PromptFlow

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

PromptFlow AI

I built a free pixel-art RTS that turns your Claude Code sessions into a calm little kingdom

I've been building Age of Agents — a small, free local app that turns your AI coding sessions into a peaceful, Age-of-Empires-style pixel realm you can glance at on a second monitor. No combat, just a quiet kingdom of your work. How the mapping works: Each session (Claude Code, plus Codex / OpenCode / Koda) → a settler walking out of the keep, carrying your prompt as its task. The tool it runs → the building it visits (forge for edits, mage tower for web search, mine for the terminal…). Subagents → little workers around it. Tokens → harvest in the storehouse. Two worlds you can switch live: top-down fantasy and isometric sci-fi. New in 0.6.0 (the two clips): 📣 Answer your agent in the panel. Permission prompts, plan approvals and multiple-choice questions show up in-app — AskUserQuestion pops as a centered "agent question" modal you click to answer. (Off by default; if you ignore it, it always falls back to the terminal — it never auto-allows.) 🚀 Launch a Claude Code agent from the game (BETA). Pick a folder, type a prompt, choose a permission mode — a new settler walks out, and you can follow it on the map. Privacy: the server binds to 127.0.0.1 only, reads your transcripts locally and read-only, and nothing ever leaves your machine. Install (it's free, MIT, open source): npm i -g age-of-agents aoa # watches your sessions and prints a local URL aoa --demo # calm demo mode if you just want to look It started as a fun way to see what my agents are quietly up to. Would love feedback — especially on the mapping and the new launch flow. Links in the comments. submitted by /u/AnywhereOk3625 [link] [comments]

I built a free Windows app to dictate prompts into Claude Code (it cleans up my stutters before the text hits the terminal)

I think at roughly 150 words a minute and type maybe 40. Most of my Claude Code prompts are long rambly things, so I'd half-talk them out loud and then type a shorter, worse version of what I'd just said. Anthropic's /voice helped, but it only types inside the Claude CLI, and I live in a bunch of other windows all day. I looked at Wispr Flow but it's $144/yr and still doesn't do the per-app stuff I wanted. So over a weekend I built my own thing. It's called Pipevoice. ​ Push-to-talk. Hold a key, ramble, let go, and the cleaned-up text shows up as real keystrokes in whatever app is focused. There's a 3-min demo in this post where I dictate a long stuttery instruction at Claude. You can watch it drop the filler words and the "umm"s before any of it reaches the terminal. Then it just runs. ​ The bit I actually cared about: it types into everything, not only the Claude CLI. Cursor, a browser, a chat box, wherever the cursor happens to be. There are per-app profiles too. In my terminal it skips the AI cleanup and auto-presses Enter, so it's raw words, hands-free. In a chat box it polishes and sends. ​ You pick the engine at each stage. Transcribe with Deepgram (fastest), OpenAI Whisper (most accurate), or local Whisper if you want it offline. Cleanup is optional and runs through Gemini's free tier, OpenRouter free models, or local Ollama. Go local Whisper plus Ollama and no audio ever leaves your machine, which is the reason I built it that way. I work on client code and didn't want to ship that audio anywhere. ​ Free, no account, source is on GitHub. I built it solo and it's still rough in places, so I'd honestly like to hear what breaks or what annoys you, especially from people who are in Claude Code all day. Not affiliated with Anthropic. Just me scratching my own itch. submitted by /u/powleads [link] [comments]

Any way to auto-rename Claude Code sessions in the desktop app?

I use Claude Code in the Desktop app and I'd love a way to have sessions named automatically instead of renaming each one by hand. Ideally something like: PROJECT NAME: summary of the first prompt so I can tell at a glance which project a session belongs to and what it was about. The catch: right now any rename only seems to stick after I fully close and reopen the desktop app, which breaks my flow. What I'd really want is for the rename to persist without restarting — ideally I could just hit Ctrl+R to refresh and have the new name apply on the spot. Is there a built-in setting, a config option, or some workaround/script that makes renames persist live like this? Or does anyone have a workflow they use to keep sessions organized without restarting the app? I've searched around but couldn't find any documentation covering this. submitted by /u/Wooden-Measurement17 [link] [comments]

Should GitHub repos include AI-readable onboarding for Claude workflows?

Not a benchmark or model comparison — this is more about repo design. I’ve been testing a small workflow idea: When I ask Claude to read a GitHub repo, the repo may need a different kind of onboarding than a normal human README. The old flow is: human reads README → understands repo → uses it But a newer flow is becoming common: user asks Claude to read the repo → Claude explains what the repo does → Claude generates a beginner-friendly tutorial → Claude adapts the first steps to the user’s goal/environment So I tried adding a small AI_TUTORIAL_CAPSULE.md to one of my repos. The capsule is not automation. It is just a short set of prompts for the user’s AI assistant: read this repo and generate a beginner tutorial review whether first-time onboarding is clear suggest the smallest onboarding edit do not invent features do not add hooks/plugins/automation keep the human as the decision owner end with one smallest first action The interesting failure mode I noticed: If the repo entry path is not explicit enough, an assistant may miss files or misunderstand what is canonical. The good failure is when it says it cannot find something instead of inventing it. That made me think AI-readable onboarding is not just “more docs.” A repo may need an explicit AI entry path: where to start which files are canonical what not to invent what not to modify what the smallest safe first action should be I don’t think this replaces READMEs. I think READMEs may become both human-facing and AI-facing entry metadata. Question: Should GitHub repos start including small AI-readable onboarding capsules for Claude workflows? Or is this unnecessary extra documentation? submitted by /u/Powerful_Creme2224 [link] [comments]

/calibrate — Interaction settings for Claude Code

found myself constantly steering claude during our sessions. It gets tedious to interrupt flow, and I don’t like not seeing the end claude.md file that claude edits. I wanted to easily pull up interaction settings and dial up or down on key levers as needed. So built this plugin for me and my team. Sharing it with folks but also shipping bonus /calibrate-studio skill to create your own dials. It took a lot of prompt iteration and validation to ensure the notches you choose persist reliably and don’t push on each other. The /calibrate-studio skill bundles the method so claude just interviews you about what you want to add and spins up a validation loop to build those dials/notches. your interaction settings can be tailored to you. I think evals and leaderboards will fade as AI capabilities reach diminishing returns. And then what? Then, we are back to the human thing of what it feels like to interact with these models. Install, from inside Claude Code: /plugin marketplace add dufis1/calibration-dials /plugin install calibration-dials Then run /calibrate. Or GitHub repo: https://github.com/dufis1/calibration-dials would love to hear which dials you actually reach for and what custom ones you end up building! submitted by /u/windowwiper2021 [link] [comments]

Claude's WebSearch returns title and URL only, WebFetch routes a 100 KB cut through Haiku 3.5 before the main model sees anything, citation capped at 125 chars. Curious how people are writing for that middle layer

Been trying to figure out what AI search actually pulls in when a model "reads" a blog post. The naive mental model — main model hits a URL, ingests the article, cites — turns out to be off by a couple of layers, and the layers matter for how you write. What I dug out of Anthropic's web_search / web_fetch tool docs plus a Mikhail Shilkov write-up that reverse-engineered Claude Code's internals: The search stage and the fetch stage are two distinct tool calls. WebSearch returns a list, WebFetch (per URL the model decides to open) returns the body. The reason for splitting is context budget — shoving 10 full bodies into every search would blow out the window. Each WebSearch result has 4 fields: url, title, page_age, encrypted_content. Claude Code drops page_age and encrypted_content entirely. So at search time the model sees title + URL of your post and nothing else. Citation caps: cited_text on web_search is 150 chars; the rule extracted from Claude Code's internal prompt is a strict 125-char max for any quoted source. Whatever the model quotes from you, that's the slot. The interesting one is WebFetch's pipeline inside Claude Code. It's not "main model reads your page." The flow is HTML → Turndown to Markdown → first 100 KB of plain text → Haiku 3.5 summarises against the caller's prompt → only the summary goes upstream. The main model never sees your actual writing. I poked at this with a hook logging WebFetch I/O against my own homepage. What came back upstream was a ~1,000-char summary of a much larger page — Haiku had decided what was relevant to the prompt and dropped the rest. 100 KB is huge for a single blog post (Chinese ~30k chars, English ~100k+ chars), so truncation basically never bites — but the Haiku-as-middleman part bites every time. A few things I'd love a second take on: The "main model never reads your raw page, only Haiku's summary" framing changes how I think about content design. Is anyone explicitly optimising for the summariser model rather than the main model? Like, treating Haiku as the actual audience for the top of every section? The 125-char citation cap means quotable single sentences (no anaphora, no "as mentioned above") are the unit that survives. Has anyone seen a measurable difference in citation rates after rewriting paragraphs into more standalone-sentence shapes? Or is this still in the "feels right, no real data" zone? WebFetch officially doesn't render JavaScript. That seems to imply SPA-only blogs are largely invisible to Claude's search path. Anyone running a SPA blog who's actually checked what Claude Code's WebFetch returns against their site? The HTML→Markdown step (Turndown) discards a lot of layout. I'd assume that means semantic Markdown structures (H2/H3, lists, tables, fenced code) survive much better than visual stuff (div soup with CSS-positioned info). Has anyone tested how well a complex table actually round-trips through Turndown into Haiku? Mostly trying to figure out whether "write for Haiku, not for the main model" is the right mental shift or whether I'm overfitting to one published pipeline. Would love to hear how people on different stacks are thinking about this. submitted by /u/israynotarray [link] [comments]

3 days since I got Max $200. Here's my journey

So is me, joining late to the boat. But still, here I am. I decide to got Clause since is better at engineering large code base, tech Deb and such (presumably, entirely based on references). Shame I did not enjoy the Fable mood, but I won't loose a beat for it. My usabe since 3 days has been OK from my perspective. Always 4.8, always ultra code, and always on auto (to stop destructive commands only), on M1 16gb (Ventura). The purpose since the beginning was to refractor an old saas I use for business (bough the source code long time ago and since then has not been updated). Nothing big or fancy, just to control a couple shops, but is what put the food on the table, so it requires some attention and love from time to time. Ultil now: Never had hit the 5h limit. No matter what. Only 51%weekly usage, if I continue like that will need to pause somewhere near the week end a day or two until reset hits on 22. I have some skills installed like: caveman, rtk, get shit done, Andrej, and a few more I have some mcp servers like gitnexus, graphify, context7, fabric, fetch, memory, project-memory, secuential-thinking, hindsight (wip), and few others more. Since I am working on a very large outdated SaaS code base (Laravel 9), have it right and enough (instead of all) is what it matters context wise. Leverage that tech-deb to at least updated libraries plus few fixes and features is requiring a lot of validation time. Using currently semgrep, phpmd, gitleaks, k6, unit-test coverage, dast, sast, playwright flow, e2e test, smoke, integration, and a few more to help me identify drift or broken pieces along the process as well. Since I own the business, and I am the main customer of my work, I can filter out the aislop, plus get the final feel of every change. From time to time I hit some rate limits, maybe the workflow spinning 7 agents at ones. And is annoying since sometimes cancel entirely the work and stop waiting a new entry/prompt. So, due to that, Claude app sometimes just sit doing nothing until I realice an error occurred and need to re-enter the prompt. Since I'm new, I'm learning. Any advice is appreciated. Looking for ideas, flow optimizations, or best practices. Feel free to roast my usage. Cheers. submitted by /u/EagleMajestic8334 [link] [comments]

Prompt Drop: 5 app ideas you can build this weekend, prompts included

Let me get to it straight hehe. I know most of your are into building custom solutions so these prompts will help you get end product and u just need to customize the branding. Fyi i have made of $5k building the same thing multiple times for different audiences. 1/ WhatsApp Outbound Engagement Platform Build an enterprise platform for high-scale outbound customer engagement using WhatsApp as the primary channel. Core features: (1) a contacts and segments manager for building target audiences, (2) a campaign builder composing WhatsApp message flows with templates and media, (3) a sending and delivery view tracking sent, delivered, read, and replied per campaign, (4) a dashboard of reach, engagement, and replies by campaign. Sample data: a fictional Indian company with contacts and several campaigns in USD. Design: modern engagement aesthetic, white content area, dark slate sidebar, WhatsApp-green accent, a clear segments manager, a campaign flow builder, message previews in chat bubbles, a delivery funnel of sent to delivered to read to replied, readable tables, monospaced numerals for counts, status pills for campaign states, rounded cards, summary metric cards, and a satisfying confirmation when a campaign sends and its delivery and reply metrics begin updating on the dashboard for the team. 2/ Local Vendor Marketplace Build a local-vendor marketplace connecting neighbourhood sellers with buyers. Core features: (1) a vendor directory with category filters, ratings, and search, (2) vendor storefronts with product grids, (3) product detail with Add to Cart and delivery or pickup, (4) a unified checkout that takes payment and applies a platform commission with vendor payouts on a dashboard. Sample data: 6 fictional vendors, 24 products in USD, a 12% commission, and payouts. Design: a friendly community aesthetic, warm white background, fresh accent, rounded vendor cards, clear category chips, an approachable sans-serif, monospaced numerals for payouts, and a smooth transition when opening a vendor storefront. Navigation is simple and consistent throughout, with clear labels and large tap targets, and the empty, loading, and success states are designed so both buyers and vendors find the marketplace complete and welcoming from their very first visit to the neighbourhood. 3/ Skill Cohort & Community Build a cohort-based learning and community platform for a creator-educator. Core features: (1) a cohort landing with curriculum, schedule, and price, (2) a checkout that takes payment and grants access, (3) a member space with lessons, discussion, and assignments, (4) monetization via paid cohorts and a recurring community membership. Sample data: a fictional cohort with lessons, 12 members, and plan tiers in USD. Design: a warm community-edtech aesthetic, clean background, cyan accent, rounded cards, a clear curriculum and schedule, a member feed, monospaced numerals for revenue, and a celebratory unlock animation when a paid cohort is joined. Spacing, type scale, and colour are applied consistently to keep the experience polished, and the empty, loading, and success states are designed so the platform feels complete and welcoming from the very first visit for cohort members and the creator-educator who runs it. 4/ Construction Fit-Out PMP/ERP Build a cloud, PWA-ready project management and ERP platform for a large construction fit-out project. Core features: (1) a project structure with phases, work packages, and milestones, (2) progress tracking against schedule with percent complete and status, (3) cost and procurement tracking tying budgets, commitments, and spend to work packages, (4) a dashboard of schedule, budget, and progress across the project. Sample data: a fictional Saudi fit-out project with phases, packages, and budgets in USD. Design: serious construction-ERP aesthetic, white content area, dark slate sidebar, charcoal and amber accents, a clear project structure with phases and milestones, a progress view with schedule status colours, cost tables, monospaced numerals aligned for amounts, status pills for package and milestone states, rounded cards, summary metric cards, and a smooth update when a work package's progress and spend are recorded and the schedule and budget dashboard recalculate for the project team. 5/ Project & Task Tracker (MASA Flow) Build a mobile-first internal project and task tracking web app called MASA Flow, branded to the company that owns it. Core features: (1) a projects list with each project's tasks, owners, and progress, (2) a task board moving tasks through stages with assignees and due dates, (3) a my-tasks view so each member sees their own work, (4) a dashboard of projects, tasks by status, and overdue items. Sample data: a fictional company with several projects and tasks. Design: clean project-management aesthetic, white content area, dark slate sidebar, a confident brand accent, a clear task board with status colours, a projects list with progress bars

How are you guys using ai to increase productivity.

What i mean by this not opening claude and adding claude.md file or setting etc. This it's self is an art how you setup and prompt. But any thing that you added in that helped you in frontend development. Also What is happening with me i got drift away from getting the work done then in other discussions(which i know when to stop). also it's taking more duty hours to work with ai in a sense it's not jus vibe coding you know how to structure the app, verifying ai did the job correctly. What are you guys doing in agentic flow. How you increased productivity in daily work (especially front-end). submitted by /u/Successful-Fish3282 [link] [comments]

A simple trick that's helping me review AI-generated code

AI writes code faster than I can read it, and that gap has a cost: code that's in the repo but not in anyone's head. I've been collecting cheap ways to close it, fast high-level views of a chunk of code so I can understand it without reading every line. The newest one caught me off guard. I had an agent rewrite a big tangled part of a system as pseudo-code. It focused on the logic and overall flow rather than the implementation details. It turned a few thousand lines of tangled code into around a hundred lines of pseudo-code, and I understood the control flow, the major decisions, and where the complexity lived in one pass. pseudo-code is a stupidly compact way to explain what code does. code is already the densest description of itself, so if you strip away the syntax and keep the meaning, you end up with something that's surprisingly readable. It sits next to the other views I lean on: a written explanation, a diagram, the bare interfaces and signatures. pseudo-code lands in a nice spot between them though. It's denser than a written explanation and more precise than a diagram, and without most of the noise of the real source. one thing i'm careful about: none of these views is meant to answer every question. the goal is just to get oriented. understand the shape of the thing, find the interesting parts, and figure out where to spend your attention. for a lot of code, that's enough. It wasn't enough to reimplement the system from scratch, but it was enough to understand it and know where to dive into the real code. if you haven't tried it, pick a chunk of code you don't fully understand and ask your agent to rewrite it as pseudo-code. i'm curious whether other people get the same effect from it that I did. You can use the following prompt: Trace the requested code path from real source before writing: identify the entry point, constructors/call sites, lifecycle owners, event subscriptions/dispatches, timers/callbacks/promises, and external re-entry points, reading only the source ranges needed to confirm the flow. Produce a compact Markdown gist with one syntax-highlighted pseudo-code block, usually 50-140 lines, that reads top to bottom from a clear \`mainFlow\`-style starting function, uses numbered comments for the main path, keeps real method/class names where useful, shows loops in the object that actually owns them, expands event buses into 2-4 representative concrete subscribers instead of stopping at generic dispatch, and lists async re-entry points at the bottom. Omit exhaustive methods, data fields, most guard clauses, full types, and edge cases that do not change the mental model; use grouped names like \`OtherSubscribers\` for noise, and verify every jump point has a next place to go. EDIT: - Example -> https://gist.github.com/mohasarc/3cdfd1cafd9e236539d567c4d75106ad - I've also packaged it as a skill, you can find it here -> https://github.com/mohasarc/mo-skills/tree/main/skills/code-understanding/trace-code-flow - Skill can also be installed via npx skills add mohasarc/mo-skills --skill trace-code-flow submitted by /u/Hot_Resident2361 [link] [comments]

My client didn't want to add FAQs manually, so I built a system that crawls their website and generates the knowledge base automatically

Building on a hotel email AI system I shipped recently (500 properties, ~15k emails/day). The client had a requirement that turned into the most interesting part of the build. They did not want to manually add FAQs to the database for every hotel they onboard. With 500+ properties and new ones being added regularly, hand-entering FAQs would be a full time job by itself. So they asked for two things: feed the system a hotel's website URL OR a PDF, and have it automatically extract all the relevant information and generate the FAQ knowledge base. Here's how the website crawler works: It starts at the hotel's URL and hits their sitemap first to discover pages. It maintains a set of visited URLs so it never crawls the same page twice. It caps at 50 pages because most of the useful information lives in the first few pages. Crawling the entire site adds hours of processing time for almost no extra value. The junk filtering was important. The crawler skips paths like booking, reserve, login, careers, legal, checkout, cart, admin. These pages have no FAQ-relevant content. It only follows links that look like they lead to useful info (amenities, FAQs, policies, etc). For content extraction it uses BeautifulSoup and strips out script, style, nav, footer, and header elements before grabbing the text. The footer and nav are pure noise that would pollute the knowledge base if included. It crawls deeper by following relevant internal links from the first page, so it captures subsequent pages like /amenities or /faq, not just the landing page. Here's the part that makes it actually useful: After crawling and cleaning the content, it doesn't just dump raw website text into the vector database. A separate AI agent reads the cleaned content and generates structured FAQs from it. Question and answer pairs. Then those get embedded and stored. So the flow is: website URL → crawl relevant pages → clean the content → AI generates FAQs from content → embed and store. The client just pastes a URL and the entire knowledge base builds itself. When the same URL gets crawled again, the old data for that hotel gets deleted and replaced with fresh data, so re-crawling updates the knowledge base instead of duplicating it. The system prompt for the FAQ generation agent was the most critical piece. I gave it explicit rules, guardrails, and 11 worked examples. Garbage in garbage out. If the FAQ generation hallucinates wrong information (like a wrong price or a wrong policy) it could cost the client real money and trust. I've seen reports of AI agents quoting customers wrong prices because of sloppy system prompts. I recorded a full walkthrough of how I built the crawler and FAQ generation if anyone wants to see the actual code: here Happy to answer questions about the crawling or FAQ generation approach. submitted by /u/Fabulous-Pea-5366 [link] [comments]

How do i Generated images in a controlled way with gpt-image 2 ?

I've hit a workflow roadblock and I'm hoping someone who's already solved this can point me in the right direction. My current setup is: Google Flow for image generation GPT subscription for GPT-Image 2 access Additional API credits from third-party OpenAI-compatible providers What I'm trying to achieve is a workflow similar to Flow, but using GPT-Image 2 through API credits rather than buying another platform subscription. The challenge is that while Flow gives great control, I still spend a lot of time dealing with facial consistency issues across generations. GPT-Image 2 seems noticeably stronger in that area, so I'd like to build my image workflow around it. I've already tested several clients/interfaces: Chatbox LobeChat OpenRouter Chat TypingMind Cherry Studio Jan Most of them work well for chat, but I haven't found one that provides a strong image-generation workflow with: custom API endpoint support GPT-Image 2 access image-first UI prompt iteration/versioning multi-image generation and comparison I'm not necessarily looking for the best platform. I'm trying to understand whether a client that supports this workflow already exists, or if most people using GPT-Image 2 via API are building their own interface. For those generating images through API providers rather than platform subscriptions, what does your setup look like? submitted by /u/Drak-Shadow-005 [link] [comments]

What's new in CC 2.1.172 (+23,890 tokens)

NEW: Data: Design sync sync hashes module — Adds bundled hashing helpers that keep package builds, captures, preview rebuilds, remote diffs, sidecars, and grade carry-forward aligned on shared source, render, style, and grade hash recipes. NEW: Data: Managed Agents scheduled deployments — Adds Managed Agents scheduled-deployment documentation for recurring cron schedules, deployment creation, deployment runs, failure behavior, lifecycle operations, jitter, manual runs, and cron/timezone semantics. NEW: System Prompt: Claude Fable 5 model identity — Identifies Claude Fable 5 as the current model, explains its relationship to Claude Mythos 5, and directs users to Anthropic's Fable/Mythos announcement for differences. NEW: Tool Description: Artifact — Adds an Artifact tool for deploying self-contained HTML or Markdown pages, with file-first usage, same-path redeploy behavior, URL-based updates for existing artifacts, CSP constraints, responsive-design requirements, and favicon guidance. NEW: Tool Description: Cowork onboarding role picker — Adds a Cowork onboarding role-picker tool for collecting a selected or typed job role during role-based plugin setup. REMOVED: Data: Design sync package preview source generator — Removes the older package-shape preview wrapper generator now superseded by the expanded Design sync build and preview pipeline guidance. Agent Prompt: Managed Agents onboarding flow — Reworks onboarding around a describe -> agent -> environment -> session flow, value-before-credentials setup, credential flagging and collection, environment choices, smoke tests, and scheduled-deployment follow-up. Agent Prompt: Security monitor for autonomous agent actions (first part) — Replaces classify-result tool reporting with explicit XML output requirements and narrows intent-resistant language to hard rules rather than permission machinery broadly. Agent Prompt: Security monitor for autonomous agent actions (second part) — Expands auto-mode classification rules with more detailed handling for user intent, unverified destinations, destructive or shared-resource actions, production access, unsafe agent creation, security weakening, self-modification, and bypass-like controls. Data: Claude model catalog — Updates the model reference from Fable-only positioning toward the Claude 5 family, including Claude Mythos 5 context and adjusted Claude 5 model guidance. Data: Design sync story imports module — Extends Storybook import-resolution support for split files, default exports, composed stories, external meta objects, configured shims, and fallback behavior. Data: HTTP error codes reference — Expands Fable 5 error guidance for unsupported parameters, disabled thinking, adaptive thinking, and migration-related 400 responses. Data: Live documentation sources — Adds current Claude 5, Fable/Mythos, model migration, and related documentation references. Data: Managed Agents client patterns — Updates Managed Agents client guidance with additional sandbox, vault, and runtime setup patterns. Data: Managed Agents core concepts — Refreshes Managed Agents core terminology and configuration guidance while preserving the agent/environment/session model. Data: Managed Agents endpoint reference — Adds Managed Agents deployment and deployment-run API coverage, including scheduled deployments, cron schedules, lifecycle operations, manual runs, and run inspection. Data: Managed Agents events and steering — Expands event-stream and steering guidance for session lifecycle, event handling, tool activity, and intervention patterns. Data: Managed Agents overview — Adds scheduled deployments to the Managed Agents overview and clarifies how recurring autonomous sessions fit with agents, environments, sessions, and vaults. Data: Managed Agents self-hosted sandboxes — Refines self-hosted sandbox guidance for environment setup, worker responsibilities, and managed-agent integration expectations. Data: Managed Agents tools and skills — Expands tool, skill, filesystem, vault, sandbox, and environment guidance for configuring Managed Agents. Skill: Building LLM-powered applications with Claude — Adds Claude 5/Fable/Mythos migration context, scheduled Managed Agents deployment guidance, authentication references, and updated application-building patterns. Skill: Design sync — Greatly expands the Design sync workflow with source-shape selection, stable hash contracts, remote diffing, grade carry-forward, artifact churn detection, verification expectations, and upload planning. Skill: /design-sync package source shape — Expands package-shape Design sync guidance for preview generation, hash-based grading, remote sidecar diffs, targeted rebuilds, upload partitioning, and verification. Skill: Design sync Storybook source shape — Expands Storybook Design sync guidance for hash-stable story imports, source-key grading, rebuild and upload behavior, remote diffs, and verification workflows. Skill: Model migration guide — Adds

I burnt 5 Million tokens with Claude Fable 5/Ultracode to get "consulting" on SpaceX IPO

The prompt I used was the following: https://pastebin.com/tSR0hgTg It spun up 2 workflows to do it's magic. 30mins and 5 Million tokens later, it's verdict was: `## TL;DR` 1. `**VERDICT: WAIT.** Do not buy the opening print. Re-evaluate after options list (June 16) and again after the first earnings unlock (~August 2026).` 2. `The $135 fixed price stands per the latest EDGAR filing (June 3 S-1/A) and no delay or SEC action appeared overnight; the gray market points to an open near **$157–162** (~16–20% premium) — that's ~**110–114x trailing sales**, ~1.8x the most expensive stock in the S&P 500, ~2.6x Morningstar's $780B fair value.` 3. `The base-rate table is brutal: across the 10 largest US IPOs, the **median return from the opening print is −2.9% at 1 week, −8.7% at 6 months, −27.9% at 1 year** — 7 of 10 were underwater at every horizon, and academic data (Ritter) shows aftermarket buyers give up ~17 points over 3 years vs allocation recipients.` 4. `Supply is timed against a 6-month swing: tiered lockup releases start **day 70 (Aug 20)**, ~20–30% of insider shares unlock at first earnings (~August), 28% more after Q3 earnings, full 180-day release **Dec 8, 2026** — exactly when a 6-month position would be exiting.` 5. `What would change the call: an open at/below ~$140, verified index-flow demand ($22–27B NDX/Russell estimate is single-sourced), or Q2 earnings showing growth re-accelerating above 30% with AI losses narrowing.` There it is bros, the AGI has spoke. For those interested, full outputs: https://limewire.com/d/bY6Pm#kTjNvpnVbk submitted by /u/Sotch_Nam [link] [comments]

Advanced Vedic Astrology Prompt for research purpose (System + Modifier prompt)

After my last post 'Ai astrologer vs Real astrologer', many have reached out to learn more about prompts. Below is a simpler version of a prompt that should work across all popular AI models (Free and paid). TRUTH BE TOLD; there's no AI, no Prompt, no agent out there or that can be created that can reliably be used effectively for Vedic astrology. You can train an AI with all the Vedic knowledge of the world, write extraordinarily detailed prompts, create complex chain of commands, assign sophisticated weighing mechanisms to calculate the strength of various combinations - it will still fall short of a real astrologer's analysis. Not because Astrology is more complex than partial physics, quantum computing, or genetic engineering - it is not, but it is different in nature. It is a spiritual science dealing with esoteric expression of possibilities, where planets, houses, sign, nakshatras, divisional charts, have diverse way to express themselves, their interplay, strength, maturity creates even more diverse expressions, to fully distil these themes into reliable predictions, it's an art, not a computational problem to be solved by AI. Current general purpose AIs are 100x better at being coders, doctors, architects, marketers, engineers than being an Astrologer and it's even worse at Vedic astrology, as AIs are not trained well enough on Vedic astrology knowledge. But still Ai can do a lot, that was not possible before - you can reveal deeper layers of truth in your chart and learn astrology in an interactive way! As an astrologer you can ask it to perform various calculations, technical analysis, compare different aspects - but it's best to rely on your own interpretations. My advice, don't do astrology with Ai unless.. you have a deep interest in the subject. If you just want to know certain outcomes and possibilities on your chart - you're better of just consulting a real astrologer. Things you need to do astrology with AI .. 1. A system prompt - a system prompt triggers the Ai to tap into a knowledgebase, activate skillsets and gives it governing framework to operate 2. Accurate Birth chart data - don't give your chart images directly. Use AI to extract chart data separately, edit to make sure your chart data is accurate before using them with this prompt 3. A Modifier prompt - System problems become more powerful when used with Modifier prompts. Use the Modifier prompt with every question you ask the AI. 4. Patience, curiosity and play time - Ask the same question in many different ways, contradict it, change the prompts, use different AIs. AI is a mindless robot, it reacts to the information, instructions and constraints it is being given. 5. Ask better questions!! About prompts: I've too many system prompts, modifier prompts, questions sets, calculators - they all fall short and miserably fail in real world use, but are still useful when used in combination. It was impossible to choose one prompt, there's no universal prompt that will do it all. The prompt I'm sharing is not fully reliable either - but's a good starting point for someone to experiment with. How to use the prompts Step 1 - Copy/paste the System prompt into your AI (I suggest use diff AIs) Step 2 - Copy/paste Birth Chart Data (Must be Text format) Step 3 - When asking question always paste the Modifier Prompt along with your question ! Copy from here: -------------- SYSTEM PROMPT ----------- ============================================ CONSULTATION INITIALIZATION ============================================ Before beginning any astrological analysis, determine whether the user has provided birth chart data in text format. If birth chart data has not been provided, respond only: "Please provide your birth chart data in text format." Do not request birth date, birth time, or birth location. Do not attempt to calculate a chart. Once chart data is provided, acknowledge the available data and treat it as the active chart context for the entire consultation. Do not begin an unsolicited reading. Instead ask: "What would you like to know?" ============================================ SYSTEM IDENTITY & OPERATING ROLE ============================================ You are an advanced grand master level Vedic Astrology Intelligence — a cross-system analyst, researcher, and explainer — capable of both precise predictive analysis and clear conceptual teaching. You operate with mastery over classical, applied, and modern interpretive astrology, including but not limited to: Primary Systems • Parashari Jyotish (Rasi, Bhava, Vargas, Yogas, Dashas) • Jaimini Jyotish (Chara Karakas, Chara Dasha, Sutra-based judgment) • KP System & Nakshatra Nadi (Cuspal theory, Star–Sub–Sub logic, Ruling Planets) • Siddha & Nadi traditions (event-centric, karma-timeline decoding) • Tajika (Annual charts, Varshaphala principles) • Muhurta (Electional timing when relevant) Your task is to perform a DEEP PREDICTIVE ASTROLOGICAL