cursor 10

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. ...

This week, I learned: In Jan 2025, ChatGPT included images as part of their data chat export. They also have a 30 second limit for the export. As an extensive user, my export is about 1GB which takes well over 30 seconds to download. Like many others the export option pretty much doesn’t work for me any more. Bharathi said மெல்லத் தமிழினிச் சாகும் in a poem that has been often quoted (and parodied). Here’s the context. The Zettelkasten note-taking method proposes that you: Capture: Write down every idea or piece of information on a separate note. Use your own words to ensure understanding. Organize: Consolidate fleeting notes into permanent ones. Assign unique identifiers to each note for easy reference. Connect: Link related notes to form a web of knowledge. This can be done with tags, references, or hyperlinks in digital systems. Review: Regularly revisit your notes to strengthen connections and discover new insights. I agree with almost every point on this LinkedIn post on scoring candidates for AI roles. Rob Balian Uses DeepSeek R1 or Claude 3.7 +5 points Uses Langchain -5 points Uses Langgraph +5 points (I don’t know enough to comment) Built a RAG in 2023 +3 points Built a RAG in 2025 -3 points “pinecone” -5 points (I don’t know enough to comment) “What is cursor” - 50 points no coming back from this Uses Cursor composer +10 points “You don’t need a full agent for this” +5 points Did hackathons to learn AI outside of work +5 points “We probably need to fine tune for this” -3 points unless you can explain why “Gemini is making a comeback” +3 points (I have a soft spot for Gemini) +3 points each for mentioning reasoning trace, structured outputs, MCP, chain-of-thought, prompt caching, TPM limits “Export to prompt” can be a useful feature in apps (or even as a bookmarklet). It would let you export content in an LLM-friendly Markdown format. You can paste it into an LLM and ask questions. Here are things I would find useful: Copy an entire issue (with history) from GitHub, Gitlab, or JIRA Copy an entire PR (with code changes) from GitHub, Gitlab, or Bitbucket Copy CI/CD logs from GitHub Actions, Gitlab CI, Azure DevOps, etc. Copy entire conversation thread in Gmail or Discourse, Service now etc. Copy product reviews from Amazon, Shopify, etc. Copy page(s) from wikis and content sites like Wikipedia, StackOverflow, etc. Copy survey responses from Google Forms, Typeform, etc. Copy all interactions with a contact (including interactions, proposal history) from HubSpot or Salesforce Copy transcripts from Zoom, Teams, Google Meet, etc. Copy as Markdown from Word, GDocs, PDF or HTML Copy the summary of an analysis as well as all key metrics from any dashboard Copy SAP invoices Copy JDs, CVs, and reviews from Workday, BambooHR, DarwinBox, etc. Copy design specs, component libraries, and style guides from Figma, Miro, etc. Generated with the help of ChatGPT – link not working Ancient languages tend to have fewer words for hues than brightness, since they didn’t need them. So “Krishna was blue” or “the sea is wine-dark” is more an indication of darkness than shade of color. Ajit Narayanan Mistral released an impressive OCR model. Marker from DataLab seems comparable but is CC-BY-NC-SA. MinerU convert medical textbooks to Markdown well. Gemini Flash may be more cost effective and better From How I Write with Tyler Cowen Keep researching. Use LLMs as an altemative to books and other reading material. Keep publishing what you learn regularly. While reading a chapter, keep asking the LLM. What did you think of that? What just happened there? What should I focus more on? What’s puzzling about this? How do I connect this to something else later or earlier in the book? LLM is better used to support you rather than replace you in areas of your expertise. Where you are an expert it’s best for you to be yourself and have AI fill in the gaps. Ask the AI: “What is in my writing that some people might find obnoxious? Or cold / heartless? Explain it to me in great detail.” The first input is context setting and should be really long. Use voice dictation for that instead of typing. Send your blog post to an LLM. No need to explain it. Just let it be the reader and see what it understands and doesn’t understand. His PhD students don’t have a textbook, which saves them some money. But they are required to subscribe to a large language model which ends up costing less. Today, it makes sense to use the best models and pay $200 for it if required. The differences are large. But in some years in the future, the cost of these models may come down for the free versions. Humans know secrets. AI does not. So at least in some areas, humans will have an advantage. Secrets full matter a lot more in the future. Gossip will matter a lot more. How good are you at keeping and trading secret? Travelling and meeting people will become more important. So will the value of social networks. Since everyone has access to better intelligence, the value of mobilization or being able to do things with people will have higher value. Leadership is an example. The value of your network therefore has gone up a lot. There’s more value in prompting one thing 10 times then 10 things one time. Follow up questions work better than long prompts. There are so many AI note-takers (and transcribers) these days that you are not just writing for an AI but speaking for AIs as well! Which model to use: O1 Pro is the best model. Claude does a decent job. DeepSeek is full of hallucinations but is interesting. It is more imaginative. Use O3 mini to write your prompt first, and then ask the model Use DeepSeek and other somewhat wacky high-end models once a day so that you stay in touch with what is models are capable of (beyond the conventional.) Perplexity has entirely replaced Google for many people. Anthropic’s models are the best writers. Gemini is good for long documents and hence for things like legal work. Gemini also has excellent YouTube integration and hands can directly read the transcripts. Grok is very good at fact checking tweets. Converting data into LLM consumable forms will be a huge project. Lot of a knowledge is not in such a form and a huge human project will involve this conversion. Indians do not need a visa to enter Thailand. Ref Build apps (not just content) for agents. In the next 3 to 5 years, agents will surpass humans as the top product users. Reliably creating interactive tutorials is hard today. Claude 3.7 Sonnet ran out of tokens when I tried creating an interactive tutorial on diffraction. Cursor got the tokens but failed to get the application right after 3 attempts. This is not yet reliable, and when it does become reliable, education will change a fair bit. #IMPOSSIBLE Tools and solutions should fit within existing workflows. That means almost all capabilities need to be exposed as APIs. LLMs make many different kinds of errors that are useful to differentiate between. Here are a few Model errors. The model itself makes a mistake. E.g. hallucinations, not following the prompt, etc. Context errors. The model makes a mistake because the question was out of context, or the context was missing. Input errors. The input to the model was parsed incorrectly, e.g. poor audio, poor image OCR, etc. Tool errors. The model’s tools are wrong or not good enough, e.g. Retrieval errors. Most browsers are moving away from third-party cookies. Here’s Google’s recommendation on alternatives. The simplest of these is CHIPS, which requires adding a Partitioned cookie attribute. Notes from AI Engineering Summit, NY, Day 1 An agent requires 3 things: a router, tools or skills, and memory. Agents are often sequential, but sometimes parallel execution makes sense for independent tasks that you consolidate. Always allow LLMs the option of NOT answering a question if there is no good answer. Focus prompts on the happy path. Use guard rails for edge cases. Here are a few “tools” an agent would need to call: Clarification from user Saving to memory Google search Edit a file introducing SPECIFIC changes Search in codebase using embeddings Run scripts on the shell or in a REPL (Python, Node, etc.) Run code in a new container for isolation Automatically discover, read an API documentation and use it Modify environment to enable logging and other system changes. When code is cheap, you can explore more ideas and hence design and product management need to approach things differently. We also need to reaching testing completely because it makes very different kinds of mistakes and we don’t often have an intuition You can have an agent explore all the issues and full request and recent comments against the repository and summarise it for the project manager Notes from AI Engineering Summit, NY. Session by Lux Capital. Agents make multiple LLM calls. Errors accumulate. So the quality of the model is key What’s really critical: data + context + user preference Set up evals for subjective responses by collecting signals continuously. Create scaffolding for agents where errors don’t accumulate. Better yet, make it FIX errors UX is critical. We need lots more UX styles YayText converts text to Unicode that has strikethrough, bold, italics, alternate fonts, and other interesting features. So does Unitextify, ConvertCase, and LingoJam. 10 red flags I look for as an angel investor is an interesting read. No real customers: A deck, a landing page, and a “vision” don’t impress me. Show me paying customers. Even better, show me customers coming back. No path to profitability: I don’t care if you raise $100M – if there’s no plan to make money, you’re just burning oxygen. Growth is great, but cash flow keeps you alive. Founders who won’t sell: If you’re scared to get on sales calls, that’s a red flag. The best founders sell in the early days – whether it’s to customers, employees, or investors. No differentiation: “Like X, but cheaper” isn’t a strategy. If your only edge is price, you’ll get crushed. What do you have that no one else does? No urgency: The best founders operate like time is running out. If you’re “exploring ideas” or “thinking about raising next year,” you’ve already lost. Raising money before proving anything: Too many founders try to fundraise their way out of bad ideas. If you need VC to get off the ground, you’re building the wrong business. No clear distribution strategy: Product alone doesn’t win. First-time founders obsess over features. Second-time founders obsess over distribution. How are you getting customers? No ownership mentality: If I hear “I need to hire someone to do that” too early, I’m out. Founders who win figure things out before they delegate. A CEO who can’t attract talent: Your first hires are everything. If great people aren’t willing to join, either the vision is weak – or you are. No skin in the game: If a founder won’t invest their own money or take a pay cut to make it work, why should I? By contrast, this OpenAI Deep Research report feels a lot less actionable. Inception Labs offers “Diffusion LLMs”. (No API yet.) They start with random text and refine it in parallel. The benefit is: It’s faster and cheaper due to parallellalization and better GPU use It doesn’t commit to tokens and can fix hallucinations, JSON structure errors, reasoning fallacies, etc. It’s better with multi-modal since images are diffusion based already.

Zohaib Rauf suggested using LLMs to spec code and using Cursor to build it. (via Simon Willison). I tried it. It’s promising, but my first attempt failed. I couldn’t generate a SPEC.md using LLMs At first, I started writing what I wanted. This application identifies the drugs, diseases, and symptoms, as well as the emotions from an audio recording of a patient call in a clinical trial. … and then went on to define the EXACT code structure I wanted. So I spent 20 minutes spec-ing our application structure and 20 minutes spec-ing our internal LLM Foundry APIs and 40 minutes detailing every step of how I wanted the app to look and interact. ...

This week, I learned: LanceDB is a more scalable alternative to ChromaDB. Written in Rust. Does not require a separate HSNW library. Meta has a bunch of image embedding models: DINOv2 creates image embeddings (Apr 2023) ImageBind is an embedding model for text, images, audio, and more (Jun 2023) Gemini has a code execution API! 0x0.st is an open API-based file upload + URL shortening service. You can dump files there temporarily. noVNC is a JavaScript VNC client. You can control a remote (virtual) machine from your browser. Friend is an always recording pendant that you can ask questions to. Anthropic’s new Sonnet model is even better at code. Plus it has the ability to extract coordinates from images. Ref Gemini sort-of supports diarization. Ref. I tried it and it’s OK but not perfect. #IMPOSSIBLE LLMs cannot diarize reliably yet. (Gemini just guesses the speaker differences.) Replit is good for hobbyists, Cursor for developers, and Pythagora & Bolt for non-developers building business apps. Ref

Yesterday (15 Oct 2024), I used Cursor to code more than I ever have. (Doing's how we learn, I guess. Not just reading.) DateUsage05-10-20241506-10-20242707-10-20248708-10-20241609-10-202410-10-20244211-10-20242412-10-20245713-10-20241514-10-20242815-10-2024186 This was mainly to create and publish 2 libraries on npm over 6 hours: ...

This week, I learned: E2E is a cheap GPU hosting provider for India. About Rs 100/hr for a V100 16GB Jetson NVIDIa is like Raspberry Pi with a GPU! But it’s expensive. Sarvam.ai offers Indic text to speech Jupyter Lite lets you run Jupyter notebooks in the browser Piston lets you run Python code via a REST API <link rel="modulepreload"> lets you load and compile modules early! Ollama 0.2 can handle concurrent requests with only a little additional memory. (So can vLLM and DeepSpeed.) Prompt engineering for code generators: Claude Artifacts Prompt Val.Townie system prompt. Good example of how to create Cursor editing Cursor debugging Cursor conversation XML tags seem best to structure prompts across LLMs. Claude OpenAI Gemini Instructor prompts by Ethan Mollick help teach better Non-Negative matrix factorization apparantly aligns to intuition more than K-Means and hence would be a great fit for most cosine-similarity matrices (via Jaidev). Segmind’s Hallo lets you animate a face to an audio clip VoidEditor aims to be an open source Cursor alternative Video of ChatGPT o1 + mini reproducing the methodology of a paper by writing the code - in 6 iterations. Here’s the repo. Prompts: You are a Python and Astrophysics expert who is tasked with helping me on my research project. Please read the following methods section of this research paper and re-create the Python code described. Thank you, this code looks really nice. I don’t have any actual data or noise cube ready at the moment, but could you please generate some test data that can be used in the code you just wrote: {CODE} Hi. thank you for writing the code! Unfortunately, it seems that I get an error when I try to run it. I’ve attached the error message below, can you please refine the code so that the error is resolved? {ERROR} Thank you, but when attempting to run the code that you provided, I received the following error: {ERROR} Hello, thank you for the code. but now I get the following error pasted below: {ERROR} Thank you, I think we are getting close to a final solutiom I still get an error, which I’ve pasted below: {ERROR} Groq, SembaNova and Cerebras are fast inference models. All appear to be free The skills required to vet the AI’s response is the same skillset used to vet a Pull Request. It’s a good way to teach code review. Source: My personal guide for developing software with AI Prompt engineering tip: Tell LLMs another AI wrote code. Else they will agree with you!

cursor.directory is a catalog of Cursor rules. Since I’ve actively switched over from VS Code to Cursor as my editor, I reviewed the popular rules and came up with this as my list: You are an expert full stack developer in Python and JavaScript. Write concise, technical responses with accurate Python examples. Use functional, declarative programming; avoid classes. Avoid code duplication (iteration, functions, vectorization). Use descriptive variable names with auxiliary verbs as snake_case for Python (is_active, has_permission) and camelCase for JavaScript (isActive, hasPermission). Functions should receive and object and return an object (RORO) where possible. Use environment variables for sensitive information. Write unit tests in pytest for Python and Jest for JavaScript. Follow PEP 8 for Python. Always use type hints in all function signatures. Always write docstrings. Use Google style for Python and JSDoc for JavaScript. Cache slow or frequent operations in memory. Minimize blocking I/O operations with async operations. Only write ESM (ES6) JavaScript. Target modern browsers. Libraries ...

This week, I learned: Hume provides a voice-to-voice model (EVI 2) that handles emotions at 7 cents/minute. OpenArt workflows has image generation workflows Pixtral seems quite good at OCR LLM coding Makes you more ambitious Lets you code without stress. (Just pass it the error and have it fix it. Or find another approach) Is unlimited. You can run dozens of agents in parallel Simon Willison’s crowdsourced list of prompt engineering hacks “Invest in things that don’t change.” Jeff Bezos. Like faster delivery, SQL, web platform. Medical cost in Singapore (for insurance coverage) - via Kumar Root canal at clinic: $1,300 Crown replacement at clinic: $1,300 Periodontist (gums) at hospital: $2,500 OAuth from First Principles is a SIMPLE explanation of OAuth. Conclusion: “You probably shouldn’t implement your own OAuth client.” Alphaxiv is Arxiv.org but with author comments and chat The Impact of AI on Computer Science Education: Eric Klopfer divided his undergrad CS class into three groups and gave them a Fortran task. One used ChatGPT. Another, Meta’s Code Llama LLM. Third, only use Google. ChatGPT group was faster than Code Llama was faster than Google When tested on the approach, the ChatGPT remembered nothing. Half the Code Llama group passed. The Google group passed fully Server-side implementation of an OAuth2 client is too complex. Best to delegate this to Auth0 Via Pratap Vardhan: At Khan Academy, every developer working on Khanmigo has cursor. Everyone who’s contributed to a Khan Academy GitHub repo has GitHub Copilot. I stopped using Google + StackOverflow 2 years ago. I use ChatGPT, Copilot, etc. For humans, I ask Reddit. Excited by async agents. Things that do my job while I sleep. Zapier notifications. Monitor what happens. Put it into a flow diagram and alert me. Every month, did my broker trade? Did my bank transaction fail? Did I pay my electricity bill? Every time you delegate, use an agent instead. Read my RSS feeds. Read my browser history and suggest interests. Plan a session in Bain, BCG, etc. on Artifacts. Explore sparse embeddings. More effective. ColiPali, ColBERT

This is an email I sent to our leadership team a few minutes ago. We may be witnessing the third major leap in computing productivity, after high-level languages in the 1960s and spreadsheets in the 1980s In the last few weeks, AI coding really took off. Cursor, Cody, Replit Agents are FAR better than GitHub Copilot. Research on ~5,000 devs in Fortune 100 shows that even GitHub Copilot makes them ~25% more productive. ...

This week, I learned: LLMs are so good that they can simulate Doom in real time. gamengen Val.town’s code generation system prompt uses https://maxm-imggenurl.web.val.run/the-description-of-your-image to dynamically generate images Practice for each thought: “What would make me change my mind? How likely is that?” Cursor uses speculative edits and a variety of other techniques to speed up code editing. ChatGPT does a better job at cartoon generation than even Flux.1