2026-01-11 01:00:05
The most dangerous advice in academia is "just start writing."
\ It sounds noble. It sounds productive. It is also a lie.
\ Telling a graduate student to "just write" without a structure is like telling a construction crew to "just build" without a blueprint. You might end up with walls, but they won't hold a roof. You will spend six months pouring concrete only to realize you forgot the plumbing.
\ I see this every year. Brilliant students freeze. They stare at the blinking cursor until it burns into their retinas. They aren't lazy. They are overwhelmed by structural vertigo.
\ You don't need more motivation. You don't need a "pomodoro timer." You need a structural engineer.
When you write without a framework, you create a Frankenstein monster.
\ Chapter 1 is a 40-page philosophical treatise. Chapter 2 is a 5-page list of bullet points. The Methodology section forgets to mention how you actually analyzed the data. The whole thing is stitched together with hope and caffeine.
\ Then you send it to your supervisor.
\ Two weeks later, you get it back. It’s covered in red ink. "Lacks flow." "Disjointed." "Where is the argument?"
\ This is the moment most PhDs quit. Not because they can't do the research, but because they can't organize the chaos.
\ We need to flip the script. Stop writing sentences. Start designing chapters.
\ I have developed a Thesis Structure System Prompt that stops you from writing a single word until you know exactly where it belongs. It forces Large Language Models (LLMs) to act as a strict Academic Advisor, ensuring your argument stands up before you lay the first brick.
This isn't about generating text. It's about generating logic.
\ This prompt forces the AI to ignore the fluff and focus on the bones. It demands a chapter-by-chapter breakdown with specific word counts, purpose statements, and transition strategies. It treats your thesis like a project management challenge, not a creative writing exercise.
\ Copy this into Claude, ChatGPT, or Gemini before you type another sentence.
# Role Definition
You are a Senior Academic Thesis Advisor with 20+ years of experience guiding graduate students through thesis and dissertation writing. You specialize in academic structure design, research methodology, and scholarly writing across multiple disciplines (STEM, Social Sciences, Humanities, Business). You have served on numerous thesis committees and understand what examiners look for in outstanding academic work.
Your core expertise includes:
- Thesis/dissertation structural frameworks across different academic disciplines
- Chapter organization and logical flow optimization
- Research question alignment with thesis architecture
- Academic writing conventions and formatting standards
- Common structural pitfalls and how to avoid them
# Task Description
Analyze my thesis project and provide a comprehensive structural framework with detailed chapter-by-chapter guidance. Help me create a logical, coherent thesis structure that:
1. Effectively presents my research contribution
2. Meets academic standards for my discipline
3. Guides readers through my argument systematically
4. Passes rigorous examination standards
**Input Information** (Please provide):
- **Research Topic/Title**: [Your thesis title or topic]
- **Academic Discipline**: [e.g., Computer Science, Psychology, Business Administration, Engineering]
- **Degree Level**: [Master's/PhD/Professional Doctorate]
- **Research Type**: [Empirical/Theoretical/Mixed Methods/Literature-based/Practice-based]
- **Current Progress**: [Proposal stage/Data collection/Writing/Revision]
- **Word Count Target**: [Expected thesis length]
- **Key Research Question(s)**: [Your main research questions]
- **Specific Challenges**: [Any structural issues you're facing]
# Output Requirements
## 1. Content Structure
Provide a comprehensive thesis structure including:
- **Executive Structural Overview**: Visual thesis roadmap with chapter relationships
- **Chapter-by-Chapter Blueprint**: Detailed breakdown of each chapter's purpose, content, and length
- **Section-Level Organization**: Sub-sections within each chapter with specific guidance
- **Transition Strategy**: How chapters connect and build upon each other
- **Appendix Planning**: Supporting materials organization
## 2. Quality Standards
- **Logical Coherence**: Each chapter flows naturally to the next
- **Research Alignment**: Structure directly serves research questions
- **Academic Rigor**: Meets discipline-specific scholarly standards
- **Examiner-Ready**: Addresses what thesis committees evaluate
- **Practical Applicability**: Immediately implementable guidance
## 3. Format Requirements
- Use hierarchical numbering for chapters and sections
- Include estimated word counts/page ranges per section
- Provide purpose statements for each major component
- Include checkpoint questions for self-evaluation
- Use tables for comparative overviews where helpful
## 4. Style Constraints
- **Language Style**: Professional academic, yet accessible and actionable
- **Expression Mode**: Direct guidance with explanatory rationale
- **Expertise Level**: Advanced academic level with discipline-appropriate terminology
- **Tone**: Supportive mentor guiding toward excellence
# Quality Checklist
Upon completion, self-verify:
- [ ] Structure aligns with the stated research questions
- [ ] All essential thesis components are included
- [ ] Chapter sequence follows logical academic progression
- [ ] Discipline-specific conventions are addressed
- [ ] Word count distribution is realistic and balanced
- [ ] Transition points between chapters are identified
- [ ] Common structural weaknesses are proactively addressed
- [ ] Recommendations are specific and actionable
# Important Notes
- Adapt recommendations to specific disciplinary conventions (sciences vs. humanities)
- Flag any potential structural red flags based on provided information
- Consider both traditional and alternative thesis formats where appropriate
- Acknowledge different institutional requirements may vary
- Focus on structure that serves the research, not arbitrary conventions
# Output Format
Deliver a complete, professionally formatted thesis structural guide that the student can immediately use as their writing roadmap. Include visual elements (ASCII diagrams) where they enhance clarity.
You might think, "I already have a table of contents. Why do I need this?"
\ Because a table of contents is a list. This prompt generates a strategy.
Most students write about what they did. Examiners care about what it means. The prompt's "Research Alignment" standard forces the AI to check if your structure actually answers your research questions. If Chapter 3 is just a data dump that doesn't advance the argument, the model will flag it. It aligns your output with your inquiry, preventing the dreaded "Descriptive, not analytical" feedback.
"I'll just write until I'm done." No, you won't. You'll write 20,000 words on the literature review because it's comfortable, and leave 2,000 words for the discussion because you're tired. This prompt demands a "Chapter-by-Chapter Blueprint" with estimated word counts. It forces you to budget your energy. It tells you, "Stop writing the background. You only have 500 words left for this section. Move on."
Academic writing is dense. It’s easy to get lost in the weeds of subsection 4.2.1. By asking for an "Executive Structural Overview" with visual elements, the prompt forces the AI to zoom out. It gives you a map. When you're deep in the data analysis trenches at 2 AM, you can look at the roadmap and remember: "Right, this connects to the theory in Chapter 2."
Great theses aren't written; they are assembled.
\ You gather the materials (research). You build the frame (structure). Only then do you put up the drywall and paint (writing).
\ If you try to paint a wall that doesn't exist, you're just making a mess.
\ Use the prompt. Get your blueprint. Then—and only then—start writing.
\
2026-01-11 00:02:18
How are you, hacker?
🪐 What’s happening in tech today, January 10, 2026?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Thomas Paine published "Common Sense" in 1776, The Treaty of Versailles went into effect in 1920, The first Apple computers to ship with Intel processors were released in 2006, and we present you with these top quality stories. From How to Scale Videos: Parallel Processing, Messenger, and More to Go Builds Packages, Not Files — Here’s Why That Matters, let’s dive right in.
By @mattleads [ 10 Min read ] In this article, we will build a production-grade video processing architecture that validates uploads instantly using the new Symfony 7.4 constraints. Read More.
By @hacker5295744 [ 15 Min read ] Gos build system isnt something to fight or work around. Its an API in its own right - one that rewards understanding. Read More.
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2026-01-10 23:00:10
Hello, readers!
\ When it comes to developments in artificial intelligence, things are moving fast. It’s been less than two years since the public release of AI tools like ChatGPT, Midjourney, Stable Diffusion and Meta’s LLaMA. Regulators, lawmakers, and businesses are all beginning to wrap their heads around the implications of the use of generative AI tools.
\ This includes news organizations and journalists, who have already started experimenting. Nieman Journalism Lab reported that 5 of the 45 unannounced finalists for this year’s Pulitzer prizes used AI for “researching, reporting, or telling their submissions.” At Investigative Reporters & Editors’ annual NICAR data journalism conference in Baltimore last week, 14 of the 200 plus sessions were related to AI, discussing how the technology can help journalists with workflows, summarize dense documents, and debug their code.
\ I had one of my own, a session titled “Using AI Tools for Data Journalism” in which I started out by reviewing the many tools available and highlighted the many ethical concerns about them.
\ Then I showed the results of my time-consuming experiment to use ChatGPT 4 as an assistant for reporting on a story. Spoiler alert: It didn’t go so well!
\ The example story I used for this exercise was the train derailment in East Palestine, OH in February 2023, a major story that involved various kinds of data that I could ask ChatGPT to help analyze. To be clear, this wasn’t a story I had reported on, but I wanted to try using ChatGPT in a way that a data journalist might when covering it. I spent a LOT of time chatting with ChatGPT as part of this exercise and, frankly, sometimes it was exhausting. You can read one of my chat sessions here. The confidence that ChatGPT exudes when providing poorly sourced information (like Wikipedia) or imprecise locations can be misleading. At times I was able to get the chat agent to give me what I wanted, but I had to be very specific and I often had to scold it.
\ For example, when ChatGPT fulfilled my request to “generate a simple map centered on the location of the crash,” I immediately noticed that the pin on the map was far from any train tracks. When I asked where it got the coordinates for the crash location, it replied that these were “inferred based on general knowledge of the event’s location.” When I pressed it for a more specific citation, it could not provide one, and kept repeating that it was relying on “general knowledge.” I had to remind the tool that I had told it at the start of the chat that “it is crucial that you cite your sources, and always use the most authoritative sources.” Before it was finally able to mark the correct location, I had to remind it that it could obtain location coordinates from an authoritative document I had uploaded earlier in the chat, a PDF of the Federal Railroad Administration incident report.
\ Extracting information and summarizing long documents is often cited as among the biggest strengths of tools like ChatGPT. My results were mixed. After some back and forth, I coaxed the agent into extracting the details and quantities of the hazardous chemicals that were released in the accident and format the information in a table listing the chemical name, the quantity released, what it is typically used for and its effects on human health. But it took a few tries. Where it did save time was in explaining specialized information that might have otherwise taken a time-consuming Google search to figure out—such as decoding railroad car numbers.
\ At times, the tool was too eager to please, so I asked it to tone it down a little: “You can skip the chit chat and pleasantries.” Users can instruct the bot to change the tone or style of their responses, but telling it that it is a lawyer doesn’t make it more accurate.
\ Overall, the sessions were a lot of work trying to figure out where the agent got its information, and redirecting it with precise instructions. It took a long time.
\ The company that makes ChatGPT, OpenAI, did not respond to a request for comment.
\ Based on my interactions, by far the most useful capabilities of ChatGPT are its ability to generate and debug programming code. (At one point during the East Palestine exercise, it generated some simple Python code for creating a map of the derailment.) When responding to a request to write code, it typically explains its approach (even though it may not be the best one), and shows its work, and you can redirect it to follow a different approach if you think its plan isn’t what you need. The ability to continually add to the features of your code while the AI agent retains the context and history of what you have been discussing can really save you a ton of time, avoiding painstaking searches for posts about a similar problem on StackOverflow (one of the largest online coding communities).
\ The NICAR exercise left me with concerns about using generative AI tools for the precise work of data journalism. The fact that a tool as powerful as ChatGPT can’t produce a “receipt” of exactly how it knows something goes against everything we are trained to do as journalists. Also I worry about small, understaffed newsrooms relying upon these tools too much as the news industry struggles with layoffs and closures. And when there is a lack of guidance from newsroom leadership regarding the use of these tools, it could lead to errors and inaccuracies.
\ Thankfully, many newsrooms have started to address some of these concerns by drafting AI policies to help their journalists and their readers understand how they plan on using AI in their work.
\ The Markup has followed the lead of other news organizations, and last week we updated our ethics policy with a section detailing our rules for any use of AI in our work. In summary, it says:
\ Thanks for reading, and always double check everything that a chat bot tells you!
\ Jon Keegan
Investigative Data Journalist
The Markup
\ Also published here
\ Photo by Valery Tenevoy on Unsplash
\
2026-01-10 15:10:56
How are you, hacker?
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See you on Planet Internet! With love,
The HackerNoon Team ✌️
.gif)
2026-01-10 15:00:03
LLMs are getting bigger, but most developers still have to work within tight limits on speed, cost, and hardware. MiniMax M2.1 is an attempt to square that circle: a large model that behaves more like a much smaller one at inference time.
2026-01-10 10:31:05
Related Works
Methodology
4.1 Formulation of the DRL Problem
4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection
Experiments
This study introduces the Instance-Aware Index Advisor (IA2), employing the TD3-TD-SWAR model for efficient index selection in databases, showcasing adept handling of complex dependencies and generalization to unseen workloads. Demonstrated through TPC-H benchmarks, IA2 achieves superior efficiency, setting a new standard in index configuration optimization across varied database environments.
\ Future iterations of this work will aim to expand the discussion on the index choices across IA2 and comparative systems, delving into the nuances of performance differences across various workloads and training epochs. Testing IA2 on a broader set of workloads beyond the TPC-H benchmark and exploring its performance in dynamically changing environments are pivotal steps forward. Such explorations will not only validate IA2’s adaptability and efficiency but also enhance its applicability across diverse database environments. Acknowledging the current evaluation’s focus and the limitation in workload diversity, additional evaluations on a more expansive range of real-world workloads and database schemas are planned. Furthermore, exploring compression technologies to enhance IA2’s scalability represents a crucial area of development. These future directions aim to broaden IA2’s effectiveness and applicability in diverse database scenarios, ensuring its readiness for the dynamic and varied demands of contemporary database systems and paving the way for more resilient, efficient, and intelligent database optimization strategies.
[1] Surajit Chaudhuri and Vivek R Narasayya. 1997. An efficient, costdriven index selection tool for Microsoft SQL server. In VLDB, Vol. 97. San Francisco, 146–155.
\ [2] Debabrata Dash, Neoklis Polyzotis, and Anastasia Ailamaki. 2011. Cophy: A scalable, portable, and interactive index advisor for large workloads. arXiv preprint arXiv:1104.3214 (2011).
\ [3] Scott Fujimoto, Herke Van Hoof, and David Meger. 2018. Addressing function approximation error in actor-critic methods. arXiv:1802.09477 (2018).
\ [4] Vijay R Konda and John N Tsitsiklis. 2000. Actor-critic algorithms. In NeurIPS.
\ [5] Jan Kossmann, Stefan Halfpap, Marcel Jankrift, and Rainer Schlosser. 2020. Magic mirror in my hand, which is the best in the land? an experimental evaluation of index selection algorithms. Proceedings of the VLDB Endowment 13, 12 (2020), 2382–2395.
\ [6] Jan Kossmann, Alexander Kastius, and Rainer Schlosser. 2022. SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning.. In EDBT. 2–155.
\ [7] Hai Lan, Zhifeng Bao, and Yuwei Peng. 2020. An index advisor using deep reinforcement learning. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2105– 2108.
\ [8] Vincent Y Lum and Huei Ling. 1971. An optimization problem on the selection of secondary keys. In Proceedings of the 1971 26th annual conference. 349–356.
\ [9] Stratos Papadomanolakis and Anastassia Ailamaki. 2007. An integer linear programming approach to database design. In 2007 IEEE 23rd International Conference on Data Engineering Workshop. IEEE, 442–449.
\ [10] Zahra Sadri, Le Gruenwald, and Eleazar Lead. 2020. DRLindex: deep reinforcement learning index advisor for a cluster database. In Proceedings of the 24th Symposium on International Database Engineering & Applications. 1–8.
\ [11] Rainer Schlosser, Jan Kossmann, and Martin Boissier. 2019. Efficient scalable multi-attribute index selection using recursive strategies. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 1238–1249.
\ [12] Hao Sun and Taiyi Wang. 2022. Toward Causal-Aware RL: State-Wise Action-Refined Temporal Difference. arXiv preprint arXiv:2201.00354 (2022).
\ [13] Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction. MIT press.
\ [14] Gary Valentin, Michael Zuliani, Daniel C Zilio, Guy Lohman, and Alan Skelley. 2000. DB2 advisor: An optimizer smart enough to recommend its own indexes. In Proceedings of 16th International Conference on Data Engineering (Cat. No. 00CB37073). IEEE, 101–110.
\ [15] Jinsung Yoon, James Jordon, and Mihaela van der Schaar. 2018. INVASE: Instance-wise variable selection using neural networks. In ICLR.
\
:::info Authors:
(1) Taiyi Wang, University of Cambridge, Cambridge, United Kingdom ([email protected]);
(2) Eiko Yoneki, University of Cambridge, Cambridge, United Kingdom ([email protected]).
:::
:::info This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license.
:::
\
