#future

• When spelling became automated, we stopped cared about spelling for its own sake
• When GPS became ubiquitous, we stopped learning geography. We just want to get there
• When photography became unlimited, most captured moments. Few perfected shots
• When fitness tracking became easy, most just track. Few exercise more

• Delegation: context engineering agents for success; parallelizing.
• Verification: efficiently reviewing and testing code/output; setting stop-points.
• Expanding scope: instead of time saved as the metric.
• Education: teaching AI-based coding, debugging, reviewing/testing.
• Product management: combining requirements + UI design + architecture + engineering + deployment.
• Cross-discipline: blending code with design, governance, finance, marketing, ... ("computational creators").

• Include a “liar agent” seeded with plausible-but-wrong policy. With limited probes, learners must detect and neutralize it; logs teach verification triggers.

const branch = await $`git branch --show-current`;
await $`dep deploy --branch=${branch}`;

• ChatGPT suggested increasing observability based on code reviews. I'm including it in my weekly codecast.
• ChatGPT suggested defining closures inmeetings. I'mn now discussing objectives at meeting starts and effectiveness at the end.

• Bias detection on declined applications tied to regulatory scrutiny (concrete, high-value, demoable on synthetic data).
  • Compute fairness metrics (selection rate, demographic parity diff, equalized odds) by protected_group; show SHAP top features per group; propose three policy levers to reduce disparity.
• Scenario “adverse action” simulator (loss/collections outcomes vs policy levers).
  • Inputs: threshold, line increase/decrease, collections intensity; Outputs: approval rate, ECL, vintage loss, roll-rate curves; include confidence bands.
• Self-learning Prompt → SQL log
  • Capture each natural-language question + generated SQL + exec time + rowcount → audit table; proves control & learning loop.

• Rewrite notes replacing "must" with "might" or "will" with "won't". See what reasoning is fragile & falls apart

• Companies-of-one will grow. Sole founder can handle support functions.
• Specialists will generalize. Consultants will code. Marketers will design.
• Wages will compress. Seniors will earn less as juniors can do more.
• Layers will compress. Organizations need fewer hierarchies as 1 person can do more.
• Shadow apps will grow. Anyone can code. Users build apps with prompts, sheets, agents, outside of IT SDLC. Like Excel sheets.
• Governance will grow. Non-experts are acting like experts. Validation is more important.
• Uneconomical apps will thrive. 1:1 tutoring. Continous decision making or A/B testing.
• Leaders will convince better. Persuasion scales.
• Brand (authenticity, trust, skill), Channel (distribution, audience) and Data are primary differentiators.

• Policy-as-code app. Checklist from doc. Apply checklist to data / doc inputs.
• Code similarity checker library based on TDS Project evals.

• Estimating tool capability & task allocation
• Task breakdown
• Spec-ing: which of user personas, user-journey maps, wireframes, technical architecture, psuedo-code
• Standards: tech stack, tools, linters, security, doc standards
• Git versioning & collaboration
• Code review. (Using AI.) Providing feedback. Modularity, naming, ...
• Automated validation
• Post-mortem. Learning from errors and successes, choices LLM made

• Delete-Driven Development. Keep deleting code until it fails, then rebuild minimally.
• No-Editor Coding. Pure prompting to build, test, and deploy the app.
• Audience-Sourced Prompt ETF. Collect micro-prompts from audience. Run in parallel. Evaluate against tests. Create index of top prompts. Compare with expert.

• 1/2: Become an API to LLMs. Create an API for LLMs to consult you. Answer based on past chats, notes, memory, etc.!
• 2/2: From a prompt, have an LLM build synthetic data, the OpenAPI spec, a CloudFlare web app, deploys it, and update your portfolio -- automatically!

• ⭐ "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.

• If there's high trust (i.e. the other person trusts you):
  • Important topic/decision: Be candid
  • Unimportant: Follow culture (e.g. in Japan, you'd be polite; in The Netherlands, you'd be candid)
• Low trust:
  • Important: Earn trust first
  • Unimportant: Be polite

• Claude for high precision / low hallucination under ambiguity. Add fallback logic for abstentions.
• GPT for aggressive answering and you’ll post‑filter. Wrap with regex/diff guards.
• Gemini / Qwen for cheap-ish long context but can tolerate noise? Enforce sanity checks and chunk shorter.

• Learn things that might grow in importance, like:
  • Data modeling
  • APIs
  • Code reviews
  • Drawing and 3D modeling
  • Narrative storytelling
  • Design
  • Movie making
  • Statistics
  • Sceptical fact checking
  • Continuous AI auditing e.g. awesome-continous-ai or automated-auditing
  • Zero knowledge proofs
  • Homomorphic encryption
  • Privacy-preserving computation
  • Fingerprinting and watermarking
  • Governance frameworks
  • Ethics and AI dilemmas
  • Negotiation
  • Change management
  • Remote working, management, hiring
  • Creating attention scarcity
  • Local cultures
• Work with people of growing importance
  • People designing products in regulated industries
  • Cross domain experts
  • Art developers, game makers, designers
  • System thinkers. Economists, ecologists, system planners. People who look for second order effects.
• Live in cities that might play a bigger role in the future
  • Cities like Singapore and learn how it builds civics trust, creates digital IDs.
  • Cities like Bangalore and Hyderabad and learn how they grow tech talent
  • Creative cities like Paris, Seoul, Mexico City, Berlin, etc. on sabbaticals to taste hubs
• Try to:
  • Build auditing credentials and IP
  • Audit your calendar for what AI can do. Have it interview you
  • Practice sceptical fact checking and audit

• Increase clarity, providing step-by-step instructions.
• Resolve conflicting instructions.
• Expand instructions to cover all scenarios and edge cases.

• TechCrunch interviewer: Anthropic released Claude Opus 4 thought it blackmailed people. Is Anthropic is becoming less safety conscious?
• Kaplan: We have very strong testing. So we're more more likely to spot AI dangers early. We share such reports to set higher standards for transparency.

• AI is like electricity. It's hard to define what is good for because it is good for so many things, most of them new that never existed before
• If experimentation is cheap, it makes sense to run far more experiments. Rather than think hard about what to prototype, explore how to build many diverse prototypes.
• Prototyping is now very fast but other steps like reliable evaluations for deployment still take time. But the speed of prototyping is putting pressure on other parts of the organization to go faster.
• While large language models and applications were serving human needs so far, increasingly they will serve the needs of AI and other tools.
• Since unstructured data is now more valuable, there will be a growth in data engineering on unstructured data.

• Sam Altman. Chase $0B ideas, not $0M ones. Weird + right > safe + crowded
• Gary Tan. Agency scales. Tools change, people/mindset don’t.
• Andrej Karpathy.
  • Instead of LLM memory to store facts, edit system prompt with general strategies, like the LLM writing a book for itself on how to solve problems.
  • Autonomy slider. Let user pick how far LLM acts by itself. Like the Tesla autopilot levels.
  • Make evals EASY and FAST for humans.

• Practice Prompt Reviews. Check if each prompt has clarity, context, and verifiability. Also, see how others would ask this. Internalize patterns

• Emerging economies trust and use AI more. It's an opportunity to leapfrog.
• 26% of students use AI daily (vs 17% employees). Efficiency is the main benefit.

• Make quick edits to repos that are not on your system (e.g. other people's repos, or via others' machines.)
• Run public workshops with a full coding environment.
• Give students assignments that have dependencies pre-installed.
• Collaborate on a work-in-progress codebase with my team.
• Share POCs with clients or public allowing them to edit it.
• Allow teams to install remote AI code extensions (e.g. Windsurf) that may be blocked inside the corporate firewall?

• A path marking app that uses cameras to draw a heatmap of people's walking paths. Popular tracks are redder.
• Using drones for machine inspection.
• Portable immigration devices that let you scan passports, face recognition, fingerprint, mic/speakers, etc.
• Using accelerometer to detect unsafe gait and improve walking habits.
• UImagine: a web app builder. Interestingly, they used Webcontainers to run Node in the browser!
• Training a drone to follow a person
• Credibility detection via micro facial expressions
• PitchMe: providing real-time feedback to pitches / presentations
• Zetesis: a platform for people to ask questions during a lecture or meeting (independent of Zoom, Meet, etc.)
• Tinyeqn: helps grade student assignments

• Form close friends. Care, ask, and share.
• Become a do-er. Stay mindful of the problem or opportunity you're deferring.

This ability to define your desired outcome in crisp, complete terms is one of the most important superpowers of the AI era.

• 2025: AI can act as autonomous agents, like Glean, Devin, Operator.
  • turn bullet points into emails
  • take instructions via Slack or Teams and make substantial code changes on their own
  • spend half an hour scouring the Internet to answer your question
• 2026:
  • automating AI R&D is the biggest enabler for AI Labs
  • job market for junior software engineers is in turmoil
  • people who know how to manage and quality-control teams of AIs are making a killing
• 2027:
  • potential demand for ~20,000 FTEs solving long-horizon tasks to train AI
  • every researcher/coder becomes the manager of an AI team
  • hiring new programmers has nearly stopped, but there’s never been a better time to be a consultant on integrating AI into your business

• Extending that thought, ANYONE who leverages assets through holistic thinking, becomes FAR more effective.

• Lead by example
• Fairness and empathy
• Clear, consistent communication
• Delegation and empowerment
• Strategic planning and foresight
• Consistent rule enforcement
• Rewarding merit
• Leadership by virtue and character

• Automate changelogs for your codebases. Convert past commits into attractive release notes automatically
• AI is going to be the consumer of many tools and logs. Build converters for these
• Speed of validation such as linting, testing, etc. will allow LLMs to iterate faster and WILL become more important

• Student: Create study plan based on course and schedule
• Student: Focus on what you need to learn more
• Student: Align with your study style and pace
• Teacher: Grading MCQs
• Teacher: Writing conceptual guides
• "New collar workers" was coined by Ginny Rometty
• Embed tutor or document in video and ask for clarification! This is a new embedded interface.

• "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."

• Claude Artifacts Prompt
• Val.Townie system prompt. Good example of how to create
• Cursor editing
• Cursor debugging
• Cursor conversation

• Makes you more ambitious
• Lets you code without stress. (Just pass it the error and have it fix it. Or find another approach)
• Is unlimited. You can run dozens of agents in parallel

• Cognition
• Factory
• Codegen

• Flux for open-weights image generation
• Runway Gen 3 for video generation
• Suno for music generation

• Defensive UX: Microsoft, Google, and Apple have guidelines for Human-AI interactions
• Collect feedback: Explicit and implicit

• solve your own problem. Don't solve other people's problems. This helps you pick what you're good at
• affordable losses. Make sure you survive
• borrow others' spares. spare time, scrap data, anything others don't use. If you can monetize it, you can pay them back
• focus on the controllable. Ignore what's outside your control don't even waste time on it

• Write tests that make people think! Like DuckDB workshop
• Share a list of exercises that people can explore

• DISCOVER mental models. Review beliefs diary.
• DIVERSIFY. Find INFLUENTIAL (not dull) people with different backgrounds.
• Experiment! New environment, approach, perspective
• Be open. Change your mind.
• APPLY. Practice regularly

• "Pre-mortem" is an analysis at the beginning of how a project failed. Then avoid that
• "Red team" or "Black hat" are designated to contradict.

• What I'd like in my obituary: Anand was happiness. A guru. Generous.
• To get what we seek we must deserve this. Build, measure, learn
• If you did the same thing daily for 50 years, would it be a great thing? If yes, do it. If not, stop. Do this in daily retrospectives
• My new role should be productivity through technology innovation. That may mean a CTO role. But be specific otherwise no one will understand it

• Just having AI is no longer a differentiator
• AI is part of life, not just work
• Explore quickdrop from Stability for Maruti
• Explore Codium VS Code plugin and Continue.dev
• Hybrid systems that combine stats, ML, DL and AI models will grow
• AGI and AutoGPT resurgence
• RAG will continue to be a focus
• GPT4 will be beaten by open source models. Special purpose models beat it already
• Self hosted and cloud hosted models will grow for security
• Small language models will grow
• Productivity will be enhanced rather than replaced
• Multi modal models will grow
• Cost efficiency will grow in focus

• GitOps
• Internal Developer Platform
• Graph Data Science
• Open Source Program Office
• Value Stream Management Platforms

• Solving complex mathematical problems
• Advanced scientific discovery (quantum computing, nanotechnology, biotechnology)
• Ultra-precise predictive modeling in complex systems (climate, economics, social dynamics)
• Optimizing global systems at high precision (logistics, traffic, energy distribution, resource allocation)
• Universal translation (unknown languages, animal communication, extraterrestrial signals)
• Deep medical personalization: individualized medical treatments from genetics, environment, and lifestyle
• Create new materials: Designing materials or chemicals with specific properties
• Complex system integration: combining AI, bio tech, nano tech in new ways
• Philosophical insights: new perspectives or solutions to age-old philosophical dilemmas
• Space exploration and colonization
• Predicting natural disasters
• Customized education at scale

• Collaborative problem solving
• Creative collaboration
• Decision support
• Personalized education
• Establishing ethical and safety protocols
• Recreational and leisure activities

• "Every text box on the internet will get an LLM"
• "Infinite interns"
• "Every UNIX function has become a company." "Every ChatGPT suggestion..."

• Singapore developers the Sea Lion 7b model
• Try VLLM with AWQ format. Can do batch inferencing. Needs a good GPU
• Amex prediction whether they can pay back in 1 year or 18 months. That choice is a business decision. In real time. Precompute individual score and use it as input to another model. Model must be explainable by regulation. Creates decision tree models therefore. Compliance team must agree if I can use a feature. Can't use gender. Age (in US, Canada);- high age is more risk. Can't use edu level in the US.
• Capture information from camera and use LLMs. Like traffic cameras mapping. Explore GIS from video cameras
• Grab tracks road closures and road accidents and whether a cycle can go on a road vs a bike vs a car
• All drivers have a front facing camera
• Drivers report road accidents by pressing a button
• Amex prices individual loans when selling to a collection agency
• buy a bike head camera!
• Playwright is a browser-based test framework. Supports recording.

• Bigger models (70b) are much better at learning from few-shot examples. They really learn.
• Bigger models will keep getting better!
• Chain of Thought prompting is a way of providing more compute to complex problems that require more compute
• Models will show emergent (completely new) behaviors that can't be predicted from extrapolation. These may not be intentional.