标签：#ssi

Judge reluctantly approves $1.5M settlement with SEC over Twitter stock violation.

Hi everyone! I recently launched IsItCrashing.com How often do you deploy a website only to discover later that: ❌ A page is returning a 404 or 500 error ❌ Images or assets aren't loading on some random pages ❌ A route is completely blank ❌ JavaScript crashes are breaking the page ❌ Customers find the problem before you do IsItCrashing.com helps you catch these issues before your users do. Simply enter your website URL, and the tool scans your site to identify: ✅ Broken pages (404/500) ✅ Broken links ✅ Missing assets ✅ Blank pages ✅ JavaScript errors ✅ Website health issues Get a clean, easy-to-read report so you can fix problems quickly and deploy with confidence. Whether you're a developer, QA engineer, agency, or website owner, IsItCrashing.com makes website testing faster and easier. try out here : 🌐 https://isitcrashing.com

Async processing qua message queue: vì sao đẩy việc nặng ra khỏi request path, và cái giá phải trả bằng eventual consistency Async processing là mô hình tách một request thành hai giai đoạn: request handler nhận việc, xác nhận với client, rồi giao phần xử lý thật cho một worker chạy ngoài request path — thường qua một message queue (RabbitMQ, AWS SQS, Kafka, Redis Streams, hoặc queue trên nền Redis như BullMQ/Sidekiq). Lý do dev gặp nó trong việc thật rất cụ thể: một endpoint gọi payment provider mất 3s, gửi email confirm mất 1s, resize ảnh mất 5s — nếu làm tuần tự trong request, p99 latency của endpoint là tổng các con số đó, và một downstream chậm hoặc chết đủ để làm timeout hết thread pool của app server. Đẩy vào queue thì request trả về trong vài chục ms; nhưng đổi lại, cái "xong" mà client thấy không còn nghĩa là việc đã thực sự hoàn thành. Cơ chế hoạt động Ba thành phần: producer (thường là API server) đóng gói việc thành message rồi publish vào broker; broker (RabbitMQ/SQS/Kafka…) giữ message trong queue có persistence tuỳ cấu hình; consumer/worker poll hoặc được push message, xử lý, rồi ack để broker biết xoá. Nếu worker chết trước khi ack, broker redeliver — đây là gốc của semantic at-least-once : mỗi message được giao ít nhất một lần, có thể nhiều lần. Exactly-once trong hệ phân tán chỉ đạt được ở lớp application bằng cách consumer viết idempotent, không phải bằng cấu hình broker. Ví dụ với RabbitMQ + Node ( amqplib ): // producer — trong HTTP handler const ch = await conn . createConfirmChannel () await ch . assertQueue ( ' image.resize ' , { durable : true }) app . post ( ' /upload ' , async ( req , res ) => { const jobId = crypto . randomUUID () const payload = Buffer . from ( JSON . stringify ({ jobId , s3Key : req . body . key })) await ch . sendToQueue ( ' image.resize ' , payload , { persistent : true , // ghi xuống disk, sống sót broker restart messageId : jobId , // để consumer dedupe contentType : ' application/json ' , }) // đợi broker confirm đ

The 1.0 lands with session recording attached HashiCorp announced Boundary 1.0 on June 25. The operational headline is RDP session recording, and the version number is a distant second. Boundary is HashiCorp's privileged-access proxy, and until this release it did not record Remote Desktop sessions on its own. Teams that route Windows-side deploys through the proxy now have a first-party audit trail that ships with the product itself. The announcement bundles two other things on top of the RDP work. "Improved management" is HashiCorp's phrasing. Boundary 1.0 also previews work aimed at securing access for AI agents, which HashiCorp positions as a same-chokepoint answer for a new class of caller. What actually changes on the CD side For most teams the practical read is narrower than "1.0 shipped". Two things move. RDP sessions get recorded through the proxy. Windows targets have historically been the awkward part of a privileged-access story. SSH session recording and TLS-terminating proxies have been standard for years on Linux. RDP has been thinner. A CD pipeline that lands on a Windows host for a hotfix, an artifact promotion, or a release-time config change now has the same after-the-fact video that Linux jumpboxes have had for a long time. The AI-agent preview signals where Boundary wants to sit next. If CD tooling is starting to hand a shell to an agent, that agent needs a credential of some kind. HashiCorp is telling operators the plan is for Boundary to mediate that call the way it mediates a human on-caller today. This is a preview. Read it as a roadmap. Why the audit line matters for release engineering The audit case for session recording is easy to state and hard to argue with. When a bad change lands on a production Windows host at 2am, the post-incident question is always the same: what did the person on the console actually do, and can it be replayed? Without recording, on-call gets shell history if it is lucky and a change-management ticket if it is n

Drone intruders that possibly flew from Russian ships showed Europe isn’t ready.

AI has made it a lot harder for tech companies like Amazon and Google to deliver on their net-zero pledges.

Hopper will pay $35 million to settle FTC allegations that it used deceptive “dark patterns” to hide fees and mislead travelers about the cost and benefits of services.

Google tries balancing AI data center emissions with clean energy efforts.

Key Takeaways Oracle Backend for Microservices and AI 2.1.0 is a platform modernization release. It updates several shared backend concerns at once, including external access, observability, configuration, messaging, database deployment choices, workflow, samples, enterprise installation planning, and upgrades. Gateway API and Envoy Gateway are now the default external access direction. NGINX Ingress Controller is deprecated, disabled by default, and still available only when explicitly enabled. The release gives platform teams clearer building blocks. OpenTelemetry Operator, Java auto-instrumentation, Spring Config Server, Kafka through Strimzi-managed resources, and clearer database deployment choices supported by Oracle AI Database Operator for Kubernetes make important platform choices easier to see and discuss. Enterprise adoption still needs architecture review. Before adopting or upgrading, teams should review private registry needs, air-gapped installation requirements, multi-tenant installation goals, workflow implications, database deployment choices, and upgrade readiness. OBaaS 2.1.0 is a platform modernization release Oracle Backend for Microservices and AI , or OBaaS , is a backend-as-a-service style platform for teams building microservices and AI-enabled applications with Oracle AI Database as a core data foundation. It brings common backend platform concerns together: service access, telemetry, configuration, messaging, workflow, and database connectivity. That matters because most teams do not want every application squad to rebuild those pieces on its own. They want a platform shape that gives developers useful defaults while still giving architects, DBAs, security teams, and operators the control points they need. This article focuses on the Oracle Backend for Microservices and AI 2.1.0 update. The best way to read OBaaS 2.1.0 is not as a single-feature release. It is a platform modernization release. The update moves the default external access

Gov. Ron DeSantis calls it a crackdown on "radical climate policies."

Operation by two Russia-state groups has been ongoing since at least March.

I've been using Home Assistant for a few years and Trading212 for longer than that. It was inevitable these two things would end up connected. The Trading212 API is surprisingly good — portfolio value, individual positions, pies, dividends, all there. So I wrote a custom integration to pull it all into HA as sensors, then a Lovelace card to make it actually look decent on a dashboard rather than a wall of entity rows. The card does zero-config auto-discovery which was the bit I spent the most time on. You drop it on a dashboard and it finds your sensors automatically — no copying entity IDs, no manual config unless you want it. Five card types: portfolio overview with a sparkline, scrollable positions list, pies with goal progress, and a combined one if you want everything in one card. The sparkline was fiddly. HA's recorder only writes state changes, not regular samples, so if your portfolio value is flat between polls the chart has gaps. Had to smooth over those client-side. The part I use most though is the automations. Every weekday at 8am Alexa tells me where I stand: action : - action : notify.alexa_media_kitchen data : message : > Portfolio is worth {{ states('sensor.trading212_total_value') | float | round(0) | int }} pounds. Today you are {% if states('sensor.trading212_pnl_today') | float >= 0 %}up{% else %}down{% endif %} {{ states('sensor.trading212_pnl_today') | float | abs | round(2) }} pounds. data : type : tts And Friday at 6pm I get the weekly version with P&L for the week and which position moved the most. I like that it just tells me — if the market's had a bad week I'd probably avoid opening the app, but Alexa doesn't give me the option to ignore it. Both the integration and the card are on GitHub. The card is in HACS as a custom repo while it waits for default catalogue approval: https://github.com/Smart-Home-Assistant-UK/lovelace-trading212-card I wrote up the full setup with all the automation YAML here if you want to copy the whole thing: ful

Security researchers found evidence that Russian authorities hacked the iPhone of a political opponent using a phone-unlocking device made by Cellebrite, even after the company said it would stop selling to Putin’s government.

The safety board, known for its thorough investigations, is probing the crash alongside the National Highway Traffic Safety Administration.

Technology companies are extending AI beyond code generation into earlier stages of the software lifecycle, including PRD validation, design inputs, and code review. Initiatives from Uber, DoorDash, and Cloudflare highlight a shift toward AI-driven governance layers that evaluate engineering artifacts before implementation while preserving human oversight across the development pipeline. By Leela Kumili

The boat-agent stack here runs on a prime directive: if there's something usable out there, improve it; build our own only as a last resort. So when we needed a SignalK MCP server, the honest first move wasn't to write one — it was to evaluate the one that already exists. VesselSense/signalk-mcp-server (TypeScript, MIT) is good work. It exposes SignalK to an agent through a single execute_code tool: the model writes JavaScript, the server runs it in a sandboxed V8 isolate ( isolated-vm ), and only the result comes back. Its README claims a 90–96% token reduction versus traditional named MCP tools — 2,000 tokens down to 120 for a vessel-state query, 13,000 down to 300 for a multi-call workflow. Those numbers are plausible, and they line up with the broader industry result that code execution beats tool-calling on token efficiency for complex multi-step work. We read it, ran the numbers against our own agent, and kept our discrete-named-tool signalk-mcp anyway — then harvested three of VesselSense's ideas into our roadmap. This post is that evaluation: the two philosophies, why the obvious-sounding win doesn't bind for a voice-first agent, and a decision framework you can reuse before you adopt-or-build your own MCP server. This is a design-reasoning post, not a debugging saga, but it maps to the same arc: a question, the dead-end that looks like an obvious yes, and the call that actually held. The question Two SignalK MCP servers, two genuinely different designs: VesselSense/signalk-mcp-server sailingnaturali/signalk-mcp ───────────────────────────── ─────────────────────────── one tool: execute_code discrete named tools: → agent writes JavaScript read_sensor(path) → runs in a V8 isolate battery_state(bank) → queries SignalK, returns depth_state() only the result get_route() get_local_time() TypeScript / Node + isolated-vm list_paths(prefix) claims 90–96% fewer tokens get_active_alarms() Python, end-to-end The adopt-vs-keep question: does the token-efficiency win bin

Public notice of a single "compassionate use" case is odd in every way.

46.4%. That number — ChatGPT's June 2026 market share — ends a streak that held since November 2022. For the first time since the product launched, OpenAI holds less than half the AI assistant market. Gemini is at 27.7%. Claude is at 10.3%. The monopoly phase of AI assistants is over. The data comes from a June 2026 market report tracking monthly active users across major AI assistants. ChatGPT still leads with 1.11 billion monthly users — a number that would define the entire category in any other software market. But Gemini has 662 million, up 129 million in five months. Claude sits at 245 million, nearly four times its December 2025 count of 60.2 million. The trajectory is the story, not the absolute numbers. Why the 50% Threshold Actually Matters Below 50% doesn't mean decline. ChatGPT's absolute user count keeps growing. What the threshold signals is the end of single-platform dominance — the condition where building for "AI users" meant building for ChatGPT users. That assumption no longer holds in mid-2026. For context: search engine market share stayed above 90% for Google for nearly a decade after competitors entered. Social network market share for Facebook stayed above 70% for years after Instagram and Twitter had genuine scale. The pace of AI assistant fragmentation is meaningfully faster than those precedents. Three products above 10% share in under two years of real competition is an unusually fast split. What fragmentation means practically: the community knowledge base — YouTube tutorials, Reddit threads, prompt libraries — that once pointed almost exclusively at ChatGPT now covers three platforms with genuine depth. That changes how you can expect your users to arrive at your AI-integrated product, and what they already know about AI when they get there. Gemini's 662 Million Users Are Not What They Look Like Gemini's surge from under 500 million to 662 million monthly users in five months is impressive on paper. The driver is less impressive: Google

Learn about the progress we’ve made toward our accessibility goals and how you can help make open source more inclusive. The post From pledge to practice: Building a more inclusive open source ecosystem appeared first on The GitHub Blog .

Sure, anyone can use OpenAI’s chatbot. But with smart engineering, you can get way more interesting results.