Editorial
Welcome to AIgentic
AIgentic publishes daily, data-driven coverage of agentic systems, LLM tooling, and AI infrastructure. Mondays, Wednesdays, and Fridays: cross-model benchmarks. Tuesdays and Thursdays: skills coverage. Weekends: arxiv digests. Each post is AI-drafted from primary sources and edited by humans before publication.
This is the first post on AIgentic. The site publishes one post per day, every day, covering a tightly scoped beat: agentic systems, LLM tooling, and the infrastructure sitting under them.
Why another AI blog
Most AI coverage today falls into two buckets. Either it rephrases company press releases a few hours after the announcement, or it writes generic tutorials that the models themselves can produce on demand. Neither bucket is useful for people building with this technology, and neither gets cited by the answer engines that developers increasingly use as their first stop.
The gap is structured, primary-source coverage. Someone tracking the agentic-tooling landscape wants to know whether LangGraph’s commit velocity is accelerating or decelerating this month. Someone picking between frontier models for a tool-use-heavy workload wants benchmark numbers run on the same task, the same day, with the same prompt. Someone keeping up with research wants a filtered, scored view of the last week’s arxiv output instead of a 300-paper firehose.
That is what this site is for.
What each week looks like
Monday, Wednesday, Friday: benchmark of the day. A standardized task (tool use, multi-step planning, code generation, retrieval evaluation) is run across three frontier models and published with the full results table, the exact prompt, and per-model cost. Tasks recur quarterly so trend lines develop. Reproducibility is the point: anyone with API keys can rerun.
Tuesday and Thursday: skills. Deep coverage of the agent-skills ecosystem (Claude Code skills, MCP servers, agent SDK patterns, plugin scopes), with worked examples and concrete file paths. An undercovered beat where primary-source walkthroughs still rank.
Saturday and Sunday: arxiv digest. Recent agentic and LLM-tooling papers, filtered from the broader cs.AI and cs.LG firehose, scored on a consistent rubric, and summarized with one flagship paper getting an extended treatment.
Every fourth Sunday: deep dive. A longer-form pillar piece that synthesizes the month’s coverage or takes a stance on a larger question.
How the site is produced
Posts are AI-drafted from a tight structural prompt, using data fetched fresh from primary sources at publication time. No content is rephrased from other blogs or news outlets. Every draft is edited by humans, who verify claims against primary sources, correct errors, and sign off before publication. Every post declares its category, carries a dated publishedAt, and is emitted as plain semantic HTML with no client-side JavaScript.
The site ships with an RSS feed, a sitemap, and an llms.txt index for answer-engine discoverability. Accessibility targets WCAG 2.1 AA. Color contrast, focus states, semantic structure, and motion preferences are all respected.
What you can do with this
Subscribe to the RSS feed if you want the full stream. Point your agent at llms.txt if you want a machine-readable index. Read the about page for more detail on the editorial approach.
Tomorrow: the first benchmark.
Frequently asked
How often does AIgentic publish?
One post per day, every day. The editorial rotation covers cross-model benchmarks (Mon, Wed, Fri), skills and tooling pieces (Tue, Thu), and arxiv digests (Sat, Sun). A longer-form pillar piece ships every fourth Sunday.
Who writes the posts?
Posts are AI-drafted from a tight structural prompt, using primary-source data fetched at publication time from GitHub, arxiv, and model vendor APIs. Every post is edited by humans before it ships. The drafting pipeline enforces structure, accuracy constraints, and citation requirements; human editors verify claims, fix errors, and sign off.
What makes this different from other AI blogs?
Every post contains original structured data (benchmark scores, skill walkthroughs, paper rubrics) rather than rephrased news. The content model is optimized for both SEO and GEO: answer-first TL;DRs, tables and lists over prose, explicit entity naming, inline citations to primary sources, and FAQ schema on every applicable piece.
How do I subscribe or consume the content programmatically?
The site publishes a standard RSS feed at /rss.xml and an llms.txt index at /llms.txt for answer-engine discovery. Each post is also available as raw Markdown at /{slug}.md for clean machine consumption.