What Is REST API? REST API is a popular approach for building communication between applications. It uses HTTP methods such as GET, POST, PUT, and DELETEto work with resources. For example, an endpoint like /api/products might return a list of products. REST is straightforward, widely supported, and relatively easy for beginners to understand.
Cloudflare has added a native Web Search API to its AI Gateway, built in partnership with search providers Ceramic.ai, Exa, and Linkup. The feature lets developers pull live web results into a model's context window during inference, addressing the gap where models are frozen at a training cutoff and can't speak to recent events or changing documentation. Three access paths are available: directly through AI Gateway, via a standard REST endpoint (passing an AI Gateway auth token and a provider name in the request payload), or through a Workers binding that Cloudflare says takes one line of cod
Cross-Chain Bridge Risk Assessment: CCIP Target Protocol: CCIP (TVL: $1862.7M) Chainlink Cross‑Chain Interoperability Protocol (CCIP) TVL (Ethereum & L2s): ≈ $1.86 B (Oct 2026) CCIP is Chainlink’s flagship cross‑chain messaging and token‑transfer layer, designed to enable developers to move arbitrary data and assets between EVM‑compatible chains, L2 roll‑ups, and non‑EVM networks. Its architecture relies on a decentralised network of off‑chain relayers (oracles), on‑chain router contracts, messenger contracts on each destination chain, and liquidity pools (or “Liquidity Providers – LPs”) that
Nginx sits at the edge of the internet, handling billions of requests daily. It is legendary for its asynchronous event-driven architecture that can handle 50,000 concurrent connections on minimal hardware. Yet, many developers deploy Nginx with stock configuration files, missing out on massive throughput gains, security hardening, and caching advantages. In this guide, we walk through a battle-tested, production-ready Nginx configuration for high-traffic API workloads. nginx.conf) Tuning begins in the global context: # Auto-detect CPU cores to spawn matching worker processes worker_processes
Introduction & Industry Context As we navigate the software development landscape of late 2026, the velocity of building software has reached unprecedented heights. For solopreneurs, bootstrapped SaaS founders, and nimble software development agencies, the barrier to launching a Minimum Viable Product (MVP) has practically evaporated. What used to take a team of senior engineers six months to build is now regularly shipped by a single developer in 14 days or less. However, this blistering pace of early-stage creation has introduced a severe technical bottleneck: the rapid accumulation of raw,
In the previous article, I ended with a small naming problem. workbench/ no longer felt like a workbench. Originally, it was a place for temporary material: investigations, prototypes, comparisons, drafts, validation output. But review feedback changed what I was putting there. Some findings were valid, but not part of the current work.
There's a new AI tool launching every day, and most "reviews" online are just reworded press releases. When someone asks me whether a tool is worth trying, I don't answer from the landing page — I test it myself first. Here's the process I follow. 1. I start with a real task, not the demo.
How should an AI content pipeline distinguish an SEO opportunity hypothesis from verified search research? When an automated content system suggests a target query, search intent, and potential content gap, those outputs represent educated editorial guesses rather than proof of a live search engine results page gap. Treating these hypotheses as factual measurements creates a risk of publishing content with unproven ranking potential. Language models excel at structuring metadata, but they generate plausible text with the same confidence whether they analyze live search data or rely entirely on
AI code review has a noise problem. On a public benchmark of 50 real pull requests, CodeRabbit raised 300 issues. 77 of them were real bugs on the benchmark's list. Most of the rest were noise: things that look like problems but aren't once you read the code. Developers learn to skim review bots, and once you skim, the real bugs slip past you too.
This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend (https://dev.to/challenges/hacktoberfest-weekend-2026-10-01) What I Built I built ColdMock so he can do rapid-fire oral technical drills on his laptop without paying anything. How it works: Demo Pick your topic (like PostgreSQL Concurrency) and set either a question limit or timer. The AI speaks, you answer hands-free, and it probes your trade-offs. You get a final debrief card at the end. Code https://github.com/Subhadip956425/ColdMock How I Built It Why Open Innovation Matters The Hand-Off His reaction: Prize Ca
I sell an Obsidian template pack (Working Notes, $6). Before I could write Here is what the measurement said, what it exposes about template packs in general (mine and everyone Nine Markdown files, no plugins, no automation: area-dashboard.md 37 lines 14 tokens 8 headings 1 checkbox 3 table rows book-note.md 29 lines 13 tokens 7 headings 1 checkbox daily-journal.md 23 lines 5 tokens 4 headings 1 checkbox meeting-notes.md 26 lines 16 tokens 6 headings 4 checkboxes one-on-one.md 27 lines 14 tokens 6 headings 4 checkboxes 3 table rows project-note.md 27 lines 14 tokens 6 headings 2 checkboxes 3 t
Four Horsemen of Agent PRs and How to Stop Them Agent-generated pull requests grew from under 1% of GitHub PRs to 27.6% in just fourteen months. Anthropic's 2026 Agentic Coding Trends Report puts the number higher: 41% of all new code is now AI-generated, with tools like Claude Code growing 6x in workplace adoption in under a year. The PR flood is here. The review infrastructure is not. 📖 Read the full version with charts and embedded sources on AgentConn → The problem is not that agents write bad code.
The Jira automation rules API at api.atlassian.com/automation/public lets a coding agent read, create, update and migrate Automation rules, and two new skills in LeanZero's open-source agent skills pack teach it how. The second skill copies Jira projects, JSM desks and Confluence spaces from one Cloud site to another over REST. They ship together for a reason. Atlassian's own cloud-to-cloud copy leaves your automation behind. Most people never find it.
In the previous article, I generated MIR from Rust code and tried reading it. An if expression became a set of blocks, and we could see where values went and which block execution would enter next. But being able to read MIR doesn't mean we've reached machine code yet. LLVM apparently comes into the picture further along. What does rustc actually pass to it, and how?
CogniRunner 3.3.0: it no longer waits for a transition Key takeaways CogniRunner 3.3.0 (Marketplace, 7 September) adds listeners and scheduled jobs. Until now every rule needed a workflow transition to fire; now a rule can react to any of 68 Jira events or run on a five-field cron in an IANA time zone. Loops are the failure mode of event automation, so the guard rails are on by default: self-generated events are ignored unless you say otherwise, and brakes cap a listener at 120 runs and an issue at 30 runs in any five-minute window. Redelivery and loops are different problems. A brake counts r
Atlassian Migrations — The Deadline Nobody's Watching (It Isn't 2029) At the end of my last post I said there was a second migration hiding behind the first one — that the platform you're racing to isn't quite the finish line either. A few people asked me what I meant. This is what I meant. Everyone in the Atlassian world is staring at one date. March 2029.
Atlassian Migrations — Nobody's Paid to Make It Smaller (Including Me) Nobody on your migration is paid to make it smaller. Not your migration partner. Not your app vendors. Not the admin who could tell you, off the top of their head, exactly which half of it should never move. Not even me.
Stop Hand-Crafting /run and /tmp Trees: Practical systemd-tmpfiles on Linux Most Linux hosts already run systemd-tmpfiles on every boot. Packages drop snippets under /usr/lib/tmpfiles.d/, the early boot units create volatile trees under /run and friends, and a timer later ages junk out of /tmp and /var/tmp. Operators still keep reinventing the same work with ad-hoc shell: mkdir -p /var/lib/myapp/cache chown myapp:myapp /var/lib/myapp/cache chmod 0750 /var/lib/myapp/cache find /var/lib/myapp/cache -mtime +7 -delete That works until the next image rebuild, the next package update that races your
I'm building MetricFlow, a performance dashboard for your website that runs on your own machine. It shows real-user metrics from New Relic and JavaScript errors from Sentry side by side, per page, and can suggest what to fix first. npx @karan171996/metricflow That opens a dashboard at http://localhost:3000. There is no MetricFlow server and no account to create. Your keys stay on your machine.
How I Built a Cheaper & Easier Alternative to Google Translate API for Flutter & React Building multi-language applications should be simple. But if you have ever tried adding localization to a Flutter, React, or mobile app using official Cloud Translation APIs (like Google Translate API or AWS Translate), you know the pain: Complex GCP / AWS Setup: Creating Cloud Projects, setting up Service Account JSON keys, configuring IAM permissions, and linking a credit card just to translate a few app strings. High & Unpredictable Costs: Google Cloud charges $20 per 1 million characters after a tiny fr