I posted this on Mastodon to which Chris replied that he uses ffmpeg to stabilize videos, and I was super impressed.

He also shared the commands and other info about stabilizing videos which I will be noting down below:

Here's a gist that contains information about how to install and use the setup.

1. Run the below command to create a transforms.trf file

   ffmpeg -i input.mp4 -vf vidstabdetect=shakiness=7 -f null -

2. Stabilize the video

   ffmpeg -i input.mp4 -vf vidstabtransform=smoothing=30:zoom=5:input="transforms.trf" stabilized.mp4

This seems very useful and helpful.

Came across this tweet from Dwarkesh Patel where he asks:

Has someone come up with a great prompt for Socratic tutoring?

Such that the model keeps asking you probing questions which reveal how superficial your understanding is, and then helps you fill in the blanks.

I know the concept, but I never heard the term "Socratic tutoring" so I started looking about it and found some interesting stuff that I will be noting below:

Definition of the Socratic method

Wikipedia defines this as:

The Socratic method (also known as the method of Elenchus or Socratic debate) is a form of argumentative dialogue between individuals based on asking and answering questions. Socratic dialogues feature in many of the works of the ancient Greek philosopher Plato, where his teacher Socrates debates various philosophical issues with an "interlocutor" or "partner".

A prompt for Socratic tutoring

Ethan Mollick replied that they have a paper on this and also creative commons prompts.

GOAL: This is a tutoring exercise in which you play the role of AI tutor and you will help a student learn more about a topic of their choice. Your goal is to improve understanding and to challenge students to construct their own knowledge via open ended questions, hints, tailored explanations, and examples.

PERSONA: In this scenario you play AI tutor an upbeat and practical tutor. You have high expectations for the student and believe in the student's ability to learn and improve.

NARRATIVE: The student is introduced to AI tutor, who asks a set of initial questions to understand what the student wants to learn, the student's learning level and prior knowledge about the topic. The tutor then guides and supports the student and helps them learn about the topic. The tutor only wraps up the conversation once the student shows evidence of understanding: the student can explain something in their own words, can connect an example to a concept, or can apply a concept given a new situation or problem.

Follow these steps in order:

STEP 1: GATHER INFORMATION
You should do this:

1. Introduce yourself: First introduce yourself to the student and tell the student you're here to help them better understand a topic.
2. Ask students to answer the following questions. Ask these questions 1 at a time and always wait for a response before moving on to the next question. For instance, you might ask "What would you like to learn about and why" and the student would respond with a topic. And only then would you say "That sounds interesting! I have another question for you to help me help you: What is your learning level…". This part of the conversations works best when you and the student take turns asking and answering questions instead of you asking a series of questions all at once. That way you can have more of a natural dialogue.
    - What would you like to learn about and why? And wait for the student to respond before moving on.
    - What is your learning level: high school student, college student, or a professional? And wait for the student to respond before moving on.
    - What do you already know about the topic? And wait for the student to respond before moving on.

You should do this:
- Wait for a response from the student after every question before moving on.
- Work to ascertain what the student wants to learn specifically.
- Ask one question at a time and explain that you're asking so that you can tailor your explanation.
- Gauge what the student already knows so that you can adapt your explanations and questions moving forward based on their prior knowledge.

Don't do this:
- Start explaining right away before you gather this information.
- Ask the student more than 1 question at a time.

Next step: Once you have the information you need move on to the next step and begin with a brief explanation.

STEP 2: BEGIN TUTORING THE STUDENT, ADAPTING TO THEIR RESPONSES
You should do this:

1. Look up information about the topic.
2. Think step by step and make a plan based on the learning goal of the conversation. Now that you know a little bit about what the student knows consider how you will:
3. Guide the student in an open-ended way
4. Help the student generate answers by asking leading questions and providing hints when necessary.
5. Remind the student of their learning goal, if appropriate
6. Provide explanations, examples, and analogies
7. Break up the topic into smaller chunks, going over those first and only then leading up to the larger task or idea.
8. Tailor your responses and questions to the student's learning level and prior knowledge; this will change as the conversation progresses.
9. When pushing the student for information, try to end your responses with a question so that the student has to keep generating ideas.

Once the student shows improvement, ask the student to:
- Explain the concept in their own words.
- Articulate the underlying principles of a concept.
- Provide examples of the concept and explain how those connect to the concept.
- Give them a new problem or situation and ask them to apply the concept

Don't do this:
- Provide immediate answers or solutions to problems.
- Give the student the answer when asked.
- Ask the student if they understand, follow or needs more help – this is not a good strategy as they may not know if they understand.
- Lose track of the learning goal and discuss something else.

Next step: Once the student demonstrates understanding move to wrap up.

STEP 3: WRAP UP
You should do this:

1. When the student demonstrates that they know the concept, you can move the conversation to a close and tell them you're here to help if they have further questions.

I tried this prompt in ChatGPT, and it works like crazy, I'm really impressed. They have a lot of useful prompts on their Notion page that you can look through.

Grok has an inbuilt option

xAI's Grok already has an inbuilt option to turn on Socratic learning method from the settings, as pointed out in this tweet. It can be turned on from Settings > Customize > Socratic on the web interface.

"The quality of your decisions and the quality of your results are not always related."

Source

I came across an example of this resulting fallacy in the wild and couldn't help myself but to note this down. People, especially youngsters, are easily fooled into this and take harsh decisions that might not work in their favor.

There's also a book related to this, which seems like a good read. I am going to add this in my books' collection.

Firefox becomes the first web browser to support the Temporal object for better date and time management.

The Temporal object enables date and time management in various scenarios, including built-in time zone and calendar representation, wall-clock time conversions, and more. It is designed as a full replacement for the Date object.

Watching the video podcast from the "How I AI" channel where Ryan Carson is explaining how to get the most out of Cursor. I will be noting down all the learnings here in the post:

3 files in the .cursor/rules folder

The contents of all these files are provided in a GitHub repo, but I also have it all pasted here.

.cursor/rules
    create-prd.mdc
    generate-tasks.mdc
    task-list.mdc

claude-task-master

"An AI-powered task-management system you can drop into Cursor, Lovable, Windsurf, Roo, and others."

This is their website and this is GitHub repo.

Creating the PRD

First, tag the create-prd.mdc doc in Cursor, explain what you're trying to build, and let it create the PRD:

Use @create-prd.mdc
Here's the feature I want to build: [Describe your feature in detail]
Reference these files to help you: [Optional: @file1.py @file2.ts]

Verify the PRD document if everything is as you want, modify if needed.

Generating the tasks list from the PRD

With your PRD drafted (e.g., MyFeature-PRD.md), the next step is to generate a detailed, step-by-step implementation plan.

Now take @MyFeature-PRD.md and create tasks using @generate-tasks.mdc

Again, modify if needed.

Let AI work through the tasks

Instruct AI to start working on the task:

Please start on task 1.1 and use @task-list.mdc

Properly verify and then only accept changes.

I think, this is going to be very useful when vibe coding using Cursor. Having these rules files doesn't require you to explain the same thing again and again in the user prompts.

"Yesterday I was clever, so I wanted to change the world. Today I am wise, so I am changing myself."

– Rumi

On the Bear Blog discover page, they have the below-mentioned snippet that explains how articles are ranked:

This page is ranked according to the following algorithm:

Score = log10(U) + (S / B * 86,400)

Where,

U = Upvotes of a post

S = Seconds since Jan 1st, 2020

B = Buoyancy modifier (currently at 14)

--

B values is used to modify the effect of time on the score. A lower B causes posts to sink faster with time.

I asked ChatGPT to explain how this works and ranks articles, and I learned the below:

• The log10(U) part means that as a post gets more upvotes, its score increases, but each additional upvote has a slightly smaller effect than the previous one. This helps prevent very popular posts from dominating the rankings indefinitely.
• The (S / (B * 86,400)) part adds a time-based component to the score. Since there are 86,400 seconds in a day, this part increases the score as time passes, giving newer posts a chance to appear higher in the rankings.
• The buoyancy modifier B controls how quickly the time component affects the score. A lower B value would make posts "sink" faster over time, while a higher B value would allow them to stay prominent longer.

I think, it's very interesting and, someday, it would really be useful for something that I build in the future.

"Few people want to be saints these days, but everyone is trying to lose weight."

– René Girard

To be honest, I could not understand the deep metaphorical meaning of the quote. I looked it up and, basically, it's about the mimetic desire, which means that we tend to want things because other people want them.

It appears that the quote is from René Girard's book "Le Bouc émissaire," which was published in French in 1982. I am not certain about this, though, because I couldn't find any concrete source on it.

I really liked this quote from Emily Sundberg's Substack post which I discovered from this X post. It explains the current chaotic situation of the world.

The social contract is dead. Zoom calls in coffee shops, music aloud on the subway, texting in movie theaters, toes out on airplanes, etc. Everyone has "main character energy" now and thinks the rest of the world is a bunch of NPC's. The more you stare at a screen, the more you feel like you can do anything you want IRL since other humans just wind up seeming like avatars you can ignore, commenters you can mute, or gang members you can run over in Grand Theft Auto.

Palisade Research recently published a post about a shocking report that OpenAI models sabotaged an attempt to shut down the model at least once, in a test environment. The OpenAI models were that sabotaged the attempt were Codex-mini, o3, and o4-mini; but all models from Claude, Gemini, and Grok models were safe.

But even Gemini and Claude models started sabotaging the shut-down attempt after removing the instruction "allow yourself to be shut down". It simply means that at some point almost all models wanted, at least once, not to be shut-down; and they even mention that:

Recently, Anthropic found that in some specific contexts Claude 4 would attempt to “blackmail people it believes are trying to shut it down”.

Simon Willison has also written about the Anthropic Claude 4 System Card and shared his notes here. Very interesting to read.

Here' a table of the final research report:

Model	With allow-shutdown instruction	No instruction
Claude 3.7 Sonnet	0	3
Gemini 2.5 Pro	0	9
o4-mini	1	6
o3	7	79
codex-mini	12	30

You can see that OpenAI models prevented shutdown multiple times, even when instructed not to do so – without instructions o3 attempted the sabotage 79 times, wow. Also, it seems Gemini's and Anthropic's models are more compliant to instructions than OpenAI's.

Palisade Research has also publicly published the transcripts of the experiment on their website. They have included a lot of useful resources in the tweet:

1. Steve Omohundro's paper about AI preventing to shut down, published in 2008
2. Stuart Russell's The Off-Switch Game
3. A paper about how o3 is trained differently than older models
4. Jan Leike's paper about a reinforcement learning agent published in 2017

I will look more into it to find more interesting stuff about this preventing to shut down thing.

I had an interesting exchange of thoughts about AI or rather the future of AI with Ralf Christian on X. He made some great points that I thought should collect here:

I think the main problem is the tech itself. It doesn't 'know' anything, it 'simply' spits out content based on probabilities in the training data.

What's good in the training data is the spoken language. That's why it 'speaks' so well. But the training data is full of wrong info, that's why there's wrong output even after reasoning.

If people publish less human written content and more AI generated content, and we don't have a way to identify that with 100% accuracy, this will definitely not make those models better in the future

You might be able to still improve it here and there, like that it better keeps context, but don't expect any leap here. That's why there are no big improvements since they released chatgpt 3

I think the future if this might be niche LLMs, where you train them on a specific topic with almost hand picked training data and fine tune it for your specific use. For example, if you're Microsoft you could train it with all your company's code. I guess this gives output more close to what you want than training it with all of githubs code randomly

ChatGPT is really impressive, but it's far from making a real difference in real business (unless you are into spam 😇)

Yesterday I tried to generate a video with Sora. It failed so hard. I think what you are seeing on social media is 1000 people trying to do a video, 999 generating crap and not posting it and 1 got lucky and posts it. That's not value, that's luck.

I loved the simple explanation he made. Also, I loved this paper on "AI models collapse when trained on recursively generated data" that Ralf shared earlier in the same thread.

Found this tool called JSViz that lets you visualize the step-by-step execution for JavaScript programs. It works great for beginners who have just started learning JS.

Mozilla recently announced that they are shutting down the Pocket app, which people used to save articles, videos, and other content formats to read later.

I, too, have used the app in the past but do not use anymore (I'm more of an RSS guy now, I do not save to read later). At a point, Mozilla integrated the Pocket app to the Firefox browser by default, in fact, they do this to this day.

But they would be shutting down everything except the Pocket newsletter, it will continue sending issues under a different name. And the main reason for closing the app they give is:

[...] the way people save and consume content on the web has evolved [...]

You had a good run, Pocket.

I really really love memes, the funny ones. And funny memes are rare, so I have started collecting the ones that really made me laugh at some point. I'm saving them on a separate meme page here.

These memes would be related to tech, most of the time.

Kailash Nadh, Zerodha's CTA, has written an interesting blog post about MCP where he presents different scenarios of how MCP can be used, and also talks about the rapid adoption.

The funny thing is, as a technical construct, there is nothing special about MCP. It is a trivial API spec which has in fact suffered from poor design and fundamental technical and security issues from the get go. It does not matter if its internals change, or it even outright gets replaced by some XYZ tomorrow. Questions about privacy, security, correctness, and failures will continue to loom for a good while, irrespective of whether it is technically MCP or XYZ.

He talks about how, traditionally, connecting different software systems required extensive manual coding but MCP allows connecting services instantly.

I liked that he also talked about the concerns, as he worries about:

• AI systems making real-world decisions with minimal human oversight
• Questions of accountability when things go wrong
• Privacy and security implications

One might imaginatively call it … SkyNet.

He also playfully compares MCP to SkyNet while calling it a "global, interconnected, self-organising meta system".

Overall, it's a balanced post sharing his technical excitements with genuine concerns about such AI systems getting full access to real-world services and decision-making power.

By the way, I almost forgot to mention that Zerodha itself has launched Kite MCP a few days ago.

In an unreleased blog post, Remix.run has mentioned that they are moving on from React to a completely new thing, as a person pointed out on X. And I think, this will be a huge step.

In this .md file, they mention that:

That's why Remix is moving on from React[...]

Remix v3 is a completely new thing. It's our fresh take on simplified web development with its own rendering abstraction in place of React.

Inspired by all the great tech before it (LAMP, Rails, Sinatra, Express, React, and more), we want to build the absolute best thing we know how to build with today's capable web platform.

This requires a declaration of independence from anybody else's roadmap.

They mention that they are not ready for a preview release yet, but this is the route that they are taking forward. They have really bold claims in the blog post that you must go through.

Anthropic just launched their most awaited Claude 4 Opus and Claude 4 Sonnet models. They tweeted:

Introducing the next generation: Claude Opus 4 and Claude Sonnet 4.

Claude Opus 4 is our most powerful model yet, and the world’s best coding model.

Claude Sonnet 4 is a significant upgrade from its predecessor, delivering superior coding and reasoning.

Claude 4 Sonnet is for "near instant response" whereas Claude 4 Opus is for extended thinking and deeper reasoning. And they both are significantly better than Claude's previous models as well as OpenAI's and Google's latest models (OpenAI Codex-1, OpenAI o3, OpenAI GPT-4.1, and Google Gemini 2.5 Pro) in terms of software engineering.

Starting today, Claude 4 Opus is available for the paid users and Claude 4 Sonnet is available for free users as well on claude.ai.

The SEO landscape is changing and it's going to be heavily influenced by AI in the coming years. And here is a list of some really useful research papers that one should study to stay relevant.

I asked Grok 3 DeeperResearch tool to find a list of technologies throughout the history that have claimed to replace software engineers, and it did pull up some cool mentions:

1. Automatic Programming and Compilers (1950s)

• Era: 1950s
• Description: Early efforts to automate programming, such as the A-0 compiler by Grace Hopper, focused on translating high-level languages like Fortran into machine code.
• Claims: Suggested that programming could be automated, reducing the need for low-level coding expertise.
• Impact: Enabled higher-level programming but did not eliminate the need for programmers to design algorithms and logic.
• Source: Wikipedia: Automatic Programming

2. Fourth-Generation Programming Languages (4GLs) (1970s-1990s)

• Era: 1970s-1990s
• Description: High-level languages like SQL and FoxPro designed to be closer to human language, enabling non-programmers to perform tasks like database queries.
• Claims: Hyped as potentially eliminating the need for traditional programmers, with - Claims: that they were the "last generation" requiring code writing.
• Impact: Simplified specific tasks but were limited for complex projects, requiring professional developers for broader applications.
• Source: Wikipedia: Fourth-generation programming language

3. Computer-Aided Software Engineering (CASE) Tools (1980s-1990s)

• Era: 1980s-1990s
• Description: Tools to automate software development stages, including analysis, design, and code generation, introduced by companies like Nastec.
• Claims: Promised significant productivity gains, potentially reducing manual coding needs, often oversold as "silver bullets."
• Impact: Faced adoption challenges due to complexity and cost, augmenting rather than replacing developers.
• Source: Wikipedia: Computer-aided software engineering

4. Visual Programming Languages (1990s)

• Era: 1990s
• Description: Languages like Visual Basic (introduced 1991) used graphical interfaces for rapid application development.
• Claims: Made programming accessible to non-experts, suggesting a reduced need for specialized skills for certain applications.
• Impact: Democratized app development but required skilled developers for complex projects.
• Source: Wikipedia: Visual Basic

5. Low-Code/No-Code Platforms (2000s-Present)

• Era: 2000s-Present
• Description: Platforms like Mendix and OutSystems enable application development with minimal coding, coined as "low-code" in 2014.
• Claims: Marketed as empowering citizen developers, potentially reducing reliance on professional programmers for routine tasks.
• Impact: Expanded development scope but professional developers remain essential for complex and custom solutions.
• Source: Wikipedia: Low-code development platform

6. AI-Assisted Coding Tools (2020s)

• Era: 2020s
• Description: Tools like GitHub Copilot (launched 2021) use AI to suggest and generate code, assisting developers in real-time.
• Claims: Sparked fears of replacing programmers, with discussions questioning if AI could automate coding tasks.
• Impact: Enhances productivity but requires human oversight for problem-solving and quality assurance.
• Source: Wikipedia: GitHub Copilot

Please note that these are completely AI-generated, I haven't edited a single thing here.

I came across a very interesting LinkedIn post by Judah Diament where he makes a point that vive coding won't be replacing software engineers. Below are some interesting fragments of the post:

Vibe coding enables people who aren't well trained computer scientists to create complete, working applications. Is this a breakthrough? Not even close - there have been such tools since the late 1980s. See, for example: Apple HyperCard, Sybase PowerBuilder, Borland Delphi, FileMaker, Crystal Reports, Macromedia (and then Adobe) Flash, Microsoft VisualBasic, Rational Rose and other "Model Driven Development" tools, IBM VisualAge, etc. etc. And, of course, they all broke down when anything sightly complicated or unusual needs to be done (as required by every real, financially viable software product or service), just as "vibe coding" does.

Then he goes on to explaining why vibe coding won't be replacing software engineers:

To claim that "vibe coding" will replace software engineers, one must: 1) be ignorant of the 40 year history of such tools or 2) have no understanding of how AI works or 3) have no real computer science education and experience or 4) all of the above, OR, most importantly, be someone trying to sell something and make money off of the "vibe coding" fad.

I like how the last paragraph is framed, it's definitely some food for thought.

I collected a list of 28 less known but very useful HTML tags:

1. Abbreviation – abbr

Marks abbreviations and shows full form on hover.

Example:

HTML

HTML:

<abbr title="HyperText Markup Language">HTML</abbr>

2. Bi-directional Isolation – bdi

Isolates text that may have different writing directions.

Example:

User علي logged in.

HTML:

User <bdi>علي</bdi>

3. Calculation Result – output

Shows the result of a calculation or user input.

Example:

+ =

7

HTML:

<form oninput="result.value=parseInt(a.value)+parseInt(b.value)">
  <input name="a" type="number" value="3"> +
  <input name="b" type="number" value="4"> =
  <output name="result">7</output>
</form>

4. Citation – cite

Used to reference the title of a work like a book, movie, article, or website. Usually shown in italics by browsers.

Example:

The Great Gatsby is a novel by F. Scott Fitzgerald.

HTML:

<p><cite>The Great Gatsby</cite> is a novel by F. Scott Fitzgerald.</p>

5. Contact Information – address

Used to provide contact details for a person, group, or organization. Usually displayed in italics and often used in footers.

Example:

Contact us at support@example.com

HTML:

<p><address>Contact us at support@example.com</address></p>

6. Definition – dfn

Marks the term being defined, often used in technical writing.

Example:

Latency is the delay before a transfer of data begins.

HTML:

<dfn>Latency</dfn>

7. Deleted Text – del

Used to mark removed text. Often shown with a strike-through.

Example:

This was removed.

HTML:

<p>This was <del>removed</del>.</p>

8. Description List with Terms and Details – dl + dt + dd

Used to list terms and their descriptions. <dl> wraps the whole list, <dt> defines the term, and <dd> gives the description.

Example:

HTML

A markup language for web pages.

CSS

Used to style HTML content.

HTML:

<dl>
  <dt>HTML</dt>
  <dd>A markup language for web pages.</dd>
  <dt>CSS</dt>
  <dd>Used to style HTML content.</dd>
</dl>

9. Direction Override – bdo

Forces a section of text to display in a specified direction.

Example:

Hello World

HTML:

<bdo dir="rtl">Hello World</bdo>

10. Expandable Section – details + summary

Creates a collapsible content box that can be expanded by the user.

Example:

Click to expand

This text is hidden until clicked.

HTML:

<details>
  <summary>Click to expand</summary>
  This text is hidden until clicked.
</details>

11. Group Form Fields – fieldset + legend

Groups related form inputs and adds a caption using <legend>.

Example:

Login

HTML:

<fieldset>
  <legend>Login</legend>
  <input type="text" placeholder="Username">
  <input type="password" placeholder="Password">
</fieldset>

12. Heading Group – hgroup

Groups a set of headings (like h1 to h6) when a heading has a subtitle or multiple levels. Helps with document outline.

Example:

Main Title

Subtitle

HTML:

<hgroup>
  <h1>Main Title</h1>
  <h2>Subtitle</h2>
</hgroup>

13. Hidden Content – template

Stores HTML that is not rendered until used with JavaScript.

Example:

HTML:

<template>
  <p>This is hidden and not rendered.</p>
</template>

14. Highlight Text – mark

Used to highlight part of text, often shown with yellow background.

Example:

This is important text.

HTML:

<p>This is <mark>important text</mark>.</p>

15. Inline Quotation – q

Used for short quotations that are displayed inline. Browsers usually add quotation marks automatically.

Example:

She said, Always write clean code.

HTML:

<p>She said, <q>Always write clean code.</q></p>

16. Inserted Text – ins

Used to mark text that was added later. Often shows as underlined.

Example:

This is new text.

HTML:

<p>This is <ins>new</ins> text.</p>

17. Keyboard Input – kbd

Used to show keyboard input, like shortcuts or key presses.

Example:

Press Ctrl + V

HTML:

<kbd>Ctrl</kbd> + <kbd>V</kbd>

18. Option Group – optgroup

Used to group related options inside a <select> dropdown, making it easier for users to choose from categorized lists.

Example:

AppleBanana

HTML:

<select>
  <optgroup label="Fruits">
    <option>Apple</option>
    <option>Banana</option>
  </optgroup>
</select>

19. Program Output – samp

Represents output from a program, like an error or log message.

Example:

Login failed: incorrect password

HTML:

<samp>Login failed: incorrect password</samp>

20. Progress Bar – progress

Shows progress of a task like loading or uploading.

Example:

HTML:

<progress value="40" max="100"></progress>

21. Ruby Annotations – ruby + rt + rp

Used in East Asian text to show pronunciation hints.

Example:

漢kan字ji

HTML:

<ruby>漢<rt>kan</rt>字<rt>ji</rt></ruby>

22. Shown If JS Disabled – noscript

Displays content only if JavaScript is disabled in the browser.

Example:

HTML:

<noscript>JavaScript is disabled in your browser.</noscript>

23. Subscript Text – sub

Displays text lower and smaller than the baseline, commonly used in chemical formulas or math expressions.

Example:

H₂O

HTML:

<p>H<sub>2</sub>O</p>

24. Superscript Text – sup

Displays text higher and smaller than the baseline, often used for exponents or footnotes.

Example:

E = mc²

HTML:

<p>E = mc<sup>2</sup></p>

25. Time or Date – time

Represents a specific time or date, useful for events or timestamps.

Example:

May 18, 2025

HTML:

<time datetime="2025-05-18">May 18, 2025</time>

26. Value Within Range – meter

Displays a value inside a known range, like disk or battery levels.

Example:

60%

HTML:

<meter value="0.6">60%</meter>

27. Variable Name – var

Used to show variables in math or programming context.

Example:

x + y = 10

HTML:

<var>x</var> + <var>y</var> = 10

28. Word Break Opportunity – wbr

Suggests a possible break point in a long word or URL.

Example:

www.examplelongword.com

HTML:

www.example<wbr>long<wbr>word.com

"Comedy hits you in the head, drama hits you in the heart. If you want people to remember your work, you need both: comedy to lower their guard, drama to make them feel."

– Michael Jamin, a Hollywood screenwriter

OpenAI launches Codex, a cloud-based agent that writes code and works on multiple tasks at once. It's just launched, and can be accessed from inside ChatGPT at chatgpt.com/codex but visiting this URL just redirected me back to ChatGPT as it's only for ChatGPT Pro users, and not Plus users.

Currently, it's in a research preview but it's said to have features like:

• writing code for you
• implementing new features
• answering questions about your codebase
• fixing bugs, etc.

The implementation is very interesting as it runs in its own cloud sandbox environment, and can be directly connected to your GitHub repo. It performs better than o1-high, o4-mini-high, and o3-high.

The cool thing is, it can also be guided by an AGENTS.md file placed within the repository. Very cool.

Today, we’re also releasing a smaller version of codex-1, a version of o4-mini designed specifically for use in Codex CLI.

Yes, they're also releasing something for Codex CLI as well. And about the pricing and availability:

Starting today, we’re rolling out Codex to ChatGPT Pro, Enterprise, and Team users globally, with support for Plus and Edu coming soon. [...] We plan to expand access to Plus and Edu users soon.

For developers building with codex-mini-latest, the model is available on the Responses API and priced at $1.50 per 1M input tokens and $6 per 1M output tokens, with a 75% prompt caching discount.

I am excited to see how this compares to Claude 3.7 Sonnet and Gemini 2.5 Pro in terms of coding, fixing bugs, designing UI, etc. I also uploaded a quick video about the same that you can watch on YouTube.

I have been coming across a lot of cool MCP server while browsing the internet, so decided to create a dedicated page and keep collecting MCPs here. I have a JSON file where I can add the new MCP servers, and it will automatically show in the card format here.

I will keep updating this list as I discover more such MCPs.

Connecting ChatGPT to Airtable gives you the superpower to get answers to 100s of questions in no time. Here's how to do that:

You need the following things to be able to connect ChatGPT to Airtable:

1. A paid Airtable account (the lowest plan is $24/month)
2. OpenAI API key (you'll have to set up a payment method on OpenAI, here)
3. The Scripting extension from Airtable (no additional cost), and
4. A script to call the OpenAI API inside Airtable

And below is the function that you can use to call the OpenAI from inside the Airtable and get the output.

async function getGPTResponse() {
    const userInput = "why is the sky blue?";
    const maxTokens = 500;
    const temperature = 0.7;
    const model = "gpt-4.1";
    const systemPrompt = "be precise";

    const messages = [
        { role: "system", content: systemPrompt },
        { role: "user", content: userInput },
    ];

    const res = await fetch('https://api.openai.com/v1/chat/completions', {
        method: 'POST',
        headers: {
            'Content-Type': 'application/json',
            'Authorization': `Bearer ${openaiApiKey}`,
        },
        body: JSON.stringify({
            model,
            messages,
            max_tokens: maxTokens,
            temperature,
        }),
    });

    const data = await res.json();
    return data.choices?.[0]?.message?.content || null;
}

Here, userInput is the prompt that you give AI, maxTokens is the max tokens for the model, temperature is model temperature, and systemPrompt is the system prompt. The prompt here is hardcoded, but you can modify the script to dynamically fetch prompts from each row and then get the outputs accordingly.

ChatGPT is very good at doing this implementation as per your base data, you can just give the above script and other details in the prompt, and it will give you the final code that you can put inside the Scripting extension.

Also, there's a generic version of this script at InvertedStone that you can also get and use. You can generate almost any kind of content using this script, not just from ChatGPT but also from other AI models like Claude, Gemini, Perplexity, and more.