RecallDesk Uses FastAPI to Build AI Memory Layer for Enterprise Customer Support

·1 views

RecallDesk is an AI-assisted customer support system whose backend was built using Python and FastAPI, coordinating between a React 19 frontend, a ticket database, and an external memory engine called Hindsight. The backend serves as a central intermediary to enforce security, redact sensitive data such as API keys from support tickets before it leaves the internal environment, and manage calls to third-party services. FastAPI was chosen for its native asynchronous I/O support, Pydantic v2 schema validation, and automatic OpenAPI documentation generation. A key architectural decision was decoupling operational ticket state — which follows a fixed lifecycle of open, pending, and resolved — from the continuous persistent memory layer. The backend also implements resilient fallback handling so that support desk operations continue uninterrupted if the external memory service faces downtime or latency.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer builds 272 client-side browser tools that never upload your files

A developer built toolspace.cloud, a collection of 272 browser-based utilities spanning PDF, image, video, audio, and developer tools, all within five weeks. Every tool runs entirely in the user's browser using WebAssembly, meaning no files are ever sent to a server. The stack relies on technologies including ffmpeg.wasm, pdf-lib, Web Workers, and the Canvas API, all of which the developer notes have been production-ready for over a year. Heavy processing tasks run off the main thread via Web Workers, while a service worker caches WebAssembly binaries after the first load to speed up repeat visits. The project requires no signup, displays no ads, and was motivated by privacy concerns over conventional online tools that process files on remote servers.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Brain-Computer Interfaces Can Now Decode Speech at 78 WPM, But Challenges Remain

Researchers have developed brain-computer interface pipelines capable of decoding attempted speech from motor cortex signals, with leading systems achieving between 32 and 78 words per minute depending on electrode type and vocabulary size. The Willett et al. (Nature 2023) study used 256 intracortical electrodes and a recurrent neural network with language model rescoring to reach 62 words per minute, while Card et al. (NEJM 2024) later sustained 97.5% accuracy over 8.4 months. A key technical challenge is electrode drift, which causes signal changes day to day and requires decoders to recalibrate regularly, often through per-day input layers. Non-invasive approaches like MEG and EEG perform significantly worse, with character error rates of 32% and 67% respectively, while Meta's surface EMG-based Neural Band shipped in late September 2025 offering a calibration-free alternative at around 21 words per minute. The output side of the equation — delivering AI-generated visuals directly into human perception via the visual cortex — remains an open and largely unsolved research problem.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer builds 37,000-page multilingual Quran site using Next.js 15 static prerendering

A developer launched qurandaily.org, a free Quran, hadith, and duas website offering content in six languages including English, Arabic, Urdu, Turkish, Indonesian, and French. All approximately 37,000 pages are fully prerendered at build time using Next.js 15 and next-intl, eliminating server-side rendering on the critical path. The build process uses generateStaticParams to enumerate 6,236 Quranic verses across six locales, producing a complete static output without live database queries. To avoid memory crashes during sitemap generation, the developer sharded sitemaps by locale, reducing peak build memory from over 1.6GB to around 600MB. The site runs on a VPS behind nginx and Cloudflare in Full TLS mode, using a self-signed 15-year origin certificate to avoid recurring renewal overhead.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

OGAD App Lets You Search Voice Notes Locally Without Cloud or Internet

A desktop application called OGAD (Off Grid AI Desktop) allows users to search and query their saved voice notes entirely offline, without uploading audio to any cloud service. The app transcribes imported audio files using a locally stored speech model, indexes the resulting text, and uses a local chat model to generate answers to natural-language questions. Users can ask conversational queries like 'What did I say about the delivery date?' instead of searching by filename or exact phrase. The workflow requires downloading a transcription model such as Whisper Base and a compatible local text model before going fully offline, with initial setup needing a one-time internet connection. The core functionality, including projects, audio uploads, and grounded chat, is available for free on Apple Silicon Macs and Windows x64 PCs.

0 comments Read more at DEV Community