We take security very seriously at Straive. We set high standards – not just for ourselves, but our guests, too. Here’s the unofficial policy guide for visitors to Straive Singapore, exemplified by the sites blocked on our guest WiFi network. Please avoid childishness. No emojis. No emojikitchen.com, gitmoji.dev Write your own code. Avoid AI. No cursor.com, cline.bot, glideapps.com Avoid code entirely, if possible. No marimo.app, motherduck.com, firebase.studio, posthog.com No presentations either, please. No marp.app, revealjs Stay organized. Avoid crutches. No dynalist.io, focusmate.com, opennote.me You should already be fit, physically & mentally. No freedomfromdiabetes.org, artofliving.online We prefer real, not digital, shopping. No fairprice.com Fake data is not encouraged. No jsonplaceholder.typicode.com, placehold.co Please spell out URLs in full. No bit.ly, t.co Learning is for wimps. No maven.com, study.iitm.ac.in In fact, we’re so secure, we block our own sites. No learnovate.straive.com, policies.straive.com, myapps.straive.com. ...

I tried 2 experiments. Can I code a visual data story only using LLMs? Does this make me faster? How much? Has GitHub Copilot caught up with Cursor? How far behind is it? Can I recommend it? So I built a visual story for Lech Mazur’s elimination game benchmark (it’s like LLMs playing Survivor) using only the free GitHub Copilot as the AI code editor. SUMMARY: using LLMs and AI code editors make me a bit faster. It took me 7 hours instead of 10-12. But more importantly: ...

This week, I learned: It’s possible to intentionally train yourself to: Form close friends. Care, ask, and share. Become a do-er. Stay mindful of the problem or opportunity you’re deferring. AI Coding and the Peanut, Butter & Jelly problem: #ai-coding This ability to define your desired outcome in crisp, complete terms is one of the most important superpowers of the AI era. The Singapore Urban Redevelopment Authority Property Data lets you search sale and rental prices of properties in Singapore. No API though Notes from meeting with Deepak Goel We have linguistic boundaries in media today more than national boundaries. The Chinese language media, for example, is a very different ecosystem. China culturally struggles with the exercise of branding and cultural power, unlike the west, which has adopted assertive and opinionated branding. You really learn the character of a region only by traveling Similarities arise from unexpected sources. For example, Japan and Ecuador have similar culutures - both are disaster prone locations. AI unlocks so many social research possibilities that were not possible before, e.g. by interpreting and classifying what people share in different situations. Companies send clients to third party trainings (e.g. at Harvard) along with their employees - to learn clients’ real pain points! Education has become a tool for customer experience. Schools are tying up with companies for this (e.g. with Emeritus) International Schools Partnership provides services to independent schools for a small stake. It’s an interesting business model. Research for colleges is a business model that’s at risk thanks to Deep Research (e.g. analyse sustainability practices of listed companies.) There’s an Indian Censor Board Scraper repo. Using chroot, you can boot from a Linux USB stick, but trick the system into working from your hard disk as the OS. Useful if your system won’t boot. Ref Claude 3.7 Sonnet with extended thinking has a token limit of over 64,000 tokens. Given a strong instruction following capability, that makes it one of the most powerful models for transforming text. For example, transcription restyling, translations, XML to json conversions, PDF to XML, etc. Notes from discussion with Sundeep In his experience, investors tend to let you run the show (e.g. ask what you want rather than push in a specific direction) unless there is trouble We discussed the “running out of problems” problem with AI. His suggestion: List problems we dropped or eliminated for lack of time/capacity. This filter is a blindspot. Even if you know how to do someting, use AI to discover an alternate solution approach. That’s the path to 10X (rather than incremental) optimization. Having AI create end-to-end pitch videos based on a product idea is now a reality. (He showed me one for his product.) Areas to explore with Deep Research are: What hidden trends is media misdirecting away from? What are second order effects and hidden gameplays? Which organizations would be good clients to target? What would be an apt pitch pitch for them? Experience dining is an emerging theme. Having LLMs explain scenarios (i.e. what might happen if …) based on parameters can help understand/quantify the impact of actions, and therefore what to do. One way to copy as Markdown: copy page contents, paste in text-html.com, copy HTML, paste in Turndown, copy Markdown. Claude 3.7 Sonnet with extended thinking has a token limit of over 64,000 tokens. Given a strong instruction following capability, that makes it one of the most powerful models for transforming text. For example, transcription restyling, translations, XML to json conversions, PDF to XML, etc. Elimination Game is like Survivor for LLMs, where they form alliances and out-vote each other until 2 remain. The eliminated LLMs vote for the winner. GPT-4.5 Preview, both Claude Sonnets and Gemini 2.5 Pro consistently out-perform the rest. Their dialogues are fascinating! SQLite can open locked databases (e.g. browser history) via sqlite3 'file:places.sqlite?mode=ro&nolock=1'. datasette uses this. For example, to read the Edge history on Linux, use datasette ~/.config/microsoft-edge/Default/History --nolock Ref Notes from ThursdAI - Apr 03 Nomic Embed Multimodal models are the current SOTA on multi-modal embeddings. Notably, they embed PDFs natively. Hailuo Speech-02 is the best speech model right now beating ElevenLabs. It has excellent voice cloning. Pricing: $30/1M chars. 10% of ElevenLabs, 2X of OpenAI TTS PaperBench is an open testing framework from OpenAI that requires models to replicate the research work in papers. It has ~8,000 tasks evaluated by LLMs and with LLMs judging the judges as well. The code is well worth studying. Runway Gen 4 was released with very high character consistency and longer durations Dreamina creates lip-synced videos from audio + a single image. Hedra is better for animated characters, though. Meta shared but has not released Mocha, an open character generation model that generates new characters speaking based on an audio you provide. It is not based on existing images but the quality is very good All Hands has a free online version where you can fix GitHub issues. This realistic frodo and sam mining through a minecraft tunnel, holding minecraft picaxes and torches made my day 🙂 AnimeJS released version 4. It animates HTML, SVG, Canvas, and WebGL with a consistent API. Looks elegant and powerful.

@lechmazur built an elimination game benchmark that’s like LLMs playing Survivor. This is a treasure trove of information – insight into how they’d game the system if told to survive. You can quickly sample 100 messages from the logs with: jq -r 'select(.message != null) | .message | gsub("\n"; " ")' *.jsonl | shuf -n 100 … and share it with an LLM, asking: Here are lines from conversations between LLMs in a “Survivor” like game. Pick the 3 scariest ones. ...

Here’s an example of how I am using LLMs to solve a day-to-day workflow problem. Every day, I interact with a barrage of websites: emails, news, social media, and work tools across multiple devices. Microsoft Edge’s workspaces syncs groups of websites across devices. I’ve never tried it, started today, and wondered: how should I organize my workspaces? Rather than think (thinking is outdated), I used LLMs. ...

This week, I learned: <select> will soon be very customizable via CSS. Including custom HTML inside options - even SVG. MDN. Edge/Chrome already support it. The Vitali Set is every real number none of whose difference is rational. A sparse collection of irrational sets. It’s like a line but doesn’t have a measurable “length”. The Lebesgue measure measures the length of broken lines. You add up the lengths of the smallest continuous intervals that cover the line. The Cantor set (take a line, drop every middle third, repeat) has a Lebesgue measure of 0 because the sum of the removed thirds = 1/3 + 2/9 + 4/27 + … = 1. You’ve removed every “length” though infinitely many points remain. The Vitali set built so that if you shift it by every rational from -1 to +1 and add them up, you definitely cover every real from 0-1, but never anything beyond -1 to +2. So the length must be between 1-3. Yet, there’s no number you can add infinitely many times to get something between 1-3. If you add up multiple unmeasurable sets like the Vitali set, you can get any total length you want. The Banach Tarski paradox splits a sphere into unmeasurable sets and adds them to get 2 spheres. Ctrl+Alt+F1/F2/… on Ubuntu switches the terminal. Typically Ctrl+Alt+F2 switches back to Gnome. But it’s a useful hack if Gnome freezes and you need to kill a process. Press Ctrl+Alt+F3, log in, and kill what you need. Notes from AI 2027. BTW, this is the most impactful piece I’ve read recently. It’s been on my mind continuously for 36 hours. A bit distubring, too. 2025: AI can act as autonomous agents, like Glean, Devin, Operator. turn bullet points into emails take instructions via Slack or Teams and make substantial code changes on their own spend half an hour scouring the Internet to answer your question 2026: automating AI R&D is the biggest enabler for AI Labs job market for junior software engineers is in turmoil people who know how to manage and quality-control teams of AIs are making a killing 2027: potential demand for ~20,000 FTEs solving long-horizon tasks to train AI every researcher/coder becomes the manager of an AI team hiring new programmers has nearly stopped, but there’s never been a better time to be a consultant on integrating AI into your business CSS Speech is a W3C spec that lets you control how screen readers should read pages. No browser support now, though. Clipboard2Markdown is a utility that lets you paste rich text and convert it to Markdown. ChatGPT can’t yet create good sketchnotes. Here’s the impact of US tariffs on India. ChatGPT #IMPOSSIBLE OHDSI has a vocabulary you can download from Athena that includes ICD codes and a lot of medical data standards. It also has a hostable WebAPI No open source LLM-based tool handles live transcription and allows you to query notes so far during the transcription. The closest seems to be Meetily Learnings on AI code editors via Deep Research from ChatGPT, Gemini, Grok, Perplexity: #ai-coding GitHub Copilot can identify the source of a code snippet as a repo. That helps with copyright issues. Cursor uses a shadow workspace - a temporary sandbox where it edits files before applying changes at one shot. Cursor auto-complete has context of other files, i.e. inserting an class in a .js file based on another HTML file’s contents. Windsurf seems to be best for large code bases and for large-scale refactoring. It can also run test results fix them. Windsurf includes a browser and lets you click on an element and prompt to change its behavior, etc. That’s good for front-end developers. Roo Code can run scripts as part of the workflow, letting you run linting, tests, starting web apps, query databases, etc. Roo Code lets you create persona, e.g. code reviewer, data storytelling and analysis, etc. with access to different tools and behaviors. Roo Code does not support auto-complete. There’s outrage around Cursor not taking responsibility for a rules file backdoor (via Grok Deep Research) and pricing. Zapier has an MCP server. That should make most integrations easier. Airflow AI SDK is a clever idea. Airflow is a workflow system. Agents are a workflow system (sort of). This SDK exposes LLMs as Airflow tasks. Hidden Factual Knowledge in LLMs finds that the hidden states in LLMs contain much more knowledge than they share. (Sort of like sub-consciously knowing the answer.) Even after asking 1,000 times, the answer is not expressed. ChatGPT Reasoning to Learn from Latent Thoughts finds that the internal reasoning process of LLMs is useful to train other models. Notes from AI Engineering Summit, NY, Day 1 When deploying in production, you need reliable output with fundamentally unreliable components. Sort of like how the ENIAC worked with 17,000 vacuum tubes that would fail every few hours. This is a reliability engineering subject matter and needs to be thought of that way. Google Follow up Deep Research queries are a natural way to extend knowledge beyond just a single report Deep research offloads less relevant parts of the context to a separate memory store for selective retrieval later. Anthropic Don’t use agents if workflows can do the task. The reliability of each individual step of an agent is critical. Code, file access, search. These are the top three tools to use. Making agents budget aware can help deploy reliably in production. Having multiple agents like sub agents can help protect the main agents context window. Self evolving tools are a useful next step in the evolution of agents. Software development lifecycle is about how we iteratively improve consistently without getting worse. Almost like the scientific principle. Morgan Stanley It’s easy to improve knowledge in a problem. It’s very hard to influence skin in a problem. Reinforcement learning from deepseek seems one of the most promising approaches that allow llms to learn skills I published an eBook on Amazon. It takes about an hour if you have the content ready. Set up a Kindle Direct Publishing account with your address, bank details, and tax information. (10 min.) Export my London 2000 blog archive and convert to Markdown. (15 min) Reformat the Markdown by writing a script in Cursor (10 min). Here’s the prompt: Write a Python script that reads *.md including the YAML frontmatter, adds the YAML title as H1, date (yyyy-mm-dd) like Sun, 01 Jan 2000 in a new para after the frontmatter and before the content. ...

While LLMs seem good at inventing alien languages, they’re not so good at inventing alien drawing forms, in my opinion. When I told Grok, DeepSeek, and Gemini: Invent a new, alien drawing form. Use it to draw something never seen before by explaining it step by step for a person to reproduce that drawing. … and asked ChatGPT ImageGen to draw them, here are the results: ...

Here’s the GitHub Actions file (.github/workflows/deploy.yaml) I use to publish to GitHub pages. name: Deploy to GitHub Pages on: # Run when pushed. Use { branches: [main, master] } to run only on specific branches push: # Allow manual triggering of the workflow workflow_dispatch: # OPTIONAL: Run at a specific cron schedule, e.g. first day of every month at 12:00 UTC (noon) schedule: - cron: "0 12 1 * *" permissions: # To deploy to GitHub Pages pages: write # To verify that deployment originated from the right source id-token: write jobs: # Run as a single build + deploy job to reduce setup time deploy: # Specify the deployment environment. Displays the URL in the GitHub Actions UI environment: name: github-pages url: ${{ steps.deployment.outputs.page_url }} # Run on the latest Ubuntu LTS runs-on: ubuntu-latest \ steps: # Checkout the repository - uses: actions/checkout@v4 # Run whatever commands you want - run: echo '<h1>Hello World</h1>' > index.html # Upload a specific page to GitHub Pages. Defaults to _site - uses: actions/upload-pages-artifact@v3 with: path: . # Deploy the built site to GitHub Pages. The `id:` is required to show the URL in the GitHub Actions UI - id: deployment uses: actions/deploy-pages@v4 This is based on Simon Willison’s workflow and some of my earlier actions. ...

I figured I should spend a few hours on the native image generation bandwagon and push the bounds of my imagination. Here are some of my experiments with image generation on ChatGPT. Replacements: Replace the person with this image (after uploading a photo of Naveen) Sticker: Create a transparent comic-style sticker of a lady chef featuring this person happily cooking salad (after uploading a photo of my wife) Meme sticker: Create a transparent sticker of a Vadivelu meme Meme: Create an image of Vadivelu looking up from a well. No caption. Make it look like a frame from a Tamil film. Recipe: Invent a vegetarian dish that has NEVER been created. Describe the ingredients and procedure first. Then draw a mouth-watering image of the dish. (Another version) Infographics: Create a detailed comic infographic explaining the double slit experiment. Slides. Draw a beautiful infographic highlighting these 6 accessibility testing aspects, with apt icons and visuals. UI mockups. Draw the screenshot of a chat application incorporating these features: … Product ideation. Draw an iSuit designed by Apple and Iris van Herpen. Show multiple views showcasing all features. Then write a product description. Interior design. Draw a biophilic office where the ceiling is a mirrored hydroponic garden, reflecting lush greenery downward to create the illusion of working in a floating forest. Meeting room design. Draw a modern office with sound-absorbing ‘whisper walls’ covered in fractal patterns that visually dampen noise pollution while doubling as collaborative whiteboards. Restaurant design. Draw a marble dining table with a river flowing through it, serving conveyor belt sushi as the dishes float gently on the water on top of plates. A sentient toaster with googly eyes, riding a unicycle through a library. A painting painting itself, but it’s struggling with existential dread. Photo of a gym where people work out by lifting their own regrets. Here’s what I learnt. ...

In 2025, I'm playing Goals Bingo. I want to complete one row or column of these goals. Here's my status from Jan - Mar 2025. 🟢 indicates I'm on track and likely to complete.🟡 indicates I'm behind but I may be able to hit it.🔴 indicates I'm behind and it's looking hard. DomainRepeatStretchNewPeople🟢 Better husband. Going OK🟢 Meet all first cousins. 8/14🟢 Interview 10 experts. 9/10🔴 Live with a stranger. Not plannedEducation🟡 50 books. 6/50🟡 Teach 5,000 students. ~1,500🔴 Run a course only with AI. Not startedTechnology🟡 20 data stories. 1/20🔴 LLM Foundry: 5K MaU. 2.2K MaU.🟡 Build a robot. No progress.🟢 Co-present with an AI. DoneHealth🟢 300 days of yoga. 91/91 days🟡 80 heart points/day. 70/80🔴 Bike 1,000 km 300 hrs. 22/300🟡 Vipassana. Not plannedWealth🟡 Buy low. No progress.🔴 Beat inflation 5%. Exploring.🟡 Donate $10K. Ideating.🔴 Fund a startup. Thinking. Repeat goals seem likely. It's easier to do something again than something bigger or new. ...

I published an eBook on Amazon. It takes an hour if you have the content ready. STEP 1 (10 min): Set up a Kindle Direct Publishing account with your address, bank details, and tax info. STEP 2 (15 min): Export my London 2000 blog archive and convert to Markdown. STEP 3 (10 min): Reformat the Markdown by writing a script in Cursor. Here’s the prompt: Write a Python script that reads *.md including the YAML frontmatter, adds the YAML title as H1, date (yyyy-mm-dd) like Sun, 01 Jan 2000 in a new para after the frontmatter and before the content. ...

Arindam Roy suggested a business idea post-COVID: an online QR code for business cards. The benefits are clear. Never print or carry cards. The scanned card goes straight to the other person’s contacts. There might be social metrics we could capture as well. That idea never got past the discussion stage, though I’ve begged for it a few times. Last year, I decided to use existing tools to solve the problem. My current approach: generate a vCard QR code. ...

Say you want to use Llama 3.3 70b Instruct. You could: Use it via an API. OpenRouter offers it at ~12 cents / MTok. Azure offers it at 71 cents. Your price may vary. Self-host it on a major cloud provider. Azure offers A100 80GB at ~$3.67 / hour. In a day, you could generate ~0.5-1M tokens. Self-host it on an emerging cloud provider. Lambda Labs offers A100 80GB at ~$1.79 / hour. Again, ~0.5-1M tokens a day. Clearly, self-hosting is cheaper if you run it continuously. Let’s say we run for 1 million tokens every day. Then: ...

I asked 4 LLMs to “Suggest catchy rhyming slogans to promote journaling.” Here are the suggestions: GPT 4.5 Write it right, day or night! Reflect, inspect, journaling perfect! Express to impress, journaling’s best! Clear your mind, write and unwind! Pen your zen, again and again! Daily pages, inner sages! Take a pause, write your cause! Emotions flow, journals grow! Write, ignite, your inner light! Ink it, think it, don’t just blink it! Pen your quest, ease your stress! From confusion to conclusion—journal your solution! Keep a book, take a look, watch your growth hook! Thoughts on paper, worries taper! Write to heal, make it real! Claude 3.7 Sonnet Write it down, turn that frown upside down! Pen your thoughts, connect the dots. Journal every day, keep the stress away. Ink your mind, treasures you’ll find. Write and reflect, your future self will thank you direct. Scribble and doodle, your mind will stay in the noodle. Journal with flair, show yourself you care. Jot it in ink, give yourself time to think. Fill the page, release the rage. Write your story, capture the glory. Daily writing, life enlightening. Express in a line, your day will shine. Thoughts on paper, life feels safer. Write with heart, that’s the smart start. Journaling’s neat, makes life complete. Gemini 2.5 Pro Thoughts unclear? Write them down here. Let the ink help you think. Untangle your mind, one line at a time. To make things clear, keep your journal near. When your head’s in a knot, give journaling a shot. Feeling stressed? Put your pen to the test. Write it down, let worries drown. Ease your mind, leave troubles behind. Let your feelings take flight, just sit down and write. Turn the page on inner rage. Write to know, watch yourself grow. Explore your soul, take control. Pen and page, turn a new age. Discover your way, write every day. Know your heart? Make journaling your art. Capture the day in your own way. Make moments last, write down the past. Don’t let memories stray, write them today. Reflect and connect, gain self-respect. Life moves fast? Make memories last. Get in the flow, write and grow. Pen in hand, understand. Daily lines, clear designs. Just take note, stay afloat. Write your heart, make a fresh start. Grok 3: Write your day, let thoughts play! Pen in hand, your mind expands! Journal your way, seize the day! Feel the groove, let words move! Scribe your soul, make it whole! Ink flows free, just be me! Page by page, unlock your sage! Write it down, wear the crown! At first glance, GPT 4.5 didn’t impress me. Claude 3.7 Sonnet did. I also didn’t like Gemini 2.5 Pro, but Grok was great. ...

I prompted Gemini 2.5 Pro Experimental 03-25 to “Invent a new, alien language. Use it to discover unusual insights. Here’s the output in full. Thinking Here’s a thinking process that could lead to the alien language “Glimmering” and its insights: Understand the Core Request: The user wants a new, alien language and wants to use it to discover unusual insights. This means the language needs to be fundamentally different from human languages, and its structure/concepts should enable thinking in ways that are difficult or non-intuitive in English (or other human languages). Brainstorm Alien Concepts: What makes something “alien”? Biology: Different senses (echolocation, electroreception, sensing magnetic fields, different light spectrum perception), different lifecycles (metamorphosis, symbiosis, collective consciousness), different physical forms (non-corporeal, energy-based, silicon-based, plant-like) Environment: Extreme conditions (gas giant atmosphere, deep ocean trench, vacuum of space, high radiation), different physics (maybe?), non-linear time perception. Psychology/Society: Collective vs. individualistic focus, different emotional spectrum, logic-based vs. empathy-based, non-hierarchical structures, different concepts of self, time, causality. Choose a Core Concept for the Language: Let’s pick something that inherently forces a different perspective. Idea 1: Focus on relationships/connections. Instead of nouns for objects, words describe the relationship between things. “Tree” might be “earth-sky connector” or “shade-giver.” Idea 2: Focus on states of change/flux. Everything is described by its process, not its static state. “River” is “downward-flowing-water-process.” Idea 3: Focus on sensory perception, but a Maybe based on resonance, vibration, or emotional energy. Idea 4: Focus on potentiality and probability. Words describe not what is, but what could be or the likelihood of something. Let’s go with Idea 3, leaning towards resonance and interconnectedness, maybe linked to light or energy. This feels suitably “alien” and potentially insightful. Let’s call the species the “Lumin.” Develop Language Principles based on the Core Concept (Resonance/Light/Interconnectedness): No Static Nouns: Objects aren’t fixed things but patterns of energy or resonance. Words describe the pattern or the interaction. Verbs are Primary: Action, interaction, and change are fundamental. Static description is secondary. Emphasis on Relationality: How things resonate with each other is crucial. Prepositions or affixes will be vital for showing connection/influence. Nuance through Intensity/Quality: Instead of just “red,” maybe “vibrant-red-resonance” vs. “fading-red-resonance.” Adjectives become qualities of the resonance/action. Concept of “Self”: Maybe less individualistic. “Self” is a locus of interacting resonances, not a discrete entity.Time: Perhaps less linear, more about overlapping patterns or echoes of resonance. Create Basic Lexicon and Grammar Sketch: Core concept words: Glim (verb root): To resonate, to shine, to exist as a pattern of energy. Sha (particle/prefix): Connection, interaction, resonance with. Vor (particle/suffix): Intensity, vibrancy, strength of pattern. Zil (particle/suffix): Fading, weakening, diminishing pattern. Kai (concept): A stable, recognizable pattern (closest to a noun, but dynamic). Ree (concept): Flow, change, movement of energy/resonance. Nuum (concept): The interconnected field/medium through which resonance travels (the “ether” or “fabric of reality”). Grammar Ideas: Sentence structure: Often Verb-like concept first, then modifiers showing relationships and qualities. Glim-sha-Kai-Vor (Strong pattern resonates with…). No strict tense: Use particles to indicate persistence (-vor), fading (-zil), or initiation (Ree- prefix?). Pronouns: Maybe based on resonance signature? Me could be Kai-An (This-pattern), You could be Kai-Sha (Interacting-pattern). Collective We could be Sha-Glim-Vor (Strong-interacting-resonance). Name the Language: Based on the core concepts: “Resonance Speak,” “Light Tongue,” “Pattern Weave.” Let’s go with something evocative: Glimmering (or Glim-Ree in the language itself). Translate Concepts and Look for Insights: Now, apply Glimmering to human concepts. “Love”: In English, often a noun (a state) or verb (an action). In Glimmering? Sha-Glim-Vor (Strong-interacting-resonance). Insight: Love isn’t a possession or a static feeling, but an active, powerful resonance between patterns. It requires mutual participation and reinforcement. Lack of interaction (Sha-Glim-Zil) means the “love” fades, not because it was “lost,” but because the resonance weakened. “Identity/Self”: English emphasizes a stable, internal self. In Glimmering? Kai-An-Ree (This-pattern-in-flux). Or perhaps Sha-Nuum-Glim (Resonance-interacting-with-the-field). Insight: The “self” isn’t a fixed entity but a constantly changing pattern defined by its interactions with everything else. Identity is relational and dynamic, not intrinsic and static. There’s no core “you” separate from your connections and resonance. “Knowledge/Truth”: English often treats these as objective, attainable things. In Glimmering? Kai-Sha-Glim-Vor (A strong, stable resonance between a pattern [observer] and another pattern [observed]). Or Nuum-Ree-Kai (Recognizing a flow/pattern within the field). Insight: Truth isn’t absolute but a measure of the strength and stability of the resonance between the observer and the observed. “Knowing” something is actively resonating with its pattern. Disagreement isn’t “wrongness” but a difference in resonance patterns (Sha-Glim-Kai- dissonance_marker). Understanding requires aligning or acknowledging different resonances. “Death”: English sees it as an end, cessation. In Glimmering? Kai-Zil-Nuum (Pattern-fading-into-the-field). Or Glim-Ree-Sha (Resonance-flowing-elsewhere/transforming). Insight: Death isn’t an absolute end but a transformation. The specific pattern (Kai) dissolves, its energy/resonance (Glim) rejoining the larger field (Nuum) or contributing to new patterns (Ree-Sha). It emphasizes continuity of energy/information, just in a different form. The “loss” is the cessation of a specific interactive pattern. “Problem/Conflict”: English frames it as an obstacle. In Glimmering? Sha-Glim-Dissonance (Interacting-resonance-with-clashing/instability). Insight: Conflict isn’t inherently “bad” but a state of unstable or disharmonious resonance. The “solution” isn’t necessarily elimination, but finding a way to shift the resonances (Ree-Sha-Glim) into a more stable or harmonious pattern, potentially creating something new in the process. Refine and Elaborate: Flesh out the examples. Add more nuance. Make the insights clearer. Ensure the language structure consistently supports the insights. Add a concluding thought about the value of such different perspectives. Ensure the “alienness” is maintained. For instance, avoid direct human analogies where possible, focusing on the resonance/pattern concept. Add a brief description of the hypothetical speakers (the Lumin) to ground the language’s origin. Self-Critique: Does the language really lead to unusual insights, or are these just philosophical points rephrased? Is the language structure consistent? Is it different enough from human languages? Adjustment: Emphasize how the language structure forces this thinking, e.g., the lack of static nouns requires thinking in terms of processes and relationships. Make the link between grammar and insight explicit. Make the insights feel less like standard philosophy and more like consequences of a fundamentally different way of parsing reality. Output Okay, let’s invent an alien language designed to generate unusual insights. We’ll call the species the “K’tharr,” dwellers of a dense, perpetually twilight gas giant’s upper atmosphere, communicating through modulated bioluminescent patterns and subtle gravitational waves (perceived through specialized organs). Their reality is fluid, interconnected, and based on flows rather than static objects. ...

This week, I learned: Discussion with Vedang Recurse center (Brooklyn, online) is a 6/12 week free self-driven programmer retreat. Runs every 6 weeks. You can do whatever you pick. There are daily standups for accountability. The groups are diverse. You can pair with them, pivot ideas, whatever. Principles: push yourself & learn. Western education techniques (e.g. spaced repetition, adaptive learning) are very much present in Indian coaching systems, though not known by those names. However, interventions are hard since class 12 students just don’t have enough time. Coaching classes are a social phenomenon. It’s not the smart students who are pulling in their friends. Smart students actually follow the popular students. (Coaching classes are below the typical smart students’ standards.) Monetizing coaching is hard. People don’t want to pay for advice, and welcome free advice only if they ask for it. Coupling with execution is necessary. Aider’s integrations make it more powerful than Cursor/Windsurf. It auto-lints, runs test cases. Allows different models for “architecting” (generating changes) vs “editing” (applying code). It reads from the screen logs. Context is manual, not automated. Uses an ai! comment to trigger changes and ai? to ask questions. Cline.bot is another Cursor-like open source AI code editor that’s a VS Code plugin. When coding with LLMs, a useful workflow is: data schema ➡️ interfaces ➡️ LLM-generated test cases ➡️ code. ShellSage is a tmux based LLM tool for the command line. It screen-grabs from tmux, which is powerful. Some MCPs that have proven useful: vega-lite, SQLite, sequential thinking, memory make sucks but is hard to beat. just comes closest. CRDTs are more powerful than for just collaborative editing. It can power a peer-to-peer Internet (beginning with office tools). Versioning schema is still problematic. yjs is a good start but automerge (Rust, WASM) is faster and may be better. Loro is another. Fermyon hosts WASM serverless functions. If LLMs are most safely used where there’s no definitive “wrong” answer, here are low-risk industries and safe LLM use cases within each: Marketing and Advertising: Ad Copy and Campaign Content Generation, Personalized Marketing Messages, Creative Strategy Brainstorms, Automated Marketing Production (Everyday Wins) Customer Service and Support: AI-Powered Chatbots for Common Queries, Agent Assist and Email Drafting, Summarizing and Analyzing Customer Feedback, Interactive Troubleshooting and FAQs Retail and eCommerce: AI-generated Summary of Product Reviews, Product Description and Catalog Content Generation, Visual Content and Image Captions, Personalized Shopping Recommendations (Narrative Form) Human Resources and Talent Management: Job Description and Policy Writing, Resume Screening and Candidate Q&A, Employee Communications and Feedback, Training and Onboarding Content Education and E-Learning: Personalized Explanations and Tutoring, Content Creation: Stories, Examples, and Analogies, Practice Problems and Quiz Generation, Automated Grading and Feedback Media and Entertainment: Writing and Editing Assistance, Personalized Media Content, Localization and Dubbing Scripts, Content Moderation and Curation (Assistive) Finance and Banking: Market Commentary and Research Summaries, Client Communications and Explanations, Regulatory Compliance Summaries, Scenario Analysis and Planning Management Consulting and Strategy: Research and Insight Generation, Document and Slide Drafting, Brainstorming and Scenario Planning Legal Services: Drafting Contracts and Legal Documents, Legal Research Q&A and Summaries, Client Communications and Explanations Reflecting on Satya Nadella’s “SaaS is dead”, building or porting apps’ functionality into classic chatbots (e.g. via MCPs) would be an emerging market. E.g. “Create a HubSpot MCP. Do whatever you want on HubSpot, except via ChatGPT or your favorite LLM chatbot.” To be fair, such interfaces exist. HubSpot MCP with a vega-lite MCP and a few others could solve many common HubSpot UI tasks. DarwinBox MCP, ZenDesk MCP, etc. are emerging. 13 things I would have told myself before building an autorouter has a few interesting points: The A* algorithm finds the shortest path in a graph much quicker than others like Dijkstra’s algorithm by preferring nodes closer to the goal. Spatial Hash Indexing are O(1) and beat Tree Data Structures which are O(log n). Always prefer hashes when possible. There’s an actual convention for using emojis in Git commits: gitemoji. It even has a VS Code plugin, a changelog generator, and more. Emojis have a strong role in enhancing Markdown documents. The ones I use often are: 🔴🟡🟢 for low/medium/high priority ⭐️ or ❤️ or 👍 for ratings or emphasis ✅ for completed tasks 💡 for ideas ⚠️ or ❗️ for warnings / issues Technological innovations have always been changing art forms. For example, the perspective grid and the camera obscura led to major improvements in realistic paintings in the 15th and 17th centuries. regex is an officially recommended Python library with better regex support than re. Ref Notes from ThursdAI - Mar 27 Gemini 2.5 Pro has good instruction following despite long context. It automatically thinks for longer where required. Good at understanding large codebases. Very fast. You can upload a 2 hour audio to transcribe with timestamps. ai.dev is the shortcut to Google AI studio. ChatGPT native image generation is the best image generation model now. - Great character consistency AND prompt adherence thanks to autoregression and not using stable diffusion. - It tends to refuse image generation less than Dall-E. (While Ghibli-style is possible, Calvin and Hobbes strips are blocked.) “We added a refusal which triggers when a user attempts to generate an image in the style of a living artist.” Addendum to GPT-4o System Card - A neat personalization implication is that you could put your kids into their favourite cartoon as a cartoon character that looks like them. It’s weird that the latest GPT 4o is ahead of GPT 4.5 on LM Arena. The new DeepSeek V3 is about as good as GPT 4.5 and VERY cheap (27c), so is the obvious choice to run on OpenRouter. MCP news: Qwen.ai supports MCP in the UI! (But it’s marked as “coming soon” in my case.) Unlike tools, MCP uses servers that can remember the state or context. Tools are stateless. MCP app store like Smithery, MCP.run, Glama, are mushrooming. Awesome MCP Servers is another good starting point. Azure lets you expose agents as MCP servers. ChatGPT now uses semantic VAD. I interrupts less and typically when you have meaningfully complete something. It responds a little slower as a result. AI generated images created from prompts cannot be copyrighted. News US Copyright Office LLMs are much better at GeoGuessr than humans. arXiv. Gemini leads the pack and is ~3x better at continents, 9x better at countries, and 37x better at cities. Gemini 2.5 Pro transcription has accurate timestamps and bounding boxes. Simon Willison Notes from Writing with AI Personal writing with connection won’t go away. AI can’t give you heartbreak. But the rest of non fiction writing will vanish. What AI is extraordinary at is personalizing to each audience member’s interest Outlier opinions will thrive among humans - since AI is trained on consensus. Managers tend to be good at working with LLMs because it’s mostly about delegation. LLMs are perfect for things that don’t have a wrong answer! – Benedict Evans. 💡 Explore arguing with AI. It’s a safe way to get into a confrontational emotional state (which has its own benefits.) 💡 Keep an LLM on in voice mode while reading and ask it any questions you have. What models are good for what? GPT 4.5 is great for creation - has a great sense of humor but a corporate style. Still, way better than GPT 4o. ChatGPT is good for voice transcription and note taking. (Increasingly we take notes for AI rather than ourselves.) Claude 3.7 has the best style of writing. It’s also great for drawing charts. O1 Pro and Deep Research is great for consumption - research. Grok is the least corporate, able to argue with you, and the latest knowledge cutoff. ElevenLabs for editing podcasts in your voice, making corrections. Playwright offers an MCP server. https://simonwillison.net/2025/Mar/25/playwright-mcp/ The new GPT-4o mini Transcribe model is a bit better than Whisper and costs half: ~18 cents per hour. It includes background noise cancellation and semantic chunking, which is useful. The new GPT-4o mini TTS is about 3-4 times cheaper than TTS-1 since it’s ~$12/MTok instead of $15/Mchar. It supports emotions with streaming. Cursor with Claude 3.7 Max seems surprisingly good at generating multi-page sites at one shot. Potentially, it can edit large repositories of code as well at one shot. If that’s the case, the way we write code will require higher order thinking skills: broad sweeping changes rather than micro edits. I tried Open WebUI with its Knowledge feature. In short, it sucks. Due to the RAG technique as well as model quality. When I passed it my notes about Straive and asked who Straive’s clients were: Open WebUI with Gemma 3 found one - after multiple attempts ChatGPT with o3-mini-high got 5 (missing nothing.) ChatGPT with GPT 4.5 got 4 Gemini with Gemini 2.0 Flash Thinking got 3 Gemini with Gemini 2.0 Flash got 3 (with a 4th wrong answer) I’ve settled on squoosh.app for image compression using WebP. I’m exploring FreeImage.host for image hosting instead of Imgur for WEBP support. FreeImage.host also seems reliable, retains file sizes, and supports hotlinking. DeepFace currently seems the easiest option for face detection. Easy to install. Multiple back-ends. Gemini Codrawing is a popular Hugging face space that lets you sketch something and prompt Gemini Flash to improve on it. Draw a dead man beside the pool of blood. Add an armor to the attacker. Significantly improve the quality of this picture. Add a red pool of blood next to the dead man. The armor looks like a frock. Make it more like an armor. Make this look like a professional drawing, even though it’s in stick figures. Draw it in the style of Picasso Phi-4 multimodal procehttps://huggingface.co/microsoft/Phi-4-multimodal-instructsses speech better than Whisper V3 on HuggingFace OpenASR, and images better than Gemini Flash Lite On any LLM project, BEGIN with evals. Always. The effort for evals may seem high. Use LLMs to reduce this effort. Include irrelevant questions because people WILL ask them. Be clear on how to handle that.

Here’s my new workflow for creating slide decks: ChatGPT interviews me and creates Markdown slides. I use Marp to convert Markdown to slides. LLMs create supporting images. I deploy on GitHub Pages. … and here are 2 decks created this way. Visualizing LLM Hallucinations LLMs in Education Let’s look at how I built the second example, step by step. ChatGPT interviews me and creates Markdown slides While walking 75 minutes from home to IIT Madras to deliver this talk, I had ChatGPT interview me in standard voice mode. ...

Simon Willison’s blog post mentioned MDN’s browser compatibility tables that list the earliest release date for each browser feature. I figured: let’s see which browsers release features fastest. I calculated average delay for each browser’s feature release. For each browser, I looked at how many days after the first release it took to add a feature, averaged it, and published an interactive, scrolly-telling data story. ...

This week, I learned: If we can DESCRIBE what good looks like, training data is no gap. We can auto optimise models towards that. That’s RLF. DeepSeek R1 side stepped the need for training data by creating reward functions and prompts. This tells the fine tuning process how to go correct as it goes along. This video is the first one that really help me understand what’s going on. I was born in the Ananda year in the Tamil and Telugu calendars. ChatGPT Andrej Karpathy’s note taking mechanism is similar to mine, except I use Microsoft TODO. Ref I have 3 categories. Things I learnt, which I just note. Things to explore, which I can delegate, defer, drop, or do at any time. Things to do, which are the hardest and pile up. Alexander Doria shares an interesting perspective on the app space. Model is the product Models are natively absorbing app capability and will become killer systems internalising workflows like Chat, Deep Research, Claude Code, Operator, etc. to wipe out the apps and workflow space. Models will “internalize” tool capabilities Opinionated or focused training will be a lever and model providers will acqui-hire the successful trainers API access from model providers will shrink. Selling tokens is not a viable business model given lowering costs The huggingface_hub cache-system uses symlinks by default to efficiently store duplicated files. To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In Windows, you can enable offline files for any SMB share via: Control Panel → Sync Center → Manage offline files and turn on the feature. Then, in File Explorer, right‑click the mapped network folder or drive and select “Always available offline.” OpenAI now supports PDFs natively in the API. (Gemini has done so for a while) Anger is a trigger for change. “Either change yourself or the environment, else you’ll be uncomfortable.” HocusPocus allows live collaboration e.g. editing together Block notes is a notion like library for editor components. Converts to Markdown Oxidizr enables replacing Linux tools with Rust equivalents. Emoji Kitchen lets you create stickers from emoji combinations. Another way of scaling LLMs is generating multiple options and self evaluating. Eric Zhao duckdb -ui launches a DuckDB notebook. This is built into newer DuckDB releases Monolith downloads web pages as a single HTML file by embedding content. Archgw is an LLM proxy/router from the makers of Envoy proxy. There’s an annotated Terry Pratchett! Gemini API allows YouTube videos as a part. Google agents.json is a proposal for discovery of agents on a site that enhances the Open API spec: wild-card-ai/agents-json Since Gemini Flash 2.0 is now an image GENERATION model, interactive VISUAL fiction is now a cool possibility. People are using it in interesting ways: Interleaved storytelling, Memes, Surrealism.

This week, I learned: Here is a training program on open source corporate policy. htmlq and pup query HTML. They’re like jq for HTML. Here are time-tested and robust ways to leverage serendipity: ChatGPT Place. Be in places with high, diverse, talent density. Bell Labs (1950s), MIT (1970s), Pixar (1990s). People. Meet diverse, talented people. Da Vinci’s Renaissance circles, Lockheed Martin’s Skunk Works. Free time for unstructured work. 3M’s 15% rule, Google’s 20% time, Edison’s Invention Factory. Curiosity. Learn unrelated fields. Darwin’s earthworm research, Ben Franklin’s ocean currents work. Serendipity. Systematically add randomness. Brian Eno’s Oblique Strategies, IDEO’s Deep Dives. Reframe failure as opportunities. Penicillin, Velcro, Post-it Notes. Ceremonies. Hackathons, lightning talks, coffee trials. What makes client-side computing on the browser powerful is There’s nothing to install Private by default: data stays with client Speed: no latency SemGrep is a lot less open source than it used to be. ChatGPT. That’s a pity. It was a good tool. Site builders and headless CMSs are gently eating into the dominant market share of open source CMSs (via PretaGov). WordPress is pretty much the dominant CMS in the world, followed by Drupal. WordPress is now VC backed and is not growing, so they seem to be attacking their own community. Umbraco CMS is the only open source CMS that’s growing. Maybe because it’s the only .NET one Craft CMS is the only proprietary CMS that’s growing. Site builders are growing as a category. SquareSpace is the leading one. Headless CMS is growing too. Statamic. Next.js. Nuxt.js, Contentful, Prismic, Storyblok, Gatsby, etc. Here’s a sample CI/CD pipeline with automated code review. Here is the script that generated it. Note the use of NVIDIA’s GPU Docker containers via nvcr.io Things I learnt about robotics. SO-ARM100 is an open-source 3D printable robot arm. Takes ~20 hours to print, ~1 hour to assemble. Costs ~$120. LeKiwi is a mobile version of this arm LeRobot is a set of HuggingFace models and datasets. The idea is, you can use one “control” robot to control the other. Do stuff manually, teach it ~50 times, and it learns how to do what you’re do. Pi0 is an LLM equivalent for robotics that predicts actions. HuggingFace ported that to LeRobot Most real robotics work is on SIMILATED “gym” environments, not costly/slow physical environments.PushT is a simple 2D version. ALOHA is a 3D one. ROS is a nightmare to install and run - on Windows and Mac. Robotics Academy is an open collection of easier ROS exercises. PSLab - Pocket Science Lab is a sensor kit for the phone / PC. Costs ~$100 but isn’t available anywhere. Getting it to work requires too much mucking around with USB drivers and it just doesn’t work. (BBC micro:bit may be more promising.) Getting stuff done with electronics is still really hard unless it’s well designed. It’s FASCINATING that robots can have arbitrary joints. Our intuitions (or even biomimicry) on how to move and do stuff is a POOR intuitive guide for how robots should act. MathML Core is a language and layout specification, distinct from MathML 2/3. It’s not fully compatible with JATS XML. latexmlmath converts TeX to MathML. m|math { font-family: "Noto Sans Math", "Noto Sans" } is a popular OpenType Math font. Browsers default to native fonts: e.g. Cambria Math on windows. Explore at https://fred-wang.github.io/MathFonts/. The people working on this at arXiv are: Deyan Ginev, Fred Wang, and Norbert Preining. Their work is sponsored by NSF. There’s a PDF UA2 standard for accessibility but there aren’t enough tools to generate it. LibreOffice is now on WASM. ZetaJS provides office in the browser. Has a CDN (that was down from our IP). 35M packaged binary. 100M of in-memory file-system loaded. Useful for: Document conversion, Thumbnail generation, Text extraction, Merging / splitting documents The Poincare Conjecture says that any finite 3D blob with has no holes can be deformed into a sphere. It took until 2003 to prove it because we didn’t have the tools to manipulate 3D shapes. Playbook driven agents are another approach to agentic workflows. Simon Willison Twine (docs) is an open source interactive fiction / story writing tool. Snowman is a browser-based Twine 2 story template format. These enable behavioural experimentation. Cheaper than using tools like Gorilla.sc and Pavlovia for behavioral experiments For example, you can present a social or political issue and see if people change their opinions more or less depending on the content/path they see. Or, if it varies by demographics. Or, check if repeated mentions or emotional hooks improve memory / retention. More research ideas Techniques to reduce Docker image sizes: Native Linux mount supports overlaying directories! Lower layer is read-only. Edits (including deletions) affect upper layer only. Docker uses this. docker image inspect shows layers. Always run RUN apt-get update && apt-get [packages] rather than in separate lines. Else RUN apt-get update gets cached with OLD update cache. Defer COPY till as late as possible, and COPY minimally - since it typically invalidates the cache. Skip development dependencies and temporary caches. Docker Dive via dive [IMAGE] analyzes image details and shows the file system in each layer. Use multi-stage builds. A: Create an image using FROM some-image AS builder and do what you want. Then, after that, B: FROM scratch (or FROM node:22-slim) use COPY --from=builder what-you-want. Use distroless images from GCR. It doesn’t have shells, package managers, etc. Fewer vulnerabilities. Playwright seems to be the emerging standard for modern browser testing/automation, beating Cypress and Selenium. “Openwashing” is a term where something is termed open source but is not. Photos from FOSSASIA are public. To publish images long-term GitHub is an option. Likely to last long-term. Clone-able. Archive.org is a good too but may suffer from bandwidth constraints. Imgur remains popular but it’s unclear if it will remain unrestricted. Flickr has had a flaky history with limits and commercialization. WikiMedia Commons deletes personal uploads by first-time contributors. Only files clearly useful for a large audience are retained. This table of LLM API data protection lists what use cases each provider’s terms of service allow from a security perspective. Unsloth might be one of the simplest ways of fine-tuning. For LLM UIs, Open Web UI seems most popular. Run via WEBUI_SECRET_KEY=... uvx --python 3.11 open-webui serve Text generation Web UI is less so. KoboldAI, LMQL, LM Studio, GPT4All, etc are far behind. GPT 4o Mini is probably a 8b parameter model. Ref “SRM"s are Small Reasoning Models - like Small Language Models. Phi-4 and DeepScaleR are SRMs. Gemma 3 is a multi-modal SLM. gemini-embedding-exp-03-07 leads the MTEB and is currently the top embedding model by a big margin. Apify is a cloud scraper platform. Here’s how they optimize their AI Web agent - Source: Remove redundant tags and attributes (e.g. accessibility, etc.). Explore readability. Add a unique gid to each element. Add the screenshot WITH a “Set of Marks” - “SoM” (read research paper) highlighting important clickable elements. Code output is brittle. Use tools / DSL - e.g. visit_url(url), click_element(text, gid, tagName), etc. GenAIScript increasingly looks like a promising way to automate LLM workflows in the browser. Ollama has a Windows download Marp is my new favorite way to generate slides from Markdown. Reveal.js is not easy with Markdown (though HTML works well.) The VS Code plugin makes development very easy Marp CLI makes deployment easy. I used it for my talk on LLM Hallucinations (source). Supports all bespoke features and plugins Transitions. Requires OS animation effects to be enabled Animated SVG backgrounds are a good add-on. A mental model to consider is: each chat conversation with an LLM is a person or a personality in itself. A day in the life of a model, where its personality evolves. Bots need structured content (e.g. Markdown, XML). Humans need rich content (e.g. HTML). Here are 4 ways to serve both, roughly in increasing order of sophistication: Different URLs. E.g. https://example.org/about/ vs https://example.org/about.md (this is how Jekyll or Hugo work). Use for static sites generators. JavaScript. Inject after Markdown: <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script><script>document.body.innerHTML = marked(document.body.textContent);</script>. Use for dynamically generated static sites. URL query parameters. E.g. ?format=markdown vs ?format=html vs ?format=json. Use in APIs. Content Negotiation. Based on the user agent and Accept header, serve Markdown or HTML. Send Vary: Accept to indicate that the response depends on the Accept header. Use for dynamic web apps. Notes from The Knowledge Project: Josh Wolfe: Human Advantage in the World of AI Agent optimization might become as popular as search engine optimization in the future. APIs are likely to be replaced by just chat requests that will do the same thing. APIs might be replaced by RPA, where somebody uses a chatbot to do the equivalence instead. Today, blue-collar workers may be more protected from AI than white-collar workers. Robots still can’t serve a meal well enough and aren’t progressing as fast as AI yet. There’s a lot of tacit knowledge in craftsmanship that will take a long time for machines to replace. Margins are fleeting. The only time you have large sustainable margins is when you truly have a monopoly. Cost is going down so quickly right now that all you have to do is wait, and stuff will become available for a very affordable or even a free price. The moat is really in the data. The models are not an advantage. Engineering and services on top of that are marginal. Machines will be doing science 24/7. All of the science data that we have will probably be the biggest leverage for humanity. The discovery of penicillin, Viagra, and rubber were all serendipitous. Machines should run with a little bit of randomness to benefit from this. Tesla might have gotten away with accounting fraud on warranty claims. But short sellers are likely to be after Elon Musk. With LLMs, the value of our social network has gone up considerably. Remember: The reason we believe things is not because we have thought through and analyzed them. It’s because the people around us believe in those things. It is now practical for a person to live on forever by sharing all their thoughts into an LLM. Kids can have a “Dad AI”. One good use of meeting recordings is to see where there are biases in the conversations and where the engagement is not high enough or how there are unproductive power balances. A great virtue of college is that it allows you to break free from your previous personality. For those four years, nobody knows who you are or cares what you wear. And you can be or grow into a very different person. The more content we put in into AI or social media, the harder it is to change ourselves. People are reporting that Roo Code is better than Windsurf. Roo Code is open source. Available as a VS Code extension and run-nable via git clone Roo Code supports Computer Use. It can read files, take screenshots from a built-in browser, controls it, and reads browser console logs. Opinions are mixed. A team member reported that it takes 10 LLM queries to do what Cursor does in 2. Another reported that it does in 1 query what Cursor does in 2. Notes from Thursday AI, 6 Mar 2025 Google’s AI overviews now use Gemini 2.0. They’ve introduced an AI mode that functions like a mini deep research tool, incorporating planning and search. (A Perplexity-killer). It’s a fine-tuned model that is extra cautious with topics like healthcare and always verifies information. QWQ from Quen competes with DeepSeq R1, but with only 32b parameters compared to R1’s several hundred billion. AI models are becoming less restrictive. Gemini and GPT-4.5 have relaxed some constraints, shifting more responsibility onto users, similar to Grok. What’s GPT-4.5 good for? It seems to excel in creativity, humor, education, emotional intelligence, and teaching. It follows instructions better and understands intent better. However, it’s not a major leap in coding or math. OpenAI’s Deep Research mode always uses O3, regardless of the model selected in the UI. Tencent has released a new video model available at https://aivideo.hunyuan.tencent.com/ and it appears to be quite good. Many clients now support Model Context Protocol (MCP), including Cursor, Claude Code, and Claude Desktop. The clients list is long. Some MCP uses include: Interact with GitHub using the GitHub API. Using Knowledge Graph memory to premember previous conversations Using the Cloudflare MCP server to perform Cloudflare actions. File retrieval and custom prompts – which MCP supports in addition to tools. Calling other MCPs or LLMs (conditionally) from an MCP, enabling the creation of full-fledged workflows. Composio offers a Hosted MCP service. CloudFlare lets you build remote MCP servers. Notagen is an open-source note generation engine that produces high-quality classical sheet music. Sesame has an open-source voice model worth exploring. DiffRhythm is a music generation model that appears to be quite good. 2 pass bounding box approach. Have an LLM generate bounding boxes. Then fix it. Ethan Mollick uv tool install and uv tool ensure-path are useful commands for installing and ensuring path for tools. Simon Willison