codex-cli 4

This week, I learned: It doesn’t always take time to learn or convey things. (Early trust can be built instantly, e.g. vulnerability.) At first, experts don’t know how to make skills explicit. But trainer effort could compress 10X via evals, practice loops, and feedback. Learner elapsed time would compress less. Everyone has something worth discovering, but not every conversation is worth my time right now. So, meet new people with trust, attention, and good questions. Continue if there’s emotional / intellectual stimulation (surprising, interesting, moving, connecting, energizing, challenging), else exit warmly with respect. To avoid getting overwhelmed in ultra-interesting conversations, mental closure helps. During the conversation, pause, name, reflect, and close. “Wait, you’re saying X. I should do Y. I’ll reflect/act tonight.” or “Wow, let’s sit with that for 5 seconds. You mean X. I feel Y. I’ll drop.” After the conversation, summarize: “What struck me were X1, X2. I’ll plan Y1, Y2 and drop Z1, Z2.” Then take a short break. Setting "markdown.editor.updateLinksOnPaste.enabled": false might fix the delay / freezing (infinite spinner) issue when pasting Markdown in VS Code. The bottleneck to quality of AI output has shifted from model quality to harness quality (and this is not obvious to many people). It is important, therefore, to optimize harness usage rather than prompts usage, i.e. harness engineering over context engineering. I use ug --smart-case --bool -Q --sort=rtime to interactively search for text in files. It’s like VS Code search-across-files. Here are the shortcuts I find useful: Alt-g: Glob (filter files to search in) Alt-[ or ]: Decrease or increase context (lines before / after) Alt-w: Word match toggle Alt-c: Count lines toggle Alt-u: Ungroup - show lines once even if multiple matches Using AI for health seems to have reached a tipping point. Three people have pitched an idea in this space to me in the last three days. One is a managed personal health provider who wants to tie-up with hospitals to gather data to improve AI health advice. Second is an enterpreneur who wants to enable the Indian Govt to use AI to improve public health - given the low proportion of trained doctors in public hospitals. The third is a colleague who is uploading personal health reports, fitness data, DNA data, wearable data, etc. and suggest daily habits such as fitness, nutrition, sleep, medication, etc. to optimize health. Changing the topic (e.g. asking a question) instead of answering a question is powerful. It lets you decline requests, avoid sensitive topics, ignore boring ones, learn rather than teach, and bring in your agenda - all at one shot. I need to un-practice my 40-year habit of answering questions. (This is selfish. I forgive myself.) bolt.diy seems like a browser-embeddable coding agent. That is, you can add bolt.diy to your web page and have it build apps. That might be a pretty powerful upgrade to generative UI - where pages build themselves based on the user input. Codex has a few new features in the last few months. Codex can generate images and have voice conversations. /goal sets an overall session goal to avoid getting side-tracked. /side is like Claude Code’s /btw - for a side task while the main task continues. /resume lets you switch to any previous session. /keymap debug lets you edit the keymap and inspect what keystrokes the terminal sends. @ lets you mention files, directories, skills, and plugins. Ctrl+R works, lets you pick a previous prompt. Ctrl+O copies the last answer as Markdown. Hooks are stable. PreToolUse lets you log every tool, SessionStart lets you inject repo-specific rules. MCPs with readOnlyHint can run in parallel. codex doctor diagnoses environment issues. codex remote-control lets you remotely control Codex, making it a server. Codex Python SDK is better and you can have Codex run as a back-end more smoothly. To change others’ behavior, embody (not preach) it visibly and consistently, make it easy to copy, and ask without forcing. It takes time, though. ChatGPT Governance is how groups keep promises when things (people, incentives, environment, pressure) change. A simple way to explain what governance is to someone who doesn’t understand why governance matters, and guide on when it does not matter. Forward Deployed Engineers are the next evolution of data scientists, IMHO. AI can do data science. Data scientists will likely act as the “Human As An Interface” (HaaI) to business, proactively identifying and solving problems - a space business analysts traditionally occupied. Of course, business analysts will likely do the same without needing data scientists to help. But since AI replaces data scientists more than business analysis, I expect that the % of data scientists who become FDEs will be higher than business analysts. The value of data exported from software is high. For example, your email, social posts, CRM / HRMS / ERP dumps, service tickets, purchases, notes etc. These let you create a personal / organizational digital brain. Hence proprietary solutions will make exports harder and open solutions will emerge. To live-preview any publicly accessible Excel file, you can embed or link to https://view.officeapps.live.com/op/embed.aspx?src=YOUR-URL The Codex app can now use the browser much better and faster since last week if you enable “Dev mode” OpenAI. THis uses CDP - which is more efficient than screenshots - and is something Codex CLI has been doing for many months. In Codex, Claude Code, etc. you can submit a prompt while the agent is working to steer it, i.e. after it completes a turn (e.g. a tool call) it will factor in the prompt. You can also queue it. Neither of these is available on ChatGPT or Claude.ai, though it’s such an important feature. On ChatGPT, submitting another prompt stops the previous run and the agent continues with the new prompt. By default, git uses ~/.config/git/ignore or %USERPROFILE%\git\ignore as the global .gitignore. You can override that with git config --global core.excludesFile PATH. StackOverflow Questions I was asked Week ending 21 Jun 2026 ...

It’s incredible how far coding agents have come. They can now solve complete exams. That changes what we should measure. My Tools in Data Science course has a Remote Online Exam. It was so difficult that, in 2023, it sparked threads titled “What is the purpose of an impossible ROE?” Today, despite making the test harder, students solve it easily with Claude, ChatGPT, etc. Here’s today’s score distribution: ...

This week, I learned: Before founding a place to do good, work in a place that does good and learn. Ben Werdmuller What should we teach when vibe coding becomes good enough for non-coders? Ethan Mollick Problem decomposition Clear communication & spec writing Core technical foundations: file systems, access control, networking, APIs, version control, data structures, databases, deployment Software development skills: Debugging, Testing, Refactoring, Design patterns, UI/UX Project management: requirements, prioritization, scoping, … Codex CLI tips: codex --add-dir $DIR lets you write into $DIR codex --full-auto is the equivalent of codex --sandbox workspace-write --ask-for-approval on-request Terse code is not necessarily easier or harder for LLMs to write. It’s about how unusual (or not aligned with training data) the code is. Gabi Teoduru How are people using browser agents like Comet / Atlas? Simon Willison Most popular: YouTube video summaries with timestamps Most useful: Form filling: Government forms, data entry, repetitive bureaucratic tasks Foreign language navigation: Applying for pension in Korea, navigating sites in other languages Time reporting auto-completion Insurance claims: Reading policy documents and drafting appeals (successfully got claim reimbursed in India) Compliance training click throughs Next most useful: Shopping / planning Energy provider comparison - Comet checked current plan vs competitors on Check24, calculated exact annual savings per provider Financial tracking: Finding Amazon orders, tracking Airbnb spending with refund calculations, analyzing bank transactions Trip planning: Mapping 50-100 places on Google Maps automatically Interesting: Airport shuttle discovery - Found shuttle that user missed in manual searching HubFS mounts GitHub repos on the file system. Every file system action directly works on GitHub via a REST API. Useful for some scenarios but less useful for note-taking than something like GitDoc which offers a delayed sync. Ernest Ryu solved an open problem in convex optimization using ChatGPT. Quotes: ChatGPT is now at the level of solving some math research questions, but you do need an expert guiding it. ChatGPT was really effective at accelerating my progress. This work took about 12 hours, spread over 3 days. In hindsight, the proof is really simple. But I iterated through so many other strategies that didn’t pan out, and ChatGPT crucially helped to quickly explore and eliminate those dead-end approaches. Also, the key successful steps were suggested by ChatGPT. ChatGPT did not produce the proof in a single prompt. The process was highly interactive. It generated many arguments, roughly 80% of which were incorrect. Yet some were genuinely novel to me. Whenever I recognized a novel idea, whether correct or only partially so, I distilled the key insight and prompted ChatGPT to develop it further. My contribution: Filtering out incorrect arguments and accumulating a set of correct facts. Identifying promising new lines of reasoning and guiding ChatGPT to explore them further Recognizing when a strategy had been fully explored and deciding when to move on. ChatGPT’s contribution: Producing the final proof argument. Significantly accelerating my (or our) exploration of the many dead-end arguments, rapidly ruling out approaches that did not work. Comparing the GPT 4.1 and 5 models at all different of reasoning, I’ve switched my default from GPT 4.1 mini to GPT 5 mini (medium). Far smarter for a slightly higher cost. Artificial Analysis python -m pdb -c continue script.py or uv run -m pdb -c continue script.py runs a script and drops into pdb on unhandled exceptions (post-mortem). ChatGPT Technology removes constraints. We then do what we really value. Claude When writing became digitized, we stopped cared about spelling/handwriting for its own sake. Spelling bees and handwriting classes declined. “ur” is acceptable. When fitness tracking became easy, many just track, few exercise more. Few people value exercise When GPS became ubiquitous, we stopped learning geography. Most value arriving, not knowing When photography became unlimited, most captured moments. Few perfected shots I had Codex scrape my ~2,000 pending invites on LinkedIn and asked ChatGPT to analyze it. Here are learnings: ChatGPT, private Power-law. 5% of inviters account for ~42% of all common connections. Top 10 people alone for ~20%. IITM student invites are high (~14%), but with 0-2 common connects, i.e. distant strangers. EdTech is tiny in count but has the highest common connections per person (outlier-sensitive but real). Among ≥20-commons, many hold VP/Head/Site-Lead titles in Data/AI or GenAI (not just recruiters). GenAI people are 7-8% and steady across months. Not a useful signal to prioritize. Premium ~ Senior. Premium accounts show ~40% senior titles vs ~29% for non-premium. Finance invites have higher seniority rate and more common connects than healthcare. Followers have higher common connections (~6 vs ~4). ⭐ Memory can be code. Agent memory is anything it choose to persist. Agents can write code on the fly to automate tasks, save them, and serve the code on the next request, potentially modifying the code as required. This is like the conscious mind saving a habit for the subconscious to execute fast. Finally: Microsoft Office has an agent mode that lets you talk to it and do stuff. The Verge

I record my conversations these days, mainly for LLM use. I use them in 3 ways: Summarize what I learned and the next steps. Ideate as raw material for my Ideator tool: /blog/llms-as-idea-connection-machines/ Analyze my transcript statistics. For example, I learned that: When I’m interviewing, others ramble (speak long per turn), I am brief (less words/turn) and quiet (lower voice share). In one interview, I spoke ~30 words per turn. Others spoke ~120. My share was ~10%. When I’m advising or demo-ing, I ramble. I spoke ~120 words per turn in an advice call, and took ~75% of the talk-time. This pattern is independent of meeting length and group size. I used Codex CLI (command-line tool) for this, with the prompt: ...