These notes describe the patch used to make Codex Mobile discover the unofficial Linux desktop port from https://github.com/ilysenko/codex-desktop-linux.
Important
You do not need to patch the Electron app to connect Codex Mobile to Codex Desktop. You can enable the bridge by adding these feature flags to the Codex config file, usually $HOME/.codex/config.toml:
[features]
remote_connections = true
Moved to my blog here: https://cedricchee.com/blog/2026-05-23-aie-sg-leadership-workshop-recap/
The Summer of AI is in full swing in Singapore.
This weekend, more than 1K builders, founders, and operators are expected to walk into the first Asia edition of the AI Engineer conference — an important conference for people looking to wield the power of AI rather than just discuss it. Super pumped for it. Feels like Christmas came early.
Personally, I'm already building, but this conference arrives at the right moment. I want to ship systems into production - go deeper on agents, workflows, infra, production patterns, orchestration layers, evals, AI operating models, governance, and org design in the agent era.
The industry is shifting from "should we use AI?" phase. The hard questions now are operational:
A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.
NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.
TL;DR: Project Glasswing is not just PR, but the interesting part is not Anthropic’s narrative, it is the underlying shift in capability and what that means for software security.
Over the past week, some of the most credible people in security have been pounding the same drum: AI-assisted vulnerability research is getting real, fast.
Thomas Ptacek (tptacek) flatly wrote that “vulnerability research is cooked.” Simon Willison (simonw) highlighted the same shift. Daniel Stenberg of curl has also said AI has gotten genuinely useful at finding bugs and vulnerabilities. Colin Percival (cperciva), former FreeBSD security officer. The most significant individual contributions in the narrative given cperciva's credibility
Date: 2026-04-04
In a new Lenny Rachitsky's podcast episode titled "An AI state of the union: We've passed the inflection point, dark factories are coming, and automation timelines".
Highlights: https://simonwillison.net/2026/Apr/2/lennys-podcast/
Transcript: https://www.lennysnewsletter.com/p/an-ai-state-of-the-union
One of the hardest parts of building an agent harness is constructing its action space.
Claude acts through Tool Calling, but there are a number of ways tools can be constructed in the Claude API with primitives like bash, skills and recently code execution (read more about programmatic tool calling on the Claude API in Lance Martin's new article).
Given all these options, how do you design the tools of your agent? Do you need just one tool like code execution or bash? What if you had 50 tools, one for each use case your agent might run into?
To put myself in the mind of the model I like to imagine being given a difficult math problem. What tools would you want in order to solve it? It would depend on your own skills!
Blog post: https://lucumr.pocoo.org/2026/3/20/some-things-just-take-time/
Author: Armin Ronacher. Published March 20, 2026.
Blog post: https://haskellforall.com/2026/03/a-sufficiently-detailed-spec-is-code
The post argues that the agentic coding movement's promise — that engineers can simply write specification documents and have AI agents generate working code — is fundamentally flawed. The central claim is captured in the title: if you make a specification precise enough to reliably generate correct code, the specification itself effectively becomes code. There is no shortcut that avoids the hard intellectual work of programming.
Podcast: www.youtube.com/watch?v=kwSVtQ7dziU
This is a comprehensive analysis of the interview with Andrej Karpathy on the "No Priors" podcast, detailing the profound shifts in software engineering driven by AI agents and Autoresearch.
This including key insights, a structured outline, and any critical nuances mentioned.