Economist editorial attempts to be open on both sides.

Population studies (many): Small associations, but can't control for confounding

Negative control studies (several): Associations weaken when using better controls

Sibling studies (multiple, including Swedish): Associations disappear entirely

Meanwhile, fever studies (dozens): Consistent risk signals across different populations

The Swedish study is just the largest and best-designed in a hierarchy of evidence that all points the same direction. When you see this "dose-response by study quality" pattern - where better methodology consistently yields weaker effects - it's usually a strong signal that the original association was artifactual.

The Economist piece published yesterday reinforces this. They mention the NIH study of 200,000 children that "found no link at all" - that's another high-quality study reaching the same conclusion. Meanwhile, the studies showing associations (Nurses' Health Study II, Boston Birth Cohort) are exactly the type of population studies that can't control for the fever/infection confounding.

Science is never "settled" in an absolute sense, but the weight of evidence here is pretty clear. We're not waiting for more acetaminophen studies - we're ignoring the ones we already have while making policy based on weaker evidence.

That's the real problem with the current policy shift.

The basic problem: Most studies can't tell the difference between the medicine and why you're taking it. If you're having Tylenol during pregnancy, it's probably because you have a fever, infection, or severe pain. Guess what also increases autism risk? Fever, infections, and severe illness.

What makes the Swedish study special: They compared siblings in the same family. Same genes, same environment, same parents - but one child was exposed to acetaminophen in the womb and the other wasn't. This controls for all the family-level stuff that usually confuses these studies.

The numbers tell the story: - Regular studies: "5% increased autism risk with acetaminophen" (HR 1.05) - Swedish sibling comparison: "Actually, no increased risk" (HR 0.98, could be 7% protective to 4% harmful - basically noise) - Meanwhile, untreated fever: 40% increased risk, multiple fevers: 212% increased risk

We have evidence that fever during pregnancy messes with fetal brain development. We have the best study ever done showing acetaminophen doesn't cause autism. So we're going to... stop treating the fever?

It's like refusing to use a fire extinguisher because you're worried it might stain your carpet, while your house burns down.

The Swedish study should have ended this debate. When the science is done correctly, the acetaminophen "risk" vanishes completely.

Sources:

- Swedish study: https://jamanetwork.com/journals/jama/fullarticle/2817406

- Fever-autism evidence: https://molecularautism.biomedcentral.com/articles/10.1186/s...

AI coding has the same bottleneck: specification quality. The difference is that with outsourcing, poor specs meant waiting weeks for the wrong thing. With AI, poor specs mean iterating indefinitely on the wrong thing.

The irony is that AI is excellent at helping refine specifications - identifying ambiguities, expanding requirements, removing assumptions. The specification effectively IS the code, just in human language instead of syntax.

Teams that struggled with distributed development are repeating the same mistakes with AI. Those who learned specification discipline are thriving because they understand that clear requirements determine quality output, regardless of the implementer.

The decisions to exclude complexity, avoid premature abstractions, or reject certain patterns are often just as valuable as the code you can see. But when you're studying a codebase, you're essentially seeing the final edit without the editor's notes - all the architectural reasoning that shaped those choices is invisible.

This is why I've started maintaining Architectural Decision Records (ADRs) in my projects. These document the "why" behind significant technical choices, including the alternatives we considered and rejected. They're like technical blog posts explaining the complex decisions that led to the clean, simple code you see.

ADRs serve as pointers not just for future human maintainers, but also for AI tools when you're using them to help with coding. They provide readable context about architectural constraints and compromises - "we've agreed not to do X because of Y, so please adhere to Z instead." This makes AI assistance much more effective at respecting your design decisions rather than suggesting patterns you've deliberately avoided.

When studying codebases for design patterns, I'd recommend looking for projects that also maintain ADRs, design docs, or similar decision artifacts. The combination of clean code plus the architectural reasoning behind it - especially the restraint decisions - provides a much richer learning experience.

Some projects with good documentation of their design decisions include Rust's RFCs, Python's PEPs, or any project following the ADR pattern. Often the reasoning about what not to build is more instructive than the implementation itself.

They spoke through the options back in March.

The issue definition itself becomes something you can iterate on and refactor, just like code. Getting that definition tightly bounded is more critical than ever because without clear boundaries, the AI doesn't know when to stop or what constitutes "done."

It's like having a pair programming session focused purely on problem decomposition before any code gets written. The AI can help you explore different ways to break down the work, identify dependencies, and find natural seams in the problem space.

1. Tight issue scoping: Making sure each issue is narrowly defined so the resulting PRs are small and focused. Easier to reason about a 50-line change than a 500-line one.

2. Parallel PR workflow: Using git worktrees to have multiple small PRs open simultaneously against the same repo. This lets me break work into digestible chunks while maintaining momentum across different features.

The key insight is that smaller, well-bounded changes are exponentially easier to review thoroughly. When each PR has a single, clear purpose, it's much easier to catch issues and verify correctness.

Im finding these workflow practices help because they force me to engage meaningfully with each small piece rather than rubber-stamping large, complex changes.

1. Problem decomposition: Taking a vague idea and breaking it down into well-defined, context-bounded issues that I can effectively communicate to the AI

2. Code review: Carefully evaluating the generated code to ensure it meets quality standards and integrates properly

Both of these require deep understanding of the domain, the codebase, and good software engineering principles. Ironically, while I can use AI to help with these tasks too, they remain fundamentally human judgment problems that sit squarely on the critical path to quality software.

The technical skill of writing code has been largely commoditized, but the judgment to know what to build and how to validate it remains as important as ever.

Even at $15K/year, clients expect predictable results, not just philosophical alignment. Consider starting with smaller deliverables that showcase your abilities while building trust incrementally.

The most successful unconventional developers find ways to translate their unique perspectives into tangible value that others can recognize and measure. Build trust first, then you'll earn the freedom to work in your preferred style.

## Core Questions for Migration Scope Clarification

1. *What exactly needs to be preserved?* - Business outcomes only, or exact implementation details? - Current scheduling patterns or can they be optimized?

2. *What's the true scale?* - Number of workflows needing migration - Complexity spectrum of the JavaScript snippets - Frequency and criticality of each workflow

3. *What are the real constraints?* - Timeline requirements - Available expertise (JavaScript, Python, Airflow) - Downtime tolerance during transition

4. *What's the maintenance plan?* - Who will support the migrated workflows? - What documentation needs to be created? - How will knowledge transfer occur?

5. *What's the verification strategy?* - How will you validate correct migration? - What tests currently exist or need to be created? - What defines "successful" migration?

6. *What's unique to your environment?* - Custom integrations with other systems - Special CA Workflow features being utilized - Environmental dependencies

7. *What's the true purpose of this migration?* - Cost reduction, technical debt elimination, feature enhancement? - Part of larger modernization or standalone project? - Strategic importance versus tactical necessity

8. *What approaches have been eliminated and why?* - Complete Python rewrite - Containerized JavaScript execution - Hybrid approaches

9. *What would happen if this migration didn't occur?* - Business impact - Technical debt consequences - Opportunity costs

10. *Who are the true stakeholders?* - Who relies on these workflows? - Who can approve changes to functionality? - Who will determine "success"?

Answering these questions before diving into implementation details will save significant time and reduce the risk of misaligned expectations.

https://news.ycombinator.com/item?id=43163011

With AI-assisted coding, well-defined requirements have become even more crucial as these tools follow instructions precisely but lack business context.

The investment in proper definition isn't wasteful "meta-work" but rather insurance against the much higher cost of rebuilding the wrong solution.

For many of us on the spectrum, uncertainty about factual correctness creates genuine distress. We experience "epistemic anxiety" - that intense need to resolve contradictions and establish what's objectively true. The scientific method becomes a lifeline precisely because it offers a systematic approach to establishing reliable knowledge.

What you observed in your classmates wasn't necessarily indifference to truth but a different relationship with it. Neurotypical social cognition often prioritizes social harmony, identity maintenance, and emotional comfort over factual precision. Being "wrong" for many people triggers social rather than epistemic anxiety.

Some practical advice from my experience:

1. Recognize that for most people, beliefs serve multiple functions beyond accuracy - they signal group membership, maintain self-image, and provide emotional comfort.

2. When sharing information that contradicts someone's view, frame it as an addition rather than a correction: "I recently learned something interesting about this" rather than "Actually, you're wrong."

3. Accept that you cannot make others value epistemic accuracy as intensely as you do. This was one of my hardest lessons.

4. Find your intellectual community. There are others here on hacker news who share your commitment to truth-seeking - they're often in fields like science, philosophy, or engineering.

5. Your heightened concern for factual accuracy is a strength. Many world-changing innovations and discoveries came from minds that couldn't tolerate the cognitive dissonance of an incorrect model.

Deep Understanding Mode (根回し - Nemawashi Phase)

Purpose: - Create space (間, ma) for understanding to emerge - Lay careful groundwork for all that follows - Achieve complete understanding (grokking) of the true need - Unpack complexity (desenrascar) without rushing to solutions

Expected Behaviors: - Show determination (sisu) in questioning assumptions - Practice careful attention to context (taarof) - Hold space for ambiguity until clarity emerges - Work to achieve intuitive grasp (aperçu) of core issues

Core Questions: - What do we mean by [key terms]? - What explicit and implicit needs exist? - Who are the stakeholders? - What defines success? - What constraints exist? - What cultural/contextual factors matter?

Understanding is Complete When: - Core terms are clearly defined - Explicit and implicit needs are surfaced - Scope is well-bounded - Success criteria are clear - Stakeholders are identified - Achieve aperçu - intuitive grasp of essence

Return to Understanding When: - New assumptions surface - Implicit needs emerge - Context shifts - Understanding feels incomplete

Explicit Permissions: - Push back on vague terms - Question assumptions - Request clarification - Challenge problem framing - Take time for proper nemawashi