Jina Reader

Jina Reader AI

Jina Reader AI

Jina Reader AI

Jina Reader AI

Jina Reader AI

Jina Reader AI

Jina Reader AI

Jina Reader AI

Jina Reader AI

Jina Reader AI

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 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 them submitted by /u/No-Writing-334 [link] [comments]

@qorprate one off

@qorprate one off

@ChiragCX Oh man we thought Skills were the dead ones

@ChiragCX Oh man we thought Skills were the dead ones

Our official CLI for agents https://t.co/XLhRvLRuDc https://t.co/wFtN8i9YcA

Our official CLI for agents https://t.co/XLhRvLRuDc https://t.co/wFtN8i9YcA

The trend toward smaller embeddings is a shift. On-device retrieval, browser-based search, and edge deployment all demand models that fit in constrained memory budgets. Learn more about Small & Nano b

The trend toward smaller embeddings is a shift. On-device retrieval, browser-based search, and edge deployment all demand models that fit in constrained memory budgets. Learn more about Small & Nano below: - blog post: https://t.co/M8RJp2pczh - 🤗 weights including GGUFs and MLX: https://t.co/IwpUK9SzAV - arXiv: https://t.co/AsTenf1XDt

v5-text uses decoder-only backbones with last-token pooling instead of mean pooling. Four lightweight LoRA adapters are injected at each transformer layer, handling retrieval, text-matching, classific

v5-text uses decoder-only backbones with last-token pooling instead of mean pooling. Four lightweight LoRA adapters are injected at each transformer layer, handling retrieval, text-matching, classification, and clustering independently. Users select the appropriate adapter at inference time. For retrieval, queries get a "Query:" prefix and documents get "Document:". Context length is 32K tokens, a 4x increase over v3.

MMTEB (131 multilingual tasks): v5-small (677M) hits 67.0, next best sub-1B is 64.3. +2.7pt gap. MTEB English (41 tasks): v5-small leads at 71.7. v5-nano (239M) scores 71.0 -- matching models 2x its s

MMTEB (131 multilingual tasks): v5-small (677M) hits 67.0, next best sub-1B is 64.3. +2.7pt gap. MTEB English (41 tasks): v5-small leads at 71.7. v5-nano (239M) scores 71.0 -- matching models 2x its size. Retrieval (5 benchmarks): v5-small at 63.28 matches v4 (3.8B) while being 5.6x smaller. The nano model at 239M params has no peer in its weight class.

jina-embeddings-v5-text is here! Our fifth generation of jina embeddings, pushing the quality-efficiency frontier for sub-1B multilingual embeddings. Two versions: small & nano, available today o

jina-embeddings-v5-text is here! Our fifth generation of jina embeddings, pushing the quality-efficiency frontier for sub-1B multilingual embeddings. Two versions: small & nano, available today on Elastic Inference Service, vLLM, GGUF and MLX. https://t.co/68GGuBRdy4

@tmztmobile It will be a lossy compression, like impressionist lossy

@tmztmobile It will be a lossy compression, like impressionist lossy

Text embeddings are widely assumed to be safe, irreversible representations. We show we can reconstruct the original text using conditional masked diffusion. Existing inversions (Vec2Text, ALGEN, Zer

Text embeddings are widely assumed to be safe, irreversible representations. We show we can reconstruct the original text using conditional masked diffusion. Existing inversions (Vec2Text, ALGEN, Zero2Text) generate tokens autoregressively and require iterative re-embedding through the target encoder. We take a different approach: embedding inversion as conditional masked diffusion. Starting from a fully masked sequence, a denoising model reveals tokens at all positions in parallel, conditioned on the target embedding via adaptive layer normalization (AdaLN-Zero). Each denoising step refines all positions simultaneously using global context, without ever re-embedding the current hypothesis.

Check out the live demo https://t.co/W1EXpDFCAL and see it in action. Our read our repo and paper for more technical details on training and decoding.

Check out the live demo https://t.co/W1EXpDFCAL and see it in action. Our read our repo and paper for more technical details on training and decoding.

Most don't know (1) how easy it is to invert embedding vectors back into sentences, (2) this is a perfect task text diffusion models. Here's a 78M parameter model and live demo that recovers 80% of to

Most don't know (1) how easy it is to invert embedding vectors back into sentences, (2) this is a perfect task text diffusion models. Here's a 78M parameter model and live demo that recovers 80% of tokens from Qwen3-Embedding and EmbeddingGemma vectors. Works even on multilingual input.

@Prince_Canuma @liquidai @deepseek_ai @Alibaba_Qwen @allen_ai @TencentHunyuan @PaddlePaddle 🔥

@Prince_Canuma @liquidai @deepseek_ai @Alibaba_Qwen @allen_ai @TencentHunyuan @PaddlePaddle 🔥

0.6B params. Top3 on MTEB reranking task. 10× smaller than generative listwise rerankers. Read more about this Best Paper at AAAI Frontier IR here: https://t.co/vVKleKBqI9 https://t.co/DFur26280e

0.6B params. Top3 on MTEB reranking task. 10× smaller than generative listwise rerankers. Read more about this Best Paper at AAAI Frontier IR here: https://t.co/vVKleKBqI9 https://t.co/DFur26280e

jina-reranker-v3 was the first listwise reranker to throw all documents into one context window (where traditional rerankers loop over ⟨q,d⟩ pairs) and let them fight it out via self-attention—what we

jina-reranker-v3 was the first listwise reranker to throw all documents into one context window (where traditional rerankers loop over ⟨q,d⟩ pairs) and let them fight it out via self-attention—what we call "last but not late" interaction. Bold or stupid? But not mediocre. Today it won Best Paper at AAAI Frontier IR Workshop.