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NOTE -> I expect answer from people who actually have experience and strong understanding of these. please give something beneficial. I'm building a SaaS platform in Sri Lanka that handles documents and other sensitive data. Each user can upload their own documents and information, and the platform uses RAG to answer questions based on that user's data. That part makes sense to me. My main concern is what happens when the user hasn't uploaded enough information. I still want the LLM to provide accurate answers using reliable information from the internet (or from a curated knowledge base), with proper citations. These are the two architectures I'm considering: Option 1: Base LLM (OpenAI/Anthropic via Azure AI Foundry or Amazon Bedrock) ↓ Platform RAG (global knowledge base managed by us) ↓ User-specific RAG In this approach, we maintain a global knowledge base that we (the platform admins) curate and update. Every user can access this shared knowledge, while their own uploaded documents are searched through their personal RAG. Option 2: Open-source LLM ↓ Fine-tuned on Sri Lankan/domain-specific data ↓ User-specific RAG Here, we fine-tune an open-source model using Sri Lankan or domain-specific data, and each user still has their own RAG for their private documents. My concerns are: Is fine-tuning actually the right solution here, or is it unnecessary? Is a global/shared RAG a better approach than fine-tuning? How would you design this architecture if you wanted: Accurate answers from domain knowledge User-private document search Citations/sources Good scalability for thousands of users I'm leaning toward Option 1 because fine-tuning seems expensive, time-consuming, and I have no experience with it yet. However, I'm not sure if I'm thinking about this correctly. I'd really appreciate hearing how others would approach this problem. submitted by /u/Fickle_Degree_2728 [link] [留言]

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Originally published at https://blog.pathvector.dev/protocol-in-code-bgp-05/ — part of the free Protocol Lab series. This post is part of Protocol in Code , a free series that reads network protocols not as configuration examples but as logic with inputs, state, and branches — actual code you can read and run. The whole series lives here: github.com/pathvector-studio/protocol-in-code . If you're newer to this material and want a more hands-on, guided on-ramp first, start with the companion Protocol Lab series and come back. Today's module is from the BGP track, Session 05. The source file is src/protocol_in_code/bgp/policy.py , and it builds directly on the origin-validation logic from Session 04. The question to keep in your head Here's the one thing to turn over as you read: What happens after origin validation returns valid , invalid , or not_found — and why does the result still need routing policy before anything happens to the route? There's a piece of folk knowledge that says "RPKI invalid means the router rejects the route." It's the kind of statement that sounds like a rule of the protocol. It isn't. It's one possible policy decision built on top of a validation result . The whole point of this session is to separate those two things in your head, and the code makes the seam impossible to miss. Two layers, not one Validation answers a factual question: does this route's origin AS match what the ROAs say it should be? That's Session 04's job, and its output is a ValidationState . Policy answers a completely different question: given that fact, what do we do ? Drop the route? Keep it but make it less preferred? Accept it normally? That's a local decision — different operators configure it differently, and the same validation result can lead to different actions on different routers. The file models the second layer with three small pieces. First, the set of actions the router can take: class PolicyAction ( str , Enum ): ACCEPT = " accept " DEPRIORITIZE = " de

Originally published at https://blog.pathvector.dev/protocol-in-code-bgp-04/ — part of the free Protocol Lab series. This post is part of Protocol in Code , a free series that reads network protocols as logic — inputs, state, and branches — rather than as configuration examples. The full source, walkthroughs, and site lessons live in the repo: pathvector-studio/protocol-in-code . If you're newer to this and want to build the protocols hands-on before dissecting them, start with the companion Protocol Lab series instead. Today we're on the BGP track, session 04, reading a single small file: src/protocol_in_code/bgp/validation.py . It's about 40 lines. The idea inside it is one that trips up a lot of engineers who've been running BGP for years. The question to keep in your head BGP's best path selection already ran. It compared local preference, AS_PATH length, MED, and the rest of the tiebreak ladder, and it picked a winner. So here's the question this module wants you turning over: Core question: How do we decide whether the origin AS is authorized — even after BGP has already selected this route as the best path? The trap is the sentence "it was the best path, so it must be fine." Best and authorized are two different words, and in the code they are two different decisions made by two different pieces of data. Best path selection asks which of these routes do I prefer? Origin validation asks is the AS at the end of this path actually allowed to originate this prefix? A route can win selection and still be a hijack. RPKI origin validation is the mechanism that answers the second question, and the file we're reading is a toy model of exactly that. Two kinds of information The first thing to read isn't a function — it's the two dataclasses, because the whole session is really about keeping them apart. @dataclass ( frozen = True ) class BGPRoute : prefix : str origin_as : int @dataclass ( frozen = True ) class VRP : prefix : str max_length : int origin_as : int BGPRout

By Suvankar Chakraborty | Principal Engineer — IAM, Modern Workplace Management & IT Operations The Collaboration Platform That Became a Governance Nightmare Microsoft Teams was deployed at extraordinary speed across the enterprise world. In most organisations I know, the deployment timeline went something like this: March 2020, global pandemic, remote work mandate, Teams switched on, everyone told to use it, governance deferred because there was no time. Five years later, the governance that was deferred in 2020 has still not been implemented in most of those environments. The result is predictable and consistent across industries: Teams sprawl at industrial scale. Hundreds of Teams that nobody owns. Channels for projects that ended three years ago. Guest users from partnerships that dissolved. Sensitive conversations in channels that include contractors who should not have visibility. Files shared in Teams chat — bypassing SharePoint governance entirely — on devices with no management policy. Meeting recordings stored in OneDrive folders that anyone with a link can access. Bot integrations that have permissions to read your Teams messages and access your calendar, approved by a user who clicked through an OAuth consent screen without reading it. In 13+ years of enterprise IAM and IT operations work, Teams governance has become one of the most consistently mismanaged areas of Microsoft 365. Not because it is technically difficult — the controls Microsoft provides are comprehensive. But because Teams sits at the intersection of IT, security, compliance, and the organisational culture of collaboration, and that intersection is where governance programmes go to die. This article is about why enterprises get Teams governance wrong, and what getting it right actually looks like — in specific, actionable, implementable terms. Why Teams Is a Governance Problem Unlike Any Other M365 Workload To understand the governance challenge, you need to understand what Microsoft Team