TLDR Dev 2026-03-11
Levels of agentic engineering 📈, AI forces good code 🙌, faster data grids ⚡️
The 8 Levels of Agentic Engineering (18 minute read)
Each level of agentic engineering (using agents effectively) represents a leap in output, with a "multiplayer effect" encouraging teams to advance together to maximize efficiency. The progression starts with basic AI assistance like tab-completion and AI-focused IDEs, then moves into more sophisticated practices such as context engineering, compounding engineering, and integrating MCPs and custom skills. The highest levels involve building autonomous agents with automated feedback loops.
Micro Frontends: When They Make Sense and When They Don't (11 minute read)
Micro frontends are really an org problem disguised as a tech problem — the actual win is letting teams own their vertical slice end-to-end without coordinating deployments. However, the complexity cost (separate CI, dependency versioning hell, and consistency overhead) isn't worth it unless you have 5+ teams actively stepping on each other. For most teams, a well-structured monorepo gets you 90% of the benefit with none of the pain.
The Cline CLI got compromised. Here is how (7 minute read)
On February 17, an attacker published a malicious cline@2.3.0 to npm using a stolen publish token, injecting a postinstall hook that silently installed OpenClaw (a background AI daemon with full disk/terminal access) on ~4,000 machines over 8 hours. The theft was pulled off via a chain of prompt injection through Cline's AI issue triage bot. The fix had already been published by a researcher 8 days earlier, but Cline revoked the wrong token, leaving the npm publish token alive for a second actor to exploit.
AI Is Forcing Us To Write Good Code (11 minute read)
All the optional best practices that devs commonly skip, such as 100% test coverage, small well-scoped files, end-to-end types, and fast ephemeral dev environments, turn out to be non-optional when agents are coding. Agents can't navigate a messy codebase and amplify messiness.
Writing my own text editor, and daily-driving it (16 minute read)
This dev built his own customized text editor over two years. He dogfooded it himself, using his editor daily despite its early pains, which drove rapid development and feature implementation over recent months. He found a lot of satisfaction and increased long-term productivity from building a tool perfectly tailored to his needs.
When AI Writes Your Code, Scanning Syntax Isn't Enough (Sponsor)
Post-commit scanners can't answer three questions that make a big difference: Who wrote this line? Why was it added? Under what security policy? Learn why AppSec needs to shift from scanning code to understanding context (origin, intent, and policy alignment) directly in the IDE.
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Checkmarx Developer Assist.RCLI (GitHub Repo)
RCLI is an on-device voice AI assistant for macOS Apple Silicon, with a complete speech-to-text, LLM, and text-to-speech pipeline that operates entirely locally without cloud services. It allows users to control their Mac with 38 voice actions, perform local document Q&A over their files, and manage models through an interactive terminal interface.
Crawl web content (14 minute read)
The Cloudflare `/crawl` endpoint allows users to scrape web content by providing a starting URL and following links up to a configurable depth or page limit. The process involves initiating a crawl job with a POST request to receive a job ID, then using that ID in a GET request to poll for job status and retrieve the results. Content can be returned in HTML, Markdown, or JSON.
Page Agent (GitHub Repo)
Page Agent is an in-page JavaScript GUI agent for controlling web interfaces using natural language. It operates directly within the webpage, removing the need for browser extensions or external tools, and uses text-based DOM manipulation.
I'm Building Agents That Run While I Sleep (6 minute read)
AI agents can write code while you sleep but there is no reliable way to verify the correctness of the AI-generated output, as AI-written tests often have the same misunderstandings as the code itself. To address this, the proposed solution involves adopting a modified Test-Driven Development approach, where specific acceptance criteria are defined by humans before the AI agent starts writing any code.
Debian decides not to decide on AI-generated contributions (11 minute read)
Debian recently had a debate regarding a proposed General Resolution aimed at establishing policies for accepting AI-generated contributions. There were deep divisions among developers, with concerns ranging from the precise definition of "AI" and "LLMs" to ethical implications, potential impacts on contributor onboarding, and copyright issues. Ultimately, the project failed to reach a consensus, with participants unable to agree on shared terminology or a unified approach.
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