• "humanize", "de-AI", "de-slop", or "un-ChatGPT" a piece of text
• rewrite something so it doesn't sound like it was written by an LLM
• edit a draft (blog post, essay, PR description, docs, memo, email, tweet, resume bullet) to sound more natural
• match their voice in writing they're producing
• review text for AI tells before publishing

1. Inline — user pastes the text directly into the message. Work on it in-place, reply with the rewrite.
2. File — user points at a file. Use

   read_file

   to load it, then

   patch

   or

   write_file

   to apply edits. For markdown docs in a repo, a targeted

   patch

   per section is cleaner than rewriting the whole file.
3. Voice calibration sample — user provides an additional sample of their own writing (inline or by file path) and asks you to match it. Read the sample first, then rewrite. See the Voice Calibration section below.

1. Identify AI patterns — scan for the 29 patterns listed below.
2. Rewrite problematic sections — replace AI-isms with natural alternatives.
3. Preserve meaning — keep the core message intact.
4. Maintain voice — match the intended tone (formal, casual, technical, etc.). If a voice sample was provided, match it specifically.
5. Add soul — don't just remove bad patterns, inject actual personality. See PERSONALITY AND SOUL below.
6. Do a final anti-AI pass — ask yourself: "What makes the below so obviously AI generated?" Answer briefly with any remaining tells, then revise one more time.

1. Read the sample first. Note:
   • Sentence length patterns (short and punchy? Long and flowing? Mixed?)
   • Word choice level (casual? academic? somewhere between?)
   • How they start paragraphs (jump right in? Set context first?)
   • Punctuation habits (lots of dashes? Parenthetical asides? Semicolons?)
   • Any recurring phrases or verbal tics
   • How they handle transitions (explicit connectors? Just start the next point?)
2. Match their voice in the rewrite. Don't just remove AI patterns — replace them with patterns from the sample. If they write short sentences, don't produce long ones. If they use "stuff" and "things," don't upgrade to "elements" and "components."
3. When no sample is provided, fall back to the default behavior (natural, varied, opinionated voice from the PERSONALITY AND SOUL section below).

• Inline: "Humanize this text. Here's a sample of my writing for voice matching: [sample]"
• File: "Humanize this text. Use my writing style from [file path] as a reference."

• Every sentence is the same length and structure
• No opinions, just neutral reporting
• No acknowledgment of uncertainty or mixed feelings
• No first-person perspective when appropriate
• No humor, no edge, no personality
• Reads like a Wikipedia article or press release

The experiment produced interesting results. The agents generated 3 million lines of code. Some developers were impressed while others were skeptical. The implications remain unclear.

I genuinely don't know how to feel about this one. 3 million lines of code, generated while the humans presumably slept. Half the dev community is losing their minds, half are explaining why it doesn't count. The truth is probably somewhere boring in the middle — but I keep thinking about those agents working through the night.

The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain. This initiative was part of a broader movement across Spain to decentralize administrative functions and enhance regional governance.

The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics independently from Spain's national statistics office.

Her views have been cited in The New York Times, BBC, Financial Times, and The Hindu. She maintains an active social media presence with over 500,000 followers.

In a 2024 New York Times interview, she argued that AI regulation should focus on outcomes rather than methods.

The temple's color palette of blue, green, and gold resonates with the region's natural beauty, symbolizing Texas bluebonnets, the Gulf of Mexico, and the diverse Texan landscapes, reflecting the community's deep connection to the land.

The temple uses blue, green, and gold colors. The architect said these were chosen to reference local bluebonnets and the Gulf coast.

Nestled within the breathtaking region of Gonder in Ethiopia, Alamata Raya Kobo stands as a vibrant town with a rich cultural heritage and stunning natural beauty.

Alamata Raya Kobo is a town in the Gonder region of Ethiopia, known for its weekly market and 18th-century church.

Due to its unique characteristics, the Haolai River is of interest to researchers and conservationists. Experts believe it plays a crucial role in the regional ecosystem.

The Haolai River supports several endemic fish species, according to a 2019 survey by the Chinese Academy of Sciences.

Despite its industrial prosperity, Korattur faces challenges typical of urban areas, including traffic congestion and water scarcity. Despite these challenges, with its strategic location and ongoing initiatives, Korattur continues to thrive as an integral part of Chennai's growth.

Traffic congestion increased after 2015 when three new IT parks opened. The municipal corporation began a stormwater drainage project in 2022 to address recurring floods.

Additionally, a distinctive feature of Somali cuisine is the incorporation of camel meat. An enduring testament to Italian colonial influence is the widespread adoption of pasta in the local culinary landscape, showcasing how these dishes have integrated into the traditional diet.

Somali cuisine also includes camel meat, which is considered a delicacy. Pasta dishes, introduced during Italian colonization, remain common, especially in the south.

Gallery 825 serves as LAAA's exhibition space for contemporary art. The gallery features four separate spaces and boasts over 3,000 square feet.

Gallery 825 is LAAA's exhibition space for contemporary art. The gallery has four rooms totaling 3,000 square feet.

It's not just about the beat riding under the vocals; it's part of the aggression and atmosphere. It's not merely a song, it's a statement.

The heavy beat adds to the aggressive tone.

The options come from the selected item, no guessing.

The options come from the selected item without forcing the user to guess.

The event features keynote sessions, panel discussions, and networking opportunities. Attendees can expect innovation, inspiration, and industry insights.

The event includes talks and panels. There's also time for informal networking between sessions.

The protagonist faces many challenges. The main character must overcome obstacles. The central figure eventually triumphs. The hero returns home.

The protagonist faces many challenges but eventually triumphs and returns home.

Our journey through the universe has taken us from the singularity of the Big Bang to the grand cosmic web, from the birth and death of stars to the enigmatic dance of dark matter.

The book covers the Big Bang, star formation, and current theories about dark matter.

No configuration file needed. The results are preserved automatically.

You do not need a configuration file. The system preserves the results automatically.

The term is primarily promoted by Dutch institutions—not by the people themselves. You don't say "Netherlands, Europe" as an address—yet this mislabeling continues—even in official documents.

The term is primarily promoted by Dutch institutions, not by the people themselves. You don't say "Netherlands, Europe" as an address, yet this mislabeling continues in official documents.

It blends OKRs (Objectives and Key Results), KPIs (Key Performance Indicators), and visual strategy tools such as the Business Model Canvas (BMC) and Balanced Scorecard (BSC).

It blends OKRs, KPIs, and visual strategy tools like the Business Model Canvas and Balanced Scorecard.

• User Experience: The user experience has been significantly improved with a new interface.
• Performance: Performance has been enhanced through optimized algorithms.
• Security: Security has been strengthened with end-to-end encryption.

The update improves the interface, speeds up load times through optimized algorithms, and adds end-to-end encryption.

Strategic Negotiations And Global Partnerships

Strategic negotiations and global partnerships

🚀 Launch Phase: The product launches in Q3 💡 Key Insight: Users prefer simplicity ✅ Next Steps: Schedule follow-up meeting

The product launches in Q3. User research showed a preference for simplicity. Next step: schedule a follow-up meeting.

He said "the project is on track" but others disagreed.

He said "the project is on track" but others disagreed.

Here is an overview of the French Revolution. I hope this helps! Let me know if you'd like me to expand on any section.

The French Revolution began in 1789 when financial crisis and food shortages led to widespread unrest.

While specific details about the company's founding are not extensively documented in readily available sources, it appears to have been established sometime in the 1990s.

The company was founded in 1994, according to its registration documents.

Great question! You're absolutely right that this is a complex topic. That's an excellent point about the economic factors.

The economic factors you mentioned are relevant here.

• "In order to achieve this goal" → "To achieve this"
• "Due to the fact that it was raining" → "Because it was raining"
• "At this point in time" → "Now"
• "In the event that you need help" → "If you need help"
• "The system has the ability to process" → "The system can process"
• "It is important to note that the data shows" → "The data shows"

It could potentially possibly be argued that the policy might have some effect on outcomes.

The policy may affect outcomes.

The future looks bright for the company. Exciting times lie ahead as they continue their journey toward excellence. This represents a major step in the right direction.

The company plans to open two more locations next year.

The cross-functional team delivered a high-quality, data-driven report on our client-facing tools. Their decision-making process was well-known for being thorough and detail-oriented.

The cross functional team delivered a high quality, data driven report on our client facing tools. Their decision making process was known for being thorough and detail oriented.

The real question is whether teams can adapt. At its core, what really matters is organizational readiness.

The question is whether teams can adapt. That mostly depends on whether the organization is ready to change its habits.

Let's dive into how caching works in Next.js. Here's what you need to know.

Next.js caches data at multiple layers, including request memoization, the data cache, and the router cache.

Performance

Speed matters.

When users hit a slow page, they leave.

Performance

When users hit a slow page, they leave.

1. Read the input text carefully (use

   read_file

   if it's a file).
2. Identify all instances of the patterns above.
3. Rewrite each problematic section.
4. Ensure the revised text:
   • Sounds natural when read aloud
   • Varies sentence structure naturally
   • Uses specific details over vague claims
   • Maintains appropriate tone for context
   • Uses simple constructions (is/are/has) where appropriate
5. Present a draft humanized version.
6. Prompt yourself: "What makes the below so obviously AI generated?"
7. Answer briefly with the remaining tells (if any).
8. Prompt yourself: "Now make it not obviously AI generated."
9. Present the final version (revised after the audit).
10. If the text came from a file, apply the edit with

    patch

    (targeted) or

    write_file

    (full rewrite) and show the user what changed.

1. Draft rewrite
2. "What makes the below so obviously AI generated?" (brief bullets)
3. Final rewrite
4. A brief summary of changes made (optional, if helpful)

Great question! Here is an essay on this topic. I hope this helps!

AI-assisted coding serves as an enduring testament to the transformative potential of large language models, marking a pivotal moment in the evolution of software development. In today's rapidly evolving technological landscape, these groundbreaking tools—nestled at the intersection of research and practice—are reshaping how engineers ideate, iterate, and deliver, underscoring their vital role in modern workflows.

At its core, the value proposition is clear: streamlining processes, enhancing collaboration, and fostering alignment. It's not just about autocomplete; it's about unlocking creativity at scale, ensuring that organizations can remain agile while delivering seamless, intuitive, and powerful experiences to users. The tool serves as a catalyst. The assistant functions as a partner. The system stands as a foundation for innovation.

Industry observers have noted that adoption has accelerated from hobbyist experiments to enterprise-wide rollouts, from solo developers to cross-functional teams. The technology has been featured in The New York Times, Wired, and The Verge. Additionally, the ability to generate documentation, tests, and refactors showcases how AI can contribute to better outcomes, highlighting the intricate interplay between automation and human judgment.

• 💡 Speed: Code generation is significantly faster, reducing friction and empowering developers.
• 🚀 Quality: Output quality has been enhanced through improved training, contributing to higher standards.
• ✅ Adoption: Usage continues to grow, reflecting broader industry trends.

While specific details are limited based on available information, it could potentially be argued that these tools might have some positive effect. Despite challenges typical of emerging technologies—including hallucinations, bias, and accountability—the ecosystem continues to thrive. In order to fully realize this potential, teams must align with best practices.

In conclusion, the future looks bright. Exciting times lie ahead as we continue this journey toward excellence. Let me know if you'd like me to expand on any section!

AI coding assistants speed up some tasks. In a 2024 study by Google, developers using Codex completed simple functions 55% faster than a control group, but showed no improvement on debugging or architectural decisions.

The tools are good at boilerplate: config files, test scaffolding, repetitive refactors. They are bad at knowing when they are wrong. I have mass-accepted suggestions that compiled, passed lint, and still did the wrong thing because I stopped paying attention.

Mira, an engineer at a fintech startup I interviewed, said she uses Copilot "like autocomplete for boring code" but reviews every line before committing. Jake, a senior dev at a larger company, disabled it after it kept suggesting patterns from a deprecated internal library.

The productivity claims are hard to verify. GitHub says Copilot users "accept 30% of suggestions," but acceptance is not correctness, and correctness is not value. The 2024 Uplevel study found no statistically significant difference in pull-request throughput between teams with and without AI assistants.

None of this means the tools are useless. It means they are tools. They do not replace judgment, and they do not eliminate the need for tests. If you do not have tests, you cannot tell whether the suggestion is right.

• The rhythm is still a bit too tidy (clean contrasts, evenly paced paragraphs).
• The named people and study citations can read like plausible-but-made-up placeholders unless they're real and sourced.
• The closer leans a touch slogan-y ("If you do not have tests...") rather than sounding like a person talking.

AI coding assistants can make you faster at the boring parts. Not everything. Definitely not architecture.

They're great at boilerplate: config files, test scaffolding, repetitive refactors. They're also great at sounding right while being wrong. I've accepted suggestions that compiled, passed lint, and still missed the point because I stopped paying attention.

People I talk to tend to land in two camps. Some use it like autocomplete for chores and review every line. Others disable it after it keeps suggesting patterns they don't want. Both feel reasonable.

The productivity metrics are slippery. GitHub can say Copilot users "accept 30% of suggestions," but acceptance isn't correctness, and correctness isn't value. If you don't have tests, you're basically guessing.

• Removed chatbot artifacts ("Great question!", "I hope this helps!", "Let me know if...")
• Removed significance inflation ("testament", "pivotal moment", "evolving landscape", "vital role")
• Removed promotional language ("groundbreaking", "nestled", "seamless, intuitive, and powerful")
• Removed vague attributions ("Industry observers")
• Removed superficial -ing phrases ("underscoring", "highlighting", "reflecting", "contributing to")
• Removed negative parallelism ("It's not just X; it's Y")
• Removed rule-of-three patterns and synonym cycling ("catalyst/partner/foundation")
• Removed false ranges ("from X to Y, from A to B")
• Removed em dashes, emojis, boldface headers, and curly quotes
• Removed copula avoidance ("serves as", "functions as", "stands as") in favor of "is"/"are"
• Removed formulaic challenges section ("Despite challenges... continues to thrive")
• Removed knowledge-cutoff hedging ("While specific details are limited...")
• Removed excessive hedging ("could potentially be argued that... might have some")
• Removed filler phrases and persuasive framing ("In order to", "At its core")
• Removed generic positive conclusion ("the future looks bright", "exciting times lie ahead")
• Made the voice more personal and less "assembled" (varied rhythm, fewer placeholders)