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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]

OpenAl Announced vs. Current Operational Compute

submitted by /u/Business_Garden_7771 [link] [comments]

These 9 Building Blocks Turned Claude Code From a Chat Into a persistent OS

Most developers Claude gurus use Claude Code one project at a time. I run 18. Not 18 sessions. 18 instances of the same OS, each running a different business, all sharing one skeleton I update once and propagate everywhere. Most developers treat Claude Code as a smarter editor. That's where it all goes wrong and you get frustrated. Claude Code becomes a real operating system the moment you stop thinking of sessions as the unit of work and start thinking of the whole environment as a substrate you build on top of. Here are 9 building blocks I use. The thesis is at the bottom. Build a skeleton with selective propagation, not a project. Most developers build one project per Claude Code workspace. I built a template instead. It has plugins, rules, agents, hooks, schemas, commands. When I start a new business I clone it and the new instance inherits the entire OS. Right now I run instances for: strategy, product, marketing website, threat intelligence, three consulting clients, a personal brand layer. Each one boots with the same DNA. Each one diverges on canonical files, memory, output, and project state. None of them bleed into the others. The sync mechanism is the load-bearing part. The update CLI pushes plugins, rules, agents, hooks, schemas. It never touches memory, output, canonical, or my-project. Those are the parts of an instance that accumulate. Without selective sync you have two options: rebuild every instance on every change, or never update. Both are dead ends. If you build features into one project, you wrote a project.If you build features into a template that propagates, you wrote an OS. I'm one person operating eighteen versions of myself. Move state out of prompts and into code. LLMs are bad at remembering. Code is designed for it. Most AI workflows leak state into the prompt. Voice rules. Style preferences. Banned words. Recent decisions. Eventually you hit context limits or contradictions. I moved as much state as possible into MCP servers. Voice linter. Lead scorer. Schedule validator. Loop tracker. They run in Python, return structured data, not hallucinations. Rule of thumb: if you've explained it to Claude more than twice, it should be code. Use receipts, not status fields. This one took me the longest to figure out. Every workflow I had was claim something is done. Issue marked closed. PRD marked shipped. Test marked passing. The problem: the LLM can claim anything. I rebuilt the system around receipts. An issue can't reach verified until a script runs and writes a verification record. A PRD can't archive until every accepted finding has a receipt. A morning routine can't close without log entries from every phase. Receipts get written by code, not by the model. The model can't lie about whether code ran. Build a wiring-check gate. Half-built features rot. In a normal repo you notice because something breaks. In an AI repo nothing breaks. The half-built feature sits there and Claude pretends it works. I built a /wiring-check command. Before any task counts as done, it checks: every new skill has a trigger, every new hook lives in settings.json, every new MCP tool sits in the server, every new bus file has a producer and a consumer. "I think it works" fails the gate. "I ran X, got Y" passes. Make rules auto-load, not slash commands. If you have to type /voice to apply voice rules, voice rules will not get applied. Rules in .claude/rules/ load automatically. The voice rule fires on outbound text. The AUDHD rule fires on anything I'll act on. The social-reaction rule fires when I share someone else's post. No remembering. No willpower. Lint style in code, not in prose. I wrote a voice document once. Claude ignored half of it. Same emdashes, same filler, same hedging. I moved the banned word list into a Python scanner. Now every outbound draft hits two linters. They block emdashes, AI hype words, and 40-something other tells. The model can't talk its way past a regex. Track file dependencies with a graph. Canonical files reference each other. Change one and three others go stale. I keep a ripple-graph.json that maps these. When I edit talk-tracks, the system flags current-state and the engagement playbook for review. Chain sessions with handoffs and memory. (This is the big one) Sessions are drafts. The work is everything that survives the session: canonical files, memory, handoffs, output. If nothing persisted, you didn't work. You chatted. Every session in my system ends with /q-wrap. Writes a handoff doc, a memory update, and a status receipt. /q-morning reads all three before doing anything else. The handoff covers: what shipped, what's blocked, what's next, what I learned. Memory files hold the longer-term version. The result: I can sleep for a week, come back, and the system reminds me where I was, what I cared about, and what the next move is.Nothing about Claude Code does this by default. You build it. Cont

Is there a way to split up Opus token spend by project?

Maybe I made a mistake by doing 'individual', but trying to figure out how to measure the cost by project. submitted by /u/read_too_many_books [link] [comments]

Released a free 9.8M doc Indic multilingual corpus — Hindi, Bengali, Tamil, Telugu + 7 more (CC0, HuggingFace) [P]

Built this over the past few weeks as part of a multilingual research project. Figured I'd share it here. Check it out! ~9.8M web documents across 11 languages — hi, bn, ta, te, mr, gu, kn, ml, pa, ur, en. ~8.4B tokens. CC0 license. 🤗 https://huggingface.co/datasets/AM0908/indic-hplt-v1 submitted by /u/ashtok897 [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]

What's the most impressive thing Claude has done for you?

Honestly I started using Claude recently and the biggest thing it has helped me with is content. Writing captions, coming up with post ideas and figuring out how to say things in a way that actually lands. Curious what it has been doing for you guys. submitted by /u/Consistent-Issue-811 [link] [comments]

Built a free Claude chat app with memory (Sonnet 4.5 is in there too)

The funny/painful timing here: I've been building this for months specifically because I wanted Sonnet 4.5 to remember everything. Then last week Anthropic pulled 4.5 from claude.ai. (I'm not a software engineer, just someone who cares a lot about AI and got obsessed with this problem and gets obsessed with things in general. Posting now because everyone seems to want sonnet back on chat and I have it.) Mneme runs on your own machine and talks to the Anthropic API directly. Because it's on the API, Sonnet 4.5 is still in the model picker. Honest catches first: The app is free. You pay Anthropic and OpenAI (for memory search) directly. Roughly $3 to $8/mo on Haiku for light use, $30 to $60 on Sonnet for moderate-highish use. No subscription. Tested mainly on Windows (one-click installer). Android browser access works over the local server/Tailscale, iPhone should work too. macOS is not packaged yet. Beta and solo dev. Things will break for someone and I'll be in the comments Setup takes about 10-20 minutes. The whole system is built non-technical people in mind, it should be relatively simple and intuitive to set up and use, and the GitHub page linked below has a PDF you can give to Claude to walk you through every step. What's actually in it (for the technically curious): There's no shortage of solid memory systems for Claude. Mneme isn't trying to win at codebase retrieval. It's a complete personal Claude client where memory is baked into the whole surface from the start, rather than added as a layer. That means: Tiered memory: Messages flow from episodic to narrative to entity summaries as relevance shifts; old context gets compressed without being lost. Daily summaries: A 7-day rolling timeline, so Claude knows what's been going on lately, not just what's semantically similar to the current message. Entity tracking: Hierarchical summaries built up over time for the people, projects, and things you keep referring to. Narrative concepts: Keyword-triggered recall for ideas you've named, surfaced when relevant. AI Notes: A persistent section Claude can write to itself between conversations. Extended thinking, file attachments, text-to-speech, a small command system (@run, artifact, etc.), autonomous python retrieval the AI can agentically use if automatic fails. Dynamic context: I wrangled with the Anthropic caching system for a while before I figured out a way to have every single message have different retrieval without breaking cache. Bon apppetit Open source (CC BY 4.0), local-first, all data in a SQLite database on your machine. It's aimed at the "journal with an AI" use case (thinking out loud, processing your week, having something that actually pays attention over time) rather than coding agents or RAG over docs. Link: Mneme-memory/MNEME-BETA: Beta version of the Claude conversational memory system Mneme (first big-ish public project, be gentle) (Video also made with Claude - shoutout to HyperFrames) (Model picker screenshot and architecture infograph in the comments if I can find a way to attach them) submitted by /u/iveroi [link] [comments]

PM running Notion MCP for 3 weeks. Should I add Linear too or is that overkill?

PM at a 60 person SaaS, not technical. got the Notion MCP server running 3 weeks ago after a friend walked me through it. the unlock has been bigger than I expected. I can ask claude code "what did we decide about the onboarding redesign across our last 4 meeting notes" and it actually reads them and answers. saved me 4+ hours of scrolling already. current setup: ● daily standup notes go into a notion db ● PRDs live in a different notion folder ● meeting transcripts auto-pipe in via fireflies with the MCP I can query across all three. asked claude this morning "did anyone raise concerns about the auth flow change in the last 2 weeks" and it pulled the exact comment from a meeting 9 days ago. felt like magic until I remembered it was just text search with extra steps. now I'm wondering if I should hook up Linear via MCP too. would be nice to ask "what tickets are blocked because of decisions we havent made yet" and have it cross-reference notion notes against linear status. but I'm worried adding another MCP makes responses slower or more confused. is it overkill for a non-coding PM? or is the value worth the setup pain? second question. anyone running 3+ MCP servers at once and finding context bleed? sometimes I worry claude doesnt know which source to trust. would love to hear from PMs specifically because most MCP content I find is engineer-focused and I'm trying to figure out the workflow for non-coding workflow people. submitted by /u/SetGuilty7210 [link] [comments]

cli issues with local models

Hello friends, I'm trying to use claude code cli with some local models that i have, and am running into a strange issue that i cannot figure out. When i use an ssh tunnel and connect to my model via localhost (ssh -L 8000:localhost:8000 aiserver) the CLI works fine, if i change it from localhost and connect directly to the server, i get an `Unable to connect to API error` i have set these env vars ANTHROPIC_BASE_URL ANTHROPIC_AUTH_TOKEN ANTHROPIC_MODEL ANTHROPIC_DEFAULT_SONNET_MODEL ANTHROPIC_DEFAULT_OPUS_MODEL ANTHROPIC_DEFAULT_HAIKU_MODEL CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC CLAUDE_CODE_ATTRIBUTION_HEADER CLAUDE_CODE_ENABLE_TELEMETRY edit: i can connect to the server from the system in question via curl and vscode using the continue.dev and zoo code extensions. submitted by /u/akp55 [link] [comments]

Tips for BI analysis with Claude? My results so far are shockingly bad compared to general coding

I have a lot of hands-on experience with developing R pipelines to ingest large, live, very dirty datasets and produce relatively straightforward BI-type analyses. Trends, completion rates, revenue etc. I am currently working on a project with a small, live, moderately dirty dataset. The output should be simple analyses eg of lead quality, time to deal, revenue per product line. I am developing this project with Python and DuckDB. I am having incredible difficulty with getting Claude (Code) to coherently do this work, even when taking the pipeline design process step by step. I am always using Opus 4.7 High, and regularly experiencing Claude contradict clear instructions I gave it even within the last 5 minutes. It gives extremely generic names to variables and then very soon will completely misunderstand what the variables mean. It leaps to fixing problems without having any understanding of them and invents generic terminology that disagrees with the established project terms. My hypothesis is that this is an artifact of the data exploration. Inevitably as I explore the dirty data while building this pipeline I'm constantly uncovering new edge cases that need to be accounted for, and I guess this likely pollutes the context very quickly. Likely also Claude is more hesitant to codify "findings" than would be normal in a data pipeline, because it's engineered for more... deterministic (?) programming situations where findings are often meant to be fixed and forgotten. I am planning a few changes to my normal workflow: Much smaller context window, potentially even clearing after every small adjustment to the pipeline Strictly aligning with enterprise-grade standards (eg OpenTelemetry, Databricks Medallions) even for this small project Developing an extremely strict and exhaustively clear variable naming structure so that as Claude writes the tokens for each variable it cannot avoid understanding its meaning (eg medallion___source_module___data_scope___data_qualifiers___stat_type___time_window). Enforce constant linting of 2 and 3 through a hook. Anything else that can be recommended? One thing I'm attempting to do is "go with the flow" and try to figure out what Claude "wants" to do, then strictly codify that... but it seems like most often Claude is just doing random things. Any advice for that? submitted by /u/unwritten734 [link] [comments]

A plugin that slows you down on purpose

Hi all. Out of respect to other humans this is written by a human. You all should take an Uber to get to the carwash. My name is Ilya and I want to share my ecosystem of skills and agents (and a couple of rules + hooks) that I've built for myself over the past 5 months because I wasn't happy with anything that the market currently offers. I use it on daily basis, and it only contains stuff that I needed to solve problems I faced, and I'm super happy with how it works. Quick context: currently I work in strategy consulting. But I got lucky enough to get consistent exposure to managing people for over 20 years. Running my own business, turning around others' businesses, playing colony management games, managing consulting teams, and most importantly - managing a mid-sized guild in an MMO (if you've done this you know). I am not a software engineer, although I do code a bit. The main idea was to organise AI in a way I would organise a team of very capable people. So this is mostly for thinking work, including coding, not just for coding. --- Why slow AI gives us speed. It's good, but the flip side - it's bad in some situations, and I see that many people miss it entirely. AI is great at following directions. If the direction is wrong because you rushed it, the wrong thing gets executed very quickly. The fix is unsexy and requires patience: spend time on the brief upfront, make the AI push back when something doesn't make sense, then check what came out before stacking the next step on top. Feels slower, is slower at first. But you end up with what you actually wanted instead of another slop-fest, so it's net faster eventually. --- The 7 principles I've built this on Slow is fast - to own the understanding you can't rush Bad communication kills results (human-to-human, human-to-AI, and human-to-self - we're often misleading ourselves thinking that we know what we want) We don't know what we don't know - AI must help you to see outside of your bubble Any computer task is doable by AI if AI is properly organised - tasks are small enough, well defined, and well assessed Solve for problems that exist now, not theoretical or aspirational ones, to stay focused (and save tokens) Context is king - shit in, shit out AI can help you deal with AI - especially by doing the boring organisational work for you --- Two examples of how it works to start with /shaping - my most-used skill. It's a small workflow where orchestrator uses 3 underlying skills in a dialogue mode and helps me to frame the problem depending on where I am in my understanding of it. It solves multiple problems - more often than desired, I think I know what the problem is, but in reality the problem is somewhere else. Often, it helps me to find a better (and simpler!) solution. This is somewhat similar to why companies pay for consulting - because they know that finding the right question is 90% of the answer. This is, as you guessed, slow - but it helps to improve defining the direction for work. Which is a big deal in management, including managing AI. /critic - this is when it comes to comparing what was produced to what was intended. It invokes a subagent, that is taught to assess the quality of stuff produced. It then gives an actionable unbiased feedback. Obviously, if the direction was wrong, there won't be much value in it, but when the direction is right - it does miracles for me. Works best for non-code artefacts (PRD, architecture, skills, slides, written documents). Together they bracket the work - shaping at the start to figure out what's actually being asked, critic at the end to check the output matches it. --- What's in it Four plugins (title is a bit misleading for controversy, sorry), MIT. Each works alone, but they compose: - rageatc-core - thinking infrastructure. Ideation, understanding, solutioning, briefing, research, producer-critic-learner loops, writing skills, persuading. The most-used plugin. - rageatc-tech (small one) - a bit of extra tools the agent can reach: browse, PDFs, with fallbacks when primary tools aren't available. - rageatc-code - software building the slow way. An improved version of Superpowers by Jesse Vincent embedded in my workflow. TDD enforced, architecture before code, scale-adaptive. Heavy on persistent project knowledge - PRD, architecture, roadmap, orchestration plan. - rageatc-design - design systems for UI work. Greenfield or extracted from existing code. This is an amazing interface-design by Damola Akinleye embedded in my workflow. Most software work uses all four. Non-coding work usually only needs core and tech. --- vs Superpowers rageatc-code draws heavily from Superpowers by Jesse Vincent - TDD enforcement, worktree isolation, verification discipline. What rageatc-code adds on top: persistent project knowledge (PRD, architecture, roadmap that survive sessions), scale-adaptive workflow (matches rigour to project size), and tight integration with rageatc-core'

Opus 4.7 broke about 40% of our team's prompts. The fix wasn't better prompts. It was finally taking CLAUDE.md seriously.

I run AI implementations for 6 mid-market companies as Fractional Head of AI. When Opus 4.7 dropped in April, about 40% of the setup degraded overnight. Token burn went up. Outputs got weirder. The Skills that had been quietly working for a year started producing oddly literal interpretations of instructions that 4.6 was clearly guessing at and filling in. The first instinct was to write better prompts. That worked for individual sessions but didn't survive the next model release in our test runs. Then I noticed something: the prompts that broke hardest had been written when 4.6 was the model. The prompts that still worked were the ones built into Skill files with explicit output format, length caps, and worked-example sections. 4.7 made the prompt-vibes-and-hope approach untenable. The model became more literal, which broke setups that relied on the model being charitable about ambiguous instructions. What I changed across the 6 setups: Skills replaced standalone prompts. Anything I'd done more than three times got moved into a Skill file. The Skill explicitly states the audience, the output format, the length, and includes a 2-3 sentence worked example. 50 to 200 lines each. The model loads them on demand instead of bloating context. CLAUDE.md got hierarchical. One global file for who the user is, what the business does, voice rules. A project-level CLAUDE.md for each engagement. Session-level instructions for one-offs. The model reads them in order and builds a mental model that survives across sessions. Memory files got broken out. I was stuffing too much into CLAUDE.md. Fix: keep the file under 400 lines. Detailed institutional knowledge lives in separate memory files that CLAUDE.md points to. The model reads on demand instead of every turn. Verification step added to long Skills. Instead of single-shot prompts, the model now generates output, checks it against a 5 to 7 item checklist, and revises. Adds 30 seconds per call. Cut downstream cleanup time by maybe 70%. The mental model that helped most: the model is the engine. The operating file under it (Skills + CLAUDE.md + memory) is the car. You do not keep buying engines and putting them on the asphalt. You build the car once, and each new engine makes it faster. Specific results across the 6 setups after 3 weeks of rebuild: Average prompt-to-acceptable-output dropped from 3-4 turns to 1-2. Token usage dropped 22% across the workspaces. The "this output is weird, let me try again" rate dropped from once-every-4-prompts to once-every-15. Most importantly: the next model release should be a net positive, not a net negative. One thing I am still figuring out: how to version CLAUDE.md so we can roll back when an edit breaks things. The project-level files are in git, but the global one lives in chat history, which is fragile. Curious if anyone has a better setup. What's working for you with 4.7? And if your prompts broke, did you go the "rewrite the prompt" route or the "build the operating file" route? submitted by /u/balance006 [link] [comments]

For the first time in years, ChatGPT falls to second place in the generative AI market, slumping behind Anthropic’s Claude. ChatGPT now lags in second place in various key metrics, including net new ARR, mobile app downloads, business adoption, daily active users, annualized revenue, etc.

Per Tech Times: “More U.S. businesses paid for Anthropic's Claude than for OpenAI's ChatGPT in April 2026 — the first time in the AI industry's short history […] Anthropic's annualised revenue run rate crossed $30 billion in early April 2026, up from roughly $9 billion at the end of 2025, placing it above the approximately $24 to $25 billion annualised figure OpenAI reported at the same time. More than 1,000 enterprise customers now spend over $1 million annually on Anthropic products — a number that doubled in under two months after the company's $30 billion Series G raise in February 2026. Eight of the Fortune 10 are now Claude customers, according to Anthropic.” submitted by /u/StarlightDown [link] [comments]

ADHD and the newer models.

I don't know if anyone is having this issue, but the last ChatGPT model that worked well for me was GPT-5.2. Everything after wants to try and fill in blanks, assume what I mean, and overwhelm me with a wall of text answer that I'm not really going to read. I have tried modifying personalty and I cannot find a good middle. I get wall of text that makes me want to flip a table after i told it to stop 20 times or two words which is very unengaging. 5.2 is one I can work with, as a tool it helps me so much. It doesn't try to figure out my intent and run in a completely different direction. With my ADHD I often forget a word or use a different meaning and it seems like 5.2 is more tolerant of that it does not jump up and go oh this is what you mean and spew a wall of text. It sucks now I have to remember to click 5.2 as you cannot set it as default. Being the last one on the list I worry it's days are numbered. It sucks and is frustrating AF to think I found a tool that genuinely helps me, and that it could be gone soon. submitted by /u/NightShaman313 [link] [comments]