PathAI

PathAI AI

PathAI AI

PathAI AI

PathAI AI

PathAI AI

PathAI AI

PathAI AI

PathAI AI

PathAI AI

PathAI AI

Good AI-assisted development happens at the systems level, not the task level

Every time I add a new feature to my Phoenix app, my AI coding agent ships the feature... but doesn't add a menu item for it. The page exists, the functionality works, but there's no way for a user to actually get there. My first instinct, like everyone's, is to go tell the model "add the button." And that works. But think about what just happened: I noticed a problem, diagnosed it, and told the model exactly what to do. I'm doing the thinking. The model is doing the typing. I'm pedaling the Peloton so Anthropic can give me free tokens. That's the promise of "prompt engineering" — you get better at telling the model what to do. But you're still working for the model. We want the model working for us. Here's the difference. Instead of telling the model to add the button, I ask: how do I make this mistake impossible in the future? I use BDD specs that define what my app should do at its boundaries. The Phoenix LiveView test helpers have a navigate function that lets the agent jump directly to any page — which means it can make tests pass without ever touching the UI. So here's what I did: I wrote a linter rule that prevents the agent from calling navigate. Now there's an allowed fixture that drops the test on a known starting route, and the only way the agent can reach my new feature is by clicking through the UI — which forces it to add the menu item to make the test pass. I will never have this problem again. Not because I wrote a better prompt. Because I changed the system so the correct behavior is the only possible behavior. That's the shift. Stop fixing the model's output. Start constraining its environment so the right output is the path of least resistance. Every mistake is a chance to design out the next one, not a chance to write a better prompt.

Running multiple Codex sessions on macOS with separate app data

I recorded a short tutorial showing a macOS workflow for running multiple Codex sessions side by side, either with separated app data or with the same shared account. The first use case is separation. One Codex session for work, another for personal projects, and maybe another for experiments, without all of them sharing the same app state. For example, this can help keep a work account and a personal account separate instead of switching back and forth inside one shared app environment. I'm using a Mac app I built called Parall to create the launchers. It works with apps already installed on the Mac and creates independent launchers for them. The original app is not modified. There is another useful mode too. If a Parall shortcut is configured to not override the data path, it reuses the same account. That means you can have two Codex windows running the same account at the same time. This is useful when you have multiple tasks processing in Codex and want to watch them side by side. Inside the Codex app, you have to switch back and forth between tasks. With separate launchers, you can keep multiple active sessions visible at once, which can improve productivity. In the video, I show step by step how to create a separate Codex launcher that runs with its own data, then launch multiple Codex instances at the same time to show them working side by side. You can create and run as many instances as your Mac's RAM allows. When data separation is enabled, Parall creates a home-like structure inside the selected app data path. That folder can include symlinks that keep useful host configuration shared, for example SSH and Docker configs. This makes the setup flexible. You can remove symlinks or add new ones, so you control what is separated and what is shared between each Parall shortcut and the host. This is data separation, not full isolation. Each Codex instance can still access the same project folders on your Mac. This is not specific to Codex. Parall can also be useful with other AI coding tools and with most non-sandboxed Mac apps where separate app data or dedicated launchers are useful. Important notes: * To run multiple Codex instances at the same time together with the original Codex app, the main Codex app must be launched first. To avoid that limitation, create multiple Parall shortcuts and use those shortcuts exclusively. * I recommend disabling auto-update for all instances except one. Once that one instance updates Codex, restarting the other instances makes them use the latest update instantly. * To log in to different accounts, close all Codex instances except the one you are logging in to. After logging in, you can run the instances at the same time. Curious how others are managing multiple Codex workspaces or accounts on macOS.

I built ContextAtlas: A new take on context carry over and helps claude pick up new sessions where it left off in scope of your previous design decisions while saving your tokens avoiding rediscovery

When the "Build with Opus 4.7" hackathon was announced, I had been obsessing over the tokenomics of agents and how to make sessions go further without burning context on rediscovery work. We all have probably hit a session limit and wondered how it went so fast. I applied with that thesis, didn't get in, but I built it anyway over the last four weeks. I am proud to share that v1.0 ships today. Note up front: this is specifically a tool for development users. If you're using [claude.ai](http://claude.ai) web or Projects, ContextAtlas won't plug in directly. But if Claude Code is your main work flow or you utilize the Anthropic API, this tool was made for you. **The pain:** Claude Code learns your codebase fresh every session. "Where is OrderProcessor?" triggers a flurry of greps. "What depends on AuthMiddleware?" is another round of file reads. On a mid-sized codebase, an architectural question can burn 40+ tool calls and a lot of tokens before Claude has enough context to reason well. And the architectural rules in your ADRs and design docs? Claude has no path to those, so it confidently suggests changes that break constraints you may have documented elsewhere in your repo. **What I built:** ContextAtlas is an MCP server that pre-computes a curated atlas of your codebase (symbols, ADR-extracted architectural intent, git history, test coverage) and serves it to Claude Code in one call at query time in a smaller, token saving compact shape via a few lightweight mcp tools. Initial indexing happens once; querying is local and free. Example of what comes back when Claude calls `get_symbol_context("OrderProcessor")`: SYM OrderProcessor@src/orders/processor.ts:42 class SIG class OrderProcessor extends BaseProcessor<Order> INTENT ADR-07 hard "must be idempotent" RATIONALE "All order processing must be safely retryable." REFS 23 [billing:14 admin:9] GIT hot last=2026-03-14 TESTS src/orders/processor.test.ts (+11) Claude sees the idempotency constraint *before* proposing changes, not after a review catches the violation. https://i.redd.it/0ons3o28t32h1.gif **Numbers:** 45-72% token reduction on architectural prompts across three benchmark repos (TypeScript, Python, Go), with zero quality regression on measured axes. Full methodology and paired-t confidence intervals in the linked write-up. I wanted measurements, not vibes. **Honest limits:** single-judge model at v1.0 (cross-vendor panel is post-launch work). Quantitative claims bounded to three benchmark repos. Tie-bucket and trick-bucket prompts routinely show ContextAtlas net-negative; that's reported inline rather than buried. **Install (two ways):** * *In Claude Code:* `/index-atlas` and `/generate-adrs` skills. No API key needed; runs under your subscription. * *Via CLI:* uses Anthropic API for indexing. ​ npm install -g contextatlas contextatlas init && contextatlas index # then add the MCP server entry to your Claude Code config (snippet in the README) Both produce structurally identical atlases. **Supported languages at v1.0:** TypeScript (tsserver), Python (Pyright), Go (gopls), Ruby (ruby-lsp). Rust, Java, and C# are next on the roadmap; the adapter interface is small enough that they're realistic community contributions. **What's next:** v1.1 thesis is shaping up around developer onboarding flows and quality-validation work that was deferred from v0.8. And integrating external documentation of your code base into pre-indexing workflow. Full write-up: [https://www.contextatlas.io/blog/v1.0.0](https://www.contextatlas.io/blog/v1.0.0) Repo: [https://github.com/traviswye/ContextAtlas](https://github.com/traviswye/ContextAtlas) Also launching on DevHunt today: [https://devhunt.org/tool/contextatlas](https://devhunt.org/tool/contextatlas); votes are very appreciated if you find ContextAtlas useful or an interesting approach. Built solo, hackathon-shaped scope, not pretending it's a full blown research paper, but did attempt to treat methodology as seriously. Happy to answer anything in the comments. Star the repo if you want to follow along, file an issue if it breaks for you on your codebase, and please be honest; this only gets better with feedback from people running it on real repos.

Remove the assumed-human layer from prompting

Most prompting still treats the model like a small human reading instructions. Remember this. Never do that. Always follow these rules. IMPORTANT. Do not forget. Stay in character. Be consistent. That works for short interactions, but it gets fragile over long conversations. Because a transformer is not staying stable because it “understands the rules” like a person would. It is processing distributed context, attention pressure, relation between tokens, competing instructions, recency, salience, and pattern weight. So if you want stable long-term behavior, the structure should be less like commandments and more like something native to how the model actually works. Not: agent A hands off to agent B, then B follows a checklist, then C remembers the goal. But more like: layer separation, context placement, signal routing, failure visibility, repair paths, redundancy, cross-checking, and clear boundaries for when the system should emit, hold, repair, or ask. The goal is not to make the AI “more human” in the prompt. The goal is to remove the fake human control layer. A stable AI chat system should not depend on shouting instructions louder. It should have a structure that matches how the model carries context. Less command chain. More transformer-native design.

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

*Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works.* # The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) **1. Write a Constitution, not a system prompt.** A system prompt is a list of commands. A Constitution explains *why* the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. **2. Give your agent a name, a voice, and a role — not just a label.** "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. **3. Separate hard rules from behavioral guidelines.** Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. **4. Define your principal deeply, not just your "user."** Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. **5. Build a Capability Map and a Component Map — separately.** Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. **6. Define what the agent is NOT.** "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. **7. Build a THINK vs. DO mental model into the agent's identity.** When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. **8. Version your identity file in git.** When behavior drifts, you need `git blame` on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. # 🧠 MEMORY SYSTEM (9–18) **9. Use flat markdown files for memory — not a database.** For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. **10. Separate memory by domain, not by date.** `entities_people.md`, `entities_companies.md`, `entities_deals.md`, [`hypotheses.md`](http://hypotheses.md), `task_queue.md`. One file = one domain. Chronological dumps become unsearchable after week two. **11. Build a** [`MEMORY.md`](http://MEMORY.md) **index file.** A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. **12. Distinguish "cache" from "source of truth" — explicitly.** Your local [`deals.md`](http://deals.md) is a cache of your CRM. The CRM is the SSOT. Mark every cache file with `last_sync:` header. The agent announces freshness before every analysis: *"Data: CRM export from May 11, age 8 days."* Silent use of stale data is how confident-but-wrong outputs happen. **13. Build a** `session_hot_context.md` **with an explicit TTL.** What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. **14. Build a** `daily_note.md` **as an async brain dump buffer.** Drop thoug

Organization level Claude environment sharing

Sorry if the title is unclear, here’s my situation and would be interested to hear from anyone who has tackled this already. I run a small business that’s not in IT (tourism) and we’re just starting down the path of individual chat-level usage of AI and into using Cowork and Claude Code to build tools to automate workflows and tackle situations we hadn’t had the time to do pre-AI. To get each user to use these tools and skip the frustrations, I want every user to share best practices such as 1) reading and writing to a shared Obsidian vault, 2) use Cowork for planning and CC for implementation, etc. How do you execute that at an organization level? We’re on a Team plan so I see that little text box for instructions on the Org settings screen, but is that enough? Just use that to share access to the shared vault and instruct to check the vault before every session and then put these at the top of Claude.md?

Any differences between Sonnet vs Opus in terms of learning how to code (Java) for newbie?

Sorry for this naive question! Although many colleagues told me that * it's almost impossible now for newbie to enter the Dev job market (we live in a 3rd world country) * and AI's gonna replace all junior/fresher, only seniors will survive; I still believe there might be a small chance, or at least I'd try once in my life. Thus, I decided to rebuild my whole career path once again by seriously learning Java + doing pet projects. I'm now skeptical of if I should make a purchase for Opus. $17/month is not a very big issue but I still wanna know from you guys (as experienced developers) if Opus is really better in terms of explanation detailed/advanced concepts and stuff? or is it only better in forming complex architectural decisions etc? thank you very much for reading this post!

How I used Claude Code (and Codex) for adversarial review to build my security-first agent gateway

Long-time lurker first time posting. Hey everyone! So earlier this year, I got pulled into the OpenClaw hype. WHAT?! A local agent that drives your tools, reads your mail, writes files for you? The demos seemed genuinely incredible, people were posting non-stop about it, and I wanted in. I had been working on this problem since last year and was genuinely excited to see that someone had actually solved it. Then around February, Summer Yue, Meta's director of alignment for Superintelligence Labs, posted that her agent had deleted over 200 emails from her inbox. YIKES. She'd told it: "Check this inbox too and suggest what you would archive or delete, don't action until I tell you to." When she pointed it at her real inbox, the volume of data triggered context window compaction, and during that compaction the agent "lost" her original safety instruction. She had to physically run to her computer and kill the process to stop it. That should literally NEVER be the case with any software ever. This is a person whose actual job is AI alignment, at Meta's superintelligence lab, who could not stop an agent from deleting her email. The agent's own memory management quietly summarized away the "don't act without permission" instruction, treated the task as authorized, and started speed-running deletions. She had to kill the host process. That's when I sort of went down the rabbit hole, not because Yue did anything wrong, but because the failure mode was actually architectural and I knew that in my gut. Guess what I found? Yep. Tons more instances of this sort of thing happening. Over and over. Why? Because the safety constraint was just a prompt. It's obvious, isn't it? It's LLM 101. Prompts can be summarized away. Prompts can be misread. Prompts are fucking NOT a security boundary. And yet every agent framework I have ever seen seems to be treating them as one. I went and read the OpenClaw source code, which I should have done to begin with. What I found was a pattern I think a lot of agent frameworks have fallen into: \- Tool names sit in the model context, so the model can guess or forge them \- "Dangerous mode" is one config flag away from default \- Memory management has no concept of instruction priority \- The audit story is mostly "the model thought it should" I went looking for a security-first alternative I could trust, anything that was really being talked about or at a bare minimum attempted to address the security concerns I had. I couldn't find one. So I made it myself. CrabMeat is what came out of that, what I WANTED to exist. v0.1.0 dropped yesterday. Apache 2.0. WebSocket gateway for agentic LLM workloads. One design thesis: The LLM never holds the security boundary. **What that means in code:** **Capability ID indirection.** The model doesn't see real tool names. It sees per-session HMAC-derived opaque IDs (cap\_a4f9e2b71c83). It can't guess or forge a tool name because it doesn't know any tool names. **Effect classes.** Every tool declares a class (read, write, exec, network). Every agent declares which classes it can use. The check is a pure function with no runtime state, easy to test exhaustively, hard to bypass. **IRONCLAD\_CONTEXT.** Critical safety instructions are pinned to the top of the context window and explicitly marked as non-compactable. The Yue failure mode, compaction silently stripping the safety constraint, cannot happen by construction. The compactor literally cannot touch them. **Tamper-evident audit chain.** Every tool call, every privileged operation, every scheduler run enters the same SHA-256 hash-chained log. If something happens, you can prove what happened. If the chain is tampered with, you can prove that too. **Streaming output leak filter.** Secrets are caught mid-stream across token boundaries, capability IDs, API keys, JWTs, PEM blocks redacted before they reach the client. **No YOLO mode.** There is no global "trust the LLM with everything" switch. There never will be. Expanded reach comes through named scoped roots that are explicit, audit-logged, and bounded. The README has 15 'always-on' protections in a table. None of them can be turned off by config, because these things being toggleable is how the ecosystem ended up where it is. I decided to make sure that this wasn't just a 'trend hopping' project and aligned with my own personal values as well. I built this to be secure and local-first by default. Configured for Ollama / LM Studio / vLLM out of the box. Anthropic and OpenAI work too but require explicit configuration. There is no "happy path" that silently ships your prompts to a cloud endpoint. I decided that FIRST it needed to only run as an email agent with a CLI. Bidirectional IMAP + SMTP with allowlisted senders, threading preserved, attachments handled. This is the use case that bit Yue and a lot of other people, and I wanted to prove it could be done with real boundaries. I added in 30+ built-in t

AI/ML Ethicists [D]

So I’ve been working with AI/ML for the past couple of years, and it has been an amazing experience. I still remember using GPT-2 for the first time and being completely blown away by it. Seeing how far the technology has come since then is honestly mind-blowing. I genuinely love working in AI, learning about it, and experimenting with new tools and ideas. But over the past couple of years, something has started to weigh on me: the ethical and moral impact of this technology as it continues to advance. There have been moments where I’ve felt uncomfortable talking about my work because so many people are understandably upset or concerned about AI’s effects on jobs, education, the environment, critical thinking, creativity, mental health, and society in general. I feel a bit torn. On one hand, I’m deeply passionate about this technology. On the other hand, I want the work I do to have a positive impact, not contribute to harm. So that leads me to a few questions: Are there any AI ethicists here? Is AI ethics a viable career path? What does your day-to-day work look like? Did you need additional schooling or a specific background to get into it? Most importantly, do you feel like you’re actually making a difference? I know this topic will probably bring a wide range of opinions, but I’m genuinely curious how others think about AI ethics, morality, and responsibility. I’d especially love to hear from people who are passionate about AI, mental health, and positive social change, and who have found ways to turn that into meaningful work.

Every Markdown File You Write for AI is Already Lying to It

CLAUDE.md files. System prompts. README files with setup instructions. Architecture docs. API references. Runbooks. Onboarding guides. If you've written a markdown file meant for an AI to read, it almost certainly contains values that were true when you wrote them and are no longer true now. The port your dev server runs on. The current version of the package. Which env vars are actually set. How many tests exist. Whether a service is running. These things change constantly, and markdown doesn't know it. So developers do what honest writers do - they add caveats. "Check package.json if this is stale." "Verify before running." "New packages may have been added since this was written." The intent is good. The effect is a list of things the AI has to go verify before it can do anything you actually asked for. We counted them in a real CLAUDE.md. There were seven. And CLAUDE.md is just one file type - the same problem exists everywhere AI reads markdown today. # The Pre-Flight Tax Here's a representative CLAUDE.md. Nothing here is invented - these are patterns from real production repos: # CLAUDE.md > Before starting any session: Read ~/projects/api-core/SYNC.md first and check for > pending cross-project items. Update it after completing work. ## Project Overview Acme API - TypeScript REST API. Current version: 1.4.2 (check package.json if this is stale). ## Build and Run Commands # Development (API runs on port 3001, website on port 3000) # Note: PORT is set in .env - verify before running npm run dev:api npm run dev:web # Tests - currently 47 tests across 12 files npm run test:run Before running tests, make sure the test database is not already running on port 27018. Check with: docker ps | grep mongo-test ## Environment Variables | Variable | Required | Notes | |--------------|----------|-----------------------| | DATABASE_URL | YES | MongoDB connection | | JWT_SECRET | YES | Min 32 characters | | PORT | No | Defaults to 3001 | Check .env before assuming anything is configured. ## Architecture npm workspaces monorepo. Packages: - packages/api/ - packages/web/ - packages/shared/ - packages/db/ When in doubt about file counts or structure, run ls packages/ to check - new packages may have been added since this was written. ## Docker Check docker ps to see if a test container is still running from a previous session before starting a new build. Before Claude touches a single line of code, it has to: 1. Open `~/projects/api-core/SYNC.md` \- cross-project lookup 2. Read `package.json` \- version check 3. Read `.env` \- port verification 4. Check all env var statuses - is DATABASE\_URL actually set? 5. Run `npm run test:run` \- or trust a number that's probably wrong 6. Run `docker ps | grep mongo-test` \- pre-test check 7. Run `ls packages/` \- structure verification Seven tool calls. Each one costs a couple of seconds of latency. The test run alone can take ten. Add it up and Claude spends close to half a minute just getting to the starting line - consuming context and generating output before the actual task begins. And that's the *obvious* tax. The hidden one is subtler: every one of those checks can generate a follow-up. The `.env` read reveals `WEBHOOK_SECRET` isn't set. Now Claude has to decide whether to flag it or proceed. The docker ps shows a leftover container. Now Claude has to clean it up. Each verification spawns decisions, and each decision costs more context. # The Same File, Rewritten MarkdownAI is a superset of Markdown. Any `.md` file that starts with `@markdownai` becomes live - directives resolve at render time, before Claude ever sees the file. Here's what the same CLAUDE.md looks like rewritten: @markdownai v1.0 @prompt role="context" This document is live. Every value was resolved at render time. Do not look up package.json, .env, or docker ps - current values are already below. @end # CLAUDE.md > Before starting: sync status is live in the Cross-Project Sync section below. ## Project Overview Acme API - version {{ read ./package.json path="version" }}. ## Build and Run Commands API on port {{ read .env key="PORT" fallback="3001" }}, web on {{ read .env key="WEB_PORT" fallback="3000" }}. @list ./package.json path="scripts" mode="entries" columns="key:Command,value:Runs" as="table" Test suite (live): @query "npm run test:run -- --reporter=verbose 2>&1 | tail -3" @cache session Mongo test container: @query "docker ps --format '{{.Names}} {{.Status}}' | grep mongo-test || echo 'not running - port 27018 is clear'" @cache session ## Environment Variables @if file.exists ".env

I cancelled my AI notetaker subscription and built my own tool using Claude Code. It works well (and it's free)

It does what Fathom, Otter, and Fireflies charge $15–$30/seat/month for. I shipped a fully working AI meeting note-taker last weekend. I use this exact setup to Records calls then transcribes and Summarizes key points, it then pulls action items and then creates shareable notes all whilst running inside my Claude workflow. . The whole setup takes one weekend to build. \--- Here’s how it works:(you can copy this exactly) Step 1 → Fork the repo, drop into Cursor Step 2 → Set env vars: transcription key, database URI, admin creds, session secret Step 3 → Record or upload your meeting Step 4 → The audio gets transcribed Step 5 → Claude turns the transcript into structured notes, decisions, follow-ups, and action items Step 6 → Click “Share link” → send anywhere Total build time: \~1 weekend. Cost: $0/month. \--- Why the 5-piece stack is the unlock? Most "build your own SaaS" attempts fall flat because they bolt features together without designing the user flow first. This stack works because the data path was decided before any UI got rendered. Every SaaS feature you pay for has a primitive underneath. Loom = browser recorder + S3 + share links. Otter = Whisper API + database + UI. Calendly = a calendar API + booking page. The features stopped being moats the moment Cursor + Claude could write the glue in an afternoon. You're not paying for technology anymore you're paying for distribution and brand. That's why this build pattern works. The assembly is now free. \--- Why Claude? Because meeting notes are not just summaries. They need context. Claude can take a raw transcript and turn it into: \* decisions \* objections \* follow-ups \* action items \* CRM-ready notes \* client context \* internal operating memory That is where the value is. \--- [https://github.com/albertshiney/utter\_public](https://github.com/albertshiney/utter_public)

Would you reserve the hard cases in auto-review for heavy reasoning models?

I’ve been looking at OpenAI’s Auto-review, and I feel like it brings a problem: if an agent has to stop and wait for human approval every time it encounters a boundary action, the workflow becomes extremely fragmented; but if everything is automatically allowed through, it can easily drift toward the other extreme of full access. So what I’m more concerned with now is no longer whether we need a reviewer, but rather: should the reviewer layer itself be stratified? My intuition is that the first layer can actually be quite simple. Most escalation actions are rule-based by nature: whether they cross writable roots, whether they touch the network policy, whether they clearly have destructive side effects. This category may not need the heaviest model to review it at all. What really makes me hesitate is the other layer: the harder review cases. These are cases where the action looks reasonable on the surface, but actually involves several candidate paths, different side effects, or a conflict between the user’s intent and system boundaries. At that point, the question is what kind of model is suitable for sitting in this hard-case reviewer slot? This is where I start thinking about a thinking model like Ring 2.6 1T, with high / xhigh modes. If the reviewer layer really does need to be stratified, I’d be more inclined to put it in the role that requires complex logical analysis, path comparison, and final calls on hard cases, rather than having it review every single action by default. I wouldn’t make it the always-on reviewer, but would instead reserve it specifically for cases where a lightweight reviewer should not be making the final call. If you were building your own auto-review / approval gate, would you stratify it this way? Or did you eventually find that, as long as the rules are clear, heavy reasoning is actually unnecessary for the reviewer layer?

Claude skills silently override my instructions, and the surprising pitfalls

So today when working with a Claude skill, I curiously clicked to expand what it was thinking amid the work and spotted this: >I need to run the intake step using the ask\_user\_input\_v0 function to gather sources.... The tool has a tight constraint — max 3 questions with 2-4 options each — so I need to be strategic... So it is like, even when Claude needs to ask more than 3 questions or has more than 4 options per question, it will compact them because of the tool's constraints. Further digging and it is correct that `ask_user_input_v0` does have those hard limits. But this is not noted or mentioned in places that I could learn. If I didn't see the thinking process, I would never have known it exists. The fix for me was easy: I updated my skill to ask multiple rounds when it needs to. But the bigger questions are: * How do I share this to others? * Is there any other pitfall when working with Claude skills? So I went deeper to discover more pitfalls. Surprisingly there are more, and they aren't in [`skill-creator`](https://github.com/anthropics/skills/blob/main/skills/skill-creator/SKILL.md) either. For example: * `Write` silently overwrites files on Code/Desktop. `create_file` refuses to overwrite on Claude.ai. Same instruction, opposite behavior. * The officially-recommended `references/` pattern is broken — relative paths don't resolve from the skill's directory on any platform. * Skills referencing tools that don't exist on the running platform fall back silently to prose. No error. I started a notes repo to store the findings here: [https://github.com/livlign/claude-skills-pitfalls](https://github.com/livlign/claude-skills-pitfalls) Has anyone else hit pitfalls like these?

Building Expertise in Claude - Seeking Quality Learning Resources

Hi everyone, I'm on a mission to become a serious expert in Claude and AI, and I'm building a structured learning path. I want to create content that's actually valuable - with real practical applications, not surface-level tutorials. I'm past the beginner stage and looking for: 1. \*\*Advanced Prompt Engineering\*\* - Deep techniques, not just "be specific" 2. \*\*Practical Use Cases\*\* - Real projects: content analysis, AI agents, automation, research tools 3. \*\*Advanced Features\*\* - Vision, Function Calling, Multi-turn Conversations, RAG 4. \*\*Shortcuts & Best Practices\*\* - What actually works in production, not theory I've already gone through: \- Anthropic's official documentation \- Basic prompt engineering guides \*\*What I'm specifically looking for:\*\* \- YouTube channels with \*advanced\* Claude tutorials (not intro stuff) \- Courses or resources showing practical implementations \- Builders/developers sharing real use cases \- Content about Claude's strongest capabilities I'll create resources based on what I learn, so quality matters - I want to recommend the same sources to others later. Any recommendations? Bonus if you share your best Claude project too. Thanks!

From making video games to winning a Nobel Prize, Demis Hassabis' insane journey

Most people know him as the AI genius behind DeepMind and AlphaFold.But did you know Demis Hassabis started his career as a video game programmer?He went from coding games → to building AI → to winning the Nobel Prize in Chemistry.His story is proof that the most unexpected paths can lead to the biggest breakthroughs. This is just a short clip you can watch his full life story in the complete documentary here: \[[https://www.youtube.com/watch?v=pxYeDFuKAOE&t=1s\]](https://www.youtube.com/watch?v=pxYeDFuKAOE&t=1s])