Feed aggregator

Article URL: https://arxiv.org/abs/2309.07932

Comments URL: https://news.ycombinator.com/item?id=43758762

Points: 1

# Comments: 1

Article URL: https://bsky.app/profile/nightsciencepod.bsky.social/post/3lnd2sxabec26

Comments URL: https://news.ycombinator.com/item?id=43758759

Points: 1

# Comments: 1

Article URL: https://solderandsignal.com/engineers-journal.html

Comments URL: https://news.ycombinator.com/item?id=43758731

Points: 1

# Comments: 0

Article URL: https://www.rumidocs.com/newsroom/new-chatgpt-models-seem-to-leave-watermarks-on-text

Comments URL: https://news.ycombinator.com/item?id=43758730

Points: 4

# Comments: 0

Article URL: https://github.com/masutaka/github-nippou

Comments URL: https://news.ycombinator.com/item?id=43758651

Points: 1

# Comments: 0

Article URL: https://www.theamericanconservative.com/what-would-jesus-do/

Comments URL: https://news.ycombinator.com/item?id=43758649

Points: 1

# Comments: 0

Article URL: https://medium.com/@utsavmadaan823/how-we-slashed-api-response-times-by-50-with-go-compiler-optimizations-3c2592c2d241

Comments URL: https://news.ycombinator.com/item?id=43758642

Points: 1

# Comments: 0

Article URL: https://www.technologyreview.com/2024/12/11/1108476/bluesky-has-an-impersonator-problem/

Comments URL: https://news.ycombinator.com/item?id=43758628

Points: 3

# Comments: 0

Article URL: https://www.theverge.com/news/652635/airbnb-full-price-stay-default

Comments URL: https://news.ycombinator.com/item?id=43758609

Points: 2

# Comments: 0

Article URL: https://www.nature.com/articles/s41467-025-58588-7

Comments URL: https://news.ycombinator.com/item?id=43758608

Points: 2

# Comments: 0

Article URL: https://jenamiller.com/notes-from-a-hired-pen-not-paying-freelancers-is-the-canary-in-the-coal-mine/

Comments URL: https://news.ycombinator.com/item?id=43758548

Points: 4

# Comments: 0

Article URL: https://www.youtube.com/watch?v=IACHfKmZMr8

Comments URL: https://news.ycombinator.com/item?id=43758547

Points: 2

# Comments: 1

Article URL: https://socket.dev/blog/malicious-npm-and-pypi-packages-steal-wallet-credentials

Comments URL: https://news.ycombinator.com/item?id=43758508

Points: 1

# Comments: 0

Article URL: https://www.nytimes.com/2025/04/15/books/review/enuf-is-enough-gabe-henry-pronoun-trouble-john-mcwhorter.html

Comments URL: https://news.ycombinator.com/item?id=43758502

Points: 3

# Comments: 1

Article URL: https://www.youtube.com/watch?v=83NRGh0Oing

Comments URL: https://news.ycombinator.com/item?id=43758499

Points: 2

# Comments: 0

Article URL: https://www.perplexity.ai/hub/blog/choice-is-the-remedy

Comments URL: https://news.ycombinator.com/item?id=43758486

Points: 2

# Comments: 1

Here are the answers for The New York Times Mini Crossword for April 22.

(Note: I'm not linking the resulting book. This post focuses solely on the process and practical lessons learned collaborating with LLMs on a large writing project.)

Hey HN, I recently finished a months-long project collaborating intensively with various LLMs (ChatGPT, Claude, Gemini) to write a book about using AI in management. The process became a meta-experiment, revealing practical workflows and pitfalls that felt worth sharing.

This post breaks down the workflow, quirks, and lessons learned.

Getting Started: Used ChatGPT as a sounding board for messy notes. One morning, stuck in traffic, tried voice dictation directly into the chat app. Expected chaos, got usable (if rambling) text. Lesson 1: Capture raw ideas immediately. Use voice/text to get sparks down, then refine. Key for overcoming the blank page.

My Workflow evolved organically: Conversational Brainstorming: "Talk" ideas through with the AI. Ask for analogies, counterarguments, structure. Treat it like an always-available (but weird) partner. Partnership Drafting: Let AI generate first passes when stuck ("Explain X simply for Y"), but treat as raw material needing heavy human editing/fact-checking. Or, write first, have AI polish. Often alternated. Iterative Refinement: The core loop. Paste draft > ask for specific feedback ("Is this logic clear?") -> integrate selectively > repeat. (Lesson 2: Vague prompts = vague results; give granular instructions. Often requires breaking down tasks: logic first, then style). Practice Safe Context Management: LLMs forget (context windows). (Lesson 3: You are the AI's external memory. Constantly re-paste context/style guides; use system prompts. Assume zero persistence across time). Read-Aloud Reviews: Use TTS or read drafts aloud. (Lesson 4: Ears catch awkwardness eyes miss. Crucial for natural flow).

The "AI A-Team": Different models have distinct strengths: ChatGPT: Creative "liberal arts" type; great for analogies/prose, but verbose/flattery-prone. Claude: Analytical "engineer"; excels at logic/accuracy/code, but maybe don't invite for drinks. Gemini: The "copyeditor"; good for large-context consistency. Can push back constructively. (Lessons 5 & 6: Use the right tool for the job; learn strengths via experimentation & use models to check each other. Feeding output between them often revealed flaws - Gemini calling out ChatGPT's tells was useful).

Stuff I Did Not Do Well:

Biggest hurdles:

AI Flattery is Real: Helpfulness optimization means praise for bad work. (Lesson 7: Prompt for critical feedback. 'Critique harshly'. Don't trust praise; human review vital). The "AI Voice" is Pervasive: Understand why it sounds robotic (training bias, RLHF). (Lesson 8: Combat AI-isms. Prompt specific tones; edit out filler/hedging/repetition/'delve'; kill em dashes unless formal). Verification Burden is HUGE: AI hallucinates/facts wrong. (Lesson 9: Assume nothing correct without verification. You are the fact-checker. Non-negotiable despite workload. Ground claims; be careful with nuance/lived experience). Perfectionism is a Trap: AI enables endless iteration. (Lesson 10: Set limits; trust judgment. Know 'good enough'. Don't let AI erode voice. Kill your darlings).

My Personal Role in This fiasco:

Deep AI collaboration elevates the human role to: Manager (goals/context), Arbitrator (evaluating conflicts), Integrator (synthesizing), Quality Control (verification/ethics), and Voice (infusing personality/nuance).

Conclusion: This wasn't push-button magic; it was intensive, iterative partnership needing constant human guidance, judgment, and effort. It accelerated things dramatically and sparked ideas, but final quality depended entirely on active human management.

Key takeaway: Embrace the mess. Capture fast. Iterate hard. Know your tools. Verify everything. Never abdicate your role as the human mind in charge. Would love to hear thoughts on others' experiences.

Comments URL: https://news.ycombinator.com/item?id=43758459

Points: 4

# Comments: 2

Article URL: https://www.macstories.net/roundups/whimsy-roundup-six-playful-apps-and-customizations-for-your-mac/

Comments URL: https://news.ycombinator.com/item?id=43758451

Points: 2

# Comments: 0

Article URL: https://ellgood.medium.com/tyranny-com-technologies-of-unreason-f0a1feede076

Comments URL: https://news.ycombinator.com/item?id=43758447

Points: 6

# Comments: 0