2025-11-25 15:51:52
For cross-border e-commerce businesses, the digital marketplace is filled with opportunities, but it also comes with its share of risks. When running an online store that serves customers in different countries, the website is constantly exposed to a range of cyber threats, from bot attacks and credit card fraud to data scraping and denial-of-service (DoS) attacks.
One such e-commerce business, an independent store selling specialized products across multiple countries, faced a growing number of security challenges. After suffering from bot-driven fraud attempts, scraping, and some SQL injection attempts, the owner turned to SafeLine, a self-hosted Web Application Firewall (WAF), to enhance security, regain control, and ensure the integrity of the business. This case study details how SafeLine helped the business secure its website and protect customer data.
As the business expanded internationally, the site began receiving higher traffic from various regions. While this was great for sales, it also attracted cybercriminals looking to exploit vulnerabilities. The site, which handled sensitive customer data and processed payments, became a target for:
Despite implementing some basic security measures through a cloud-based firewall, the owner quickly realized that the generic solutions in place weren’t sufficient for the unique needs of an e-commerce business. Bots continued to evade detection, and attackers were targeting payment processing routes with more sophisticated techniques.
After researching different options, the e-commerce business decided to implement SafeLine, a modern, self-hosted WAF that combines intelligent threat detection with full control over web traffic. Unlike traditional cloud-based WAFs, SafeLine’s self-hosted approach allows businesses to retain full ownership of their security infrastructure, ensuring data privacy and providing advanced customizability.
The key reasons the business opted for SafeLine were:
The deployment process for SafeLine was straightforward. The business used Docker for installation, which allowed for a clean, quick setup on their server infrastructure.
Here’s how SafeLine was integrated:
Once set up, SafeLine acted as a reverse proxy, analyzing every incoming request and blocking malicious traffic before it could reach the e-commerce platform. The business could now monitor traffic in real-time through SafeLine’s intuitive dashboard.
The first notable success came when SafeLine detected and blocked an ongoing credential stuffing attempt. Automated bots were trying to log into customer accounts using large datasets of stolen usernames and passwords. SafeLine’s intelligent rate-limiting feature was able to detect the abnormal login patterns and block the bots before they could cause any damage.
This proactive protection saved the business from a potential data breach and reduced customer frustration by ensuring legitimate users could still log in without interference.
Soon after, a bot scraper started scraping product listings and reviews from the site. This content was being harvested to sell competing products at a lower price point. SafeLine’s advanced bot detection, which analyzes behavioral patterns such as the speed and accuracy of user actions, identified the scraper and blocked it. By challenging the scraper with a dynamic bot verification page, SafeLine neutralized the attack without impacting real users.
The business was now able to protect its intellectual property and prevent competitors from undercutting its prices based on stolen data.
Another successful intervention came when SafeLine blocked a complex SQL injection attempt targeting the backend database. The attacker embedded malicious code within the URL parameters, attempting to gain access to sensitive customer data. SafeLine’s semantic analysis engine, which looks at the context of requests, was able to identify this as an attack and block it before it reached the application layer. This prevented potential data leaks and further fortified the website's security posture.
Since implementing SafeLine, the business owner has been able to sleep better at night, knowing that the site is protected from various types of attacks. The ability to monitor security events in real-time through the SafeLine dashboard has been invaluable. The owner can see which attacks were blocked, track any unusual traffic patterns, and tweak rules when needed.
For growing e-commerce businesses, especially those operating internationally, securing the website from malicious traffic and protecting sensitive customer data should be a top priority. SafeLine’s self-hosted WAF provided a flexible, scalable, and cost-effective solution that enabled the business to defend against advanced attacks like credential stuffing, scraping, and SQL injection.
Unlike cloud-based WAFs, SafeLine offered the e-commerce business full control over its security infrastructure, ensuring data privacy while allowing for precise customization. As the business continues to expand, SafeLine remains an essential part of its security strategy.
For e-commerce businesses looking for a robust, self-hosted solution to protect their web assets, SafeLine is an excellent choice.
Want to protect your e-commerce site?
Learn more about SafeLine: SafeLine WAF
2025-11-25 15:51:35
It was 2 AM on a Tuesday when my phone exploded with alerts. Our e-commerce platform was dying. Response times had ballooned from 200ms to 45 seconds. The CPU graphs looked like a heart monitor during a cardiac arrest.
I logged in, hands shaking, coffee forgotten. The culprit? A seemingly innocent query:
SELECT * FROM products
WHERE description @@ to_tsquery('wireless bluetooth headphones')
ORDER BY created_at DESC
LIMIT 20;
My "fix" from earlier that day: adding a B-Tree index on the description column.
The problem: I'd brought a lockpick to a safe-cracking job.
This disaster taught me something crucial: PostgreSQL indexes aren't just about "making queries faster." They're specialized tools, each designed for specific types of heists—I mean, queries. Using the wrong one is like trying to break into Fort Knox with a butter knife.
Let me tell you about the six types of indexes in PostgreSQL's arsenal, when to use each, and more importantly, when NOT to.
Before we choose our tools, let's understand what we're dealing with. Think of your database like a massive library:
PostgreSQL Database = Library
├── Tables = Bookshelves
├── Rows = Books
└── Indexes = Finding Systems
But here's where it gets interesting: not all finding systems are created equal.
B-Tree indexes are like the Dewey Decimal System—perfect for ordered searches.
Hash indexes are like a magical catalog that teleports you directly to one book.
GIN indexes are like a full-text search engine that knows every word in every book.
GiST indexes are like a map showing spatial relationships between books.
SP-GiST indexes are like a hierarchical filing system for weird organizational schemes.
BRIN indexes are like sticky notes saying "Books 1-1000 are roughly in this area."
Now, let's plan our heists.
The Target: Ordered data, range queries, equality searches
The Tool: B-Tree (Balanced Tree)
B-Tree is the Swiss Army knife of indexes. It's the default, and honestly, it solves 80% of your problems. But that's also why developers (including past-me) slap it on everything without thinking.
-- The classic: finding a user by ID
CREATE INDEX idx_users_id ON users(id);
SELECT * FROM users WHERE id = 12345;
-- Range queries on dates
CREATE INDEX idx_orders_created ON orders(created_at);
SELECT * FROM orders
WHERE created_at BETWEEN '2024-01-01' AND '2024-12-31';
-- Sorting operations
CREATE INDEX idx_products_price ON products(price);
SELECT * FROM products ORDER BY price DESC LIMIT 10;
[50]
/ \
[25] [75]
/ \ / \
[10] [40] [60] [90]
/ \ / \ / \ / \
[...leaves with actual data pointers...]
Why it works: B-Trees maintain balance and order. Search, insert, and delete operations are all O(log n). They're predictable, reliable, and fast for most operations.
Remember my production disaster? Here's what I learned:
-- DON'T DO THIS
CREATE INDEX idx_description_btree ON products(description);
-- Why? Because:
SELECT * FROM products WHERE description LIKE '%bluetooth%';
-- B-Tree can't optimize this! It needs to scan the entire index.
-- Also terrible:
CREATE INDEX idx_json_btree ON logs(data);
-- JSON data isn't ordered in a meaningful way for B-Tree
Real-world gotcha: B-Trees are fantastic for equality and range queries, but they're nearly useless for:
%term)Performance benchmark from my disaster:
Before B-Tree on description: 200ms (seq scan)
After B-Tree on description: 45 seconds (index scan + sort nightmare)
After switching to GIN: 12ms (proper full-text search)
The Target: Full-text search, arrays, JSON documents
The Tool: GIN (Generalized Inverted Index)
GIN is what I should have used. Think of it as an inverted index—instead of "Document 5 contains words X, Y, Z," it stores "Word X appears in documents 3, 5, 7."
-- Add a tsvector column for full-text search
ALTER TABLE products ADD COLUMN description_tsv tsvector
GENERATED ALWAYS AS (to_tsvector('english', description)) STORED;
-- Create the GIN index
CREATE INDEX idx_description_gin ON products USING GIN(description_tsv);
-- Now this flies
SELECT * FROM products
WHERE description_tsv @@ to_tsquery('english', 'wireless & bluetooth & headphones')
ORDER BY ts_rank(description_tsv, to_tsquery('english', 'wireless & bluetooth & headphones')) DESC
LIMIT 20;
Word "bluetooth": → [doc_1, doc_5, doc_7, doc_23, ...]
Word "wireless": → [doc_1, doc_8, doc_12, doc_23, ...]
Word "headphones": → [doc_1, doc_5, doc_23, doc_45, ...]
Query: bluetooth AND wireless AND headphones
Result: intersection → [doc_1, doc_23]
-- 1. Full-text search (my use case)
CREATE INDEX idx_articles_content ON articles
USING GIN(to_tsvector('english', content));
-- 2. Array operations
CREATE INDEX idx_tags_gin ON posts USING GIN(tags);
SELECT * FROM posts WHERE tags @> ARRAY['postgresql', 'performance'];
-- 3. JSONB queries
CREATE INDEX idx_metadata_gin ON events USING GIN(metadata);
SELECT * FROM events WHERE metadata @> '{"user_type": "premium"}';
-- 4. Multiple columns
CREATE INDEX idx_multi_search ON products
USING GIN(to_tsvector('english', name || ' ' || description));
Warning: GIN indexes are LARGE. Like, really large.
-- Check your index sizes
SELECT
schemaname,
tablename,
indexname,
pg_size_pretty(pg_relation_size(indexrelid)) AS index_size
FROM pg_stat_user_indexes
ORDER BY pg_relation_size(indexrelid) DESC;
In my case:
Trade-off: GIN indexes also slow down inserts and updates because they need to update the inverted index structure. For high-write workloads, this matters.
Pro tip: Use GIN(description_tsv) WITH (fastupdate = on) to batch updates and reduce write overhead.
The Target: Geometric data, ranges, custom types
The Tool: GiST (Generalized Search Tree)
GiST is the elegant solution for "nearness" problems. Finding restaurants within 5km? Checking if date ranges overlap? GiST is your friend.
-- Enable PostGIS extension
CREATE EXTENSION IF NOT EXISTS postgis;
-- Create a table of coffee shops
CREATE TABLE coffee_shops (
id SERIAL PRIMARY KEY,
name VARCHAR(200),
location GEOGRAPHY(POINT, 4326)
);
-- The magic: GiST index on geographic data
CREATE INDEX idx_location_gist ON coffee_shops USING GiST(location);
-- Find coffee shops within 1km of a point
SELECT
name,
ST_Distance(location, ST_MakePoint(-122.4194, 37.7749)::geography) as distance_meters
FROM coffee_shops
WHERE ST_DWithin(
location,
ST_MakePoint(-122.4194, 37.7749)::geography,
1000 -- 1km in meters
)
ORDER BY location <-> ST_MakePoint(-122.4194, 37.7749)::geography
LIMIT 5;
[Bounding Box: Entire City]
/ \
[Downtown Area] [Suburbs]
/ \ / \
[Block A] [Block B] [Region 1] [Region 2]
/ \ / \ / \ / \
[shops....] [shops....]
-- Range overlap queries
CREATE TABLE bookings (
id SERIAL PRIMARY KEY,
room_id INT,
booking_period TSTZRANGE
);
CREATE INDEX idx_booking_period ON bookings USING GiST(booking_period);
-- Find overlapping bookings (double-booking detection)
SELECT * FROM bookings
WHERE booking_period && '[2024-12-25 14:00, 2024-12-25 16:00)'::tstzrange;
-- Exclusion constraints (prevent overlaps)
ALTER TABLE bookings ADD CONSTRAINT no_overlap
EXCLUDE USING GiST (room_id WITH =, booking_period WITH &&);
Use case: IP address range lookups
CREATE TABLE ip_locations (
ip_range inet,
country VARCHAR(2)
);
CREATE INDEX idx_ip_gist ON ip_locations USING GiST(ip_range inet_ops);
-- Lightning fast IP geolocation
SELECT country FROM ip_locations
WHERE ip_range >>= '192.168.1.1'::inet;
The Target: Hierarchical or partitioned data
The Tool: SP-GiST (Space-Partitioned GiST)
SP-GiST is the hipster of indexes—less commonly used but perfect for specific scenarios. It's designed for data with a natural hierarchy.
CREATE TABLE phone_directory (
id SERIAL PRIMARY KEY,
phone_number VARCHAR(20)
);
-- SP-GiST for prefix searches
CREATE INDEX idx_phone_spgist ON phone_directory
USING SPGIST(phone_number);
-- Fast prefix matching
SELECT * FROM phone_directory
WHERE phone_number LIKE '415%';
[Origin]
/ | \ \
[NW] [NE] [SW] [SE]
/ \ | | / \
[...] [...] [...] [...]
-- 1. IP network hierarchies
CREATE INDEX idx_network_spgist ON networks USING SPGIST(cidr_column);
-- 2. Quadtree spatial indexes (alternative to GiST)
CREATE INDEX idx_point_spgist ON locations USING SPGIST(point_column);
-- 3. Text prefix matching
CREATE INDEX idx_prefix_spgist ON words USING SPGIST(word text_ops);
Performance comparison (prefix search on 10M phone numbers):
Why? SP-GiST naturally partitions the search space by prefixes, while B-Tree has to scan all matching prefixes.
The Target: Huge tables with naturally ordered data
The Tool: BRIN (Block Range INdex)
BRIN is the minimalist's dream. Instead of indexing every row, it stores summaries of data blocks. Tiny index size, surprisingly effective for the right use case.
-- Imagine a logs table with 1 billion rows
CREATE TABLE application_logs (
id BIGSERIAL PRIMARY KEY,
timestamp TIMESTAMPTZ NOT NULL,
level VARCHAR(10),
message TEXT
);
-- Traditional B-Tree index: ~40GB
-- BRIN index: ~40MB (1000x smaller!)
CREATE INDEX idx_timestamp_brin ON application_logs
USING BRIN(timestamp) WITH (pages_per_range = 128);
-- Still fast for time-range queries
SELECT * FROM application_logs
WHERE timestamp BETWEEN '2024-12-01' AND '2024-12-02';
Block Range 1 (rows 1-10000): timestamp MIN: 2024-01-01, MAX: 2024-01-05
Block Range 2 (rows 10001-20000): timestamp MIN: 2024-01-05, MAX: 2024-01-10
Block Range 3 (rows 20001-30000): timestamp MIN: 2024-01-10, MAX: 2024-01-15
...
Query plan: Postgres skips entire block ranges that couldn't contain the target data.
-- BAD: Random updates destroy correlation
UPDATE application_logs SET timestamp = NOW() WHERE id = 5;
-- Now block 1 might have rows from 2024 and 2025!
-- GOOD: Append-only tables
INSERT INTO application_logs (timestamp, level, message)
VALUES (NOW(), 'INFO', 'User logged in');
-- My time-series data table: 500M rows, 80GB
-- Comparing index approaches:
CREATE INDEX idx_ts_btree ON metrics(timestamp);
-- Index size: 15GB, Query time: 45ms
CREATE INDEX idx_ts_brin ON metrics USING BRIN(timestamp);
-- Index size: 15MB, Query time: 52ms
-- For a 1000x size reduction, 7ms slower? I'll take it.
Golden rule: If your data has natural physical ordering (timestamps, sequential IDs, geographic regions in order), BRIN is your secret weapon.
The Target: Exact equality matches only
The Tool: Hash Index
Hash indexes used to be the "don't use these" option in PostgreSQL (pre-v10 they weren't WAL-logged). Now they're viable, but their use case is narrow.
-- UUID lookups
CREATE TABLE sessions (
session_id UUID PRIMARY KEY,
user_id INT,
data JSONB
);
-- Hash index for exact UUID lookups
CREATE INDEX idx_session_hash ON sessions USING HASH(session_id);
-- Perfect for:
SELECT * FROM sessions WHERE session_id = 'a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11';
-- Test on 50M UUID rows
-- B-Tree index: 8.5GB, Query time: 0.8ms
-- Hash index: 6.2GB, Query time: 0.6ms
-- But...
SELECT * FROM sessions WHERE session_id > 'a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11';
-- B-Tree: 45ms
-- Hash: Can't do this at all!
Use them when:
Don't use them when:
Controversial take: In PostgreSQL 16, hash indexes are rarely worth the specificity. B-Trees are "good enough" for equality checks, and they give you flexibility. I've stopped using hash indexes entirely.
Here's how to choose your tool:
┌─────────────────────┬──────────────────────────────────────────┐
│ Query Type │ Best Index │
├─────────────────────┼──────────────────────────────────────────┤
│ =, <, >, <=, >= │ B-Tree │
│ BETWEEN │ B-Tree │
│ LIKE 'abc%' │ B-Tree │
│ LIKE '%abc%' │ GIN (with trigram extension) │
│ Full-text search │ GIN (with tsvector) │
│ @>, <@, &&, ? │ GIN (arrays, JSONB) │
│ Geometric queries │ GiST (PostGIS, ranges) │
│ && (overlap) │ GiST (ranges) │
│ Prefix search │ SP-GiST │
│ IP address lookup │ GiST or SP-GiST │
│ Time-series (huge) │ BRIN │
│ = only (UUID) │ Hash (or just use B-Tree) │
└─────────────────────┴──────────────────────────────────────────┘
| Index Type | Size | Insert Speed | Query Speed | Best For |
|---|---|---|---|---|
| B-Tree | Medium | Fast | Fast | General purpose |
| GIN | Large | Slow | Very Fast | Search, arrays, JSON |
| GiST | Medium | Medium | Fast | Geometric, ranges |
| SP-GiST | Small | Fast | Very Fast | Hierarchical data |
| BRIN | Tiny | Very Fast | Medium | Huge ordered tables |
| Hash | Medium | Fast | Fast | Equality only |
-- Only index active users
CREATE INDEX idx_active_users ON users(email)
WHERE status = 'active';
-- Only index recent orders
CREATE INDEX idx_recent_orders ON orders(created_at)
WHERE created_at > NOW() - INTERVAL '90 days';
-- Combine with other index types
CREATE INDEX idx_recent_posts_gin ON posts
USING GIN(to_tsvector('english', content))
WHERE published = true AND created_at > '2024-01-01';
Result: Smaller indexes, faster queries, less maintenance overhead.
-- Index on computed values
CREATE INDEX idx_email_lower ON users(LOWER(email));
SELECT * FROM users WHERE LOWER(email) = '[email protected]';
-- Index on JSON extraction
CREATE INDEX idx_user_type ON events((metadata->>'user_type'));
SELECT * FROM events WHERE metadata->>'user_type' = 'premium';
-- Order matters!
CREATE INDEX idx_user_created ON logs(user_id, created_at);
-- Works great for:
SELECT * FROM logs WHERE user_id = 123 AND created_at > '2024-01-01';
SELECT * FROM logs WHERE user_id = 123;
-- Doesn't use the index:
SELECT * FROM logs WHERE created_at > '2024-01-01'; -- No user_id!
-- Find unused indexes
SELECT
schemaname,
tablename,
indexname,
idx_scan as scans,
pg_size_pretty(pg_relation_size(indexrelid)) as size
FROM pg_stat_user_indexes
WHERE idx_scan = 0
AND indexrelname NOT LIKE '%pkey%'
ORDER BY pg_relation_size(indexrelid) DESC;
-- Find duplicate indexes
SELECT
indrelid::regclass as table_name,
array_agg(indexrelid::regclass) as indexes
FROM pg_index
GROUP BY indrelid, indkey
HAVING count(*) > 1;
B-Tree isn't always the answer. It's the default, not the optimal.
Index type matters more than having an index. Wrong index can be worse than no index.
Size matters. GIN indexes can be 40% of your table size. BRIN can be 0.1%.
Test with production-scale data. My B-Tree index worked great on 1,000 rows. At 10M rows, it collapsed.
Monitor your indexes. You'll be surprised how many are unused.
Partial indexes are underrated. If you're querying WHERE status = 'active' 99% of the time, don't index inactive rows.
Read the query planner. EXPLAIN ANALYZE is your best friend:
EXPLAIN (ANALYZE, BUFFERS)
SELECT * FROM products
WHERE description_tsv @@ to_tsquery('wireless & bluetooth');
Right now:
This week:
This month:
The best index is invisible. Users don't notice it. Developers forget it's there. It just works, quietly making queries fast, without consuming too much space or slowing down writes.
Choose wisely. Monitor religiously. Test thoroughly.
And please, for the love of Postgres, don't put a B-Tree index on a text column for full-text search. Learn from my 2 AM phone call.
What's your index horror story? Drop a comment below. Let's learn from each other's disasters—I've shared mine.
Tool I built after my disaster: A simple index suggestion script:
-- Save this as index_advisor.sql
WITH table_stats AS (
SELECT
schemaname,
tablename,
seq_scan,
idx_scan,
n_live_tup
FROM pg_stat_user_tables
WHERE n_live_tup > 10000
)
SELECT
schemaname || '.' || tablename as table_name,
n_live_tup as rows,
seq_scan,
idx_scan,
CASE
WHEN seq_scan > idx_scan * 10 THEN '⚠️ Consider adding indexes'
WHEN idx_scan = 0 AND seq_scan > 100 THEN '🚨 Definitely needs indexes'
ELSE '✅ Looks good'
END as recommendation
FROM table_stats
ORDER BY seq_scan DESC
LIMIT 20;
Happy indexing, and may your queries be fast and your 2 AM phone calls be rare! 🎯
2025-11-25 15:49:13
Understanding the hidden failure mechanisms in Li-ion/Li-polymer batteries for better hardware design.
Lithium batteries power almost everything from IoT devices and wearables to drones and robotics. However, many developers struggle with unexpected battery failures — sudden drops in runtime, swelling, overheating, or even thermal runaway.
Understanding why lithium batteries fail is crucial for makers, engineers, and hardware developers to build reliable devices. This article breaks down the technical causes, symptoms, and engineering considerations.
What happens:
Charging a Li-ion battery beyond its maximum voltage (usually 4.2V per cell) leads to chemical stress:
Engineering tip:
Always use a CC/CV (Constant Current / Constant Voltage) charger designed for your battery chemistry. For high-density cells, check the manufacturer’s maximum charge voltage; some allow 4.35V, others 4.4V, but this shortens cycle life.
What happens:
Discharging below the cut-off voltage (usually 2.75–3.0V per cell) can:
Engineering tip:
What happens:
Heat accelerates chemical reactions inside the cell:
Engineering tip:
What happens:
Cold temperatures (below 0°C) slow down ion movement, causing:
Engineering tip:
What happens:
Batteries can deform under:
Effects include:
Engineering tip:
What happens:
Drawing current above the recommended continuous or peak discharge rate:
Engineering tip:
What happens:
Even high-quality batteries can fail due to defects:
Engineering tip:
What happens:
All lithium batteries degrade over time and usage:
Engineering tip:
Lithium batteries are powerful and versatile but also sensitive to voltage, current, temperature, and mechanical stress. Developers and engineers can prevent many failures by:
Understanding these failure modes not only improves device reliability but also keeps your products safe.
2025-11-25 15:39:35
The drive towards a circular economy in the UK is gaining momentum, with the Government's 'Circular Economy Growth Plan' for England now anticipated in the new year. This strategic shift, despite a slight delay from its initial autumn 2025 consultation target, signals a significant evolution in how industries, including commercial real estate (CRE), will approach resource management. For property owners and asset managers, understanding this impending framework is crucial, as it will shape future operational practices, compliance requirements, and investment decisions, moving away from a traditional 'take-make-dispose' model towards one that values reuse, repair, and recycling.
The traditional linear economy model, prevalent for decades, has led to immense waste generation, resource depletion, and significant environmental impact. In commercial real estate, this manifests as substantial construction and demolition waste, inefficient material usage, and a lack of foresight regarding a building's end-of-life cycle. The new Circular Economy Growth Plan, spearheaded by the Circular Economy Taskforce and chaired by the influential Andrew Morlet of the Ellen MacArthur Foundation, aims to reverse this trend. With roadmaps planned for key sectors including construction, the initiative seeks to foster a systemic change, making resource efficiency and waste reduction not just an aspiration but a regulated expectation. This shift is vital for the property sector, which traditionally accounts for a considerable portion of national waste streams.
The impending Circular Economy Growth Plan brings a wave of implications for commercial property. Firstly, it will necessitate a fundamental reassessment of current waste management strategies. Mere waste disposal will no longer suffice; focus will shift to waste prevention, material recycling, and extending product lifecycles. This directly impacts building design, procurement of materials, and operational management. Secondly, tighter regulations will likely emerge, demanding more rigorous ESG reporting and transparency around material flows and waste outputs. Property owners will need robust data to demonstrate compliance and progress, influencing tenant engagement and potential green financing opportunities. This is particularly relevant for those navigating frameworks like CSRD, GRI, and GRESB, where granular, verifiable data is becoming indispensable. Inaccurate or estimated waste data will become a significant liability, paving the way for advanced solutions like AI-powered ESG reporting to ensure precision and compliance.
Transitioning to a circular economy within a commercial property portfolio may seem daunting, but modern technology offers a clear pathway. The core challenge often lies in a lack of real-time, accurate waste data. Without understanding what, when, and how much waste is being generated, effective circular strategies are impossible. This is where platforms like Wastify AI become invaluable. By providing automated, granular waste tracking, commercial buildings can move beyond estimates to actionable insights. This data empowers facility managers to identify opportunities for waste segregation improvement, optimise recycling programmes, and even automate tenant recharging based on actual waste contributions. Such a data-centric approach not only supports future circular economy mandates but also enhances operational efficiency, reduces costs, and bolsters a building's environmental credentials. Adopting real-time waste tracking technologies is no longer just an option, but a strategic necessity for future-proofing assets in a circular economy. This technological shift enables seamless integration of circular principles, from initial design concepts through to day-to-day operations, ensuring compliance and driving genuine environmental impact.
The UK's Circular Economy Growth Plan signals a pivotal moment for commercial real estate. It underscores a future where resource efficiency and waste reduction are paramount, transforming how buildings are designed, operated, and managed. For property professionals, preparing for this shift means prioritising robust data collection, embracing technological solutions, and rethinking traditional waste practices. By moving away from guesswork and towards verifiable, real-time insights, you can not only meet impending regulatory demands but also unlock significant operational efficiencies and enhance your portfolio's sustainability profile. Don't let your waste data hold you back from a circular future; discover how Wastify AI can transform your waste management and ESG reporting today. Visit wastify.co.uk to learn more.
2025-11-25 15:39:12
When it comes to mechanical keyboards, choosing the right size and layout can make a huge difference in your typing or gaming experience. With so many options available, it’s easy to get overwhelmed by the choices. Whether you need a compact keyboard for portability or a full-sized one for a complete set of keys, understanding the key differences between keyboard sizes and layouts is essential.
In this ultimate guide, we’ll explain the various keyboard sizes and layouts, their features, and how to choose the right one based on your needs. By the end of this article, you’ll have all the information you need to make an informed decision when picking your next keyboard.
The size of a keyboard refers to the number of keys and the general layout configuration. Keyboards come in different sizes, each designed to cater to specific needs, such as gaming, typing, or portability. Here’s a breakdown of the most common keyboard sizes:
Key Features:
Best For: Office work, data entry, and anyone who needs the full functionality of a keyboard, especially those who frequently use the number pad.
Key Features:
Best For: Gamers, typists, and anyone who doesn’t frequently use the number pad but still needs all the other functions. It’s also perfect for those looking to save desk space.
Key Features:
Best For: Portability, minimalists, and gamers who want a compact keyboard but still need most of the standard keys.
Key Features:
Best For: Traveling gamers, minimalists, and those who need a portable, space-saving keyboard without sacrificing basic functionality.
Key Features:
Best For: Extreme minimalists or those looking for a customizable layout for specific tasks such as coding or advanced gaming.
Once you’ve decided on a keyboard size, it’s time to choose a layout. The layout refers to the arrangement of keys on the keyboard. The two most common layouts are ANSI and ISO. Understanding these layouts will help you choose the best one for your typing style and region.
Key Features:
Best For: North American users, those who are familiar with the standardized layout found in most keyboards.
Key Features:
Best For: European users, especially those who type in languages that require additional characters (e.g., German, French).
Consider the tasks you perform on your keyboard. If you type a lot of numbers or do data entry, you might want a full-sized keyboard with a number pad. For gaming, a TKL or 75% keyboard might offer the perfect balance of functionality and space-saving.
If you value portability, the 60% or 40% keyboards are ideal, especially if you’re on the go or need a setup that doesn’t take up too much desk space.
Comfort is subjective, but a full-sized keyboard typically provides the most ergonomic setup, with enough space for comfortable key presses. However, smaller keyboards like TKL or 60% layouts can be more comfortable for some, as they encourage a more compact typing position and reduce the distance your hands need to move.
If you enjoy customizing your setup, ANSI layouts are generally more compatible with a wide range of keycap sets and accessories. ISO layouts are more niche and may limit your options for customizations, but they’re still great for users who need a layout that supports European characters.
Choosing the right keyboard size and layout depends on your needs, typing style, and desk space. Here’s a quick recap of the most common options:
Ultimately, the best keyboard layout and size are those that fit your typing habits, space constraints, and customization needs. Whether you're a gamer, typist, or someone who spends long hours at their desk, there’s a keyboard size and layout out there for you.
Q1: What is the difference between ANSI and ISO layouts?
ANSI is common in North America with a horizontal Enter key, while ISO is used in Europe with a vertical Enter key and an additional key between Shift and Z.
Q2: Which keyboard size is best for gaming?
TKL or 75% keyboards are often preferred by gamers as they offer a compact layout while still maintaining all the essential keys.
Q3: What is the best keyboard size for typing?
Full-sized keyboards are best for those who do extensive typing and need access to the number pad and function keys.
Q4: Can I switch between keyboard layouts?
Yes, you can easily switch between different keyboard layouts in your device’s settings, especially on Windows and Mac computers.
2025-11-25 15:37:51
正直に言うと、APIテストを書くのは地味に手間がかかります。
異常系や境界値まで全部書こうと思うと、時間がいくらあっても足りません。
そんなとき、「ApidogでAIがテストケースを自動生成できる」と聞いて、
半信半疑ながら実際に試してみました。
Apidogは、APIの設計・ドキュメント・テスト・モック・管理をひとつのプラットフォームで完結できるオールインワンのAPI開発ツールです。
Postman、Swagger Editor、Mockサーバー、APIテストなど
これまでバラバラだった作業をまとめて扱えるのが大きな特徴です。
最近はAIによるテストケース自動生成にも対応し、テスト設計の初稿づくりを大幅に効率化できます。
手動でテストケースを作成するとき、いつもこんな悩みに直面していました：
「AIが初稿を作ってくれるなら、この負担が軽くなるかも」と思ったのが使い始めたきっかけです。
APIを定義したあとに 「テストケース」タブ を開くと、中央付近に 「AIで生成」 ボタンがあります。
クリックすると、次の画面で生成したいテストタイプを選べます：
一括生成も、必要な種類だけを選択して生成も可能です。
生成後は各テストケースを確認し、採用／破棄 を選んで最終調整します。
最終的には、テストレポートをエクスポートしてチームで共有することもできます。
ちょっとしたポイント
- 生成後は必ずレビューする（誤解・抜け漏れ防止）
- 生成精度は使うAIモデルの性能に依存
- AI機能は初回のみ有効化が必要
- 特に異常系や境界値は完全ではないので手動補完が必要
Apidog自体はAIモデルを提供していません。
使用するには、OpenAIやClaudeなど任意のAIモデルのAPIキーを設定する必要があります。
ここで重要なのは、AIが生成するテストケースの精度はモデルの性能によって決まる ということです。
モデルが高性能であれば、より実務に近いテストケースが生成されます。
今回の体験で感じたのは、AIに丸投げするのではなく、AIと協力してテストを作る ということです。
AIが下書きを作ってくれることで、
開発者は仕様やロジックの確認により集中でき、
テストの質とスピードがどちらも向上しました。
忙しい開発者ほど、
一度AI生成で試してみる価値は十分にあると実感しました。
この記事が役に立ったら、ぜひシェアしてください。
質問やコメントがあれば、お気軽にどうぞ。
https://docs.apidog.com/jp/apidog%E3%81%AEai%E6%A9%9F%E8%83%BD%E6%A6%82%E8%A6%81-1237382m0