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Curated links from external sources — not 360Softy original articles.

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When a human edits an agent's output, where does that decision go?

An agent drafts a reply to an insurance claim. A human reads it, changes one sentence that would have committed the insurer to something it did not mean, and ships it. That edit is the single most valuable thing that happened in the whole interaction. It is a human catching an error a model could not. Now ask where that edit is recorded. In most production systems, the answer is: in application code, a chat thread, a ticket comment, or nobody's memory at all. The judgement that mattered most lea

aiopensourceshowdev
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ExternalSoftware Engineering
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I Tried Kimi K3 for a Week. Here’s What Happened.

A few days ago I posted my first impressions of Kimi K3 on LinkedIn. Since then, I’ve been using it daily for real work — not benchmarks, not demos. Here’s what actually held up. The Context Window Isn’t Marketing Frontend Coding: The Hype Checks Out It’s also #1 on nextjs.org/evals — the first open model to beat all proprietary ones on that benchmark. Agentic Work: Good, Not Magic The Honest Truth Why the Open Weights Matter Bottom Line If you want to try it: kimi.com P.S. — Original first impr

aiprogrammingdevelopment
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ExternalSoftware Engineering
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We built Multi-Repo Workspaces for AI coding

Most production applications aren't monorepos. A feature often spans the frontend, backend, shared packages, infrastructure, and more. While polyrepos work well for ownership, AI coding tools typically only see one repository at a time. We wanted to change that. With Multi-Repo Workspaces in DevSwarm, you can bundle multiple Git repositories into a single workspace while keeping each repository independent with its own history, branches, remotes, CI, and pull requests. Your AI agent, whether it'

devtoolsvibecodingcoding
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ExternalSoftware Engineering
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Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10 Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small, top-k=5, stuff into context. It works for demos. Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks drown signal in noise Customer tickets: Conversational c

llmaievaluation
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ExternalSoftware Engineering
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Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics

Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics How we replaced "looks good to me" with automated evaluation catching 92% of hallucinations before deployment Three months ago, our team shipped a RAG-based customer support assistant. It worked great in testing — we'd ask it questions, read the answers, and say "yeah, that looks right." Then it hit production. A customer asked about their billing cycle. The assistant confidently cited a policy that didn't exist. Another

llmaievaluation
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ExternalSoftware Engineering
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.DAT to .DXF Converter

NACA 2412 Airfoil Databases are in .dat file. The problem? There are no clean .dat -> .dxf converters. The solution? I built one. This post is directly linked to my ongoing project at the time of writing: Comparative Aerodynamic Analysis Of Wing Planforms. I needed to model a NACA 2412 wing which had .dat coordinate files available. But OnShape needed .dxf in order to import the coordinates. I couldn't find a clean converter online, so I decided to build one for the sake of th

pythongithubcoding
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ExternalSoftware Engineering
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200+ Tested AI Prompts I Use to Run My SaaS (And How I Tested Them)

I've spent the last year building and running a SaaS almost entirely on AI-generated output — launch copy, code reviews, cold outreach, changelogs, support replies. Along the way I've written, tested, and thrown away something like 400+ prompts. About half of them are now in a curated pack I sell. The other half were just bad. This post isn't a sales pitch. It's the actual lessons from testing hundreds of prompts: why most of them fail, what a good one looks like, and how to test them systematic

aipromptengineeringsaas
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ExternalTechnology Trends
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Jellyfin founder Andrew leaves team

Article URL: https://forum.jellyfin.org/t-project-leadership-changes Comments URL: https://news.ycombinator.com/item?id=48986091 Points: 359 # Comments: 321

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