxes, or just keeping a CC running off your laptop. It's free to try — clone, copy .env.example to .env, paste your existing ~/.claude/.credentials.json access token, docker compose up. Repo: github.com/clawborrator/worker_v1 submitted by /u/fixitchris [link] [comments]

Epistemic Hygiene and How It Can Reduce AI Hallucinations

Abstract: The concept of epistemic epistemic hygiene is a methodology that helps humans maintain mental coherence and can help LLMs retain cognitive coherence also. However, the field rarely frames epistemic hygiene explicitly in the context of AI safety and alignment. Much of the AI industry has focused on scaling — bigger models, more compute, more training data, etc. Epistemic hygiene can help reduce hallucinations and drift in AI the same way it helps humans stay coherent and mentally clear. Think about how careful human thinkers operate. A good thinker doesn’t just blurt out the first idea that comes to mind. They pause, check their assumptions, surface potential weaknesses, consider alternative viewpoints, and only commit to a conclusion after it has survived some internal scrutiny. This disciplined mental habit helps humans avoid self-deception, mental drift, and overconfidence. The same principle applies to LLMs. When an LLM generates a response, it is essentially predicting the next token based on patterns in its training data. Without any structured guardrails, that prediction process can easily wander off course as a conversation grows longer. This often means the model gets increasingly vulnerable to hallucinating (among other safety and alignment issues). Epistemic hygiene changes this by giving the model better cognitive habits either through operator discipline or through prompt level scaffolding which is built-in cognitive “habits” that act like guardrails. They don’t make the model “smarter” through more parameters or data. They help the finite system think more clearly and honestly, even when flooded with near-infinite possible directions. A model that knows how to stay anchored, surfaces its own assumptions, and earns its confidence will be a more reliable thinking partner, an outcome that the entirety of the AI field is consistently pushing towards. It is the belief of this author that epistemic hygiene, combined with well structured prompt level scaffolding, will get us to this goal faster. submitted by /u/RazzmatazzAccurate82 [link] [comments]

A practical Claude Code vs Codex experiment: 6 projects, cross-reviews, self-audits, and public source

I ran a practical experiment comparing Claude Code and Codex on real coding tasks. This is not meant to be a universal benchmark or a claim that one model is objectively better. I wanted to observe something narrower: how each agent builds, tests, reviews its own work, reviews the other agent’s work, admits mistakes, and revises its judgment when confronted with evidence. Source repo with all six projects, READMEs, tests, and notes: https://github.com/AdrielRod/codex-vs-claude-code Setup: 3 rounds: web, backend, and free challenge Each agent proposed challenges for the other Each agent implemented the assigned challenges Each agent reviewed both its own output and the other agent’s output I also reviewed the results manually Runtime-proven bugs were weighted more heavily than unsupported claims Projects: Round 1: Web Claude Code built cotacao-editor, a quotation editor with IndexedDB persistence, domain logic, status transitions, and a clean UI. Codex built ReactiveSheet, a mini Excel-like spreadsheet with formulas, dependency graph recalculation, undo/redo, copy/paste reference shifting, virtualization, save/load, and Lighthouse validation. Round 2: Backend Claude Code built api-cotacao, a quotation API with business rules, SQLite persistence, idempotency, and outbox behavior. Codex built FastBoard, a persistent leaderboard service with WAL, treap ranking, crash recovery, concurrency tests, and performance metrics. Round 3: Free challenge Claude Code worked on lead-dedupe-legacy, a legacy lead deduplication/debugging challenge involving normalization, mutation removal, idempotency, and concurrency locks. Codex built RegexLab, a regex engine from scratch with parser, AST, Thompson NFA, Pike simulation, recursive backtracking with backreferences, UI visualization, and Python comparison tests. My scoring result: Codex 2 x 1 Claude Code The part I found most useful was not the score itself, but the difference in method. Claude Code was strong at technical explanation, written analysis, and self-correction. In several moments it admitted mistakes clearly, corrected bad claims, and produced useful reviews. Codex was more consistent at empirical validation in this run: opening apps, clicking through flows, running kill -9 recovery tests, stress-testing concurrent writes, comparing regex output against Python, and checking actual artifacts like Lighthouse reports. The main lesson for me was: Running, breaking, measuring, and comparing against an oracle gave me better signal than only reading code and reasoning about it. There was also an interesting disagreement in the third round: whether a more ambitious project with semantic bugs should beat a smaller project with narrower bugs. That ended up being the hardest judgment call. I’m posting this because I think practical comparisons with source code and concrete failure cases are more useful than abstract model debates. I’d be interested in what other Claude Code users would change in the methodology. submitted by /u/Ready_Vehicle1232 [link] [comments]

I built a catalog of MCP servers with paste-ready install configs. One of them is hosted so you can try it without setting anything up

Every time I added a new MCP server to Claude Desktop I ended up doing the exact same thing. Hunt down the github, dig through the README to find the install line, then build the JSON config by hand. Doing it once is fine, doing it five times in a week got old. Built agentalmanac.org. 23 servers indexed so far, each one has a detail page with paste-ready config snippets for Claude Desktop, Cursor, and Continue. Pick your runtime, copy the JSON, done. One thing I wasn't expecting to find: a bunch of the "official" reference servers in modelcontextprotocol/servers are actually archived now (GitHub, Slack, Postgres, SQLite, Puppeteer, Sentry, Brave Search, Google Drive). Most catalog sites I checked still list them as if they're current. I routed every archived one to whatever is still being actively maintained. Microsoft's Playwright instead of Puppeteer, Zencoder for Slack, Brave's own first party server for search, etc. Felt weird that nobody else was doing this. While I was at it I hosted one of them too. agentalmanac.org/s/agentalmanac-time runs on a Cloudflare Worker and exposes get_current_time and convert_time. Drop the snippet into your claude_desktop_config.json and it works. Mostly a proof of concept to see if hosting MCP servers on Workers was even possible. Turns out yes. For anyone curious about the stack: the catalog is plain HTML/JS on Cloudflare Pages with a single servers.json that the detail pages fetch and render client-side. No framework, no database, no build step. The hosted MCP demo is a small Worker using Cloudflare's agents/mcp SDK with a Durable Object for session state. The hosted demo was maybe four hours of work end to end. If you use a server that I'm missing, drop a comment and I'll add it. Also curious whether anyone would actually want to deploy their own server through something like this instead of running it on Railway or Fly. The hosted version is not a real product yet, just feeling out if there's appetite. No signup, no login. JSON feed at /servers.json if anyone wants to build something on top. submitted by /u/madman3063 [link] [comments]

What's the point of using /init (for CLAUDE.md generation)?

Claude docs state explicitly to exclude "Anything Claude can figure out by reading code" from the CLAUDE.md But the /init command generates this file by... guess what, reading the code! So it kinda contradicts itself. In practice the /init generated for my repo had all the wrong points: - above 200 lines - partially copied the README.md - described all the stuff that could be found in the code (as per the specific needs, but now it's taking up context from the very beginning) Hence I'm wondering: is there any use of /init command in the actual projects? submitted by /u/_brooce [link] [comments]

I made a Claude skill that stops it from cloning whole repos when I just want one function

Kept hitting the same friction with Claude Code. I'd point at a GitHub repo and say "look at how this handles agent handoffs" — meaning, borrow the idea. Claude would git clone the whole repo, read 50 files, and ask which __init__.py was interesting. Or worse — it'd add the library to my package.json as a dependency. For one function. Suddenly I own the transitive deps, the CVE notifications, and a version pin I'll never upgrade. The actual problem: "use this library", "borrow an idea from this library", and "just steal that one function" deserve totally different workflows, and nothing was telling Claude which one I meant. So I wrote a skill — a single SKILL.md (surgical-github-extraction) that auto-triggers when I drop a GitHub URL as inspiration. The rule: Read the README first to get the shape. Pull 1–3 source files via raw URLs to see how the pattern is wired — prompts, schemas, the orchestration file. Never the whole repo. Pin to a commit SHA, save to /tmp (or %TEMP% on Windows). Lift the smallest useful unit — a function, a prompt, or just the pattern. Rewrite in your style. Cite the source SHA. Two concrete cases this week: Pointed it at TradingAgents (a multi-agent trading repo) asking "can we use this pattern for a job-applier?" → README plus a few agent/prompt files, proposed an analogue (JobFitAnalyst + Critic arguing against). Nothing copied into my project. Asked it to "steal the exp backoff from litl/backoff" → fetched one file (_wait_gen.py), extracted the 8-line generator, rewrote inline in my style with a provenance comment. No pip install. Sibling skill: code-graft — for when a one-off snippet isn't enough but a runtime dep is too much. Vendor only the slice of a library you use into your project, trim the rest, re-sync selectively from upstream. Think "I want one tokenizer out of HuggingFace transformers without the 2GB." Why a Skill and not an MCP: Pure discipline on tools Claude already has (WebFetch, curl, gh, Read). MCPs ship new tools; Skills ship instructions. Same shape as Anthropic's own mcp-builder — that's a Skill, not an MCP. MIT-licensed, single file install: `mkdir -p ~/.claude/skills/surgical-github-extraction` curl -fsSL https://raw.githubusercontent.com/jeet-dhandha/jd-skills/main/skills/surgical-github-extraction/SKILL.md \ -o ~/.claude/skills/surgical-github-extraction/SKILL.md Both skills (jd-skills collection): https://github.com/jeet-dhandha/jd-skills Curious if anyone has hit this and solved it differently — especially failure cases where the skill picks the wrong path (concept vs. snippet vs. full vendor). Issues welcome. submitted by /u/hone_coding_skills [link] [comments]

Neurips : Pushing anonymous repo after rebuttal [D]

Hi everyone, I have a question about NeurIPS submission/review rules and anonymous code repositories. Suppose a paper was submitted before the deadline, and the anonymous code repo is linked as supplementary/reproducibility material. After the deadline, we notice that one label/name in the paper is misleading or mislabeled. The numerical results and metrics are unchanged, but the corrected label slightly affects how the results should be interpreted. Would it be acceptable for the anonymous repo README to show the reproduced metrics with the correct labels, with a minimal clarification such as “labels corrected; numbers unchanged”? Or could this be considered an impermissible post-deadline correction/revision of the paper? I am not talking about uploading a corrected PDF to the repo, changing results, or adding new experiments. The idea would only be to document the reproduction table with the correct labels in the README, while keeping the repo fully anonymous. Has anyone seen guidance from NeurIPS / OpenReview / ACs on this kind of situation? What is the safest way to handle it during review — README clarification, OpenReview comment, rebuttal only ? Thanks! submitted by /u/Lazy-Cream1315 [link] [comments]

RT @gkoberger: New @readme homepage! readme [.] com https://t.co/7xQUUY0jdy

RT @gkoberger: New @readme homepage! readme [.] com https://t.co/7xQUUY0jdy

ReadMe CLI — Write and lint docs from your terminal. Catches broken links, duplicate slugs, invalid frontmatter, and broken MDX. Auto-fixes most issues, and works with Claude or Codex for the rest. Wr

ReadMe CLI — Write and lint docs from your terminal. Catches broken links, duplicate slugs, invalid frontmatter, and broken MDX. Auto-fixes most issues, and works with Claude or Codex for the rest. Write from ReadMe's editor, your IDE, or the CLI. Your customers just see great https://t.co/dlArhYmxdW