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This story was originally published on HackerNoon at: https://hackernoon.com/158-blog-posts-to-learn-about-search-engine. Learn everything you need to know about Search Engine via these 158 free HackerNoon blog posts. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #search-engine, #learn, #learn-search-engine, and more. This story was written by: @learn. Learn more about this writer by checking @learn's about page, and for more stories, please visit hackernoon.com.
This story was originally published on HackerNoon at: https://hackernoon.com/how-to-build-production-ready-passkey-authentication-in-symfony-81. Build a passkey login in Symfony 8.1 with a hybrid password/passkey form, passkey revocation, recovery codes and fixes for the three errors you will hit. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #passkeys, #webauthn, #symfony, #php, #authentication, #cybersecurity, #web-development, #tutorial, and more. This story was written by: @mattleads. Learn more about this writer by checking @mattleads's about page, and for more stories, please visit hackernoon.com. A step-by-step Symfony 8.1 build of passkey login for an app that already has passwords. In 17 files you get a hybrid password/passkey form with browser autofill, an account page to add, track and revoke passkeys, one-time recovery codes behind login throttling, and fixes for the three errors you will hit. No Node.js. The full demo is on GitHub.
This story was originally published on HackerNoon at: https://hackernoon.com/decentralized-ai-inference-needs-a-boring-job-queue. A backend engineer's proposal for reliable decentralized GPU inference using job queues, retries, and idempotency, so remote AI jobs don't fail or bill twice. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #decentralize-ai-hackathon, #decentralized-ai, #software-engineering, #fault-tolerant-systems, #distributed-computing, #bullmq, #inference-api, #duplicate-billing, and more. This story was written by: @mirabilis. Learn more about this writer by checking @mirabilis's about page, and for more stories, please visit hackernoon.com. Decentralized AI needs reliable backend plumbing, not just GPUs. This proposal treats remote GPU inference like a normal backend service: an API returns a job ID, BullMQ, Redis and PostgreSQL track the work, and a provider adapter (starting with Nosana) runs it. Idempotency keys, deadlines, and recovery checks prevent duplicate runs and double billing. It's a proposal, not a benchmarked product.
This story was originally published on HackerNoon at: https://hackernoon.com/stop-trusting-notification-urls. If I call your impression URL twice, what happens? If the answer is anything other than “the second call is rejected,” you are paying for the second call. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #notifications, #notification-urls, #dsp, #dmp, #url, #digital-signal-processing, #bidding, #audience-matching, and more. This story was written by: @hichem-hannafi. Learn more about this writer by checking @hichem-hannafi's about page, and for more stories, please visit hackernoon.com. Every bid response a DSP sends carries URLs that anyone who has seen the response can call: impression, click, win, billing. Most DSPs count whatever comes in, which is where discrepancy reports come from. We treated a notification URL like a cheque: valid only if we issued it, for that bid, and it hasn’t been cashed. The bidder mints hashes and a spend reservation at bid time; every callback is checked against them, duplicates and replays are rejected, and unclaimed reservations expire after 45 minutes. Result: about 6% of callbacks rejected as unverifiable, and reported impressions within 1% of the exchanges’ figures.
This story was originally published on HackerNoon at: https://hackernoon.com/meet-the-new-home-of-the-hackernoon-brand. Explore HackerNoon's new brand hub, access official logos and creative resources, and discover our free open-source brand kit template for building your own. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #hackernoon-brand-hub, #brand-guidelines, #open-source-brand-kit, #brand-kit-template, #open-source-tools, #hackernoon-product, #hottest-features-deployed, #branding, and more. This story was written by: @product. Learn more about this writer by checking @product's about page, and for more stories, please visit hackernoon.com. HackerNoon has a new brand hub featuring official brand assets, design resources, writing guidelines, social media information, and partnership details. We've also open-sourced the website as a free, customizable brand kit template that anyone can use to build and deploy their own. Explore the site, check out the GitHub repo, and support our Product Hunt launch!
This story was originally published on HackerNoon at: https://hackernoon.com/how-to-write-technical-seo-tickets-developers-can-implement-and-test. Learn how to write technical SEO tickets with clear acceptance criteria, regression checks, and verifiable evidence that developers and reviewers can use. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #technical-seo, #technical-seo-audit, #seo-best-practices, #seo-acceptance-criteria, #regression-testing, #seo-for-developers, #spec-driven-development, #good-company, and more. This story was written by: @boxhallmarketing. Learn more about this writer by checking @boxhallmarketing's about page, and for more stories, please visit hackernoon.com. Vague technical SEO requests leave developers guessing about implementation and make it difficult to verify whether a change was delivered correctly. This article demonstrates how to turn an internal-linking request into a testable engineering ticket by specifying the affected page, expected HTML, acceptance criteria, regression boundaries, and release evidence. It also explains why verifying a deployed SEO change is different from proving that it improved search performance.
This story was originally published on HackerNoon at: https://hackernoon.com/femtech-needs-to-move-beyond-period-tracking-apps-for-its-next-breakthrough. Femtech needs to move beyond period trackers. Explore how wearables, faster diagnosis, menopause care, and privacy could shape women's health technology. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #femtech, #women's-health-technology, #oura-ring, #wearable-technology, #future-of-femtech, #menopause-tracking-tech, #healthtech, #femtech-wearables, and more. This story was written by: @shradhapuri. Learn more about this writer by checking @shradhapuri's about page, and for more stories, please visit hackernoon.com. Femtech is moving beyond period-tracking apps toward more personalized, connected health insights. The next wave could use wearable data, AI, and physiological trends to better understand women's health across different life stages, while prioritizing accuracy, privacy, and individual needs.
This story was originally published on HackerNoon at: https://hackernoon.com/making-local-llms-usable-on-8gb-gpus. Learn how to optimize local LLM inference on 8GB GPUs using KV-cache quantization, Flash Attention, batching, and practical latency benchmarks. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #local-llm, #llm-optimization, #circuit-based-noise-mitigation, #multilingual-llm, #model-quantization, #llama.cpp, #python-benchmarking, #hackernoon-top-story, and more. This story was written by: @nareshwaghela. Learn more about this writer by checking @nareshwaghela's about page, and for more stories, please visit hackernoon.com. This guide explores practical ways to run local LLMs efficiently on GPUs with only 8GB of VRAM. It compares baseline settings with Flash Attention, KV-cache quantization, batching, and context-size adjustments. You’ll learn how to measure VRAM usage, latency, throughput, and stability using reproducible llama.cpp commands and GPU monitoring tools.
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