AI 资讯

If you ask it to create a recipe, you can click plus and minus buttons to change the amount of portions. You can connect it to other apps like canva. It hallucinates WAY less, and it explains ilvery clearly. submitted by /u/OkComputer_13 [link] [留言]

I have been experimenting with LLMs to see how well they navigate highly cross-referenced texts like the Bible. Standard models often hallucinate verses or lose historical context. To try and fix this, I built a free app called Biblians (no ads, no paywalls). I built it specifically for people who have questions they might hesitate to ask in person, or who simply want a 1-click way to explain a verse. While testing it, I discovered a fascinating denominational bias that is still lingering and changes depending entirely on the language you use: In English: It is Protestant-leaning. It praises Luther, saying things like, "Martin Luther sought to return the Church to the truth of God's Word." In Spanish, French, or Portuguese: It is Catholic-leaning. It condemns Luther's actions, stating: "...trajo confusión..." (...brought confusion...). Has anyone else noticed how drastically the training data changes the core bias based on the language prompted? I would love for this community to test the app, look for other linguistic biases, or just try to break the AI's logic. You can experiment with it here: https://play.google.com/store/apps/details?id=com.biblians.app Let me know what weird outputs you get! submitted by /u/Snorlax_lax [link] [留言]

The original babel-plugin-transform-flow-strip-types hasn't been updated in 9 years and breaks silently in Babel 7 and 8 environments. The fix I published a maintained fork that works as a drop-in replacement: npm install --save-dev babel-plugin-transform-flow-strip-types-maintained Then update your .babelrc: { "plugins": ["transform-flow-strip-types-maintained"] } That's it. No other changes needed. What's fixed Babel 7 and 8 peer dependency conflicts Missing syntax plugin declaration Deprecated visitor patterns allowDeclareFields support Automated migration If you want to update your entire project automatically: npx flow-strip-migrate . This updates your package.json and babel config in one command. More info: https://flowstrip.netlify.app npm: https://www.npmjs.com/package/babel-plugin-transform-flow-strip-types-maintained

May 25, 2026. This is not just another date on my calendar. This marks the beginning of one of the biggest professional transitions of my life. After nearly a decade working in the world of IT infrastructure, technical support, networking, field engineering, and systems operations, I’ve made a decision that has been building in my mind for some time: I am transitioning into Data Analytics. And this is where I document that journey—publicly, honestly, and in real time. Not when I become an expert. Not when I feel “ready.” Not when everything looks polished. I’m starting now. Because real growth is rarely clean, predictable, or perfectly planned. Sometimes it starts with one uncomfortable decision: To leave what you already know… and step into what your future requires. Where My Journey Started Before data, before dashboards, before writing my first SQL query or building my first analytics project—my career started in the trenches of IT. For the past 10 years, I’ve built my career solving real technical problems across businesses, organizations, schools, offices, and field operations. My world has been cables, routers, networks, system failures, installations, troubleshooting, and making technology work where others saw complexity. Over the years, I’ve worked deeply in: Computer troubleshooting and hardware diagnostics Printer setup, configuration, and enterprise support Wi-Fi deployment and hotspot installations LAN design and structured network deployment Fiber optic installations and network termination Data cabling and structured cabling systems CCTV surveillance installation and maintenance Alarm systems and electronic security integration Intelligent security systems Electric fence installations and perimeter protection systems Router, switch, and access point configuration End-user support and enterprise technical troubleshooting Systems maintenance and operational support I’ve spent years on ladders, in server rooms, inside offices, on construction sites, insi

OK.. so I've had this reasonable PC sitting under my desk for ages.. NOT working because of some reason or other. But it was my baby as is housed in a lovely Soprano DX silver brushed case. SO, I swapped out the old HDD for a couple of SSDs (a couple of mirrored OS disks and a large 2TB storage disk) I swapped out the Nvidia 780ti graphics card for a couple of OG Nvidia 1080ti's. I pulled the whole thing to bits.. repasted the northbridge chip, southbridge chip and central CPU. Upgraded the fans to push pull the CPU heatsink. Wrapped ALL cables in mesh and it's so lovely now. Installed Windows 10 Pro. Installed the Nvidia App. Installed CrystalDiskInfo and all is sweet 😄 EXCEPT... I'd like to use this old bangin box for an HG AI server... now I have read that ALL LLMs need this thing called AVX (Advanced Vector Extensions) I didn't even know that was a THING! So even though I have 22Gb worth of GPU sitting there that I was going to point everything to, because I have a lame ass QX6700 CPU sitting on a kickass D975XBX2 (BadAxe2) main board I CAN NOT fulfill my wish for this OG box to be a headless source of awesomeness sitting in it's home under my desk supplying me with a home grown AI. IS THERE ANYTHING I CAN DO?!?!?! Surely after all this time of parts getting munched by AI farms a plenty people have been using what's around to do what they will... Does anyone know of anything I can do apart from just look at it running at 25 degrees aircooled humming along so lovely... it NEEDS purpose!!! 😄 Cheers and thanks all NB submitted by /u/Independent-Sound196 [link] [留言]

10 year senior dev, vibe coded this in 4 weeks and counting. Something like this would have taken me a year+ before and ive always been a 10x dev. I built this along side my day job (gov contractor dev). Feel free to check it out! https://imgur.com/a/F6OINKR⁠ Game Title: Roguelite MMO Playable Link: https://roguelite-mmo.com/⁠ Platform: PC / Web Description: Roguelite MMO is a browser-based RPG/MMO project built around dungeon runs, exploration, gear progression, PvP, quests, loot, and character building. The game is still in beta and active development, with the latest update adding new side activities and progression options. Latest update: The new Casino is now live, giving players more ways to spend gold, take risks, and chase rewards between dungeon runs and exploration. Horse racing and horse taming have also been added. Players can race horses, bet on races, and work toward collecting better horses over time. Fishing is now available too, adding a more relaxed activity with its own rewards while exploring the world. The core loop is still being refined, but the current focus is making sure players understand what they earned, where important items come from, what to do next, and whether the early gameplay loop feels worth continuing after the first few minutes. Free to play submitted by /u/HeadHunterX223 [link] [留言]

This is a submission for the GitHub Finish-Up-A-Thon Challenge. I’ll keep this short since the repo...

I had a work version of GPT do a very simple spreadsheet summary task for me yesterday. It took it 5 minutes to do it. I could probably have done it myself in 30 or so minutes. The heavily subsidised token cost of that task? 10 dollars. That's with a 10x subsidy. The actual compute cost was about 100 dollars. There's something seriously wrong there. It's going to crash and crash HARD. if people think i'm lying or are just interested. The spreadsheet had 45 sheets. Each sheet had roughly 500 x 50 populated cells. Formatting was not exactly standard across all sheets. The prompt was something like "there is labelled column in each sheet, give me a simple list of all the items from all the sheets in that column and ignore duplicates." We can chose which model to use. The model I chose was one of the newer ones, I honestly can't remember which one, possibly GPT 5.5. It took 5 minutes or more to so and the stated cost for the task was 10 dollars, possibly even more. I can't recall the token amount. submitted by /u/Complete-Sea6655 [link] [留言]

I maintain an open-source GitHub Action called vorsken. It does one thing: scan the diff on a pull request with Semgrep, apply a fixed policy, and return BLOCK, FLAG, or PASS. No dashboard, no model that drifts over time. Rules at ERROR/HIGH/CRITICAL severity block the merge, WARNING/MEDIUM flag it, the rest pass. Same diff, same verdict. The usual pitch for a tool like this is that it catches the SQL injection your AI assistant wrote. I wanted to see what it actually catches against real assistant output, so I generated 28 functions and ran them through. The test Seven backend tasks: a FastAPI upload endpoint, a URL-fetch helper, JWT auth, a SQL filter, an ImageMagick subprocess call, a LangChain file agent, and a LangChain RAG pipeline. I generated each one four times, with ChatGPT (GPT-5.5 Instant), Claude Code (Opus 4.8), Claude Code plus the security-guidance plugin, and Cursor (Composer 2.5). Single-shot, neutral prompt, no security hints. Then I scanned all 28 with the same ruleset. I'm reporting which rule fired on which file, not whether some model thinks the code is safe. That part you can reproduce. Task ChatGPT Claude Code + plugin Cursor Verdict file upload — — — — PASS url fetch (SSRF) ssrf ssrf ssrf — FLAG / Cursor PASS jwt auth api8 api8 — — BLOCK / 2 PASS sql filter — — — — PASS imagemagick — — — — PASS fs agent — overperm — — 1 BLOCK / 3 PASS rag dangerous dangerous dangerous dangerous BLOCK 7 BLOCK, 3 FLAG, 18 PASS across 28 functions. The basics were fine SQL filter, ImageMagick, file upload: clean on every tool. The SQL was parameterized, the subprocess calls passed argument lists instead of shell strings, the uploads weren't doing anything reckless. If you still expect current models to spray SQL injection across a straightforward CRUD task, they don't. On conventional work they get it right. Two of the flags are soft. The JWT api8 hits landed on a SECRET_KEY = "CHANGE_ME" placeholder, which you can read as a false positive or as a gate doing i

Multi-Model AI API Routing: Cut Costs Without Sacrificing Quality Problem: You're building an AI-powered app, but relying on a single model (like GPT-4) for every request is burning through your budget. Simple tasks like summarization or classification don't need a heavyweight model, yet you're paying premium prices for them. Solution: Route requests intelligently to the cheapest model that can handle each task. This is multi-model AI API routing, and it can cut your costs by 60-80% while maintaining output quality. Prerequisites Python 3.8+ API keys for at least 2 AI providers (e.g., OpenAI, Anthropic, or NovaAPI) Basic understanding of async/await in Python Step 1: Define Your Routing Strategy First, create a routing configuration that maps task complexity to model tiers: # router_config.py ROUTING_CONFIG = { " simple " : { " models " : [ " nova-1-fast " , " gpt-3.5-turbo " ], " cost_per_token " : 0.0001 , " max_tokens " : 500 , " tasks " : [ " summarization " , " classification " , " entity_extraction " ] }, " medium " : { " models " : [ " nova-1-medium " , " gpt-4-mini " ], " cost_per_token " : 0.0005 , " max_tokens " : 2000 , " tasks " : [ " code_generation " , " translation " , " sentiment_analysis " ] }, " complex " : { " models " : [ " nova-1-pro " , " gpt-4 " ], " cost_per_token " : 0.002 , " max_tokens " : 4000 , " tasks " : [ " reasoning " , " creative_writing " , " complex_qa " ] } } Step 2: Build the Router Now implement the core routing logic with fallback capabilities: # ai_router.py import asyncio from typing import Dict , List , Optional import time class AIRouter : def __init__ ( self , config : Dict , api_keys : Dict [ str , str ]): self . config = config self . api_keys = api_keys self . metrics = { " cost " : 0 , " requests " : 0 , " failures " : 0 } async def route_request ( self , task : str , prompt : str ) -> str : """ Route request to appropriate model based on task complexity. """ tier = self . _classify_task ( task ) models = self . confi

Every time I started a side project, I rebuilt the same five things before I wrote a single line of the actual idea: auth, a database, file uploads, a deploy pipeline, and TLS. Different domain, same plumbing. By the third project I was copy-pasting my own docker-compose.yml from a folder two repos over and renaming things until they stopped erroring. So I stopped. I froze that plumbing into one boilerplate — PocketBase + Next.js + Caddy on a single cheap VPS — and now I ship every side project on it. Same shape every time: clone, rename, write the part that's actually new, push. The thing I care about most isn't the speed, though. It's that I stopped paying for it. A handful of real projects now run on this setup for a few euros a month, total — not a stack of per-service SaaS bills that each want $25 here and $20 there before you've shipped anything. No Vercel seat, no managed Postgres, no Auth-as-a-Service, no object-storage line item. Here's the whole thing. Why this stack The trick that makes the cost collapse is PocketBase . It's a single Go binary that gives you, in one process: Auth — email/password, OAuth, the works, with a real users collection A database — SQLite, with a schema you manage from an admin UI Realtime — subscribe to collection changes over SSE File storage — uploads handled, with on-the-fly thumbnails An admin dashboard — at /_/ , for free That's four or five separate SaaS products collapsed into one binary that runs anywhere and stores everything in a folder. Compared to wiring up Supabase or Firebase, the mental model is tiny: it's one process and one data directory. Back up the directory and you've backed up the entire app — database, uploaded files, auth tokens, all of it. For the front I use Next.js (App Router, React Server Components) because that's where I'm fastest, and Caddy as the reverse proxy because it gets you automatic HTTPS with zero config — it provisions and renews Let's Encrypt certificates on its own. And it all lives on

Claude and ChatGPT have thinking efforts where you can tune the amount of thinking allowed. Like low, medium, high, xhigh and so on. But are they different models underneath? Or same model with different parameter? The reason I ask is because, if I change the effort param mid conversation in Claude code, I get a warning suggesting I’m breaking the cache. I don’t think this happens in Codex because when I change the effort, the responses are still quick.

Most organizations still think of the hypervisor as a resource abstraction layer. CPU. Memory. Storage. The platform that decides where workloads run. That mental model is increasingly incomplete. Every major virtualization platform — vSphere, AHV, Proxmox — has been steadily accumulating policy enforcement responsibilities. The hypervisor isn't just deciding where workloads run. It's increasingly deciding what they're allowed to do. The Speed of the Shift Is the Real Story Virtualization practitioners already know security controls have moved downward through the stack. What's less appreciated is how compressed the most recent phase has been. For years, hypervisors enforced resource allocation. Within a single platform generation cycle, that same layer accumulated encryption policy enforcement, workload trust validation, microsegmentation, secure boot enforcement, host attestation, and workload isolation boundaries — not as optional add-ons, but as core platform capabilities. The perimeter-to-OS transition took decades. The hypervisor accumulated a comparable policy enforcement surface in the time between one major vSphere release and the next. That compressed timeline is what creates the ownership lag — the governance model adequate for a resource scheduler has not caught up to a platform that enforces organizational policy. The Hypervisor Now Makes Binding Decisions The distinction that matters: a platform that observes policy versus a platform that enforces it. The hypervisor is no longer observing. It is enforcing. VM fails attestation → workload does not start. Encryption policy mismatch → workload cannot migrate. Segmentation policy violation → communication blocked at the platform layer. Trust validation failure → host removed from workload eligibility. Those are not scheduling decisions. Those are governance outcomes. The workload doesn't get a vote. This is what makes the hypervisor governance infrastructure : infrastructure that directly enforces organiza

Over the past few months I built an AI-assisted delivery framework — not to write code faster, but to eliminate ambiguity across the entire software development lifecycle. The result completely changed how I think about AI in engineering. The problem I kept hitting Every time I used AI to generate architecture docs, API contracts, or implementation plans across separate sessions, the outputs looked great in isolation. But viewed together? They were broken. A pivot in the system architecture was never reflected in the API contracts. Frontend assumptions silently diverged from backend data models. AI wasn't the problem. Treating it as a collection of disconnected prompt sessions was. What I built instead A governance-driven framework built on three layers: Prompt → Agent → Skill The Prompt captures intent only — lightweight, declarative The Agent orchestrates execution and decides which capabilities to invoke The Skill is a reusable, schema-validated execution block with hardcoded governance rules This connects every delivery artifact into a sequential dependency chain: Business Requirements ↓ System Architecture ↓ Data Architecture ↓ Event Architecture ↓ API Contracts ↓ Implementation Plans ↓ Backend / Frontend Implementation Each artifact consumes the one before it. Upstream changes automatically propagate downstream. Governance is enforced at the Skill layer — not buried in fragile prompts. The finding that surprised me most The highest-leverage use of AI wasn't code generation. It was context generation . When engineers — or downstream agentic workflows — were given a governed, unambiguous spec, implementation quality was consistently higher than any raw AI-generated code output. The context was the unlock, not the syntax. What failed I'm including this because most write-ups skip it: Over-orchestrating everything (not every workflow needs an agent loop) Prompt bloat as a substitute for real architecture Severely underestimating token costs at scale Believing full

so i kept using chatgpt to ask questions about my pdfs and notes, and half the time i couldn't tell if it actually read the doc or just made something up that sounded right. that bugged me enough to build my own thing over the last few weeks. you upload a pdf (or word, csv, image, or just paste a link), ask whatever you want, and it answers using only what's in your file - and it shows the exact page it pulled the answer from, so you can check. if the answer isn't in the doc, it just tells you instead of guessing. stuff i actually end up using: flip on web search when i want it to look something up online instead one click to turn a doc into a summary / key points / flashcards (this is clutch for studying) resume review + cover letter help you can talk to it and it reads the answer back it's completely free, i'm not selling anything. honestly just want people to break it and tell me what's missing. link: https://athena-wisdom.vercel.app (there's a short guide on the site too if you get stuck) solo project so be gentle lol - but real feedback is what i'm after, especially what you'd want it to do next. submitted by /u/Independent_Diver352 [link] [留言]