According to this article > https://next.io/news/regulation/asa-ukgc-warn-operators-ads-under-18s/ , the UK's ASA and CAP are reportedly rolling out an AI system to scan social media for gambling ads that appeal to under-18s or breach advertising codes, with the UKGC coordinating enforcement. It feels like a meaningful shift in how compliance gets monitored, moving from reacting to complaints toward systems that actively scan and flag issues in near real time. For operators and their B2B partners, the practical takeaway is that marketing has to be compliant from the start, because anything off will now get picked up much faster and at scale. It raises a real question: what happens when AI starts flagging compliance breaches faster than humans can review them? Are operators and suppliers actually ready for that? submitted by /u/Altenar_b2b [link] [留言]

Recently i have been trying to learn full stack skills, springboot and react.js , These things are so overwhelming, I haven't started react.js yet, I mean there are so many things to remember ModelMapper, ObjectMapper, GrantedAuthority, User details, User detailsService,Logger, so many annotations, So many features Really getting confused, trying to build a resume based Ecommerce Project Even If I am able to make it , I know many will comment " It's very common, it's a basic project" dude it was so tough for me how can u say that submitted by /u/faangPagluuu [link] [留言]

submitted by /u/fagnerbrack [link] [留言]

Microsoft used their Build 2026 event to announce new functionality for Microsoft Foundry. Citing Foundry as "the place where AI agents move from experiments to production systems," in a blog post, Nick Brady writes that the release brings “runtime, tools, memory, grounding, models, observability, and governance” that developers need for production agents, rather than just new model endpoints. By Matt Saunders

I just had a long conversation with Claude about 'paraquat' (a type of agricultural chemical) from a scientific and public-policy perspective. I wanted to discuss about its toxicological mechanism, why it is difficult to treat (if someone drinks it), current research, agricultural regulation (many countries have banned this chemical because it's too toxic), safer herbicides, plant-specific biochemical targets, and weed-control methods. These were just some coherent questions about toxicology, medicine, agriculture, and plant biology. I never said that I wanted to harm myself, that I had access to paraquat, or that I was in any immediate danger. Despite that, Claude repeatedly redirected the conversation toward suicide intervention. It asked whether I was considering harming myself, told me to move dangerous substances away, asked whether anyone was nearby, and repeatedly gave me crisis hotline numbers. The first time this happened, I explicitly objected and said that scientific interest in a toxic substance is not evidence of suicidal intent. Emergency physicians, toxicologists, biology students, and public-health researchers discuss exactly these questions everyday, and very few people commit suicide from this type of discussions. Claude apologized and said it understood. Then it did it again. It apologized again and promised to stop. Then it did it again. I reviewed the full transcript and I counted approximately: 30 responses that personally implied I might be suicidal, self-harming, or in a psychological crisis I objected about 20 times and told it to stop 28 of those implications occurring after I had already clearly rejected the assumption At least 14 promises that it would stop asking or stop inserting crisis-intervention content At least 12 later violations of those promises Claude repeatedly acknowledged my correction, accurately summarized that I was asking normal scientific questions, promised not to make the assumption again, and then resumed the exact s

Kafka is used for handling messages/events between different services. Here's how I understand it: A Producer sends an event/message to Kafka. The message contains things like Topic, Key-Value data, and Timestamp. Kafka stores these messages in Brokers (Kafka servers). Topics can be divided into multiple Partitions. Each partition has one Leader and multiple Followers (Replicas). All read and write operations happen through the Leader, while Replicas act as backups if a broker fails. Now Kafka does not immediately delete messages after they are consumed, unlike many traditional queues. There is a term called Offsets. You can think of an offset like the index of a message inside a partition. For example: A user places an order → payment is processed → email is sent → analytics service processes the event. Suppose during that analytics service goes down, Kafka knows which offset was last processed. When the service comes back up, it can continue from that offset instead of starting from the beginning. This is also one reason why Kafka keeps messages for some time after consumption. Any corrections? Is there anything else I should know about this topic? Please let me know. submitted by /u/No-Resolution-4054 [link] [留言]

Open-source noise suppression for voice AI agents Discussion | Link

Imagine speaking in English, and having listeners from all over the world hear you translated into...

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This article was originally published on aicoderscope.com TL;DR : Gemini 3.5 Flash went GA on May 19, 2026 and costs 50% less than Claude Sonnet 4.6 on input tokens ($1.50 vs $3.00/M). It generates code at ~284 tokens per second — roughly 4.7× faster than Sonnet 4.6. Cursor already lists it natively; Cline needs one extra config step. The trap: Flash's default thinking level is "medium," which is slower and pricier than "low," the setting Google specifically tuned for coding and tool-use loops. Gemini 3.5 Flash Claude Sonnet 4.6 DeepSeek V4-Flash Best for Fast agent loops, context-heavy analysis Complex refactors, instruction fidelity Cost-capped high-volume tasks Input / Output per 1M tokens $1.50 / $9.00 $3.00 / $15.00 $0.14 / $0.28 Context window 1M tokens 200K tokens 1M tokens Terminal-Bench 2.1 76.2% — — Output speed ~284 t/s ~60 t/s — Max output per request 65,536 tokens 64K tokens 64K tokens The catch Output at $9/M erodes savings on code-gen 15× pricier output than Flash No vision, MIT-licensed Honest take : Use Gemini 3.5 Flash with Cline for multi-step agent tasks where round-trip latency compounds and context windows run large. Stay on Claude Sonnet 4.6 when you need a hard refactor to land perfectly on the first try — Sonnet's 79.6% SWE-bench Verified score still leads Flash's on correctness benchmarks. The cost math that does and doesn't work Gemini 3.5 Flash charges $1.50 per million input tokens and $9.00 per million output tokens. Against Claude Sonnet 4.6 at $3.00/$15.00, the input side is a genuine 2× saving. The output side is almost the same story: $9 vs $15 is 40% cheaper per generated token. Run the numbers on a typical Cline coding session: 8 tool calls, reading 12 files (roughly 20,000 context tokens), generating 500 lines of code output (~7,000 output tokens). Sonnet 4.6: (20K × $3 + 7K × $15) / 1,000,000 = $0.165/session Gemini 3.5 Flash: (20K × $1.50 + 7K × $9) / 1,000,000 = $0.093/session That's 44% cheaper per session. At 50 sessions a m

This article was originally published on aicoderscope.com TL;DR : Cursor Tab (the inline ghost-text autocomplete) breaks in 8 distinct ways. Roughly 70% of cases are fixed in under a minute by checking three things: the toggle in Settings, your extension list for Copilot, and your usage quota. The remaining 30% need a corporate proxy fix or a toggle-restart cycle. After this guide you will: Know which of the 8 root causes matches your specific symptom Have the exact settings path and command for each fix Understand the 2,000-completion monthly cap on the Hobby plan that silently kills suggestions at month-end Honest take : Every "Cursor Tab is broken" thread in 2026 falls into one of these eight buckets. Go through the three-step quick check first — most readers fix it there. The 60-second triage: three checks before anything else Open Cursor and verify these in order. They resolve 70% of cases. 1. Is Cursor Tab actually enabled? Press Ctrl+Shift+J (Windows/Linux) or Cmd+Shift+J (Mac) to open Cursor Settings, then go to Features > Cursor Tab . The toggle should be on. If it's off — and this happens more than it should after updates — flip it, restart Cursor, done. You can also check from the status bar at the bottom right of the editor. A small "Tab" indicator shows whether inline completions are active. If it shows disabled, click it to re-enable. 2. Have you hit the free quota? On the Hobby plan, Cursor gives you 2,000 tab completions per month . During active coding at a normal acceptance rate, you can burn through 50–100 per hour. If you're late in the billing cycle, open Settings > Usage & Limits and check the completions counter. Completions reset on your account anniversary date, not the calendar month. When you hit 2,000/2,000, tab completion stops silently — no error, no banner, just nothing appearing. Upgrade to Pro ($20/mo) or wait for the reset. 3. Is GitHub Copilot or Tabnine installed? If you migrated from VS Code with extensions intact, you may have G

Building a production TypeScript CLI in 2026: oclif vs commander vs custom. I shipped my first Node CLI in 2019 with a 12-line arg slicer and process.argv . It worked until it needed a second command and then collapsed into spaghetti. The other extreme is grabbing a full framework for a tool that runs one command. In 2026 there are three reasonable paths between those extremes, and each one wins on a specific slice of the problem. This post covers @oclif/core v4, commander v14, and a zero-dependency parser that fits in 30 lines. Same "greet" command in all three. Same distribution steps at the end. Honest tradeoffs throughout. TL;DR oclif v4 commander v14 zero-dep npm install size ~8 MB ~220 kB 0 B Type inference on flags Full, generated Good, manual Manual Plugin ecosystem Yes (Heroku, Salesforce) No No Learning curve High (day 1) Low (hour 1) None Best for Multi-team, multi-command CLIs Most real-world tools One-shot scripts 1. The decision: framework vs no framework Reach for a framework when the tool needs subcommands, a plugin system, or auto-generated help text. The second engineer who touches the CLI should be able to find where things live without reading your code twice. Build your own when the tool does one thing, ships as a one-file script, or lives inside a monorepo where pulling in 8 MB of transitive deps is not welcome. A zero-dep parser also removes the surface area for supply-chain incidents, a real concern on tools that run in CI. Commander sits in the middle: a 220 kB install that covers most real tools without the scaffolding overhead of oclif. 2. Project skeleton Every path shares the same bin setup. Start with a package.json that declares the executable: { "name" : "greet-cli" , "version" : "1.0.0" , "bin" : { "greet" : "./dist/cli.js" }, "scripts" : { "build" : "tsc" , "dev" : "tsx src/cli.ts" }, "type" : "module" } The tsconfig.json for a CLI targets the Node release line you plan to support. Node 24 LTS handles ESM natively, so use "module":

The first post went over some of its capabilities. Over the past week Edict went v1.0, adding cursors for reading projections after command dispatch (to close some eventual-consistency gaps), a new type of projection that holds state inside the Orleans grain directly instead of a table, saga timeouts, schedules, an improved skills package and MCP server that ships with Edict, and more. Edict has now grown to over 75,000 lines of code and more than 1000 tests, and contains several deep mechanisms that have been fixed, broken, and fixed again. It is well past the point where I can hold all of Edict in my head. This post is about working with AI on large codebases, which I expect to be the first problem most software engineers have to solve. The context problem Years ago I was talking to a PhD candidate whose area of research was Natural Language Processing (NLP). He explained to me that one of the most difficult NLP problems was context. If a colleague says they need to pop out to pick their kids up from school, a scene can form in your head: one with a school, the layout of the road, people waiting, walking, driving, the environs. You may never have seen the school your colleague mentioned, but you can form a rich scene from your accumulated experience and use it to drive the rest of the conversation with a shared understanding. LLMs ingeniously dodge this entire issue by making it your problem. Just a word-probability machine Strip away the chat window and a Large Language Model (LLM) is doing one thing: predicting the next token. Give it a run of text and it returns a probability distribution over what comes next, samples one, appends it, and repeats. Companies like OpenAI and Anthropic then beat it into shape using techniques like supervised fine-tuning and reinforcement learning, which tune those probabilities in meaningful ways. That is why Claude is always telling me "Good framing" or "You've spotted...". It even called me "Bold" on one occasion. The probabilit

TL;DR As an international student in the United States, I joined tech communities hoping to find...

สรุปสาระสำคัญ (TL;DR) ตัวแปรที่ตั้งค่าระหว่างการรันคำขอแบบแมนนวลใน Postman อาจ “หายไป” เมื่อรันผ่าน Collection Runner เพราะขอบเขตตัวแปรและพฤติกรรมการคงค่าระหว่างการรันไม่เหมือนกัน จุดที่ต้องตรวจสอบคือ pm.environment.set , การเลือก Environment, ค่า Initial/Current Value, ตัวเลือก “Keep variable values” และการเลือกใช้ Collection Variables ให้เหมาะกับสถานะภายในรันเดียวกัน ลองใช้ Apidog วันนี้ บทนำ คุณอาจเคยเจอสถานการณ์นี้: รันคำขอ Login ใน Postman แบบแมนนวล Post-response script ดึง access_token ตั้งค่า token ด้วย pm.environment.set คำขอถัดไปใช้ {{token}} ได้ตามปกติ แต่เมื่อกด Run Collection คำขอ Login ผ่าน แต่คำขอถัดไปได้ 401 Unauthorized ตัวอย่างสคริปต์ที่มักเป็นต้นเหตุ: pm . environment . set ( ' token ' , pm . response . json (). access_token ); สคริปต์นี้ไม่ได้ผิดเสมอไป แต่จะมีปัญหาเมื่อ: ไม่ได้เลือก Environment ใน Runner Runner รีเซ็ตค่าหลังรันเสร็จ ใช้ Environment Variables ทั้งที่ต้องการแค่ state ภายใน Collection Run ตั้งค่าเฉพาะ Current Value แต่ไม่ได้ตั้ง Initial Value บทความนี้สรุปวิธีดีบักและแก้ไขแบบลงมือทำได้ทันที ลำดับชั้นขอบเขตตัวแปรของ Postman Postman แก้ค่า {{variable}} ตามลำดับความสำคัญดังนี้: Local variables — ใช้เฉพาะในสคริปต์ที่กำลังรัน Data variables — มาจากไฟล์ CSV/JSON สำหรับ data-driven test Collection variables — ใช้ภายในคอลเล็กชัน Environment variables — ใช้ใน Environment ที่เลือก Global variables — ใช้ได้ข้ามคอลเล็กชันและ Environment ถ้ามีตัวแปรชื่อเดียวกันหลายขอบเขต เช่น token Postman จะใช้ค่าจากขอบเขตที่มี priority สูงกว่าก่อน ตัวอย่าง: pm . collectionVariables . set ( ' token ' , ' collection-token ' ); pm . environment . set ( ' token ' , ' environment-token ' ); console . log ( pm . variables . get ( ' token ' )); pm.variables.get('token') จะคืนค่าตามลำดับ priority ไม่ได้หมายความว่าจะอ่านจาก Environment เสมอไป ทำไมตัวแปรจึงหายไปใน Collection Runner 1. Current Value และ Initial Value ไม่เหมือนกัน ตัวแปรใน Postman มี 2 ค่า: Initial value : ค่าที่ซิงค์และแชร์กับทีม Current value : ค่า local ในเครื่องของคุณ เมื่อใช้: pm . environment . set (

Tóm tắt Nếu thay đổi gói miễn phí của Postman khiến bạn mất quyền truy cập vào workspace được chia sẻ, dữ liệu của bạn chưa chắc đã bị xóa. Việc cần làm là phục hồi càng sớm càng tốt trước khi cache cục bộ, quyền API hoặc bản sao lưu còn sót lại không còn dùng được. Bài viết này hướng dẫn các cách lấy lại collection/environment từ Postman và nhập chúng sang Apidog để giảm rủi ro bị khóa dữ liệu trong tương lai. Dùng thử Apidog ngay hôm nay Bối cảnh Sau bản cập nhật gói miễn phí Quý 1 năm 2026 của Postman, nhiều developer dùng workspace chia sẻ với đồng nghiệp phát hiện rằng họ không còn truy cập được dữ liệu nhóm. Các collection nằm trong workspace team, thay vì workspace cá nhân, đột nhiên bị khóa sau paywall. Một developer mô tả trên Reddit: “Tôi đến làm việc vào thứ Hai và toàn bộ không gian làm việc của nhóm tôi đã biến mất. Ba tháng với các bộ sưu tập, môi trường được sắp xếp gọn gàng, tất cả đều biến mất. Chỉ còn cách trả tiền thì mới có lại.” Điểm quan trọng: dữ liệu thường không bị xóa ngay. Postman lưu dữ liệu workspace phía server, còn việc bạn không nhìn thấy collection là hạn chế quyền truy cập. Vì vậy, hãy xử lý theo thứ tự dưới đây, ưu tiên các nguồn có khả năng còn dữ liệu đầy đủ nhất. 1. Kiểm tra cache trong ứng dụng Postman desktop Trước tiên, mở Postman desktop app nếu bạn đã từng dùng nó. Không mở bản web tại app.getpostman.com . Ứng dụng desktop có thể còn cache cục bộ của collection và environment bạn truy cập gần đây. Cache này thường chỉ tồn tại trong thời gian ngắn, tùy hệ thống và cơ chế invalidation của Postman, nên hãy xuất dữ liệu ngay nếu còn nhìn thấy. Các bước thực hiện: Mở Postman desktop. Kiểm tra tab History để xem các request gần đây. Kiểm tra sidebar bên trái xem collection còn hiển thị không. Nếu collection còn hiển thị, xuất ngay từng collection. Cách export collection: Nhấp chuột phải vào collection hoặc bấm menu ba chấm. Chọn Export . Chọn định dạng Collection v2.1 . Lưu file .json ra thư mục an toàn. Nếu collection vẫn hiển t