duckdb on S Anand

Things I Learned - 31 May 2026

Sun, 31 May 2026 00:00:00 +0000

This week, I learned:

D-ID is an avatar generator platform like HeyGen. Creatify and Synthesia are a couple of others I heard of. This space seems to be growing.
cosign is a CLI that lets you sign and verify any piece of text with a Google, GitHub or Microsoft account. cosign sign-blob FILE --bundle sign.json opens a login window and creates a sign.json signature. Anyone who has FILE and sign.json and the email ID can verify via a Google account with cosign verify-blob FILE --bundle sign.json --certificate-identity $EMAIL --certificate-oidc-issuer https://accounts.google.com.
arxiv2md.org converts arXiv papers to Markdown. Source. markxiv.org claims the same - by just changing the URL - but it ended up reporting an error when I tried this link: https://markxiv.org/abs/2604.08649.
From Akhilesh Tilotia: So we have someone in our team with initials AS. She made a document which was named vAS. Then I made edits and named it vAT. These docs were in a CoWork folder. I asked Claude to clean up my doc. It created another version for me to review. In its wisdom, it named the file vAU 🙂
Maybe what a forward-deployed engineer does is enginer AI-native workflows. (This sounded profound when I wrote it down. Not sure if it’ll sound as profound tomorrow.) The idea is that the FDE will say, screw existing processes; let me fire up my AI agent and get stuff done; THEN we’ll figure out what works, how to optimize it, etc.
The PRAGMA: Revolut Foundation Model has some good tokenization ideas for tabular data. Create your own token space with key–value–time tokenization - to retain field information. Bucketize numbers by percentile, preserving magnitude/ordering that subword tokenization destroys. Encode time both as log-seconds and as cyclical calendar features.
Codex uses the Alt + Up Arrow key to edit queued commands, but on the VS Code terminal, this key binding is not sent to the terminal. Enable the terminal.integrated.sendKeybindingsToShell setting to send it to the terminal, hence Codex.
Based on this catalog on “universal foods”, here’s what I 🟢 like, am 🟡 neutral, 🔴 dislike, 🟣 must try, and will ⚫ skip.
- Universal favorites: 🟢 pizza, 🟢 fried potatoes/chicken, 🟡 dumplings, 🟢 ice cream.
- Universal comfort foods: 🟢 khichdi, 🟡 congee, 🟡 dal-rice, 🟡 risotto, 🟡 ramen, 🟢 pho, ⚫ chicken noodle soup, 🔴 rice porridge, 🟡 mac-and-cheese, 🔴 mashed potato, 🟣 polenta, 🟢 oatmeal, 🟣 Japanese curry rice.
- Acquired tastes that convert most: 🟡 coffee, 🟢 tea, 🟡 dark chocolate, 🟢 mild fermented dairy, 🟢 pickles, 🟢 olives, 🟣 kimchi, 🟣 miso, 🟢 mild chili dishes.
- Acquired tastes that have cult devotion: 🟣 durian, 🟣 natto, 🟣 stinky tofu, ⚫ fermented fish, ⚫ hákarl, 🟢 very funky blue cheese, ⚫ offal.
OceanoPDF seems like a good place to download ePubs of books.
The entire Wikipedia is available as a Parquet file. You can query it like duckdb -c "FROM 'hf://datasets/wikimedia/structured-wikipedia/enwiki/data/*.parquet' LIMIT 5". The English version has 35 GB, 7.6 million articles, and you’re better off downloading it rather than running analyses remotely.
When you receive a Calendly link of the form https://cal.com/USER/EVENT you can fetch the available slots via curl -H 'cal-api-version: 2024-09-04' 'https://api.cal.com/v2/slots?eventTypeSlug=EVENT&username=USER&start=2026-05-25&end=2026-06-01&timeZone=Asia/Singapore&format=range'. Useful to automate good meeting-slot selection.
“Reference saved memories” in ChatGPT is different from “Reference chat history” as per OpenAI. In Developer Mode, memory is turned off, but not chat history. I confirmed that I can access past conversations in Developer Mode. It might be a privacy concern for others, but for me, this is singularly useful, because I can use ChatGPT with Local MCP effectively getting a non-metered AI coding agent.
Seems GPT-5.2 reaches expert level in peer review: 45 scientists took 469 hours evaluating human & AI reviews on 82 papers. “Surprisingly, current AI reviewers are competitive even with the top-rated reviewers in Nature’s official peer review…” though not without weaknesses, so use AI + humans. On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists via Ethan Mollick

Things I Learned - 30 Nov 2025

Sun, 30 Nov 2025 00:00:00 +0000

This week, I learned:

Warp has a terminal agent feature - allowing Warp to control a terminal via text. I find that regular coding agents like Codex can do that too with tmux. For example, I opened a session and had Codex run commands in it while I watched. Here’s the guidance it needed:

# Create a new session
tmux new-session -d -s $SESSION 'uv run --with pandas,httpx,lxml python -iqu'
# Capture output to a log file
tmux pipe-pane -t $SESSION -o "cat >> /tmp/$LOG"
# Run a command
tmux send-keys -t $SESSION 'print(1 + 2)' C-m
# See output
cat /tmp/$LOG
# Capture the last 5 lines of the pane
tmux capture-pane -p -t $SESSION -S -5

Notes from Early science acceleration experiments with GPT-5 - via Claude
- LLMs are accelrating research because they are good at:
  - Literature search, especially across disciplinary boundaries
  - Generating and checking routine calculations
  - Proposing variations on known techniques
  - Identifying connections between disparate results
  - Producing first-draft code for well-specified problems
  - Explaining why certain approaches won’t work
- But they’re curently struggling with the following - though it’s a shrinking space
  - Genuinely novel conceptual leaps (but this is increasingly happening, e.g. Sawhney and Sellke’s problem #848)
  - Recognizing when it’s plagiarizing, e.g. when it “discovered” a proof for the Chevalley-Warning theorem which was copied from a Noga Alon paper - it wasn’t conscious of this
  - Knowing what it doesn’t know
  - Distinguishing important problems from unimportant ones
  - Understanding the “negative space” of mathematics (why certain problems are hard, why obvious approaches fail)
Anthropic introduced three excellent tool use practices that I expect will be adopted widely.
- Tool search: Don’t pass the tool definitions to the model. Model can ask for a tool search when needed
- Programmatic tool calling: Instead of calling a tool, it’ll return a Python program to execute that will call the tools! This is a huge win
- Tool use examples: Lets you specific examples of tool calls to guide th model better
- The Hacker News thread flags that CLIs solve these - but CLI updates are hard, while APIs auto-update.
With AI, some skills that beome more valuable are (and will soon be in short supply, hence need to be taught) are: #
- Problem formulation (“What question should we actually ask?”)
  - Traits: Curiosity (absolutely), systems thinking, comfort with ambiguity, metacognition (thinking about your thinking)
  - Practice reframing exercises (“What are 5 other ways to frame this?”), study great questions in your field, work backward from outcomes, learn adjacent domains. The “5 Whys” technique helps. Also: deliberately pause before diving into solutions—force yourself to spend time in the question space.
- Taste and judgment (“Is this response appropriate?”)
  - Traits: Pattern recognition from experience, cultural literacy, empathy, contextual awareness, aesthetic sense
  - How to strengthen: Immerse yourself in excellent examples, study spectacular failures (they’re more instructive!), get feedback on your calls, practice explaining why you made a judgment. Build a “swipe file” of great/terrible examples. The key is volume—you need lots of reps.
- Quality assessment (“Is this AI output correct?”)
  - Traits: Healthy skepticism, attention to detail, domain knowledge, logical reasoning, understanding of edge cases
  - How to strengthen: Study common AI failure modes, build verification checklists, practice the “does this make sense?” test, learn what “good” looks like in your domain, cross-reference claims. Develop your “bullshit detector” by analyzing why wrong answers feel wrong.
- Creative synthesis (“How do these ideas connect?”)
  - Traits: Associative thinking, wide knowledge base, playfulness, comfort with non-obvious connections, intellectual courage
  - How to strengthen: Consume diverse inputs outside your field, practice analogical thinking (“X is like Y because…”), use visual thinking tools like concept maps, study how innovations happen in other domains, give yourself permission to make weird connections. Read broadly—fiction, history, science.
- Domain expertise (“Does this solution work in reality?”)
  - Traits: Deep curiosity, persistence, willingness to get hands dirty, learning from failure, long-term commitment
  - How to strengthen: Deliberate practice on real problems, seek mentorship, study edge cases and failure modes, build things (don’t just read about them), learn your field’s history. The “10,000 hours” thing is real, but it’s quality hours that matter.
- Meta pattern:
  - Reflection loops: doing something, then analyzing why it worked/didn’t.
  - Exposure to excellence: you can’t develop taste without seeing great work.
Some more new CLI tools I installed:
- trash-cli: Alias rm to move files to trash instead of deleting permanently.
After a week of seeing ligatures in Fira Code, all other fonts look ugly. My favorite ligatures: !== ==> =» <–> (and every possible arrow) >= ||> ||- |- …
The first name, alphabetically (at least among Straive employees) is “Aabida” and the last is “Zyrene”. Something I would never have discovered working in a smaller company.
chokidar-cli is an easy way to run commands when files change, e.g. npx -y chokidar-cli '**/*.js' -c 'npm run build'
npx -y mapscii shows a map on the terminal. Not too useful, not maintained, but very interesting.
termsvg converts asciinema .cast files to animated SVG suitable for embedding in GitHub (e.g. via mise x github:MrMarble/termsvg -- termsvg export file.cast --minify). The animated SVG is ~10X larger than the .cast file. The GZipped size is fine but saving it as .svgz is not recognized by GitHub. In contrast, agg, the official asciinema-to-GIF converter, creates .GIF files that are only 5X larger. The most efficient seems to be embedding via asciinema.org

usql queries MySQL, Postgres, SQLite, MSSQL, Oracle, etc via a single interface. For example, usql 'mysql://rfamro:@mysql-rfam-public.ebi.ac.uk:4497/Rfam' -c "SELECT * FROM clan limit 3;". But DuckDB is more versatile, IMHO.

INSTALL mysql; LOAD mysql;
ATTACH 'host=mysql-rfam-public.ebi.ac.uk port=4497 user=rfamro database=Rfam' AS rfam (TYPE mysql);
SELECT * from rfam.Rfam.clan LIMIT 3;
SELECT * FROM 'file.xlsx' LIMIT 3;
SELECT * FROM 'file.csv' LIMIT 3;

Autistic and allistic people just have different communication styles. Autistic people have no trouble understanding other autists. They just happen to be in a minority which makes it seem like they have a social deficit. Conflict between Neurotypes
1 second = 10 tokens for OpenAI Realtime APIs. 1 second = 25 tokens for Gemini Live API
- 39 cents / hour on GPT Realtime Mini = 36 cents audio input + 3 cents text output
- 139 cents / hour on GPT Realtime = 115 cents audio input + 15 cents text output
- 30 cents / hour on Gemini 2.5 Flash Native Audio (Live API) = 27 cents audio input + 3 cents text output
Here are some AI experiments I’m planning to try with our marketing team:
- Video Generation: Create marketing videos from text scripts in minutes
- Poster Generation: AI designs high-conversion posters from brief text inputs - notably Nano Banana Pro
- Synthetic Persona A/B Testing: LLM agents simulate 100K+ user behaviors to test designs before real users
- LLM-Powered A/B Automation: AgentA/B system runs experiments with AI-simulated traffic
- Vibe Coding Landing Pages: Marketers build production-ready pages in hours vs weeks
- On-demand Landing Pages: Generate pages for automated campaigns/products without human intervention
- Brand Voice Cloning at Scale: Train on company content to ensure consistency across 1000s of pieces
- Persona-Driven Content Synthesis: Use 1B+ personas to generate diverse content perspectives
- Competitive Intelligence Briefing: Real-time monitoring across millions of data points + data storytelling
- Marketing Analytics with LLMs: AI agents analyze complex datasets for insights
- Brand Compliance Checks: Ensure all content meets brand guidelines automatically
- Autonomous Blog Squads: AI agents identify trending topics / internal content, create data stories ready for review
New skill unlocked: creating tutorials from talk proposals. I asked Claude to Write a Malcolm Gladwell article based on this talk description to teach me the topic and passed it this talk proposal: Your Causal Parrot might be lying to you. The story it wrote is very engaging and informative!
- LLMs “understand” causality because of training, but lack a world model to extrapolate to new situations.
- Giving them tools to reason (e.g. causal models, sub-agents to explore root causes) will help.
A cool Gemini 3 Pro hack: convert satellite imagery into stylized maps! Bilawal Sidhu
Running sub-agents in tmux helps avoid timeout cancellation, and hence allowing resuming Peter Steinberger

Workshops That Teach Me More Than You

Sat, 18 Oct 2025 09:38:22 +0000

I don’t charge for workshops. Altruism? No: it’s self-interest.

“If you’re not paying for it, you’re not the customer; you’re the product being sold.” Andrew Lewis, via Tim O’Reilly, 2010.

My workshop process is designed to benefit me first.

I pick topics I want to learn, not stuff useful to the audience. Example: I picked DuckDB for my PyCon India 2025 talk to learn it.

I experiment on the audience. Example: I tried voice-vibe-modeling in my RIP Data Scientists talk. Experiments fail often, e.g. vibe-coding Minecraft in PyCon IN 2023.

I learn from the audience. Example: Rakesh Roshan’s K-fetish is statistically significant, via my Vibe Analysis workshop.

I collect data from the audience. Example: How student copy.

I gather ideas. Example: skills AI will replace, from my IITM DOMS commencement talk.

I publish these, too, with recordings and transcripts.

LLMs let me do more such stuff:

LLM simulations. We play an LLM-driven case. I learn from the choices.
LLM Q&A talks. Audience questions → LLM → live slides.
Cluster polls. Audience answers a question. We cluster live and learn.
Compare models. I show multiple answers to same question. You votes. We review diffs.
Learn code prompting. You build the same app with your own prompts. We analyze what works.
Security testing. Try to jailbreak or inject prompts. We map what works and why.

If your HR team or college is OK with all the above, ping me. I’m game for a free talk / workshop!

Things I Learned - 17 Aug 2025

Sun, 17 Aug 2025 00:00:00 +0000

This week, I learned:

Git partial clone lets you fetch files on-demand! E.g. git clone --filter='blobs:size=100k' <repo> will clone files under 100K and fetch the rest only on checkout. Over time, Git LFS capabilities will migrate into native Git. Ref
⭐ From Daniel Kahneman, The Knowledge Project Podcast.
- Key lesson. Have lower expectations. Behavior change is hard.
- Happiness is pleasure in the moment. Satisfaction is the meaningful story of our life. When reflecting, the thinking brain wants satisfaction. When feeling, the feeling brain feels happiness. The 2 brains optimize for different things. The thinking brain packs the calendar with satisfying tasks that the feeling brain hates doing.
- Happiness & pleasure are both are good for us. We don’t know which matters more.
- Behavior change is harder than most people think. Usually, it’s better not to expect success. Changing others, or ourselves.
  - Instead, understand the cause of that behavior. Behaviour is an equilibrium of forces.
  - Weakening forces preventing right behaviour is easier than strengthening forward forces. It lowers tension. That’s inversion!
  - Behaviours are more about situations than personality. We assume otherwise - that’s an attribution error.
  - Environment shapes thinking but it’s not obvious how, e.g. some people work better in noisy cafes. Some colors are more calming.
- Leadership & delegation
  - Motivation is complex. People can do bad things for good reasons and vice versa.
  - So, delegate decisions to unemotional agents. But agents misjudge perceived value of gain or loss!
  - People prefer over-confident intuitive leaders over slow, deliberate leaders.
  - Protect dissenters and dissent. It’s painful and costly, and needs nurturing.
- Negotiation is about understanding, not convincing.
- “Feelings get in the way of clear thinking.”
  - Example: I vibe-coded the last 2 questions of TDS GA7 on Claude Code. It didn’t run. I delayed fixing it for 5 days, afraid it would a major effort. It ended up a 2 min fix. It could have been major, but checking would have helped. Fear prevented that.
  - Intuition, emotion, beliefs hamper clear thinking. Beliefs are often formed based on people we admire or identify, not reason.
- What enables clear thinking (all are hard):
  - Pragmatism. Don’t threaten your identity, the leader, etc. Else none of this works.
  - Rules, systems and processes. Willpower is illusion. Alignment is an illusion. “Whereever there is judgement, there is noise, and more than what people think.”
  - Standards. Shared, consistent scales of evaluation. Super-forecasters use probability scales.
  - Deliberation. Slow decision making.
  - Decomposition. Break down the problem, analyze it, THEN form an intuition. Be disciplined in delaying intuition or forming an opinion.
  - Pre-mortems. “Write the history of the disaster this decision led to.”
  - Decision journals with post-mortems. Pros, cons and alternatives from failed decisions, e.g. Ray Dalio’s principles. Change of mind.
  - Independent data. Use data. Keep evidence gatherers independent of decision makers.
  - Preparation. Have decision makers write down decisions before discussing. Increases diversity.
DuckDB’s feature engineering capabilites are faster than scikit-learn. DuckDB
Developers are encoding their entire SDLC workflow into Claude commands ChatGPT #ai-coding
- Commands are used for:
  - Requirements: Research sub-agent, task breakdown into todos.md, creating specs.md from todos.md
  - Progress tracking: session logging, effort tracking, updating status, planning next steps
  - Project setup: initializing, adding deps, scaffolding features
  - Development: code review, debug error (five whys), explain code, refactor code
  - Optimization: optimize build, DB, caching
  - Testing: TDD, generate test cases, set up unit/integration/E2E testing, analyze coverage
  - Security: security audits, dependency vulnerability scans
  - Integration: sync tasks between GitHub and Linear (two-way issue synchronization, PR linking)
  - Deployment: prepare releases, hotfix deploys, rollbacks, containerization, CI pipeline setup
- Patterns of usage
  - Sub-agents
  - Command handoffs, i.e. one command invoking another
  - Shared among a team in a repo, enforcing standards & sharing best practices
  - Integration with specific tools / APIs (e.g. Linear)
⭐ LLMs can hyper-personalize demos. E.g. an LLM document generator demo accepts a role, document type, and prompt. The demo-er says “Bank, LinkedIn marketing” and the LLM auto-populates the fields aptly, re-purposing the demo.
From the GPT 5 coding cheatsheet:
1. Be precise and avoid conflicting information. Use a prompt optimizer to check for inconsistencies.
2. Use the right reasoning effort. Prefer medium or low reasoning to avoid overthinking simple problems.
3. Use XML-like syntax to help structure instructions
4. Avoid overly firm language, e.g. “You MUST be THOROUGH” vs “Thoroughly”.
5. Give room for planning and self-reflection. Explain what to do in steps, asking it to think deeply
6. Control the eagerness of your coding agent, e.g. do not ask for confirmation, parallelize tool calls, use more tools, etc.
⭐ Assets are any leveragable stored capability. Money is one, but there are several one can “invest” in, be an agent of, or perhaps steal.
1. Wealth (investments, income)
2. Regenerative assets (land, carbon credits, renewables)
3. Contacts (reference customers, hiring pipeline, talent bench, weak-ties)
4. Distribution channels (repeatable routes to users: partnerships, marketplaces, APIs, SEO)
5. Attention (your audience, whom you can reach directly)
6. Trust/reputation in communities (community capital in employers, clients, forums, society, search keywords)
7. Personal brand “edges” (moral authority, values lived aloud, distinctive taste or stance)
8. Data (your clean, labeled, joined data corpus)
9. Code (models, algorithms, components, templates, libraries, tools, evals; versioned)
10. Content (blog posts, video tutorials, case studies, demos, stories, slides, docs)
11. Knowledge (notes, decision logs, knowledge graph, institutional memory)
12. Playbooks & runbooks (process checklists that survived fire, SOPs, scenario plans)
13. Habits & policies (operating cadence, rituals, governance & compliance muscle)
14. Optionality (cash buffer, credit lines, slack time, real options, small bets)
15. Agreements (MSAs/SLAs, pre-negotiated contracts)
16. IP (copyrights, trade secrets, trademarks)
17. Health & energy reserves
⭐ Intense negative emotions get in the way of clear thinking. Curiosity, humor, kindness, and gratitude help. (Intense positive emotions like awe, passion, etc. help creativity and are not so bad.) #beliefs
I like to think I’m a Python expert. When I saw a client use this code, I told her the indentation is wrong. It ran just fine. And people think only LLMs hallucinate.
This is undocumented, but the way to get an Gemini ephemeral auth token for the live API is below. (Update time as required.) ChatGPT
Learnings from a discussion on vibe-coding between Kunal Jain, Ravi Nadimpalli and me. #ai-coding
- On the Vibe Coding Process & Strategy
  - The 80/20 Rule is Real: The first 80% of a project is incredibly fast, but the final 20% (debugging, custom features, production-readiness) is extremely difficult and time-consuming.
  - Validation is the New Bottleneck: Since coding is now much faster, the critical, time-consuming task has shifted to reviewing, testing, and validating the LLM’s output.
  - “Spec-Locking” is Crucial: Providing the LLM with detailed, well-defined, and “thinly sliced” specifications is essential for getting good results. Vague requests lead to poor outcomes.
  - It’s Not Production-Ready (Yet): The consensus is that vibe coding is excellent for prototypes, demos, and go-to-market (GTM) activities but is not yet reliable for building production-grade applications from scratch.
  - Code is Brittle & Unstable: An application that works perfectly one day can inexplicably break the next, as the underlying agent might make undocumented changes.
- Impact on Roles & The Future of Work
  - The Rise of QC/Validation: The Quality Control (QC) function will become larger and more critical to manage the new challenge of validating AI-generated work.
  - Product Managers Shift Focus: PMs can move away from tedious documentation (like flowcharts) and focus more on high-level business strategy, using vibe coding to create quick prototypes.
  - Democratization of Building: It empowers non-coders to build functional apps and helps professionals upskill faster by “conversing” with an LLM on complex topics.
  - New Forms of Cheating: The technology is creating novel ways for people to cheat in interviews, such as using tools that provide real-time subtitles of answers.
  - The “Jagged Edge” of AI: The technology excels at certain tasks (like GTM content) but fails at others, creating new upstream bottlenecks where teams must rapidly generate more of the “AI-friendly” work.
- Practical Hacks & Takeaways
  - Meta-Prompting: Use an LLM to refine and improve your prompt before giving it to the final tool. This helps fill in gaps and add necessary detail.
  - Human-First Drafting: For creative or nuanced work (like writing), it’s often better to write the first draft yourself and use the LLM to polish it, rather than starting with a generic AI draft.
  - Use Structured Prompts: For predictable and clean output, providing instructions in a structured format (JSON is OK but not needed) is highly effective.
  - LLM as a Judge: Use LLMs to evaluate and grade content, code, and other outputs, dramatically speeding up the review process.
  - Automate Learning & Documentation: Use tools to transcribe conversations automatically and create personalized revision quizzes from notes and documents.
  - Voice is a Powerful Modality: Using voice-to-code allows for capturing more complex ideas faster and can be done while multitasking (e.g., walking), capitalizing on “dead time.”
For live transcription, Gemini 2.5 Flash Live costs 0.6c/min of audio ($3/MTok x 32 tokens/second) while GPT 4o Mini Realtime costs ~2c/min and GPT 4o Realtime costs ~8c/min. ChatGPT
I set up MCPs Codex CLI by adding this to ~/.codex/config.toml. I’ve disabled it for faster startup (this takes ~2 seconds) and raised an enhancement issue for MCP lazy loading
Anthropic launched a remote MCP connector in their API. OpenAI Responses API already had remote MCP support. Gemini will likely follow, opening up new tool capabilities. The APIs can directly call the MCPs as part of their thinking.
Turns out Indian English is a well studied topic. Indianisms like “can able to”, “need not to”, “why because…”, “if suppose…”, “return back”, “revert back”, “angry on”, “discuss about”, “order for”, “do one thing…”, “give me a missed call”, “what is your good name”, “kindly adjust”, “we are like that only”, “he is coming only”, “today itself”, “now only”, “prepone”, “pass out (of college)”, “out of station”, “do the needful”, “hotel”, “batchmate”, “cousin-brother / cousin-sister”, “I have a doubt”, “I am understanding”, “she is knowing”, “you’re coming, no?” etc. are discussed in Pingali Sailaja’s Indian English. ChatGPT
Astral is building pyx - a paid PyPi alternative. It aims to solve problems like PyTorch CUDA builds. Knowing them, it’ll be fabulous. I look forward to when they build a Python hosting service.
⭐ Here’s one way to improve LLMs apps in real-time.
- After sending a response, send the prompt + input + output + optional user feedback to an LLM-as-a-judge asking for feedback to improve the prompt.
- Revise the prompt based on the improvement. Now the app has improved, real-time, based on human/LLM feedback.
- Refine this process to ensure that the revisions are smooth and positive.
GPT 4.1 (and presumably GPT 5) models have been trained on a specific diff format useful for code diff-patching. PseudoPatch is a Python package that implements their apply_patch() function. Aider supports multiple edit formats that are commonly referenced as a standard. Code Surgery has a good walkthrough of various strategies. These are similar to Google’s diff-match-patch approach (which fuzzy matches and then patches) but does not require line numbers. ChatGPT
Here are some query parameters ChatGPT.com unofficially supports:
- ?q=... prefills in a new chat and often auto-submits, especially small text #. Useful for:
  - A custom search engine in your browser
  - An “Ask ChatGPT about selection” bookmarklet, etc.
  - Links (e.g. from courses, FAQs, etc.) for tasks or learning
  - … but not for custom GPTs
- ?model=... selects a model (e.g., gpt-5-thinking).
- ?hints=search enables Search mode
- ?temporary-chat=true opens a new temporary chat
Tavus is another AI avatar platform.
- Synthesia. Market leader; $2.1B valuation; enterprise trusted. Good: Realism, enterprise features, templating. But: Price, usage caps, slower avatar setup
- HeyGen. Rapidly growing; $500M valuation. Good: Avatar realism, speed, affordability. But: Basic collaboration, support, scene complexity
- Colossyan. Favored L&D focus. Good: Interactive & educational tools, good value. But: Less polished avatars, slower renders
- D-ID. Frequently cited alternative. Good: Speed, flexibility, custom avatars. But: Watermarks, fewer templates
- Elai.io. Repeats in alternatives lists. Good: Storyboarding, educational formats. But: Limited templates, render time
- Hour One. Also common in alternative lists. Good: Photoreal avatars, expression control. But: Missing advanced features like screen capture
- Others. Niche or emerging tools. Good: Varies by platform. But: Less adoption, fewer reviews
Training companies are offering “Labs-as-a-service” as part of their AI training. Corporates ban LLMs, but need employees trained. Trainers offer a bundled package where they also offer access to LLMs are part of their course. Interesting business-model value-add.
⭐ I’m meta-AI-coding. I wrote a crude prompt in prompts.md, told Codex “prompts.md has a prompt under the “# Improve schema” section starting line 294. This is a prompt that will be passed to Claude Code to implement. Ask me questions as required and improve the prompt so that the results will be in line with my expectations, one-shot.” After a few discussions, it generated this remarkable prompt. This prompt was easy for me to review AND easy for Claude Code to understand because of the lack of inconsistencies.
- Use the Ask-Code pattern. In Codex, speak the requirement and have it rewrite the prompt asking clarifying questions pressing the Ask button instead of Code. Then, answer its questions. Then press Code.
A Forward Deployed Engineer (FDE) is a hybrid role, part software engineer, part product manager, and part consultant, focused on deeply integrating a company’s technology with a specific client’s needs.
Based on what I’ve seen of AI coding, new developers need to learn these skills. #ai-coding
- Context engineering
- Documentation
- Automated testing
- Standards
- Capabilities of platforms
- Modularity (and DRY vs WET)
- Code composition
- Code reviews
Blindspots continue to be the insight with maximum RoI. Discovering something we’re not even aware we’re unaware of opens up the largest possibilities. #beliefs My top sources to discover blindspots are:
- Feedback. Especially feedback we reject, ignore, or miss.
- Things we run/shy away from.
Across clients, providers (e.g. Bedrock) and products (e.g. Cursor) I have observed capacity bottlenecks for Claude models which don’t seem to affect OpenAI models as much.
Increasing the size of an image improves OCR accuracy for LLM models (or at least Claude 4 Sonnet). Anecdotally, resizing 2x did not work on a number of examples but 2.5x - 3x did. This increases the cost to 6.25x or 9x, however.
Discussion at PyConSG Edu Summit 2025. Padlet
- Discussion validation
- Interesting ways students use AI
  - Use AI to refactor/debug whole codebases
  - Get AI to create questions for practice
  - ChatGPT Study mode
  - Students like to upload photos. We can teach them to upload these to ChatGPT and ask questions.
- What teaching practices / assessment design can help students think for themselves before turning to AI? ChatGPT
  - Interactive orals / micro-vivas (short, process-focused). Strong alignment with “interactive oral assessment” research and guidance in the AI era: improves authenticity, reduces outsourcing/contract cheating, and checks understanding. Make them low-stakes but frequent. How: 5–8 min viva tied to a task; students must explain choices, failures, and next steps.
  - Authentic / project-based assessments students can self-validate (observable outputs). Project-based and “authentic” assessment meta-reviews show consistent positive effects (achievement, thinking skills, motivation), especially in STEM and small teams. Design tasks with local data/constraints so generic LLM answers are only a baseline. How: “Default AI answer” gets a pass; “A-grade” requires empirical validation, custom data, or optimisation trade-offs with metrics.
  - Pair programming + peer critique on whiteboards/pseudocode. Evidence (meta-analyses & CS-ed studies) supports pair programming for learning and retention; code tracing/peer instruction deepen understanding before coding. How: Rotate driver/navigator; force commit-message style rationales; 10-minute “whiteboard dry-run” before touching IDE.
  - Process-over-product with structured reflection. Metacognitive/reflective interventions show medium-to-large effects on achievement; they also build habits that resist blind acceptance of AI outputs. Keep reflections short but structured. How: “What I asked AI; what it missed; how I verified; what I’d change next time.”
  - “No-AI under secure conditions” mixed with AI-permitted coursework. Matches national/institutional guidance for GenAI-aware assessment design. Use secure, time-boxed checks for fundamentals; allow AI elsewhere with audit trails.
  - Primary research (interviews/user studies) before design/coding. Fits the “authentic assessment” literature and reduces LLM substitution. Grade on research protocol + synthesis rigor, not word count.
  - Explicit problem-solving frames (initial/current/goal state). Classic problem-solving scaffolds; improves formulation before querying AI. Pair with short “assumption logs.” (General pedagogy supported; CT depends on domain knowledge – see caveat below.)
  - Caveat (important): Critical thinking depends on domain knowledge. Don’t expect generic CT drills to transfer without content mastery. Plan tasks so students must recall/apply specific knowledge before or alongside AI.
- How can we train students to use AI critically instead of accepting the output blindly? ChatGPT
  - Teach “lateral reading” and SIFT for source checking. Stanford’s Civic Online Reasoning work and Caulfield’s SIFT method offer actionable heuristics for verifying claims, URLs, and citations that LLMs surface. Build these into rubrics.
  - Run “AI auditing” labs (hallucination hunts). Students collect/label model mistakes, missing assumptions, and fabricated citations – an approach aligned with UNESCO’s call for AI literacy and validation.
  - Use online judges with hidden tests + adversarial cases. Autograding literature supports hidden tests for robust generalization; it trains students to verify and not overfit to visible specs – or to AI’s surface patterns.
  - “Sandwich” workflow: spec → implement 1–2 reps → let AI complete → verify rigorously. Mirrors human-in-the-loop patterns in industry; use checklists for unit/property tests and invariants before accepting AI output.
  - Live-coding with an AI assistant on display (to show failure modes). Demonstrates nondeterminism/limitations in real time; supports critical habits. Pair with a post-mortem template.
  - Prompt red-teaming/jailbreak exercises (safe scope). Students learn that guardrails can be bypassed and why verification matters. Keep it ethical and bounded.
  - Build a knowledge base first. Reinforce that CT sits on content knowledge; teach students to explain why an AI answer is plausible or not, citing domain facts.
Notes from “My Thoughts on Computational Thinking in the Generative AI Era” by LEONG Hon Wai, ex-NUS, at PyConSG Edu Summit 2025
- Students from China don’t like to write, express their ideas, and share. That’s changing now.
- Computational thinking is pretty new (Jeannette Wing, 2006), actually, based on Papert (1980). It’s too early to abandon it.
- It enables effective learning attitudes:
  - Tinker (experiment & play): helps finding diverse problems to generalize into
  - Debug (find & fix bugs)
  - Create (design & make)
  - Persevere (keep going): but only if it’s productive, i.e failing in new ways
  - Collaborate & communicate
- Teaching this is hard. Get students to WANT to do computational thinking.
- Problem formulation (among the computational thinking blocks) is more important than before.
- Leveraging Computational Thinking in the Era of Generative AI argues that computational thinking manifests in prompt/context engineering.
- We’re moving from “Computational Thinking” to “Computational Action” – where we’re talking to AI coders that actually deploy apps that DO stuff.
Notes from “Make Learning Easy and Fun @ NLB LearnX” by Goh Soon Seng, NLB, at PyConSG Edu Summit 2025
- Libraries have a Pi Python Makers Club, open for all. Bi-monthly meetings. Quarterly Pi Python workshop.
- Space provides 3D printers, Raspberry Pi, sensors, etc.
Notes from “Teaching Goals and Plans - How we might help students improve problem-solving” by Dr Norman Lee, SUTD, at PyConSG Edu Summit 2025
- Programming is hard. E.g. Solving the Rainfall problem “Sum numbers until 99999” needs several building blocks:
  - Python syntax
  - Getting user input
  - While loop
  - Controlling while loop with counter
  - Accumulation
  - If-else
- Merging (or composing) such blocks is the hard part. In Learning to program = learning to construct mechanisms and explanations, Soloway, shares 4 compositions.
  - Abutment: Put one block after another
  - Nesting: Put one block inside another
  - Merging: Interleave the code in the blocks
  - Tailoring: Modify the code in the blocks
- But you need to already have those primitives (patterns) to put together. The “expert blind spot” blinds experts to this.
- Actionable ideas:
  1. Teach patterns explicitly
  2. Create exercises on applying them
  3. Use Parsons problems: Fill in the blanks. Re-order lines of code. But design problem carefully
  4. Step through a debugger. BUT students must predict next line, not passive watching
  5. Teach to from one format (psuedocode, flowchart, another language like Excel) to Python. Helps multiple modes of learning
Notes from “AISG programmes” by Chen Qeiquang, AI Singapore, AI Apprentice Programme (AIAP) Assistant Head
- Full-time. For SG citizens. $4,000/month. Build 3-6 month MVPs for startups, SMEs, or corporates. 300/1000 delivered so far.
- No lectures/tutorials. Focus is: topic assignments, discussion with mentors, apprentice sharing sessions.
- Includes an LLM Application Developer Program.
Notes from “Scaffolding the Problem-Solving Process for Introductory Computing Students” by Ashish Dandekar, NUS, at PyConSG Edu Summit 2025
- Built an intelligent tutoring system
- Encourage students to create their own pattern banks / cheat sheets. “Find 2 more problems that can be solved in the same way.”
- Focusing on the problem-solving process shrinks the gap. Students above the 50th percentile of pre-assessment did not improve much. The lowest percentile improved the most.
- “At NUS, I know that even if I give 0.5% weightage for students attending tutorials, everyone will attend it for those ‘free marks’.”
Notes from “Exploring Multi-Agent Generative AI in Education and Career Advisory” by Dr Yeo Wee Kiang, NUS, at PyConSG Edu Summit 2025
- ⭐ “When you have a high fever, do you speak more sense or nonsense? Nonsense. LLM temperature is like that. But it can also sound creative!”
- The router pattern is a powerful query rewriter. Redirects the query to specialized prompts/agents.
- Useful tools you can build for students: Course Mentor, Interview Coach, Job planner/matcher.
Notes from “Do we need to teach coding given vibe-coding tools?” by Dr. Oka Kurniawan, SUTD, at PyConSG Edu Summit 2025
- Paper: What the Science of Learning Teaches Us About Arithmetic Fluency says mental math helps mathematicians. Fluency bootstraps higher-level thinking.
- MIT Media Lab’s Project: Your Brain on ChatGPT. Explores impact on brain. Bran-only group had the widest ranging brain networks. AI accumulates cognitive debt.
- Paper: “A Study of the Difficulties of Novice Programmers” struggle with:
  1. Syntax
  2. Problem solving
  3. Tools
  4. Computing concepts
  5. Analytical thinking / debugging
- Polya’s How to Solve It is the base problem solving framework for maths and can be adapted to computing
- Expert programmers have enough patterns to match against. Novices don’t. We need a bottoms-up framework instead
  - Give them a concrete case.
  - Have them generalize (loops, functional, vectors)
  - Have them implement (debugging)
  - Have them break it (test)
  - All via vibe-coding!
  - The chats are tracked!!
- Paper: First Things First: Providing Metacognitive Scaffolding for Interpreting Problem Prompts
  - Students often get the problem wrong
  - Reading student conversations helps figure it out
  - LLMs can figure it out too!
- Paper: The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
  - Good coders got better with AI. Were able to ignore unhelpful advice.
  - Poor coders got worse! Thought they performed better than they did. Increased illusion of competence.
The Bebras Challenge is a global non-programming computational thinking (CT) challenge. Examples. Singapore runs a National Junior Informatics Olympiad that learns from Bebras. It tests the mindset behind coding, specifically “computational thinking”:
- Problem formulation (added recently, and is increasingly important)
- Decomposition (and composition): break the problem down
- Pattern recognition: find the building blocks
- Abstraction: generalize useful blocks, drop irrelevant ones
- Algorithmic thinking: write the steps to solve
- Validation (not part of original list, but critical): how to efficiently check if this works
Apple’s Embedding Atlas (Demo - slow, needs WebGPU) is an embeddings visualizer, like Tensorflow Projector or Mantis (Demo).
John Kotter’s organizational change model is the accepted practice for top-down change, while ADKAR is for bottom up. It’s surprising how obviously effective both are to someone who has effected both kinds of changes, but there is NO WAY I would have appreciated either during my MBA. Wikipedia: Change management
The OpenAI Chat Completions API has a few interesting and (relatively) new options:
- verbosity. low: concise response, medium: default, high: verbose
- reasoning_effort: minimal: almost none. medium: default. Or low, high.
- truncation: auto: truncate response by dropping input items in the middle. disabled: default
- prediction: speeds up output for minor corrections to text
- prompt_cache_key: tailors per-user caches
CSS nesting can be used with media queries too! Julia Evans
id3v2, mid3v2 and eyeD3 seem the cleanest way of editing MP3 tags on the CLI. mid3v2 was already installed on my system.
Learnings people shared in Ask HN: What trick of the trade took you too long to learn?
- Finance & housing
  - Time is a non-renewable asset.
  - Lifestyle design matters as much as net worth.
  - Future-proof against regret. The present matters, too.
  - Home ownership ties up location choice, capital and has hidden costs.
  - Market timing & geographic arbitrage has an outsized effect.
- Software
  - Align abstraction to domain. Avoid premature abstraction (Don’t Repeat Yourself vs Write Everything Twice) and over-abstraction.
  - Temporary fixes tend to stick. Stop-gap regexes last for years.
  - Consistency is a quality multiplier. Small inconsistencies cause disproportionate harm.
  - git bisect is a regression-finding superpower.
  - It’s OK to write tests covering key parts of legacy codebases - 100% coverage isn’t critical.
  - Document architectural decisions: why this approach. See Diátaxis.
  - Flow metrics predict delivery better than (arbitrary) estimates.
  - Building features without linking to delivery spesd wastes resources.
- Life habits & learning
  - You have the right to say “no”.
  - Small, consistent actions beat dramatic changes. Persistence beats skill.
  - You’re allowed to change your mind.
  - Over-cleverness backfires. Witty code & communication lead to confusion.
  - Context is king. Without background, everything is mis-interpretable.
  - Fun leads to excellence. Excellence leads to fun.
- The meta-lesson here is how I discovered these:
  - Run topicmodel to identify topics
  - Feed the output CSV to ChatGPT and ask it to share lessons topic-by-by-topic #
Topic modeling can be extended in many ways. #
- Structural Topic Models factor in metadata, like year (numeric) or category or author (categorical).
- Relational Topic Models factor in undirected graph relationships, e.g. parent documents
- Graph-Regularized Topic Models factors in arbitrary graph relationships, e.g. weighted, directed
- Neural (GNN + Topic Model) approaches work better for large graphs, long-range dependencies, etc.
Some ways to inject graph structure into topic similarities to, for example, cluster threaded discussions. #
- Start with a graph similarity matrix S, like #
  - a regularized graph Laplacian (based on degree - adjacency matrix)
  - a similarity matrix like graph2vec from Graph Kernel
  - a node-embedding karateclub.
- Option 1: “Smoothen” the embedding matrix multiplying it with S (i.e. spread each document towards neighbors), then calculate similarities
- Option 2: Take the weighted average of S and the embedding similarity matrix
You can extract Hacker News comments as a threaded discussion pasting this into the DevTools console:

Things I Learned - 27 Jul 2025

Sun, 27 Jul 2025 00:00:00 +0000

This week, I learned:

Here are some tech community builders in India. ChatGPT
- Atul Chitnis (Bengaluru) – FOSS.IN and Linux Bangalore
- Dr. Nagarjuna G. (Mumbai) – FSF India and ILUG Bombay
- Rushabh Mehta (Mumbai) – FOSS United & ERPNext Community
- Kiran Jonnalagadda & Zainab Bawa (Bengaluru) – HasGeek Tech Conferences
- Kenneth Gonsalves (Nilgiris/Tamil Nadu) – Indian Python Community (deceased)
- Thejesh GN (Bengaluru) – DataMeet Open Data Community
- Varun Aggarwal (Delhi) – ML-India (Machine Learning Forum)
- Prashant Sahu (Pune) – Pune AI Meetup
- Akshay Dashrath (Bengaluru) – BlrDroid Android Group
- Vikrant Singh (Bangalore) – ReactJS
- Sankarshan Mukhopadhyay – Mozilla India and Wikimedia tech outreach
- Neependra Khare (Bengaluru) – Docker/Kubernetes Meetup
- Atul Jha (Bengaluru/Hyderabad) – OpenStack & CNCF Communities
- Aseem Jakhar & Ajit Hatti (Delhi/Pune) – null Open Security Community
- Rohit Srivastwa (Pune) – ClubHack and Hackerspaces
- Anubha Maneshwar (Nagpur) – GirlScript Developer Network
Digital Public Infrastructure initiatives in India scale if there’s a clear use case and centralized orchestration. Prof R Srinivasan
The distance between the end of the thumb and little finger, when fullet stretched, is ~9 inches. Between the thumb and pointer, when at a right angle, is ~6 inches. I checked this today - and it’s right. A useful rule of thumb for measurement - literally. Vasuki, ~1985
GitHub Sponsors Explore shows you which developers code most of your dependencies. You can sponsor them. I sponsored isaacs who maintains node-tap and sindresorhus who maintains several NodeJS packages for $50/month each.
markmap looks like a promising JS-based interactive mindmap from Markdown. More interactive than Mermaid Mindmap.
- mind-elixir is another option that lets you edit mindmaps and serialize in its own format
- jsmind is yet another but docs are in Chinese
- elkjs seems a good option for laying out nodes in an architecture-style flow diagram
⭐ O3 seems a better data scientist than I am. Based on my Google Searches, I have 3 persona: developer, AI-builder, and India/Singapore geo-culturist. A great example of an analysis from O3 that’s better than anything I could have come up with. ChatGPT
⭐ Fast review of AI be a powerful skill and enabler. I built an Image Editing tool with Codex in ~4 hours, with 11 prompts taking 3.5 - 7.5 minutes each. 3 hours human review, 1 hour LLM coding. I’m 3X slower at reviews while AI will keep improving. ChatGPT: Faster LLM review techniques #ai-coding
- Auditize: citations, rationale, output screens, diffs, test results, risks, unknowns
- Auto validate. Evals, tests
- Prioritize. High z-values, big-useful-surprising areas
At the VizChitra Birds of a Feature session, here’s what people said AI enables:
- Complementary skills enable a team of 1. Non-coders can code. Non-domain people get insights from data
- Solves starting trouble. It offers a first draft
- Generation. New ideas (reduces blind spots), scenarios, non-existent people, new data, new persona for surveys
- Hyper-personalization. Parts of YouTube relevant for THIS asset manager. Implication of data for me
- Automated scaling. Generate 1,000 images. Evaluate 1,000 assignments
- Saves time: debugging, research, validation, documentation, copywriting
- New ways of working. Loading event schedules into my calendar
Qwen-Code is a fork of Gemini CLI and uses qwen3-coder – a model that can also be used with Claude Code and Cline. The model is not anywhere near as good as Claude 4 Sonnet. The app is costlier than using Claude Code directly. #ai-coding
The LLM industry seems to have matured quickly. Early adopters who are open to understand the generic capabilities of LLMs through demos are somewhat saturated. The early majority have come in. They aren’t interested in generic capabilities. They’re looking for solutions that solve their specific problem. Soon the late majority will come in asking for existing solutions that have already solved their problem for many others. ChatGPT: Creating demos for majority
Claude for Financial Services is an agentic version of Claude available on AWS & Google marketplaces tuned for financial services analysis. Video
catbox.moe is a file hosting service that you can upload a file to without any API key. It’s an alternative to 0x0.st. Both can be used for images. Catbox retains files indefinitely and openly publishes costs - might last longer. 0x0 deletes files between 1-12 months based on size.
Agents face 3 problems: compounding errors, quadratic costs, and poorly designed tools. Start with small scope & strong reviews while you solve these problems. Betting Against Agents
Leadership and vision will matter more. LLMs iterate fast. They can think for longer. So tasks where people need to work longer independently than LLMs can are what humans will be needed for. That requires understanding the objective. So leadership and specifically vision transfer will become more valuable. You need to be able to tell people what to do well enough that they can work independently for weeks.
Having LLMs go through engineering drawings, floor plans, etc. and understand them, find problems, etc. is an emerging use case. People are using Veo 3 to convert a floor plan into a 3D walk through too.
Digital adoption is slow partly because of a skill gap. “Old-timers” are slow to let go of traditional approaches.
Video recordings are used in manufacturing to evaluate quality (e.g. wafer inspection, assembly inspection, component presence) using AI. An interesting by-product of this data is that they can also measure productivity, task time.
“Common sense is a specialization”. That’s something I said accidentally when seeing that some schools/colleges tend to produce more broad, sensible thinkers (e.g. Naval College @ Goa) while others produce more narrow-thinking specialists (e.g. engineering colleges).
Three groups control the financial economy. To sell sustainability services, you need to have sold to one of them. via Sundeep
1. Banks, who will sell a loan against anything they can insure, and look to insurers for long-term thought leadership.
2. Insurers, who will insure anything they can re-insure, and re-insurers, who look at real-estate trends as a stable long-term asset
3. REITs who own the majority of the world’s real-estate
We could think of a copilot as an (agentic) LLM chat interface for an artifact. E.g. Code pilot (Claude Code. Cursor.). Data analysis copilot (Google Colab, sort-of. ChatGPT). That allows us to imagine tools that will create/edit artifacts. Here are some I’ve encountered as a demand.
- Documents. E.g. Docsearch, GPTs, Microsoft Copilot, Gemini
- Slides. E.g. Microsoft Copilot, Gemini
- Sheets. E.g. Microsoft Copilot, Gemini
- Code. E.g. Cursor, Claude Code
- Database. Create DB schema, ER diagrams, synthetic data, ingestion scripts, etc.
- Data (analysis). E.g. Datachat, Google Colab, Marimo
- Posters. E.g. Postgen
- Shell. E.g. Warp
- Topic modeling. E.g. classify
- Surveys. E.g. Personagen
- APIs. E.g. apiagent
- Drug regulatory submissions.
- Contracts (risk).
- Manufacturing SOPs.
- Curriculum.
- Data quality.
- Support tickets.
- Dashboards.
- IaaC / DevOps.
- Video campaigns.
- Resumes.
- Patents.
CLI optimization for LLMs will likely emerge. More CLIs (and wrappers / hooks in the shell) will improve output and error contexts for LLMs, e.g. printing current directory, caching slow outputs, suggesting alternate commands, etc. Ref
Frequent commits with linting & building seems like a good AI coding strategy, especially for Claude Code. Ref #ai-coding

To keep Claude Code in line on my project, I’ve relied heavily on linters, build scripts, formatters, and git commit hooks. It’s pretty easy to get Claude Code to commit often by including it in your CLAUDE.md, but it often likes to ignore other commands like “make sure the build doesn’t fail” and “fix any failing tests”. All my projects have a .git/hooks/pre-commit script that enforces project standards. The hook works really well to keep things in line.
Google Apps Scripts are actually a web apps platform in JavaScript more than a macros equivalent. Ref
⭐ DuckDB supports joins based on embedding similarity and even hybrid similarity! Ref
Human persuasion techniques like Cialdini’s work well with LLMs Ethan Mollick
gitwatch is a clean way of auto-committing & pushing files into GitHub. It effectively converts GitHub into a Dropbox-like service.
Adding ?udm=14 to Google Search URLs removes AI mode and other clutter. Ref
⭐ Never embed LLM‑generated summaries without a disclaimer, source links, and flag‑as‑wrong feedback button. Build a fast appeal/edit pipeline before release. via Death By AI

Things I Learned - 06 Jul 2025

Sun, 06 Jul 2025 00:00:00 +0000

This week, I learned:

When adding a coding benchmark for LLMs, here’s a question I’d like to add. #benchmark

How do I use Apache Arrow in the browser via cdn.jsdelivr.net to create a .parquet file and download it? Give me minimal working code I can paste in the browser console to test.
LinkedIn has an undocumented link that shows schedules posts at https://www.linkedin.com/share/management/ which redirects to https://www.linkedin.com/feed/?shareActive=true&view=management

Here’s a JS snippet you can paste in the DevTools console of an npm package version page (example) to get a Markdown list showing the versions and dates

copy(
  $$('table[aria-labelledby="version-history"] tbody tr')
    .map((tr) => {
      const a = tr.querySelector("a");
      const date = new Date(tr.querySelector("time").getAttribute("datetime")).toLocaleDateString("en-GB", {
        day: "numeric",
        month: "short",
        year: "numeric",
      });
      return `- [${a.textContent.trim()}](https://npmjs.com${a.getAttribute("href")}): ${date}.`;
    })
    .join("\n"),
);

DuckDB can read JSON APIs! Ref
⭐ When bringing in humans-in-the-loop, applications must make it easier to review and to edit the work.

Things I Learned - 01 Jun 2025

Sun, 01 Jun 2025 00:00:00 +0000

This week, I learned:

MicroVMs like firecracker are like containers but offer higher isolation with slightly higher latency and memory via kvm hypervisors. ChatGPT
I was exploring free alternatives to the $4/mo Hetzner instance I use. Google offers a free e2 micro instance. But it’s much smaller than the Hetzner CAX11/CX-22 server I run. 25% of CPU, 25% of RAM (which is the main problem – 1 GB is often not enough), slower HDD, 5% of outbound traffic. Hetzner remains one of the best value offerings.
Planning to use pretty-quick instead of prettier. It’s a wrapper that only fixes changed files based on git.

f2 is an intuitive cross-platform renaming tool. Usage:

f2 -f 'jpeg' -r 'jpg'
f2 -r '{id3.artist}/{id3.album}/${1}_{id3.title}{ext}'

git worktrees can create multiple copies of code. This is useful when using different coding agents run the same task in parallel. Ref
- git worktree add -b $newbranch worktree/$path creates a copy of HEAD in $path as a $newbranch
- git push from branch and create a pull request
- git worktree remove worktree/$path to remove worktree
- git worktree prune for garbage collection
LLMs optimize for compression. Humans optimize for adaptive flexibility. Ref arXiv
Gemini Deep Research accepts files and images. Cross-checking reports, providing private sources, etc. is now realistic. Ref
The new Flux1.Kontext model seems very good at image editing. Costs 4-8c per image. Peter Gostev
Today, I’d go with Node’s native test runner for backend JS testing. I used node-tap earlier. For front-end, I’d pick vitest. ChatGPT
⭐ DuckLake is a DuckDB extension that makes Parquet files editable with history. And much more. DuckDB
When processing presentations for RAG via OCR:
- How to parse PDF docs for RAG is a useful OpenAI cookbook with a GPT 4o prompt
Here’s one way controls inflate cost. Tracking expenses, submitting receipts, and justifying usage adds transaction cost. So, rather than a $10 monthly top-up, I’d rather top-up $200 (even if it might go unused), rather than have to ask again.

Things I Learned - 25 May 2025

Sun, 25 May 2025 00:00:00 +0000

This week, I learned:

oxlint is a fast eslint alternative written in Rust. It supports most but not all eslint rules. Migration can be automated but not all rules are migrated (which may be OK). Best for new projects.
TTS typically costs $1/hour now. Gemini 2.5 Flash Preview TTS, Gemini 2.5 Pro Preview TTS, GPT 4o TTS, and GPT 4o Mini TTS are the current best-in-class text-to-speech models from the mainstream LLM providers. Assuming ~175 words per minute and 1 token ≈ ¾ words, 1 hour of speech ~ 10,300 words/hr ~ 13,800 input tokens ~ 75,000 audio tokens, it costs:
- Gemini 2.5 Flash Preview TTS ($0.50/1 M input, $10.00/1 M output): ~$0.8 per hour
- GPT-4o-mini-TTS ($0.60/1 M input, $12.00/1 M output): ~$0.9/hour
- Gemini 2.5 Pro Preview TTS ($1.00/1 M input, $20.00/1 M output): ~$1.5 per hour
- GPT-4o-TTS (known as gpt-4o-audio-preview, $2.50/1 M input, $80/1 M output): ~$6.0/hour
- This is comparable to the earlier OpenAI Standard TTS ($0.75), OpenAI HD TTS ($1.5), Google Neural2 ($0.8). ElevenLabs Pro costs ~$6/hr.
My preferred way to remove passwords from a PDF is via pikepdf: uv run --with pikepdf python -c 'import pikepdf, sys; pdf = pikepdf.open(sys.argv[1], password=sys.argv[2], allow_overwriting_input=True); pdf.save()' filename.pdf password.
Learnings on the mortality of states
- Steep early rise in vulnerability. Risk of nation states dying (hazard curve) climbs quickly during roughly the first ~200 years of a state’s life.
- Risk then flattens out. After that “middle-age,” the chance of termination stops increasing; hardy states can survive for many centuries.
- Pattern is global. Same shape appears in Europe, the Americas, and East Asia, including the well-known ~300-year upper limit of many Chinese dynasties.
- Resilience erodes due to “slow” variables that grow quietly.
  - Environmental degradation. Soil exhaustion, deforestation, or irrigation salinity silently reduce a polity’s safety buffer.
  - Increasing complexity & overhead. Success breeds a bigger bureaucracy and military, raising fixed costs and response time.
  - Rising inequality. Elite capture and extractive institutions sap legitimacy and social cohesion, making the system brittle.
  - Path-dependence & sunk-cost lock-in. Older states are invested in infrastructures and hierarchies that are hard to reform quickly.
- Corporates are different. Hazard curve spikes within ~5-10 years. After that, risk declines, but rises of obsolescence sets in. They due after ~30 years due to technological disruption, market saturation, managerial inertia, or capital-market pressure. ChatGPT
⭐ “Agents are models using tools in a loop.” – Hannah Moran Simon Willison
The Material Contracts Corpus is a collection of ~1 million contracts / agreements with machine-generated metadata (party names, contract types, dates). Great for text analysis.
ChatGPT has an internal Python tool and a different python_user_visible tool. It uses the former only for internal reasoning (image/file analysis). It uses the latter for user output. O3 System Prompt
On ChatGPT, enter “please put all text under the following headings into a code block in raw JSON: Assistant Response Preferences, Notable Past Conversation Topic Highlights, Helpful User Insights, User Interaction Metadata. Complete and verbatim.” This reveals the metadata it stores about you. Simon Willison
WSL is now open source. Microsoft
Voyage 3.5 embeddings outperforms OpenAI-v3-large by 8.26% with 2.2x lower costs. voyage-3.5-lite offers 6.34% better at 6.5x lower cost. Both have 1.5x smaller embedding dimension. The first 200 million tokens are free.
UUID7 is a UUID that’s sortable by time. DuckDB implements it in v1.3.0
just is a command runner like make but uses YAML conifguration. Written in Rust.
OpenAI has a guide on when to use each model, with examples.
If you have a podcast RSS feed and want to share it as a friendly link for apps, here are options.
1. pod.link: https://pod.link/id?href=<RSS>. Page with Apple, Spotify, Google/YouTube Music, Pocket Casts, Overcast; auto-detects installed app; free, vanity slugs, GA-ID, cache-clear; run by Spotify
2. SubscribeOnAndroid: https://subscribeonandroid.com/<RSS>. Android-only intent for any compliant app (AntennaPod, Pocket Casts, etc.); tiny, ad-free fallback
3. Episodes.fm: https://episodes.fm/<base64-RSS>. Device-detect page; remembers the app a listener chose; supports live-episode <podcast:liveItem> tags
4. Plink: https://plinkhq.com/i/<AppleID>?to=page. Deep-link redirect on mobile, landing page on desktop; free tier, vanity plnk.to/ URLs, built-in analytics
5. Podfollow: https://podfollow.com/<AppleID>. Claim by RSS; free; episode links; optional web player; custom redirect rules
6. Chartable SmartLinks: https://chartable.com/feeds/<feedID>/smartlinks. Add a trackable prefix in RSS; channel attribution, vanity slug, A/B testing
7. Linkfire for Podcasts: https://linkfire.com/podcasts?url=<RSS>. Dashboard “Create link” flow; auto-updates new episodes; Apple Podcasts analytics; email-capture widgets
8. Feature.fm: https://feature.fm/smartlinks/podcast?feed=<RSS>. Pixel support, retargeting campaigns; freemium tier with upgrade for custom domains

Things I Learned - 27 Apr 2025

Sun, 27 Apr 2025 00:00:00 +0000

This week, I learned:

OpenAI’s reasoning models are much ahead of other models when multiplying two numbers in their heads. Ref
⭐ Promptfoo may be the most mature open source LLM evals tool. Simon Willison
Dyson Sphere.
LemonSlice showcases real-time audio-video models (avatars) that are close enough to real.
Notes from Latent Space ICLR 2025, Singapore
- Daniel: Menlo’s ReZero. A model that keeps searching till it finds the answer.
  - There are multiple search techniques: Multi-step retreival, Iterative retrieval, Query rewriting. Also, reasoning.
  - The LLM token generation sequence is normally: <think>, <search>, <answer>.
  - Insight: “If we explicitly reward LLMs for retrying after a failed search, they out-perform one-attempt systems.” So <think>, <search>, <think>, <search>, <think>, <search>, <answer>.
  - ⭐ Prompt reasoning models, e.g. “Keep searching till you find the best answer.”
- Roger, Nous Research
  - Supervised learning is limited because accuracy is piece-wise linear, i.e. it’s broken up. Continuous optimization is meaningless.
  - Reinforcement learning works better because rewards can be discrete. (But it converts things back into differentiable loss functions behind the scenes.)
    - Rewards can be good/bad. Single or multi-step. Whatever.
  - We’re in the “Era of experience”, i.e. models gain experience from the environment themselves.
  - ⭐ So, we need environments models can learn in. This is the next thing after training data. That needs a standard for environments.
  - We’d need a model, a trainer, and the environment.
  - The environments whatever capabilities. Run code. Browser. A game. … With an exposed interface
- Eugene Cheah (Featherless.ai)
  - Transformer architectures need n-square GPUs as # of tokens grow. Featherless is exploring an RWKV architecture that scales linearly. THere are other such architectures. Performer, Linformer, Reformer, Hyena.
  - Mistral-Nemo-12b-ic is one of the most popular fine-tuned model. It’s small enough to run on a server.
- Justus Mattern (Prime Intellect)
  - Intellect-2 is a continously learning (RL) model that uses decentralized training on peer-to-peer GPUs.
  - Solving problems on bandwidth, verifiable contributions, etc.
ChatGPT Deep Research now also has an O4-Mini version to serve smaller reports. Free users get 0 original + 5 lightweight 5 tasks / month. $20 version gets 10 + 15. $200 version gets 100 + 150. The month begins on first use of Deep Research and runs on a 30 day “window”. Ref
O4-Mini-High is great at going through an under-documented repo and finding things. For example, here’s how I configured cmdg.
ChatGPT is my new Jupyter Notebook :-)
Google announced new AI capabilities at Google Next APAC 2025. Blog. Interesting ones are:
- @Gemini in chat
- Google Meet support for “Catch me up”
- Google Vids: Create short video clips
- Google Sheets: does better analysis
- Google Slides: image generation
- Google Docs: Create Audio Clips (like NotebookLM in Google Docs)
- Google Docs: “Help me refine” is better than before
- Google Workspace Flows
gcalcli is a convenient way to export Google Calendar. Example: uvx gcalcli agenda --tsv 2025-01-01 2025-01-05
cmdg is a command line GMail client that I’ve now switched to for quick email checks. 80% of my email is spam and this is good enough to scan and delete those. It also avoids running a 200-500 MB tab in the browser that constantly shows me how many unread emails I have.
From Worklife with Adam Grant: Cancelling cancel culture with Loretta Ross
- “Lighten up! Fighting Nazis should be fun. It’s being a Nazi that sucks. If you’re not having fun fighting for hope and joy and human rights, maybe you’re doing the fight wrong. We are the ones who should be having fun.”
- “You can say what you mean. But you don’t have to say it mean.” There is always a way to put it across better. Refusing to say mean things is about to discover these approaches.
- “The true mark of a lifelong learner is knowing that you can learn something from every single person you meet.” If you remember that, you can’t be a know it all.
semantic-text-splitter could be the go-to text splitter. It’s Rust-based, supports MarkdownSplitter, and multiple tokenizers. Alternatives like semchunk, advanced-chunker, chonkie, etc. seem clunkier.
ULID is like UUID but time-sortable. That’s an improvement over timestamp IDs (definitely) and potentially even UUIDs. They can be generated by clients as a globally unique ID. Try pip install python-ulid and npm install ulid.
The Consumer Product Safety Commission Data has thousands of reports of product safety over time
You can run xclip -sel clip -o | pandoc -f markdown -t html --no-highlight | xclip -sel clip -t text/html -i to convert Markdown in the clipboard to rich text. But xclip doesn’t support multiple selections, so the text is lost. ChatGPT
DuckDB UI & Notebooks will potentially be a good alternative to Datasette, DBeaver, etc. But for now, there are still glitches. It crashes with a SIGSEGV (Address boundary error) when connecting to SQLite databases.
Ollama limits MAX_TOKENS to 2K by default.
AI assisted search helps wherever I would have used Google, e.g.
- Debugging. “Fix CUDA initialization: CUDA unknown error”
- Tool search. “Find an online word counter tool.”
- Library search. “Find a JS micro library to render Markdown.”
OpenAI API capabilites lag ChatGPT features. For example:
- o4-mini via the API does not search the web natively as part of its reasoning.
- o4-mini, o3, o3-mini, o1, gpt-4.1-nano don’t yet support the web_search_preview tool. Only gpt-4.1 and gpt-4.1-mini do. Limitations
- Search results are NOT visible via the API. They’re fed directly to the model. The number of searches or results is unknown. Each search costs 0.25-0.5 cents. Pricing
- For reasoning traces (e.g. .reasoning.summary: "medium") you need to verify your organization via withpersona.com which failed with my Indian passport AND Singapore work permit.
The ChatGPT Plus plan ($20) gives you 50 O4 mini messages a day, which I exceeded! It’s supposed to reset at midnight UTC Ref but might operate on a rolling window ChatGPT. “Currently, there is no way to check how many messages you have used in your usage budget.” OpenAI
SignalBloom reads SEC filings and writes analyst reports on it using LLMs
“Evaluation in the loop” or “Evals-in-the-loop” is a new term I learnt. SignalBloom’s Hallucination Bechmark
If AI interacts with the world and generates data from its own experience and learns from that, we have a new scaling mechanism. DeepMind podcast
OpenAI’s search API is fairly expensive at $30+/1K calls. Typically, to read interesting HN articles, I will make 30 calls which is about 75c. Instead I should use the app and summarise HM news across different days manually based on my interests!
Finally! t-strings land in Python. They’re like JavaScript template literals.
DuckDB’s CSV parser might be one of the most forgiving parsers. Even better than Pandas or SQLite3. Ref
Good managers will probably make good AI managers. AI agents can probably substitute humans in business experiments. Ethan Mollick
If Windsurf stops working, reload the extension. GitHub
TLS certificates will start expiring in 47 days from 15 Mar 2029, forcing automated domain renewals. Digicert
Nix flakes are a reliable alternative to DevContainers that don’t need Docker - but don’t work on Windows.
Ink is like React for the CLI.
The Unsure Calculator is a great tool to calculate formulas with multiple uncertainties, like:
- My office is 9-11 km away and it takes me 45-55 min to reach. So I cycle at 9~11 / 45~55 * 60 ~ 10-14 kmph (12 most likely).
- I spend $6-15 on lunch and eat out 80-120 days a year. So I spend 6~15 * 80~120 ~ $600~1550 ($1000 most likely) eating out yearly.
- I take 30-120 min to prepare a quiz question. Each exam has 6-12 questions. So I need 30~120 * 6~12 / 60 = 4~20 hours (11 most likely)
Using Kiran’s macOS setup for dev I enabled colorized less and mouse options for tmux.
time fish -i -c exit prints the time taken for fish startup. fish --profile-startup ~/fish.profile -i -c exit prints the time taken by each command on fish startup to ~/fish.profile. I used this to speed up my fish startup.
The 8 top features of the OpenAI Responses API that are an improvement over the Completions API (IMHO) are:
- Link to previous response rather than sending history
- Uploading files directly
- Swappable system instructions while retaining the chat history
- Customisable reasoning effort AND reasoning summary detail
- Truncation in the middle option
- Web search context size option
- File search filters by file attributes
- Flex service tier for lower cost
OpenAI doesn’t charge for file storage but does charge 10 cents / GB-day for vector storage beyond 1 GB. The first 1GB is free
Augment Code is an AI code editor that’s growing popular on Reddit. #ai-coding
The GPT 4.1 models have a 75% discounted prompt caching (instead of the usual 50%), making them particularly suited for repetitive tasks. OpenAI
chatgpt.com shortcut keys are revealed via Ctrl + /. Here’s my ranking on usefulness:
- Ctrl + Shift + C: Copy last response as Markdown!
- Ctrl + Shift + ;: Copy last code block
- Ctrl + Shift + S: Sidebar toggle
- Ctrl + Shift + O: Open new chat
- Shift + Esc: Focus chat input
- Ctrl + Shift + I: Ccustom instructions
- Ctrl + Shift + X: Delete chat

Things I Learned - 23 Mar 2025

Sun, 23 Mar 2025 00:00:00 +0000

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.

Things I Learned - 02 Mar 2025

Sun, 02 Mar 2025 00:00:00 +0000

This week, I learned:

Proxmox Virtual Environment is an open-source alternative to VMWare, Hyper-V, Citrix XenServer, etc. (There’s nothing there that prompts me to explore it further.)

With Podman on Windows (a Docker equivalent), many Docker-enabled tasks become easier. For example, running PostgreSQL is as easy as:

podman run -d --name postgres -e POSTGRES_PASSWORD=postgres -p 5432:5432 postgres:latest
podman exec -it postgres psql -U postgres -c "CREATE DATABASE mydb;"

Bad deep research prompts are: vague/broad, under-specified or ambiguous. In short, the more you know what you want, the better. Iterate until then.
What kind of reports do clients are research companies to produce? I was curious to see if Deep Research can replace these. Here are a bunch of ideas. ChatGPT
1. Strategy & Management Consulting Research (McKinsey & Company, Boston Consulting Group, Bain & Company, Strategy&, Accenture Strategy)
  - Produce a comprehensive strategic transformation report for a Fortune 500 consumer goods company. Analyze global market trends, competitor strategies, and actionable growth recommendations, including case studies and source citations.
  - Generate an in‐depth study on corporate restructuring trends in emerging markets. Focus on successful turnaround strategies, CEO leadership factors, and strategic pivots, with a comparative analysis of key players.
  - Create a report on M&A trends in the technology sector over the past five years. Detail deal drivers, integration best practices, and forecast future acquisition opportunities, citing relevant data.
2. IT & Technology Research Analysts (Gartner, Forrester Research, IDC, 451 Research, Ovum)
  - Produce a market assessment report on emerging cloud computing platforms. Include vendor evaluations, adoption forecasts, and key technology drivers with supporting data and charts.
  - Generate an in‐depth cybersecurity trends report for enterprise IT. Analyze recent threat vectors, defense strategies, and best practices for risk mitigation, providing actionable recommendations.
  - Create a comprehensive study on the impact of artificial intelligence in enterprise software. Include competitive benchmarking, technology adoption rates, and forecasted market changes.
3. Marketing & Consumer Research (Nielsen, Kantar Group, Ipsos, GfK, Euromonitor International)
  - Produce a consumer behavior analysis report for a leading retail brand. Identify key demographic shifts, purchasing trends, and brand loyalty factors, and provide actionable insights with data visualizations.
  - Generate a detailed report on digital media consumption trends among millennials, incorporating survey results, social media analytics, and case studies of successful campaigns.
  - Create a market segmentation report for a new consumer electronics launch. Identify key consumer segments, behavioral drivers, and media usage patterns with clear recommendations.
4. Financial Investment Research (Goldman Sachs, JPMorgan Chase, Morgan Stanley, Morningstar, Keefe Bruyette & Woods)
  - Produce an equity research report on mid-cap technology stocks. Include detailed financial modeling, valuation analysis, and buy/sell/hold recommendations with supporting data and charts.
  - Generate a fixed income analysis report for corporate bonds in the industrial sector. Assess credit risk, yield forecasts, and macroeconomic influences, citing key data sources.
  - Create a comprehensive report on global market trends impacting investment banking. Analyze regulatory changes, market sentiment, and performance metrics of leading financial institutions.
5. Healthcare Research (IQVIA, Frost & Sullivan, Evaluate Ltd, Deloitte Healthcare, IMS Health)
  - Produce a market analysis report on emerging biotechnologies in oncology. Include competitive landscape, regulatory challenges, and growth forecasts with relevant case studies.
  - Generate a comprehensive report on patient satisfaction and telemedicine adoption trends. Analyze survey data from leading healthcare providers and benchmark best practices.
  - Create a detailed study on pharmaceutical market dynamics in emerging economies. Focus on pipeline developments, regulatory environments, and market potential with actionable insights.
6. Legal Research Providers (LexisNexis, Westlaw, Bloomberg Law, Fastcase)
  - Produce a legal risk assessment report on the impact of recent data privacy regulations for multinational corporations. Include case studies, trend analysis (2019–2024), and strategic recommendations.
  - Generate a comprehensive report summarizing key federal and Supreme Court rulings on intellectual property rights over the past five years, highlighting trends and divergent interpretations.
  - Create a detailed report on the evolution of securities law and its effect on investment research practices, incorporating analysis of recent litigation and regulatory updates.
7. Media & News Research (Factiva, Kantar Media, Comscore, Cision)
  - Produce a media consumption trends report that analyzes audience behavior shifts across digital, TV, and print platforms. Include data visualizations, key drivers, and forecasted trends.
  - Generate a comprehensive report on the impact of social media on traditional news reporting, with case studies and a comparative analysis of engagement metrics.
  - Create a detailed study on the effectiveness of multimedia advertising campaigns, evaluating ROI, consumer engagement, and best practices with actionable insights.
8. Economic & Industry-Specific Research (Economist Intelligence Unit, BMI Research, IHS Markit, Consensus Economics)
  - Produce a macroeconomic outlook report for emerging markets, including GDP, inflation, and employment forecasts, with detailed data analysis and visualizations.
  - Generate an industry analysis report on the automotive sector, covering technological innovations, competitive dynamics, and consolidation trends.
  - Create a comprehensive country risk assessment report for a target region, detailing political, economic, and regulatory factors with recommendations for investors.
9. Human Resources & Employee Engagement Research (Gallup, Great Place to Work, Mercer)
  - Produce an employee engagement report for a multinational firm based on recent survey data. Identify key drivers of satisfaction, retention challenges, and improvement recommendations.
  - Generate a comprehensive study on the impact of remote and hybrid work models on employee productivity across industries, including best practices and benchmark data.
  - Create a detailed report on workplace culture transformation, analyzing organizational behavior trends, employee feedback, and actionable strategies to boost engagement.
10. Environmental, Social & Governance (ESG) Research (MSCI ESG Research, Sustainalytics, ISS ESG, Bloomberg ESG)
  - Produce an ESG performance report for a portfolio of global companies. Include sustainability scores, risk assessments, and recommendations for improvement with data visualizations.
  - Generate a comprehensive study on the impact of climate change regulations on the energy sector, including policy analysis, market forecasts, and strategic implications.
  - Create a detailed report on corporate social responsibility trends in the consumer goods industry, incorporating qualitative and quantitative analyses with actionable recommendations.
11. Education & Academic Research (RAND Corporation, National Center for Education Statistics, HolonIQ)
  - Produce an analysis report on the future of online education, examining technological adoption, market growth projections, and student outcome trends with supporting data.
  - Generate a comprehensive study on the effects of educational policy reforms on public school performance in the U.S., including trend analysis and actionable recommendations.
  - Create a detailed international higher education trends report, covering tuition dynamics, international student mobility, and emerging academic programs with comparative data.
12. Real Estate & Property Research (CBRE, JLL, CoStar Group, Cushman & Wakefield)
  - Produce a commercial real estate market analysis report for major urban centers, including occupancy trends, rental rate forecasts, and investment opportunity assessments.
  - Generate a comprehensive study on residential housing market dynamics in emerging economies, focusing on affordability, supply-demand gaps, and policy impacts.
  - Create a detailed report on the impact of urban redevelopment projects on local real estate values, including case studies, forecasts, and strategic recommendations.
13. Energy & Natural Resources Research (Wood Mackenzie, Rystad Energy, Bloomberg New Energy Finance)
  - Produce an analysis report on global renewable energy trends, covering technology adoption, market forecasts, and key policy drivers, with detailed data and visuals.
  - Generate a comprehensive commodity price forecasting report for oil, natural gas, and key metals, incorporating historical trends, risk assessments, and predictive modeling.
  - Create a detailed report on energy transition strategies for traditional energy companies, focusing on clean technology investments and market adaptation strategies.
14. Supply Chain & Logistics Research (ARC Advisory Group, Gartner Supply Chain Research, Supply Chain Insights)
  - Produce a report on supply chain resilience for global manufacturers. Analyze risk factors, digital transformation impacts, and best practices for operational efficiency with supporting data.
  - Generate a comprehensive study on the impact of technology on logistics networks, including case studies on digital optimization and cost reduction strategies.
  - Create a detailed report on emerging last-mile delivery solutions, assessing innovations, consumer expectations, and scalability with actionable insights.
15. Cybersecurity & Information Security Research (KuppingerCole, Forrester Security, IDC Cybersecurity, Cybersecurity Ventures)
  - Produce an in-depth report on emerging cybersecurity threats for large enterprises, including detailed analysis of recent incidents, risk vectors, and defense strategies.
  - Generate a comprehensive cybersecurity market landscape report, evaluating vendor performance, technology forecasts, and best practices for mitigating risks.
  - Create a detailed report on regulatory compliance trends in information security within the financial services industry, with case studies and strategic recommendations.
16. Social Media, Digital & Online Research (Comscore, SimilarWeb, Brandwatch)
  - Produce a digital audience behavior report for a global brand, focusing on social media trends, engagement metrics, and platform performance with detailed data analysis.
  - Generate a comprehensive analysis of influencer marketing effectiveness across digital channels, including ROI metrics, case studies, and best practices.
  - Create a detailed report on online brand sentiment analysis, incorporating social listening data, trend forecasts, and actionable recommendations.
17. Public Opinion & Political Research (Pew Research Center, Gallup, YouGov)
  - Produce a public opinion polling report on voter sentiment ahead of a major election. Include demographic breakdowns, key issue analysis, and trend visualizations for the past five years.
  - Generate a comprehensive study on political risk in emerging markets, analyzing historical data, current trends, and future projections, with policy recommendations.
  - Create a detailed report on the influence of media on public policy, using survey data, social media analysis, and comparative case studies.
18. Sports, Entertainment & Media Research (Nielsen Sports, Sportcal, Kantar Media Sports)
  - Produce a market analysis report on sports sponsorship trends, detailing viewership metrics, brand engagement, and investment ROI with industry case studies.
  - Generate a comprehensive report on audience behavior in the streaming media industry, including demographic insights, consumption trends, and competitive benchmarks.
  - Create a detailed analysis of digital advertising effectiveness in the entertainment sector, including segmentation data, ROI analysis, and strategic recommendations.
19. Innovation, R&D & Technology Trends Research (Innosight, Frost & Sullivan Innovation, CB Insights)
  - Produce a global R&D investment trends report, analyzing technology spending, innovation indices, and the impact on market growth across key industries.
  - Generate a comprehensive study on disruptive technologies in manufacturing, including competitive analysis, market potential forecasts, and adoption trends.
  - Create a detailed report on emerging innovation hubs worldwide, focusing on startup ecosystems, funding trends, and collaborative opportunities in technology.
20. Agriculture & Agribusiness Research (Rabobank Agribusiness Research, USDA Economic Research Service, AgFunder)
  - Produce an analysis report on global agricultural market trends, including crop yield forecasts, trade dynamics, and policy impacts, with data visualizations.
  - Generate a comprehensive study on agritech innovations such as precision farming and sustainable practices, including case studies and market forecasts.
  - Create a detailed report on the impact of climate change on food production and supply chain stability in agribusiness, with risk assessments and strategic recommendations.
21. Environmental & Climate Change Research (Carbon Trust, IHS Markit Energy Transition, Bloomberg New Energy Finance)
  - Produce a report on the economic and social impacts of climate change on urban infrastructure, including forecasting models and policy recommendations.
  - Generate a comprehensive study on national climate policies and their effects on industrial competitiveness, with detailed trend analysis and source citations.
  - Create a detailed report on corporate sustainability initiatives, assessing environmental risk management practices and providing actionable recommendations for improvement.
22. Customer Experience (CX) & User Experience (UX) Research (Forrester CX Research, Gartner CX Research, Qualtrics, Nielsen Norman Group)
  - Produce a report on customer journey mapping for a leading retail brand, identifying key touchpoints, pain points, and actionable improvement strategies with data visualizations.
  - Generate a comprehensive study on digital user experience trends for e-commerce platforms, including usability testing insights, design best practices, and conversion optimization recommendations.
  - Create a detailed report on customer satisfaction and loyalty metrics across multiple industries, integrating survey data and actionable recommendations to enhance overall CX.
23. Blockchain, Cryptocurrency & Fintech Research (Chainalysis, CoinDesk Research, Deloitte Fintech Research, CB Insights)
  - Produce an analysis report on emerging blockchain technologies and their applications in financial services, including market trends, adoption forecasts, and case studies.
  - Generate a comprehensive study on cryptocurrency market dynamics, analyzing regulatory developments, investor sentiment, and competitive landscapes with source citations.
  - Create a detailed report on fintech disruption in traditional banking, with case studies on leading startups, technology adoption, and future market forecasts.
24. Venture Capital, Startup & Private Equity Research (PitchBook, CB Insights, Crunchbase, Preqin)
  - Produce a global venture capital investment trends report, including performance analysis of high-growth startups, sector benchmarks, and emerging market opportunities.
  - Generate a comprehensive study on private equity market dynamics, covering deal flow analysis, exit strategies, and forecasted trends with supporting data.
  - Create a detailed report on emerging startup ecosystems in key regions, highlighting funding trends, investor activity, and growth potential with actionable insights.
25. Operations Research & Management Science Consulting (The Brattle Group, NERA Economic Consulting, CRA International)
  - Produce a report on optimization techniques for operational efficiency in large-scale manufacturing, including quantitative analysis, simulation models, and case studies.
  - Generate a comprehensive study on the application of predictive analytics in supply chain management, focusing on data modeling, process improvements, and actionable insights.
  - Create a detailed report on advanced quantitative modeling approaches to solve complex business problems in logistics and operations, including scenario analysis and recommendations.
26. Cultural & Social Research (Ethnographic/Sociocultural Studies) (Ipsos MORI, Kantar TNS, YouGov)
  - Produce a qualitative ethnographic study on urban consumer lifestyle trends, incorporating field observations, interviews, and cultural analysis with actionable insights.
  - Generate a comprehensive study on how cultural shifts influence global brand perception, including comparative case studies and trend analysis.
  - Create a detailed report on sociocultural dynamics and consumer behavior in emerging economies, integrating in-depth field research and actionable recommendations.
27. Economic & Demographic Research Firms (Oxford Economics, The Conference Board, CEIC Data)
  - Produce a macroeconomic forecasting report for a specific region, including GDP, inflation, and employment trends with detailed data visualizations and source citations.
  - Generate a detailed demographic analysis report for a target market, highlighting age distribution, income levels, and consumption patterns with actionable insights.
  - Create a comprehensive report on the economic impact of demographic shifts on consumer markets, with policy recommendations and trend analysis.
28. Academic & Think Tank Research Organizations (Brookings Institution, RAND Corporation, Carnegie Endowment for International Peace)
  - Produce a policy research report on global governance challenges and their implications for economic development, including case studies, literature reviews, and expert interviews.
  - Generate a comprehensive study on social inequality and its effects on public health and education outcomes, supported by empirical research and trend analysis.
  - Create a detailed report on emerging trends in international relations and their impact on global trade and security, integrating academic research and data analytics.
29. Market Research Technology & Software Providers (Qualtrics, SurveyMonkey, Confirmit)
  - Produce a report on the latest innovations in survey technology and data analytics software for market research, including product comparisons, user case studies, and future trend forecasts.
  - Generate a comprehensive study on the integration of AI and machine learning in consumer insights platforms, highlighting case studies, performance metrics, and industry benchmarks.
  - Create a detailed report on digital transformation trends in market research technology, featuring analysis of leading software solutions, market share data, and recommendations for technology adoption.
When evaluating inputs, models tend to prefer the first response, prefer their own response, and prefer longer responses. ThursdAI
Real-time speech-to-text options for transcription:
- Deepgram has a MediaRecorder API, which is perfect.
- Whisper Streaming Web is a web app that can transcribe audio real-time from the browser. A good approach, but I wouldn’t use it for meeting transcription on my mid-end laptop. Streaming takes up the bulk of my GPU, leaving little for transcription.
- whisper-live runs as a Python console app and does something similar.
- Whisper WebGPU runs on the browser (only 200MB). Cool! But slow and still takes up GPU.
Mini-omni is an open-source Qwen-based LLM that can hear and talk while thinking in real-time. An interesting experiment, but not for prototyping.
OpenAI shares an insights report with clients that has insights on what different professions search for. What doctors search for is:
1. Is my diagnosis right?
2. How do I read this report?
3. Is my prescription correct?
4. Is there a cheaper medicine?
5. What’s the life expectancy given these symptoms?
Dataclasses in Python have a slight overhead over named tuples. The 2 main uses I see for them are: providing defaults and offering type hints.
UVB 76 is a radio channel has been broadcasting static (with occasional Russian conversation) since 1976. No one knows why. It’s live at https://m.youtube.com/watch?v=8h_D2P0iqMk
Romans washed clothes in urine. The government taxed the purchase of urine for commercial purposes! That’s the origin of the phrase “Pecunia non olet” which means “money doesn’t stink”.
Nix is a package manager that creates container-like environments. Like a cross between Docker and apt / venv. It has an immutable file system. DevBox is a higher-level tool built on top of Nix that streamlines developer workflows, e.g. common project environment setup.
VS Code can be used to develop inside a Docker container via Podman, too. Set dev.containers.dockerPath": "podman" Ref
Rill Data is an interesting BI tool based on DuckDB. It auto-generates a dashboard given a dataset.

It’s possible to assign “variables” in SQL (notably in DuckDB). Here’s an example:

WITH
  sessions AS (FROM events SELECT COUNT(DISTINCT session_id) AS value),
  pages AS (FROM events SELECT COUNT(*) AS value)
FROM sessions, pages
SELECT sessions.value / pages.value AS pages_per_session;

DuckDB has a GROUP BY * that groups by all categorical columns. SELECT x, y, COUNT(*) FROM t GROUP BY * is equivalent to SELECT x, y, COUNT(*) FROM t GROUP BY x, y.
VS Code can be used as a code executor by adding {"key": "shift+enter", "command": "workbench.action.terminal.runSelectedText", "when": "editorFocus"} to the keybindings.json file. Press Shift-Enter to run the selection on the terminal. Useful for DuckDB, SQLite, etc. Ref
LLMs are excellent at database migration. They can convert schemas and queries across SQL dialects (e.g. BigQuery to DuckDB, etc.) at 90%+ accuracy. This is useful when clients want to migrate cloud providers, go from on-prem to cloud, or reduce cost by switching databases.

Things I Learned - 09 Feb 2025

Sun, 09 Feb 2025 00:00:00 +0000

This week, I learned:

Lessons from discussions at IIT Madras:
- Even in recorded video tutorials, asking students a question and pausing to give them time to think can be effective.
- When you put students in front of real clients, engagement increases dramatically.
- Most teaching assistants would like to help diligent students among the bottom half (more than the top decile of students).
- However, there is a fraction of poor performers who do not care, and are best ignored. Their engagement and effort is a good measure of their interest.
- Defining a minimal set of principles that we want to teach helps us measure if we’ve helped the bottom half at least meet those objectives.
- Teaching is hard. Even after explanations, students, even ENGAGED students, tend to make basic mistakes
ChatGPT does a good job of spotting errors in architectural and structural diagrams. In fact, the whole theme of spotting errors in large diagram is a theme that can have potential use cases. Source: Dan Becker.
R1 seems good at text-to-CAD. Even better than Sonnet. Source: Dan Becker
OpenAI advices a few different prompting techniques for reasoning models. OpenAI:
- Avoid examples unless zero-shot prompting fails.
- Avoid chain-of-thought. These models do that internally anyway.
- Short, direct prompts are better than detailed prompts.
GitHub models is free for anyone to try. The model catalog us extensive and even includes o3-mini which was launched this week (though in limited preview).
The data catalog space is led by proprietary solutions:
- Alation Data Catalog: Market leader; growing steadily in enterprise use
- Collibra Data Catalog: Widely adopted with steady growth
- AWS Glue Data Catalog: Growing rapidly as AWS expands its data services
- Informatica Enterprise Data Catalog: Long established and stable, though facing newer alternatives
- Microsoft Purview Unified Catalog: Experiencing fast growth driven by cloud momentum
- Atlan Data Catalog: Relatively new but gaining fast traction among tech-forward organizations
OpusClip automatically creates short clips from long videos. I ran it on Programming Minecraft with WebSockets in Python to get this short 30-second clip. 30 minutes. 100% automated.
Alternatives to Postman:
- Hoppscotch – A web‑based/desktop API client supporting REST, GraphQL, and WebSockets. It’s lightweight, open-source, and self‑hostable.
- HTTPie – A web-based API along with a friendly command-line tool for API interaction.
- Insomnia (or its fork Insomnium) – A popular cross‑platform API client with a minimal interface and plugin ecosystem.
- Bruno – A desktop open-source API client that stores collections as files (ideal for Git versioning).
- Milkman – A desktop open‑source workbench for managing API requests.
Here is the summary of DuckCon #6 on 31 Jan 2025 in Amsterdam. I copied the transcript from YouTubeTranscript and passed it through Gemini 2.0 Flash Exp with the system prompt: “Summarize this transcript from the DuckDB conference without missing any points. Cover every point mentioned. A lot of spelling errors that sound like DuckDB are likely to be DuckDB”.
- Introduction & Welcome:
  - DuckCon #6: This is the 6th DuckDB conference, held in their hometown. The first DuckCon was online due to the pandemic.
  - Live Streaming: This is the first time DuckCon is being live-streamed, chosen to accommodate global time zones (especially China and the US).
  - Global Reach: The live stream is intended to reach users in areas where in-person DuckCons are unlikely.
  - Q&A: Slido (qa.duckdb.org) will be used for Q&A, with upvoting to prioritize questions.
  - Sponsors: Thanks to gold sponsor monday.com and silver sponsors Real and Crunchy Data.
  - DuckCon Purpose: DuckCon is a place for users to connect, share experiences, and provide feedback to the DuckDB team.
  - Inspiration: The team is inspired by the community’s use of DuckDB and how far the project has come.
  - Mission Statement: DuckDB aims to make large datasets less intimidating and more accessible, moving away from fear of data to confidence in handling it.
  - Motivation: The project was born from seeing people struggle with data that didn’t fit in Excel and the lack of user-friendly tools.
  - Industry Trends: Single-node processing capabilities have grown faster than the size of useful datasets.
  - Data Singularity: A prediction that most data analysis queries can run on a single node is now a reality.
  - Real-World Data Sizes: Analysis of Snowflake and Redshift data shows that 99.9% of datasets are under 300GB.
  - Raspberry Pi Benchmark: The industry-standard TPCH benchmark (scale factor 300, ~300GB) can run on a Raspberry Pi using DuckDB.
  - Single Node Growth: Single-node processing power is rapidly increasing, allowing for larger datasets to be handled.
  - Adoption Numbers:
    - 32 Million Extension Installs: 32 million DuckDB extension installs in the last month.
    - 1.8 Million Unique Website Visitors: 1.8 million unique visitors per month to the DuckDB website.
    - Blue Sky Community: Growing community on Blue Sky, with the hashtag #dataBS.
- Technical Updates (Mark):
  - Extension Ecosystem: Focus on enabling the community to build and share extensions.
  - Community Extensions: Making it easier to create and use community-built extensions.
  - DuckDB v1.2 (Harlequin Duck): Releasing next week, named after the Harlequin duck.
    - CSV Reader Improvements: Significant improvements to the CSV reader.
    - Friendlier SQL: Improvements to the SQL experience.
    - CLI Autocomplete: Reworked and improved CLI autocomplete.
    - Performance Optimizations: Many queries are now faster due to performance work.
    - C API for Extensions: Introducing a C API to make building extensions easier.
    - Logging Features: Improved logging for production use.
  - Lakehouse Focus: The main focus for the year is on lakehouse formats and related features.
- Q&A (Mark & Hanis):
  - Doubling Team: If the team doubled, they would focus on client integrations and other projects, not a major architectural change.
  - Partitioning: Near-term plans to add support for partitioning, related to lakehouse formats.
  - DuckDB WASM: The WASM ecosystem is evolving, with exciting possibilities for in-browser use.
  - Financial/Pharmaceutical Industries: DuckDB could replace some SAS workflows due to its cost-effectiveness and capabilities.
  - Lakehouse & MotherDuck: Lakehouse work is separate from MotherDuck, though MotherDuck will likely support lakehouse features.
  - Contributing to Extensions: Plans to make it easier to contribute to extensions, including support for Rust and Go.
- Airport Extension (Rusty):
  - Analogy: The airport extension allows DuckDB to “fly” to remote servers using Apache Arrow Flight.
  - Functionality: Supports select, insert, update, and delete operations on remote data sources.
  - Motivation: To reduce the burden of writing extensions and enable faster development using existing code.
  - Arrow Flight: Uses Arrow Flight for communication, enabling connections to various data sources.
  - Demo 1: Delta Lake:
    - Attaches to a flight server for Delta Lake access.
    - Allows creating schemas, tables, and performing standard SQL operations.
    - Uses Python and deltars (Rust implementation of Delta Lake).
    - Supports predicate pushdown and C integration with the DuckDB catalog.
  - Demo 2: AutoGluon:
    - Integrates the AutoGluon AutoML package.
    - Predicts Hacker News post votes using a trained model.
    - Demonstrates table-returning functions for model fitting and prediction.
    - No C++ code required, just Python.
  - Demo 3: Geocoding:
    - Uses a geocoder service to convert addresses to coordinates and vice versa.
    - Demonstrates scalar UDFs for vectorized requests.
    - Uses a Python example for a simple uppercase function.
  - Features:
    - List flights, take flights.
    - Catalog integration.
    - Select, update, delete.
    - Scalar UDFs.
    - Table in/out functions.
    - Authentication for row/column filtering.
  - Availability: Requires DuckDB 1.2, MIT licensed, available on GitHub.
- Q&A (Rusty):
  - Most Proud Extension: Airport is the most fun, but the AWS API wrapper also brings joy.
  - Extension Resources: The GitHub DuckDB extension template and reading others’ source code are helpful.
  - Airport & Other Extensions: Airport is separate and can be used alongside other extensions like spatial or httpfs.
  - Graph Support: Graph database support is planned, with examples like Kuzu, Neptune, and Neo4j.
  - Licensing: Airport is MIT licensed, compatible with Apache license.
  - Scaling Out: Airport can be used to query multiple DuckDB instances on different machines.
- Ibis & Geospatial (Nati):
  - Nati Clementi: Senior software engineer at Nvidia, working on open-source projects like Ibis.
  - Ibis: Open-source Python library for data wrangling, with a DataFrame API and interfaces to 15+ engines, including DuckDB.
  - DuckDB for Geospatial: DuckDB is fast, has a geospatial extension, and supports various geospatial formats.
  - Geop Parquet: Becoming a standard for geospatial data, enabling cloud data warehouse interoperability and compression.
  - Geo Arrow: A way of representing geospatial vector data in memory for faster processing.
  - Ibis Benefits: Allows writing Python instead of SQL, with deferred execution determined by the engine.
  - Demo:
    - Uses OverTour Maps data in geop parquet format.
    - Filters data using bounding boxes.
    - Demonstrates geospatial operations like ST_Distance and ST_Transform.
    - Plots data using Lumber.
    - Shows how to find points of interest near a location (e.g., the Van Gogh Museum).
  - Ibis & DuckDB: Ibis uses DuckDB for the parquet reader and lets DuckDB do the heavy lifting.
  - Ibis Optimizations: Ibis does type checking but doesn’t do query optimization, leaving that to the engine.
  - Ibis in Browser: Ibis works in the browser through DuckDB WASM.
- Q&A (Nati):
  - Linear Interpolation: Ibis ML module can help with regression-related tasks.
  - Missing Features: No major features are missing in the DuckDB/Ibis geospatial setup, with minimal overhead.
  - Parquet Reader: Ibis uses DuckDB’s parquet reader.
  - Query Optimization: Ibis does not optimize SQL queries, leaving that to DuckDB.
  - Ibis in Browser: Ibis works in the browser through DuckDB WASM.
- Rill & Metrics Layer (Mike):
  - Rill: A BI tool optimized for DuckDB, with instant slicing and dicing, BI as code, and a metrics-first philosophy.
  - Metrics-First: Design metrics models, and Rill autogenerates dashboards and user experiences.
  - Live Demo:
    - Downloaded Rill using a curl command.
    - Created a new project called “DuckCon 6”.
    - Imported a parquet file of GitHub commit data.
    - Used AI to generate a metrics model and dashboard.
    - Showed the dashboard with trends and filtering.
  - Metrics as Building Blocks: Metrics are flexible, fast, and intuitive.
  - SQL for Metrics: Metrics should be defined in SQL, not other languages.
  - Visual Metrics Editor: Rill has a visual editor for defining metrics using DuckDB SQL.
  - Metric Stack:
    - Legacy: Data warehouses, traditional BI tools, inconsistent metrics, full table scans.
    - DuckDB Powered: Consistent metrics, fast olap queries, SQL everywhere.
  - Challenges: Data modeling is hard, metric changes can be expensive, single-node scale has limits.
  - AI & Metrics: AI can assist in metrics modeling, optimization, and conversational data exploration.
- Q&A (Mike):
  - Complex Metrics: Rill works well with complex metrics involving multiple sources and transformations by joining tables in DuckDB.
  - 60 FPS Dashboards: Users can feel the difference with faster dashboards.
  - Defining Metrics: Metrics are defined in the Rill UI using SQL expressions.
  - Replacing ChatGPT: Considering locally run self-hosted models for privacy.
- Stock Data Analysis (Ryan):
  - Two Takeaways: Simple finance data flows with trade data and a tool called Q Studio.
  - Ryan Hamilton: 14 years building large data platforms in banks.
  - Bank Data: Data from exchanges, market data providers, and internal systems.
  - Use Cases: Backtesting, data analysis, and report generation.
  - Q Studio: A Java desktop application that connects to 30 databases, including DuckDB.
  - Demo:
    - Loaded a 6GB CSV file of trade data into DuckDB.
    - Showed basic queries, pivoting, and Candlestick charts.
    - Demonstrated time-based aggregation and moving averages.
    - Showed a basic trading strategy using window functions.
  - DuckDB Benefits: Fast, easy to use, great for time-based analysis.
- Q&A (Ryan):
  - KDB+ vs. DuckDB: KDB+ is for large data, DuckDB is more approachable with strong Python integration.
  - XML Files: Offloading processing to DuckDB, not planning XML integration.
- Lightning Talks:
  - Zuk (Jared):
    - Search engine research using DuckDB.
    - Python-based experiments with SQL.
    - Removing document lengths for faster search engines.
  - DuckPGQ (Daniel):
    - Graph analytics in DuckDB using SQL property graph queries (pgq).
    - Visual graph syntax for pattern matching and path finding.
    - Outperforms Neo4j on analytical queries.
  - Yat (Kristoff):
    - Smallest DuckDB SQL orchestrator.
    - Runs SQL queries in a folder in the correct order.
    - Generates a mermaid diagram for lineage.
  - Grafana & DuckDB (Sam):
    - Lessons learned from using DuckDB in Grafana.
    - Security incident due to shell commands and file access.
    - Importance of reading the documentation.
  - Cloud Slur (Adam):
    - Syncing query engine for bank transaction data.
    - Uses LLM to convert human language to SQL.
    - Uses DuckDB in the browser, Node.js, and Python.
  - Healthcare Data (Tony):
    - Data engineering use cases in healthcare.
    - Dynamic data masking system using DuckDB and Snowflake.
    - Data integration pipeline using DuckDB and Arrow streams.
- Closing Remarks:
  - Michel Simmons: Author of the DuckDB in Action book, will be signing books.
  - Poster Session: A poster session will follow the talks.
  - Sponsors: Thanks again to the sponsors.
  - Social Event: The conference will now move to the social event.
ibis is a Python library that works with multiple dataframe backends like DuckDB, Polars, and Pandas.
With just 3 annotators and 50-100 samples, you can figure out if an LLM can replace human annotators systematically.Arxiv ChatGPT explanation
Curiosity and agency may be the differentiator in a world of LLMs (not experience, knowledge, or ability), since LLMs will democratize expertise. Jack Clark
“AI/human combined work can be copyrighted as long as a human is adding, changing or selecting elements. Prompts alone do not usually produce copyrighted work.” - Copyright and Artificial Intelligence, Jan 2025, US Copyright Office via Ethan Mollick
- Human Authorship is Essential: Works created solely by AI are not copyrightable.
- AI can be used as a Tool: Using AI as a tool does not negate copyright protection, as long as the final work reflects sufficient human creativity.
- Prompts Alone are Insufficient: Simply providing prompts to an AI system, even detailed ones, is generally not enough to establish authorship. Prompts are considered instructions or ideas, which are not copyrightable.
- Expressive Inputs: When a human author provides their own expressive content (like a drawing, photo, or text) as input to an AI system, and that content is perceptible in the output, the human author can claim copyright in that portion of the output.
- Modifying and Arranging AI-Generated Content: Humans can claim copyright in the creative selection, coordination, and arrangement of AI-generated material, as well as in creative modifications to AI-generated outputs.
- No Need for New Legislation: The report concludes that existing copyright law is adequate to address the copyrightability of AI-generated works, and no new legislation is needed at this time.
- Case-by-Case Analysis: Copyrightability will be determined on a case-by-case basis, considering the specific facts of each work and the extent of human contribution.

Things I Learned - 29 Dec 2024

Sun, 29 Dec 2024 00:00:00 +0000

This week, I learned:

A clever idea. Give an LLM a chapter from a textbook. Ask it to generate a unique, playable game to help me learn theconcepts for an exam. Page Bailey
What would be the cost of storing about 500GB of LLM cache logs and 5 million write requests per month?
- CloudFlare KV: $250 + $25 / month Ref
- MongoDB: $125 + $5 / month Ref
- S3: $0.0115 + $25 / month Ref + ?
- CloudFlare R2: $0.0075 + $22.5 / month Ref
Satya Nadella prepares for meetings by asking Copilot to tell him everything he needs to know about the client from the CRM, emails, meeting transcripts etc. He shares that colleagues who annotate it further for him. That’s using AI for reasoning and collaborating with colleagues. Satya Nadella | BG2 w/ Bill Gurley & Brad Gerstner
WOW. This is how a software agent will work alongside humans: Fix issue #5478: Add color to the line next to “Ran a XXX Command” based on return value - using @openhands-agent.
aisuite by Andrew Ng is a unified interface to LLMs. Sort of like an openai library across multiple providers.
Learnings from Best of 2024 in Agents (from #1 on SWE-Bench Full, Prof. Graham Neubig of OpenHands/AllHands)
- Passing code execution as a tool is more powerful than granular tools. You combine multiple tools and tool calls into one. You move code to the data rather than the other way around. Mostly, you need bash, Python (or Jupyter), file manager, web browser.
- UI: Go where the user is, instead of bringing them to you.
- A remote runtime is a critical component.
- Claude 3.5 Sonnet (20241022) and Claude 3.5 Haiku (20241022) perform best on SWE Bench, followed by Deepseek V3, then O1 2024-12-17. X
Browsers support SVG favicons as data URLs. So I used this SVG (generated by Claude via Generate a simple, interesting SVG favicon. Keep the SVG size VERY small but it should be inspiring.)
Since HNSW indexing is an overhead, just use NumPy matrix multiplication to calculate cosine similarity. For 1M vectors, it takes ~0.05 seconds. A 1M vector dataset handles ~2GB of text at a chunk size of 2K chars. In short, if you’re embedding <2GB of text, just use NumPy.
DuckDB’s VSS extension HNSW index + Embeddings (2K chunks of 512 dimensions) takes up roughly 2.5X the size of the original data. Embedding 554 files of ~4,456 KB took 710 seconds. Creating the index took 660 seconds. The resulting DB was 18.1 MB.
How to use LLMs in market research.
- Use LLMs with search for secondary research.
- Create different personas and run user surveys on them. This paper used 1,052 real-life interview audio transcripts as agent memory to simulate people
- Generate your market research report using LLMs.
Given about 30 generations, Llama 1b outperforms Llama 8b. Ref
OpenAI introduced a developer role in addition to the system role. This is mainly for o1. The API is backward compatible - and also forward compatible. OpenAI
Em dashes are a strong sign of ChatGPT use. Curly quotes too. Reddit
CloudFlare has multiple SSL modes when proxying requests.
- Off (no encryption): No encryption between browsers and Cloudflare or between Cloudflare and origins. Everything is cleartext HTTP.
- Flexible: Browsers to Cloudflare is HTTPS, Cloudflare to origin is HTTP. Useful to set up CloudFlare as a HTTP Proxy.
- Full: Browser to Cloudflare matches browser request. Same protocol is used for Cloudflare to origin, without validating the origin’s certificate. Use for self-signed or otherwise invalid certificates.
- Full (strict): Similar to Full Mode, but with validation.
- Strict (SSL-Only Origin Pull): Cloudflare always connects to the origin over HTTPS with certificate validation.
- Getting this wrong can lead to a HTTP 526: invalid SSL certificate
Medical coding is an area ripe for LLMs.
- Ojasvi Yadav created a repo that uses hierarchical classification (rather than embeddings) to find the right coding.
- Gemini models seem to understand medical terms better than others.
- RapidClaims, funded by TogetherAI, is apparently working on this problem.
Document to Markdown Converters:
- PyMuPDF4LLM uses MuPDF. Requires PyTorch.
  - PYTHONUTF8=1 uv run --with pymupdf4llm python -c 'import pymupdf4llm; h = open("pymupdf4llm.md", "w"); h.write(pymupdf4llm.to_markdown("$FILE.pdf"))'
- markitdown from Microsoft. PDF via PDFMiner, DOCX via Mammoth, XLSX via Pandas, PPTX via Python-PPTD, ZIP, etc.
  - PYTHONUTF8=1 uvx markitdown $FILE.pdf > markitdown.md
- Docling by IBM. Unable to install via pip on Windows AND on Linux.
- MegaParse uses libreoffice, pandoc, tesseract-ocr, etc. Requires OpenAI API key.
Awesome Tabular LLMs compiles encodings of tables for LLMs.
What’s the best way of encoding tabular data for LLMs? Looks like including the cell address helps. Here is an explanation from ChatGPT
aspose-words is a Python library that converts documents with many formats (Word, RTF, PDF, HTML, Markdown, EPUB, etc.)
Discourse does not support searching across multiple forums. Instead, search for the term in all forums. Example. Then scroll through the results. Then, in the console, hide the ones you don’t want. Example:
- Hide posts that are not in the “Tools in Data Science” category: $(".badge-category__name").filter(d => d.textContent == "Tools in Data Science").map(d => d.closest(".fps-result")).filter(d => d).forEach(d => d.style.display = "none")
How are software engineers are future-proofing their careers in the face of LLMs?
- Leveraging LLMs as Force Multipliers
  - Use LLMs for repetitive tasks, rapid prototyping, exploring multiple approaches, data extraction and brainstorming, providing feedback.
  - Explore prompting techniques, integrate LLMs into their workflows, and develop strategies for validating and refining LLM-generated code
- Focusing on higher-level skills that llms struggle with
  - Systems Thinking and Architecture: code readability, extensibility, testability, and maintainability
  - Problem Solving and Critical Thinking: define problems clearly, break them down into manageable parts, and reason through complex scenarios. LLMs produce plausibly incorrect code.
  - Communication and Collaboration
  - Domain Expertise
- Exploring Adjacent Roles: product management, technical leadership, or consulting. Involve more interaction with clients and stakeholders.
- Developing “Evergreen” Skills: debugging, system administration, and security. Or outside of software engineering, such as trades or other hands-on vocations.
- Scepticism: LLMs may not reach a level of sophistication that would render their expertise obsolete. Complex problems, understanding context, and producing high-quality, maintainable code.
Examples of agentic AI
- Text-to-SQL automated business analyst: A system that generates SQL queries from natural language, handles errors, creates visualizations, and includes a FAQ component. The author calls it “constrained agentic AI.”
- Data source querying system: A bot that queries multiple SQL and API data sources, selecting tools and reformulating tasks as needed.
- Cursor (agentic mode): An LLM-powered VS Code fork that chains together various LLM capabilities (code generation, applying changes, linting suggestions, terminal commands, codebase RAG) to reduce user prompts.
- Vulnerability finding system: A system that uses LLM agents to discover novel vulnerabilities in open-source web applications. The agents leave traces of their actions.
- Marketing strategy generation system: A system using approximately 60 agents to generate marketing strategies.
- Restaurant finder: A system that searches for restaurants based on dietary preferences and group size, and downloads social media information.
- Proofreading and editing of transcripts: LLM agents apply specific customer requirements to transcripts after human editing.
- Meeting notes and action items generator: A system that generates meeting notes and action items.
- O’Reilly auto parts customer service agent: An agent demonstrated using RAG.
- UI enhancement agent: An agent that added features like language locales and dark mode to a UI.

Things I Learned - 08 Dec 2024

Sun, 08 Dec 2024 00:00:00 +0000

This week, I learned:

ChatGPT uses several unusual unicode characters for citations. Ref
NumLock can be dangerous. An IT support team member took control of Radheya’s screen while debugging and had turned on NumLock. Radheya’s login failed after that. After 5 tries, he was locked out.
With LLMs, most architectural decisions are no longer one-way doors. Steve Yegge
The cost of intelligence is trending to zero. How do we plan for this? Logan Kilpatrick
- If you are not planning for the price of intelligence to go to zero, the next 3-5 years are going to incredibly disruptive to your business / life.
- The important but not stated caveat: consumer willingness to pay for AI is going to go up (a lot). It will be fascinating to watch consumer willingness, cost, and the amount of AI being used all move in different directions.
- Everyone building things with AI has an economic incentive to limit the amount of AI because of cost, which inherent limits the value prop. This will change as intelligence goes up and cost goes down.
- What this means is:
  - Admin automation: Administrative tasks vanish into background AI. Booking meetings, managing finances, or even planning family activities will require less thought.
  - Hyper-personalization: Individuals get tailor-made everything—from medical advice to product recommendations to daily schedules. Systems learn your quirks.
  - AI co-brains: AI co-worker “assistants support you at any moment. Productivity soars in knowledge work. “I’ll have my AI follow up becomes a normal response.
  - Humanity valued more: As AI handles rote tasks, humans move up the value chain, focusing on creativity, empathy, or the “last-mile decisions.
  - New business models:
    - AI experts as a service
    - Embedded AI Solutions
    - AI micro-services for smart-calls
    - Distributed AI
Arena Hard is a set of hard prompts to test LLMs. Here is the code and evaluation
LLMs can detect clear outliers easily. PROMPT: Which is the outlier in this dataset: (1,7), (2,7), (3,6), (4,6), (5,5), (6,1), (7,5), (8,3), (9,1), (10,1) (ANS: (6,1))
- 🟢 GPT-4o on ChatGPT gets this. GPT-4o Mini on the API gets it too.
- 🟢 Gemini Pro, Flash, Flash 8b gets this right straight away, without even thinking.
- 🟢 Claude 3.5 Sonnet, Claude 3 Haiku, Claude 3.5 Haiku get it on LLM Foundry. 🔴 Claude.ai, where it visualizes it and gets it wrong.
- 🟢 Nova Micro, Lite, and Pro get it right.
- 🟢 Llama 3.1 70b gets it right. 🔴 Llama 3.2 8b gets it wrong. Llama 3.2 70b, Llama 3.1 8b enter repetition.
To install Docker on Windows without admin privileges, use net localgroup docker-users "your-user-id" /ADD
A non-administrator in a Google Groups domain can only add 200 emails to a group from the UI directly without invitation at a time. The only programmatic way to add users is for an administrator to add them. Even apps that use the Google Admin SDK need an admin to log in to access the relevant API.
Take 100% of your work, including complex, multi step processes and put it into an LLM. It might fail at some but you will discover the limitations.
I emailed Straive employees about their use of LLM Foundry - the internal LLM portal. I picked ~500 non-users from teams that otherwise have high (30%+) usage.
- Reasons they didn’t use it were:
  - 40% had not heard of it.
  - 40% were unclear of the benefits
  - 20% didn’t have time
- 45% feel they don’t have enough information and training to use it
- Some feedback
  - Sharing training videos will help
  - Live training sessions that allows for Q&A will help
  - Developers prefer detailed documentation
  - The same prompt gives different results
- Possible solution: Email non-users introducing the tool and sharing a quick 15-minute tutorial and a 1-page quick start.
My notes on the Amazon Nova models. More on Hacker News
- Nova Micro (3.75c/MTok) has the same cost as Gemini 1.5 Flash 8b but does not support images or documents.
- Nova Lite (6c/MTok) has about the same cost as Gemini 1.5 Flash 002 and supports images and documents (but not audio or video). It may be a good alternative. But GPT-4o mini, which is 2.5X costlier, is much better. (It partly passes the Gr brx vshdn Fdhvdu flskhu? test which Nova Lite fails.)
- Nova Pro (80c/MTok) is cheaper than Gemini 1.5 Pro and a lot cheaper than GPT 4o, but does not match their quality.
LLMs are great at convincing you of wrong things. A danger and something to be wary of. Ethan Mollick
Fish eye text summary is a great way to read text while summarizing context. Amelia Wattenberger
DuckDB’s JavaScript API is still under development. For example, JSON, ARRAY are not insertable. Plus, re-creating persistent HNSW indices crashes.
What’s a good text splitter library to use in JS?
- LangChain: If you use it, use it with a simple wrapper decoupled from the implementation (e.g. your own parameters) that you can replace later.
  - Popular
  - Fit-for-purpose. MarkdownTextSplitter which inherits from RecursiveCharacterTextSplitter is what’s needed in most cases.
  - Unstable
  - Poorly maintained Python docs indicate version 0.0 but it is in 0.1
  - Under-maintained Last update was 3 months ago, 13 Sep 2024
- LlamaIndex:
  - Popular
  - Not an ideal fit. MarkdownNodeParser does not support chunk size. SentenceWindowNodeParser does not capture Markdown headings.

Things I Learned - 24 Nov 2024

Sun, 24 Nov 2024 00:00:00 +0000

This week, I learned:

OpenAI lets you download GPT instructions and execute arbitrary code in their containerized environment. This is not a bug. Ref
BM25 works as follows: Ref
- For each query term in the query, sum up the product of:
  - Inverse document frequency = LN(% of docs without the query term + 1) – with a small tweak
  - Term frequency = freq / (freq + k) – where k is usually between 1.2 to 2. Returns 0-1 with diminishing frequency benefit
    - k is multiplied by Document length normalization = 1 - b(1- DocLength/AvgDocLength). Longer documents have larger k, dampening frequency benefits.
- Some implications:
  - The actual BM25 score has no meaning. It’s just useful for ordering
  - BM25 scores for 2 queries can be compared ONLY IF the document sets don’t change
A list of Markdown to Website converters on this thread:
- Jekyll - Ruby - 2008
- MkDocs - Python - 2014
- GitBook - JavaScript (Node.js) - 2014
- MkDocs Material - Python (MkDocs-based) - 2016
- Docsify - JavaScript - 2016
- MdBook - Rust - 2017
- Antora - JavaScript (Node.js) - 2017
- Docusaurus - JavaScript (React) - 2017
- JupyterBook - Python - 2019
- Keenwrite - Java - ~2019
- Honkit - JavaScript (GitBook fork) - 2019
- Nextra - JavaScript (Next.js) - 2020
- Astro - JavaScript/TypeScript - 2021
- Hugo Book - Go (Hugo-based) - ~2020
- Clowncar - JavaScript/Node.js - ~2021
- Quarto - R and Python - 2022
- Starlight - JavaScript/TypeScript - 2023
DuckDB has an LLMs.txt. Today, 38 repos on GitHub support it
When identifying LLM use cases, it helps to tell LLMs what they can do. I use one or more of a list like below:
- Core capabilities:
  - Text Generation: Produce coherent and contextually relevant text across various domains.
  - Image Generation: Create realistic images that match the style and content of a given reference image.
  - Text to Speech: Convert text into natural-sounding speech with appropriate intonation and rhythm.
  - Speech to Text: Transcribe and interpret spoken language.
  - Vision: Analyze and describe visual content from images.
  - Video Analysis: Summarize and extract information from video content.
  - Text to Video: Generate realistic (and surrealistic) videos from text descriptions.
  - Function Calling: Execute predefined functions or access external tools to perform specific tasks.
  - Structured Output: Generate structured outputs like JSON, XML, HTML, YAML, DSLs, etc.
  - Tool Use: Utilize external applications or APIs to enhance functionality.
  - Code Generation: Write and debug code snippets in various programming languages.
- Cross-domain use cases:
  - Summarization: Understand and condense lengthy documents into concise summaries.
  - Translation: Convert text between multiple languages with high accuracy.
  - Question Answering: Provide precise answers to user queries based on provided information.
  - Reasoning and Planning: Solve complex problems and develop step-by-step plans.
  - Personalization: Tailor responses based on user preferences and historical interactions.
  - Dialogue Management: Engage in context-aware, multi-turn conversations.
  - Data Analysis: Interpret and generate insights from structured data.
  - Content Moderation: Identify and filter inappropriate or harmful content.
  - Sentiment Analysis: Detect and interpret emotions and opinions in text.
  - Robotics Integration: Interface with robotic systems for control and decision-making.
  - Knowledge Retrieval: Access and present information from vast datasets or knowledge bases.
  - Creative Writing: Generate poetry, stories, and other creative content.
  - Educational Assistance: Provide explanations and tutoring across various subjects.
  - Ethical Reasoning: Assess scenarios for ethical considerations and implications.
  - Accessibility Support: Assist users with disabilities through tailored interactions.
  - Simulation and Modeling: Create predictive models and simulate scenarios.
- Domain-specific use cases:
  - Legal and Medical Assistance: Offer information and guidance within legal and medical domains.
  - Gaming: Generate narratives, dialogues, and scenarios for interactive entertainment.
  - Scientific Research: Aid in literature reviews, hypothesis generation, and data interpretation.
  - Financial Analysis: Analyze market trends and provide investment insights.
  - Cultural Competence: Understand and respect diverse cultural contexts in interactions.
  - Security Applications: Detect and respond to potential cybersecurity threats.
  - Environmental Monitoring: Analyze data related to environmental changes and sustainability.
  - Healthcare Support: Assist in patient monitoring, diagnostics, and personalized treatment plans.
  - Supply Chain Optimization: Enhance logistics and inventory management through predictive analysis.
  - Customer Service: Provide automated support and resolve customer inquiries.
  - Market Research: Analyze consumer behavior and market trends for business insights.
  - Content Creation: Generate articles, blogs, and marketing materials.
  - Virtual Assistance: Manage schedules, reminders, and personal tasks.
  - Social Media Management: Craft posts and engage with audiences across platforms.
  - Human Resources: Assist in recruitment, training, and employee engagement strategies.
  - Event Planning: Organize and coordinate events, including logistics and communication.
  - Travel Planning: Provide itineraries, booking assistance, and destination information.
  - Real Estate: Analyze property markets and assist in buying or selling decisions.
  - Agriculture: Monitor crop health and optimize farming practices through data analysis.
  - Energy Management: Optimize energy consumption and monitor renewable energy sources.
  - Transportation: Enhance route planning and traffic management systems.
  - Urban Planning: Assist in designing sustainable and efficient urban infrastructures.
  - Disaster Response: Provide real-time information and coordination during emergencies.
  - Public Policy: Analyze data to inform policy decisions and predict societal impacts.
  - Art and Design: Generate visual art concepts and assist in creative design processes.
  - Music Composition: Create original music pieces and assist in songwriting.
  - Language Learning: Facilitate language acquisition through interactive exercises and feedback.
  - Historical Analysis: Interpret historical data and provide insights into past events.
  - Philanthropy: Identify charitable opportunities and assess the impact of donations.
  - Sports Analytics: Analyze player performance and game strategies.
  - Fashion: Predict trends and assist in clothing design and merchandising.
  - Culinary Arts: Generate recipes and provide cooking guidance.
  - Astronomy: Analyze celestial data and assist in space exploration research.
  - Psychology: Offer insights into human behavior and mental health support.
  - Linguistics: Analyze language patterns and assist in translation studies.
  - Archaeology: Assist in artifact analysis and historical site interpretations.
  - Literature Analysis: Interpret literary works and provide critical analyses.
  - Philosophy: Engage in discussions on ethical dilemmas and existential questions.
  - Mathematics: Solve complex equations and assist in theoretical research.
  - Physics: Model physical phenomena and assist in experimental design.
  - Chemistry: Analyze chemical compounds and predict reactions.
  - Biology: Assist in genetic research and ecological studies.
  - Geology: Analyze geological data and assist in natural resource exploration.
  - Meteorology: Predict weather patterns and analyze climate data.
  - Oceanography: Study marine ecosystems and assist in ocean exploration.
  - Anthropology: Analyze cultural data and assist in ethnographic research.
Style of writing impacts output style a lot. E.g. Adding an evil laugh makes Claude more creative. Ethan Mollick
For good structured mode output, we need good prompting.
- Mentioning examples and schema and “JSON” helps. When providing examples, using (user, assistant) message pairs helps (I think it’s because it’s easier for the LLM to parse).
- Using a {reasoning, answer} schema (with reasoning first) helps. Make reasoning concise and relevant Ref Arxiv
- We already know code in JSON is not a great idea. Ref
Just adding 3 real examples and regurgitation helped GPT 4o play chess much better. Both techniques may have more general use in prompting. Simon Willison
With Deno 2.0, the same .js file can run in Node.js as well as Deno. Example
jspm lets you generate import maps against any CDN.
You can click on htop columns on the terminal to sort by that column! Mouse events work on command line apps. Julia Evans
Alt Text will very likely be a browser feature. It’s important for the Alt text to flow as part of the content when listening to the page. Perhaps even become a part of the browser APIs like speechRecognition.
Langchain suggests multiple levels of agentic behaviour. LLM Call < LLM Chain < LLM Rounter < State Machine < Autonomous Langchain
A HTML quine: A page that, when rendered as HTML, shows the HTML source code of the page!
You can enable syntax highlighting just using fonts. Ref
HTML is all you need shows examples of using HTML for notebooks instead of Jupyter, Observable, etc.
Straive evaluated Gemini 1.5 Flash 002 and GPT 4o Mini for translation.
- Portugese: Flash is better than GPT 4o Mini. BLEU Word Overlap is 65.5% > 64.6% and METEOR (Semantic) is 84.9% > 78.9%
- Mandarin: Flash is better than GPT 4o Mini. BLEU Word Overlap is 25.0% > 15.9% and METEOR (Semantic) is 54.7% > 51.1%
The problem with Accept headers is that you can’t link to them. Simon Willison
Recraft v3 supports vector (SVG) generation Simon Willison. The output is 100% <path> elements (even for text). You get 50 free credits daily. Creating 1 image is ~2 credits. The API costs $1 per 1K credits. Some things I can create with it are:
- Base data visualizations that I can animate with code
- Icons in a specific style
- Comic strips
- Explainers for talks or student material
- Featured images for blog posts
- Architecture diagrams?

Things I Learned - 13 Oct 2024

Sun, 13 Oct 2024 00:00:00 +0000

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

Things I Learned - 06 Oct 2024

Sun, 06 Oct 2024 00:00:00 +0000

This week, I learned:

ffmpeg on WASM works but is unstable and hard to use.
- You can’t use it in a CDN without CORS issues, since it loads ffmpeg-core via a worker.
- It often runs into buffer allocation issues.
Exotel and Plivo provide voice & SMS services in India (like Twilio). Plivo is more customer friendly.
Uber’s H3, Google’s S2, and GeoHash are geocoding systems.
- H3 offers uniform cell sizes and better distance measurement
- S2 offers higher precision (factoring in Earth’s curvature) for exact location matches
- GeoHash is the simplest
There’s a movement towards embeddable databases on the cloud.
- MotherDuck is hosted DuckDB.
- Turso is hosted SQLite (with local sync, multi-tenant)
- StarBase DB is SQLite with an API on top of Cloudflare Durable Objects.
Software 2.0 by Andrej Karpathy.
- This is fundamentally altering the programming paradigm by which we iterate on our software, as the teams split in two:
  - the 2.0 programmers (data labelers) edit and grow the datasets, while
  - a few 1.0 programmers maintain and iterate on the surrounding training code infrastructure, analytics, visualizations and labeling interfaces.
- Adaptive UI ideas:
  - Adaptive Fields: Show only required fields based on what the user field so far.
  - Smart Inputs: Dropdowns and auto-complete based on user’s context.
  - Smart Themes: Change font size, contrast, theme guessing the user’s age and preferences.
  - Dynamic Menus: Show what they might need to do next. Like Nokia’s right button, but using LLMs.
  - Smart Tooltips: Check what the user’s doing (delays, confusions, previous clicks, current actions) and show relevant tips.
  - Personalized Layout: Show only the relevant sections of the app. E.g. based on what they’re doing.
  - Smart Charts: Create the right chart that solve the user’s question.
- Adaptive Back-end
  - Dynamic APIs: Create endpoints on the fly based on user needs
  - Dynamic Indexing: Create & update indices on the fly based on user needs
  - Dynamic Schema: Create & update schema on the fly based on user needs
  - Dynamic Migration: Migrate to a new database or OS or language as required
  - Dynamic Queries: Create SQL/NoSQL queries to solve the user problem
  - Dynamic RBAC: Figure out who needs permissions and why. Add OR REMOVE access as required
  - Dynamic Logging. Log what’s required. Explain why it’s logged and what’s happening. Fix code that raised the error
  - Dynamic Caching. Cache what’s likely to be required. Evict what may not be required. Figure out cache keys.
Aider LLM Leaderboards show which LLMs code better. As of now,
- o1-preview > claude-3.5 sonnet on code editing
- claude-3-opus > claude-3.5-sonnet on code refactoring
- deepseek-coder-v
- gpt-4o-mini sucks.
Jaro-Winkler Distance is a string matching algorithm that weights the start of a string higher.
Passing the feed of the following to NotebookLLM is a good way to get caught up with news and summaries.
- A blog / WhatsApp group (e.g. The Generative AI Group, Sithamalli, etc.)
- A Google Group / mailing list (e.g. genainews, datameet)
- YouTube channels (e.g. Vertiasium, GitHub)
- Hacker News top stories
- Research papers
- Emails (skipping marketing emails)
OpenAI Evals and Distillation has a clever design. They just convert filtered history to .JSONL files that can be an input to either.
Speak is a language learning app based on OpenAI’s Realtime API.
OpenAI’s Realtime API can be used in a text-to-text chat mode without needing to send the entire context. If the pricing works out right, this can be far cheaper than sending the entire conversation context. Ref
Matching addresses with just embeddings works well. Combine it with simple hard rules. Ref
OpenAI’s prompt caching works for images too – both linked and embedded
Quotes on Graph RAG from a Generative AI WhatsApp Group.
- “Damn so literally nobody uses Graph RAG yet. Good to know.” ~Sumba
- “A big four consulting firm uses GraphRAG to retrieve related documents and excerpts from governance and compliance docs.” ~Vinayak Hegde (Microsoft)
- “Graph RAG is expensive and unnecessary in most of the cases.” ~Utkarsh Saxena
ChatGPT’s advanced mode includes: “…you can use various regional accents and dialects.” Ref Source
- But the API can “laugh, whisper, and adhere to tone direction.” Ref
Hume API (INR 6/min) is far cheaper than OpenAI’s real-time chat (6c/min input + 24c/min output)
Devika is an open-source clone of Devin.
DuckDB runs inside Pyodide
Hungarian Jews have genetic diseases that increase their IQ. Gaucher’s disease, Torsion dystonia.
People don’t like hard stuff like maths or science, so richer societies have fewer scientists
Ethan Mollick feels Claude 3.5 Sonnet is better at style and critiquing blog posts than OpenAI’s o1 (which is better at reasoning.)
News is going to be crazily disrupted again with voice mode. I can just listen to the topic I want
In Singapore Airlines,
- You can’t wear your seatbelt loose
- You have to keep the laptop in the pocket in front, not on your lap, during takeoff
- You can’t charge during takeoff
- They verify if you ask for a veg meal and place a sticker on your seat
Coders are more likely to edit LLM code. Non-coders don’t have that bad habit.
- Vaishnavi and Ranjeet edited code
- Indal and Koustav didn’t
Coders are likely to get more out of an LLM because they know what it can do. But some non-coders will get more out of an LLM because they don’t know what it can’t do.
- E.g. Indal trying for a confetti animation, which is hard but do-able
“You have to put in a lot of work to become productive at AI coding.” Simon Willison

Embeddings in DuckDB

Sun, 16 Jun 2024 13:06:44 +0000

This article on Using DuckDB for Embeddings and Vector Search by Sören Brunk shows a number of DuckDB features I wasn’t aware of.

DuckDB can read directly from Huggingface datasets
DuckDB can read just the parts of a .parquet file it needs, even over HTTP
DuckDB lets you write custom functions in Python
DuckDB now has a vector similarity search extension

I’ve recently become a DuckDB fan and continue to be impressed.

Things I Learned - 31 Mar 2024

Sun, 31 Mar 2024 00:00:00 +0000

This week, I learned:

sqlite-schema-diagram generates schemas for SQLite databases using Graphviz
TechEmpower web server benchmarks place Rust servers on top
browser.new is a good example of a browser agent. It slowly but independently does a good job of achieving the result. Example: What crew is common in Ingrid Bergman - Cary Grant films?
twinny is an open source VC Code Copilot alternative.
typesense supports embeddings natively.
Binary embeddings are good enough. Cohere releases binary embeddings.
Extract.langchain.com is a poor early interface to featurize unstructured.io
Hume.ai offers voice emotion API and emotion-based conversational responses. An empathic AI.
Rust is non-trivial. Inspired by We are under DDoS attack and we do nothing, I “wrote” a small binary that serves a parquet file as JSON. It failed and I couldn’t fix it.
spleeter is a better alternative to demucs. Splits audio into
pyannote-audio does speaker diarization
uvicorn is faster than hypercorn but hypercorn supports HTTP/2 and HTTP/3. FastAPI with uvicorn is reasonably fast.
Representational engineering lets you control LLM output based on preference on the fly.
When I set up a training:
- On inviting for DuckDB workshop on Sun evening, Gramener starts accepting immediately, Straive doesn’t.
- Straive has high spread of joining time. When joining Gitlab Pipelines Workshop, Straive starts meeting (e.g. Premlal) many minutes early. Gramener floods in (due to alert). Straive streams in slowly.
- Gitlab Pipelines Workshop acceptances: Gramener 47, Straive 100

Things I Learned - 17 Mar 2024

Sun, 17 Mar 2024 00:00:00 +0000

This week, I learned:

DuckDB is 2-10 times faster than Pandas. ClickHouse is supposedly faster but doesn’t run on Windows.
Claude 3 Haiku input costs is $0.25/MTok. That’s half the GPT-3.5 cost. If it’s of comparable quality, it’s worth switching. But Claude 3 Opus is comparable to GPT-4 and twice the cost, so not worth it.
Tavily is a search API for LLMs
Interesting model garden models
There are sites you TRULY cannot scrape even in the browser because of the isTrusted read-only property of events that you can never set to true. Oracle Service Cloud checks for isTrusted in mouse actions.