Signal AI via Every Dream Has a Limit Sato 2026年6月29日 06:21 Hot: 50 ℃ Abstract: Using an agent feels like a dream. The limit is what wakes you up. Everyone's talking about loops, goals, agents, subagents, yada, yada, yada. But barely anyone mentions what to do when the hand of time slaps you in the face and the dream you were dreaming vanishes like an island cloud. Then You Hit the Wall It feels really nice to have that kind of power on your side — to command multiple entities working for you. O... Forum RSS 阅读全文
Signal 安全 via Building a Production Grade Authentication System with NestJS Sato 2026年6月29日 06:11 Hot: 55 ℃ Abstract: Building a Production Grade Authentication System with NestJS Security is not a feature you bolt on after the fact. It is an architectural decision that shapes every layer of a system, from how requests are received to how identities are verified and how access is enforced. NestJS, with its opinionated structure and enterprise level design philosophy, makes it possible to build authentication systems that are not jus... Forum RSS 阅读全文
Signal AI via Building a RAG System from Scratch — Wrap-up and What Comes Next Sato 2026年6月28日 06:21 Hot: 57 ℃ Abstract: In this final article, we'll recap what we built across the series, consolidate the design decisions, and point to where to go next. What We Built Starting from a blank Python project, we built a complete AI system step by step: 01_setup_db.py pgvector table + extension 02_create_index.py HNSW index (m=16, ef_construction=64) 03_ingest.py Embed documents → store in pgvector 04_search.py Cosine similarity search 05_ra... Forum RSS 阅读全文
Signal Web via Scaffold - Microservice's generator Sato 2026年6月28日 06:15 Hot: 59 ℃ Abstract: 🚀 I built a microservice that generates microservices. Yes — you read that right. After watching teams lose hours to repetitive scaffolding and inconsistent project structures, I built the tool I always wished existed. Introducing my Spring Boot Scaffold Generator — a Spring Boot microservice with its own UI that generates fully structured Spring Boot projects in seconds. 🎥 In this video, I walk you through exactly h... Forum RSS 阅读全文
Signal AI via Build a Simple RAG App with Telnyx AI Inference Sato 2026年6月27日 06:11 Hot: 72 ℃ Abstract: RAG is one of those patterns that sounds more complicated than it has to be. At its core, retrieval-augmented generation is just: Store some documents Embed the user’s question Find the most relevant docs Send those docs to the model as context Return an answer with sources I built a small Python example that shows that flow end to end with Telnyx AI Inference. Repo: https://github.com/team-telnyx/telnyx-code-example... Forum RSS 阅读全文
Signal AI via Extract Structured JSON from Messy Text with Telnyx AI Inference Sato 2026年6月27日 06:03 Hot: 63 ℃ Abstract: Messy text is everywhere: support tickets, lead forms, emails, contracts, incident reports, call notes, Slack messages. The annoying part is that the useful data is usually in there somewhere, but not in a shape your app can trust. I built a small Python example that uses Telnyx AI Inference to turn unstructured text into structured JSON. Repo: https://github.com/team-telnyx/telnyx-code-examples/tree/main/extract-str... Forum RSS 阅读全文
Signal Web via This Brilliant Tool Reimagines Searching the Web as a Distraction-Free Social Feed Sato 2026年6月26日 06:05 Hot: 55 ℃ Abstract: * The traditional way of searching the web is broken. You type a query, look at a list of blue links, click one, get bombarded by cookie banners and ads, go back, and repeat. Arabinum is a fresh, innovative project that completely fixes this broken user experience. 🛠️ The Magic Inside Arabinum: See Before You Click: It aggregates results from Google Search, and other platforms, but reformats them into a fluid social... Forum RSS 阅读全文
Signal AI via Three Loops, No Ship Sato 2026年6月26日 05:59 Hot: 60 ℃ Abstract: I spent three iterations on an auto-fix pipeline that still doesn't work reliably. Here's what I learned. Loop 1 Wrote a background script. Pull tickets from Azure DevOps, run them through a local model, hand to a coding agent, push the result. Poll → triage → fix → push. Worked 40% of the time on trivial tickets. Anything that crossed file boundaries or needed real context — stalled or hallucinated. I shipped it any... Forum RSS 阅读全文
