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The problem: counting unique viewers per second is a row explosion A viewer scrubs to 4:12 of a 9-minute trending clip, watches for 40 seconds, jumps back to the intro, then bounces. Multiply that by the few hundred thousand sessions a day that hit a mid-size aggregator and you get the question every product person eventually asks: which parts of this video do people actually watch, and how many distinct people watched each part? The naive answer is a watch_events table: one row per (user, video, second) . It works until it doesn't. A 9-minute video is 540 seconds. One viewer who watches the whole thing generates 540 rows. A million viewers across our catalog generate hundreds of millions of rows per day , and the only query anyone runs against them is COUNT(DISTINCT user_id) GROUP BY second . That COUNT(DISTINCT) is a sort-or-hash over the entire partition every single time someone opens the analytics tab. At TopVideoHub we aggregate trending video across Asia-Pacific, so a single popular clip can spike from zero to half a million sessions in an afternoon when it lands in the JP and KR feeds simultaneously. We did not want a fact table that grew by hundreds of millions of rows a day to answer a question whose answer is approximately fine. "Roughly 41,000 unique viewers saw the hook at 0:08" is just as actionable as "41,287". That tolerance for approximation is exactly what HyperLogLog is built for, and Postgres has a battle-tested extension for it. This post is the design we landed on: fixed-size HLL sketches, one per (video, time_bucket) , that you can merge, slice, and union across regions in milliseconds. The main app is PHP 8.4 on LiteSpeed behind Cloudflare, with our search layer on SQLite FTS5; the analytics store is a separate Postgres instance, and HLL is what made that store affordable. Why HyperLogLog instead of COUNT(DISTINCT) HyperLogLog estimates the cardinality of a set using a fixed amount of memory regardless of how many elements you throw at it. Th

Cross-border purchasing platforms commonly use marketing tactics such as coupons, registration points, order rebates, and spend-based discounts to acquire new users and boost engagement. However, public promotions are prime targets for “wool hunters” (fraudsters) who exploit batch account registration, fake orders, malicious order spamming, and combined discount abuse to drain platform benefits, causing direct financial losses. Most purchasing systems lack dedicated event risk controls, making them highly vulnerable to batch exploitation as soon as a promotion goes live. This not only leads to financial losses but also crowds out genuine user benefits and distorts campaign effectiveness. This article details Taocarts’ comprehensive anti-fraud risk control framework, which uses multi‑dimensional behavior detection, rule‑based blocking, and account risk assessment to accurately distinguish real users from malicious exploiters, ensuring fair and controllable marketing activities. First, we identify common malicious exploitation scenarios and system vulnerabilities in cross‑border platforms: Batch account registration – Using new‑user exclusive coupons and registration points to harvest benefits repeatedly. Fake order placement and cancellation – Repeatedly claiming limited‑time discounts or rebates by placing and then canceling orders. Multiple accounts from the same device/IP – Spamming orders to consume activity quotas. Illegal discount stacking – Violating platform rules by combining multiple coupons or point deductions. Activity volume manipulation – Generating fake orders to earn activity rewards or points, creating false engagement data. Traditional systems have no risk rules and cannot detect batch operations or abnormal behavior, leading to wasted marketing spend and campaigns that actually lose money. Taocarts builds a fine‑grained anti‑exploit rule engine based on four dimensions: user behavior, device information, network characteristics, and order data, ena

AI aside, Golden Gate includes a bunch of subtle-but-helpful improvements.

WWDC26 brought a substantial round of updates to SwiftUI — not a ground-up redesign, but a lot of small limitations removed, new APIs that were clearly driven by real-world pain points, and meaningful performance improvements. This post walks through every major announcement so you know exactly what's available and when to reach for it. Look and Feel: Liquid Glass and the 2027 Releases The most immediately visible change costs you zero code. Apps built with SwiftUI automatically pick up the updated Liquid Glass appearance on the 2027 OS releases. The glass tint responds to the new system-level Liquid Glass slider without any changes on your part. On iPad, windows now dim when inactive, reinforcing which window has focus — again, automatic. On Mac, custom interactive Liquid Glass elements respond more fluidly to the mouse pointer. There are a few opt-in refinements available when you want tighter control: Responding to active state — use the appearsActive environment value to reduce opacity on custom elements when the window is inactive: struct SidebarFooterView : View { @Environment (\ . appearsActive ) private var appearsActive var body : some View { MyAccountView () . opacity ( appearsActive ? 1 : 0.5 ) } } Menu bar icons — the menu bar now shows a minimal set of icons by default. Add .labelStyle(.titleAndIcon) to a specific menu item to make its icon visible: CommandMenu ( "Stickers" ) { Button { openStore () } label : { Label ( "Store" , systemImage : "bag.fill" ) . labelStyle ( . titleAndIcon ) } } Resizability on iPhone iPhone apps become resizable on iOS 27, which matters for iPhone Mirroring and running iPhone apps on iPad. Xcode 27's Live Previews now include resize handles so you can test this interactively without running on a device. If your app mixes UIKit and SwiftUI, check the session "Modernize your UIKit app" for specifics around screen geometry, size classes, and orientation handling. Toolbar APIs The toolbar has been a source of friction on smalle

PostgreSQL Partitioning for Multi-Tenant Audit Logs: Querying 100M Events Without Table Scans I'll be direct: if you're running a SaaS with compliance requirements and your audit_logs table is approaching 50M rows, you're three months away from pain. I've watched audit queries go from 200ms to 8 seconds in production at 2am because someone ran a "give me all logs for tenant X" report. Partitioning isn't optimization theater—it's table-stakes infrastructure. At CitizenApp, we store 9 months of audit logs across 50+ tenants. Without partitioning, a single compliance query would full-table scan 100M+ rows. With it, we hit the same data in <100ms. This post is exactly how we do it. Why Partitioning Matters (The Reality Check) Most developers treat audit_logs like any other table. You add an index on tenant_id and created_at , call it done, and move on. Then your compliance officer runs a query like: SELECT * FROM audit_logs WHERE tenant_id = 'acme-corp' AND created_at >= '2024-01-01' ORDER BY created_at DESC ; At 50M rows, even with a composite index, PostgreSQL has to: Index scan → finds millions of matching rows Random I/O all over the table Spill to disk if sorting is large Hope the OS cache is warm Partitioning solves this by eliminating the data you don't need from day one . Instead of scanning a 100GB table and filtering it down, PostgreSQL can skip entire partitions. A query against January 2024 data simply ignores partitions for February–December. I prefer partitioning because it's native PostgreSQL—no external caching layer, no read replicas, no Redis gymnastics. It's boring infrastructure that works. The Partitioning Strategy: Composite Partitioning (Range + List) I use a two-level partitioning scheme: Range partition by month ( created_at ) — keeps each partition to ~5–10GB List subpartition by tenant — ensures compliance queries are single-partition scans This is deliberately opinionated. You could do range-only, but then a multi-tenant query still scans the

PostgreSQL Error 2200G: Most Specific Type Mismatch PostgreSQL error code 2200G ( most_specific_type_mismatch ) is a SQL-standard data exception that occurs when a value's type does not match the most specific (most derived) type expected in a context involving type hierarchies, XML schema types, or user-defined structured types. It most commonly appears when working with composite types, domain hierarchies, or XML processing functions where type inheritance or derivation is in play. While relatively rare in everyday CRUD operations, it can be a significant pain point in enterprise applications with complex type systems. Top 3 Causes and Fixes 1. Composite or Domain Type Hierarchy Mismatch When a function expects a specific domain or composite type but receives a parent/base type, PostgreSQL raises 2200G. Always cast explicitly to the most specific required type. -- Define types CREATE TYPE base_info AS ( name TEXT , value INTEGER ); CREATE DOMAIN specific_info AS base_info ; -- Function expecting the specific domain type CREATE OR REPLACE FUNCTION handle_info ( data specific_info ) RETURNS TEXT AS $$ BEGIN RETURN ( data ). name || ': ' || ( data ). value ; END ; $$ LANGUAGE plpgsql ; -- WRONG: passing base type causes mismatch -- SELECT handle_info(ROW('test', 42)::base_info); -- CORRECT: explicit cast to the most specific type SELECT handle_info ( ROW ( 'test' , 42 ):: specific_info ); 2. XML Type Processing Mismatch Using XML functions like XMLTABLE or XMLCAST without explicitly matching the expected schema type can trigger this error. Always declare column types explicitly. -- Correct: explicitly typed columns in XMLTABLE SELECT * FROM XMLTABLE ( '//product' PASSING XMLPARSE ( DOCUMENT ' <products> <product> <id>1</id> <price>29.99</price> </product> </products> ' ) COLUMNS product_id INTEGER PATH 'id' , price NUMERIC PATH 'price' ); -- Explicit XMLCAST to resolve type ambiguity SELECT XMLCAST ( XMLQUERY ( '//price/text()' PASSING XMLPARSE ( DOCUMENT '<data><pri

I run a production multi-agent AI system on a single M1 Mac in Jamaica. 6 autonomous agents. 26 cron workflows. 5-layer persistent memory. All containerized, all running 24/7. I checked my OpenRouter dashboard last week and realized something: I'd processed 2.4 billion tokens across 52 different AI models and spent a total of $0.52 . That's not a typo. Here's exactly where that money went and what it means. The Numbers Metric Value Total Requests 26,600+ Tokens Processed 2.4 Billion Models Used 52 Total Cost $0.52 Cost per Token $0.00000021 Tokens per Dollar 4.6 Million For context: GPT-4 Turbo costs about $0.00001 per token at scale. I'm running at roughly 50x below that rate. Where the $0.52 Actually Went Here's the breakdown by model: Model Requests Tokens Cost openrouter/owl-alpha 1,334 251.2M $0.00 nvidia/nemotron-3-super-120b 32 1.8M $0.00 google/gemma-4-31b-it 47 1.8M $0.00 openai/gpt-5 1 2.8K $0.03 google/gemini-3.1-pro-preview 1 3.2K $0.04 anthropic/claude-opus-4 1 2.0K $0.13 qwen/qwen3.5-plus 1 6.3K $0.01 z-ai/glm-5-turbo 1 3.0K $0.01 moonshotai/kimi-k2.5 2 4.1K $0.01 google/gemini-2.5-flash 2 5.5K $0.01 +42 other models ~125 ~8.5M ~$0.28 99.6% of my requests cost exactly $0.00. They ran on free-tier models or local inference. The $0.52 comes from a handful of premium model calls: Claude Opus, GPT-5, Gemini Pro. These are reserved for specific high-quality tasks — not everyday inference. What This Would Cost on Cloud Approach Hardware Monthly Cost Annual Cost My setup (M1 Mac) M1 Mac 16GB, local + free tier ~$0.09 ~$1.04 OpenRouter Paid Tier API-only, no local $15-30 $180-360 AWS (g4dn.xlarge + API) 1x T4 GPU, on-demand $350-500 $4,200-6,000 AWS (g5.xlarge + API) 1x A10G GPU, on-demand $700-1,000 $8,400-12,000 A $1,200 laptop replaces $500-1,000/month in cloud bills. The break-even point is about 2 weeks. How the Architecture Works The key insight: not every task needs a $20/month model . My system routes tasks intelligently: Local inference (free): Ollama

Imagine you lose your work laptop on a commute. It holds 3 years of customer PII, internal product roadmaps, and access keys to your company's cloud infrastructure. Without full disk encryption enabled, anyone who finds the device can access every file in 10 minutes or less with a free bootable USB tool. With encryption enabled? They'll never access your data, even if they brute-force the password for decades. Per IBM's 2025 Cost of a Data Breach Report, organizations that use encryption save significantly on breach costs compared to teams that skip encryption. As cyber threats grow more sophisticated, and quantum computing edges closer to breaking legacy cryptographic standards, encryption is no longer an optional add-on—it's a core requirement for every digital system. This guide breaks down everything you need to know about data encryption, from core concepts to 2026's latest post-quantum developments, with actionable best practices for teams of all sizes. Table of Contents Core Concepts of Data Encryption How Does Data Encryption Work? Key Data Encryption Algorithms (2026 Approved & Deprecated) Encryption for All 3 Data States: At Rest, In Transit, In Use Real-World Data Encryption Use Cases Encryption Standards & Compliance Regulations Data Encryption Best Practices Common Encryption Mistakes to Avoid 2024-2026 Encryption Trends & Future Developments Conclusion & Key Takeaways References Core Concepts of Data Encryption Data encryption is a cryptographic process that converts human-readable plaintext into unreadable scrambled ciphertext using mathematical algorithms and secret keys. Only authorized parties with the correct decryption key can reverse the process to recover the original plaintext. Core Benefits of Encryption Encryption provides three non-negotiable security properties: Confidentiality : Only authorized users can access sensitive data Authentication : Verifies the origin of encrypted data Integrity : Confirms encrypted data has not been tampered w

The update brings a low-latency profile, speeds up search, and patches hundreds of flaws.

Microsoft's Xbox division will be hit with significant layoffs next month, according to people familiar with Microsoft's plans. The company has been preparing for the layoffs internally for weeks, with Xbox CEO Asha Sharma hinting about "making hard choices" last month. Sources suggest the cuts could even involve a studio closure, or changes to the […]

Recently open-sourced by Microsoft, pg_durable is a PostgreSQL extension that enables durable workflows to run natively inside the database, eliminating the need for external orchestration systems. By Sergio De Simone

New college graduates around the country have been booing and heckling commencement speakers who hype up AI. Microsoft would like everyone to talk it out. In a blog post running more than 3,100 words, Microsoft vice chair and president Brad Smith addressed the recent spate of viral clips from graduation ceremonies, like former Google CEO […]

The US military struck Iran again after an Iranian drone’s lucky midair strike.

Anthropic released Claude Fable, its first Mythos-class AI model, yesterday and it's already causing concerns inside Microsoft. Sources tell me that Microsoft is limiting the use of Claude Fable 5 for employees because of Anthropic's new data retention requirements. While Microsoft quickly rolled out Claude Fable 5 to its GitHub Copilot and Foundry customers, I'm […]

ServiceNow is used by thousands of enterprises to automate their internal processes, but says several customers had data accessed because of a security bug.

Ambrosia Energy wants to build power plants in less than 12 months while undercutting natural gas. It hopes to build gigawatts worth by 2030.

The RAMageddon crisis has got Microsoft rethinking its Xbox console hardware business. Xbox CEO Asha Sharma and Xbox strategy chief Matthew Ball have both revealed this week that Microsoft is reevaluating plans for its next-generation Project Helix console and exploring "radically different" console business models in the meantime. "We are working very hard to rethink […]

Azure API Management shipped a Unified Model API that lets clients speak one format while APIM transforms requests to Anthropic, Vertex AI, and other backends. Content safety policies now cover MCP tool calls and Agent-to-Agent payloads alongside LLM traffic. Token metrics expanded to track reasoning, cached, and audio tokens across providers. By Steef-Jan Wiggers

Microsoft's Office 2019 apps for Mac will stop working next month, because the company isn't renewing a certificate that validates Office licenses. Owners of Office 2019 for Mac are being warned they'll have to purchase Office 2024 or a Microsoft 365 subscription if they want to continue editing documents. Microsoft previously promised that "all your […]

PostgreSQL Error 2201X: invalid row count in result offset clause PostgreSQL error code 2201X ( invalid_row_count_in_result_offset_clause ) is thrown when the value provided to an OFFSET clause is not a valid non-negative integer. This commonly surfaces in applications that implement pagination using dynamic queries or user-supplied parameters, where the offset value may be negative, NULL, or a non-integer type. Top 3 Causes 1. Negative OFFSET Value The most frequent cause is a miscalculated page offset. When computing (page - 1) * page_size , a bad page number can produce a negative result. -- Triggers error 2201X SELECT * FROM orders ORDER BY created_at DESC OFFSET - 5 LIMIT 20 ; -- ERROR: invalid row count in result offset clause -- Fix: Use GREATEST to clamp the value to 0 SELECT * FROM orders ORDER BY created_at DESC OFFSET GREATEST ( 0 , - 5 ) LIMIT 20 ; 2. NULL Passed as OFFSET When an application fails to initialize or bind the offset parameter, NULL gets passed to the query. This often happens with ORMs or query builders where optional parameters are not explicitly set. -- Triggers error 2201X SELECT * FROM products ORDER BY id OFFSET NULL LIMIT 10 ; -- ERROR: invalid row count in result offset clause -- Fix: Use COALESCE to provide a default value SELECT * FROM products ORDER BY id OFFSET COALESCE ( NULL , 0 ) LIMIT 10 ; -- Parameterized query with safe fallback SELECT * FROM products ORDER BY id OFFSET COALESCE ( $ 1 :: BIGINT , 0 ) LIMIT COALESCE ( $ 2 :: BIGINT , 10 ); 3. Non-Integer Type (Float or String) Passing a float or a string that cannot be cleanly cast to a whole number causes this error. This typically happens when Python float values or unvalidated user input strings are interpolated directly into a query. -- Triggers error 2201X SELECT * FROM employees ORDER BY last_name OFFSET 10 . 7 LIMIT 5 ; -- ERROR: invalid row count in result offset clause -- Fix: Use FLOOR and explicit cast to BIGINT SELECT * FROM employees ORDER BY last_name OFFSET F