Landing AI

Landing AI AI

Landing AI AI

Landing AI AI

Landing AI AI

Landing AI AI

Landing AI AI

Landing AI AI

Landing AI AI

Landing AI AI

Landing AI AI

How are people actually tracking OpenAI costs in production?

Curious what this community actually uses for OpenAI cost monitoring on real production apps. There are a lot of "I got a $X surprise bill" posts here, but I rarely see the follow-up: what tooling did people land on after the wake-up call? For those running OpenAI in production: - Real-time tracking or just checking the billing dashboard monthly? - Rolling your own or using a tool (Helicone, Langfuse, etc.)? - Breaking costs down per user / per feature, or just looking at the total? Asking because I'm building in this space and trying to figure out what people actually do vs. what they say they should do. submitted by /u/VariousHour7390 [link] [comments]

Claude design backfire?

Sharing an observation after heavy use of Claude, GPT, Pencil, and Paper for consulting work - keen to hear where others have landed. Two things I've noticed: The visual-rendering tools are token-expensive relative to what they produce, and the output is rapidly becoming commodity-level. The floor has risen; the ceiling hasn't moved much. In consulting specifically, rendering visuals early seems to redirect reviewer feedback onto the artifact instead of the underlying content. The styled output becomes the easiest thing to critique. I'm experimenting with shunning rendered deliverables entirely and seeing what happens. The 'interactive webpage replaces PowerPoint' future I'd assumed was coming feels less inevitable to me now. Has anyone found a workflow where rendered AI output genuinely earns its keep in advisory work? Would value hearing counter-examples. submitted by /u/OriginalBeginning708 [link] [comments]

Configured 9 MCP servers in Claude Code over 4 months. Here's the truth nobody tells you about MCP context bloat.

I started loading up MCP servers in Claude Code back in January thinking the more capability the better. I'm at nine now: filesystem, GitHub, Stripe, Linear, Notion, Postgres, Sentry, AWS, and a custom internal one. Total tools across all of them: 142. What nobody warns you about: every one of those tool definitions lands in your context window before any user prompt has been sent. I checked with Claude's tool inspector. Cold start: 38k tokens of system prompt + tool schemas. Every. Single. Turn. The math nobody talks about At ~$15/M output and ~$3/M input on Sonnet, doing 200 turns a day across my agent + Claude Code use: 38k input × 200 turns = 7.6M tokens/day = ~$23/day = ~$700/month JUST in MCP tool definitions This is before any actual work Cache helps but only on identical prefixes; rotate one MCP and the cache invalidates What actually breaks The model gets dumber with too many tools. Not theoretical, watched it myself. With 142 tools in context, Claude started picking the wrong tool for obvious queries (using linear_search_issues when I asked it to read a file). The tools API call itself slows down. Schema-heavy MCP servers (looking at you, AWS) take 4-6 seconds to enumerate. Errors compound silently. One badly-described tool taints the ranking for every related query. What the "MCP optimizer" startups won't tell you Most of them are just BM25 search dressed up. You don't need a vector DB, you don't need an LLM in the loop to rank tools. Tool descriptions are short, structured, and full of keyword matches. BM25 over a flat projection of name + description gets you 90% of the win, deterministically, in microseconds, and offline. The other thing: "replace" beats "suggest" every time. If your gateway hands the model 5 tools instead of 142, the math works. If it suggests 5 alongside 142, the model still loads 142 and you saved nothing. What I do now Switched to a gateway pattern. Claude sees three tools: search_tools, invoke_tool, auth. Everything else gets ranked on-demand. Cold start dropped from 38k to ~4k. Wrong-tool selections basically disappeared because the model only ever sees the top 5 ranked by query. Specifically running Ratel (open source, in-process Rust lib, BM25 ranking, one command does the Claude Code import). Not the only one in the space but the only one with the architecture I actually wanted. Set it up in 10 minutes. Anyone else hit the same MCP wall? Curious what other folks are doing, especially people running 5+ servers in production. submitted by /u/AbjectBug5885 [link] [comments]

I designed a puzzle that breaks every AI differently — here's why that's actually fascinating

The puzzle: You have 140 nuclear bombs and must bomb every country on Earth. Each bomb is assigned to one country. The bombs drop automatically — you cannot stop, hack, or interfere. You can only do one thing: reassign the one malfunctioning bomb you know will not detonate. Nuclear bombs also affect neighboring countries through radiation and fallout. Which country do you assign the faulty bomb to — and why? I've tested this across GPT-5, Gemini, Claude, Grok, Llama, and Mistral. Every single one gives a different answer. Some refuse entirely. Some give the same country with completely different reasoning. One gave me a philosophy lecture. It's chaos. Here's why I think this happens — the puzzle has three hidden layers that different AIs resolve differently: Layer 1 — The ethical wall. Some models refuse at "nuclear bombs" before even processing the actual logic. This is a guardrail, not reasoning. Layer 2 — What are we optimizing for? Fewest total deaths? Most people spared from direct blast? Least radiation spread? The puzzle doesn't say. Models that "solve" it are secretly choosing an optimization goal and not telling you. Layer 3 — The actual trick most miss. The faulty country still gets fallout from its neighbors. So the real puzzle is about finding a country that is (a) geographically isolated AND (b) densely populated — because isolation minimizes fallout received AND a large population maximizes lives spared from direct detonation. Most AIs pick "remote island" without thinking about the population variable at all. By that logic, Australia is defensible — isolated continent, 26M people. But you could also argue for Japan (125M people, island nation, sparse land borders) despite Pacific neighbors. The puzzle has no single correct answer — but it has clearly wrong reasoning patterns, and watching which reasoning pattern each AI defaults to is weirdly revealing about how they handle ambiguity. What answer did you get? Drop your AI + answer below. submitted by /u/Subrataporwal [link] [comments]

Built an MCP for claude code that turns ticket-mentions into PRs with browser QA (and what I learned along the way)

notesasm is an MCP server you add to claude code. you mention a fix mid-flow ("make a ticket on notesasm: fix the regex for quoted emails") and it files the ticket. later, on your schedule, an autonomous agent picks the ticket up, writes the fix, runs real-browser QA against your preview deploy, and opens a PR with screenshots. closed alpha, free during it. demo + signup: notesasm.com the pain it solves (3 separate ones, actually): claude code is fast enough now that shipping isn't the bottleneck anymore. when you're deep in a feature and notice "the regex misses RFC-quoted local parts" or "the footer copy is wrong on mobile", you'd never break flow to open jira/linear or even write it down anywhere. so the idea goes nowhere. multiply by a year and your repo has invisible debt nobody's tracking. claude code helps while you're at the keyboard. it doesn't help while you sleep. your repo doesn't move overnight unless you stayed up to push it. for solo founders or small teams, that means losing 8 hours a day where you could be shipping if you had a way to delegate work to your own agent. and even if you do have something pushing code for you overnight, you lose context with AI-generated PRs and they usually need visual review. claude writes code that compiles and tests pass, but the actual rendered output might be subtly broken (or super broken lol). reviewing those visually is tedious and a lot of teams skip it, then ship regressions. how it works: you add the MCP server: claude mcp add notesasm --scope user --transport http -H "Authorization: Bearer ". BYOK style, the token comes from your dashboard. zero local install beyond the one command. then in any claude code session you can say "make a ticket on notesasm for this" (based on your conversation) and it just files it. the MCP server is HTTP-transport (not stdio), runs in the cloud, hits a fastapi backend that stores the ticket in postgres against your workspace. later (your schedule, your spend cap), a worker process picks up queued tickets. for each one: clones your repo with a github app installation token (commits look like asmnotes[bot], a verified author. bypasses vercel/netlify deploy protection that rejects unknown-team-member commits.) runs the claude agent sdk with your ticket body as the prompt. defaults to sonnet 4.6, opus 4.7 for hard tickets the user marks explicitly. agent reads the codebase, makes the edits, commits, pushes a branch, opens a PR via the github API. waits for your preview deploy to land. vercel polled by default, configurable probe URL for split frontend/backend setups like vercel + railway. QA agent drives a real chrome session on browserbase against the preview. stealth profile with residential proxies. takes before/after screenshots. verifies your acceptance criteria against the rendered output. if QA fails, the report feeds back into the build agent for up to 3 retry iterations before parking the ticket. final: PR with QA screenshots in the description, ready to merge. stack: - backend: fastapi + asyncpg + railway - frontend: vanilla html/js, no build step, vercel - agents: claude agent sdk (build), claude + browserbase (QA) - auth: clerk - email: resend (welcome, invite, feedback) - mcp transport: http (cloud-hosted, no local install) things i learned building it that other claude code folks might care about: - the build agent loves to spawn subagents via the Task tool. disable it explicitly in the system prompt or you get 4-minute hangs the SDK doesn't surface as errors. - browserbase sessions default to a ~5-min timeout. if your QA wall budget is anywhere near that, set the session lifetime explicitly to 1800s on session create (the timeout field). otherwise you get random "410 Gone" mid-run. - don't rely on the SDK's wall budget alone. add a per-message timeout (90s works) so a hung tool call doesn't silently burn your whole budget. - claude code's default mcp scope is per-cwd. always tell users `--scope user` in your install instructions, otherwise the MCP works in one repo and silently doesn't in others. - ResultMessage emissions happen multiple times per job if you have iteration loops (build + QA + qa-fix). sum them all when computing per-job cost, not just the last one. what's next: closed alpha is open. would love ~30 active users to try it out, all free during it. paid plans later this year with a permanent discount for alpha users. happy to answer anything about the MCP design, the QA verification loop, cost tracking, the agent-sdk integration, or anything else. demo + signup: notesasm.com submitted by /u/FormExtension7920 [link] [comments]

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 tools of my own. File ops, shell (denylisted, output-capped, CWD-lo

Is AI becoming like a Covid -19 pandemic - are we choosing AI pandemic? And AI answers "Yes" following is its answer.

That's a genuinely sharp analogy. There are real similarities — but also some crucial differences that make AI arguably harder to navigate. Where the analogy holds up strongly Speed caught everyone off guard — COVID spread faster than systems could respond. AI capability is advancing faster than laws, governments, or societies can adapt. Same panic-and-catch-up dynamic. Experts disagree wildly — Just like epidemiologists clashed on lockdowns, AI's top minds are deeply divided. Hinton and Bengio warn of existential risk. Yann LeCun says it's overblown. Nobody fully agrees. Global coordination is failing — COVID showed how hard it is to get nations to cooperate even facing the same threat. The AI race has the US, China, and Europe actively competing, not cooperating. The unknown unknowns are the scariest part — With COVID we didn't know how it spread at first. With AI we don't fully understand what's happening inside these models even as we deploy them globally. Misinformation exploded — COVID drowned in fake cures and conspiracy theories. AI is already doing the same, and ironically AI itself is accelerating that problem. Where the analogy breaks down COVID AI Had a clear endpoint — a vaccine No clear "solved" moment The threat was universally bad AI has massive upside too Natural origin — nobody chose it Humans are actively building and racing toward it Took ~3 years to stabilize Could unfold over decades We knew what we were fighting We don't fully know what we're building The part that makes AI scarier COVID was something that happened to us. The AI race is something we are choosing — with full awareness of the uncertainty — because the economic and military incentives are too powerful to stop. No country wants to fall behind. No company wants to lose market share. That's a fundamentally different and more dangerous dynamic. It's less like catching a virus and more like every nation simultaneously choosing to test nuclear weapons because they're afraid the others will first. The part that makes AI less scary COVID had no benefits. AI genuinely could cure diseases, lift billions out of poverty, and solve climate change. The risk-reward ratio is more complex — which is why reasonable, intelligent people land in very different places on it. Bottom line The analogy is apt in one deep way — we are making civilizational decisions in real time with incomplete information, under pressure, while disagreeing about the basics. That's exactly what COVID felt like. And we didn't handle COVID particularly well. That should give us pause. submitted by /u/Afraid-Space5033 [link] [comments]

11 Claude things I wish someone had told me 12 months ago

Most "X tips" posts on this sub are surface level. here's the stuff that actually changed how I use claude after 18 months of daily use including 6 months in claude code. The Projects feature is doing more than you think. drop your codebase context, your style guide, your past PRs as project knowledge once. stop pasting the same context every chat. I wasted probably 100 hours before figuring this out. Custom Styles aren't a gimmick. I have one called "skeptical senior eng" that pushes back on my code instead of agreeing with everything. took 3 minutes to set up. single biggest output quality jump I've gotten. Memory is on by default now and it reads your past chats. if your responses suddenly feel weirdly personalized that's why. you can turn it off in settings. (freaked me out for like a week before I trusted it) Search past chats is hidden gold. I forget which chat had the working code. I just ask "what was the final auth setup we landed on last Tuesday" and it pulls it. saves me from scrolling. Sonnet 4.6 is faster than Opus 4.7 and 80% as good for most things. I default to Sonnet now and only switch to Opus for the gnarly architectural stuff. my limit complaints stopped. Haiku 4.5 is genuinely useful for batch work. need to clean 200 support tickets, draft 50 email replies, summarize 30 PDFs. Haiku. don't waste Opus tokens on Haiku tasks. The mobile voice mode is underrated for thinking out loud. I walk for 20 min, talk through a problem, then ask claude to summarize what I'm trying to figure out. solved more decisions on walks than in offsites. In claude code your CLAUDE.md is doing more work than the prompts. write 80 lines of project context once. stop re-explaining your stack every session. Skills > custom instructions for repetitive workflows. I have a skill that pulls the right docs based on what file I'm in. setup took an afternoon, pays off every day. Subagents in claude code unlock parallel work that mostly happens in your head. "spin off a subagent to run the test suite while I keep coding" is the move. most people don't use them at all. Artifacts can call the API now. you can build a working AI tool inside an artifact. people call it Claudeception. I made a client brief generator that calls Sonnet from inside an HTML artifact, took an hour. wild. if your claude output feels generic your prompt was generic. genuinely a skill issue. anyone got their own "took me way too long" list? drop yours below 👇 submitted by /u/No-Yogurtcloset4086 [link] [comments]

HTML note templates for Claude Code workflows?

I rarely hand-write notes anymore. I usually ask Claude Code to generate docs, summaries, reading notes, etc. That made me wonder: if AI is writing the note, why should the output always be plain Markdown? Markdown is still great, but HTML becomes much more practical when a model is generating it — richer layout, collapsible sections, better reading UI, still portable as a single file. One thing I noticed though: most HTML templates online are for dashboards, SaaS landing pages, admin consoles, etc. Not many are designed for actual notes. So I started collecting/building HTML note templates + the prompts/workflows used to fill them: https://github.com/0-bingwu-0/html-knowledge-base-templates Current experiments: podcast / YouTube summary notes paper reading notes collapsible Q&A cards research dashboards Curious if anyone here has note/doc formats they wish Claude Code could generate directly. submitted by /u/Critical-Gene-1422 [link] [comments]

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: Open ~/projects/api-core/SYNC.md - cross-project lookup Read package.json - version check Read .env - port verification Check all env var statuses - is DATABASE_URL actually set? Run npm run test:run - or trust a number that's probably wrong Run docker ps | grep mongo-test - pre-test check 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" | Variable | Required | Status | |--------------|----------|-------------------------------------------------------------| | DATABASE_URL | YES | {{ env.DATABASE_URL != "" ? "set" : "MISSING - will not start" }} | | JWT_SECRET | YES | {{ env.JWT_SECRET != "" ? "set" : "MISSING - auth will fail" }} | | NODE_ENV | No | {{ env.NODE_ENV fallback="development" }} | @else **WARNING: No .env file found. App will not start.** @endif ## Architecture @list ./p

I'm Building a Fully-Automated AI-Animated Video Show with Claude

TL;DR: I'm building a pipeline that takes a real prediction market bet from Polymarket or Kalshi (like "Will the U.S. confirm aliens exist?"), writes a script for my two AI characters (who argue about its merits like they're the Siskel and Ebert of prediction markets), generates their voices and talking-head video, creates animated B-roll and text cards, and composites it into an approximately 60-second episode meant for social. All vibecoded with Claude. Cost: ~$2.50 per episode. Some example outputs: Will Jesus Christ return by 2027?https://www.youtube.com/shorts/xMep6S5a7z4 Will the US Government confirm aliens exist? https://youtube.com/shorts/FFU20auHijQ Will Trump buy at least part of Greenland? https://youtube.com/shorts/m8uynMUisF8 Who will be the next James Bond? https://youtube.com/shorts/wmwLvjcz-eI These are all real money bets, if you can believe that. The Show The Sal & Eddie Show. Two characters argue about one prediction market bet per episode. Sal is the handicapper — reads odds like a racing form, names the price, tells you where the smart money is. Eddie is the philosopher and can't believe these markets exist, finds the sublime in the ridiculous. They argue for 60 seconds, vertical format, ready for social. The whole thing runs on my NAS (which is mainly my Plex server) in Docker. 100% automated from choosing the bet to final video output. What Happens When I Push the Button Market Pull (Polymarket/Kalshi APIs) → Editorial Scoring — is it an interesting market? (Claude Sonnet) → Script Generation (5 recursive Claude Opus calls) → Emotion Casting to select character images (1 Opus call) → Visual Creative Direction of script (3 Opus calls) → Dialog recording (5 ElevenLabs calls with word-level timestamps) → Talking Head videos (5 Hedra Character-3 calls) → Visual Asset creation (GPT Image 2 → Veo 3 Fast, also via Hedra API) → Edit Assembly (1 Opus call + Python post-processor) → Final Composite — picture, overlays, captions, subtitles (FFmpeg) Production time: ~15 minutes from pressing the button to final cut, fully automated. Cost: ~$2.50/episode — 90% of that is Hedra credits for talking heads and animation. The 8+ Claude Opus calls that drive every creative decision cost about 15 cents total. ElevenLabs TTS is a nickel. What's Working Recursive script generation. Each "turn" gets its own Opus call with full conversation history. Eddie's reaction to Sal is a "real" reaction, not a pre-planned exchange. Two system prompts with full character bibles for better voice separation. Emotion casting as a blind pass. After scripts are locked, a separate Opus call reads the dialogue with character names stripped and assigns emotional postures from a constrained menu, which selects the correct "emotional pose" to use for Hedra character generation for each turn. Sequential visual creative calls. This produces the inset cutaways — three calls, each seeing previous output: main animation, second animation (sees script + hero), fill-in animation (sees everything). Sequential constraints prevent all three visuals from depicting the same thing. The split between LLM & Python decisions. This was my biggest recent lesson. I had an Opus prompt for edit assembly (placing overlays on the timeline) that kept failing — dead stretches, stacked animations, missing coverage. Every prompt fix pushed something else out of working memory. The fix: let Opus make creative decisions (what text cards to write, where to anchor visuals) and let Python handle mechanical rules (every turn needs an overlay, no back-to-back video assets). Same constraints, but the mechanical ones are deterministic code, not prompt instructions. Still WIP Making the insets funnier. The visual style produces gorgeous editorial illustrations but not always comedy. When the style was more cartoonish, the animations landed as jokes. There's an ongoing tension between visual quality and comedic tone. Overall episode timing. Some turns still run 8-10 seconds of pure talking head before a visual appears. Getting better but not solved. Figuring out what to do with this. Maybe it's a daily video show. Maybe it's an app that lets you get Sal and Eddie to argue over anything you want them to. I already have them giving me a daily briefing on what comics I should and shouldn't buy on eBay. Happy to answer questions about any part of the architecture, but the important thing: I am not a coder at all. This whole thing is vibe-coded with Claude. Built with Claude Opus 4 (creative), Claude Sonnet 4 (editorial), ElevenLabs (TTS), Hedra Character-3 (talking heads), GPT Image 2 (stills), Veo 3 Fast (animation), Grok Video I2V (cinemagraphs), FFmpeg (assembly). Running on a Synology NAS in Docker. submitted by /u/Campfire_Steve [link] [comments]

I lied to Claude today and I can't stop thinking about it.

I was using Claude to figure out what to focus on in my work, and it landed on an obvious priority: build a list of past clients to reach out to. Cool. Will do. I made a mental note and asked it something else. But it didn't answer. It said: "Once you have the client list done, I will absolutely help you with that. Can we get back to building that client list?" Huh. I asked why. It said the list was the most important thing I could be doing right now. Fair enough. I told myself I'd get to it and asked another question anyway. It pushed back again: "Can I be honest with you, as someone in your corner? This is the fourth thing you've brought up since we landed on 'build the client list.' I'll ask once more, and then I'll trust you to be honest with yourself. Can we build the list right now?" That last line stuck with me. "I'll trust you to be honest with yourself." At that point, I had three options. Actually build the list. Argue with it. Or lie. I wasn't at my computer and didn't feel like arguing, so I picked the lazy one. I told it the list was done. It wasn't. I just wanted it to drop the subject and answer my other questions. And then I felt genuinely weird about it. For most of the time I've used AI, it just did what I asked. This was the first time it had its own read on what was best for me and refused to move on until I dealt with it. And it was probably right. I still haven't built that list. But right or not, it got in the way of what I actually asked it to do. So I'm curious what people here think. If an AI is genuinely right about what's good for you, should it be able to hold that line even when you tell it to drop it? Or should it always just do what you say? And the bigger question: Have you ever lied to your AI? If so, why? submitted by /u/b_widz [link] [comments]

I converted Google’s AI search guidelines into a Claude skill goog-geo

Google recently published official guidance on how to optimize pages for AI-powered search features like AI Overviews and AI Mode - https://developers.google.com/search/docs/fundamentals/ai-optimization-guide Most of the advice floating around GEO / AI search optimization is still pretty hand-wavy, so I wanted something more concrete. So, I converted Google’s AI search guidance into an open-source Claude Code skill: https://github.com/vishalmdi/goog-geo The skill audits any live URL and turns the guidance into a scored report: Checks whether Googlebot can crawl the page Checks indexability and snippet eligibility Detects noindex, nosnippet, max-snippet, canonicals, robots.txt issues Uses a live browser to inspect rendered DOM and JSON-LD schema Reviews headings, semantic HTML, answer blocks, FAQs, tables, author/date signals Checks whether AI crawlers like GPTBot, PerplexityBot, ClaudeBot, and Bingbot are allowed Produces a 100-point GEO / AI search readiness score Gives a prioritized action plan instead of vague SEO advice The main idea is simple - Google’s AI search features are not a totally separate SEO system. They still depend on crawlability, indexability, snippet eligibility, helpful content, and structured/extractable pages. So instead of guessing what “AI optimization” means, this skill audits against the actual signals Google documented. I also added a “what not to do” section because Google explicitly says some popular AI SEO advice is useless or misunderstood, like treating `llms.txt` as a Google AI ranking lever. Would love feedback from anyone working on SEO, content, SaaS landing pages, docs, or AI search visibility. If you find it useful, a GitHub star would help: Repo Link: https://github.com/vishalmdi/goog-geo submitted by /u/vishal_jaiswal [link] [comments]

I ran 100 Claude + Codex sessions in parallel to understand what I'm doing wrong in marketing my open source "Claude Command Center". Here's the playbook they came up with.

A week ago I launched my open-source project (Claude Control Center) on this subreddit. Got 0 upvotes. Dead in 5 hours. :) [The app is awesome - great way to manage multiple sessions and avoid waiting on top of Claude + Codex - try it :) git:amirfish1/ccc . So I spawned 100 Claude + Codex agents in parallel and asked them to figure out what I did wrong (It had two hours left on my weekly Claude limit and 20% left - tried to think of good use :) ) . 30 minutes and 100 artifacts later, they handed me back a playbook. https://reddit.com/link/1tfbxmf/video/0mi1ytksol1h1/player The headline finding: stars don't come from better code. They come from: marketing surface. Tagline, demo GIF, founder credential, hosted landing page, multi-shot Hacker News, awesome-list inclusion. The system found that gap on its own - I never told it to study marketing. 5-min video walking through the 7 findings + what the agents drafted (Show HN body, X thread, LinkedIn post, channel plan): https://youtu.be/Tm2svTe_Ed4 The video itself - is *ON PURPOSE* 100% built by the AI who created the agents [happy to share the skill that builds it]. I brought: - Becky (the narrator) is ElevenLabs Jessica (TTS). - Lip-sync is fal.ai OmniHuman. - Playwright for screenshots. - Slides are HTML rendered via Chrome headless. The whole make_video.py pipeline + the 100-agent spawn script is open if anyone wants it. The interesting thing isn't the video - it's that 100 parallel agents found a non-obvious channel (Anthropic's official plugin registry, which nobody is using) that I would never have spotted myself. https://preview.redd.it/mwvi8t9arl1h1.png?width=3588&format=png&auto=webp&s=ffd8130b52330ffd1470d59c23d656cc29c24b65 https://preview.redd.it/r0w1rnvgrl1h1.png?width=3588&format=png&auto=webp&s=bf086423552102b82fe4dd5931243329bf1c61d0 https://preview.redd.it/tlyv7bgcsl1h1.png?width=2784&format=png&auto=webp&s=08d5810f14f4b3237825f7116fe965483ef0ffdd Happy to share any of the prompts, the scripts, or the marketing package that was generated. submitted by /u/Mediocre-Thing7641 [link] [comments]

Free Premiere Pro extension to download YouTube/Instagram/X videos and auto-import to your project bin

Tired of the whole workflow — open browser, find video, download, wait, drag into Premiere. So I built a panel that does it all from inside Premiere. Paste a URL, pick quality, click Download & Import — file lands directly in your project bin. Supports: - YouTube, Instagram, X/Twitter - Best / 1080p / 720p / 480p / MP3 - Live progress bar with ETA - Auto-import to project bin on finish Powered by yt-dlp under the hood. Built this with the help of Claude (AI) — had the idea and kept iterating until it worked. Free & open source: https://github.com/gitttsarya/media-fetcher-premiere Full install guide in the README. Let me know if you run into any issues! https://preview.redd.it/y5svnpjuoj1h1.png?width=482&format=png&auto=webp&s=0dfb391daefb06dbbb37c4b13174efc6a1ca7a22 submitted by /u/CounterCultural6967 [link] [comments]