cpu

Attention over time

7-day rolling share of analyzed posts/comments mentioning this entity · 90 daily points ending 2026-08-28

Final complete day: 2026-08-28. Attention was 0.2% — this entity appeared in 5 of 2,594 analyzed posts/comments in the seven days ending that day (6 mentions).

Each point is a seven-day rolling window of complete UTC days; a point is comparable only when at least 100 analyzed posts or comments fall inside it. A day with no analyzed records counts as zero; missing coverage is marked and never shown as zero. The series ends at the latest complete analyzed day — it never extends into the current incomplete day. How attention is measured.

Recent mentions

1. 2026-08-28 15:33 UTCComment

   the ip cycling thing is what breaks every conventional approach here -- rate limit by ip, block ips, iptables rules, all the same dead end when you're dealing with a botnet rotating through hundreds of addresses. using cpu as the trigger sidesteps the classification problem entirely. you're not asking "is this a bot" you're just asking "is something hammering us right now" and responding

2. 2026-08-28 13:12 UTCPost

   Won a bot battle. - We support a client site with ~20,000 pages. It has an SSR search page that uses the url querystring to execute the search/sort/filter. We have of course disallowed bots on that page because the 20k pages are meaningful and should be scraped/indexed, but the billions of possible query combinations on a search page are not. Most of the bots just ignore the robots rule and this results in waves of traffic hammering every possible query combination on the search page. These waves are 10x-50x the human traffic. Rather than let the bill autoscale along with the server cluster, I wrote a little bash script that crons 1/m: - Hits DO's metrics endpoint to check the CPU usage on the VPSs/DBs - Each minute any of the CPUs stays above a given threshold, bump Cloudflare's security level, eventually ending in "under attack" + "bot fight" - When the cpu drops below the threshold for a given amount of time, start backing off until we're back to normal operation. Works a treat. It's nothing particularly flashy or sophisticated, but it was satisfying to think through an under-engineered solution to a problem that a lot of people end up just chucking money/clusters at. Edit: more info for context: We do have pretty aggressive WAF rules especially on the search page, but we've been seeing an increasing amount of bot traffic get past CF's "known bots". Rate-limiting has been minimally effective too because the bots that get through are cycling through 100s of IPs in a given scrape.

3. 2026-08-26 17:55 UTCComment

   I must say my CPU is struggling and it is somewhat laggy. The only test I've done is an image PDF which then ran your OCR text recognition. then I scrolled through and clicked on some bits of text. The scrolling and clicking would be laggy and my CPU was clearly doing a lot of work

4. 2026-08-23 11:16 UTCPost

   Trazor: an image vectorizer that runs fully in your browser - Hi r/webdev! I'm building an open source app that turns images into clean SVG, and it all runs in your browser. Nothing gets uploaded, there is no back end, and there is no account to make. I am trying to get the quality close to paid tools while keeping it easy to use. There are a lot of settings, but an "Auto settings" button picks good ones for you and explains why. It does not use any existing vectorization library. Each step is built from published research papers (there is a list in the repo if you want to read them). There are also some optional machine learning tools that run on your own device. Right now that is just an edge step that helps find outlines more cleanly. The other ML models are not ready yet (my GPU is on fire). The whole process for building the models is written down and scripted in the repo. It is still early, but I would really like to hear how the results compare to other free or paid tools for your own images. For now it is just the web app. Nothing is on npm yet, but the code is already split into clean packages, so I will release the libraries and a command line tool if people want them. One note, since it always comes up: yes, I built this with a lot of help from Claude. But the design is mine, every step comes from published research, and I understand and stand behind all of it. Source code: The app: It works in any modern browser. The optional ML tools use your GPU when it is there and fall back to the CPU/WASM when it is not.

5. 2026-08-22 07:21 UTCPost

   I built an in-browser PDF engine that actually rewrites embedded text layers, converts Office docs with WASM, repairs broken files, and runs OCR (0 bytes sent to a server) - I spent the past few months compiling and writing a massive client-side PDF suite so everything runs directly inside your browser tab without any backend processing: Most web tools out there do cheap tricks (like drawing a canvas overlay or sending your private files to a remote server). I wanted to see how far I could push pure in-browser JavaScript, PDF.js, and WASM binaries to build something actually capable. Here is what the engine actually handles right inside the client: * **True text layer rewriting:** It doesn’t just drop a text box on top. It reads the document's text stream, extracts embedded font glyphs/metrics (or calculates the closest fallback), and lets you literally retype paragraphs directly in the original layout. * **Heavy Office conversions in WASM:** Local Word (.docx), Excel, and PowerPoint to PDF conversion running straight in browser workers without needing any remote microservices or headless instances. * **Destructive redaction & security:** The redact tool actually deletes underlying text objects and vector data instead of just painting a black rectangle over it. Also handles local AES-256 encryption/decryption. * **Corrupted PDF repair:** Built a local parser that rebuilds damaged/broken cross-reference tables (XREF) so corrupted PDFs that crash standard readers can be recovered. * **In-browser OCR & vector signatures:** Background web workers for text extraction from raw scans, plus signature extraction with automatic background removal if you snap a photo with your phone camera. * **28+ utilities total:** Reorganize, split, merge, crop, metadata stripping, compression, page numbering, etc. Since everything is executed locally using the client's CPU and RAM, there are zero server costs for file processing, which means no subscriptions, no file size throttles, no daily limits, and no account requirements. The web version is fully live at and I’m wrapping up the native Android package for the Play Store (should be live in \~15 days / early September).