Since launching EvoBlog internally, I’ve wanted to improve it. One way of doing this is having an LLM judge the best posts rather than a static scoring system.
I appointed Gemini 2.5 to be that judge. This post is a result.
The initial system relied on a fixed scoring algorithm. It counted words, checked readability scores, & applied rigid style guidelines, which worked for basic quality control but missed the nuanced aspects of good writing.
AI coding assistants like Cursor & Replit have rewritten the rules of software distribution almost overnight.
But how do companies like these manage margins? Power users looking to manage as many agents as possible may find themselves at odds with their coding agent providers.
Let’s create a hypothetical million user AI coding company & play around with some numbers.
Let’s assume this company has four pricing plans: $20 per month, $50 per month, $500 per month, & $1,500 per month. We assume a 1% conversion rate for the first two plans, a 0.5% conversion rate for the $500 per month pricing plan, & 0.1% for the $1,500 plan.1
Groupon grew from a staff of a few dozen to over 350. Revenue and bookings also grew swiftly, and the company was valued at over $1 billion after just 16 months in business, the fastest company ever to reach this milestone.[20]
A little-known Midwest company called The Point became the fastest-growing company ever up until that age.
By developing a collective coupon, the idea that if a certain number of people agreed to buy a product or a service, all of them would receive a discount, the Point surged on explosive viral growth.
One of the hardest mental models to break is how disposable AI generated content is.
When asking me to generate one blog post, why not just ask it to generate three, pick the best, use that as a prompt to generate three more, and repeat until you have a polished piece of content?
This is the core idea behind EvoBlog, an evolutionary AI content generation system that leverages multiple large language models (LLMs) to produce high-quality blog posts in a fraction of the time it would take using traditional methods.
GPT-5 achieves 94.6% accuracy on AIME 2025, suggesting near-human mathematical reasoning.
Yet ask it to query your database, and success rates plummet to the teens.
The Spider 2.0 benchmarks reveal a yawning gap in AI capabilities. Spider 2.0 is a comprehensive text-to-SQL benchmark that tests AI models’ ability to generate accurate SQL queries from natural language questions across real-world databases.
While large language models have conquered knowledge work in mathematics, coding, and reasoning, text-to-SQL remains stubbornly difficult.
Perplexity AI just made a $34.5b unsolicited offer for Google’s Chrome browser, attempting to capitalize on the pending antitrust ruling that could force Google to divest its browser business.
Comparing Chrome’s economics to Google’s existing Safari deal reveals why $34.5b undervalues the browser.
Google pays Apple $18-20b annually to remain Safari’s default search engine¹, serving approximately 850m users². This translates to $21 per user per year.
The Perplexity offer values Chrome at $32b, which is $9 per user per year for its 3.5b users³.
In 1999, the dotcoms were valued on traffic. IPO metrics revolved around eyeballs.
Then Google launched AdWords, an ad model predicated on clicks, & built a $273b business in 2024.
But that might all be about to change : Pew Research’s July 2025 study reveals users click just 8% of search results with AI summaries, versus 15% without - a 47% reduction. Only 1% click through from within AI summaries.
Cloudflare data shows AI platforms crawl content far more than they refer traffic back : Anthropic crawls 32,400 pages for every 1 referral, while traditional search engines scan content just a couple times per visitor sent.
GPT-5 launched yesterday. 94.6% on AIME 2025. 74.9% on SWE-bench.
As we approach the upper bounds of these benchmarks, they die.
What makes GPT-5 and the next generation of models revolutionary isn’t their knowledge. It’s knowing how to act. For GPT-5 this happens at two levels. First, deciding which model to use. But second, and more importantly, through tool calling.
We’ve been living in an era where LLMs mastered knowledge retrieval & reassembly. Consumer search & coding, the initial killer applications, are fundamentally knowledge retrieval challenges. Both organize existing information in new ways.
2025 is the year of agents, & the key capability of agents is calling tools.
When using Claude Code, I can tell the AI to sift through a newsletter, find all the links to startups, verify they exist in our CRM, with a single command. This might involve two or three different tools being called.
But here’s the problem: using a large foundation model for this is expensive, often rate-limited, & overpowered for a selection task.
What force could dethrone AWS after more than a decade of unchallenged dominance?
For years, Amazon Web Services ruled the cloud infrastructure market. It was the default choice without a question for every startup.
Then OpenAI released GPT-4. Microsoft’s exclusive partnership with OpenAI transformed Azure from a second-place player into the obvious choice for AI-first companies. With this week’s earnings, we are seeing the ultimate impact of that strategic decision.