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Essays

Theodore Dalrymple, Truth-Teller

Source: City Journal
Author: Rob Henderson (foreword to the 25th-anniversary edition of Life at the Bottom)
Date: May 8, 2026


Dalrymple's Central Thesis

Theodore Dalrymple worked as a doctor in British prisons and inner-city hospitals. He saw a poverty not just of money but of meaning, responsibility, and hope. His core argument: the underclass is shaped by ideas from elite intellectuals — mockery of family, self-restraint, and police, alongside celebration of "liberation." Welfare incentives alone don't explain the squalor; you need the ideological scaffolding peddled by intellectuals.

The "Luxury Belief" Class

Rob Henderson's signature concept:

  • Definition: Views that confer social status on the affluent at little cost to them but inflict real damage on the poor (e.g., denouncing marriage, effort, police).
  • Reverse Hypocrisy (JFK vs. Modern Elites):
  • JFK: Flawed in private (unfaithful, absent father) but preached public virtue.
  • Modern Elites: Live stable, disciplined private lives (marriage, hard work, family) but publicly dismiss these values as boring or oppressive.
  • Mechanism: The rich kid experiments with drugs and is fine. The poor kid hits meth and self-destructs. Both hear elite culture say "judge nothing."

Nonjudgmentalism's Toll

Refusing to say some actions are better than others destroys the poor who lack structure: - A woman dismissed advice to leave an abusive boyfriend as "sexist," returned, and was beaten again. - Academic criminologists declare criminals "addicted" to crime; inmates immediately adopt the excuse. - The pattern: deny personal choice, blame systemic forces, equate judgment with oppression.

The Behavioral Gap

Norms used to flatten the behavioral divide between rich and poor (marriage, work, lawfulness). As elites became insular and stopped modeling/enforcing norms, the gap widened massively.

"The choice is never between having an elite or not. It is between having an elite that accepts responsibility and provides leadership and an elite that does neither."

Key Anecdotes

  • Tyler (San Quentin): Friend from Henderson's past. Quit a job because he "didn't feel like it," crashed his motorcycle drunk, sentenced to 18 months. Upper-middle class excuses the choice as understandable — but studying for a Ph.D. or working 80-hour weeks "isn't fun" either.
  • Tesco Shoplifting (England): Two native-born boys stuffing pockets; white cashier bored. South Asian immigrant security guard intervenes. Boys shout "racism" and leave. Immigrants still believe work matters.
  • Cambridge Double Standard: A fellow doctoral student says publicly of a poor kid skipping class — "maybe it's good he didn't go" — but privately forces her own son to attend. "Our elites have isolated themselves from the world I grew up in, while paying lip service to inequality."
  • Doctors from Mumbai and Manila: Arrive brimming with sympathy for the British welfare state and the poor. Over time, they are shocked by the ingratitude and absence of basic decency from patients.

The Imperative

  • Elites must publicly preach the discipline that governs their private lives. Share values (marriage, family, responsibility) equally with wealth.
  • A young person from a deprived background should be held to higher standards, not lower.
  • The luxury belief class "walks the Fifties and talks the Sixties" — enjoying the warm glow of liberation while those at the bottom pay the price.

To Have Machines Make Math Proofs, Turn Them Into a Puzzle

Source: Quanta Magazine
Interview with: Marijn Heule (Carnegie Mellon University)
Date: November 10, 2025


Core Idea

Marijn Heule uses SAT (Satisfiability) — a symbolic AI technique that turns math problems into giant binary constraint puzzles (think Sudoku with millions of cells) — to solve long-standing open problems in pure mathematics. His track record includes the Empty Hexagon, Schur Number 5, and Keller's Conjecture (dimension 7), problems that resisted proof for 90+ years.

His vision is a three-part pipeline that could produce the first mathematical proof ever discovered by AI that humans cannot verify independently:

  1. LLM — Carves a big mathematical statement into smaller, plausible lemmas (high-level "big picture" work).
  2. SAT Solver — Proves or refutes each lemma, returning minimal counterexamples that act like a human learning from failure.
  3. Lean — A formal proof checker that glues all certified pieces together into a watertight whole.

"A SAT tool is not computing with zeros and ones. Instead, it is searching for a combination of them that satisfies all the constraints."


The "Understanding vs. Trust" Debate

The philosophical heart of the piece. Timothy Gowers (Fields Medalist) called Heule's Pythagorean triples proof "the most disgusting proof ever" because it offered no human-comprehensible insight.

Heule's counter: Understanding in mathematics is highly overrated. No single mathematician understands all of math — we rely on chains of trust. Automated reasoning can produce proofs more trustworthy than most pen-and-paper proofs.

"LLMs can do all of their bullshitting, but as soon as automated reasoning is able to say, 'OK, but this part is actually correct, and here's a proof,' this is actually more trustworthy than most of the pen-and-paper proofs out there."


AI as Co-Author, Not Replacement

Heule emphasizes humans remain essential — his successes came from collaborating with mathematicians who spent years developing conceptual insights, which he then encoded for the SAT solver. The future is LLMs helping more mathematicians learn to encode problems, not removing humans from the loop.

"The creative intuition, the conceptual reframing, that's still something people are uniquely good at. The magic comes from the collaboration."


Key Takeaways

SAT ≠ Neural Networks SAT is symbolic GOFAI — hard-coded logical rules, not pattern matching. It searches rather than computes.
Minimal counterexamples SAT solvers return small, interpretable refutations, providing insight into why a conjecture fails.
The bottleneck Encoding a math problem for SAT is currently an expert skill. Heule wants LLMs to automate this, opening the pipeline to more mathematicians.
Trust > Understanding Heule provocatively flips the traditional mathematical value system — certified correctness matters more than human-comprehensible narrative.
Future goal The first AI-discovered proof of a problem that no human can independently verify.

Why Birth Rates Are Falling Everywhere All at Once

Source: Financial Times — John Burn-Murdoch (Chief Data Reporter, The Big Read)
Date: 2026-05-16


TL;DR

In over two-thirds of the world's 195 countries, fertility is now below replacement rate. The primary driver has shifted: it's no longer that couples have fewer children — it's that fewer couples are forming at all. Housing costs explain up to half the decline in the US/UK, while smartphones and social media are the global accelerators, with birth rates plunging in country after country immediately following 4G rollout. The result is a K-shaped fertility collapse hitting the least educated hardest, with profound economic and political consequences.

Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?

Source: arXiv:2605.10848
Authors: Tz-Huan Hsu (UWaterloo), Jheng-Hong Yang (Stencilzeit), Jimmy Lin (UWaterloo)
Date: May 2026


Core Research Question

"Does a lexical retriever suffice as LLMs become more capable in an agentic loop?"

The paper argues that prior BM25 baselines scored low because of poor parameter configuration and shallow retrieval depth, not because lexical retrieval is fundamentally inadequate.

Answer: Yes. A well-configured lexical retriever (BM25 with k1=25, b=1, depth=1000) matches or beats dense-retriever baselines when paired with a capable LLM through a proper tool interface.


Key Results

System Accuracy Surfaced Recall Total Cost
Pi-Serini (gpt-5.5) 83.1% 94.7% $291.6
Prior dense baseline (gpt-5 + qwen3) 73.0% 79.0% $360.7
Pi-Serini (deepseek-v4-flash) 68.1% 94.5% $28.9
Pi-Serini (claude-opus-4.7) 69.8% $246.6

Tuning impact (default BM25 → tuned BM25): Accuracy +18.0%, surfaced recall +11.1%
Depth impact (k=5 → k=1000): Surfaced recall +25.3%
Cost reduction vs. dense baselines: 3.3×–10×


The Pi-Serini System

Pi-Serini is a deliberately minimal search agent that isolates the agent–retriever interaction. It has three components:

1. Retrieval Controller

The main abstraction layer between the LLM agent and the Anserini BM25 backend. It manages: - A cached, session-local ranking (up to 32 search IDs) - Paginated access to results - Spill files for large outputs

2. Tool Interface (Three Distinct Tools)

Decouples retrieval from context management:

  • search(query) — Issues a BM25 query, retrieves up to 1000 documents, caches the ranking, exposes only top 5 excerpts
  • read_search_results(search_id, offset, limit) — Browses the cached ranking without a new backend query
  • read_document(docid, offset, limit) — Reads a document in line-based chunks

3. Time-Budget Steering (Instead of Fixed Iterations)

  • Hard timeout: 300 seconds per query
  • Submit steer at 0.7T: Injects a message forcing the agent to stop using tools and answer immediately. All three tools are blocked afterwards.
  • Prevents runaway loops — prior work used ~74 tool calls/query; Pi-Serini uses ~15–24

Trajectory Logging (Four Document Sets)

To understand agent behavior, Pi-Serini tracks: - Dsurfaced — returned by search - Dpreviewed — excerpts shown via read_search_results - Dopened — full document read via read_document - Dcited — used in final answer


BM25 Tuning: The Critical Finding

The paper's central insight is that default BM25 parameters are optimized for passage retrieval (~100 words), not long documents (BrowseComp-Plus has median ~2k tokens, 90th percentile ~14k tokens).

Parameter Default (Anserini) Tuned (Pi-Serini)
k1 0.9 25
b 0.4 1.0

A grid search over k1=[0–32] and b=[0–1] shows Anserini's default (k1=0.9, b=0.4) sits in a low-performing region. The tuned parameters adapt term-frequency saturation (high k1) and length normalization (b=1.0) for long documents.

Result on a 100-query subset: accuracy jumps from 64.0% (default) to 82.0% (tuned).


Retrieval Depth Matters

k (depth) Surfaced Recall
5 70.5%
50 ~89%
1000 95.8%

Previewed recall saturates at k=50 (~74.7%). Deeper rankings offer more opportunity, but agents don't automatically inspect them. The bottleneck shifts from "Can the retriever find it?" to "Can the agent recognize and spend context on evidence it already has?"


Failure Mode Analysis: Premature Branch Commitment

A key behavioral difference between models with similar costs:

  • GPT-5.5 keeps candidate-specific probes reversible. If a probe fails (e.g., "Warrington", "Vinegar Strokes"), it returns to original clues (town population, spelling history).
  • Claude Opus 4.7 tends to commit to a branch early and doesn't revisit alternatives even when initial probes are unproductive.

This suggests agent architecture matters as much as retriever choice.


Cost Efficiency

Pi-Serini achieves massive cost reduction through two mechanisms: 1. No dense retriever overhead — BM25 is cheap to query 2. Prefix-cache-friendly loop — The same system prompt and repeated context means 82–90% of total tokens are served from cache at 10% of the input price

Model Input price Output price Cache read
deepseek-v4-flash $0.14 $0.28 $0.028
gpt-5.5 $5.00 $30.00 $0.50

Implications

  1. Lexical retrieval is not dead for agentic search. A tuned BM25 with sufficient depth is competitive with dense retrievers at a fraction of the cost.
  2. The bottleneck has moved up the stack. The agent's ability to use surfaced evidence — not the retriever's ability to find it — is now the primary constraint.
  3. Default configurations are misleading. Papers comparing against BM25 should confirm they're using appropriate (not default) parameters for their document domain.
  4. Time budgets beat iteration caps. Steering with a hard timeout prevents runaway tool calls and reduces variance.

Paper Details

  • Dataset: BrowseComp-Plus — 830 queries, 100,195 long documents (avg. 5,179 words/doc; 6.1 evidence docs, 2.9 gold docs per query)
  • Subjects: Information Retrieval (cs.IR), Artificial Intelligence (cs.AI), Computation and Language (cs.CL)
  • Code: github.com/justram/pi-serini
  • Pages: 15 pages, 4 figures

GitHub Is Sinking

Source: dbushell.com
Author: David Bushell
Date: 2026-04-29


TL;DR

"GitHub used to be cool and now it's a lame slop graveyard."

Under Microsoft, GitHub has deteriorated into an unreliable, "enshittified" platform flooded with AI slop and bots. The author argues it is now an expensive liability rather than a useful tool, and urges users to begin migrating immediately.

TextGen by oobabooga

Source: github.com/oobabooga/textgen
Author: oobabooga
Date: Active development (5,600+ commits, 112 releases)


Core Project

TextGen is an open-source desktop application designed to run large language models (LLMs) locally on consumer hardware. It provides both a privacy-first web interface and an OpenAI/Anthropic-compatible API, enabling users to self-host chat, vision, tool-calling, and web-search capabilities without relying on third-party cloud services or sending data externally.


Key Features

  • Local-First & Private: No telemetry; all inference runs on your own machine.
  • Multiple Interaction Modes: Supports instruct, chat-instruct, and chat modes.
  • OpenAI-Compatible API: Drop-in replacement for OpenAI/Anthropic APIs, enabling integration with existing tools.
  • Vision & Tool-Calling: Supports multimodal inputs (vision) and agentic tool use.
  • Web Search: Built-in web search capabilities within the UI.
  • Training & Extensions: Supports model fine-tuning, image generation via diffusers, and extensions for TTS, voice input, and translation.
  • Cross-Platform: Available for Linux, Windows, and macOS (Intel and Apple Silicon).

Installation Methods

1. Portable Desktop App (Easiest)

Download a pre-built portable release from GitHub Releases. Includes all dependencies (CUDA, Vulkan, ROCm, CPU-only). Compatible only with single-file GGUF models.

2. One-Click Installer (Web UI in Browser)

Run the OS-specific start script (start_windows.bat, start_linux.sh, start_macos.sh). Creates a local environment and launches the UI at http://127.0.0.1:7860. Supports environment variables for silent/automated installs.

3. Full Conda Installation (Most Flexible)

For users needing multi-file model support (Transformers, EXL3), training, or extensions: - Requires Conda (Miniforge recommended) and Python 3.13 - Install PyTorch 2.9.1 with hardware-specific wheels (NVIDIA CUDA 12.8, AMD ROCm, Apple MPS, CPU-only) - Clone repo and install from requirements/full/ - Requires ~10GB disk space


Model Support

Format File Structure Portable Build Full Install
GGUF Single .gguf file in user_data/models ✅ Yes ✅ Yes
Transformers Multi-file folder in user_data/models/<name>/ ❌ No ✅ Yes
EXL3 Multi-file folder ❌ No ✅ Yes

Users are directed to resources like LocalBench for recommended GGUF quantizations and a VRAM calculator for memory planning.


Architecture & Backends

  • llama.cpp (GGUF inference)
  • Transformers (Hugging Face models)
  • ExLlamaV3 (Optimized inference for specific architectures)
  • Supports CPU, NVIDIA CUDA, AMD ROCm, Apple Metal (MPS), and Vulkan

Privacy & Philosophy

TextGen is explicitly built for users who want full control over their AI workloads. By running locally, users avoid data leakage, API rate limits, and subscription costs. The project emphasizes ease of use for beginners while offering deep configurability for advanced users through command-line flags, persistent settings, and an extension ecosystem.


Ecosystem

Code-to-Paper Mapping Assessment: Local LLM Evaluation

Source: github.com/nathanlgabriel/paper_code_mapping_assessment
Author: Nathan L. Gabriel
Date: 2025-07-01


Core Thesis

Local LLMs have advanced dramatically enough to perform a task previously impossible for small local models: mapping computational simulation code to its corresponding academic research paper with high accuracy. However, the more surprising finding is that a decent local model combined with an intelligent human can outperform even overpowered frontier models.


The Task

Evaluate local LLMs on their ability to map computational simulation code to its corresponding research paper, tracking iterative refinement from initial outputs to corrected mappings.

"Ultimately, my assessment is that the hype is real. Qwen 3.6, Gemma 4, and Nemotron Nano were all able to do reasonably well at a task that was impossible for small local models a few months ago."


Critical Finding: Local Model + Human > Frontier Model

The author discovered a substantive oversight in Claude Sonnet's mapping of replicator dynamics related to continuous vs. discrete population representation and rounding bias prevention.

  • Claude Opus 4.7 failed to identify the issue even after a follow-up prompt specifically asking about omissions related to avoiding statistical biases (despite explicit Python code comments stating the modification purpose).
  • Qwen 3.6 35B A3B, using more detailed guided prompts, successfully identified the relevant code sections and produced the actually definitive code-to-paper mapping.

"Conclusion: A decent local model + an intelligent human can still be smarter than an overpowered frontier model."


Models Evaluated

Model Performance
Qwen 3.6 35B A3B Standout performer. Captured 75-80% of the definitive mapping. Fast inference.
Qwen 3.6 27B Reasonable baseline; solid structural understanding but required more extensive correction.
Gemma 4 26B Generated initial mappings requiring substantial correction; good grasp of simulation pipeline.
Nemotron Nano Delivered reasonable baseline outputs.
Gemma 4 31B Failed: Exceeded context/VRAM limits; resulted in inference crashes.

Methodology

  • Context Window: ~160k tokens used by each model; maximum configured up to 262,144 tokens.
  • Iterative Process: Models received structured prompts; outputs were corrected and refined across multiple attempts.
  • Source Materials: Research paper PDF, supplementary appendices (partial and full), and two Jupyter notebooks (reinforcement learning and replicator dynamics).

Key Insight

The hype around local LLMs is justified — they can now handle complex code-to-paper mapping tasks that were impossible months ago. But the more profound insight is about human-AI collaboration: careful prompting and human oversight with a capable local model can catch errors that even the most powerful frontier models miss.

Deep Research is now Open — Agent-ModernColBERT

Source: LightOn Blog
Date: 2026-05-12


TL;DR

Agent-ModernColBERT is a 149M-parameter late-interaction retriever that adds ~10% accuracy over Reason-ModernColBERT on BrowseComp-Plus by treating agent reasoning traces as first-class retrieval signals. Paired with GPT-OSS-120B, it matches the original GPT-5 + Qwen3-Embed-8B stack while being 54× smaller than dense alternatives.

ELF: Embedded Language Flows

Source: arXiv:2605.10938
Authors: Keya Hu*, Linlu Qiu*, Yiyang Lu, Hanhong Zhao, Tianhong Li, Yoon Kim, Jacob Andreas, Kaiming He (MIT) · equal contribution
Code:* github.com/lillian039/ELF
Date: 2026-05-11


TL;DR

Continuous diffusion models can beat discrete ones for language — if you stay in embedding space until the very last moment. ELF achieves Gen. PPL ~24 with just 32 sampling steps, using 10× fewer training tokens than leading discrete DLMs, with no distillation required.

How to Do Great Work

Source: paulgraham.com
Author: Paul Graham
Date: July 2023


Core Project

Graham collected techniques for doing great work across many fields and found their intersection. The result is a definite shape — not just a point labeled "work hard." The essay is a recipe for the very ambitious, organized around four steps: choose a field, reach the frontier, notice gaps, explore them.


1. Choosing What to Work On

The work must have three qualities: natural aptitude, deep interest, and scope for great work. The third is rarely a problem for ambitious people.

How to find it: By working. If unsure, guess and start. Develop a habit of working on your own projects — great work almost always happens on projects you drive yourself.

"What are you excessively curious about — curious to a degree that would bore most other people? That's what you're looking for."

The Four Steps:

  1. Choose a field (or let curiosity choose it)
  2. Learn enough to reach the frontier of knowledge — its edges are full of gaps
  3. Notice the gaps (resist your brain's tendency to simplify)
  4. Explore promising gaps, especially those ignored by others

Steps two and four require hard work. Interest drives harder work than diligence ever could. The three most powerful motives: curiosity, delight, and the desire to do something impressive. When they converge, the combination is the most powerful of all.


2. The Difficulty of Deciding

You can't know what a field is like without doing it — so the four steps overlap. You may work at something for years before knowing if it's right. Ambition comes in two forms: one that precedes interest (makes deciding harder) and one that grows out of it.

Educational systems pretend choosing is easy and expect early commitment. In reality: "When it comes to figuring out what to work on, you're on your own."

Be a big target for luck: try many things, meet many people, read many books, ask many questions. When in doubt, optimize for interestingness. A field should become increasingly interesting as you learn more. If it doesn't, it's probably not for you.

Signs of fit: you enjoy even the parts others find tedious or frightening. Strange tastes are often strong ones — and strong taste means you'll be productive.

Make what you yourself want. Write the story you want to read; build the tool you want to use. Many people get this wrong, trying to please an imaginary sophisticated audience.

Forces that lead you astray: pretentiousness, fashion, fear, money, politics, other people's wishes, eminent frauds. If you stick to genuine interest, you're proof against all of them.


3. Strategy: Stay Upwind

Following genuine interests requires boldness — risking rejection and failure. But you don't usually need much planning. The recipe is: work hard on excitingly ambitious projects, and something good will come of it.

Planning only works for achievements you can describe in advance (gold medals, wealth). You can't plan discovery of natural selection. Instead, "stay upwind": at each stage do what seems most interesting and gives the best options for the future.


4. Working Techniques

Work hard, but not too hard. For the hardest types, you may only manage 4–5 focused hours a day. Arrange large contiguous blocks — you'll shy away from hard tasks if interruption is likely.

Activation energy: Starting is harder than continuing. Trick yourself: "I'll just read over what I've got so far." Similar lies work for starting projects: "How hard could it be?" The young have an advantage here — their optimism (sometimes born of ignorance) helps overcome activation energy.

Finish what you start. Much of the best work happens in what was meant to be the final stage. Exaggerating the importance of your work (in your own mind) is another permissible lie — if it helps you discover something new, it may turn out not to have been a lie after all.

Two forms of procrastination: - Per-day procrastination — obvious, sets off alarms - Per-project procrastination — far more dangerous. It camouflages itself as work: you're industriously busy on something else. Ask regularly: "Am I working on what I most want to work on?"

Consistency is king. Writing a page a day = a book a year. People who do great things don't get a lot done every day — they get something done, rather than nothing. We underestimate cumulative effects. Exponential growth (learning, audience) feels flat early on — push through the initial unrewarding phase.

Undirected thinking: Letting your mind wander (while walking, showering) solves hard problems, but only when interleaved with focused work that feeds it questions. Avoid distractions that push your work out of the top spot in your mind. (Exception: Don't avoid love.)


5. Taste, Style, and Earnestness

Aim high. Cultivate taste — know the best in your field and what makes it so. Trying to be the best simplifies things and is often easier than merely trying to be good. Use the 100-year test: will people care in a century?

Style emerges from doing your best work; don't try to be distinctive. Trying to be is affectation — pretending someone other than you is doing the work. The fakeness shows.

"Be the one who puts things out there rather than the one who sits back and offers sophisticated-sounding criticisms."

Earnestness is the positive expression: intellectual honesty, willingness to admit error, informality. The core is intellectual honesty — to see new ideas you need an exceptionally sharp eye for the truth. Maintain a slight positive pressure toward admitting you're wrong. Once you admit a mistake, you're free. Till then you carry it.

Nerds have an advantage: they expend little effort on seeming anything. Any energy that goes into how you seem comes out of being good.


6. Consistency and Elegance

Great work is consistent with itself. When facing a decision, ask: "If I'd already made the change, would I want to revert to what I have now?"

Have the confidence to cut. Don't keep something that doesn't fit just because you're proud of it or it cost effort.

Elegance is a useful standard beyond math. Laborious solutions often have more short-term prestige — they cost effort and are hard to understand. But the best work sometimes seems like it took little effort, because it was in a sense already there, just waiting to be seen.

When you're doing work that could be seen as either creation or discovery, err on the side of discovery. Think of yourself as a conduit through which ideas take their natural shape.

Make tools gratuitously unrestrictive — a powerful tool will be used in ways you didn't expect. Express ideas in the most general form; they'll be truer than you intended.


7. Originality and New Ideas

Originality is not a process — it's a habit of mind. Original thinkers throw off new ideas about whatever they focus on, like an angle grinder throwing off sparks. They can't help it.

Ideas come from working on something slightly too difficult — not from trying to have original ideas. Writing is a powerful generator: when you try to put ideas into words, a missing idea creates a vacuum that draws it out of you. There's a kind of thinking that can only be done by writing.

Changing context helps: visiting new places, going for a walk. Also travel in topic space — explore different topics. Distribute attention according to a power law: be professionally curious about a few things and idly curious about many more.

New ideas are simultaneously novel and obvious. The contradiction resolves because seeing them requires changing your model of the world. Models both help and constrain us. To find new ideas, be stricter than others — notice where models bash against reality. This is what Einstein did with Maxwell's equations.

Rule-breaking is required because new models break at least implicit rules of old ones. Two ways to be comfortable breaking rules: aggressive independence (enjoying it) and passive independence (not caring or not knowing the rules exist). Novices and outsiders often make discoveries because their ignorance acts as passive independent-mindedness.

"A good new idea has to seem bad to most people, or someone would have already explored it."

Look for ideas that seem crazy but the right kind of crazy — they tend to be exciting and rich in implications.

Choosing problems > solving problems. People show more originality in solving than in deciding which problems to solve. Unfashionable problems are undervalued. One of the most interesting kinds: problems people think have been fully explored, but haven't. Ask yourself: if you took a break from "serious" work to work on something purely because it would be interesting, what would you do? The answer is probably more important than it seems.

Questions matter more than we think. People imagine big ideas are answers, but often the real insight was in the question. A really good question is a partial discovery. Be rich in unanswered questions — they grow in the answering.


8. Execution: Start Small, Evolve

Being prolific is underrated. Try lots of things — you can't have many good ideas without also having many bad ones. Err on the side of starting. Great things are made in successive versions: start small and evolve.

Begin with the simplest thing that could possibly work. Surprisingly often, it does. Don't cram too much into one version. An early version being dismissed as a "toy" is a good sign — it has everything a new idea needs except scale, and that tends to follow.

Planning is a necessary evil — a response to inflexible media or coordinating many people. If you keep projects small and use flexible media, designs can evolve instead.

Take as much risk as you can afford. In an efficient market, risk is proportionate to reward. If you're not failing occasionally, you're probably being too conservative.


9. Youth and Age

Advantages of youth: energy, time, optimism, freedom. Advantages of age: knowledge, efficiency, money, power. The young often don't realize how rich they are in time. Spend it lavishly but not wastefully — learn something out of curiosity, build something because it's cool, become freakishly good at something.

Fresh eyes: When learning something for the first time, pay attention to things that seem wrong or missing. There's a 99% chance the problem is with you, but if the misgiving survives as your knowledge grows, it may represent an undiscovered idea.

Unlearn school-induced nonsense: Schools induce passivity (authority tells you what to learn and tests you), give a misleading impression of work (problems are pre-defined and always soluble), and train you to win by hacking the test. You can't trick God. Stop looking for shortcuts — focus on problems others have overlooked.

Don't take rejection by committees to heart. The qualities that impress admissions officers differ from those required to do great work.


10. Copying and Influence

There's a good way to copy and a bad way. Copy openly, not furtively or unconsciously. Originality is the presence of new ideas, not the absence of old ones. Early work is almost inevitably based on others' — you have no previous work of your own to react to yet.

Don't copy the flaws. The features easiest to imitate are most likely the flaws. Some talented people are jerks — being a jerk is not part of being talented, it's merely how they get away with it.

Cross-pollinate by copying ideas from one field into another. Negative examples can be as inspiring as positive ones — sometimes you learn more from things done badly.


11. Colleagues and Morale

Seek the best colleagues. Quality over quantity — one or two great ones beat a building full of pretty good ones. Work with people you want to become like, because you will. Sufficiently good colleagues offer surprising insights — they can see and do things you can't.

Husband your morale like a living organism. Morale compounds: high morale → good work → higher morale → even better work. The cycle also runs in reverse. When stuck, switch to easier work just to get something done.

Treat setbacks as part of the process. Solving hard problems always involves backtracking. Never let setbacks panic you into backtracking more than necessary. Learn to distinguish good pain (effort) from bad pain (damage).

An audience matters for morale — but it doesn't need to be big. A handful of people who genuinely love what you're doing is enough. Avoid intermediaries between you and your audience whenever possible.

People affect your energy. Seek those who increase it, avoid those who drain it. Don't marry someone who doesn't understand that you need to work.

Morale is physical. Exercise, eat and sleep well, avoid dangerous drugs. Running and walking are particularly good for thinking.


12. Motivation and Competition

People who do great work aren't necessarily happier than everyone else — but they're happier than they'd be if they didn't. For the smart and ambitious, not being productive is dangerous — it breeds bitterness.

Fame adds noise. The opinion of people you respect is signal. The prestige of a type of work is at best a trailing indicator. The right question: how well could it be done? not how much prestige does it have?

Competition can motivate, but don't let it choose your problem for you. Don't let competitors make you do anything more specific than work harder.


Conclusion: Curiosity Is the Key

The factors in doing great work are literally mathematical: ability, interest, effort, and luck. Luck you can't control, and effort is assumed if you genuinely want to do great work. So it boils down to ability and interest combining to yield new ideas.

Curiosity is the word that appears most often. It's the key to all four steps: it chooses the field, gets you to the frontier, makes you notice the gaps, and drives you to explore them. The whole process is a kind of dance with curiosity.

The essay's length itself acts as a filter — if you made it this far, you're already further along than you realize. Many more people could try to do great work than do. What holds them back is a combination of modesty and fear.

"The discoveries are out there, waiting to be made. Why not by you?"


Notable Quotes

  • "The three most powerful motives are curiosity, delight, and the desire to do something impressive."
  • "When it comes to figuring out what to work on, you're on your own."
  • "Instead of making a plan and then executing it, you just try to preserve certain invariants."
  • "Any energy that goes into how you seem comes out of being good."
  • "A good new idea has to seem bad to most people, or someone would have already explored it."
  • "Originality isn't a process, but a habit of mind."
  • "Being prolific is underrated."
  • "Curiosity is the best guide. Your curiosity never lies, and it knows more than you do about what's worth paying attention to."