A colleague told me a near-miss horror story. As Google began deprecating Gemini 2.0, we moved to Gemini 2.5 Pro. But reasoning is enabled by default and cannot be turned off. For our specific problem statement, reasoning was not required. Token costs increased 10x and speeds were 3-4x slower. We moved the client to Gemini 2.5 Flash Lite, which has reasoning turned off by default and offers much lower latency. ...
This week, I learned: Warp has a terminal agent feature - allowing Warp to control a terminal via text. I find that regular coding agents like Codex can do that too with tmux. For example, I opened a session and had Codex run commands in it while I watched. Here’s the guidance it needed: # Create a new session tmux new-session -d -s $SESSION 'uv run --with pandas,httpx,lxml python -iqu' # Capture output to a log file tmux pipe-pane -t $SESSION -o "cat >> /tmp/$LOG" # Run a command tmux send-keys -t $SESSION 'print(1 + 2)' C-m # See output cat /tmp/$LOG # Capture the last 5 lines of the pane tmux capture-pane -p -t $SESSION -S -5 Notes from Early science acceleration experiments with GPT-5 - via Claude LLMs are accelrating research because they are good at: Literature search, especially across disciplinary boundaries Generating and checking routine calculations Proposing variations on known techniques Identifying connections between disparate results Producing first-draft code for well-specified problems Explaining why certain approaches won’t work But they’re curently struggling with the following - though it’s a shrinking space Genuinely novel conceptual leaps (but this is increasingly happening, e.g. Sawhney and Sellke’s problem #848) Recognizing when it’s plagiarizing, e.g. when it “discovered” a proof for the Chevalley-Warning theorem which was copied from a Noga Alon paper - it wasn’t conscious of this Knowing what it doesn’t know Distinguishing important problems from unimportant ones Understanding the “negative space” of mathematics (why certain problems are hard, why obvious approaches fail) Anthropic introduced three excellent tool use practices that I expect will be adopted widely. Tool search: Don’t pass the tool definitions to the model. Model can ask for a tool search when needed Programmatic tool calling: Instead of calling a tool, it’ll return a Python program to execute that will call the tools! This is a huge win Tool use examples: Lets you specific examples of tool calls to guide th model better The Hacker News thread flags that CLIs solve these - but CLI updates are hard, while APIs auto-update. With AI, some skills that beome more valuable are (and will soon be in short supply, hence need to be taught) are: # Problem formulation (“What question should we actually ask?”) Traits: Curiosity (absolutely), systems thinking, comfort with ambiguity, metacognition (thinking about your thinking) Practice reframing exercises (“What are 5 other ways to frame this?”), study great questions in your field, work backward from outcomes, learn adjacent domains. The “5 Whys” technique helps. Also: deliberately pause before diving into solutions—force yourself to spend time in the question space. Taste and judgment (“Is this response appropriate?”) Traits: Pattern recognition from experience, cultural literacy, empathy, contextual awareness, aesthetic sense How to strengthen: Immerse yourself in excellent examples, study spectacular failures (they’re more instructive!), get feedback on your calls, practice explaining why you made a judgment. Build a “swipe file” of great/terrible examples. The key is volume—you need lots of reps. Quality assessment (“Is this AI output correct?”) Traits: Healthy skepticism, attention to detail, domain knowledge, logical reasoning, understanding of edge cases How to strengthen: Study common AI failure modes, build verification checklists, practice the “does this make sense?” test, learn what “good” looks like in your domain, cross-reference claims. Develop your “bullshit detector” by analyzing why wrong answers feel wrong. Creative synthesis (“How do these ideas connect?”) Traits: Associative thinking, wide knowledge base, playfulness, comfort with non-obvious connections, intellectual courage How to strengthen: Consume diverse inputs outside your field, practice analogical thinking (“X is like Y because…”), use visual thinking tools like concept maps, study how innovations happen in other domains, give yourself permission to make weird connections. Read broadly—fiction, history, science. Domain expertise (“Does this solution work in reality?”) Traits: Deep curiosity, persistence, willingness to get hands dirty, learning from failure, long-term commitment How to strengthen: Deliberate practice on real problems, seek mentorship, study edge cases and failure modes, build things (don’t just read about them), learn your field’s history. The “10,000 hours” thing is real, but it’s quality hours that matter. Meta pattern: Reflection loops: doing something, then analyzing why it worked/didn’t. Exposure to excellence: you can’t develop taste without seeing great work. Some more new CLI tools I installed: trash-cli: Alias rm to move files to trash instead of deleting permanently. After a week of seeing ligatures in Fira Code, all other fonts look ugly. My favorite ligatures: !== ==> =» <–> (and every possible arrow) >= ||> ||- |- … The first name, alphabetically (at least among Straive employees) is “Aabida” and the last is “Zyrene”. Something I would never have discovered working in a smaller company. chokidar-cli is an easy way to run commands when files change, e.g. npx -y chokidar-cli '**/*.js' -c 'npm run build' npx -y mapscii shows a map on the terminal. Not too useful, not maintained, but very interesting. termsvg converts asciinema .cast files to animated SVG suitable for embedding in GitHub (e.g. via mise x github:MrMarble/termsvg -- termsvg export file.cast --minify). The animated SVG is ~10X larger than the .cast file. The GZipped size is fine but saving it as .svgz is not recognized by GitHub. In contrast, agg, the official asciinema-to-GIF converter, creates .GIF files that are only 5X larger. The most efficient seems to be embedding via asciinema.org usql queries MySQL, Postgres, SQLite, MSSQL, Oracle, etc via a single interface. For example, usql 'mysql://rfamro:@mysql-rfam-public.ebi.ac.uk:4497/Rfam' -c "SELECT * FROM clan limit 3;". But DuckDB is more versatile, IMHO. INSTALL mysql; LOAD mysql; ATTACH 'host=mysql-rfam-public.ebi.ac.uk port=4497 user=rfamro database=Rfam' AS rfam (TYPE mysql); SELECT * from rfam.Rfam.clan LIMIT 3; SELECT * FROM 'file.xlsx' LIMIT 3; SELECT * FROM 'file.csv' LIMIT 3; Autistic and allistic people just have different communication styles. Autistic people have no trouble understanding other autists. They just happen to be in a minority which makes it seem like they have a social deficit. Conflict between Neurotypes 1 second = 10 tokens for OpenAI Realtime APIs. 1 second = 25 tokens for Gemini Live API 39 cents / hour on GPT Realtime Mini = 36 cents audio input + 3 cents text output 139 cents / hour on GPT Realtime = 115 cents audio input + 15 cents text output 30 cents / hour on Gemini 2.5 Flash Native Audio (Live API) = 27 cents audio input + 3 cents text output Here are some AI experiments I’m planning to try with our marketing team: Video Generation: Create marketing videos from text scripts in minutes Poster Generation: AI designs high-conversion posters from brief text inputs - notably Nano Banana Pro Synthetic Persona A/B Testing: LLM agents simulate 100K+ user behaviors to test designs before real users LLM-Powered A/B Automation: AgentA/B system runs experiments with AI-simulated traffic Vibe Coding Landing Pages: Marketers build production-ready pages in hours vs weeks On-demand Landing Pages: Generate pages for automated campaigns/products without human intervention Brand Voice Cloning at Scale: Train on company content to ensure consistency across 1000s of pieces Persona-Driven Content Synthesis: Use 1B+ personas to generate diverse content perspectives Competitive Intelligence Briefing: Real-time monitoring across millions of data points + data storytelling Marketing Analytics with LLMs: AI agents analyze complex datasets for insights Brand Compliance Checks: Ensure all content meets brand guidelines automatically Autonomous Blog Squads: AI agents identify trending topics / internal content, create data stories ready for review New skill unlocked: creating tutorials from talk proposals. I asked Claude to Write a Malcolm Gladwell article based on this talk description to teach me the topic and passed it this talk proposal: Your Causal Parrot might be lying to you. The story it wrote is very engaging and informative! LLMs “understand” causality because of training, but lack a world model to extrapolate to new situations. Giving them tools to reason (e.g. causal models, sub-agents to explore root causes) will help. A cool Gemini 3 Pro hack: convert satellite imagery into stylized maps! Bilawal Sidhu Running sub-agents in tmux helps avoid timeout cancellation, and hence allowing resuming Peter Steinberger
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