marimo 3

This week, I learned: The devcontainers.json spec encapsulates everything you need to get a codebase running for development - as opposed to production. E.g. VS Code extensions, linters, etc. Practical use for GitPod are: Make quick edits to repos that are not on your system (e.g. other people’s repos, or via others’ machines.) Run public workshops with a full coding environment. Give students assignments that have dependencies pre-installed. Collaborate on a work-in-progress codebase with my team. Share POCs with clients or public allowing them to edit it. Allow teams to install remote AI code extensions (e.g. Windsurf) that may be blocked inside the corporate firewall? AI coding can teach us new tech. For example I learned that tqdm.pbar can print logs while showing progress. It’s worth noting such learnings until it becomes a habit. #ai-coding If English is the new coding language, should prompts be versioned? Or at least stored, perhaps in a PROMPTS.md? #ai-coding marimo new "prompt" generates an entire new notebook using your prompt. Video Google Sheets now has an =AI(prompt, [range]) function Help Codex is more a proof-of-concept for agentic coding than a coding tool. #ai-coding You can’t run commands. Only prompts. You need to exit codex to run commands. So you can’t use it like a shell, e.g. like Warp.dev. It doesn’t index local code. It runs commands to figure out stuff. Code diffs and applying changes are clunky. The output is hard to read with text scrolling. codex.md can only handle 32K. ⭐ O3 and O4 have built-in tool use covering all of OpenAI’s tools, including containers. This allows them to manipulate images and natively understand them improving vision capabilities dramatically. GPT 4.1 can handle videos Notes from discussion with Balaji T: Zero-day options are options that expire on the same day. They are priced low. It’s almost just a gamble or a lottery ticket. But since the price is low, retail investors can invest. NIFTY is one of the largest markets for zero day options, surprisingly. There are several college grads who trade writing Python scripts. CoreWeave has taken over all the compute from OpenAI. Though the stock price has fallen, buying CoreWeave is the closest equivalent to buying OpenAI pre-IPO. However, every OpenAI product lost money, despite their 75% discounted compute from Microsoft. (With CoreWeave, the cost would be higher.) So their profitability depends on wiping out competition long-term. For investment research companies (hedge funds, VCs, etc.) increasing the number of companies they research is an advantage. So using AI for research is key. However, the quality of LLMs is too poor for financial analysis accuracy. We need better LLMs for spreadsheet analysis. We suffer from the Gell-Mann’s amnesia effect with LLMs. “You read a newspaper article in your field and find it’s rubbish. You turn the paper and believe it’s perfectly accurate on the next page”. Domain expertise will therefore become even more valuable in the near future. People don’t like AI being forced down their throats. MAS is forcing AI down banks whose execs are forcing it down the org. Bankers and analysts are grumbling about this. I visited SUTD InspireCon 2025. Here were some exhibits that caught my eye. A path marking app that uses cameras to draw a heatmap of people’s walking paths. Popular tracks are redder. Using drones for machine inspection. Portable immigration devices that let you scan passports, face recognition, fingerprint, mic/speakers, etc. Using accelerometer to detect unsafe gait and improve walking habits. UImagine: a web app builder. Interestingly, they used Webcontainers to run Node in the browser! Training a drone to follow a person Credibility detection via micro facial expressions PitchMe: providing real-time feedback to pitches / presentations Zetesis: a platform for people to ask questions during a lecture or meeting (independent of Zoom, Meet, etc.) Tinyeqn: helps grade student assignments The dynamic between domain experts and coders has changed. Now, rather than domain experts pitching ideas to developers who build the apps, developers are creating interfaces that allow the domain experts to shape the app. Ref Since even the cheapest LLMs do a good job of converting unstructured text into a JSON schema, for all practical purposes, adding a full text search on top of any structured API is a trivial exercise. (Of course, it can’t handle complex questions but that’s what agents are for.) Ref ⭐ Marp supports bespoke transitions which includes morphing animations. This can create a bar chart race just using Markdown! Nick Lansley, who I know from my work with Tesco, wrote a great article that includes advice for aspiring consultants: Re-connect with ex-colleagues Leave on good terms with your employer Have a 6-12 month financial buffer Hire an accountant / legal advisor to set up your business Focus on what you enjoy Have a 30-second elevator pitch Build a brand with blogs, social media, or talks Create a portfolio to reinforce your skills DeepCoder is currently the best 14b coding model, i.e. best if you want to code while on a flight. Ref #ai-coding docker model run can run models. Currently, only on Docker Desktop on Mac Ref

This week, I learned: DuckDB supports function chaining DuckDB lets you create functions = macros HTML for People is a nice introduction to HTML. FlightRadar24 lets you watch airplanes live. sq is like jq but for SQL. Deno 2 is fully backward compatible with Node! via O1 is good at solving problems where the solution is easy to verify and generating options helps get closer to the solution Reverb ASR does diarration as well as transcription. It seems the state of art right now. Gemini Flash and Gemini Flash 8b can be fine-tuned at zero cost. Inference is at the same price! Ref Flux 1.1 Pro is released. I tried my Calvin & Hobbes test on it. Not great. ImageGen3 is better, ChatGPT is the best. Ref Revisiting text to speech models. Nothing much has changed since July 2024. OpenAI TTS: $15/1M chars Ref Deepgram Aura: $15/1M chars Ref Azure AI Speech: $15/1M chars Ref Google TTS Neural2: $16/1M chars Ref AWS Polly Neural TTS: $16/1M chars Ref Cartesia Pro: $50/1M chars Ref Elevenlabs Scale: $300/1M chars Ref GitHub co-pilot workspaces let you code using your mobile with AI and deploy it at one shot If you need an Ubuntu Docker container with Python, install it via uv rather than compiling from source. via VTracer is an open source library (and tool) to convert raster images to SVGs. via If you want to create a console.llm() function, a browser extension is the best way, because some pages have Content-Security-Policy that block eval, form submission, fetch from other domains, and script execution. PyPi lets you publish from GitHub Actions without a token. Also from Gitlab.com CI/CD and Google Cloud. ActiveState which made ActivePython, ActivePerl, etc. made these products paid for commercial use around 2013 after a series of acquisitions. Marimo supports: Publishing any notebook to static.marimo.app as a static app Creating a SINGLE link that embeds the ENTIRE notebook in the URL! Runnable via uvx marimo edit Parables on the Power of Planning in AI: Giving models about 30 seconds of thinking time consistently improves results - as much as increasing parameter size by a factor of 1,000 to 100,000! This works particularly well for verifiable results (code, math, etc.) Technique: Ask an LLM hundreds of times at low temperature and pick the most common one. (Google’s Minerva used this on the MATH dataset.) Better Technique: Ask an LLM hundreds of times. Pick the best solution based on an evaluation metric (reward model) Better Technique: Apply a reward model at EACH step of the process. OpenAI’s “Let’s Verify Step by Step” Late chunking is an interesting approach to adding context to embeddings. (I don’t understand it, but it’s cheap and effective.) DeepInfra offers embedding models as APIs at about 0.5 to 1 cent per MTok in an OpenAI compatible API. It also supports text-to-image models like flux.dev and speech recognition models like Whisper. Jake Heller: “One of the things we learned is (an LLM app) after it passes passes frankly even 100 tests, the odds that it will do, on any random distribution of user inputs, the next 100,000 100% accurately is very high.” OpenAI’s O1 is like Daniel Kahneman’s System 2 thinking - as against other LLMs’ System 1 thinking. Continue.dev is another AI coding editor. It supports OpenRouter. So now I have heard good things about: Github Copilot Cursor Cody Continue.dev (supports OpenRouter) Aider (supports OpenRouter) Maybe: Codeium Not: Amazon Q Developer

This week, I learned: Transparent LED screens will be useful in windshieds to display maps as we drive. Marimo is a reactive alternative to Jupyter notebooks that saves files as pure Python. To run an org-specific chatbot on your own LLM: (via awesome-chatgpt) opengpts - but it doesn’t support auth chatbot-ui - but Supabase is hard to install anse - but it doesn’t support auth ChatGPT-Next-Web - but it doesn’t support auth Python 3.13 gets a store and copy JIT If an npm package adds another package as a dependency with version “*”, target package cannot unpublish ANY version! So this is a way of freezing EVERY repo and preventing unpublishing of EVERY version – an unintentional flaw in the npm design. via Quantization is better than fewer parameters. So prefer high parameters (e.g. 70b) and quantize to 4-bit. In-browser playgrounds has compiled WASM versions of Python, PHP, SQLite. Happiness Lab podcast. Happiness lessons of the ancients Talking to strangers makes us happy Giving money makes us happy Free time makes us happier than working hard Tangi Domain-specific models being beaten by general purpose models is a phase. It will reverse towards domain. AI will potentially help build and understand domain-specific models Models are evolving so rapidly that humans cannot interpret models. We need a process to interpret models! xAI, Responsible AI, Physics-guided or Knowledge-guided models (called grey box models) are therefore a trend CS papers Don’t review other papers, certainly not other fields. Disregard measurement errors. When CS papers get applied to climate, manufacturing or biology, we’ll worry about Interpretability Domain-specific mechanics. (Introduce that into the training as a constraint.) Many domain experts are using AI to UNDERSTAND their process. Need to explore Uncertainty IB adds context to make learning applicable. But that distracts from the core learning, and if there’s a gap it widens Most data science courses teach “Python science”, not data science. They teach a bunch of models. They don’t teach how even one kind of model e.g. LSTM works. Most coaching programs today teach FAMILIARITY with problems, not critical thinking Most of current education will become redundant thanks to LLMs. For students AND teachers Coding will become irrelevant Cognitive thinking, reasoning, human relations, systems thinking will become more relevant Troubleshooting will become more important. AI is not self-diagnosing. I would hire someone who can figure out something is going wrong, diagnose what’s going wrong, and fix it #TODO Hire for troubleshooting ability. Give a Q, an A, and ask them to figure out if it’s wrong, why, and fix it All my exams and quizzes are open book, open ChatGPT. Onus is on me to give a problem that forces you to think. #TODO Write a question paper that is ChatGPT proof. Exploring AI could be a ToK subject. “How to interact with an AI?” We need a manual on how to use AI. Like Simon Willison says Content doesn’t suffice. You need pedegogy. What to serve you at what time, how, how to assess. Lots of businesses are filling this gap Students get great confidence when a teacher points to online content and says, I"ll tell you WHAT to see" and COMPLEMENTS that in their class “The map is not the territory.” Most people confuse sample mean for the actual. #ASK Parameter estimation -> Signal estimation -> State estimation Stats vs DL differ in that There is no notion of a defined “truth”. Hence reliability is not measurable Parameters have no value. Hence interpretability is ignored. #TODO Read 2020 National Education Policy. It’s quite modern. We need a manual on self-learning too Listening is not learning. You know only if you implement. Levels for students: I can solve it. I can explain why it works. I can find alternatives. I can apply it to a new area, reformulating (requires imagination.) For teachers, you also need: Responsible learning (extra careful about what to teach and how to teach, to exceite them, to teach at THEIR level). Show the universality and connecting to other concepts. E.g. noise reduction with FT is like using water to remove dirt. Transform to water domain, remove dirt, transform back to air domain. It’s better than dusting clothes to remove clothes. Washing machine programs are just different models of removing noise in the water domain. Teach people who WANT to learn AND who will APPLY it long-term. That’s what maximizes impact Grad students are more satisfying that way. Else, it is WASTED effort. (Not that it’s a bad thing for the student, but the effort IS wasted for the teacher) Therefore, I believe students should have general engineering first, and let students pick specialization later. Some universitie are doing that. #THINK Students remember my philosophy more than my content. We impart character, not just knowledge. Astrology and horoscopes serve a different function. They provide explainability, not predictive ability. As the world becomes less explainable, the need for astrology will grow. Explainability is about creating STORIES that fits data plausibly. It has nothing to do with data or truth. Explainability and predictive ability and reproducibility are all different. Maybe, Science is about the latter two, less about explainability. Astrology is a model. The map is not the territory. It’s an explanatory, not a predictive model. #THINK Therefore, my lessons are just explanations. Stats about experiments are STILL explanations. They are NOT reproducible or predictive. Hence not yet science The meaning of our life is the transformation we undergo in our lives #TODO Read “The Journey of Souls” by Michael Newton. A hypnotherapist #TODO Try regression therapy / hypnosis. Record it and listen to it. Just for fun! Rohini Deshpande Slam book was the Facebook of the 1900s Prepared mind is an extremely powerful tool for learning. Practice prepared mind When women drop out of education or career, that is also a waste from the teacher and system perspective The time for career growth is the same as child bearing time for women. That’s not true for men. But child rearing can be done by either. That’s not recognised. It’s 0K for a man to raise the child and make the home and 0K to treat that as the default Since men are more senior, it’s usually logical for them to stay in their jobs. That’s a systematic bias. When seniors advise women to step back. they respect it. That widens the barrier. Why not eliminate that situation? Be proud of the working women in the family Stats are just a symptom. They don’t explain the cause. (Map is not the territory.) Explanations are what really helps us fix the cause. Hence stories are important. Read Tinker Tailor Soldier Spy RV Athimber health tips: Eat foods with low glycemic index Eliminate free salt completely Voyage AI Embeddings have a higher quality, similar price compared to OpenAI embeddings. There’s a clear benefit to replacing text-embedding-3-large with voyage-3-lite. There’s a 200 MTok free tier currently. mixtral-offloading cleverly loads only the model layer required at any point, letting you run Mixtral 8x7b on Colab Free and on 16GB GPUs. This notebook runs on Colab Free too. CodeGPT is an alternative to Github Copilot that can use any LLM.