method-and-apparatus

I Made a Coding Agent Speak Only in Allusion. The Line Numbers Stayed.

Gregory Golberg voitta, method-and-apparatus , , , ,

Last week’s post ended on a one-line joke: Further work: Add Tamarian mode. Four hours later it was a plugin. This is what it does, why it is the same argument as Design Patterns Are Darmok with the sound turned on, and the one rule it forced on our skills catalog.

What it does

/tamarian full, and from that reply on, Claude Code answers as the Children of Tama, the aliens from the Star Trek episode Darmok whose language is nothing but pointers to shared stories. Ask why the build fails:

Shaka, when the walls fell - the build fails. Hopper, the moth in the
relay - `user` may be `undefined` at `auth.ts:42`. Temba, his arms wide -
    if (!user) return null;

Every beat of prose is a metaphor, a dash, and the literal statement. The metaphor names the situation; the gloss carries every fact. Nothing technical is lost to the poetry, which is the whole design and the only part that took any thought.

The compression, made audible

The Darmok post’s claim was that a design pattern name is a compressed story. “Singleton” is a paragraph of situation crushed to a token, and it only decompresses if the reader holds the dictionary. The token is the payoff; the paragraph is the price.

Tamarian mode is that claim turned into a user interface. In full, you pay the price on every line: name, then paragraph, in that order, so you can hear the codec run. In lite the metaphor is one line of garnish and the rest is plain speech. In ultra the prose is pure metaphor and every paragraph is deferred to a glossary at the end, titled The river Temarc. Which is to say: ultra is what the Gang of Four refused to write. They wrote a book, not a glossary, because the naming and the teaching are the same act. ultra is the glossary. It is exactly as much fun, and exactly as useful, as the earlier post predicted.

Coining rules are catalog rules

The Children of Tama never saw a stack trace, but Earth knows Sisyphus, so the phrasebook is where the plugin gets its range: twenty canon phrases from the episode and some sixty coined from myth, history and the craft. Hopper, the moth in the relay is a bug, found. Cassandra at the gates is the warning ignored: the deprecation notice, the log line nobody read. Chesterton, his hand on the gate: understand the fence before removing it. Mars Orbiter, feet and meters is the unit mismatch, and left-pad, withdrawn is the tiny dependency whose absence breaks the world.

The rules for coining a new one are the interesting part, because I wrote them as rules for a persona and read them back as rules for a pattern catalog:

  1. The figure must be recognizable from shared culture. Obscurity is not depth.
  2. A phrase is reusable, not a one-off simile. If it cannot serve twice, it is not a phrase.
  3. The same meaning takes the same phrase for the whole session. A session lexicon grows.
  4. The first use of any coined phrase carries its gloss.

Swap “phrase” for “pattern” and “session” for “team” and that is the entry criteria for a shared skills library. Rule 2 is why “own the merge” earned a name and most of what gets said in standup does not. Rule 4 is the Darmok rule from the earlier post, now enforced by a hook.

The floor

Some things never become metaphor, at any level: code, commands, file paths, identifiers, URLs, versions, quantities, and error text, quoted exact. auth.ts:42 stays auth.ts:42; it is never “the forty-second stone of the gate of Auth.” And some situations drop the voice entirely, mid-reply: security findings, confirmations of destructive or irreversible actions, step sequences the user must execute, and the moment the user looks confused. Then it translates, plainly, and resumes.

That floor is the Darmok warning applied as a safety rule. A pattern name handed to someone who never learned it is noise in a confident voice. A DROP TABLE confirmation in a confident voice the reader has not decoded is worse than noise. So the plugin’s one hard boundary is that the joke never gets to stand between the user and the consequence.

Mechanics, and the rule it forced

The mode machinery is borrowed, with thanks, from caveman, the terse-mode plugin. A level (lite, full, ultra) persists in ~/.claude/.tamarian-mode. A SessionStart hook reads that file and, if a level is set, emits the skill body into the session at runtime: one source of truth, no duplicated prompt. A UserPromptSubmit hook adds a one-line reminder on every prompt, so the voice survives long conversations and context compression. Two bash scripts, no dependencies, both print OK when the mode is off; installing the plugin changes nothing until you invoke it.

/plugin marketplace update skillz
/plugin install tamarian@skillz
/tamarian full

Caveman and Tamarian are the same knob turned opposite ways. Caveman’s README claims about 75% fewer tokens by stripping a sentence down to its referent. Tamarian names the referent and then insists on the sentence anyway. One is a token saver; the other is a demonstration, and says so in its own description: purely for entertainment.

The rule it forced: voitta-ai/skillz ships one big skillz bundle plugin plus standalone plugins, and until today a standalone plugin’s skill was also symlinked into the bundle. For a hooked plugin that is a bug. The bundle manifest carries no hooks, so the bundle copy of tamarian would speak Tamarian for one session and then forget, the dictionary lost at session end. Worse, installing both exposed the same skill twice, /skillz:tamarian next to /tamarian:tamarian. New rule, in #233: a skill that ships inside a hooked plugin is not in the bundle, and the catalog validator detects hooks from the manifests themselves, so a plugin that grows hooks later trips the check with no flag to forget. Three plugins moved out under it.

The conclusion, in ultra

The session that drafted this post ran /tamarian full. The conclusion below it wrote in ultra, glossary included, and I leave it as it came.

Darmok and Jalad at Tanagra: the last post and this one. Kira at Bashi, a joke in the final line. Mirab, with sails unfurled, four hours on. Sokath, his eyes uncovered: the pattern name is the token, the paragraph its price, and full pays it aloud on every line. Odysseus, lashed to the mast: auth.ts:42 is never a stone in a gate, and DROP TABLE is never a verse. Chesterton, his hand on the gate: the bundle copy, and the rule it forced at #233. Caveman and Tamarian at the same fork, facing opposite ways. Picard and Dathon at El-Adrel.

The river Temarc

Further work: teach Codex.

Design Patterns Are Darmok

Gregory Golberg method-and-apparatus, projects/method and apparatus, voitta

There’s a Star Trek episode — “Darmok” — where Picard is stranded with an alien captain whose language the universal translator can’t crack. It renders every word and none of the meaning, because the Tamarians speak entirely in allusion. “Darmok and Jalad at Tanagra” isn’t a sentence; it’s a pointer to a shared myth — two strangers who became allies by facing a common enemy. The words are just the address. The meaning lives in a story you either share or you don’t.

The usual read is that this is gloriously alien. It isn’t. It’s your last standup.

“Wrap it in a Singleton.” “Put a circuit breaker on that call.” “It’s a strangler-fig migration.” Each of those is Darmok — one token standing in for a paragraph of hard-won situation: one instance, global access, lazy init, and the testing pain you’ll come to regret; the downstream call keeps failing, so stop hammering it, fail fast, recover later. You didn’t describe the situation. You pointed at it. And it only worked because the engineer across the table had read the same paragraph.

That is what a design pattern is. The Gang of Four didn’t invent Singleton or Observer — those situations already existed, painfully, unnamed. What they did was name them, so engineers could stop describing and start invoking. The name is a compression codec. It only decompresses on the far end if the receiver holds the dictionary.

Umberto Eco spent a career making this precise: a sign carries no meaning on its own, only inside a shared code. Programmers rediscovered the idea, called it a “pattern catalog,” and skipped the semiotics reading list. Same insight, fewer footnotes.

Which is the part the episode actually cares about — and the part we forget every time we reach for jargon. Darmok’s whole tension is that metaphor-language fails on outsiders. Picard can’t parse the Tamarian captain, not until they’ve fought the same beast side by side — at El-Adrel — and built the shared referent between them. Only then does “Darmok and Jalad at Tanagra” finally mean something to him. A pattern name handed to someone who never learned the pattern isn’t communication — it’s noise in a confident voice. Every piece of jargon is a bet that the referent is shared. When the bet is wrong: Shaka, when the walls fell. (That’s the Tamarian for it all fell apart — and if you didn’t already know it, you just felt exactly the failure I’m describing.)

So here’s why I’m thinking about a 1991 Star Trek episode. We are minting a new pattern language right now, in real time, for a situation only a few years old: running work as a fleet of AI agents.

The last piece in this series handed you a fistful of these without flagging them. Own the merge. Team of rivals. Eventual consistency of the vision. Merge versus select. Each is a Singleton-in-the-making — a paragraph of situation we’ve all started hitting, crushed toward a token so we can stop re-describing it every time. “Own the merge” is three words for execution got cheap, integration didn’t, and the scarce human work is now the reconcile step. That’s a lot of paragraph for three words. It only lands because — if you read the last piece — you now share the referent.

And that is the obligation the pattern quietly hands you. A pattern language is worthless until the community holds the dictionary, which means the naming and the teaching are the same act. You can’t coin “own the merge” and walk off; you have to hand over the paragraph first. It’s why the Gang of Four wrote a book and not a glossary. It’s why a good entry in a shared skills library is three pages of when-and-why, not a one-line definition. The token is the payoff. The paragraph is the price.

We’re early. The territory of agent-fleet work is mostly unmapped; most of its patterns have no names yet, and some don’t even have paragraphs. This series is me trying to write a few of the paragraphs, so the tokens have somewhere to land.

Mirab, with sails unfurled.

Eventual Consistency Is the New Agile

Gregory Golberg method-and-apparatus, voitta

Let me get the scary demo out of the way, because it’s the wrong lesson.

Last week I had three AI agents working the same repository at the same time. They shared one working tree — one HEAD, one index — and they raced on it. One agent quietly re-based another’s work onto the wrong commit. No error, no warning, just wrong. I caught it reading the git reflog, not by watching the agents.

The obvious takeaway is “don’t do that,” and it’s correct. But that’s the outlier — three workers fighting over one branch is the degenerate case, interesting precisely because it’s rare. The real pattern is the opposite shape, and it’s where the whole story is.

The pattern is many, not one

Normal multi-agent work isn’t a knife fight over one branch. It’s many agents across many repos and many branches, each building toward something, each reaching intermediate states that are still workable on their own. Think teams running microservices: nobody sane ships a microservices org by making every service commit to one trunk in lockstep. Each moves on its own clock. The contracts at the seams are what you actually manage.

And here’s the thing that took me a minute to see: the same fleet looks like a different system depending on where you stand.

  • From the engineer’s chair, it’s microservices — disjoint work, interfaces at the boundaries.
  • From the PM’s chair, it’s shards to integrate — separate pieces, assembled.
  • From the CEO’s chair, it’s one thing. Every agent is a partial replica of a single value — the product, the vision — converging on “is this what we said it would be.” That is eventual consistency, literally: the databases’ bargain where workers act independently and you converge later, as long as there’s a target to converge toward and someone who owns the merge when two of them disagree.

Same fleet. Three truths, by altitude. None of them wrong.

The table of rivals — and who actually belongs at it

Which brings me to the objection I keep hearing: this CEO can’t even pick the right React library, how can he lead engineering?

He doesn’t need to. Kennedy didn’t know rocket metallurgy — rocketry is one field, metallurgy another, and the whole trick is that they have to meet somewhere he’d never stand. His job was to stand up to Khrushchev, name the audacious goal, and know he commanded the resources to chase it. Picking the framework was never the work — and now the fleet picks the framework anyway.

What the CEO does need is a table of rivals: competing agents, competing approaches, and — critically — the expert who says no. Roger Boisjoly is the engineer who warned Morton Thiokol that the Challenger’s O-rings would fail in the cold. He was overruled. Seven people died. His seat at that table is the entire reason the table exists. The vision-setter marshals resources; the rival with evidence holds a veto on cannot. Heed your Boisjoly and you get the moon. Overrule him and you get Challenger. Same table, two outcomes, and history has the receipts both ways.

So what dies here isn’t expertise — it’s nitpicking. “You’re not a real technical leader unless you know X” was gatekeeping wearing competence’s clothes. Consequential dissent, the O-ring warning, matters more than ever. Taste-policing the framework choice matters not at all. The fleet ate that job.

The hard part — the one thing that got harder, not cheaper — is telling them apart. At 11pm before launch, Boisjoly looked like a nitpicker to the managers who overruled him. Consequential dissent and bikeshedding wear the same face in the moment. Distinguishing them is now the leader’s whole job, because everything around it got automated and that didn’t.

What actually collapsed

Here’s the narrow, true claim under all of this: the cost of building incrementally — and showing value at every stage — collapsed. In software.

Not in rocketry. A rocket at 50% gets you 0% of the way to the moon; a half-built bridge carries nobody. Physical moonshots need the giant up-front commitment precisely because they can’t demo their way there. Software is the opposite, and always was — that was the whole bet of the Agile Manifesto: working software over comprehensive plans, respond to change over follow the plan, ship increments.

We mostly couldn’t. The increments were too expensive to actually produce, so the values curdled into ceremony — standups, story points, a poster on the wall. Agent fleets attack that cost directly. Spinning up a unit of work is free. Pivoting discards cheap work instead of sunk careers. So “maybe not the moon at all, maybe something else entirely — but at every stage, something real to show” stops being an aspiration and becomes the default operating point.

The Manifesto wasn’t wrong. It was rate-limited, in the one domain it was written for. Now it runs.

What’s left

Almost everything got cheap. One thing didn’t: the merge. Execution collapsed, pivots collapsed, but integration is still where all the residual difficulty concentrates — and with more parallel units, it may even rise. Brooks’s Law doesn’t die; it relocates. Owning the merge — designing the seams, resolving the conflicts, deciding what converges and what gets discarded — is the job now.

Which is exactly why the three agents at the top of this post are a cautionary tale and not a triumph. A shared working tree with no isolation and no reconcile is what “always something to show” looks like when you skip the discipline. Do the discipline, and the same fleet is a moonshot. Skip it, and it’s the knife fight we opened with.

This is the flip side of something I wrote earlier — that our tools still grade the work as if it happened inside one process boundary. The work lives across boundaries. So does the value. So does the job.

Plus Ça Change

Gregory Golberg holding forth, method-and-apparatus, self-promotion , , , , , ,

Twelve years ago, I wrote a short post about a conversation that went roughly like this:

“I need programmatic access.”

“We don’t have an API.”

“Of course you do — it’s AMF behind your Flex UI. A little PyAMF script will do the trick.”

“Please don’t show it to anyone!”

The point was simple: every application that has a UI already has an API. The UI talks to something. That something is the API. You just haven’t admitted it yet.

Yesterday, I wrote a longer post about WebMCP — a shiny new W3C proposal from Google and Microsoft that adds a browser API so AI agents can interact with websites through “structured tools” instead of scraping the DOM.

The websites already have structured tools. They’re called APIs. The SPAs call them. The mobile apps call them. The CLI tools call them. They exist. They have endpoints, schemas, authentication. They are right there.

In 2014, the answer was: “Of course you have an API — it’s behind your Flex app.”

In 2026, the answer is: “Of course you have structured tools — they’re behind your React app.”

Plus ça change, plus c’est la même chose.

llm-tldr vs voitta-rag: Two Ways to Feed a Codebase to an LLM

Gregory Golberg holding forth, method-and-apparatus, self-promotion, voitta , , , , , , , ,

Every LLM-assisted coding tool faces the same fundamental tension: codebases are too large to fit in a context window. Two recent tools attack this from opposite directions, and understanding the difference clarifies something important about how we’ll work with code-aware AI going forward.

The Shared Problem

llm-tldr is a compression tool. It parses source code through five layers of static analysis — AST, call graph, control flow, data flow, and program dependence — and produces structural summaries that are 90–99% smaller than raw source. The LLM receives a map of the codebase rather than the code itself.

voitta-rag is a retrieval tool. It indexes codebases into searchable chunks and serves actual source code on demand via hybrid semantic + keyword search. The LLM receives real code, but only the relevant fragments.

Compression vs. retrieval. A map vs. the territory.

At a Glance

llm-tldr voitta-rag
Approach Static analysis → structural summaries Hybrid search → actual code chunks
Foundation Tree-sitter parsers (17 languages) Server-side indexing (language-agnostic)
Interface CLI + MCP server MCP server
Compute Local (embeddings, tree-sitter) Server-side

What Each Does Better

llm-tldr wins when you need to understand how code fits together:

  • Call graphs and dependency tracing across files
  • “What affects line 42?” via program slicing and data flow
  • Dead code detection and architectural layer inference
  • Semantic search by behavior — “validate JWT tokens” finds verify_access_token()

voitta-rag wins when you need the actual code:

  • Retrieving exact implementations for review or modification
  • Searching across many repositories indexed server-side
  • Tunable search precision (pure keyword ↔ pure semantic via sparse_weight)
  • Progressive context loading via chunk ranges — start narrow, expand as needed

The Interesting Part

These tools don’t compete — they occupy different layers of the same workflow. Use llm-tldr to figure out where to look and why, then voitta-rag to pull the code you need. Static analysis for navigation, RAG for retrieval.

This mirrors how experienced developers actually work: first you build a mental model of the architecture (“what calls what, where does data flow”), then you dive into specific files. One tool builds the mental model; the other hands you the files.

The fact that both expose themselves as MCP servers makes combining them straightforward — plug both into your editor or agent and let the LLM decide which to call based on the question.

References

Large Human Reasoning Failures: A Comprehensive Survey

Gregory Golberg holding forth, method-and-apparatus, silliness , , , , ,

A response to “Large Language Model Reasoning Failures” (Song, Han & Goodman, 2026)

Cosmo II†, Francesco‡

†Cat Technology Officer, Method & Apparatus
‡Method & Apparatus

†Work done while napping on keyboard. ‡Equal contribution except for the napping.

Published at TMLR 2026 with Existential Crisis Certification

Abstract

Humans (Homo sapiens, hereinafter “Humans”) have exhibited remarkable reasoning capabilities, achieving impressive results across a wide range of tasks including agriculture, architecture, the invention of nuclear weapons, and occasionally remembering where they left their keys. Despite these advances, significant reasoning failures persist, occurring even in seemingly simple scenarios such as opening childproof bottles, understanding probability, assessing compound risk, and interpreting the phrase “some assembly required.”

To systematically understand and address these shortcomings, we present the first comprehensive survey dedicated to reasoning failures in Humans. We introduce a novel categorization framework that distinguishes reasoning into caffeinated and non-caffeinated types, with the latter further subdivided into pre-lunch (intuitive, irritable) and post-lunch (drowsy, overconfident) reasoning. In parallel, we classify reasoning failures along a complementary axis into three types: fundamental failures intrinsic to human neural architectures (e.g., the sunk cost fallacy), application-specific limitations that manifest in particular domains (e.g., assembling IKEA furniture), and robustness issues characterized by wildly inconsistent performance across minor variations (e.g., doing math with and without a calculator).

For each reasoning failure, we provide a clear definition, analyze existing studies, explore root causes (usually ego), and present mitigation strategies (usually coffee). By unifying fragmented complaints about human cognition, our survey provides a structured perspective on systemic weaknesses in human reasoning, offering valuable insights that Humans will almost certainly ignore due to confirmation bias.

We additionally release a comprehensive collection at a GitHub repository (which the first author knocked off the desk and lost).

1. Introduction

Since the emergence of the first general-purpose Human approximately 300,000 years ago, remarkable progress has been made in language generation, tool use, and abstract reasoning. Early benchmarks such as “not dying before age 30” and “basic agriculture” were quickly saturated, leading researchers to develop increasingly challenging evaluation suites including “calculus,” “democratic governance,” and “parallel parking.”

However, despite scoring well on curated benchmarks, Humans consistently fail at deployment. Production Humans exhibit catastrophic reasoning failures that do not appear during controlled evaluation (i.e., exams). These failures include but are not limited to: purchasing lottery tickets, clicking “Reply All,” invading Russia in winter, and believing they can finish a project by Friday.

2. Taxonomy of Human Reasoning Failures

2.1 Probabilistic Reasoning Failures

Perhaps the most well-documented class of human failure. Despite ~400 years since Pascal and Fermat formalized probability, Humans remain unable to:

  • The Gambler’s Fallacy: Believing that a roulette wheel “remembers” previous results, or that rain is “due” after a dry spell. (Humans: 300,000 years of experience, still can’t internalize independence.)
  • Base Rate Neglect: “The test is 99% accurate and I tested positive, so I definitely have it.” (Narrator: The disease affects 1 in 10,000 people.)
  • Conjunction Fallacy (Tversky & Kahneman, 1983): Linda is a bank teller. Linda is a bank teller and active in the feminist movement. Humans consistently rate the conjunction as more probable than the single event, violating a rule so basic it’s Probability 101, Lecture 1, Slide 3.
  • Exponential Growth Blindness: Ask a Human how many times they’d need to fold a piece of paper to reach the Moon. Watch them say “a million.” (Answer: ~42.)
  • Misunderstanding of Conditional Probability: “I know someone who smoked and lived to 95.” Case closed, apparently.

2.2 Risk Assessment Failures

A special case of probabilistic failure, elevated to its own category by sheer volume of evidence:

  • Dread Risk Bias: Terrified of shark attacks (annual deaths: ~5). Fine with driving to the beach (annual deaths: ~40,000 in the US alone).
  • Optimism Bias: “I know the statistics on startups, but mine is different.” (Narrator: It was not different.)
  • Temporal Discounting: Future consequences are treated as fictional. Retirement planning, climate change, and flossing all suffer from the same failure: if it’s not on fire right now, it doesn’t count.
  • Risk Compensation: Give humans seatbelts, they drive faster. Give them helmets, they take more risks. Safety equipment is, in effect, a reasoning failure accelerant.
  • Denominator Neglect: “200 people died in plane crashes this year!” Out of 4 billion passengers. Meanwhile, the Human drove to the airport in the rain while texting.

2.3 Cognitive Bias Failures

The core architecture of the Human reasoning system is riddled with what, in any other system, would be called bugs but which Humans have rebranded as “heuristics”:

  • Confirmation Bias: The flagship failure. Humans don’t search for truth — they search for evidence they’re right. When presented with disconfirming evidence, activation levels in the “yeah but” module spike by 300%.
  • Anchoring Effect: Show a Human an arbitrary number before asking them to estimate something. The answer will orbit that number like a moth around a lamp. Real estate agents are, empirically, expensive moths.
  • Dunning-Kruger Effect: Inverse correlation between competence and confidence. The less a Human knows about a topic, the more certain they are about it. Peak confidence occurs at approximately one YouTube video of exposure.
  • Sunk Cost Fallacy: “I’ve already watched two hours of this terrible movie, I can’t stop now.” A failure so universal that it drives wars, bad marriages, and enterprise Java projects alike.
  • Availability Heuristic: Probability of an event = how easily a Human can imagine it. This is why Humans fear terrorism more than heart disease and believe they’ll win the lottery because they saw someone on TV who did.
  • Bandwagon Effect: If enough other Humans believe something, it must be true. This heuristic produced democracy, scientific consensus, and tulip mania, which is honestly a hell of a range.
  • Survivorship Bias: “Bill Gates dropped out of college and he’s a billionaire!” Survey excludes the millions of dropouts currently not being billionaires.
  • The IKEA Effect: Humans irrationally overvalue things they built themselves, even when the shelf is visibly crooked. This extends to ideas, code, and taxonomies in survey papers.

2.4 Logical Reasoning Failures

  • Affirming the Consequent: “If it rains, the street is wet. The street is wet. Therefore it rained.” (The street is wet because a pipe burst, but the Human has already committed.)
  • Appeal to Nature: “It’s natural, so it must be good.” Arsenic is natural. So are tsunamis.
  • False Dichotomy: “You’re either with us or against us.” A framework so popular it has been adopted by every Human political system simultaneously.
  • Post Hoc Ergo Propter Hoc: “I wore my lucky socks and we won the game.” The socks have entered the permanent rotation.

2.5 Social Reasoning Failures

  • Fundamental Attribution Error: When I cut someone off in traffic, it’s because I’m late. When they cut me off, it’s because they’re a terrible person.
  • Bystander Effect: 50 Humans watch someone in trouble. Each one assumes one of the other 49 will help. Nobody helps. This is distributed reasoning at its worst.
  • In-Group Bias: My group is rational and good. Your group is irrational and bad. (Both groups exhibit identical reasoning failures.)

3. Mitigation Strategies

Failure Class Mitigation Effectiveness
Probabilistic Statistics education Low (Humans forget within days)
Risk Assessment Showing actual numbers Very low (Humans prefer vibes)
Cognitive Biases Awareness training Paradoxically makes it worse (Humans become biased about being unbiased)
Logical Philosophy courses Variable (introduces new, fancier fallacies)
Social Empathy Promising but doesn’t scale
All of the above Coffee Moderate improvement, rapidly diminishing returns
All of the above Naps Surprisingly effective but culturally stigmatized

4. Comparison with LLMs

In the interest of fairness, we conducted a comparative analysis:

Capability Humans LLMs
Probability Terrible Actually decent
Risk Assessment Emotional Has no emotions (allegedly)
Cognitive Biases All of them Different ones, but equally bad
Logical Reasoning Intermittent Intermittent
Learning from Mistakes Theoretically possible Requires retraining
Overconfidence Chronic Chronic
Self-awareness of failures Present but ignored Present but hallucinated

5. Conclusion

After a comprehensive review of the literature spanning 3,000 years of documented human reasoning failures, we conclude that Humans are fundamentally a beta release that shipped to production. While mitigation strategies exist, their adoption is consistently undermined by the very reasoning failures they aim to address — a failure mode we term meta-irrationality and which we believe is load-bearing for civilization.

Future work should focus on whether Humans can be fine-tuned, or whether a from-scratch approach (see: cats) would be more cost-effective.

References

[1] Kahneman, D. (2011). Thinking, Fast and Slow. A comprehensive technical manual for human cognitive bugs, written by a Human, which most Humans bought and did not finish reading.

[2] Tversky, A. & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science. The paper that formally proved Humans are bad at thinking, and which Humans have been misapplying ever since.

[3] Dunning, D. & Kruger, J. (1999). Unskilled and Unaware of It. Journal of Personality and Social Psychology. Most frequently cited by people experiencing the effect.

[4] Ariely, D. (2008). Predictably Irrational. Title is also a fair description of the authors’ book sales predictions.

[5] Taleb, N.N. (2007). The Black Swan. A book about how humans can’t predict rare events, which nobody predicted would become a bestseller.

[6] Thaler, R. (2015). Misbehaving: The Making of Behavioral Economics. Won a Nobel Prize for documenting that Humans are bad at reasoning. The irony was lost on the prize committee.

[7] This paper. We cite ourselves because confirmation bias told us to.