标签：#Cloud

Google's AlphaEvolve reached general availability on the Gemini Enterprise Agent Platform, turning the DeepMind research project into an evolutionary code optimization service. Evaluators run client-side so code never leaves the customer's infrastructure. Klarna doubled ML training throughput; practitioners note it only works where a measurable evaluation function exists. By Steef-Jan Wiggers

AWS has recently introduced CloudFormation express mode, a deployment option that can reduce infrastructure deployment times by marking stack operations complete once resource configuration is applied, rather than waiting for full resource stabilization. By Renato Losio

Running out of API quota in the middle of a production deployment is a frustrating rite of passage. If you are using the Google Custom Search API, you have likely hit that 100 free daily queries wall. Once you do, your application throws a 403 Quota Exceeded error, stalling your features unless you link a billing card and risk uncapped charges of $5 per 1,000 queries. In my experience, relying on Google's default limits without safeguards is a major liability. Here is how I protect my cloud budget, stretch the free tier using Redis, and transition to scalable alternatives when 100 queries are no longer enough. Step 1: Enforce a Hard Billing Cap in GCP Never rely on email alerts alone; they do not stop API requests. If a recursive loop in your code or a malicious bot targets your search endpoint, your credit card will bear the brunt. To set up a hard stop: Log into your Google Cloud Console . Navigate to APIs & Services > Enabled APIs & Services . Select Custom Search API , then click the Quotas tab. Locate Queries per day and click the edit pencil icon. Set your maximum limit to 95 (not 100). Pro Tip: This 5-query cushion gives you a safe buffer for emergency local debugging without triggering paid overages. Step 2: Implement Redis Caching Middleware Over 40% of search queries in typical web applications are repetitive. Implementing a Redis database to cache these searches can cut your API consumption by up to 80%. Here is a simple Python middleware pattern to normalize queries and cache them with a 24-hour Time-To-Live (TTL): import redis import requests # Connect to local Redis instance cache = redis . Redis ( host = ' localhost ' , port = 6379 , db = 0 , decode_responses = True ) def fetch_search_results ( query , api_key , search_engine_id ): # Normalize input to avoid duplicate cache keys normalized_query = query . strip (). lower () cache_key = f " search:cache: { normalized_query } " # 1. Check local cache first cached_data = cache . get ( cache_key ) if cach

This started as a comment @wrencalloway left on my last post. It was sharp enough that it deserved more than a reply — so here's the full version. You did the work. The page is server-rendered. The JSON-LD is in the raw response. curl returns the whole article, headline and all. A crawler that fetches your URL gets everything it needs. Except the crawler never fetches your URL. It asks the CDN, and the CDN says 403. Your content is perfect and unreachable. This is the layer underneath the one everyone talks about — and it's invisible in every tool you'd normally reach for. Two different kinds of "no" robots.txt is a note taped to the door. It says "please don't come in." A polite crawler reads it and turns around. A rude one ignores it and walks straight past. Either way, the note never touches your bytes — it's a request, enforced entirely by the visitor's own manners. A WAF or CDN rule is the door. It answers the request itself, at the edge, before anything reaches your origin: 403 Forbidden , 429 Too Many Requests , or a JavaScript challenge the bot can't solve. The HTML behind that door could be a masterpiece or a blank page. The bot sees neither. It sees the status code. People spend weeks perfecting the note and never check whether the door is locked. This isn't hypothetical — here's the data Cloudflare Radar publishes what actually happens to AI-crawler traffic across its network. From the AI Insights dashboard (7-day view, as of 18 July 2026): Of all HTTP responses served to AI bots and crawlers: 200 OK — 73.6% 403 Forbidden — 5.2% 429 Too Many Requests — 1.3% 503 — 1.1% AI-crawler response codes. Source: Cloudflare Radar, AI Insights, 7-day view, 18 Jul 2026. Read that again. More than one in fifteen requests from AI crawlers is being actively refused at the edge — 403 or 429 — before it ever touches the content. Not deprioritised. Refused. And most of that isn't a decision. It's a default — a managed WAF ruleset, a "block AI bots" toggle flipped in 2024, a

Most AI assistants forget everything after each session. Memoria remembers, forgets, and evolves—extracting personal facts, resolving contradictions, and reflecting on what it knows. This post shares the journey of building a production‑ready MemoryAgent for the Qwen Cloud Hackathon, Track 1 . Inspiration Every conversation with a typical chatbot starts from zero. You tell it you're allergic to peanuts on Monday, and by Wednesday it recommends pad thai with crushed peanuts. The model doesn't forget; it never had long‑term memory in the first place. Without durable knowledge about who you are, real personalisation is impossible. We built Memoria to solve that problem: a personal AI with human‑like memory that remembers what matters, forgets what fades, resolves contradictions, and evolves its understanding of you over time. Real memory isn't a bigger context window—it's extraction, prioritisation, decay, consolidation, and reflection. The hackathon challenged us to deliver a memory‑efficient, production‑grade MemoryAgent, and we built one from the ground up on Alibaba Cloud. What Memoria does Memoria organises knowledge in three deliberate tiers: Session Memory (Redis) – the last 10 messages of the active chat. Personal Memory (PostgreSQL 16 + pgvector) – user‑centric facts embedded with text-embedding-v3 , ranked by hybrid scoring, and subject to decay, consolidation, and conflict resolution. Context Archive – full transcripts stored for on‑demand search, never polluting routine retrieval. Other key features: Autonomous memory lifecycle : daily decay, weekly consolidation, and background reflection. Personal Intelligence toggle : global memory access vs. session‑only. Memory‑Less incognito mode : no memory reads or writes. MCP skills server : exposes get_core_memories , get_user_preferences , forget_memory , and strengthen_memory to any Qwen agent. Conflict detection & versioning : contradictory facts are automatically flagged and superseded. Persona customisation :

If someone asked me ten years ago what it takes to break into cloud, I would have said "get certified and hope someone gives you a chance." I was wrong. And I watched dozens of freshers follow that exact advice, collect a certification, then sit in interviews unable to explain why they chose one architecture over another. The problem was never knowledge. It was proof. Proof that you can gather requirements from a confused client, design something that works, and actually build it with your own hands. AWS just launched something that helps close that gap. And two of these credentials cost nothing. Table of Contents What AWS SimuLearn Badges Actually Are Why This Matters More Than Another Certification The 12 Badges Available Right Now The Free Starting Path I Would Follow Today The LinkedIn Advantage Nobody Is Talking About For Career Switchers: Your Existing Skills Are the Cheat Code My Honest Take After 10 Years in Cloud What AWS SimuLearn Badges Actually Are SimuLearn is not another video course. Not another multiple-choice exam. You sit in a simulated client meeting powered by generative AI. A virtual customer explains their business problem. You ask questions, uncover requirements, handle objections, and propose an architecture. The AI evaluates your communication, your technical accuracy, and your decision-making in real time. Then you build the solution. In a live AWS environment. Not a sandbox with three buttons. The real console. After that, an automated validation confirms your solution actually works. Complete every assignment in a learning plan, and AWS issues you a badge through Credly. Automatically. No exam booking. No proctored test. Just demonstrated capability across the full workflow. Each badge represents the entire journey: customer conversations, architecture design, hands-on building, and validated outcomes. Not a single quiz. Not one lab. The whole thing. Why This Matters More Than Another Certification I hold 7 AWS certifications. Let me tell

Patreon is strengthening its defenses against AI scraping by working with Cloudflare to block bots that train AI models on creators’ content without permission. The move marks a shift away from relying on websites using robots.txt alone to actively block unauthorized AI training.

✍️ This post was written with two hands. The story — the first part — is Murilo's, lived and told by the person who was there. The technical manual , at the end, was written with AI. The split is intentional and marked in the text. Nothing hidden about the seam: part is human, part is machine, and the reader sees both. If you've ever managed or logged into a web server and never set up SSH keys, it's because you don't yet know the real risks of a break-in — and that's okay. Until you find out what can happen. Logging into a server over SSH with a username and password is like locking the front door of a house that faces the street, with nobody keeping watch. Anyone can try as many combinations as they want, freely. And setting this up takes almost as much time as typing a username and password — and it makes getting into the server much faster and easier afterwards. Ignorant of best practices, I managed my servers for a long time by typing: ssh user@server-ip password That nearly cost me dearly, the day I found out my server had been broken into. After that incident, I realized just how vulnerable a username and password are on SSH. Today I can't say I sleep soundly — no system is completely break-in proof — but I sleep a lot better (and honestly, I always slept well, until I started managing servers). Waking up on a fine Sunday morning to do some maintenance on the server, and finding out it was broken into through the front door because you left a combination padlock facing the street — that is not the kind of surprise I'd wish on anyone. I have a degree in Law. I worked for 15 years in the legal field at a public institution, until I decided to venture into the world of programming. And where did I end up? Managing systems at the institution I work for, after spending some time building automations in Python. Managing systems wasn't exactly what I had in mind when I wanted to learn to code and understand the world of programming. But that opportunity ended up tea

A new technical analysis published by the Cloud Native Computing Foundation (CNCF) argues that the future of agentic AI will be built not on entirely new infrastructure, but on the mature cloud-native ecosystem that already powers modern distributed applications By Craig Risi

Today I'm excited to launch RegionCheck , a tool for testing, monitoring, and debugging endpoints from cloud regions around the world. The idea is simple - test an endpoint's DNS and HTTP connectivity and response from a defined set of cloud regions. Whether you're troubleshooting an API, validating a deployment, checking DNS propagation, or investigating latency, seeing the results from multiple cloud regions can quickly reveal issues that aren't obvious from your own machine. What is RegionCheck? RegionCheck lets you run endpoint checks from AWS, Azure, and Google Cloud regions without provisioning infrastructure or maintaining test instances. Current capabilities include: HTTP endpoint testing DNS lookups TLS certificate validation Continuous monitoring with alerts Side-by-side comparison across cloud providers and regions Shareable result pages for collaboration API/MCP access for automation and agents Why I built it When debugging production issues, I often wanted to answer questions like: Is DNS returning the same result everywhere? Or is geo-DNS returning the results intended? Is TLS certificate propagation for my CDN working as intended? Is one region significantly slower than another? Is my CDN caching working as expected? Are my geo-HTTP redirects working as intended? (For some interesting examples try www.yahoo.com and www.cnn.com in non-US regions) There are many tools that exist that provide these answers, but nothing that answers all of these questions in one place. That's what RegionCheck aims to provide. Who it's for RegionCheck is designed for engineers who work with cloud infrastructure, including: DevOps engineers Site Reliability Engineers (SREs) Platform engineers Backend developers Anyone who likes to take a peek at backend infrastructure Try it out RegionCheck is available at https://regioncheck.io You can run free checks directly from the website; or create an account to access monitoring, alerting, the API, and additional features. I'd love

Amazon Web Services has recently introduced AWS Continuum, a new integrated security platform to automate the discovery, enforcement, and remediation of security issues across codebases, dependencies, and applications. AWS Continuum launches with four agentic capabilities, aiming at the entire vulnerability lifecycle: penetration testing, code review, threat modelling, and code vulnerabilities. By Gianmarco Nalin

If you've deployed your application on Render and noticed that emails are not being sent, you're definitely not the only one. I recently faced this issue while deploying my project: https://rizzzler.onrender.com After spending hours debugging my code, checking environment variables, testing SMTP credentials, and reading logs, I finally discovered the real cause: The hosting environment was restricting outbound SMTP connections, preventing my application from connecting to the mail server. To solve this, I moved the email-sending functionality to Google Cloud , where the SMTP connection worked correctly. This article explains how I diagnosed the issue, common mistakes to avoid, and the solution that worked for me. Symptoms You might experience one or more of the following: Password reset emails are never received. OTP emails aren't delivered. Email verification doesn't work. Nodemailer throws timeout errors. SMTP connection fails. Everything works on localhost but fails after deployment. Typical errors include: ETIMEDOUT ECONNREFUSED Connection timeout Greeting never received Step 1: Verify Your SMTP Credentials Before assuming the issue is with Render, verify your SMTP configuration. Check that the following are correct: SMTP Host SMTP Port Username Password Even one incorrect character can prevent emails from sending. Step 2: Check Environment Variables Ensure all required environment variables are configured in Render. Example: SMTP_HOST=smtp.example.com SMTP_PORT=587 SMTP_USER=your-email@example.com SMTP_PASS=your-password Also remember to: Restart your Render service after updating variables. Never hardcode credentials in your source code. Step 3: Test Locally If your application sends emails successfully on your local machine but fails only after deployment, your application code is probably not the problem. This is an important clue. Step 4: Read the Logs Open your Render logs and look for SMTP-related errors. Common messages include: ETIMEDOUT ECONNREFUSED Co

A three-person agency received a $14,000 AWS bill in one day after attackers extracted static access keys and burned Claude invocations on Bedrock. Combined with May's DN42 incident, where an autonomous agent provisioned $6,531 of oversized infrastructure in 24 hours, practitioners warn that cloud billing lags roughly a day behind agent-speed spend. By Steef-Jan Wiggers

AWS and Anthropic have released the Claude apps gateway for AWS, a self-hosted control plane that centralizes identity, policy, telemetry, routing, and spend caps for Claude Code and Claude Desktop. The gateway runs as a single stateless container and routes inference to Amazon Bedrock or Claude Platform on AWS. By Steef-Jan Wiggers

The Google Cloud Workbench Notebooks extension for VS Code is a new tool that enables developers to connect their local IDE directly to managed Jupyter notebook environments on Google Cloud. By Sergio De Simone

The Linkerd community has announced the release of Linkerd 2.20, introducing a series of performance, observability, and traffic management enhancements that further strengthen the CNCF-graduated service mesh's position as a lightweight alternative for Kubernetes networking. By Craig Risi

Google released the Genkit Agents API in preview for TypeScript and Go. The open-source framework packages message history, tool loops, streaming, and state persistence behind a single chat() interface. Detached turns let agents work after clients disconnect. Interruptible tools provide human-in-the-loop control with anti-forgery validation on resume. By Steef-Jan Wiggers

A scraper fails, someone swaps the User-Agent, someone else adds a proxy, then the job starts passing locally but fails again in CI. That usually happens because Cloudflare did not block “scraping” as one thing. It evaluated several signals, and each failure needs a different fix. This is about authorized automation: your own sites, customer-approved workflows, testing, monitoring, data access you are allowed to perform. If you do not have permission to automate against a site, changing fingerprints or rotating IPs does not make it okay. Start with the failure you actually see Cloudflare failures often get collapsed into “403”, but the page body matters. Common cases: Error 1020 : usually an access denied page from a Cloudflare rule or bot score decision. The HTTP status may still be 403, so inspect the HTML. 403 without a 1020 page : often IP reputation, firewall rules, geo restrictions, or an auth problem. 429 : rate limit exhaustion. Slowing down can help here, but it will not fix a fingerprint problem. Endless Just a moment... page : your client did not complete the browser-side challenge. CAPTCHA or Turnstile loop : Cloudflare still considers the session borderline after earlier checks. Add classification before you add workarounds. Even a basic classifier saves time: import time import requests CLOUDFLARE_MARKERS = { " 1020 " : " cloudflare_access_denied " , " Just a moment " : " cloudflare_js_challenge " , " cf-turnstile " : " cloudflare_turnstile " , " cf-error-code " : " cloudflare_error_page " , } def classify_response ( resp : requests . Response ) -> str : body = resp . text [: 5000 ] if resp . status_code == 429 : return " rate_limited " for marker , label in CLOUDFLARE_MARKERS . items (): if marker in body : return label if resp . status_code == 403 : return " forbidden_unknown " return " ok " if resp . ok else f " http_ { resp . status_code } " def get_with_backoff ( url : str , max_attempts = 4 ): for attempt in range ( max_attempts ): resp = request

✓ Human-authored analysis; AI used for formatting and proofreading. Scott Piper published a twenty-year retrospective on cloud security research in March 2026. It's the most useful structural history of the field I've seen — four eras, each with defining milestones, each with the tools and research that shaped cloud security. If you work in cloud security, read it first. What follows is a question about what the history reveals when you examine one detail it doesn't discuss. The four eras Piper divides two decades into four eras: 2006–2016, Foundational. Cloud providers built the security primitives — IAM (2011), CloudTrail (2013), Organizations and SCPs (2016). Before these existed, there was no mechanism for least privilege, no audit trail, and no organizational boundary. Security research in this era was part-time work from people with broader careers. 2016–2021, CSPM. Cloud security became a full-time job. CIS Benchmarks standardized what to check. Open-source tools proliferated — Prowler, CloudMapper, Pacu, Cloud Custodian, ScoutSuite. Cloud security during this time largely meant deploying a CSPM. 2021–2025, CNAPP. Point solutions gave way to platforms. Vendors integrated CSPM with container scanning, vulnerability management, and workload protection into a single product category. Research teams at vendors began finding cross-tenant vulnerabilities in the cloud providers themselves. 2025–present, AI. AI accelerates both attack and defense. Exploits that required deep language expertise are generated in minutes. A CTF challenge was solved by an AI within minutes of release. The industry is speed-running the cloud eras. This is a well-evidenced narrative. Every era is defined by a change in what tools could do and who was building them. The verb that didn't change Look at what each era's defining tools do. The direct action each tool performs on its direct object. In the CSPM era, the defining tools match API responses against rule databases. Prowler, ScoutSuit

TL;DR Traditional SaaS streaming requires exporting sensitive data to a vendor cloud, creating security risks and egress costs. BYOC reverses this model by running the data plane inside the customer’s cloud while the vendor manages the control plane. This keeps data within the enterprise perimeter while still providing a managed platform. Condense builds on this model with AI-driven automation, unified monitoring, and marketplace deployment, enabling private, compliant, and cost-efficient real-time data streaming. The enterprise data landscape is currently defined by a conflict between real-time AI data streaming utility and the strict requirements of data sovereignty . For years, the standard SaaS model forced a compromise. To access premium analytics, companies had to export sensitive telemetry to a vendor cloud. This created massive cloud egress costs and introduced significant security vulnerabilities. Bring Your Own Cloud (BYOC) for data streaming platforms has emerged as the professional solution to this dilemma. It allows a business to keep data within its own perimeter while benefiting from a fully managed, high-performance ecosystem. The BYOC Architecture: Privacy by Design An experienced analyst views BYOC as a clean separation of concerns. The architecture splits the environment into two distinct layers to ensure raw data never leaves the authorized environment. SaaS Control Plane: This is the management layer hosted by the provider. It handles the brain of the operation. It manages orchestration, user access, and pipeline configuration without ever seeing the actual data packets. Private Data Plane: This is the muscle. The managed Kafka clusters , Kubernetes (K8s) nodes, and storage engines like ClickHouse live inside the customer Virtual Private Cloud (VPC) . By keeping the data plane inside the customer perimeter, telemetry collection remains private. This architecture is the most direct path to satisfying internal security audits and global regulatory