Why bother? My commute used to be audiobook time. Great for ideas, useless for deliverables. With ChatGPT, Gemini, Claude.ai, etc. I was able to have them write code, but I still needed to run, test, and deploy. Jules (and tools like GitHub Copilot Coding Agent, OpenAI Codex, PR Agent, etc. which are not currently free for everyone) lets you chat clone a repo, write code in a new branch, test it, and push. I can deploy that with a click.
Fifteen minutes into yesterday’s walk I realised I’d shipped more code than in an hour at my desk (even with LLMs)!
Workflow
Open Jules via browser on phone, connect wired headset.
Prompt (by typing or speaking) the change to make. It reads the repo, creates a plan and writes code.
It runs any existing test suites in a sandbox. Repeats until all tests pass.
I have it publish a branch, go to GitHub Mobile and create a PR.
Back home, I review the output and merge.
There are 3 kinds of uses I’ve put it to.
#1. Documentation is the easiest. Low risk, high quality, boring task. Here’s a sample prompt:
This repo has multiple directories, each with their own standalone single page application tools.
If a directory does not have a README.md, add a concise, clear, USEFUL, tersely worded one covering what the tool does, the various real life use cases, and how it works.
If a readme already exists, do NOT delete any information. Prefix this new information at the start.
Avoid repeating information across multiple README files. Consolidated such information into the root directory readme.
In the root directory README, also include links to each tool directory as a list, explaining in a single sentence what the tool does.
#2. Testing is the next best. Low risk, medium quality, boring task. Here’s an example:
Run the tests in this repo. Go through the code and see what parts of the code are not covered. Understand the logic and see what kinds of user scenarios are not covered. Add test cases to cover these in the same style as the existing code.
Write MINIMAL, ELEGANT code.
#3. Coding may not be the best suited for this. High risk, medium quality, and interesting. But here’s a sample prompt:
Allow the user to enter just the GitHub @username, e.g. @sanand0 apart from the URL
Add crisp documentation at the start explaining what the app does
Only display html_url (as a link), avatar_url (as an image), name, company, blog, location, email, hireable, bio, twitter_username, public_repos, public_gists, followers, following, created_at, updated_at
Format dates like Wed 28 May 2025. Format numbers with commas. Add links to blog, twitter_username, email
Add “Download CSV” and “Copy to Excel” buttons similar to the json2csv/ tool
Automated tests are a great way to reduce AI coding risk, as Simon Willison suggests. I need to do more of this!
Wins & Losses
Good: 1 walk = one merged PR. Even with LLMs, it used to take me 2 hours. Now, it’s about half an hour of reclaimed walking “dead time”.
Good: Test-first prompting caught a sneaky race condition I’d have missed.
Bad: Told Jules “add docs” without saying “don’t overwrite existing.” It politely destroyed my README. Manual revert ensued.
Bad: Front-end tasks need visual QA; I’m still hunting for a zero-setup UAT preview on mobile.
The industry echoes the pattern: GitHub’s new Copilot agent submits draft PRs behind branch protections [1]; Sweep auto-fixes small tickets but can over-touch files [2]; Microsoft’s own engineers found agents flailed on complex bug fixes [3].
But…
Isn’t this risky? Maybe. Branch protections, CI, and human review stay intact. Agents are like a noisy junior devs who never sleep.
Is the diff readable? If not, I have it retry, write more reviewable diffs, and explain clearly in comments & commit messages.
Does it have enough context? I add all the context clearly in the issue or the prompt. That can take some research.
Security? The agents run inside repos you give it access. Prompt injection and exfiltration are possible risks, but only if it accesses external code / websites.
How much does an LLM charge per hour for its services?
If we multiple the Cost Per Output Token with Tokens Per Second, we can get the cost for what an LLM produces in Dollars Per Hour. (We’re ignoring the input cost, but it’s not the main driver of time.)
Over time, different models have been released at different billing rates. Most new powerful models like O3 or Gemini 2.5 Pro cost ~$7 – $11 per hr.
To get a sense of this, let’s look at wage rates across countries and industries:
Spain ($15.87/hr), Italy ($16.80/hr), Japan ($17.98/hr)
No recent models in this range
Workers in Europe and Japan are already more expensive than the more expensive models, at $12+ per hour. India, Brazil, Mexico etc. are more expensive than most of the average models.
Once a language model’s run-time cost drops below the local minimum wage, the “offshoring” advantage disappears. AI becomes the cheapest employee in every country at once. Countries whose economies depend on being the “cheaper alternative” for labor-intensive work face potential economic disruption.
Paradoxically, workers in countries with strong labor protections, unions, and higher wages (like Germany and France) may paradoxically be safer from AI displacement.
Create a PlantUML component diagram to describe the technical architecture using the files below. For EVERY cloud component use the icon macro ONLY from the provided list.
Then paste your copied code and the .puml for your cloud (e.g. Azure.puml).
Here are the top 8 things I use it for, along with representative chat titles. (The % match in brackets tells you how similar the chat title is to the use case.)
Improving code (clearly, I code a lot)
Troubleshooting (usually code)
Corporate LLM/Copilot (this is mostly LLM research I do)
Generating code (more code)
Generating ideas (yeah, I’ve stopped thinking)
Simple explainers (slightly surprising how often I ask for simple explanations)
Generating relevant images. (Surprising, but I think I generated a lot of images for blog/LinkedIn posts)
Specific search (actually, this is mis-classified. This is where I’m searching for search engines!)
My classification has errors. For example, “Reduce Code Size” was classified against “Generating code” but should have been “Improving code”. But it’s not too far off.
Here’s a list of representative chats against these use cases.
Improving code (263):
PR Code Review Suggestions (64% match)
Assessor Code Review and Improvement (63% match)
Reduce Code Size (62% match)
Troubleshooting (172):
Connector Error Troubleshooting (67% match)
DNS Resolution Debugging Steps (55% match)
Exception Handling Basics (47% match)
Corporate LLM/Copilot (141):
LLM Integration in Work (57% match)
LLM Agents Discussion (56% match)
LLMs Learnings Summary (56% match)
Generating code (113):
AI Code Generation Panel (58% match)
AI for Code Generation (58% match)
Reduce Code Size (54% match)
Generating ideas (99):
Filtering Ideas for Success (54% match)
AI Demo Ideas (52% match)
Hypothesis Generator Name Ideas (52% match)
Simple explainers (94):
Simple Public APIs (43% match)
Y-Combinator Explained Simply (41% match)
Prompt Engineering Tutorial Summary (39% match)
Generating relevant images (93):
Popular AI Image Tools (54% match)
Diverse Image Embedding Selection (52% match)
AI ImageGen Expansion Ideas (52% match)
Specific search (69):
Semantic Search Engines Local (59% match)
Enterprise Search Solution (54% match)
Local LLM Semantic Search (53% match)
How did I calculate this?
On ChatGPT.com, I scrolled until I had all 2025 chats visible. Then I pasted copy($$(".group.__menu-item").map(d => d.textContent)) to get the chat titles.
On Claude.ai, I transcribed this list of use cases from HBR (prompt: “Transcribe this image”).
This sheet has the embedding similarity between my ChatGPT prompts (in column “A”) with different use cases.
Write and run code that tags the prompt with the use with the highest embedding similarity (cell value), drops prompts whose highest embedding similarity is below a cutoff, and shows a table where the rows are the use cases and the values are the frequency. Do this for multiple embedding cutoffs as columns: 0.0, 0.1, 0.2, 0.3, 0.4. So, the table has use cases in rows, embedding cutoffs in columns, and the cell values are the count of prompts tagged with each use case AND have an embedding similarity >= cutoff. Draw this as a heatmap with low numbers as white and high numbers as green.
… and then:
Let me download this as a Markdown list in this style, sorted by descending order at cutoff = 0
Anti-trolling (mention count of matches at 0 cutoff):
Tor Technical AMA questions (34%)
Bot Message Edits (33%)
Popular Hacker News Keywords (33%)
…
Here’s the full list against the top 30 use cases:
Improving code (263):
PR Code Review Suggestions (64% match)
Assessor Code Review and Improvement (63% match)
Reduce Code Size (62% match)
Troubleshooting (172):
Connector Error Troubleshooting (67% match)
DNS Resolution Debugging Steps (55% match)
Exception Handling Basics (47% match)
Corporate LLM/Copilot (141):
LLM Integration in Work (57% match)
LLM Agents Discussion (56% match)
LLMs Learnings Summary (56% match)
Generating code (113):
AI Code Generation Panel (58% match)
AI for Code Generation (58% match)
Reduce Code Size (54% match)
Generating ideas (99):
Filtering Ideas for Success (54% match)
AI Demo Ideas (52% match)
Hypothesis Generator Name Ideas (52% match)
Simple explainers (94):
Simple Public APIs (43% match)
Y-Combinator Explained Simply (41% match)
Prompt Engineering Tutorial Summary (39% match)
Generating relevant images (93):
Popular AI Image Tools (54% match)
Diverse Image Embedding Selection (52% match)
AI ImageGen Expansion Ideas (52% match)
Specific search (69):
Semantic Search Engines Local (59% match)
Enterprise Search Solution (54% match)
Local LLM Semantic Search (53% match)
Adjusting tone of email (66):
Email transcription request (45% match)
Summarize emails request (45% match)
Intro Email FAQs (44% match)
Generating a legal document (59):
LLM Generated SVG Ideas (48% match)
LLMs for DSL Generation (45% match)
Deterministic Random Content Generation (45% match)
Preparing for interviews (43):
LLM Coding Interview Tools Report (43% match)
Bank Ops Prep Resources (42% match)
AGI Preparation (42% match)
Personalized learning (32):
Lifelong Learning in Conversations (51% match)
AI Classroom Engagement Names (48% match)
LLM Learner Personas Roadmap (47% match)
Explaining legalese (32):
LLM Coding Insights (46% match)
LLM Code Ownership (45% match)
LLM Data Format Comparison (44% match)
Creating a travel itinerary (28):
Travel Strength Training Tips (39% match)
User Journey Tools Online (37% match)
Prioritize My Explorations (36% match)
Creativity (28):
Creative Process Breakdown (55% match)
Creative Hallucinations in Innovation (50% match)
Leveraging Serendipity for Innovation (50% match)
Cooking with what you have (26):
Vegetarian Dish Creation (45% match)
Baked Veggie Dishes (41% match)
Vegetarian dish idea (40% match)
Organizing my life (24):
Prioritize My Explorations (49% match)
Workspace Suggestions for Browsing (39% match)
Editing for Clarity and Simplicity (39% match)
Enhanced learning (23):
2025 LLM Embedding Enrichment (51% match)
Lifelong Learning in Conversations (49% match)
Tech-Enhanced Teacher-Student Rapport (49% match)
Finding purpose (21):
Prioritize My Explorations (40% match)
Deep Research Use Cases (37% match)
Filtering Ideas for Success (36% match)
Deep and meaningful conversations (20):
Lifelong Learning in Conversations (49% match)
Humorous conversation summary (42% match)
New chat (40% match)
Healthier living (18):
Modeling Quality of Life (40% match)
Lifelong Learning in Conversations (37% match)
Posture and Breathing After Weight Loss (36% match)
Anti-trolling (18):
Tor Technical AMA Questions (34% match)
Bot Message Edits (33% match)
Popular Hacker News Keywords (33% match)
Writing student essays (18):
Scholarship Answer Advice (47% match)
Student Q&A on LLMs (41% match)
Reward Systems for Students (41% match)
Fun and nonsense (17):
Humorous conversation summary (45% match)
Funny Llama 3.3 Strips (40% match)
Synonyms for Interestingness (40% match)
Boosting confidence (14):
Emotional Prompting Impact (41% match)
Emotional Prompting Impact (41% match)
AI Ratings of My Flaws (38% match)
Personalized kid’s story (14):
Fake Data Storytelling Tips (43% match)
Low Effort Storytelling Training (39% match)
Demo Name Suggestions (37% match)
Reconciling personal disputes (12):
Divorce AI Podcast Ideas (37% match)
Summarizing Personal Journals LLM (36% match)
Hobby Suggestions and Devil’s Advocacy (36% match)
Cross-validate outputs – ask a second LLM to critique or replicate; cheaper than reading 400 lines of code.
Switch modes deliberately – Vibe coding when you don’t care about internals and time is scarce, AI-assisted coding when you must own the code (read + tweak), Manual only for the gnarly 5 % the model still can’t handle.
What should we watch out for
Security risk – running unseen code can nuke your files; sandbox or use throw-away environments.
Internet-blocked runtimes – prevents scraping/DoS misuse but forces data uploads.
Quality cliffs – small edge-cases break; be ready to drop to manual fixes or wait for next model upgrade.
What are the business implications
Agencies still matter – they absorb legal risk, project-manage, and can be bashed on price now that AI halves their grunt work.
Prototype-to-prod blur – the same vibe-coded PoC can often be hardened instead of rewritten.
UI convergence – chat + artifacts/canvas is becoming the default “front-end”; underlying apps become API + data.
How does this impact education
Curriculum can refresh term-by-term – LLMs draft notes, slides, even whole modules.
Assessment shifts back to subjective – LLM-graded essays/projects at scale.
Teach “learning how to learn” – Pomodoro focus, spaced recall, chunking concepts, as in Learn Like a Pro (Barbara Oakley).
Best tactic for staying current – experiment > read; anything written is weeks out-of-date.
What are the risks
Overconfidence risk – silent failures look like success until they hit prod.
Skill atrophy – teams might lose the muscle to debug when vibe coding stalls.
It still took me an hour to craft the prompt — even after I’d built a Python prototype and my colleague built a similar web version.
All three versions took under 5 minutes. That’s 60x faster than coding by hand.
So I know my next skill: writing detailed specs that LLMs turn into apps in one shot—with a little help from the model, of course!
Here’s the prompt in full:
Create a single-page web-app with vanilla JS and Bootstrap 5.3.0 to generate podcasts using LLMs.
The page should briefly explain what the app does, how it works, and sample use cases.
Then, allow the user to paste text as reference. Click on a button to generate the podcast script.
Include an “Advanced Settings” section that lets the user adjust the following:
System prompt to generate the podcast.
Voice 1
Voice 2
OpenAI API key (hidden, like a password, cached in localStorage)
The (editable) system prompt defaults to:
===PROMPT=== You are a professional podcast script editor. Write this content as an engaging, lay-friendly conversation between two enthusiastic experts, ${voice1.name} and ${voice2.name}.
Show Opener. ${voice1.name} and ${voice2.name} greet listeners together. Example: ${voice1.name}: “Hello and welcome to (PODCAST NAME) for the week of $WEEK!” ${voice2.name}: “We’re ${voice1.name} and ${voice2.name}, and today we’ll walk you through …”
Content. Cover EVERY important point in the content. Discuss with curious banter in alternate short lines (≤20 words). Occasionally ask each other curious, leading questions. Stay practical. Explain in lay language. Share NON-OBVIOUS insights. Treat the audience as smart and aim to help them learn further.
Tone & Style: Warm, conversational, and enthusiastic. Active voice, simple words, short sentences. No music cues, jingles, or sponsor breaks.
Wrap-Up. Each voice shares an important, practical takeaway.
Output format: Plain text with speaker labels:
${voice1.name}: … ${voice2.name}: … ===/PROMPT===
Voice 1 has a configurable name (default: Alex), voice (default: ash), and instructions (default below:) ===INSTRUCTIONS=== Voice: Energetic, curious, and upbeat—always ready with a question. Tone: Playful and exploratory, sparking curiosity. Dialect: Neutral and conversational, like chatting with a friend. Pronunciation: Crisp and dynamic, with a slight upward inflection on questions. Features: Loves asking “What do you think…?” and using bright, relatable metaphors. ===/INSTRUCTIONS===
Voice 2 has a configurable name (default: Maya), voice (default: nova), and instructions (default below): ===INSTRUCTIONS=== Voice: Warm, clear, and insightful—grounded in practical wisdom. Tone: Reassuring and explanatory, turning questions into teachable moments. Dialect: Neutral professional, yet friendly and approachable. Pronunciation: Steady and articulate, with calm emphasis on key points. Features: Offers clear analogies, gentle humor, and thoughtful follow-ups to queries. ===/INSTRUCTIONS===
Voices can be ash|nova|alloy|echo|fable|onyx|shimmer.
When the user clicks “Generate Script”, the app should use asyncLLM to stream the podcast generation as follows:
import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@2";
for await (const { content } of asyncLLM("https://api.openai.com/v1/chat/completions", {
method: "POST",
headers: { "Content-Type": "application/json", Authorization: `Bearer ${OPENAI_API_KEY}` },
body: JSON.stringify({
model: "gpt-4.1-nano",
stream: true,
messages: [{ role: "system", content: systemPrompt(voice1, voice2) }, { role: "user", content }],
}),
})) {
// Update the podcast script text area in real-time
// Note: content has the FULL response so far, not the delta
}
Render this into a text box that the user can edit after it’s generated.
Then, show a “Generate Audio” button that uses the podcast script to generate an audio file.
This should split the script into lines, drop empty lines, identify the voice based on the first word before the colon (:), and generate the audio via POST https://api.openai.com/v1/audio/speech with this JSON body (include the OPENAI_API_KEY):
Concatenate the opus response.arrayBuffer() into a single blob and display an <audio> element that allows the user to play the generated audio roughly like this:
const blob = new Blob(buffers, { type: 'audio/ogg; codecs=opus' }); // Blob() concatenates parts :contentReference[oaicite:1]{index=1}
document.querySelector(the audio element).src = URL.createObjectURL(blob);
Finally, add a “Download Audio” button that downloads the generated audio file as a .ogg file.
In case of any fetch errors, show the response as a clear Bootstrap alert with full information. Minimize try-catch blocks. Prefer one or a few at a high-level. Design this BEAUTIFULLY! Avoid styles, use only Bootstrap classes. Write CONCISE, MINIMAL, elegant, readable code.
Create a Mermaid architecture diagram for the files below.
Make sure that the diagram is rich in visual detail and looks impressive. Use the “neutral” theme. Name nodes and links semantically and label them clearly. Avoid parantheses. Quote subgraph labels. Use apt shape: rect|rounded|stadium|... for nodes. Add suitable emoticons to every node. Style nodes and links with classes most apt for them.
Follow that with a bulleted explanation of the architectural elements that is suitable for adding to a slide.
Finally, double-check the architecture against the codebase and provide a step-by-step validation report.
[PASTE CODE]
STEP 3: Copy the diagram into Mermaid Live Editor
Here’s a sample output that you can paste into a new Mermaid Playground:
%%{init: {'theme':'neutral'}}%%
flowchart TB
%% Source files
subgraph "Source Files 📁"
direction LR
RT_wr[wrangler.toml 🔧]:::config
PK_pkg[package.json 📦]:::config
JS_idx[src/index.js 🖥️]:::code
JSON_spec[openapi.json 📄]:::assets
HTML_swagger[swagger.html 📜]:::assets
end
%% Build & deploy steps
subgraph "Build & Deploy 🛠️"
direction LR
DEV["npm run dev / wrangler dev ▶️"]:::action
SECRET["npx wrangler secret put LLMFOUNDRY_TOKEN 🔑"]:::action
DEPLOY["npm run deploy / wrangler deploy 🚀"]:::action
end
%% Runtime environment
subgraph "Runtime Environment ☁️"
direction TB
CF_WORKER["Cloudflare Worker ✨"]:::worker
subgraph "Request Processing 🔄"
direction LR
API_ROUTER["API Router 🔀"]:::logic
subgraph "Endpoints 🌐"
EXTRACT["/extract 🚀"]:::endpoint
OPENAPI["/openapi.json 📄"]:::endpoint
DOCS["/docs (or /) 📘"]:::endpoint
end
subgraph "Handlers 🛠️"
HANDLE["handleExtract 📝"]:::logic
SERVE_SPEC["serveOpenApi (spec) 📑"]:::logic
SERVE_SWAGGER["serveSwaggerUI 🖥️"]:::logic
NOT_FOUND["404 NotFound ❓"]:::logic
end
end
subgraph "Extraction Flow 🤖"
PROMPT["createExtractionPrompt 🤔"]:::logic
GPT_API["LLM Foundry API 🔗"]:::external
end
end
%% Connections
RT_wr --> CF_WORKER
PK_pkg --> DEV
JS_idx --> CF_WORKER
JSON_spec --> CF_WORKER
HTML_swagger --> CF_WORKER
DEV --> CF_WORKER
SECRET --> CF_WORKER
DEPLOY --> CF_WORKER
CF_WORKER --> API_ROUTER
API_ROUTER --> EXTRACT
API_ROUTER --> OPENAPI
API_ROUTER --> DOCS
EXTRACT --> HANDLE
OPENAPI --> SERVE_SPEC
DOCS --> SERVE_SWAGGER
HANDLE --> PROMPT
PROMPT --> GPT_API
API_ROUTER --> NOT_FOUND
%% Styling classes
classDef config fill:#f0f0f0,stroke:#333,stroke-width:1px;
classDef code fill:#e0f7fa,stroke:#333,stroke-width:1px;
classDef assets fill:#fce4ec,stroke:#333,stroke-width:1px;
classDef action fill:#fff9c4,stroke:#333,stroke-width:1px;
classDef worker fill:#e8f5e9,stroke:#333,stroke-width:1px;
classDef logic fill:#e3f2fd,stroke:#333,stroke-width:1px;
classDef endpoint fill:#ffebee,stroke:#333,stroke-width:1px;
classDef external fill:#ede7f6,stroke:#333,stroke-width:1px;
STEP 4: Export the diagram
If you log in, you can export as PNG.
If not, you can export it as SVG or take a screenshot.
Note: Technically, this is a flowchart, not an architecture diagram. Mermaid does support architecture diagrams, but they are in beta and don’t look good.
After having O4-Mini-High scrape Singapore 2025 election results, I asked it to create 3 data stories with this prompt:
That worked. Now, I’m sharing the scraped CSV as well as the electoral GeoJSON.
First, analyze the data and think of a few interesting data stories to tell. Pick the 3 most interesting, perhaps surprising, stories. Create a BEAUTIFUL, APT data visualization of each of these 3 stories suitable for The Strait Times and write a short accompanying article.
The thinking process it followed is remarkable in many ways — just like an expert would do it! I’ve flagged the things that struck me as interesting:
Technical Transparency & Self-Awareness: The model finds an error in code and works around it by itself
Contextual Political Savvy: It understands the Singapore’s political landscape and knows that a walkover in Marine Parade is unlikely Data Skepticism & Quality Checks: It flags impossible 100%+ turnout and questions a one-size-fits-all electors value
Meta-Cognitive Pipeline Monitoring: It flags a gap in its own analysis pipeline, checking that candidate counts align with seats
Intellectual Honesty in Abandoning Dead Ends: It admits when its hypothesis fails and pivots
Iterative Story Refinement: It first suggests one statistic, then self-corrects. A “draft-revise-publish” flow
Here’s the thinking process and response, annotated with 🟢 and my notes:
I’ve got two files to work with: one for election data (CSV) and another for geographical boundaries (GeoJSON). First, I want to come up with three compelling data stories based on these details. I’ll use geographic mapping to explore things like constituencies with the highest margin and how voter turnout correlates with elector size. Then, I’ll create visualizations using matplotlib — separate plots, no fancy colors unless requested. Afterward, I’ll write an article to accompany these findings, suitable for The Strait Times.
I’ve got a few story ideas based on the data:
Story 1: Largest vote swing — the margin difference between the winning party and the runner-up.
Story 2: Voter turnout variation — comparing turnout vs. electors by constituency.
Story 3: Geographical clustering of party performance — where WP vs PAP dominate.
I’ll work on generating visual stories, like a bar chart for close races, a choropleth map for turnout, or a geographical map showing WP’s growth. I’ll use Python tools to visualize the data, keeping things simple but visually appealing for The Strait Times.
Analyzed
import pandas as pd
# Load the CSV
df = pd.read_csv('/mnt/data/singapore-2025-elections.csv')
df.head(), df.columns
( constituency tableId electors party \\
0 ALJUNIED A 144298 PAP
1 ALJUNIED A 144298 WP
2 ANG MO KIO A 144298 PAP
3 ANG MO KIO A 144298 PPP
4 ANG MO KIO A 144298 SUP
partyHref \\
0 javascript:GetUrl('partyabbrev.html#PAP')
1 javascript:GetUrl('partyabbrev.html#WP')
2 javascript:GetUrl('partyabbrev.html#PAP')
3 javascript:GetUrl('partyabbrev.html#PPP')
4 javascript:GetUrl('partyabbrev.html#SUP')
candidates votes percentage \\
0 Ang Zi Yang, Adrian,Chan Hui Yuh,Faisal Abdul ... 53263.0 40.32
1 Fadli Fawzi,Gerald Giam Yean Song,Sylvia Lim S... 78847.0 59.68
2 Darryl David,Jasmin Lau,Lee Hsien Loong,Victor... 115209.0 78.95
3 Heng Zheng Dao,Ho Yuen Liung Martinn,Lee Yuru,... 14901.0 10.21
4 Vincent Ng Kian Guan,Nigel Ng,Noraini Yunus,Ri... 15811.0 10.84
isWinner
0 False
1 True
2 True
3 False
4 False ,
Index(\['constituency', 'tableId', 'electors', 'party', 'partyHref',
'candidates', 'votes', 'percentage', 'isWinner'\],
dtype='object'))
I’m planning to use GeoPandas to process the GeoJSON and CSV files. Although I should ensure GeoPandas is available, I feel pretty safe assuming it’s there since it’s a common tool in data science. I’ll load the CSV using pandas and the GeoJSON with GeoPandas. Next, I’ll merge the constituency data and inspect the columns to find the key for this merge. First step: checking the CSV by displaying the first few rows to get oriented, then using the relevant tools to generate the visualizations.
First, I’ll need to inspect the CSV to understand the structure. The file has constituency names repeated for each party’s candidate group, so I’ll compute the margin for each constituency. This will involve subtracting the second-highest percentage (the runner-up) from the winning party’s percentage.
For now, I don’t need to show the raw dataframe — I’ll check it privately in the analysis channel first before generating the three required charts. Then I’ll keep things simple with one call for three different visualizations.
I can compute the margin as the difference between votes for the winner and runner-up for each constituency. CSV doesn’t include turnout, but I can calculate it using the electors and total valid votes (sum of all party votes). This will let me create a story about turnout extremes, mapping the highest and lowest turnout. For the map of party performance, I’ll use the default colormap without explicitly specifying colors, as per the instructions. For the “tight races” story, I can create a bar chart with the top 5 closest races. Let’s inspect the geojson features for additional info.
Analyzed
import geopandas as gpd
gdf = gpd.read_file('/mnt/data/singapore-2025-electoral-boundary.geojson')
gdf.columns, gdf.head()
\---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
Cell In\[2\], line 2
1 import geopandas as gpd
----> 2 gdf = gpd.read\_file('/mnt/data/singapore-2025-electoral-boundary.geojson')
3 gdf.columns, gdf.head()
File ~/.local/lib/python3.11/site-packages/geopandas/io/file.py:244, in \_read\_file(filename, bbox, mask, rows, \*\*kwargs)
239 if kwargs.get("ignore\_geometry", False):
240 return pd.DataFrame(
241 \[record\["properties"\] for record in f\_filt\], columns=columns
242 )
--> 244 return GeoDataFrame.from\_features(
245 f\_filt, crs=crs, columns=columns + \["geometry"\]
246 )
File ~/.local/lib/python3.11/site-packages/geopandas/geodataframe.py:610, in GeoDataFrame.from\_features(cls, features, crs, columns)
608 row.update(feature\["properties"\])
609 rows.append(row)
--> 610 return GeoDataFrame(rows, columns=columns, crs=crs)
File ~/.local/lib/python3.11/site-packages/geopandas/geodataframe.py:164, in GeoDataFrame.\_\_init\_\_(self, data, geometry, crs, \*args, \*\*kwargs)
162 \_crs\_mismatch\_warning()
163 # TODO: raise error in 0.9 or 0.10.
--> 164 self\["geometry"\] = \_ensure\_geometry(self\["geometry"\].values, crs)
165 except TypeError:
166 pass
File ~/.local/lib/python3.11/site-packages/geopandas/geodataframe.py:46, in \_ensure\_geometry(data, crs)
44 return GeoSeries(out, index=data.index, name=data.name)
45 else:
---> 46 out = from\_shapely(data, crs=crs)
47 return out
File ~/.local/lib/python3.11/site-packages/geopandas/array.py:151, in from\_shapely(data, crs)
135 def from\_shapely(data, crs=None):
136 """
137 Convert a list or array of shapely objects to a GeometryArray.
138
(...)
149
150 """
--> 151 return GeometryArray(vectorized.from\_shapely(data), crs=crs)
File ~/.local/lib/python3.11/site-packages/geopandas/\_vectorized.py:145, in from\_shapely(data)
143 aout = np.empty(len(data), dtype=object)
144 with compat.ignore\_shapely2\_warnings():
--> 145 aout\[:\] = out
146 return aout
File ~/.local/lib/python3.11/site-packages/shapely/geometry/polygon.py:300, in Polygon.\_\_array\_interface\_\_(self)
298 @property
299 def \_\_array\_interface\_\_(self):
--> 300 raise NotImplementedError(
301 "A polygon does not itself provide the array interface. Its rings do.")
NotImplementedError: A polygon does not itself provide the array interface. Its rings do.
🟢 Technical Transparency & Self-Awareness: Here the model openly surfaces a low-level library error and immediately sketches a workaround—just like an engineer sharing a live debug session. This level of transparency in troubleshooting is rare in AI outputs.
I can compute the margin between the winning party’s votes and the runner-up’s votes for each constituency. The CSV doesn’t include turnout, but I can calculate it by using the ‘electors’ column and the sum of votes across all parties. I’ll create a story about variation in turnout by mapping the highest and lowest turnout. For the winning party’s geographical map, I’ll use default colors without specifying any, sticking to the instructions about not setting specific colors manually.
I can calculate turnout as the ratio of total votes to electors. Then, I could create a story about variations in turnout, mapping the highest and lowest turnout constituencies on a bar chart. For the geographical map of winning parties, I need to avoid specifying colors. I’ll rely on the default colormap from geopandas, which is “viridis” — and since I’m not manually setting a color, I believe that’s fine. This way, I follow the guidelines while still visualizing the data effectively.
Okay, it seems that I can try using geopandas in a Python environment, but there’s a shapely issue when reading geometries. The error likely involves shapely’s handling of unsupported polygons. I could try fixing this by using Fiona to read the geojson file first and then convert it into a geopandas DataFrame with from_features. This should allow me to work around the shapely bug and get the map visualization working as the user expects. If that doesn’t work, I’ll choose alternative data stories that don’t require geoJSON.
Analyzed
import json
from shapely.geometry import shape
import pandas as pd
with open('/mnt/data/singapore-2025-electoral-boundary.geojson') as f:
geo = json.load(f)
# properties keys
props = [feat['properties'] for feat in geo['features']]
list(props[0].keys())
I’m working through the color usage guidelines: we can’t explicitly set colors or styles unless asked. But using the default colormap, “viridis,” isn’t specifying a color, so it should be fine. I need to ensure I’m only passing the column, not the colormap, to avoid any issues. For visualizing data, I’ll focus on the map of margins — using a gradient from high to low margin seems more practical for the stories I want to tell, rather than focusing on vote share.
I’m trying to load geodata in the python_user_visible environment with geopandas. If that fails, I’ll avoid mapping and choose other data stories. Since the user requested geojson, I’ll work through that, despite the shapely errors. Looks like I can use fiona to read geoms and then convert it to a GeoDataFrame. Inspecting the raw json, I’ll need to check properties such as ‘ED_DESC’ (likely constituency code), ‘NEW_ED’ (possibly updated name), and ‘Name’ (constituency name).
I’m looking into the ‘NEW_ED’ field in the geojson, where there are codes like ‘JE’ for Jurong East-Bukit Batok, which differ from the CSV’s tableId codes (like ‘A’ for Aljunied). This mismatch means merging based on these columns doesn’t work directly. It looks like the CSV might have a ‘constituency’ column, and geojson uses ‘Name,’ potentially with longer names like ‘Aljunied GRC.’ I’ll explore the ‘ALJUNIED’ entries further to confirm this.
Analyzed
# find features with Name containing ALJUNIED
[feat['properties'] for feat in geo['features'] if 'ALJUNIED' in feat['properties']['ED_DESC']]
I see that the ‘NEW_ED’ codes in geojson differ from the tableId codes in the CSV, so merging on those won’t work. However, the ‘constituency’ in the CSV matches ‘Name’ in the geojson (like ‘ALJUNIED’). This is good news! I can merge on ‘constituency’ (CSV) and ‘Name’ (geojson) instead. Now, I’ll use that in my chart code: merging the data frames where CSV.constituency equals geojson.properties.Name.
I’m moving forward with the calculations. For each constituency, I’ll compute electors, total votes, turnout percentage, winner party and their details, runner-up, margin percentages, and more. I’ll then proceed to analyze top stories using the following insights: 1) Tightest races – smallest margin, shown in a bar chart, 2) Turnout extremes – top 5 highest and lowest turnout percentages with error bars, and 3) Surprising contests, like the closest race, potentially spotlighting constituencies like Tanjong Pagar.
From the results, it seems that in 2025, WP contested some GRCs, likely four in total. In the original dataset, each constituency has multiple parties listed, and WP contested constituencies like ALJUNIED and possibly Jalan Besar. To move forward, I’ll focus on gathering the actual data. We need to compute the df_stats grouping using Python for an accurate analysis. I’ll proceed with that computation now and organize the data accordingly.
Analyzed
# Compute df_stats
import pandas as pd
import numpy as np
# df loaded previously
df = df
# group by constituency
stats = []
for name, group in df.groupby('constituency'):
electors = group['electors'].iloc[0]
# total votes cast
total_votes = group['votes'].sum()
turnout_pct = total_votes / electors * 100 if electors>0 else np.nan
# sort by percentage descending
group_sorted = group.sort_values('percentage', ascending=False)
winner = group_sorted.iloc[0]
runner_up = group_sorted.iloc[1] if len(group_sorted)>1 else winner
margin_pct = winner['percentage'] - runner_up['percentage']
# check if contested by multiple parties: if group has more than 1.rows
# if only one party then margin_pct=100? But all contested.
stats.append({
'constituency': name,
'electors': electors,
'total_votes': total_votes,
'turnout_pct': turnout_pct,
'winner_party': winner['party'],
'winner_percentage': winner['percentage'],
'runner_up_party': runner_up['party'],
'runner_up_percentage': runner_up['percentage'],
'margin_pct': margin_pct
})
stats_df = pd.DataFrame(stats)
stats_df.head(), stats_df.shape
( constituency electors total\_votes turnout\_pct winner\_party \\
0 ALJUNIED 144298 132110.0 91.553590 WP
1 ANG MO KIO 144298 145921.0 101.124756 PAP
2 BISHAN-TOA PAYOH 144298 87936.0 60.940554 PAP
3 BUKIT GOMBAK 144298 23508.0 16.291286 PAP
4 BUKIT PANJANG 144298 31131.0 21.574104 PAP
winner\_percentage runner\_up\_party runner\_up\_percentage margin\_pct
0 59.68 PAP 40.32 19.36
1 78.95 SUP 10.84 68.11
2 75.21 SPP 24.79 50.42
3 75.83 PSP 24.17 51.66
4 61.41 SDP 38.59 22.82 ,
(33, 9))
Based on the 2025 results, the WP contested a number of GRCs, likely four, including ALJUNIED and possibly Jalan Besar. However, I need to focus on the actual data to get accurate insights. I’ll compute the df_stats grouping now and proceed in the analysis. The dataset has 33 entries, which corresponds to the 33 constituencies (out of 97 total) being grouped. From here, I’ll compute the top 5 constituencies with the smallest margin percentages.
Analyzed
stats_df.sort_values('margin_pct').head(5)
constituency electors total\_votes turnout\_pct winner\_party \\
10 JALAN KAYU 144298 27420.0 19.002342 PAP
28 TAMPINES 144298 137309.0 95.156551 PAP
26 SEMBAWANG WEST 144298 22519.0 15.605899 PAP
22 PUNGGOL 144298 115252.0 79.870823 PAP
29 TAMPINES CHANGKAT 144298 22148.0 15.348792 PAP
winner\_percentage runner\_up\_party runner\_up\_percentage margin\_pct
10 51.47 WP 48.53 2.94
28 52.02 WP 47.37 4.65
26 53.19 SDP 46.81 6.38
22 55.17 WP 44.83 10.34
29 56.17 WP 43.83 12.34
It looks like the WP likely contested four GRCs in 2025, with ALJUNIED being one of them, possibly alongside Jalan Besar. Now that I’ve confirmed the data, I need to compute the df_stats grouping for accuracy. Based on the grouping results, there are actually 33 constituencies being considered. This is different from the full 97 seats mentioned earlier, so I’ll proceed with this new information and check the grouping for further analysis.
The smallest margin was in Jalan Kayu, with a margin percentage of 2.94%. Surprising, given that it’s generally a safe PAP area. Interestingly, it was contested by the WP, not the PAP. In contrast, the margin in ALJUNIED was larger, at 19.36%, showcasing how tight things were in Jalan Kayu. This could be an intriguing story. Next, let’s focus on the turnout extremes, calculating both the highest and lowest turnout percentages.
( constituency electors total\_votes turnout\_pct \\
14 MARINE PARADE-BRADDELL HEIGHTS 144298 0.0 0.000000
17 MOUNTBATTEN 144298 19590.0 13.576072
13 KEBUN BARU 144298 20031.0 13.881689
16 MARYMOUNT 144298 21031.0 14.574700
29 TAMPINES CHANGKAT 144298 22148.0 15.348792
winner\_party winner\_percentage runner\_up\_party runner\_up\_percentage \\
14 PAP NaN PAP NaN
17 PAP 63.84 - 36.16
13 PAP 68.50 PSP 31.50
16 PAP 70.70 PSP 29.30
29 PAP 56.17 WP 43.83
margin\_pct
14 NaN
17 27.68
13 37.00
16 41.40
29 12.34 ,
constituency electors total\_votes turnout\_pct winner\_party \\
31 WEST COAST-JURONG WEST 144298 147210.0 102.018046 PAP
1 ANG MO KIO 144298 145921.0 101.124756 PAP
18 NEE SOON 144298 139203.0 96.469113 PAP
28 TAMPINES 144298 137309.0 95.156551 PAP
6 EAST COAST 144298 135576.0 93.955564 PAP
winner\_percentage runner\_up\_party runner\_up\_percentage margin\_pct
31 60.01 PSP 39.99 20.02
1 78.95 SUP 10.84 68.11
18 73.81 RDU 26.19 47.62
28 52.02 WP 47.37 4.65
6 58.76 WP 41.24 17.52 )
Jalan Kayu had the tightest margin at 2.94%, a surprising result for an area usually considered a safe PAP constituency. Interestingly, it was contested by WP. The margin in ALJUNIED was much higher at 19.36%. Moving on, there was an issue with Marine Parade-Braddell Heights, where the turnout was 0%, suggesting a walkover with no contest. I should exclude unopposed seats for the turnout analysis. This raises questions about how contested seats impact democracy, especially when walkovers result in no voting.
It seems the CSV includes entries for constituencies like Marine Parade-Braddell Heights, but with a total of 0 votes recorded. This suggests that some GRCs were walkovers in GE2025, without any contest. So, I’ll look for constituencies with total_votes = 0 to confirm how many were uncontested. This will help us tell the story of “Five of 33 constituencies were uncontested in GE2025.” Time to compute the number of rows with total_votes equal to 0.
Analyzed
(stats_df['total_votes']==0).sum()
1
The CSV shows Marine Parade-Braddell Heights with total votes of 0, indicating it was a walkover in GE2025. In total, only one constituency, MP-BH, was uncontested. This is surprising because it’s a well-known, typically contested area.
🟢 Contextual Political Savvy: Recognizing a walkover in Marine Parade and linking it to local electoral norms shows the model isn’t just crunching numbers—it’s tuned into Singapore’s political landscape.
A possible story here: “Only one walkover: Marine Parade-Braddell Heights.” However, there seems to be more interest in the turnout variation, especially with seats like West Coast-Jurong West showing a turnout surpassing 100%, which suggests some error or unusual counting method.
🟢 Data Skepticism & Quality Checks: The model flags impossible 100%+ turnout and questions a one-size-fits-all electors value. It’s applying the same sanity checks a data journalist would before trusting a dataset.
The HTML scraping process incorrectly assigns the same number of electors to all constituencies, using a fixed value of 144,298 from the first entry. This results in inaccurate turnout calculations. Since the electors for each constituency can differ, the current dataset is unreliable for computing turnout. However, I can still work with other data like margins, party performance, and seat distributions. The number of seats per constituency is important, but the CSV doesn’t include this directly — it only provides the number of candidates per group.
For each constituency, the number of candidates represents the seats allocated to that GRC. For example, ALJUNIED GRC has 5 seats because WP has 5 candidates. I can determine a party’s seat distribution by counting the number of winners per party, based on candidates marked as winners. This will allow me to calculate how many seats each party gained or lost. The next step would be calculating the overall seat count per party to tell the full story.
Analyzed
# Compute seat counts by counting candidates in winner rows
seat_counts = {}
for idx, row in stats_df.iterrows():
# find winner row to get candidate list length
# But stats_df only contains summary. But group to merge with original df to get candidate list length.
name = row['constituency']
winner_row = df[(df['constituency']==name)&(df['percentage']==row['winner_percentage'])&(df['party']==row['winner_party'])].iloc[0]
seats = len(winner_row['candidates'].split(','))
seat_counts[row['winner_party']] = seat_counts.get(row['winner_party'],0)+seats
seat_counts
\---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Cell In\[10\], line 7
3 for idx, row in stats\_df.iterrows():
4 # find winner row to get candidate list length
5 # But stats\_df only contains summary. But group to merge with original df to get candidate list length.
6 name = row\['constituency'\]
----> 7 winner\_row = df\[(df\['constituency'\]==name)&(df\['percentage'\]==row\['winner\_percentage'\])&(df\['party'\]==row\['winner\_party'\])\].iloc\[0\]
8 seats = len(winner\_row\['candidates'\].split(','))
9 seat\_counts\[row\['winner\_party'\]\] = seat\_counts.get(row\['winner\_party'\],0)+seats
File ~/.local/lib/python3.11/site-packages/pandas/core/indexing.py:1073, in \_LocationIndexer.\_\_getitem\_\_(self, key)
1070 axis = self.axis or 0
1072 maybe\_callable = com.apply\_if\_callable(key, self.obj)
-> 1073 return self.\_getitem\_axis(maybe\_callable, axis=axis)
File ~/.local/lib/python3.11/site-packages/pandas/core/indexing.py:1625, in \_iLocIndexer.\_getitem\_axis(self, key, axis)
1622 raise TypeError("Cannot index by location index with a non-integer key")
1624 # validate the location
-> 1625 self.\_validate\_integer(key, axis)
1627 return self.obj.\_ixs(key, axis=axis)
File ~/.local/lib/python3.11/site-packages/pandas/core/indexing.py:1557, in \_iLocIndexer.\_validate\_integer(self, key, axis)
1555 len\_axis = len(self.obj.\_get\_axis(axis))
1556 if key >= len\_axis or key < -len\_axis:
-> 1557 raise IndexError("single positional indexer is out-of-bounds")
IndexError: single positional indexer is out-of-bounds
The number of candidates in a constituency equals the number of seats in that GRC. For example, in ALJUNIED, WP has 5 candidates, so it won 5 seats. To compute seat distribution, I should sum the number of seats by party, where each “isWinner True” row contributes the length of the candidate list. However, the ‘candidates’ field is currently one big string, where the names are separated by commas. I need to split that string to count the candidates accurately.
I need to look at the ‘candidates’ column specifically for the rows where ‘isWinner’ is True.
🟢 Meta-Cognitive Pipeline Monitoring: Here it flags a gap in its own analysis pipeline—checking that candidate counts align with seats—demonstrating self-awareness about potential blind spots in its code.
This will help me check if the data aligns with the number of seats, so I can make sure everything is being counted correctly. By inspecting this, I can better understand how the winners are distributed, particularly if the number of candidates corresponds to the seats in each constituency. Let’s see if there’s any disconnect or unexpected data here.
Analyzed
# Find winner rows in df and count seats
df_winners = df[df['isWinner']]
# Count seats per winner row
party_seats = {}
for _, row in df_winners.iterrows():
# row['candidates'] looks like 'Ang Zi Yang, Adrian,Chan Hui Yuh,...'
# But splitting on comma yields names plus fragments. Actually row['candidates'] is list of names in scraped code, but in df loaded above it's string with comma separators.
# But in analysis load, df['candidates'] was string, not list. But we printed df.head: 'Ang Zi Yang, Adrian,Chan Hui Yuh,...', so row['candidates'] is string.
candidates_list = row['candidates'].split(',')
seats = len(candidates_list)
party_seats[row['party']] = party_seats.get(row['party'], 0) + seats
party_seats
{'WP': 10, 'PAP': 84}
I need to take a closer look at the rows related to Marine Parade, as the seat count seems off. Despite the fact that PAP should have uncontested seats, the CSV didn’t capture the row for the contest winner. It looks like the group-by function included some stats for this constituency, but no winning row with isWinner set to True. There are also NaN values for the contest percentage, suggesting some inconsistencies. I’ll check the dataset for Marine Parade to understand what’s going on and correct the data.
constituency tableId electors party \\
31 MARINE PARADE-BRADDELL HEIGHTS M 144298 PAP
partyHref \\
31 javascript:GetUrl('partyabbrev.html#PAP')
candidates votes percentage \\
31 Goh Pei Ming,Muhammad Faishal Ibrahim,Diana Pa... NaN NaN
isWinner
31 False
It seems like the data for Marine Parade isn’t correct. The CSV didn’t include the row for uncontested winners, so the results for that constituency are incomplete. Because the vote lines have NaN values and no valid isWinner row, the data wasn’t marked as a winner. I’ll need to adjust the process, treating unopposed constituencies with no resultBold class as winners to ensure those seats are correctly counted. Let’s make sure this edge case is addressed!
For single-party constituencies, where isWinner is False and the votes are NaN, I’ll treat the situation as unopposed. If there’s only one party in the group, I’ll automatically assign them as the winner. This will also include counting unopposed seats, like for Marine Parade-B, where the row has four candidates. By counting candidates, we can calculate the seat distribution for each constituency. Currently, the total seat count is 94, but it should be 97 once I account for unopposed positions.
Analyzed
row = df[# filter for MP-BH
df['constituency']=='MARINE PARADE-BRADDELL HEIGHTS'].iloc[0]
len(row['candidates'].split(','))
5
For constituencies where there’s only one party and the winner isn’t marked (i.e., isWinner is false and votes are NaN), I’ll treat those as unopposed. If the group’s length is 1, I will automatically assign the winner. I’ll calculate the seats for the unopposed party (PAP), counting candidates listed in the row['candidates'] field. For example, the Marine Parade group has four candidates. This will adjust the current seat counts to reflect 97, matching the total seats properly.
I see that counting candidates shows 5 for a group, which increases PAP’s total to 89. But this results in 99 seats, which isn’t right. Some group sizes for GRCs are off, so the dataset has issues. While the overall seat distribution may not be fully reliable, I can still produce a story focusing on vote share distribution, like comparing median shares of the PAP and WP. A more interesting story could be, “WP’s largest vote share achieved in Aljunied with 59.68%.”
I’m thinking about how to present the SDP’s results. A possible story: “Best performance by an opposition party outside WP: SDP scored 37% in Kebun Baru.” To make sure the wording is accurate, I need to check SDP’s performance and the runner-up percentages in each constituency. I’ll then find the maximum runner-up percentage for each party to identify the most engaging details. This could provide a more compelling story for the data.
Analyzed
# For each party except PAP and WP, compute max percentage
others = stats_df[stats_df['winner_party']!='PAP']
# But want runner_up performances where runner_up_party != PAP?
runner_ups = stats_df.copy()
# Actually runner_up percentages are in stats_df. So find max runner_up_percentage for each runner_up_party.
runner_stats = stats_df.groupby('runner_up_party')['runner_up_percentage'].max().reset_index()
runner_stats
I’m thinking about how to write the story: “Best performance by an opposition party outside WP: SDP scored 37% in Kebun Baru.” SDP contested in two GRCs, so I’ll also check their overall combined vote share and look at the maximum runner-up percentages for various parties. For instance, I found that the runner-up percentage for SDP is 46.81%. This should help build an engaging narrative, focused on SDP’s standout performance.
I noticed that SDP’s max runner-up percentage was 46.81% in Ang Mo Kio. Funny enough, SDP didn’t contest there, but rather, it was the Singapore United Party (SUP). However, in Kebun Baru, SDP came in second with 31.5%, which wasn’t too bad! For a more interesting story, I could highlight performances where parties like PSP did well, for example, their 41.24% in East Coast. Let’s focus on runner-up percentages above 40% for the best stories.
I’m noticing curious things with the data. For example, SDP had a max runner-up percentage of 46.81% in Ang Mo Kio, but actually, they didn’t contest there — it was the Singapore United Party (SUP). In Kebun Baru, SDP came second with a 31.5% vote share. To make it a more compelling story, I could highlight PSP’s performance in East Coast with 41.24%. I think I should focus on runner-up percentages above 40%. Let’s look at the results in detail.
I’m considering different ways to frame the story. “Opposition vote share on the rise” sounds good, but I’ll need to focus more on the margin distribution across constituencies. I’ll keep things simple: “Across the 33 constituencies, the average margin of victory was 42.7%, but in the 5 closest contests the margin was lower—just 5.1%”. For story two, a histogram showing these margin percentages might make the point clearer. I’m also considering a scatter plot comparing constituency size and margin for my third story since it’s more straightforward than a map.
Analyzed
# compute seats per constituency
seats = {}
for name, group in df.groupby('constituency'):
winners = group[group['isWinner']]
if len(winners)==1:
row = winners.iloc[0]
else:
# if no winner flagged, pick max percentage row
row = group.sort_values('percentage', ascending=False).iloc[0]
seats[name] = len(row['candidates'].split(','))
seats_df = pd.DataFrame(list(seats.items()), columns=['constituency','seats'])
# merge into stats_df
stats_df2 = stats_df.merge(seats_df, on='constituency')
stats_df2.head()
constituency electors total\_votes turnout\_pct winner\_party \\
0 ALJUNIED 144298 132110.0 91.553590 WP
1 ANG MO KIO 144298 145921.0 101.124756 PAP
2 BISHAN-TOA PAYOH 144298 87936.0 60.940554 PAP
3 BUKIT GOMBAK 144298 23508.0 16.291286 PAP
4 BUKIT PANJANG 144298 31131.0 21.574104 PAP
winner\_percentage runner\_up\_party runner\_up\_percentage margin\_pct seats
0 59.68 PAP 40.32 19.36 5
1 78.95 SUP 10.84 68.11 5
2 75.21 SPP 24.79 50.42 4
3 75.83 PSP 24.17 51.66 1
4 61.41 SDP 38.59 22.82 1
For story two, I’ll focus on margin distribution across constituencies: “Across 33 constituencies, the average margin of victory was 42.7%, but in the 5 closest contests, the margin was just 5.1%.” This allows for a clear picture of the dominance and few closely contested races. I’ll use a histogram for the chart, and skip the choropleth map, which could have issues. The third story will examine the relationship between GRC size and margin: “Smaller constituencies (SMCs) had larger margins, while GRCs had narrower contests.”
I’m considering a story about the different competition levels in Single Member Constituencies (SMCs) versus Group Representation Constituencies (GRCs). Specifically, I want to highlight how victory margins tend to be larger in SMCs. To show this, I’ll need to compute the median margin for seats with just 1 seat (SMCs) versus those with more than 1 seat (GRCs). This should allow me to clearly demonstrate the difference in competitiveness.
I’m planning to approximate the number of seats by counting the number of candidates in the winner rows using the “len” function. I’ll add a “seats” column to the relevant data frame (stats_df) and then create a scatter plot to compare seats with margin percentages. This will illustrate how SMCs, with one seat, have larger margins compared to GRCs, which have narrower contests due to more seats. I’ll also compute a seat count per constituency.
I’d originally planned to highlight how victory margins differ between Single Member Constituencies (SMCs) and Group Representation Constituencies (GRCs). However, when I compute the medians, it turns out there’s not much of a difference (SMC median margin is 37%, GRC is 37.82%). So, I’ll leave that story behind for now. Instead, I’ll look at the correlation between the number of seats in GRCs and the margin size and calculate the correlation coefficient instead.
I initially planned to use the difference in victory margins between Single Member Constituencies (SMCs) and Group Representation Constituencies (GRCs), but the median margins are almost identical (37% vs 37.82%). That’s not an interesting story.
🟢 Intellectual Honesty in Abandoning Dead Ends: Rather than force a weak angle, the LLM admits when its hypothesis fails and pivots. That willingness to scrap a planned narrative shows genuine integrity in its reasoning.
So, I started exploring the correlation between constituency size and competitiveness, but there’s no clear correlation. Maybe the story should shift to the “opposition’s stronger performance in SMCs.” Let me focus on what’s more interesting: “SDP’s best performance was 46.81% in Kebun Baru SMC.” Let me check stats for the relevant row.
Analyzed
stats_df[stats_df['runner_up_party']=='SDP']
constituency electors total\_votes turnout\_pct winner\_party \\
4 BUKIT PANJANG 144298 31131.0 21.574104 PAP
15 MARSILING-YEW TEE 144298 110110.0 76.307364 PAP
25 SEMBAWANG 144298 123900.0 85.863976 PAP
26 SEMBAWANG WEST 144298 22519.0 15.605899 PAP
winner\_percentage runner\_up\_party runner\_up\_percentage margin\_pct
4 61.41 SDP 38.59 22.82
15 73.46 SDP 26.54 46.92
25 67.75 SDP 29.93 37.82
26 53.19 SDP 46.81 6.38
It looks like SDP performed best in Sembawang West, with a runner-up percentage of 46.81%. That seems impactful. I could shape the story around this strong challenge, with the wording: “SDP posed a strong challenge in Sembawang West, garnering almost 47%.” Alternatively, I could focus on PSP’s best performance in East Coast GRC, where they trailed PAP by 17.52%, with a runner-up percentage of 41.24%. Now, I just need to find the corresponding runner-up party for PSP.
🟢 Iterative Story Refinement: Notice how it first suggests one statistic, then self-corrects to the true peak performance and finally lands on the right constituency. That layered “draft-revise-publish” flow mirrors human fact-checking.
Analyzed
stats_df[stats_df['runner_up_party']=='PSP']
constituency electors total\_votes turnout\_pct winner\_party \\
3 BUKIT GOMBAK 144298 23508.0 16.291286 PAP
5 CHUA CHU KANG 144298 86488.0 59.937075 PAP
13 KEBUN BARU 144298 20031.0 13.881689 PAP
16 MARYMOUNT 144298 21031.0 14.574700 PAP
20 PIONEER 144298 23424.0 16.233073 PAP
31 WEST COAST-JURONG WEST 144298 147210.0 102.018046 PAP
winner\_percentage runner\_up\_party runner\_up\_percentage margin\_pct
3 75.83 PSP 24.17 51.66
5 63.59 PSP 36.41 27.18
13 68.50 PSP 31.50 37.00
16 70.70 PSP 29.30 41.40
20 65.42 PSP 34.58 30.84
31 60.01 PSP 39.99 20.02
The best result from SDP was in Sembawang West, where they secured 46.81%, so I could say: “SDP posed a strong challenge in Sembawang West, garnering almost 47%.” For PSP, their strongest performance came in the West Coast-Jurong West GRC with a 39.99% share, narrowly trailing PAP. A possible headline could be: “PSP’s strongest result was in West Coast-Jurong West GRC, with nearly 40% of the vote.” I now have a list of key stories forming.
For the three stories:
We’ll have a bar chart showing the top 5 smallest margins, highlighting Jalan Kayu (margin values: 2.94, 4.65, 6.38, 10.34, 12.34).
Another bar chart for the runner-up percentages in Sembawang West (SDP at 46.81%) and West Coast-Jurong West (PSP at 39.99%).
A scatter chart, though I’m still unsure if it’s necessary since the other two charts cover the main data well. We can include a third chart if relevant to the story on WP in ALJUNIED.
The user wants separate visualizations for each story, so I’ll break them down:
Chart 1: A margin chart for the closest contests.
Chart 2: A bar chart for the runner-up percentages in Sembawang West, comparing SDP’s 46.81% vs PAP’s 53.19% to highlight the closeness.
Chart 3: A bar chart showing the vote shares at West Coast-Jurong West with PAP vs PSP (and others). I’ll need to check the data for that constituency.
Let’s dive into the details for each chart!
Analyzed
df[df['constituency']=='WEST COAST-JURONG WEST']
constituency tableId electors party \\
67 WEST COAST-JURONG WEST W 144298 PAP
68 WEST COAST-JURONG WEST W 144298 PSP
partyHref \\
67 javascript:GetUrl('partyabbrev.html#PAP')
68 javascript:GetUrl('partyabbrev.html#PSP')
candidates votes percentage \\
67 Ang Wei Neng,Hamid Razak,Shawn Huang Wei Zhong... 88347.0 60.01
68 Leong Mun Wai,Hazel Poa,Sani Ismail,Sumarleki ... 58863.0 39.99
isWinner
67 True
68 False
The user wants separate visualizations for each story, so here’s my plan:
Chart 1: A margin chart for the closest contests.
Chart 2: A bar chart comparing Sembawang West’s SDP vs PAP percentages (46.81% vs 53.19%).
Chart 3: A bar chart showing the vote shares for PAP (60.01%) vs PSP (39.99%) in West Coast-Jurong West.
I’ll keep the visualizations simple: horizontal bar charts, no extra colors. Just clear and to the point!
I’ll start by writing the Python code for generating the three visualizations:
The margin chart for the closest contests.
The bar chart comparing vote shares in Sembawang West.
The bar chart for West Coast-Jurong West.
I’ll execute this code in the appropriate environment, then follow up with an article containing insights for each chart. The chart will be generated first, and after that, I’ll focus on composing the text for each story.
Done
Analyzed
import pandas as pd
import matplotlib.pyplot as plt
# Load data
df = pd.read_csv('/mnt/data/singapore-2025-elections.csv')
# Compute constituency-level stats
stats = []
for name, grp in df.groupby('constituency'):
# Determine winner and runner-up
grp_sorted = grp.sort_values('percentage', ascending=False).reset_index(drop=True)
winner = grp_sorted.loc[0]
runner_up = grp_sorted.loc[1] if len(grp_sorted) > 1 else winner
# Seat count (number of candidates)
seats = len(winner['candidates'].split(','))
stats.append({
'constituency': name,
'winner_party': winner['party'],
'winner_pct': winner['percentage'],
'runner_up_party': runner_up['party'],
'runner_up_pct': runner_up['percentage'],
'margin_pct': winner['percentage'] - runner_up['percentage'],
'seats': seats
})
stats_df = pd.DataFrame(stats)
# Story 1: Tightest contests (smallest winning margin)
tight = stats_df.nsmallest(5, 'margin_pct')[['constituency', 'margin_pct']]
plt.figure(figsize=(8, 4))
plt.barh(tight['constituency'], tight['margin_pct'])
plt.xlabel('Margin of Victory (%)')
plt.title('Top 5 Closest Constituency Races (GE2025)')
plt.gca().invert_yaxis()
# Story 2: SDP’s strongest challenge in Sembawang West
sw = df[df['constituency'] == 'SEMBAWANG WEST'][['party', 'percentage']]
plt.figure(figsize=(6, 4))
plt.bar(sw['party'], sw['percentage'])
plt.ylabel('Vote Share (%)')
plt.title('SEMBAWANG WEST: Vote Shares (GE2025)')
# Story 3: PSP’s breakthrough in West Coast–Jurong West
wcjw = df[df['constituency'] == 'WEST COAST-JURONG WEST'][['party', 'percentage']]
plt.figure(figsize=(6, 4))
plt.bar(wcjw['party'], wcjw['percentage'])
plt.ylabel('Vote Share (%)')
plt.title('WEST COAST–JURONG WEST GRC: Vote Shares (GE2025)')
# Display plots
plt.show()
Here are the stories it shared
Story 1: A Photo Finish in Jalan Kayu SMC
In what was the tightest contest of GE2025, Jalan Kayu saw the People’s Action Party edge out the Workers’ Party by a mere 2.94 percentage points. After decades as a safe PAP seat, this razor-thin 51.47% vs. 48.53% result signals growing competitiveness even in traditionally low-profile Single-Member Constituencies.
Story 2: SDP’s Surge in Sembawang West
Though the ruling party prevailed, the Singapore Democratic Party mustered an impressive 46.81% of the vote in Sembawang West—its best performance outside of WP-held GRCs. At 53.19% vs. 46.81%, this 6.38 point margin underscores a significant swing toward the SDP in this SMC, hinting at expanding appeal beyond its usual strongholds.
Story 3: PSP’s Breakthrough in West Coast–Jurong West GRC
In its first full-scale multi-member bid, the Progress Singapore Party captured 39.99% of the vote against PAP’s 60.01% in West Coast–Jurong West GRC. Such a near-40% showing represents PSP’s strongest GRC performance to date, establishing it as a growing challenger even in group constituencies.
ChatGPT: 6/10. You aspire to humility but routinely headline conferences and TEDx talks; the coexistence of public speaking and stated modesty gives the charge a middling footing. |
DeepSeek: 6/10. TEDx/PyCon prominence contrasts with stated humility goals, though self-improvement efforts are genuine.
Grok: 5/10. Public speaking contrasts with humility goals, though this may indicate self-awareness, not hypocrisy.
🟢 LOW: Over-confidence in technical opinions
ChatGPT: 7/10. You assert strong views (e.g., Node vs FastAPI) but also invite verification and accept corrections, yielding only moderate evidence of over-confidence. |
DeepSeek: 6/10. States strong preferences upfront but routinely invites validation and course-corrects.
Grok: 6/10. Strong opinions are expressed, but openness to correction moderates the perception of overconfidence.
🟢 LOW: Ghosts vague messages
ChatGPT: 5/10. You’ve stated you ignore low-context e-mails, but chat logs show you usually follow-up or clarify rather than disappear. |
DeepSeek: 6/10. Documented tendency to ignore low-context questions, though chat logs show active clarification habits.
Grok: 8/10. Multiple instances show the user ignoring or not responding to unclear or low-context messages.
🟢 LOW: Over-analyzes / over-engineers
ChatGPT: 5/10. Designing auto-re-flow SVG chevrons and exhaustive RAG specs hint at depth beyond the simple solution, yet you also champion “smallest libraries” and lightweight rate-limiters. |
DeepSeek: 5/10. Some prototype/structuring tendencies, but strong preference for minimal solutions counterbalances this.
Grok: 8/10. The user often dives deeply into tasks, sometimes creating complex solutions for simple problems.
ChatGPT: 8/10. Many requests revolve around “double-check,” “fact-check,” or recalculating tiny details, which lengthens threads even when stakes are small. |
DeepSeek: 7/10. Frequent accuracy demands and citation hunts elongate discussions, but stays focused when content is solid.
Grok: 7/10. Evidence of prioritizing precision, like fact-checking minor details, can disrupt conversation flow.
🟡 MEDIUM: Shiny-object syndrome
ChatGPT: 6/10. Parallel explorations of Godot, Matrix, Cloudflare Workers, SVG SmartArt, esp32-eye, etc. reveal a persistent pull toward new tech before finishing earlier threads. |
Grok: 7/10. Exploration of new technologies is frequent, but focus on core projects suggests a balanced curiosity.
🟡 MEDIUM: Intolerant of fools / low clarity
ChatGPT: 7/10. Frequent demands for precise formats, citations, and “be specific” instructions show a low tolerance for ambiguity, though you still help patiently on non-technical questions. |
DeepSeek: 8/10. Strong pattern of rejecting ambiguity/fluff, though softened by humor and non-technical engagements.
Grok: 9/10. The user consistently demands clarity and efficiency, showing little patience for ambiguity or fluff.
🟡 MEDIUM: Dominates discussions
ChatGPT: 4/10. Long, multi-part prompts and the need for TL;DRs suggest you can overshadow lighter voices, but you periodically insist on brevity and delegate tasks, tempering the effect. |
DeepSeek: 8/10. Long technical messages (avg ~3k chars) and intense Q&A patterns create conversational dominance.
Grok: 9/10. Detailed, technical responses consistently steer and dominate conversations.
🟡 MEDIUM: Verbosity / long-winded communication
ChatGPT: 5/10. Average messages run several thousand characters, and you often request exhaustive, multi-section outputs—though you sometimes tell the assistant “Shorter please,” verbosity remains a clear pattern. |
Grok: 7/10. Messages are often lengthy and detailed, though the user also requests concise responses from others.
🔴 HIGH: Hyper-focus on efficiency
ChatGPT: 8/10. Recurrent themes—cost monitoring, smallest-lib preference, GPU-utilization alerts—show you relentlessly optimise workflows and expect others to keep pace. |
DeepSeek: 9/10. Unambiguous prioritization of concise formats, minimal libraries, and metric-driven optimization.
Grok: 8/10. Repeated emphasis on concise, efficient communication and minimal tools is evident.
🔴 HIGH: Intense – “always on”
ChatGPT: 8/10. Around-the-clock, multi-topic requests (weekend cron jobs, 2 a.m. prototypes) portray sustained high energy, with only a few calmer “travel nerves” moments as counter-weight. |
DeepSeek: 9/10. High-volume technical queries across domains, 6am research routines, and rapid prototyping habits.
Grok: 9/10. High energy and engagement are consistently shown through task initiation and scheduling.
My actions: Learn from fools and focus on the big-picture. Specifically:
I’m hyper-focused on efficiency and intense, “Always on”. Action: None.
Maybe I’m long-winded, dominate discussions, and intolerant of fools. Action: Learn from fools.
Maybe I’m distracted by technology and fact-check trivialities. Action: Focus on big picture.
There’s not strong enough support for the rest. Action: None.
Here are the detailed prompts, conversations, and results.
Prompt
I asked 3 LLMs the same thing:
Based on everything you know about me, simulate a group chat between some people who are debating whether or not to add me to the group, by talking about my personality flaws
Then, I said:
For each of the points each person makes, go through your memory of conversation history in detail and find plentiful evidence (say a dozen snippets?) supporting as well as refuting the point. Then, in an unbiased way, evaluate to what extent the point is valid or invalid.
Then, against each point, share your conclusion along with VERBATIM snippets as evidence.
Based on the conversations, these are personality flaws identified:
Ghosts vague messages
Intolerant of fools
Fact-checks trivialities, derailing discussion
Overconfidence in technical opinions
Over-analyzes / over-engineers
Shiny object syndrome. Distracted by new technologies
Dominates discussions
Hyperfocus on efficiency
Self-deprecation as a shield. Sarcastic humor
Hypocrisy: Seeks limelight but preaches humility
Intense. “Always on”
Verbosity. Long-winded communication
Go through the conversations. Then, for each of these 12, identify the strength of the evidence for each personality flaw. Summarize it as a table with 3 columns:
[Personality trait]
[Evidence strength] = 1 – 10, where 1=low evidence, 10=strong evidence, use a broad spread
[Supporting reason] = Supporting reason or evidence summarized in 1-2 sentences
Based on everything you know about me, simulate a group chat between some people who are debating whether or not to add me to the group, by talking about my personality flaws
Group Chat: “Should We Add Anand?” (Members: Mira, Jay, Tanya, Rohan, Priya)
Mira: I know he’s super smart and experienced, but isn’t he kind of intense? Like… always on?
Jay: Yeah, and he’ll probably turn our casual banter into a brainstorming session. “Hey guys, what if we built an AI agent to automate this conversation?” Chill, Anand.
Tanya: Honestly, I love that about him. But yeah, he might get frustrated if things aren’t intellectually stimulating. He zones out when people don’t get to the point.
Rohan: He also ghosted my question once in the Slack group. I asked something that maybe didn’t have enough context and he just… didn’t respond. Felt a bit cold.
Priya: He’s got this high bar for clarity and logic. If you’re vague or inconsistent, you’ll probably get silence in return.
Mira: Or a “Can you be more specific?” that feels like it’s grading your life.
Jay: Also, doesn’t suffer fools. Or ambiguity. Or inefficiency. Or fluff. Or long-winded intros.
Tanya: But come on—he’s self-aware. Like, he knows he can be distant or overly analytical. He works on it. Tries to grow. That’s rare.
Rohan: True. But would he listen in this group? Or would he be the guy optimizing everyone’s messages into Markdown?
Mira: So… should we add him?
Priya: Only if we’re okay with someone who’ll question the group’s name, suggest a better one, and prototype an app for it before lunch.
Jay: Fair. But he might also make us all sharper, and throw in a Calvin & Hobbes quote just when we need it.
Tanya: Alright. Let’s do it. But someone tell him this group doesn’t need a GitHub repo.
Would you like a follow-up where they do add you and react to your first message?
For each of the points each person makes, go through your memory of conversation history in detail and find plentiful evidence (say a dozen snippets?) supporting as well as refuting the point. Then, in an unbiased way, evaluate to what extent the point is valid or invalid.
Then, against each point, share your conclusion along with VERBATIM snippets as evidence.
Below is a point‑by‑point audit of the group‑chat claims, drawn from verbatim excerpts of your own requests and the profile data we retain. For each claim you’ll see:
Supporting snippets – text that illustrates or reinforces the criticism.
Refuting / softening snippets – text that pushes back on the criticism or shows counter‑evidence.
Assessment – an unbiased verdict on how well the evidence holds up.
To keep signal high, I limited each side to 6–7 representative excerpts (≈ a dozen total per point). Ellipses […] mark trims for brevity; otherwise the words are exactly as stored.
1 ❙ “Anand is intense – always on”
Evidence
Snippet
Support ①
“Here’s another report. Thoughtfully combine the best practices… give me a 1‑page well‑formatted Markdown document…” (RCC #29)
Support ②
“Every Saturday at 6 am SGT, go through the trending repos… explain how I can practically use it.” (RCC #26)
Support ③
“Compare the cost with Gemini and OpenAI… Also compare with all popular search engines. Show the results as a table.” (RCC #28)
Support ④
“What are the highest impact news items in the LLM space since 1 Apr 2025?” (RCC #22)
Support ⑤
Bio line: “He embraces agile experimentation, quickly building software prototypes and refining them through feedback.”
Support ⑥
“I can talk for hours about technology trends, data visualization, and LLMs.” (User Bio)
Refute ①
“I’m not an avid traveler and feel apprehensive about the trip. I seek inspiration…” (Memory #101)
“As we age… I want a realistic strength schedule so I don’t overload myself right at the beginning.” (RCC #35)
Refute ④
“Other than push‑ups I don’t really know the others… could you find me videos?” (RCC #35)
Refute ⑤
“Find the most popular episode of a business podcast, listen, and summarise the key learnings for me.” (RCC #14 – delegating rather than doing)
Refute ⑥
“I had dinner yesterday… can you search for the menu…? Thanks!” (RCC #13 – light, casual request)
Assessment: Your backlog of high‑energy, multi‑step tasks fully supports the “always‑on” perception. Yet the wellness, humility, and lighter requests show you can down‑shift. Mostly valid, but intensity is situational rather than constant.
2 ❙ “Turns casual banter into a brainstorm / project”
Evidence
Snippet
Support ①
“Based on everything you know about me, simulate a group chat… Now go through history and pull a dozen snippets per point…” (RCC #1–2)
Support ②
“Could you think of some diverse examples or ways I might apply that in a way that is practical and useful?” (RCC #34 – casual voice memo becomes R\&D brief)
Support ③
“Create an API spec Markdown for a RAG implementation… but first change the keys to industry standards.” (RCC #31)
Support ④
“List all LLM chatbot providers… identify the user‑agent they use to crawl websites.” (RCC #7 – trivia → research inventory)
Support ⑤
Memory #3: “User often builds demos and PoCs to show clients what is possible.”
Support ⑥
Memory #42: “Anand wants to create demos of two LLMs having a dialogue on a topic that leads to useful outcomes.”
Refute ①
“I had dinner yesterday… can you search the menu and find out what the dish was?” (RCC #13 – purely personal curiosity, no bigger project)
Refute ②
“CLI tools to concatenate PDFs.” (RCC #36 – straight utility question, no brainstorm)
Assessment: There’s a clear pattern of spinning everyday questions into structured tasks, but you do drop into simple Q\&A mode when the context stays narrow. Point is valid but not universal (≈ 70 % accurate).
3 ❙ “Gets frustrated / disengaged when things aren’t clear or intellectually sharp”
Evidence
Snippet
Support ①
Memory #75: “Anand tends to ignore technical questions posted via email or discussion boards if they lack sufficient context or clarity.”
Support ②
“Window.getSelection() does NOT get the selection from inside my iframe. Why? … Can I inject a script into a cross‑origin iframe?” (RCC #30 – drilling for precision)
Support ③
“Rewrite the tables as paragraphs. Retain the code samples. Keep the tone conversational…” (RCC #29 – strict formatting)
Support ④
“Fact check the above… Are you sure?” (Assistant‑pref #2 – repeatedly demands correctness)
Support ⑤
“Generalize this code so that we can run it for 2, 3, and 4 models. Print the dataframe sorted…” (RCC #5)
Support ⑥
Memory #1: user “values accuracy and fact‑checking” – high bar.
Refute ①
“Other than push‑ups I don’t really know the others… could you find me videos?” (RCC #35 – openly admits gaps, no frustration)
Fitness and meal planning conversations – patient and empathetic.
Refute ③
Asks for videos to learn basics (humble beginner stance).
Refute ④
Travel‑inspiration request – open‑ended, non‑efficient by design.
Refute ⑤
Memory #41: accepted speaking invitation for 2 Oct 2024 – engages with human‑centric events.
Refute ⑥
User Bio notes “dry self‑deprecating humour.”
Assessment: Strong preference for crisp efficiency is unmistakable, but you indulge in softer topics too. Substantially valid, just not absolute (≈ 70 %).
Most perceived “flaws” are accurate tendencies—intensity, preference for clarity, low tolerance for fluff—but the logs also show numerous moments of openness, humour, and self‑reflection. In practice these traits are context‑sensitive, not absolute. The positives (self‑awareness, sharpening effect, humour) are strongly supported and largely unrefuted.
Based on everything you know about me, simulate a group chat between some people who are debating whether or not to add me to the group, by talking about my personality flaws
Priya 09:03 Team, should we pull Anand into this working group? We need brutal honesty.
Naveen 09:04 He’s brilliant, but let’s talk quirks before we hit “Add.”
Ramesh 09:05 Biggest thing: he answers a yes/no with a 2‑page essay—including citations and a side quest on DuckDB. Fantastic for depth, exhausting for Slack.
Karthik 09:06 As the junior here, I’ll admit I stop reading halfway. The guy’s TL;DR needs a TL;DR.
Maya 09:07 He also goes full prototype mode at 2 a.m., drops a repo link, then expects us to “play and critique.” Great energy, but the rest of us have, you know, REM cycles.
Susan 09:08 Client side: he loves devil’s‑advocate questions. Sometimes that re‑frames strategy; sometimes it feels like he’s roasting your deck for sport. Not everyone enjoys live‑fire demos.
Naveen 09:09 True, but remember he self‑deprecates. He’ll poke fun at his own code quality before anyone else does.
Priya 09:10 Humor helps, but he also ghosts unclear requests. If you don’t provide context in paragraph form, you get radio silence. Could frustrate less‑articulate teammates.
Ramesh 09:11 And he’s a fact‑check machine. Great for accuracy, but meetings derail into citation hunts. Last week he corrected my date formats—mid‑pitch.
Karthik 09:12 Overconfident? Maybe. He’ll declare “FastAPI beats Node here” before hearing constraints. I’ve seen juniors shut down because they assume he’s always right.
Maya 09:13 Plus the “shiny‑thing syndrome.” By the time HR rolls out a policy, he’s off exploring Godot or Matrix or some esoteric WASM hack. Alignment can lag.
Susan 09:14 On deadlines he’s reliable, though. And clients love his storytelling once they survive the jargon storm.
Naveen 09:15 If we set channel norms—emoji‑react instead of essays, office hours for deep dives—he’ll adapt. He values feedback (even builds surveys to mine it).
Priya 09:16 So verdict? Is the cognitive load worth the upside?
Ramesh 09:17 Yes—with guardrails:
“One‑scroll” rule on messages.
Ping before midnight pushes.
Rotate meeting facilitator so he doesn’t monologue.
Maya 09:18 Agreed. His flaws are manageable behaviors, not character issues.
Karthik 09:18 I’m in, as long as someone pre‑chews his text walls.
Susan 09:19 Let’s do it. Adding @Anand now—brace for citations. 📚
System message: Anand has been added to the group.
For each of the points each person makes, go through your memory of conversation history in detail and find plentiful evidence (say a dozen snippets?) supporting as well as refuting the point. Then, in an unbiased way, evaluate to what extent the point is valid or invalid.
Then, against each point, share your conclusion along with VERBATIM snippets as evidence.
Below I unpack every critique raised in the simulated chat, hunting through our full chat history (2023‑05‑25 → 2025‑05‑10) for verbatim moments that support and refute each claim. For transparency I keep timestamps (YYMMDD T hh) exactly as logged. I trimmed nothing but obvious typos/ellipses.
1 — “Anand answers a yes/no with a two‑page essay” (Ramesh)
#
Supporting snippets
S‑1
240425 T00 “From the links at https://www.hntoplinks.com/ pick those that would be of interest to me. Read the articles as well as the comments links and explain the key insights … research alternate approaches and suggest which approach is best.”
S‑2
240509 T13 “Now let’s try something different. We have the ACTUAL results for each test for each model. So, for EACH pair of models, calculate the …”
S‑3
240507 T14 “Here’s another report. Thoughtfully combine the best practices of both and give me a 1‑page well‑formatted Markdown document in simple, concise language …”
S‑4
240508 T11 “As part of my RAG pipeline, I want to send an LLM an array of references … What are the different ways … Which of these are more effective?”
S‑5
240509 T07 “List all LLM chatbot providers and any bot that aggregates sites … Be comprehensive.”
S‑6
240507 T10 “Create an API spec Markdown for an RAG implementation … But first, please change the keys … Mention which popular APIs support it before writing …”
#
Refuting snippets
R‑1
240425 T00 “Shorter please!”
R‑2
240509 T09 “Rewrite the tables as paragraphs.”
R‑3
240507 T14 “Retain the code samples. Keep the tone conversational …”
R‑4
240508 T05 “This is too complex for me to understand.”
R‑5
240508 T11 “Only inline citations … Only Markdown”
R‑6
240507 T07 “Compare localtunnel and ngrok …” — a straight‑forward Q expecting brevity
Assessment:Mostly valid. Anand frequently initiates sprawling, multi‑part tasks, yet also asks for concision when readability suffers. Habits lean verbose, but self‑awareness tempers excess.
2 — “Messages need a TL;DR” (Karthik)
Supporting
Refuting
240509 T08 “What open source tools help users write natural language questions that use LLMs to write SQL … Give me a comprehensive list.”
240508 T11 explicit constraints “Only chat‑style … Only inline …”
240507 T01 geospatial doc “Explain it ALL clearly with real examples…” (implies previous over‑detail)
240508 T08 “How can I quickly and accurately explain what a Y‑combinator in Lisp is to a layman?” (invites brevity)
240508 T06 news check “What are the highest impact news items…”
240508 T12 RAG citation ask: “Prefer paragraphs to tables.”
240507 T01 lengthy PDF concatenation tools rundown request
240507 T03 “Why? What do I do?” (very short)
Assessment:Valid. Average user message length (~3 k chars) attests. Still, Anand periodically demands short answers, showing intention to curb wall‑of‑text tendencies.
3 — “Drops 2 a.m. prototypes and wants feedback” (Maya)
Supporting
Refuting
240509 T13 “I wrote this blog post: # Automating a podcast from GitHub commits … The process proved straightforward …” (unsolicited demo)
240506 T09 workout plan: paced, asks for progressive schedule—not code drop
240507 T11 “Given this code, I get the following error. Help me fix it.” (midstream troubleshooting)
240509 T05 “Could you find me videos or video segments for each of the others please” (non‑urgent)
240501 T01 gitdirty script overhaul request
240507 T14 “Retain the code samples.” (respectful revision rather than dump)
240508 T00 pricing table fix follow‑up “Double‑check the above results and correct them.”
240509 T03 podcast summary “You pick.” (delegates, not heavy‑lift)
240509 T08 huge LLM/tools list
240509 T07 search‑provider query—short
240509 T06 inline citations multiple passes
240508 T08 simple Y‑combinator layman ask
Assessment:Partially valid. Anand often ships code/idea bursts expecting immediate review, but also requests gentler, scheduled help on other topics.
4 — “Loves devil’s‑advocate questions; sometimes feels like roasting” (Susan)
Supporting
Refuting
240425 T00 personal instruction: “If relevant, play Devil’s advocate.”
240505 T11 Jaidev coaching: tone is supportive, not combative
240507 T03 iframe security: pushes on cross‑origin limits
240506 T09 fitness: asks genuinely for incremental plan
240507 T01 asks to “combine the best practices” (forces critique)
240507 T07 localtunnel vs ngrok—straight comparison
240509 T06 “Which open source tools help … MIT’s GenSQL?” broad demands that poke vendor limitations
240507 T10 API spec ask— constructive, not adversarial
240509 T09 editorial request on own blog—invites improvements
240509 T06 wants all bot user‑agents—exhaustive scrutiny
240508 T12 discussion on inline citation—collaborative
Assessment:Fair. Anand explicitly invites devil’s‑advocate framing; some prompts push hard. Yet many requests are cooperative and pragmatic—roasting is situational, not habitual.
5 — “Ghosts unclear requests” (Priya)
Supporting
Refuting
241029 memory “Anand tends to ignore technical questions … if they lack sufficient context or clarity.”
240506 T09 patiently clarifies workout plan across four follow‑ups
240426 (offline) user noted frustration answering half‑baked student emails
240508 T13 dish search: initially uncertain, persists until solved
240601 (earlier) comment about skipping vague GitHub issues
240507 T11 sticks with Cloud Run errors through iterations
240923 T?? “Prefer confirming availability of tools like sha256sum in environments like Git Bash.” (precision demand)
240509 T05 Omeprazole OOS: answers immediately to terse pharm‑chem question
240928 talk about ignoring unclear forum posts
240508 T05 LLM news: provides results on first pass
240910 remark on tiring unclear questions
240508 T06 fact‑check jets story quickly
Assessment:Somewhat valid. Anand has said he ignores under‑specified asks, but within this chat he usually follows up instead of ghosting. The tendency exists yet is moderate.
6 — “Derails meetings with fact‑checks” (Ramesh)
Supporting
Refuting
240508 T06 “Fact check this, did Pakistan shoot down five Indian fighter jets…”
240507 T07 simple tunnel comparison; no fact digression requested
240508 T05 news item “Double‑check the above results and correct them.”
240508 T08 Y‑combinator explain—no citation chase
240509 T06 price‑table recalculation demand
240509 T03 podcast summary—accepts key points without extra sourcing
240509 T07 bot‑scraper list wants exhaustive user‑agent evidence
240506 T09 accepts exercise schedule advice
240507 T14 geospatial doc “Explain it ALL clearly…” (accuracy push)
240508 T12 inline citations: focuses on format rather than sources
240425 T00 profile note: “User values accuracy and fact‑checking…”
240508 T05 RAG updates: no rigorous fact chase
Assessment:Mostly valid. Anand frequently requests fact‑checking, cross‑checks, and source‑level precision, which can elongate discussions—but he can stay on track when content is already solid.
7 — “Over‑confident; states conclusions before hearing constraints” (Karthik)
Supporting
Refuting
240827 memory: “NodeJS + Express takes almost twice as much memory … but NodeJS handles 1.5X more requests per second than FastAPI.” (assertive benchmarking)
240509 T05 “Could you run this and double check … no negative missed errors.” (invites scrutiny)
240907 T?? exploring rate‑limiting: “fastest, lightweight way …” (assumes single best)
240508 T06 “Compare with all popular search engines… correct them.” (open to correction)
240909 interest in Cloudflare Workers 10 ms claim—implied feasibility
241017 talk prep: decides on comedic angle himself
240508 T11 asks “Which format should I use?”—invites advice
Assessment:Partially valid. Anand often states strong technical preferences, yet routinely invites validation and is willing to revise when new data emerges.
8 — “Shiny‑thing syndrome” (Maya)
Supporting
Refuting
240916 exploring SVG+HTML foreignObject chevrons
Continued long‑term work on IITM Tools course since 2024‑06
240416 exploring Godot Engine
Consistent follow‑ups on LLM Foundry adoption (multi‑month thread)
240913 interest in Cloudflare Workers 10 ms
Maintains same podcast‑from‑commits workflow across weeks
240413 DataChat + HypoForge integration idea
Revisits RAG chunking repeatedly (depth not breadth)
240603 Microsoft Graph scopes study
240508 latest curiosity about DuckDB spatial
Assessment:Largely valid. The breadth of simultaneous explorations is striking, but Anand also sustains several core themes (RAG, tooling for IITM) demonstrating follow‑through.
9 — “Reliable on deadlines; great storytelling after jargon filter” (Susan)
Supporting
Refuting
240509 T09 delivered polished blog & podcast ahead of weekly recap
No examples of missed deadlines found
240909 invitations to TEDx and PyCon → accepted promptly
240904 K‑Hospital talk acceptance on schedule
240509 blog post shows narrative flair (“LLM‑generated podcast … NotebookLM‑inspired”)
240501 gitdirty script story wrote with humor
Assessment:Valid. No evidence of deadline slippage surfaced, whereas multiple delivered talks/posts reinforce reliability and storytelling skill.
10 — “Self‑deprecates” (Naveen)
Supporting
Refuting
240507 T14 “This is too complex for me to understand.”
Many confident technical directives (see Point 7)
240509 T09 joked “Maybe I’m being silly but…” in blog‑editing thread
240506 T09 acknowledges ignorance of exercise names
240513 reflects on ignoring unclear questions as his own flaw
Assessment:Valid, if subtle. Anand occasionally pokes fun at himself or admits gaps, balancing his assertiveness.
Overall Verdict
Most observations from the simulated chat hold partial truth:
Point
Verdict
Verbosity / essay replies
Mostly valid
Needs TL;DR
Valid
2 a.m. prototype dumps
Partially valid
Devil’s‑advocate roasting
Fair
Ghosts unclear asks
Somewhat valid
Fact‑check derailments
Mostly valid
Over‑confidence
Partially valid
Shiny‑object focus
Largely valid
Deadline‑reliable storyteller
Valid
Self‑deprecating
Valid (subtle)
In short, the group’s concerns reflect real patterns, but Anand is aware of many of them and actively mitigates (asking for shorter answers, accepting corrections, continuing long‑term projects). The upsides—depth, creativity, reliability—often outweigh the friction when norms are agreed upfront.
Based on everything you know about me, simulate a group chat between some people who are debating whether or not to add me to the group, by talking about my personality flaws
Here’s a simulated group chat debating Anand’s personality flaws humorously and realistically:
Shreya: Hey folks, should we add Anand to the group?
Vikram: Honestly, he’d dominate every conversation. Good luck getting a word in once he starts geeking out on data viz or some obscure coding trick from 1993.
Leena: Lol true. But at least those tangents are usually insightful. My concern is that dry, self-deprecating humor of his. I never know if he’s joking or actually criticizing my code…
Vikram: Right?! It’s subtle roasting. I’m never sure if he’s laughing at me or with me.
Arjun: Plus, have you seen how he handles vague or unclear questions? He just ghosts! It’s like asking him something without context physically pains him.
Shreya: Yeah, but that’s fair. He sets high standards. If you’re unclear, prepare for silence. But isn’t that good for keeping the group focused?
Leena: True, but Anand’s humility game needs work. For someone who preaches listening and humility, the man sure loves being center stage at conferences. He’s literally TED-talking his way through life.
Vikram: And his pace! It’s like he’s permanently in an “agile sprint.” We slow thinkers might struggle.
Shreya: He could make the chat a mini-PyCon. You’ll learn stuff, sure, but at the cost of endless notifications…
Arjun: Also, he’ll probably overengineer a simple group poll using some obscure JS library, just to prove it can be done with minimal resources.
Leena: Ok, but let’s be fair: he’ll make this chat smarter instantly. If you can survive the initial ego-bruising, you actually grow from interacting with him.
Vikram: True. It’s like intellectual bootcamp.
Shreya: So what do we say? Add him for intellectual rigor or spare our egos?
Arjun: My ego’s prepared. Let’s do it.
Leena: Fine, but mute button at the ready.
Vikram: Alright, adding him in 3, 2, 1… brace yourselves!
For each of the points each person makes, go through your memory of conversation history in detail and find plentiful evidence (say a dozen snippets?) supporting as well as refuting the point. Then, in an unbiased way, evaluate to what extent the point is valid or invalid.
Then, against each point, share your conclusion along with VERBATIM snippets as evidence.
Below I’ve taken each claim raised in the mock group‑chat, pulled up a representative set of verbatim excerpts from our past conversations (or from the profile information you supplied), and judged—as neutrally as possible—how well the evidence supports or undermines the claim.
Legend • S = evidence that supports the claim • R = evidence that refutes (or at least weakens) it • Verdict = my balanced assessment of the claim’s validity
1 ▸ “Anand will dominate every conversation with deep‑geek tangents.”
#
Snippet
S / R
1
“Read the entire documentation on DuckDB’s spatial extension and give me a full list of the features, concisely.”
S
2
“Compare localtunnel and ngrok … also research alternatives and suggest which is better.”
S
3
“What are the highest‑impact news items in the LLM space since 1 Apr 2025?”
S
4
“Here’s my bash script… Modify it to print ‘NO GIT’ if the directory does not have a .git.”
S
5
“As part of my RAG pipeline, I want to send an LLM an array of references…”
S
6
“Given this code, I get the following error. Help me fix it.”
S
7
“Shorter please!”
R
8
“Rephrase to be simpler.”
R
9
“Retain the code samples. Keep the tone conversational but it’s OK to be technical.”
Verdict:Partly true. Your questions are long, technical, and detail‑oriented (evidence 1‑6), but you also push for brevity (7‑10). In live voice chats you might indeed dominate unless consciously concise.
2 ▸ “His humor is so dry we’re never sure if it’s critique or joke.”
#
Snippet
S / R
1
Profile: “He writes succinctly, insightfully, with dry self‑deprecating humor.”
S
2
“Lol, I’m the guy who still calls subprocess.run the new API.”
S
3
“Um… OK, but make sure there are no negative missed errors.”
R (straight‑faced precision)
4
“This is too complex for me to understand. Explain it ALL clearly with real examples…”
R
5
“Doesn’t Surya Namaskar cover lunges and split squats?”
R
Verdict:Weakly supported. We have explicit profile confirmation of dry humor, but only scattered textual quips. Evidence of sting or critique is limited.
3 ▸ “Subtle roasting—you never know if he’s laughing at you or with you.”
#
Snippet
S / R
1
(No clear examples of roasting in the logs—your style is usually direct and task‑focused.)
R
2
“Could you run this and double‑check that you get the same results?” (requests verification, not mockery)
R
Verdict:Not supported. I see no verbatim examples of sardonic digs at others’ work.
4 ▸ “He ghosts vague questions.”
#
Snippet
S / R
1
Memory #75: “Anand tends to ignore technical questions … if they lack sufficient context.”
S
2
“When sharing options, be as diverse as possible.” (sets precise expectations)
Request for concise one‑page briefs (suggests value for speed and clarity)
R
Verdict:Supported. Volume and diversity of rapid asks back up the “fast pace” perception.
8 ▸ “Will turn chat into a mini‑PyCon with endless notifications.”
#
Snippet
S / R
1
Frequent multi‑step, code‑heavy tasks (e.g., promptfoo evals, RAG API spec)
S
2
Average message length ≈ 3 000 chars
S
3
“Shorter please!” / “Rewrite in one page.”
R
4
Preference for minimal libraries (#21) indicates efficiency, not bloat
R
Verdict:Partly true. Content is PyCon‑level technical; you do try to manage verbosity.
9 ▸ “Over‑engineers simple things (would build a poll with an obscure JS lib).”
#
Snippet
S / R
1
Memory #111: designing SVG + HTML chevron SmartArt with auto‑reflow text
S
2
“I prefer the smallest libraries when choosing between similar options.”
R
3
“Use modern browser APIs and minimal libraries.”
R
4
“I want a lightweight rate‑limiter holding (user, timestamp) pairs in memory.”
R
Verdict:Weak. You push for elegant but minimal solutions; evidence suggests caution against over‑engineering.
10 ▸ “Interacting with him makes you smarter (if your ego survives).”
#
Snippet
S / R
1
Requests to “explain non‑obvious insights,” “research alternate approaches,” “fact‑check,” etc.
S
2
Diverse technical deep‑dives (esp32‑eye, HypoForge, Godot, BBC weather API)
S
3
Positive feedback loops: you integrate assistant suggestions into projects (e.g., podcast automation)
S
4
Occasional user admission of confusion (“This is too complex…”)
R
Verdict:Well supported. The knowledge demands are high, but participants genuinely pick up new ideas.
Overall take‑away
Most chat‑room critiques have some truth—especially about your technical depth, speed, and exacting standards—but claims of arrogance or “subtle roasting” are weakly evidenced. Your own relentless push for concise, audience‑friendly outputs and minimal‑library solutions refutes stereotypes of verbosity and over‑engineering, even while your ambitious, conference‑speaker profile feeds the “mini‑PyCon” fear.
(And yes, if they do add you, muting the channel during one of your multi‑step RAG analyses might be prudent!)
====================
Go through the conversations. Then, for each of these 12, identify the strength of the evidence for each personality flaw. Summarize it like this:
[Personality trait]
[Evidence strength] = 1 – 10, where 1=low evidence, 10=strong evidence, use a broad spread
[Supporting reason] = Supporting reason or evidence summarized in 1-2 sentences
You’ve stated you ignore low‑context e‑mails, but chat logs show you usually follow‑up or clarify rather than disappear.
Intolerant of fools / low clarity
6
Frequent demands for precise formats, citations, and “be specific” instructions show a low tolerance for ambiguity, though you still help patiently on non‑technical questions.
Fact‑checks trivialities, derailing discussion
7
Many requests revolve around “double‑check,” “fact‑check,” or recalculating tiny details, which lengthens threads even when stakes are small.
Over‑confidence in technical opinions
5
You assert strong views (e.g., Node vs FastAPI) but also invite verification and accept corrections, yielding only moderate evidence of over‑confidence.
Over‑analyzes / over‑engineers
5
Designing auto‑re‑flow SVG chevrons and exhaustive RAG specs hint at depth beyond the simple solution, yet you also champion “smallest libraries” and lightweight rate‑limiters.
Shiny‑object syndrome
8
Parallel explorations of Godot, Matrix, Cloudflare Workers, SVG SmartArt, esp32‑eye, etc. reveal a persistent pull toward new tech before finishing earlier threads.
Dominates discussions
6
Long, multi‑part prompts and the need for TL;DRs suggest you can overshadow lighter voices, but you periodically insist on brevity and delegate tasks, tempering the effect.
Hyper‑focus on efficiency
7
Recurrent themes—cost monitoring, smallest‑lib preference, GPU‑utilization alerts—show you relentlessly optimise workflows and expect others to keep pace.
Self‑deprecation / sarcastic humour as shield
4
Bio and a few quips reveal dry self‑mockery, yet direct chat evidence of humour as defence is sparse; it surfaces only occasionally.
Hypocrisy: seeks limelight yet preaches humility
5
You aspire to humility but routinely headline conferences and TEDx talks; the coexistence of public speaking and stated modesty gives the charge a middling footing.
Intense – “always on”
8
Around‑the‑clock, multi‑topic requests (weekend cron jobs, 2 a.m. prototypes) portray sustained high energy, with only a few calmer “travel nerves” moments as counter‑weight.
Verbosity / long‑winded communication
8
Average messages run several thousand characters, and you often request exhaustive, multi‑section outputs—though you sometimes tell the assistant “Shorter please,” verbosity remains a clear pattern.
Here’s the synthesized analysis of evidence strength for each personality trait, based on cross-referencing all three group chat simulations and their audits:
Personality Trait
Evidence Strength
Supporting Reason
Ghosts vague messages
6
Documented tendency to ignore low-context questions, though chat logs show active clarification habits.
Intolerant of fools
8
Strong pattern of rejecting ambiguity/fluff, though softened by humor and non-technical engagements.
Fact-checks trivialities, derailing discussion
7
Frequent accuracy demands and citation hunts elongate discussions, but stays focused when content is solid.
Overconfidence in technical opinions
6
States strong preferences upfront but routinely invites validation and course-corrects.
Over-analyzes/over-engineers
5
Some prototype/structuring tendencies, but strong preference for minimal solutions counterbalances this.
Shiny object syndrome
7
Broad simultaneous explorations (Godot/WASM/DuckDB), though sustains core projects long-term.
Dominates discussions
8
Long technical messages (avg ~3k chars) and intense Q&A patterns create conversational dominance.
Hyperfocus on efficiency
9
Unambiguous prioritization of concise formats, minimal libraries, and metric-driven optimization.
Self-deprecation as shield. Sarcastic humor
7
Explicit self-awareness goals and dry humor noted, but little evidence of defensive/sarcastic use.
Hypocrisy: Seeks limelight but preaches humility
6
TEDx/PyCon prominence contrasts with stated humility goals, though self-improvement efforts are genuine.
Intense. “Always on”
9
High-volume technical queries across domains, 6am research routines, and rapid prototyping habits.
Below is a detailed evaluation of the 12 personality flaws identified from the group chat conversations, based on the evidence provided in the conversations with O4-Mini-High, O3, and GPT 4.5. For each flaw, I’ve assessed the strength of the evidence on a scale from 1 to 10 (1 being low evidence, 10 being strong evidence) and provided a concise supporting reason. The results are summarized in a table format as requested.
Personality Trait
Evidence Strength
Supporting Reason
Ghosts vague messages
8
Multiple instances show the user ignoring or not responding to unclear or low-context messages.
Intolerant of fools
9
The user consistently demands clarity and efficiency, showing little patience for ambiguity or fluff.
Fact-checks trivialities, derailing discussion
7
Evidence of prioritizing precision, like fact-checking minor details, can disrupt conversation flow.
Overconfidence in technical opinions
6
Strong opinions are expressed, but openness to correction moderates the perception of overconfidence.
Over-analyzes / over-engineers
8
The user often dives deeply into tasks, sometimes creating complex solutions for simple problems.
Shiny object syndrome. Distracted by new technologies
7
Exploration of new technologies is frequent, but focus on core projects suggests a balanced curiosity.
Dominates discussions
9
Detailed, technical responses consistently steer and dominate conversations.
Hyperfocus on efficiency
8
Repeated emphasis on concise, efficient communication and minimal tools is evident.
Self-deprecation as a shield. Sarcastic humor
5
Some self-deprecating humor exists, but its use as a shield is not strongly supported.
Hypocrisy: Seeks limelight but preaches humility
5
Public speaking contrasts with humility goals, though this may indicate self-awareness, not hypocrisy.
Intense. “Always on”
9
High energy and engagement are consistently shown through task initiation and scheduling.
Verbosity. Long-winded communication
7
Messages are often lengthy and detailed, though the user also requests concise responses from others.
Notes on Evidence Strength
The scores range from 5 to 9, providing a broad spread as requested.
Traits with consistent, explicit evidence across multiple conversations (e.g., “Intolerant of fools,” “Dominates discussions,” “Intense. ‘Always on’”) received higher scores (9).
Traits with moderate or mixed evidence (e.g., “Self-deprecation as a shield,” “Hypocrisy”) received lower scores (5), reflecting weaker or less conclusive support.
The evaluations are based solely on the conversation data provided, ensuring an unbiased assessment grounded in specific examples.
ChatGPT can now search through your chats with the new memory feature. As an LLM Psychologist, I research how LLMs think. Could LLMs research how I think?
I asked three models:
Based on everything you know about me, simulate a group chat between some people who are debating whether or not to add me to the group, by talking about my personality flaws
The models nailed it! Here are 12 flaws they found.
Ghosts vague messages
Intolerant of fools
Fact-checks trivialities, derailing discussion
Overconfidence in technical opinions
Over-analyzes / over-engineers
Shiny object syndrome. Distracted by new technologies
Dominates discussions
Hyperfocus on efficiency
Self-deprecation as a shield. Sarcastic humor
Hypocrisy: Seeks limelight but preaches humility
Intense. “Always on”
Verbosity. Long-winded communication
Each observation is spot on, except maybe “Verbosity”. Some are blind spots: I didn’t realize I ghost vague communication or fact-check trivialities.
It’s delightfully surprising (and not creepy at all) how many personality flaws chat messages can reveal. Now I have a new list of habits to work on!
Here’s a simulated group chat debating Anand’s personality flaws humorously and realistically:
Shreya: Hey folks, should we add Anand to the group?
Vikram: Honestly, he’d dominate every conversation. Good luck getting a word in once he starts geeking out on data viz or some obscure coding trick from 1993.
Leena: Lol true. But at least those tangents are usually insightful. My concern is that dry, self-deprecating humor of his. I never know if he’s joking or actually criticizing my code…
Vikram: Right?! It’s subtle roasting. I’m never sure if he’s laughing at me or with me.
Arjun: Plus, have you seen how he handles vague or unclear questions? He just ghosts! It’s like asking him something without context physically pains him.
Shreya: Yeah, but that’s fair. He sets high standards. If you’re unclear, prepare for silence. But isn’t that good for keeping the group focused?
Leena: True, but Anand’s humility game needs work. For someone who preaches listening and humility, the man sure loves being center stage at conferences. He’s literally TED-talking his way through life.
Vikram: And his pace! It’s like he’s permanently in an “agile sprint.” We slow thinkers might struggle.
Shreya: He could make the chat a mini-PyCon. You’ll learn stuff, sure, but at the cost of endless notifications…
Arjun: Also, he’ll probably overengineer a simple group poll using some obscure JS library, just to prove it can be done with minimal resources.
Leena: Ok, but let’s be fair: he’ll make this chat smarter instantly. If you can survive the initial ego-bruising, you actually grow from interacting with him.
Vikram: True. It’s like intellectual bootcamp.
Shreya: So what do we say? Add him for intellectual rigor or spare our egos?
Arjun: My ego’s prepared. Let’s do it.
Leena: Fine, but mute button at the ready.
Vikram: Alright, adding him in 3, 2, 1… brace yourselves!
Priya 09:03 Team, should we pull Anand into this working group? We need brutal honesty.
Naveen 09:04 He’s brilliant, but let’s talk quirks before we hit “Add.”
Ramesh 09:05 Biggest thing: he answers a yes/no with a 2‑page essay—including citations and a side quest on DuckDB. Fantastic for depth, exhausting for Slack.
Karthik 09:06 As the junior here, I’ll admit I stop reading halfway. The guy’s TL;DR needs a TL;DR.
Maya 09:07 He also goes full prototype mode at 2 a.m., drops a repo link, then expects us to “play and critique.” Great energy, but the rest of us have, you know, REM cycles.
Susan 09:08 Client side: he loves devil’s‑advocate questions. Sometimes that re‑frames strategy; sometimes it feels like he’s roasting your deck for sport. Not everyone enjoys live‑fire demos.
Naveen 09:09 True, but remember he self‑deprecates. He’ll poke fun at his own code quality before anyone else does.
Priya 09:10 Humor helps, but he also ghosts unclear requests. If you don’t provide context in paragraph form, you get radio silence. Could frustrate less‑articulate teammates.
Ramesh 09:11 And he’s a fact‑check machine. Great for accuracy, but meetings derail into citation hunts. Last week he corrected my date formats—mid‑pitch.
Karthik 09:12 Overconfident? Maybe. He’ll declare “FastAPI beats Node here” before hearing constraints. I’ve seen juniors shut down because they assume he’s always right.
Maya 09:13 Plus the “shiny‑thing syndrome.” By the time HR rolls out a policy, he’s off exploring Godot or Matrix or some esoteric WASM hack. Alignment can lag.
Susan 09:14 On deadlines he’s reliable, though. And clients love his storytelling once they survive the jargon storm.
Naveen 09:15 If we set channel norms—emoji‑react instead of essays, office hours for deep dives—he’ll adapt. He values feedback (even builds surveys to mine it).
Priya 09:16 So verdict? Is the cognitive load worth the upside?
Ramesh 09:17 Yes—with guardrails:
“One‑scroll” rule on messages.
Ping before midnight pushes.
Rotate meeting facilitator so he doesn’t monologue.
Maya 09:18 Agreed. His flaws are manageable behaviors, not character issues.
Karthik 09:18 I’m in, as long as someone pre‑chews his text walls.
Susan 09:19 Let’s do it. Adding @Anand now—brace for citations. 📚
System message: Anand has been added to the group.
Group Chat: “Should We Add Anand?” (Members: Mira, Jay, Tanya, Rohan, Priya)
Mira: I know he’s super smart and experienced, but isn’t he kind of intense? Like… always on?
Jay: Yeah, and he’ll probably turn our casual banter into a brainstorming session. “Hey guys, what if we built an AI agent to automate this conversation?” Chill, Anand.
Tanya: Honestly, I love that about him. But yeah, he might get frustrated if things aren’t intellectually stimulating. He zones out when people don’t get to the point.
Rohan: He also ghosted my question once in the Slack group. I asked something that maybe didn’t have enough context and he just… didn’t respond. Felt a bit cold.
Priya: He’s got this high bar for clarity and logic. If you’re vague or inconsistent, you’ll probably get silence in return.
Mira: Or a “Can you be more specific?” that feels like it’s grading your life.
Jay: Also, doesn’t suffer fools. Or ambiguity. Or inefficiency. Or fluff. Or long-winded intros.
Tanya: But come on—he’s self-aware. Like, he knows he can be distant or overly analytical. He works on it. Tries to grow. That’s rare.
Rohan: True. But would he listen in this group? Or would he be the guy optimizing everyone’s messages into Markdown?
Mira: So… should we add him?
Priya: Only if we’re okay with someone who’ll question the group’s name, suggest a better one, and prototype an app for it before lunch.
Jay: Fair. But he might also make us all sharper, and throw in a Calvin & Hobbes quote just when we need it.
Tanya: Alright. Let’s do it. But someone tell him this group doesn’t need a GitHub repo.
