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When shopping for an electric vehicle, affordability is becoming a more common trait. But affordable and energy efficient is truly a rare breed. Often you have to sacrifice one for the other. Want something affordable? Great, here's a range loser. Want something that goes the distance? Be ready to pay the price. Surprisingly, here comes […]

At this point, there are very few situations in which you'll want to use an Ethernet splitter.

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Hey guys. I am a GPT Plus user, and I use Freebuff a lot to execute my tasks for free, so I don't use up usage limits at all. Freebuff, if you don't know, is a desktop and CLI agent that gives u a bunch of models for free (DeepSeek V4 Flash, GLM 5.3 Flash, 5.6 Luna, Solar 4 Pro), and it is really good at executing tasks that you give it, imo. I searched for connectors or MCPs that connect to it so I can seamlessly integrate it with Luna or Terra as the planner and Freebuff as the implementer. There was nothing online, so I created my own MCP ( https://github.com/Praket7/freebuff-mcp ). If you guys could check it out, try it out, and let me know if I need to make any security changes or to make it work better, and if you guys could star and test it, I'd appreciate it. I am currently adding some more features, like ChatGPT or Claude being able to check live progress, but let me know if you would like something else or if it doesn't work. thanks! submitted by /u/Swimming_Ask3859 [link] [留言]

The UK's National Cyber Security Centre says employees using AI tools outside an organisation's approved systems can expose company or customer data and reduce the organisation's visibility and control over that information. It cites research saying 71% of employees use AI tools that their employer has not approved. The NCSC also warns that AI agents add another risk: if an agent has a vulnerability or bad configuration, an attacker may gain the same data, services, and privileges the agent can access. The practical point is less 'ban AI' and more 'make the approved path usable'. The NCSC says teams should understand why people use shadow AI, provide safer alternatives, and reduce the risk rather than assume it will disappear. Sources: https://www.ncsc.gov.uk/blogs/the-hidden-risks-of-shadow-ai https://ukstories.microsoft.com/features/rise-in-shadow-ai-tools-raising-security-concerns-for-uk/ submitted by /u/Codeblix_Ltd [link] [留言]

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

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I Have a Job — I Just Need to Do It A few months ago, I left my last job as a remote Unity developer. Before leaving, I had already started working on a freelance project to build a multi-tenant security and workforce management system . It became a fairly large system involving web applications, mobile apps, real-time tracking, scheduling, reporting, GPS, notifications, and more. The project is now mostly completed, but the client wants to continue adding maintenance, business logic changes, UI modifications, and new development under the same maintenance fee. That doesn't work for me. Maintenance and development are two different things, and when the amount of new development keeps growing while the price stays the same, eventually it stops being sustainable. So I started thinking about what I would do next. The Fear of Not Having a Job For the last few days, I was genuinely worried. I have more than 250,000 BDT in savings , so I'm not in an immediate financial crisis. But money slowly disappears when there is no income. And freelancing isn't exactly comforting right now either. I've been using Upwork, but the experience has become increasingly frustrating. You apply for jobs and often hear nothing. Some clients post a job and never hire anyone. Some jobs get dozens of proposals and disappear quickly. Some invites arrive, but someone else gets hired almost immediately. And every application costs money. After a while, it starts feeling like you're continuously putting money into a machine that promises a job somewhere in the future. You keep applying. You keep waiting. You keep hoping. And eventually, I realized something. What I Was Actually Missing I wasn't missing money. I was missing a job . And there is an important difference. I already have the skills. I already know how to build software. I already have ideas. I already have projects I want to work on. I was simply thinking that a "job" had to come from someone else. Then I thought: I can create my own job

Everything you need to know to decide which set of Sony earbuds are right for you.

I was testing out CH-Ops - an admin GUI for self-hosted ClickHouse - on a simple setup: 1 shard, 2 replicas. Stumbled onto the replication queue almost by accident. Here's what I did: I stopped one of the nodes (let's call it Node B), then inserted some data through the other one (Node A). Just wanted to see what would happen. Then, while Node B was still down, I checked it in CH-Ops. It had stuff sitting in its replication queue. My first assumption was: okay, this must be showing what's left to replicate across the cluster - the total pending replication work. So I switched over and checked Node A, the one that was actually up and had just received the insert. Its queue was empty. That didn't match what I expected at all. If the queue was a cluster-wide "here's what still needs to replicate" view, Node A should've shown something too - it was the one that had the fresh data now waiting to reach Node B. Instead it was Node B, the down one, sitting there with pending tasks. That mismatch is what sent me digging. Turns out the queue isn't cluster-wide at all - it's specific to each ClickHouse instance. Once I brought Node B back up, its queue drained in seconds and the data showed up. That whole experiment is basically the entire post in miniature. Here's the mental model I ended up with. A Queue Belongs to a Replica, Not to the Table This is the first thing to get straight. With a ReplicatedMergeTree table, you can have multiple replicas holding copies of the same data. It's tempting to think of replication as one shared pipe between them. It isn't. Each replica keeps its own local replication queue . So if you see: Replica 1 → queue_size = 0 Replica 2 → queue_size = 25 that doesn't mean 25 operations are waiting somewhere in the middle for both replicas to pick up. It means Replica 2, specifically, has 25 tasks it hasn't finished yet. Once that clicked for me, the rest of the system made a lot more sense. So Where Do These Tasks Come From? Replication in ClickHouse

Hey HN! Ilya and Nikita here. We’re building HomeCat, a tool that lets you design a backyard office space - https://myhomecat.com We started out by helping people design sheds, and quickly realized that many people actually want to build a backyard office. There wasn't really an easy way to do this, so we thought we'd take on the challenge. Right now, you can design a backyard shed and our tool will make it code compliant. It's maybe 90% of the way there, since you also have zoning/HOA and other

I build text-to-SQL agents on Oracle and Postgres for a living. Every one of them had the same bug, and it wasn’t in my code. It was in the order of operations. The bug The schema goes into the prompt before the query runs. Row-level security runs when the query runs. So the model sees a table the user can’t read, writes perfectly valid SQL against it, the database returns zero rows, and the agent says “no records found”. A wrong answer, delivered with confidence. Vanna (23k stars, archived March 2026) applied identity exactly there: at execution, after the model had seen everything. The fix Apply identity at selection. Decide which tables the model is shown, per caller, before any SQL exists. A restricted table isn’t ranked low — it’s absent. from schemagate import Catalog, Principal cat = Catalog().bootstrap("postgresql://localhost/app") cat.restrict("hr_compensation", roles=["payroll"]) analyst = Principal("okta:jdoe", roles={"analyst"}) cat.select("salary by employee", principal=analyst).table_names # no hr_compensation pip install schemagate — one dependency, no API key, any SQLAlchemy database. The side effect that pays for it You’re now sending ~6 tables instead of the schema dump. Measured on the test schemas: 65–79% fewer prompt tokens on small ones, 97% on a 260-object one (16,095 → 444 per question). The selector never calls a model — BM25 plus a hashed embedder, offline, milliseconds. What broke while building it Six invented schemas found ten bugs before release. My favourite: a three-column orders_bkp outranked the real orders table, because short documents win cosine similarity. Backup and staging copies now rank below the object they shadow. The full list is in TESTING.md. Where it plugs in MCP server for Claude Desktop and Cursor, a LangChain retriever, a native Oracle 23ai VECTOR store, and a browser demo that needs no install: https://ashishsinha1602.github.io/schemagate/ Repo: https://github.com/ashishsinha1602/schemagate — tell me where it break

AI can now generate functions, components, tests, SQL queries, APIs, and sometimes entire applications from a short description. For developers, this has changed the daily workflow faster than almost any previous programming tool. Need a React component? AI can generate one. Need to debug an error? AI can suggest possible fixes. Need unit tests? AI can create a first draft. Need documentation for an unfamiliar API? AI can summarize it in seconds. The result is obvious: developers are writing code faster. But faster code generation raises an important question: If AI can generate code, why do human developers still matter? The answer is simple. Writing code is only one part of software development. Software engineering involves understanding problems, making architectural decisions, evaluating tradeoffs, validating requirements, securing systems, debugging unexpected behavior, and taking responsibility for what eventually runs in production. AI can generate code. Human developers still need to decide what should be built, why it should be built, whether the generated code is correct, and whether it is safe to deploy. This article explores why AI-generated code still requires human developers and why the future of programming is likely to involve developers working with AI rather than being completely replaced by it. AI Is Already Changing How Developers Work There is no serious argument that AI coding tools are irrelevant. Developers are using them. According to Stack Overflow's 2025 Developer Survey, 84% of respondents were already using or planning to use AI tools in their development workflow , and 51% of professional developers reported using AI tools daily . ([Stack Overflow Developer Survey][1]) AI can significantly reduce the time required for tasks such as: Generating boilerplate code Creating unit tests Explaining unfamiliar code Writing documentation Refactoring simple functions Generating SQL queries Debugging common errors Creating initial prototypes This