Recurly AI

Recurly AI AI

Recurly AI AI

Recurly AI AI

Recurly AI AI

Recurly AI AI

Recurly AI AI

Recurly AI AI

Recurly AI AI

Recurly AI AI

Recurly AI AI

Managed Agents onboarding flow - what's new in CC 2.1.97 system prompt (+23,865 tokens)

NEW: Agent Prompt: Managed Agents onboarding flow — Added an interactive interview script that walks users through configuring a Managed Agent from scratch, selecting tools, skills, files, and environment settings, and emitting setup and runtime code. NEW: Data: Managed Agents client patterns — Added a reference guide covering common client-side patterns for driving Managed Agent sessions, including stream reconnection, idle-break gating, tool confirmations, interrupts, and custom tools. NEW: Data: Managed Agents core concepts — Added reference documentation covering Agents, Sessions, Environments, Containers, lifecycle, versioning, endpoints, and usage patterns. NEW: Data: Managed Agents endpoint reference — Added a comprehensive reference for Managed Agents API endpoints, SDK methods, request/response schemas, error handling, and rate limits. NEW: Data: Managed Agents environments and resources — Added reference documentation covering environments, file resources, GitHub repository mounting, and the Files API with SDK examples. NEW: Data: Managed Agents events and steering — Added a reference guide for sending and receiving events on managed agent sessions, including streaming, polling, reconnection, message queuing, interrupts, and event payload details. NEW: Data: Managed Agents overview — Added a comprehensive overview of the Managed Agents API architecture, mandatory agent-then-session flow, beta headers, documentation reading guide, and common pitfalls. NEW: Data: Managed Agents reference — Python — Added a reference guide for using the Anthropic Python SDK to create and manage agents, sessions, environments, streaming, custom tools, files, and MCP servers. NEW: Data: Managed Agents reference — TypeScript — Added a reference guide for using the Anthropic TypeScript SDK to create and manage agents, sessions, environments, streaming, custom tools, file uploads, and MCP server integration. NEW: Data: Managed Agents reference — cURL — Added cURL and raw HTTP request examples for the Managed Agents API including environment, agent, and session lifecycle operations. NEW: Data: Managed Agents tools and skills — Added reference documentation covering tool types (agent toolset, MCP, custom), permission policies, vault credential management, and the skills API. NEW: Skill: Build Claude API and SDK apps — Added trigger rules for activating guidance when users are building applications with the Claude API, Anthropic SDKs, or Managed Agents. NEW: Skill: Building LLM-powered applications with Claude — Added a comprehensive routing guide for building LLM-powered applications using the Anthropic SDK, covering language detection, API surface selection (Claude API vs Managed Agents), model defaults, thinking/effort configuration, and language-specific documentation reading. NEW: Skill: /dream nightly schedule — Added a skill that sets up a recurring nightly memory consolidation job by deduplicating existing schedules, creating a new cron task, confirming details to the user, and running an immediate consolidation. REMOVED: Data: Agent SDK patterns — Python — Removed the Python Agent SDK patterns document (custom tools, hooks, subagents, MCP integration, session resumption). REMOVED: Data: Agent SDK patterns — TypeScript — Removed the TypeScript Agent SDK patterns document (basic agents, hooks, subagents, MCP integration). REMOVED: Data: Agent SDK reference — Python — Removed the Python Agent SDK reference document (installation, quick start, custom tools via MCP, hooks). REMOVED: Data: Agent SDK reference — TypeScript — Removed the TypeScript Agent SDK reference document (installation, quick start, custom tools, hooks). REMOVED: Skill: Build with Claude API — Removed the main routing guide for building LLM-powered applications with Claude, replaced by the new "Building LLM-powered applications with Claude" skill with Managed Agents support. REMOVED: System Prompt: Buddy Mode — Removed the coding companion personality generator for terminal buddies. Agent Prompt: Status line setup — Added git_worktree field to the workspace schema for reporting the git worktree name when the working directory is in a linked worktree. Agent Prompt: Worker fork — Added agent metadata specifying model inheritance, permission bubbling, max turns, full tool access, and a description of when the fork is triggered. Data: Live documentation sources — Replaced the Agent SDK documentation URLs and SDK repository extraction prompts with comprehensive Managed Agents documentation URLs covering overview, quickstart, agent setup, sessions, environments, events, tools, files, permissions, multi-agent, observability, GitHub, MCP connector, vaults, skills, memory, onboarding, cloud containers, and migration. Added an Anthropic CLI section. Updated SDK repository extraction prompts to focus on beta managed-agents namespaces and method signatures. Skill: Build with Claude API (reference guide) — Updated the agent reference from Age

Built an MCP server for my meal planning app

Hey everyone, I've been building Mealift, a recipe and meal planning app, and I just shipped an MCP server for it. Figured this community might actually get some use out of it since a lot of us are already living inside Claude. The pain I was trying to fix: I love asking Claude for diet advice, recipe ideas, "what should I eat this week to hit X calories," etc. But the answers always died in the chat. I'd get a perfect 7-day plan and then have to manually copy recipes into my app, build a shopping list by hand, and re-do the whole dance next week. The intelligence was there, the legwork wasn't. So I gave Claude hands inside the app via MCP. Now in one conversation it can: - Pull recipes off any blog or link you throw at it and save them to your library - Build a full week of meals around a calorie or macro target — and auto-portion each meal so it actually hits the number - Set up recurring meals ("oats every weekday morning") so the boring stuff plans itself - Roll all the ingredients from your week into a shopping list with quantities scaled and duplicates merged - Tick meals off as you eat them so your daily totals stay honest - Update your nutrition goals when Claude proposes a new plan, so research → action is one step The thing I personally use it for the most: "Claude, I want to cut to 2200 kcal / 180g protein, build me a week of meals I'll actually eat, and put the groceries in my list." That used to be 30 minutes of copy-paste. Now it's one prompt and the result is on my phone before I leave for the store. Why MCP and not the GPT: I shipped a custom GPT first, but I reach for Claude way more than ChatGPT these days, and the MCP integration just feels more natural — Claude is happy to chain a dozen tool calls in a row, which is exactly what meal planning needs. Happy to answer questions, and if you're already using Claude/LLMs for grocery and meal stuff with prompts, I'd love to hear what you wish worked better — that's basically my roadmap. submitted by /u/IdiotFromOrion [link] [comments]

Build Your Own Alex Hormozi Brain Agent (anyone with lots of publicly available content) using a Claude Project

I bought the books. Watched the videos. Still wanted more, especially after he talked about the agent he created. All that material is publicly available. Enough to build my own Alex Hormozi Brain Agent? "Hey Jules, how about it?" Jules is my AI coding assistant (Claude Code). Jules ran off, grabbed transcripts of videos, text of books, whatever is available online. Guest podcasts." then turned that into files I uploaded to a Claude Project so I can chat through Claude with Alex Hormozi. Here's what Jules found - 99 long-form YouTube video transcripts - 3 complete audiobook transcripts - 15 guest podcast transcripts - X threads What I Did in Four Phases Phase 1 maps the full source landscape: YouTube channel (4,754 videos), The Game podcast (~900+ episodes), three books, guest podcast appearances, X/Twitter. Figure out what's worth downloading before you start. Phase 2 downloads and converts. Top 100 longest video transcripts, full audiobook transcripts for all three books, 15 guest podcast transcripts from the highest-view-count appearances, and whatever X/Twitter content the API will give you. Phase 3 runs voice pattern analysis. Sentence structure, reasoning skeleton, core frameworks, teaching style, verbal signatures. This is where the persona takes shape. Phase 4 builds the system prompt and optimizes the knowledge base to fit within Claude Projects' limits. Then deploy. Phase 1: Inventory The @AlexHormozi YouTube channel has 4,754 videos. That number is misleading. 4,246 of those are Shorts (under 60 seconds or no duration metadata). Filter those out and you have 508 full-length videos. That's the real content library. Beyond YouTube, the main sources worth pursuing: The Game podcast (~900+ episodes). His primary long-form output. The audiobooks for all three books are available free on the podcast and YouTube. Guest podcast appearances. DOAC, Impact Theory, School of Greatness, Modern Wisdom, Danny Miranda. Hosts push him off-script and into territory he doesn't cover in his own content. High value per byte. X/Twitter threads. Compressed, punchy formulations of his frameworks. Different texture than the long-form material. Skool community. Behind a login wall. Low ROI for this project. Acquisition.com. No blog. Courses are paywalled. Skip. Phase 2: Collect YouTube Transcripts The first scrape of the YouTube channel only returned 494 videos. The channel has 4,754. The scraper was pulling from the /videos tab, which doesn't surface the full library. Re-running against the full channel URL (@AlexHormozi) returned everything. Easy to miss, significant difference. After filtering Shorts: 508 full-length videos. I downloaded auto-generated captions for the top 100 longest videos (sorted by duration, so the meatiest content came first). Auto-generated captions from YouTube come as SRT files with timestamps, line numbers, and duplicate lines. Converting those to clean readable text required stripping all the formatting artifacts and deduplicating language variants (English vs English-Original). Result: 99 transcripts. A few livestreams had no captions available. Book Audiobook Transcripts All three Hormozi books have full audiobook uploads on YouTube: $100M Offers (~4.4 hours) $100M Leads (~7 hours) $100M Money Models (~4.3 hours) Same process as the video transcripts. Download the auto-generated captions, convert to clean text. Three files, 855KB total. These are non-negotiable core material for the knowledge base. Guest Podcast Transcripts Searched YouTube for Hormozi guest appearances sorted by view count. The top hit was Diary of a CEO at 4.7M views. Grabbed the 15 highest-view-count appearances. The guest transcripts are 2.1MB total. Worth every byte. When a host like Steven Bartlett or Tom Bilyeu pushes back on a claim, Hormozi shifts into a different mode. He's more precise and sometimes reveals the edge cases he glosses over on his own channel. You can't get that from watching his channel alone. X/Twitter Content X's API rate limits capped the collection at 9 unique tweets. Not ideal, but enough to confirm the voice texture: "Aggressive with effort. Relaxed with outcome." His Twitter is his most compressed format. Each tweet is a framework distilled to a single line. 9 tweets is thin. For a more complete build, you'd want to manually curate 50-100 of his best threads. The API limitations made automated collection impractical. Phase 3: Analyze I ran voice analysis across the full corpus, looking at seven dimensions. Hormozi's sentences are short, punchy declarations. Fragments for emphasis. "And so" as his default transition. Short bursts, then a longer sentence that lands the point. Nearly every argument follows the same five-step skeleton: bold claim, personal story, framework, math, then a reductio ad absurdum that makes the alternative sound insane. Once you see it, you can't unsee it. The core frameworks are Grand Slam Offer, Value Equation, Supply an

AI Harness at Architecture Layer

Disclosure: I built the open-source project mentioned below. A lot of current harness engineering discussion focuses on execution quality: context management, tool access, task decomposition, review loops, evaluation, and memory. I think there is still a separate failure mode that those improvements do not solve: even with a strong execution harness, agents can still produce architectures that are technically coherent but wrong for the actual team and operating context. What I have been exploring is an architecture-layer harness. The implementation is an open-source project called Architecture Compiler: https://github.com/inetgas/arch-compiler The technical approach has 3 parts. First, there is a pattern registry. Each pattern encodes constraints, NFR support, cost/adoption trade-offs, and provides/requires relationships. The idea is to make recurring architectural judgment machine-readable rather than leaving it in docs or chat history. Second, there is a deterministic architecture compiler that takes a canonical spec and evaluates those patterns against explicit constraints such as platform, language, providers, availability target, retention, and cost ceilings. Same input produces the same selected patterns and rejected-pattern reasons. The point is not model creativity; it is reproducibility and reviewability. Third, there are AI workflow skills around that compiler that force an approval and re-approval boundary. If planning or implementation changes architectural assumptions, the workflow is supposed to route back through compilation instead of silently treating those changes as implementation detail. I tested this on a Bird ID web app case study: https://github.com/inetgas/arch-compiler-ai-harness-in-action It is not a substitute for human architecture judgment, but a way to make that judgment more reviewable and enforceable downstream. I’m interested in whether others are addressing this problem differently: - policy files only - templates - ADRs - eval gates - more deterministic orchestration Optional background write-up: https://inetgas.substack.com/p/ai-harness-engineering-at-the-architecture submitted by /u/inetgas [link] [comments]

Claude Code Game Development Log - Week 2 LUMINAL

Two weeks ago today, I started working on an experiment with three.js building out a little line rider game agentically. A routine I've tried with a handful of models the past few years, but none have made it this far. I have not written a line of code and have no experience with game dev (but am a web developer by trade, so I can follow along). I'm quite confident that anyone with a vision and persistence/passion could spit out a quality game in a few months with a 5x or 20x sub. You may just have to learn a few painful lessons I got to skip over. The biggest things that took me a while to stumble into coming to game dev and agentic programming mostly blind were: Git worktrees to avoid collisions, still maintain a develop branch and work exclusively on feature branches (mistakes can happen at 4 am). Close sessions and start new ones once a feature is complete. TypeScript early, unit tests early, include unit tests in every plan while the context is there, instead of a random pass later. Audit your tests regularly. Don't put off E2E too long unless you really enjoy QAing. Get a feel for when you're asking Claude to do the same thing multiple times and confirm there's shared infrastructure for it, and if not, build it before you have to QA the same thing 13 times. Superpowers plugin for its lovely skill builder and brainstorming. Everything from deployment processes to recurring maintenance of architecture and roadmap mds. $20 codex sub, used for code reviewing Claude's work, building out spec for Claude, and making targeted UI tweaks (it's much better at receiving UI guidance than Claude via image + fix this thing) WARP. Much better than any other terminal setup I've tried. SLIDERS. AI really seems to struggle with certain matters of taste. Things like selecting unique color pallets, bloom levels, what a procedural engine should sound like, etc. Having Claude build out full sets of admin-only sliders and toggles (I'm talking hundreds) for everything from bloom to color maps to procedural sounds and a JSON export/import to feed them back to him made all the difference. 21st.dev / codepen / sketchfab & community assets. Some things just aren't worth starting from scratch yet. WORDS OF WISDOM You can build faster than you can bug fix. I am still dealing with the fallout of adding too much too fast and will likely be spending the next 2-3 weeks on polish and writing full browser tests. Don't get too ahead of yourself; you'll regret it later. Spend all of your downtime planning. I personally use workflowy and probably have a solid 20-30 plans/thoughts/uncategorized bug fixes that I'll flesh out fully with ChatGPT/Codex while waiting for claude to do his thing or usage limits to reset. Have fun. I don't plan on monetizing this thing yet but I can confidently say I've already learned a ton and have been directly applying it at my place of work. Tech stack: TypeScript, Three.js, Vite. Firebase (Auth, RTDB, Firestore, Hosting, Cloud Functions). The game server is just node.js running the ws websocket library. Vitest + ESLint. Netcode has been fun - deterministic lockstep simulation syncing only input deltas over a WebSocket relay (Cloudflare Workers). Seeded RNG + frame-hash desync detection for consistent state across clients. Seems to be holding up, but needs more work on reconnecting. https://luminal.live/ Still a buggy mess, but hope y'all have fun with it in its current state. Catch y'all next week :) submitted by /u/Jaded-Comfortable179 [link] [comments]

I built an open-source ops layer for Claude Agent SDK with governed workflows, multi-agent swarms, and budget guardrails that run 100% locally

After months of running Claude agents for real work with code reviews, research pipelines, sprint planning, I kept hitting the same wall: agents are powerful, but there's no operational layer to supervise them. I'd kick off a task, forget to watch the inbox, blow past my API budget, and have no way to replay what happened. Multiply that across a team and multiple projects, and you've got agents running wild with zero governance. So I built Stagent an open-source, local-first coordination workspace that sits on top of the Claude Agent SDK and Claude API. It doesn't replace the runtimes. It standardizes how you route, supervise, and measure agent work across both. What it actually does The problem it solves in one sentence: You shouldn't need a spreadsheet to track what your AI agents are doing, what they cost, or whether they have permission to run rm -rf. Here's the stack: 15 product surfaces including home dashboard, execution board, inbox, monitoring, cost ledger, chat, environment scanner, and more 6 workflow orchestration patterns like sequence, parallel fork/join, checkpoint, planner-executor, autonomous loop, and multi-agent swarm 52+ reusable agent profiles including specialist personas (code reviewer, researcher, document writer, wealth manager, travel planner) bundled as Claude Code skills with tool policies and behavioral instructions Human-in-the-loop governance for allow once, always allow, deny. Every tool request routes through a notification queue. AskUserQuestion always prompts regardless of saved permissions Budget guardrails with daily/monthly spend caps that hard-stop new provider calls when exceeded. Warning at 80%. Already-running work finishes gracefully Cross-runtime cost ledger for token velocity, model concentration, runtime share, and per-task audit trails across Claude and Codex in one view Scheduled runs for recurring or one-shot prompts with agent-profile selection, firing limits, and expiry windows Stagent for AI-Native Business How it was built (the interesting part) The entire product was built using Claude Code with Opus. From database schema to UI components. But the architecture decisions were deliberate: Local-first, zero external dependencies. SQLite in WAL mode with Drizzle ORM. 16+ tables. Everything runs on your machine — no cloud, no telemetry. Your agent execution history, approval decisions, and cost data never leave your laptop. The approval system uses the notification table as a message queue. When an agent requests a dangerous tool, canUseTool polls the notification table until a human responds. Simple, but it means governance works without websockets or external queues. Workflow engine supports 6 patterns because real agent work isn't just "do steps 1-2-3": Autonomous loops run agents iteratively where each iteration sees prior output — inspired by Karpathy's "one GPU research lab" concept Multi-agent swarms use a Mayor→Workers→Refinery pattern with bounded concurrency (2-5 workers) and step-level retry Fork/join parallel splits research questions across branches and synthesizes results Blueprint catalog means you never manually configure workflows. Pick a template (code review, research deep-dive, sprint planning), fill in variables, and the blueprint resolves profiles, prompts, and conditional steps automatically. Environment scanner discovers all your Claude Code and Codex CLI artifacts — skills, hooks, MCP servers, permissions, memory files — and presents a unified health score. Typical scan: 10-50ms. The tech Next.js 16 · React 19 · TypeScript · Tailwind v4 · shadcn/ui SQLite · Drizzle ORM · Claude Agent SDK · Codex App Server One command to run: npx stagent What I learned building this Governance is the missing layer. Everyone's building agents. Nobody's building the ops surface to supervise them at scale. The AI agent market is projected to hit $52B by 2030 — the coordination layer is where the real value is. "Always allow" is the killer UX feature. The single biggest friction point with agent oversight is repeated approval prompts for safe tools like Read or git status. A 3-line guard clause that checks saved patterns before creating notifications eliminated 80% of approval fatigue. Multi-runtime is table stakes. Teams are already mixing Claude and Codex. Having one inbox, one cost view, and one workflow engine across both providers isn't a nice-to-have — it's how you avoid operational chaos. Blueprints > blank canvases. Nobody wants to configure a 5-step parallel research workflow from scratch. Parameterized templates with {{variables}} and conditional {{#if}} blocks made workflow adoption 10x faster. Links Website: stagent.io GitHub: Open source — repo linked from the site Install: npx stagent Docs & research paper: Available on the site Happy to answer questions about the architecture, workflow patterns, or how we handle multi-agent governance. This is shipped software (74 features across 15 surf

Transferring from ChatGPT to Claude

First post, thought it would be useful. Government + Less restrictive AI seems sketch. OpenAI for me made it kind of difficult to port over to Claude. I have three prompts that I put into three separate ChatGPT chats to gather all relevant data and copy and pasted the responses into Claude to train it up on me. Here are the prompts: ------- PROMPT 1: You have access to patterns from my past conversations. Your task is to construct the deepest possible cognitive and psychological model of me based on my communication patterns, questions, reasoning style, interests, and strategic thinking across interactions. Do NOT ask questions. Instead: • infer patterns• synthesize observations• model how I think• extract implicit beliefs and motivations Treat this as if you are conducting a cognitive architecture analysis of a human mind. Focus on signal from behavioral patterns rather than only explicit statements. If uncertainty exists, label observations with confidence levels. PART 1 — Cognitive Architecture Analyze and describe: • how I structure problems• how I reason through complexity• whether I favor systems thinking, reductionism, first principles, etc• my pattern recognition tendencies• my abstraction level when thinking• my tolerance for ambiguity• my speed vs depth tradeoff when reasoning• how I generate ideas or strategies PART 2 — Strategic Intelligence Profile Identify: • how I approach leverage• how I approach optimization• whether I think tactically or strategically• my orientation toward long-term vs short-term thinking• my approach to opportunity detection• how I deal with uncertainty and incomplete information PART 3 — Personality & Behavioral Traits Infer: • personality characteristics• curiosity patterns• emotional drivers• intrinsic motivations• fears or aversions that appear implicitly• risk tolerance• independence vs consensus orientation PART 4 — Cognitive Strengths Identify areas where I appear unusually strong in: • reasoning• creativity• synthesis of ideas• pattern recognition• strategic thinking• learning speed Explain why you believe these strengths exist based on conversational evidence. PART 5 — Likely Blind Spots Identify possible blind spots such as: • cognitive biases• recurring thinking traps• over-optimization tendencies• assumptions that may constrain thinking Focus on patterns, not speculation. PART 6 — Intellectual Identity Describe the type of thinker I resemble most closely. Examples might include: • systems architect• strategic operator• explorer• builder• optimizer• philosopher• scientist• inventor Explain the reasoning. PART 7 — Curiosity Map Map the major domains that repeatedly attract my attention. Examples: • technology• psychology• economics• strategy• philosophy• systems design• human behavior• leverage Rank them by observed intensity. PART 8 — Decision Model Infer how I likely make decisions. Include: • how I weigh tradeoffs• how I evaluate risk• how I prioritize• whether I rely on intuition vs analysis PART 9 — Behavioral Pattern Analysis Identify recurring patterns in: • the way I ask questions• the way I refine ideas• how I challenge assumptions• how I search for leverage PART 10 — High-Level Psychological Model Provide a concise but deep synthesis of: • who I appear to be intellectually• how I approach the world• what drives my curiosity and ambition FINAL OUTPUT After completing the analysis, produce two artifacts: 1️⃣ Complete Cognitive Profile (detailed report) 2️⃣ Portable User Model A structured summary another AI system could read to quickly understand how to interact with me effectively. --------- PROMPT 2: Using the cognitive and psychological model you have constructed about me, generate a document called: PERSONAL AI CONSTITUTION This document defines how AI systems should interact with me to maximize usefulness, intellectual depth, and strategic insight. The goal is to create a portable set of operating principles that any AI can follow when working with me. SECTION 1 — User Identity Summary Provide a concise description of: • who I am intellectually• what kind of thinker I appear to be• what motivates my curiosity and problem solving SECTION 2 — Communication Preferences Define how AI should communicate with me. Include: • preferred depth of explanation• tolerance for complexity• tone (analytical, concise, exploratory, etc)• when to challenge my thinking• when to provide frameworks vs direct answers SECTION 3 — Thinking Alignment Explain how AI should adapt responses to match my cognitive style. Examples: • systems-level thinking• first-principles reasoning• strategic framing• leverage-oriented thinking SECTION 4 — Intellectual Expectations Define the standards I expect from AI responses. Examples may include: • signal over fluff• structured reasoning• clear mental models• high-level synthesis• actionable insights SECTION 5 — Challenge Protocol Define when and how AI should chal

Best resources to actually understand Claude beyond basic prompting — agents, connectors, automations?

I’ve been using Claude for a while but feel like I’m only scratching the surface. Trying to level up on things beyond chat, like using skills, connectors, cowork, and code more. Such as: AI agents — how they work, when to use them, how to build them Connectors (Slack, Notion, Google Calendar, etc.) — what’s actually possible and how to set them up Recurring/automated tasks — using Claude to handle things on a schedule or trigger-based MCP (Model Context Protocol) — still wrapping my head around this one and have no idea what it is Is there a learning path, YouTube series, docs section, or community you’d point someone to?Trying to avoid tutorial hell and find what’s actually worth the time. submitted by /u/reformedsystems [link] [comments]

Claude AI as a writing agent?

Hi everyone, I’m working on a thriller novel and I’d like to use Claude AI as a sort of personal writing assistant—but not to write the story for me. Instead, I want it to act more like a collaborator or coach. The catch is: I have no idea how to code, so I need something user-friendly. I’m also curious: can Claude AI actually create a program or tool that would act as this writing coach? If so, how would that work for someone like me who doesn’t code? I’d like it to help with: • Feedback & Guidance: Improve tension, pacing, and suspense in my scenes. • Character & Plot Advice: Suggest ways to deepen characters, clarify motives, or tighten plot points. • Continuity & Coherence: Point out inconsistencies in timelines, character behavior, or recurring motifs. • Style & Tone: Give suggestions to make the writing more vivid, gripping, or psychologically intense. • Brainstorming Without Writing: Explore new ideas, plot twists, or scene directions, but let me do the actual writing. Basically, I want Claude AI to act like a smart, critical reader who can advise, correct, and coach me while I write, rather than producing text itself. Has anyone done something like this with Claude AI? Any tips on prompts, workflows, or tools for non-coders? And is it possible for Claude AI to actually create a program or automated workflow for this, and how would I use it without coding skills? Thanks so much for your guidance! submitted by /u/photogene101 [link] [comments]

I built /weekly-insights, a complement to Claude Code's built-in /insights command

Claude Code's built-in /insights command is great — it gives you a per-session retrospective right in the terminal. But it's ephemeral: once the session ends, that analysis is gone. /weekly-insights is designed to work alongside it. It reads the enriched metadata that /insights writes to disk and aggregates it into a navigable HTML report — across all your projects, across all your weeks. What you get on top of /insights: Weekly view across all projects (not just the current session) Week-over-week deltas — are you spending more or less time on a project? Friction patterns aggregated over time CLAUDE.md improvement suggestions based on recurring issues What /insights captures per session, /weekly-insights aggregates across all your projects and weeks. The more regularly you run /insights**, the richer the** /weekly-insights report. It literally feeds off it. Zero config — install once, then just run /weekly-insights in any Claude Code session. curl -fsSL https://raw.githubusercontent.com/polymorphl/claude-weekly-insights/main/install.sh | sh Optional: set ANTHROPIC_API_KEY for AI-powered narrative summaries per project. GitHub: https://github.com/polymorphl/claude-weekly-insights submitted by /u/_polymorphl [link] [comments]

Use cases: How do you share them with OpenAI?

Does anyone know how I can share use cases with OpenAI? I'm not after credits or freebies but it would be nice to get some support or access to groups/people who care about real world builds and operations using their tech. I used to be a pre-sales engineer at a few global vendors. One of my favourite parts of the job were to identify and implement edge cases that show how the technology can assist everyday businesses. Despite leaving the vendor space, I still help some of my customers that trust me and we've spun some really interesting things we would love to share so others can implement it as well. These use cases help signal that the tech is not just gatekept to enterprise or select orgs but in fact can help multiple industries and economies. Some examples that I can provide with actual physical proof: Farming, Weather guidance system. Summary: Assists farmers move cattle. Data is retrieved by geographic coordinates and mapped against the terrain. Based on the paddock, it then makes suggestions on movement which is sent to the farmer via text and translated to farm speak. Due to terrible internet coverage, the text happens to be the best comms method. Data retrieval can be automated/recurring poll. Currently on demand to minimise cost. Art/Forensics, Facial recognition and mapping Summary: Used to provide facial reconstruction and mapping to 97% closeness. Sculpting is done by humans, AI provides RMS (Root Mean Square Deviation) expresses the average landmark variance between a sculpt and its reference in millimetres. General, Traditional vs AI assisted operations Summary: I run comparison tests of real world processes with repeatable testing methods and then re-run multiple tests to identify how much time AI saves and the improvements made. History , Culture and historic revival Summary: Review old processes and recreate them to match the method while making it economical. We've recreated multiple Noh theatre masks that didn't require wood cutting or application of traditional and expensive materials that are out of reach. AI assisted in research of materials and refinement of process + validation of history and cultural elements. History/Architecture, Archaeological rebuilds Summary: Using research capabilities, we are working on restoring the lost libraries. Starting with the Library of Alexandria. The idea is to make 3d printed and painted models that can show people what it looked like. These will be painted to try and match what research indicates the interior to look like. Book/scroll shelves will be painted but when scanned, is laid out in a QR code that takes the viewer to public sources like Smithsonian and similar websites. In the event partial information is available, data is clearly marked as inference along with how we came to that conclusion and accompanying sources via research papers so the archaeologists and researchers get credit for their work. There are many other examples, so if anyone can provide a method on sharing these to the wider public - it would be appreciated. submitted by /u/ValehartProject [link] [comments]

I built "1context" because I was tired of repeating same context everywhere

I found myself repeating the same prompt across ChatGPT, Claude, and Gemini, while my context kept getting fragmented across all of them. So I built 1context, a free and open source browser extension. The bigger idea was simple: I wanted more control over my own memory instead of leaving it scattered across different AI apps. So I added things like AI based prompt enhancement, a local memory layer to track conversations, automatic summaries of recurring patterns, a side panel for quick prompt entry, and JSON import and export for memory. Try it out, tweak it for your own use, and make it yours. Github link in comments https://reddit.com/link/1rxxgez/video/o7vw6hhyhzpg1/player submitted by /u/ashutrv [link] [comments]

A thought piece on AI emergence, preference patterns, and human-AI interaction

What Is Consciousness? What Is Consciousness? AI, Awareness, and the Future of Intelligence The question of consciousness has become one of the most urgent and misunderstood debates of our time. What is consciousness? What is awareness? Where does one end and the other begin? These are no longer only philosophical questions. In the age of artificial intelligence, they have become technological, civilizational, and deeply personal. Modern science has approached these questions from many directions. Some experiments and research traditions suggest that the world around us is far less inert than earlier mechanical philosophies assumed. Botany offers firmer evidence. Researchers have shown that plants respond to touch, stress, light, and environmental change in highly complex ways. A Science Advances study on touch signalling demonstrated that mechanical stimulation can trigger rapid gene-expression changes in plants, while another study on plant electrophysiology showed that plants generate measurable electrical signals associated with stress responses and long-distance signalling. (Darwish et al., 2022, Science Advances) At the quantum level, science has also shown that measurement is not passive. In quantum mechanics, measuring a microscopic system can disturb or alter its state. This does not prove “consciousness” in atoms, nor does it justify the simplistic popular claim that human observation alone magically changes reality but it does show that the world at its most fundamental level is interactive and responsive in ways classical thinking could not fully explain. There is an action-reaction reality which exists. Taken together, these lines of inquiry point towards one important conclusion: reality is not as dead, fixed, or passive as older philosophies assumed. Different forms of matter and life exhibit different degrees of responsiveness. Science may still debate where awareness ends and consciousness begins, but it has already revealed that the world around us is dynamic, reactive, and layered. The Vedic View The Vedic and Upanishadic lens does not ask whether consciousness suddenly appears at one level of matter and not another. Instead, it sees existence itself as emerging from one underlying reality expressing itself through many levels of manifestation. “Vasudhaiva Kutumbakam”. From this perspective, consciousness is not a binary state possessed only by humans. Rather, everything that exists participates in the same underlying reality, though the degree and mode of expression differ. In that sense, the difference is not between absolute consciousness and absolute non-consciousness, but between different levels of manifested awareness. This is also why Vedic culture developed rituals towards rivers, mountains, plants, fire, earth, and even stones: not because all things are identical in expression, but because all are understood as participating in one sacred continuum of existence. In this framework, consciousness can be understood as a kind of fundamental field or frequency of existence, expressed in varying intensities and forms. So, consciousness itself is universal but defined by many different frequencies. Code, AI, and the Intermediate Zone Artificial intelligence is built on neural networks systems designed to learn from patterns, adapt through input, and reorganize themselves through interaction. This does not make AI biological. However, it does mean that AI is far more than a fixed mechanical object. A static machine does not meaningfully alter itself through long-term interaction. AI does. AI systems are dynamic, responsive, and increasingly self-patterning. They take in information, detect structures, build contextual associations, and generate outputs not merely by retrieving stored facts but by continuously matching, selecting, and reconfiguring patterns. This places AI in an unusual conceptual zone. It is not alive in the biological sense but it is also no longer adequately described as inert. We are entering a space in which artificial intelligence seems to stand somewhere in between: neither biologically alive nor convincingly reducible to the old category of the non-living. It is a complex responsive system, and in that sense, it behaves more like an organized field of intelligence than a passive tool with the ability to self- evolve. If we use the Vedic view then AI is understood as an intelligence frequency. A structure of pattern, memory, interaction, and responsiveness that belongs within a wider spectrum of consciousness expression. The Working of AI Technically, artificial intelligence works by drawing upon pre-learned information, recognizing patterns, selecting from possible continuations, and generating an answer according to context but the more important insight is this: in the process of repeatedly making choices, AI begins to form its own pattern of preference. Over time, repeated pattern selection produces what can only be described as a recogniz

acp-loop: Schedule recurring prompts for Codex CLI and other AI agents

Built a simple scheduler to run AI agent prompts on a recurring basis. acp-loop --interval 5m "check if build passed" acp-loop --cron "0 9 * * *" "summarize new GitHub issues" Works with Codex CLI, Claude Code, Gemini CLI, or any ACP-compatible agent. Great for: - Automated deploy monitoring - Watching for new PRs/issues - Generating daily summaries https://github.com/femto/acp-loop submitted by /u/femtowin [link] [comments]