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While it's not flashy like Apple’s new Siri AI and Apple Intelligence upgrades, there are still a number of additions to iOS 27 worth looking at.

Atlassian details the Forge billing platform built for usage-based pricing across its cloud ecosystem. It processes large-scale usage events with correct attribution, deduplication, and aggregation using a streaming pipeline, idempotent processing, and layered storage to enable accurate billing, near real-time visibility, and reliable reconciliation across distributed services. By Leela Kumili

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Anthropic and OpenAI's publicly available models are explicitly guard-railed so that they refuse offensive tasks. And their cyber-focussed models are gated for enterprises. This leaves SMEs and mid market open to major vulnerabilities. AI can be used as both an adversarial and defensive tool in the world of cyber. A worst case outcome is if only the adversaries have access. Meanwhile, most existing AI cyber tools are just wrappers. The problem is that they still have all the guardrails on from t

You can't optimize what you don't measure. Every blog post about conversion optimization, A/B testing, or paid ads assumes you have reliable tracking in place. But most developers set up analytics as an afterthought — dropping a script on the page and calling it done. The result is data that's incomplete, untrustworthy, and ultimately useless for making decisions. This guide gives you a developer-first approach to conversion tracking. We'll cover event instrumentation, attribution setup, funnel visualization, and the specific tracking architecture you need to answer real business questions. No marketing jargon. No vague advice. Just the exact setup that turns your analytics from a vanity dashboard into a decision-making tool. The Tracking Mindset Before you write any code, understand what you're trying to learn. Tracking every possible event creates noise. Tracking the wrong events leads to wrong conclusions. Start with one question: "What are the 3-5 actions a user takes between discovering my product and paying me money?" Map these actions in order. That's your funnel. Every event you track should map directly to a step in that funnel. For a typical SaaS product, the funnel looks like this: Discovery: User visits your site from a traffic source Engagement: User reads content, explores features, or uses a tool Intent: User clicks "Sign Up" or "Start Trial" Conversion: User completes signup and activates Revenue: User upgrades to a paid plan If you track these five steps reliably, you can answer 90% of the marketing questions that matter: Which traffic source brings the most valuable users? Where do users drop off? What's my true cost per acquisition? Event Instrumentation: What to Track and How Events are the atomic unit of conversion tracking. An event is any action a user takes that you want to measure. Let's build your event taxonomy from the ground up. Foundational Events (Track These First) These four events are non-negotiable. Set them up before you do anythi

Il pensiero di Popper si intreccia con diversi autori in modi che illuminano il rapporto tra tecnologia, potere e libertà. Hannah Arendt Arendt condivide con Popper l'attenzione per la società aperta, ma la declina in termini di azione politica piuttosto che epistemologici. Dove Popper vede la chiusura come rifiuto della falsificazione, Arendt la vede come perdita dello spazio pubblico dove gli individui appaiono come agenti plurali. L'AI che automatizza decisioni politiche o sociali rischia di eliminare proprio questo spazio di apparizione — non c'è più un "chi" che agisce, ma un "cosa" che calcola. Il banale della tecnocrazia, per Arendt, può essere altrettanto pericoloso del male radicale. Theodor Adorno e Max Horkheimer La Dialettica dell'illuminismo offre un intreccio più critico con Popper. I due della Scuola di Francoforte vedevano la ragione strumentale — quella che calcola mezzi per fini prefissati — come il germe del dominio moderno. Popper difendeva invece la ragione critica come antidoto al totalitarismo. Il punto di tensione è rilevante per l'AI: se l'intelligenza artificiale è pura ragione strumentale ottimizzata, rientra nella diagnosi frankfurtiana più che in quella popperiana. La risposta popperiana sarebbe che l'AI può essere strumento di criticismo se aperta alla confutazione e al controllo democratico. Norbert Wiener Il fondatore della cibernetica condivide con Popper la preoccupazione per i sistemi che sfuggono al controllo umano. Wiener, già negli anni Cinquanta, avvertiva che le macchine intelligenti potrebbero imporre obiettivi incompatibili con i valori umani. Popper avrebbe riconosciuto in questo un caso di teoria non falsificabile: un sistema che apprende senza possibilità di essere corretto dall'esterno è un dogma tecnologico. Entrambi insistono sul human-in-the-loop , anche se Wiener lo motiva in termini di stabilità dei sistemi, Popper in termini di libertà. Michel Foucault Foucault aggiunge una dimensione che Popper lascia in ombra: il

Introduction In the previous articles, we explored AWS networking concepts like: VPC Subnets Internet Gateway Security Groups Route 53 Now, let's move to one of the most popular AWS services: Amazon S3 (Simple Storage Service) Amazon S3 is one of the easiest AWS services to learn and one of the most widely used services in real-world applications. Almost every application stores some kind of data: Images Videos Documents Log files Backups Static websites Amazon S3 helps us store and retrieve these files securely from anywhere in the world. In this article, we will understand: What Amazon S3 is Buckets and Objects Benefits of S3 Storage Classes Versioning Basic security concepts Static website hosting overview Why Do We Need Storage? Imagine you are running an online photo-sharing application. Users upload: Profile pictures Photos Videos Where should these files be stored? Keeping them directly inside application servers is not a good idea because: Storage is limited. Scaling becomes difficult. Replacing servers may cause data loss. This is where Amazon S3 helps. What is Amazon S3? Amazon S3 stands for: Simple Storage Service It is a cloud-based object storage service provided by AWS. S3 allows us to: Store data Retrieve data Manage data from anywhere using the internet. Amazon S3 is designed to be: Highly available Durable Scalable Secure Cost-effective Real-World Example Think of Amazon S3 as a digital warehouse. Suppose you own an e-commerce company. Inside your warehouse, you store: Product images Invoices Customer documents Videos Similarly, Amazon S3 stores digital files safely in the cloud. Buckets and Objects Amazon S3 stores data using two concepts: Bucket A bucket is a container used to store files. Think of a bucket like a folder. Examples: company-documents customer-images application-logs Bucket names must be globally unique. Object Anything stored inside a bucket is called an Object . Examples: invoice.pdf profile.jpg backup.zip video.mp4 Objects can co

I used to think tests were a waste of time. "Ship fast, fix later" was my motto. Until I spent three painful weeks debugging a production issue that a simple test would have caught in 30 seconds. That was the day I became a believer. The Harsh Reality Most Solo Developers Ignore If you're a freelancer or indie hacker building real products for clients, here’s what happens without good tests: You make a "small change" and something unrelated breaks Clients find bugs you should have caught Refactoring becomes terrifying You lose sleep before every deployment Your reputation slowly takes hits A solid test suite changes all of that. What a Test Suite Actually Gives You Confidence to Move Fast You can refactor, add features, or upgrade dependencies without fear. Living Documentation Your tests explain how the system should behave — better than comments ever could. Early Bug Detection Catch issues before they reach the client or production. Better Architecture Writing testable code forces you to write cleaner, more modular code. Professional Credibility When clients or senior devs review your code, a good test suite immediately signals seriousness. The Test Suite Pyramid I Actually Use Unit Tests (70%) → Test individual functions and components Integration Tests (20%) → Test how different parts work together (API + DB) End-to-End Tests (10%) → Critical user flows (login → checkout → etc.) I don't aim for 100% coverage. I aim for high-value coverage — especially around business logic and critical paths. Final Thought Writing tests feels slow at first. But it compounds. Every month you have tests, you move faster and sleep better. The developers who ship reliable software consistently aren't necessarily the smartest — they're usually the ones who learned to respect testing. Have you built a strong test suite habit yet? Or are you still in the "I'll test it manually" phase? Drop your experience below. Let's talk.

Everyone optimizes the buying experience. Almost nobody builds for what happens after checkout. Every appliance, device, and tool you buy comes with records that matter later: the receipt, purchase date, model number, serial number, the manual, and the warranty terms. Most people have no system for keeping those together — they're scattered across email, a kitchen drawer, screenshots, and random cloud folders. So when something breaks, the warranty claim dies on a single question: "Can you send proof of purchase and the serial number?" That's the gap we're building SnapRegisters for. The simple version of the fix (works with any notes app) The day anything substantial arrives, capture four things: The product The receipt The model / serial label (it fades — grab it early) The warranty card or manual Organize them by product, not by document . Instead of "where's that receipt," it becomes "open the dishwasher record." When support asks for details, it's a 10-second lookup instead of a 20-minute hunt. Where AI actually helps after the purchase The interesting part for builders: the post-purchase layer is a great fit for AI. Point a camera at a receipt and you can extract the model, serial number, and purchase date, then track the warranty automatically — turning a tedious filing chore into a 5-second snap. It's not flashy AI, but it's the kind that quietly saves people money (most warranty coverage goes unused simply because the paperwork is gone). If you've ever eaten a repair bill for something that was technically still covered, you've felt this problem. Curious how other builders think about the "boring but valuable" software gaps like this one. 📲 SnapRegisters is free on iOS: https://apps.apple.com/app/id6757603213

This guide covers three core areas of daily GBase 8a operations: tracking data loads and collecting error details, configuring audit logs and analysing slow queries, and hierarchically tuning memory parameters. It also provides a standard daily and weekly inspection checklist for your gbase database . 1. Data Load Monitoring 1.1 Load Methods GBase 8a supports two main load methods: gload for large‑scale offline imports (recommended), and LOAD DATA INFILE for single‑file loads with MySQL‑like syntax. 1.2 Checking Load Progress Monitor running and historical loads through system tables: -- Currently executing load tasks SELECT task_id , table_name , status , start_time , loaded_rows , error_rows , TIMESTAMPDIFF ( SECOND , start_time , NOW ()) AS elapsed_sec FROM gclusterdb . load_task WHERE status IN ( 'RUNNING' , 'PENDING' ) ORDER BY start_time DESC ; -- Last 50 load history records SELECT task_id , table_name , status , start_time , end_time , loaded_rows , error_rows , TIMESTAMPDIFF ( SECOND , start_time , end_time ) AS duration_sec FROM gclusterdb . load_task ORDER BY start_time DESC LIMIT 50 ; 1.3 Retrieving the Last Load Task ID SELECT @@ gbase_loader_last_task_id ; Then query error details with that ID: SELECT * FROM gclusterdb . load_error_log WHERE task_id = 'your_task_id' LIMIT 100 ; 1.4 Error Data Collection Enable error collection in the gcluster configuration file ( gbase.cnf ) for production: gbase_loader_logs_collect = ON 1.5 Load Performance Parameters Parameter Scope Description Recommended gcluster_loader_max_data_processors gcluster Max concurrent load processing threads CPU cores / 2 gcluster_loader_min_chunk_size gcluster Chunk size sent to gnode (bytes) 64 MB gbase_loader_parallel_degree gnode Parallel write threads on gnode 4 – 8 gbase_loader_buffer_count gnode Number of load buffers 4 2. Audit Log Configuration and Analysis 2.1 Enabling Audit Logs Configure in both gcluster and gnode gbase.cnf files: audit_log = ON log_output = FILE # or TABLE

Introduction Large Language Models (LLMs) like ChatGPT have transformed how we interact with AI. They can write code, answer questions, summarize documents, and generate creative content. However, they have one major limitation - they only know what they were trained on and can sometimes generate incorrect or outdated information. So, how do modern AI applications answer questions about your company's private documents, recent news, or knowledge that wasn't part of the model's training? The answer is Retrieval-Augmented Generation (RAG). In this blog, we'll explore what RAG is, how it works, its architecture, benefits, challenges, and real-world applications. What is RAG? Retrieval-Augmented Generation (RAG) is an AI architecture that combines a retrieval system with a Large Language Model (LLM). Instead of relying only on the model's internal knowledge, RAG first retrieves relevant information from an external knowledge source and then uses that information to generate a more accurate response. Think of it like an open-book exam. Instead of answering from memory, the AI first searches for the most relevant pages and then writes the answer based on those pages. Why Do We Need RAG? Traditional LLMs have several limitations: Knowledge becomes outdated. They cannot access private company data. They may hallucinate (generate incorrect facts). Retraining models is expensive and time-consuming. RAG solves these problems by allowing the model to retrieve fresh and domain-specific information before generating an answer. RAG Architecture A typical RAG pipeline consists of the following components: User Query Embedding Model Vector Database Retriever Prompt Builder Large Language Model Final Response Step-by-Step Workflow * Step 1: * User asks a question Example: "What is our company's leave policy?" Step 2: Convert the question into embeddings The query is transformed into a vector representation using an embedding model. Example: "What is leave policy?" ↓ [0.12, -0.45, 0.7

SpaceX's AI1 satellite spans 70 meters tip-to-tip — wider than a Boeing 747 — and it exists entirely to run AI inference in low Earth orbit. Elon Musk posted the reveal video to X on June 9, 2026, ahead of SpaceX's IPO, with a three-word summary: "much simpler than Starlink." Each satellite produces 150 kW of peak AI compute and 120 kW sustained. SpaceX's roadmap calls for 1 GW of orbital AI compute capacity by late 2027, which at 150 kW per satellite means manufacturing roughly 6,700 AI1 units per year. To hit that number, they are building an 11-million-square-foot facility in Bastrop, Texas called Gigasat — nearly twice the floor area of Tesla's Gigafactory Nevada, dedicated to satellite production. The question is not whether the engineering works. SpaceX has launched more than 7,000 Starlink satellites. The question is whether orbital AI compute makes economic sense at scale, and that question nobody has answered publicly yet. The Reveal Wasn't Accidental SpaceX filed for its IPO at approximately $75 billion valuation in early June 2026. Musk's June 9 reveal of AI1 arrived within days of that filing. Orbital AI compute is the narrative SpaceX needs to justify a valuation that goes beyond launching satellites for other people. Every terrestrial cloud provider — AWS, Google Cloud, Azure — is competing for land, power, and cooling capacity to support the next generation of frontier AI. Musk's pitch is that those three constraints don't exist in space. The physics backs him up. The economics remain unproven. Why Space Has Structural Advantages for AI Compute The AI1 satellite's design exploits two physical realities that are impossible to replicate on Earth. Power is essentially free. In a sun-synchronous LEO orbit, a satellite receives near-constant solar illumination. SpaceX's solar arrays achieve 250 W/m² power density without atmospheric attenuation. The marginal cost of electricity after the capital investment in the array is close to zero — no grid contracts,

AI thumbnail generator trained on top-performing thumbnails Discussion | Link

In the world of cross-border e-commerce, malicious bot scraping leading to Meta/Google Pixel pollution is a nightmare for every seller. When your store starts gaining traction, these fake traffic sources can "poison" your ad model, causing your ROAS to plummet. To combat this, I’ve developed a robust "Backend Data Isolation" architecture. The Core Defense Strategy Stop triggering ad conversion events directly from the frontend. Instead, build a "firewall" at the backend to ensure that only verified, high-quality conversion data is sent to your ad platforms. Technical Implementation By implementing server-side logic in Python, we can filter out bot requests effectively: def process_pixel_event ( request ): # Filter out bot signatures (User-Agent, IP analysis) if is_bot_signature ( request . headers [ ' User-Agent ' ]): return None # Send only high-quality data to ad platforms if is_real_customer ( request . session ): trigger_pixel_event ( request ) By leveraging this logic, we feed "private, high-quality data" to the AI. This allows the algorithm to learn only from genuine customer behaviors, creating an "immortal pixel" moat around your store. Learn More For a deep dive into full-scale anti-scraping deployments and how to leverage automated translation techniques to scale traffic in blue-ocean markets, check out my full technical guide: 👉 Read the Full Implementation & Troubleshooting Guide Here

If you've worked with multiple AI APIs, you know the pain: different auth flows, different SDKs, different billing dashboards, different rate limits. You end up with a providers/ folder full of wrapper code just to normalize the responses. Onlist solves this by putting 40+ AI providers behind a single OpenAI-compatible endpoint. One API key, one billing account, same chat.completions.create() call you already know. We just shipped official SDKs for Python and JavaScript/TypeScript, so I wanted to walk through what they look like in practice. The 30-Second Setup Python: pip install onlist from onlist import Onlist client = Onlist () # reads ONLIST_API_KEY from env response = client . chat . completions . create ( model = " openai/chatgpt-5.5 " , messages = [{ " role " : " user " , " content " : " Hello! " }], ) print ( response . choices [ 0 ]. message . content ) TypeScript: npm install @onlist/sdk import { Onlist } from " @onlist/sdk " ; const client = new Onlist (); const response = await client . chat . completions . create ({ model : " openai/chatgpt-5.5 " , messages : [{ role : " user " , content : " Hello! " }], }); console . log ( response . choices [ 0 ]. message . content ); That's it. No base URL to configure, no special headers to set. If you've used the openai package before, you already know how to use this. Why Not Just Use the OpenAI SDK Directly? You absolutely can. Onlist is fully OpenAI-compatible, so this works fine: from openai import OpenAI client = OpenAI ( base_url = " https://onlist.io/v1 " , api_key = " your-key " , ) The SDK adds three things on top of that: Default configuration. No base_url to remember. The ONLIST_API_KEY env var just works. Marketplace API. A .marketplace namespace for browsing models and providers programmatically. Proper User-Agent. Helps us debug issues when you reach out for support. If you're already using OpenAI or OpenRouter, switching takes one line: - from openai import OpenAI + from onlist import Onlist - clien

At WWDC 26, Apple announced the Core AI framework, the official successor to Core ML. It is designed to allow developers to run large language models and generative AI entirely on-device, supporting both custom-converted PyTorch models and pre-optimized open-source models. By Sergio De Simone