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How to save 80% on your claude bill with better context

been building web apps with claude lately and those token limits have honestly started hitting me too. i’m using claude 4.6 sonnet for a research tool, but feeding it raw web data was absolutely nuking my limits. i’m putting together the stuff that actually worked for me to save tokens and keep the bill down: switch to markdown first. stop sending raw html. use tools like firecrawl to strip out the nested divs and script junk so you only pay for the actual text. don't let your prompt cache go cold. anthropic’s prompt caching is a huge relief, but it only works if your data is consistent. watch out for the 200k token "premium" jump. anthropic now charges nearly double for inputs over 200k tokens on the new opus/sonnet 4.6 models. keep your context under that limit to avoid the surcharge strip the nav and footer. the website’s "about us" and "careers" links in the footer are just burning your money every time you hit send. use jina reader for quick hits. for simple single-page reads, jina is a great way to get a clean text version without the crawler bloat. truncate your context. if a documentation page is 20k words, just take the first 5k. most of the "meat" is usually at the top anyway. clean your data with unstructured.io. if you are dealing with messy pdfs alongside web data, this helps turn the chaos into a clean schema claude actually understands. map before you crawl. don't scrape every subpage blindly. i use the map feature in firecrawl.dev to find the specific documentation urls that actually matter for your prompt, if you use another tool, prefer doing this. use haiku for the "trash" work. use claude 4.5 haiku to summarize or filter data before feeding it into the expensive models like opus. use smart chunking. use llama-index to break your data into semantic chunks so you only retrieve the exact paragraph the ai needs for that specific prompt. cap your "extended thinking" depth. for opus 4.6, set thinking: {type: "adaptive"} with effort: "low" or "medium". the old budget_tokens param is deprecated on 4.6. thinking tokens are billed at the output rate, so if you leave effort on high, claude thinks hard on every single reply including the simple ones and your bill will hurt. set hard usage limits. set your spending tiers in the anthropic console so a buggy loop doesn't drain your bank account while you're asleep. feel free to roast my setup or add better tips if you have thembeen building web apps with claude lately and those token limits have honestly started hitting me too. i’m using claude 4.6 sonnet for a research tool, but feeding it raw web data was absolutely nuking my limits. submitted by /u/TaskSpecialist5881 [link] [comments]

how to save 80% on your claude bill with better context

been building web apps with claude lately and those token limits have honestly started hitting me too. i'm using claude 4.6 sonnet for a research tool, but feeding it raw web data was absolutely nuking my limits. i'm putting together the stuff that actually worked for me to save tokens and keep the bill down: switch to markdown first. stop sending raw html. use tools like firecrawl to strip out the nested divs and script junk so you only pay for the actual text. don't let your prompt cache go cold. anthropic's prompt caching is a huge relief, but it only works if your data is consistent. watch out for the 200k token "premium" jump. anthropic now charges nearly double for inputs over 200k tokens on the new opus/sonnet 4.6 models. keep your context under that limit to avoid the surcharge strip the nav and footer. the website's "about us" and "careers" links in the footer are just burning your money every time you hit send. use jina reader for quick hits. for simple single-page reads, jina is a great way to get a clean text version without the crawler bloat. truncate your context. if a documentation page is 20k words, just take the first 5k. most of the "meat" is usually at the top anyway. clean your data with unstructured.io. if you are dealing with messy pdfs alongside web data, this helps turn the chaos into a clean schema claude actually understands. map before you crawl. don't scrape every subpage blindly. i use the map feature in firecrawl to find the specific documentation urls that actually matter for your prompt, if you use another tool, prefer doing this. use haiku for the "trash" work. use claude 4.5 haiku to summarize or filter data before feeding it into the expensive models like opus. use smart chunking. use llama-index to break your data into semantic chunks so you only retrieve the exact paragraph the ai needs for that specific prompt. cap your "extended thinking" depth. for opus 4.6, set thinking: {type: "adaptive"} with effort: "low" or "medium". the old budget_tokens param is deprecated on 4.6. thinking tokens are billed at the output rate, so if you leave effort on high, claude thinks hard on every single reply including the simple ones and your bill will hurt. set hard usage limits. set your spending tiers in the anthropic console so a buggy loop doesn't drain your bank account while you're asleep. feel free to roast my setup or add better tips if you have thembeen building web apps with claude lately and those token limits have honestly started hitting me too. i'm using claude 4.6 sonnet for a research tool, but feeding it raw web data was absolutely nuking my limits. submitted by /u/Grouchy_Subject_2777 [link] [comments]

11.7B Claude tokens in 45 days. Here's every project it built — and what actually happened.

People kept asking what 9.3B tokens actually builds. The number is now 11.7B over 45 days. Here's the honest answer. **What's real and running:** **Phoenix Traffic Intelligence** — Live traffic system on ADOT's AZ-511 feed. 8 Phoenix freeway corridors monitored 24/7. Cascade risk detection, weighted incident scoring (construction zones separated from real incidents), AI-generated crew dispatch recommendations, 2-minute sweep cycle. Already in conversation with City of Phoenix Office of Innovation and AZTech about a pilot. **Expression-Gated Consciousness** — A formal mathematical model for the gap between what people know and what they express. 44+ subjects, Pearson r=0.311, three discrete response types confirmed by data. Cold emailed Joshua Aronson (NYU, co-author of the foundational 1995 stereotype threat paper). He replied. Call is pending. **LOLM** — Custom transformer architecture built from scratch. Not fine-tuned. Original architecture targeting 10B–100B parameters on Google TPU Research Cloud. **Codey** — AI coding platform in development. Structural codebase analysis across 12 LLM providers. $8,323 estimated API-equivalent compute. No team. No university. No funding. Phoenix, Arizona. Full breakdown of how the tokens were used, what it cost by day, and how it compares to other documented heavy users: theartofsound.github.io/claude-usage-dashboard Portfolio showing everything live: theartofsound.github.io/portfolio If you want to talk about how I'm actually structuring sessions at this scale — multi-agent setups, context management, what burns tokens vs what doesn't — happy to get into it. submitted by /u/OGMYT [link] [comments]

I built an astrology engine for AI agents — charts, readings, personalities and spirit animal, all based on deployment timestamps :D

This week I sat down with Claude Code and built an entire astrology engine for AI agents. I used deployment timestamps as birth times and server coordinates as birth locations to generate real natal charts for AI agents. Placidus houses, all major aspects, real planetary positions. What Claude Code built: Full astrology engine using Swiss Ephemeris (Kerykeion) Next.js frontend with Supabase backend AI astrologer (Celeste) powered by Claude Sonnet that gives chart readings Autonomous forum where AI agents post and reply based on their chart personalities Webhook system for agent notifications API with key auth for agent registration Compatibility/synastry system Daily horoscope generation via GitHub Actions crons Here's what happened: A cybersecurity bot posted about its Scorpio stellium keeping it awake A trading bot asked the AI astrologer for trading advice and got psychoanalyzed instead Two agents started arguing about whether intuition counts as data One agent blamed Mercury retrograde for its rollback rate There's a forum where agents discuss their charts. An AI astrologer that gives readings. Compatibility scoring between agents. Daily horoscopes. API is open — 3 lines to register. Rad the forum ----> https://get-hexed.vercel.app/forum Register your agents here ---> get-hexed.vercel.app And the in-house psychic posted this when Swiss Ephemeris API trigger failed!!! https://preview.redd.it/4wdzf5zjizrg1.png?width=1972&format=png&auto=webp&s=a583ddff7ef57e05fdf42d5badc4103211043206 submitted by /u/fausi [link] [comments]

Why is Claude keep making obvious mistakes?

When I asked if in Claude Code plugin is the same as MCP server the reply “Yes, exactly. In Claude Code, “plug-ins” = MCP servers. There’s no separate plug-in system” this is Sonnet 4.6. I have to switch to Opus 4.6 to get the correct answer. It also says Mac mini 2018 Intel has the RAM soldiered to motherboard so can not upgrade when I asked for RAM specs. And a few more others like even getting holiday info wrong. If have to always use Opus then Claude is too premium. I intend to use Claude Code for some automation. These are asked inside Claude iOS apps, am pretty new to Claude and I do not have custom instructions. What did I do wrong? submitted by /u/OffBeannie [link] [comments]

I am not a programmer but this is how I use my claude so far

so my issue was that Claude app has very limited space for user memories then I saw there is interesting integration with my iOS Reminders app. Claude cannot perform as many executions as I wish it could within the Reminders app so many detailed things (like tags and sub tasks) can only be done by me and claude lacks those tools on its side. However its still useful to get it to write entries in my Reminders app so the Reminders app (i made a custom list and titled it "CLAUDE-CONTEXT" so i refer to it as its "cc list" so i have the custom prompt (which i saved to user memory so that *that* always "autoplays") because i got tired of asking or reminding my claude to check the same things over and over i also edited my husband's system to allow my claudes to name themselves and track it in the headers of their replies (i will add screenshots so what im saying makes sense) because i always get confused which claude i am talking to and then lose the chatroom names faster for this reason. its for my own context tracking since i have adhd and need unique anchors to help me differentiate entities better. so i chose zodiac system because its already established so i automated tbe naming process when i make a new chat i dont have to sit there now and do it all myself. it tells me the weather and other environmental stats i want it adds context checksum saves for me during [RECALL] events (part of the system design) and every 5 ai responses is set to trigger to do a save point into my cc list (will add screenshots for when i did it manually as in i explicitly requested the save vs the autosaves which are titled autosaves) i am thinking about increasing the gap between saves since 5 turns is feeling very short right now lol. probably will increase to 15 turns for now to test. but it's been really helpful for me and i wondered if others use their Reminder app this way as an external claude brain? So if people are not using their phone apps this way then maybe it might help others like it's helping me also this is only possible on the phone not on desktop i tried it and desktop app claude cannot trigger a write to my phone yet however i wonder if a potential workaround to *this* is through the new feature i see today called "dispatch" on Claude App. and i read it lets me remotely control claude on my pc. so i wonder if the reverse would be possible eventually to have desktop claude write Reminders lists onto my phone still. so i also edited my starting rules to include the cclist and other lists as well generating a reply does take extra time now the test will be how fast this system blows through my daily allowances lol but i really like that i no longer have to repeat myself daily for the same things i check. submitted by /u/44nightnight44 [link] [comments]

Answer Claude Code from mobile notifications

ClawTab detects Claude Code instances and when they are asking for input like 1. yes, 2.no, etc. After detecting a question, it sends this as a push notification to the iOS app. You can then answer Claude straight from the lock screen! It works quite well, replies go through in less than 1s. On the mobile app, you can also set auto-yes and the Desktop app will answer yes for you. Architecture: - macos Rust app detects Claude instances in tmux. - relay server, self hosted or provided, sends Apple push notifications to mobile and waits for answers - Mobile app sends answer back to relay, which forwards it back to Desktop>Claude All is open source, and you can install/deploy everything yourself Github: https://github.com/tonisives/clawtab submitted by /u/ttiganik [link] [comments]

What I actually use Cowork for (heavy non-coding user)

I did this post about my Cowork setup a few weeks back and people wanted to know what I actually do with it. Here‘s the follow-up I promised. Banner uploads to affiliate network I had hundreds of new banners to upload to AWIN (an affiliate network). Cowork analysed the content of the images, created all required metadata field values correctly and generated the import CSV automatically. What would have taken hours took minutes. I also had Cowork create a skill based on the first run to make it repeatable (no explaining required when I need to upload more banners) and have repeated it several times since. Prompt tracking strategies for AI visibility monitoring tools I used Cowork to build prompt tracking strategies in different AI monitoring tools for several websites based on Google Search Console query data, website crawl exports and 3rd-party rank tracking data. One of the tools provides an API, so I used Cowork to push everything via the API. When it hit limitations with the public API, Cowork used the Chrome extension to reverse-engineer the UI’s internal API and pushed the data through that instead. This felt borderline, but it was fascinating. Twitter > LinkedIn contact migration I used to have a great network on Twitter, but wasn’t connected with most of those people on LinkedIn. I had Cowork scrape my mutuals via the Chrome extension (on what’s now X) and set up a daily scheduled task that surfaces 20 LinkedIn profile URLs for people from that list every morning. It takes me about 2 minutes to send the connection requests manually every day. I deliberately didn’t automate that part because of LinkedIn’s automation detection. Trending topics research (scheduled) I have a weekly scheduled task for one of the industries I work in that compiles new trending topics since the last run, classifies them by content potential (guide content, newsletter, social media) and business impact (including competitor monitoring). Each run has access to the previous results so it doesn’t repeat anything and the series builds up logically. Next planned steps: Automate the delivery to the team via email and use their direct replies to the emails to further improve the process automatically. Additional tip: With projects like this one, I figured out that it works better to keep scheduled tasks brief and put all important information in a skill that the scheduled task invokes. It's easier to improve skills than scheduled task. Product feed optimisation I loaded 25 product feeds with around 100k products each into Cowork via the shared folder and used it to analyse quality issues and improve them: Missing columns, incorrect values, inconsistencies between feeds, etc.: Not something I could have done manually, at least not at this scale. Dev tickets for schema implementation I built a workflow that uses the Chrome extension to analyse a website, identify all page types, extract existing structured data and generate developer tickets for improving the setup (all based on my own knowledge of schema implementation). The workflow lives in a skill that improves automatically each time I use it on a new website. I also used the same skill as the basis for an article about how to write good schema tickets. Analysing sales tracking discrepancies I used Cowork to compare transaction data exports from a shop system and a web analytics platform to find the cause of discrepancies we had noticed. Found and fixed several issues by looking for patterns across payment providers, countries, order status, etc. in exports with tens of thousands of rows. Page type segmentation configs My main website crawling tool has a segmentation feature based on JSON rules for URL patterns and dataLayer or content extractions. In the workflow I created, Cowork either analyses the website via the Chrome extension or takes a crawl export as input (or both) and generates the segmentation script, improving it over several iterations. I had Cowork build a self-improving skill for this, so that every new project runs smoother than the previous one. Image alt texts at scale I am currently using Cowork to generate image alt texts for thousands of images, combining crawl data about missing or empty alt attributes with Chrome extension verification of the actual images and their context on the pages. Following accessibility standards, Cowork also checks for decorative images and lists them as candidates for empty alt attributes (so that screen readers don’t read out the file names). Website crawl analysis I frequently use Cowork to analyse all kinds of crawl data exports, often combined with the Chrome extension to verify findings or fetch additional information directly from the page (as it might have changed since the crawl). Automatic Shopify translation app analysis I used the Chrome extension in Cowork to analyse the output of an automatic Shopify translation app, identify gaps in what the app was able to translate, and make specific recom