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Cloudflare recently added support for Claude Managed Agents, allowing developers to run and manage Claude agents within Cloudflare. Developers can connect agents to private systems, choose their runtime environment, and monitor agent activity using Cloudflare services. By Renato Losio

Two years ago, when I got my first freelance client, I was still in my final semester of college. A...

Last year, I was doing something that felt increasingly absurd: manually reading AI-generated content to decide if it was "good enough." PostAll — the content automation tool I've been building — was producing hundreds of blog posts per week for clients. And I had no systematic way to evaluate quality at scale. I was spot-checking. Vibes-checking, really. That doesn't work at volume. So I built a programmatic quality analysis pipeline, ran it over 1,000 AI-generated posts, and let the numbers tell me what my gut was missing. The findings surprised me. A few of them genuinely changed how I think about AI content quality. What I Actually Measured First, a definition of terms, because "quality" is almost meaninglessly vague in this space. I broke quality into five measurable dimensions: Readability — Flesch-Kincaid grade level and reading ease score Keyword density — Target keyword frequency and distribution across the post Grammar error rate — Errors per 1,000 words, caught via LanguageTool's API Factual accuracy — Claims that could be verified programmatically (dates, statistics, named entities cross-referenced against a knowledge base) Structural consistency — Presence of expected elements: intro hook, subheadings, conclusion, CTA I used 1,000 posts across three categories: SaaS product descriptions, long-form "how-to" articles (1,200–2,000 words), and listicles (500–900 words). All were generated by PostAll using GPT-4o, with various prompting strategies. The Setup The analysis pipeline isn't complicated, but the piece that makes it useful is the batch processing layer: import anthropic import language_tool_python import textstat from dataclasses import dataclass from typing import Optional import json @dataclass class QualityReport : post_id : str flesch_reading_ease : float flesch_kincaid_grade : float grammar_errors_per_1000_words : float keyword_density : float structural_score : int # 0–5 based on element presence flagged_claims : list [ str ] overall_score :

Why Does Using an ORM Decrease Database Performance? While trying to optimize the shipping module in a production ERP, I noticed that database queries were incredibly slow. At first, I examined the SQL queries and checked the indexes. However, I couldn't get the performance boost I expected. The problem lay in the Object-Relational Mapper (ORM) library, which was the cornerstone of our application. ORMs make things easier for software developers by providing an abstract layer for database operations, but this convenience often comes at a performance cost. In this post, I will explain why using an ORM decreases database performance, using concrete examples from my own field experience. The core promise of ORMs is to keep developers away from SQL and allow them to interact with databases in a way that is more aligned with the object-oriented paradigm. This is a huge advantage, especially in small and medium-sized projects or rapid prototyping processes. However, when things get complex and performance becomes critical, the efficiency of the queries generated by ORMs starts to be questioned. In many cases I have encountered, especially in enterprise software development processes, the default behaviors of ORMs created an unexpected load on database servers. Query Inefficiencies Generated by ORMs ORMs usually manage database relationships using mechanisms like "eager loading" or "lazy loading". Depending on the developer's preference, these mechanisms either fetch all related data at once (eager loading) or fetch it in pieces as needed (lazy loading). However, ORMs may not always perform these loads in the most optimized way. For example, while only a few fields like ID and name are sufficient in a list view, the ORM might query the entire table or all related tables. This situation causes unnecessary data transfer and unnecessarily overloads the database server. To give an example, on the order list screen of an e-commerce site, we needed to display the customer inform

The company has finally set a date for the first customer deliveries of what CEO RJ Scaringe has said is "maybe the most important thing we’ve launched to date."

The Series B round was led by Battery Ventures.

Ferrari fans don’t like the design of the new Luce EV, an electric four-door sedan that just doesn’t look like the Ferraris of old. It was designed with help from Jony Ive’s LoveFrom, but what worked for Ive at Apple isn’t working for Ferrari. The Luce’s launch immediately preceded a stock drop that even an […]

Amid ongoing economic anxieties, BLK and other companies are giving away basic essentials to appeal to the public.

Motorola says that recently discovered behavior, which saw some of its phones sending users to an affiliate tracking website before opening the Amazon app, was "unintended" and has been "promptly corrected." The company didn't explain how the error was introduced in the first place. "Recently, Motorola acted quickly to resolve an issue that was identified, […]

Starship is grounded until SpaceX finds out why the first V3 booster failed during its first test flight.

Designed by Jony Ive and a host of ex-Cupertino colleagues, the Luce shows us what might have been had Apple made good on its $10 billion bet.

These display smart glasses can connect to a phone, laptop, or gaming handheld and project the screen to your eyeballs.

China's AI boom is producing world-class talent, and Beijing is increasingly reluctant to let them go elsewhere.

The database provider is eyeing a public debut within the next few years.

Until we get something like ::nth-letter , there are still some really cool text effects we can make from existing CSS features, like letter-spacing , ::first-word and ::first-line . Revealing Text With CSS letter-spacing originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

The company is mapping Alexandria and, soon, Arlington—right across from the power center of Washington, DC.

The feds are raising the alarm about a new category of threat.

The email instructs workers to report behavior that predates Donald Trump’s second inauguration. One employee tells WIRED it felt like a “reminder to narc on your coworkers.”

An octopus about the size of a golf ball was first spotted in 2015 near Darwin Island. A new study gives it both a formal description and a name.

Aaron Erickson discusses the evolution of AI workflows, shifting from "vibe checking" to building reliable, multi-agent frameworks. He explains how to combine deterministic software guardrails with agentic discovery, optimize agent hierarchies, leverage time-series foundation models, and implement rigorous evaluation pyramids to ensure architecture scales effectively in production. By Aaron Erickson