Tirthankar Ray

An observer, a student & a curious learner .

[Tirthankar Ray @Tirthankar_Ray]

You're not going to master the rest of your life in one day. Just relax. Master the day. Than just keep doing that every day. ” ― Anonymous

[The Bugged Dev @thebuggeddev]

Only if education could be this interactive ❤️‍🔥

I've had a looong wish to build something genuinely useful through vibe coding, and I finally did it.

A 3D human anatomy application built with @threejs using GPT 5.6 Sol.

It all started with a single design image that I created using GPT Image 2.0. I then used it to generate every 3D organ image, one by one.

Next, I converted each of those images into 3D models using @tripoai (and no, they didn't sponsor this 😄).

After that, I opened Codex, wrote a master prompt based on the design, and gave it the prompt, the design image, and all the 3D models.

Codex built the first version beautifully, but there was one big problem.

Every single 3D model was nearly 120-150 MB. That obviously wasn't practical for the web and was giving a performance of 16fps.

After a few iterations, Codex optimized each model down to roughly 2–5.5 MB while preserving the visual quality, reducing the total asset size from ~900 MB to just 28.6 MB. And each model loads on demand.

Along the way, Codex also generated those anatomical illustrations showing where each organ sits in the human body, and even created the interactive hotspot markers that explain different parts of every organ. It handled all of that.

The process wasn't exactly one shot, but it also wasn't difficult. You just have to do it step by step.

It genuinely felt like building something that could make learning anatomy much more engaging.

The inspiration came from @DilumSanjaya's 3D animal plant cell project. I remember seeing it and thinking, "I want to build something like this one day."

And I did it :D

Live: https://t.co/gIFgkj8UVB
Code: https://t.co/nijvyWcyxO

[Sucheta Dalal @suchetadalal]

Another heartwarming story. Remember coach Param Asawar who took a loan to run a coaching camp? His girls are doing india proud! Way to go!! Liked any of our commonwealth winners these girls come from incredibly humble backgrounds! Posting more.

[Satya Nadella @satyanadella]

https://t.co/xv6csf1SbV

[Sridhar Vembu @svembu]

I love this. I wish I was as driven and resourceful as these IIT Bombay students at their age!

[Sivori @sivori]

Anthropic is buying millions of rare books, scanning and destroying them because legally destruction is the safest option. This was a plot element in the Vernor Vinge novel, "The Rainbow's End", which I read 20 years ago.

[The Shift Journal @TheShiftJournal]

The Invisible Glass Experiment

Scientists once conducted a fascinating experiment with a pike and an aquarium.

They placed a transparent glass barrier in the middle of the tank. On one side was a large, hungry pike. On the other side swam several small fish.

As soon as the pike spotted the smaller fish, it launched itself forward to attack.

Bang! It crashed headfirst into the invisible glass and was thrown backward.

Undeterred, the pike tried again... and again. Each attempt ended the same way a painful collision. After repeated failures , its head became bruised and some of its scales were knocked loose.

Eventually, the pike gave up. It retreated to a corner of the tank, clearly frightened and defeated.

Then, the scientists quietly removed the glass barrier.

The small fish now swam freely around the entire aquarium some even passing right in front of the pike’s mouth.

But the pike never attacked again.

Even though it was starving, it refused to strike. In its mind, the invisible wall was still there.

A few days later, the pike died of starvation surrounded by abundant food it could no longer bring itself to eat.

This phenomenon is known as the Pike Effect (or Pike Syndrome).

It serves as a powerful metaphor for how repeated failures and setbacks can create invisible mental barriers that limit us long after the real obstacles have disappeared.

[Massimo @Rainmaker1973]

She turned sadness into smile. A true winner.

[Kaito | 海斗 @_kaitodev]

5 minutes ago, @karpathy just dropped karpathy/jobs!

he scraped every job in the US economy (342 occupations from BLS), scored each one's AI exposure 0-10 using an LLM, and visualized it as a treemap.

if your whole job happens on a screen you're cooked.

average score across all jobs is 5.3/10.
software devs: 8-9.
roofers: 0-1.
medical transcriptionists: 10/10 💀

https://t.co/7MWRgdtLDI

[Pratyush Kumar @pratykumar]

📢 Open-sourcing the Sarvam 30B and 105B models! Trained from scratch with all data, model research and inference optimisation done in-house, these models punch above their weight in most global benchmarks plus excel in Indian languages.

Get the weights at Hugging Face and AIKosh. Thanks to the good folks at SGLang for day 0 support, vLLM support coming soon. Links, benchmark scores, examples, and more in our blog - https://t.co/DcCG3zlN8p

[Ilya Sutskever @ilyasut]

It’s extremely good that Anthropic has not backed down, and it’s siginficant that OpenAI has taken a similar stance.

In the future, there will be much more challenging situations of this nature, and it will be critical for the relevant leaders to rise up to the occasion, for fierce competitors to put their differences aside. Good to see that happen today.

[Vishal Khandelwal @safalniveshak]

If you have a father who can still pick up your call...call.

[Andrej Karpathy @karpathy]

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow.

Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes.

As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now.

It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.

[The Sigma Mindset @thesigmamindset]

This forgotten mouse experiment is now more relatable than ever.

[Tirthankar Ray @Tirthankar_Ray]

I just wrote my first note on Substack
https://t.co/hmVncKOddV

[Matt Shumer @mattshumer_]

https://t.co/ivXRKXJvQg

[Greg Brockman @gdb]

Software development is undergoing a renaissance in front of our eyes.

If you haven't used the tools recently, you likely are underestimating what you're missing. Since December, there's been a step function improvement in what tools like Codex can do. Some great engineers at OpenAI yesterday told me that their job has fundamentally changed since December. Prior to then, they could use Codex for unit tests; now it writes essentially all the code and does a great deal of their operations and debugging. Not everyone has yet made that leap, but it's usually because of factors besides the capability of the model.

Every company faces the same opportunity now, and navigating it well — just like with cloud computing or the Internet — requires careful thought. This post shares how OpenAI is currently approaching retooling our teams towards agentic software development. We're still learning and iterating, but here's how we're thinking about it right now:

As a first step, by March 31st, we're aiming that:

(1) For any technical task, the tool of first resort for humans is interacting with an agent rather than using an editor or terminal.
(2) The default way humans utilize agents is explicitly evaluated as safe, but also productive enough that most workflows do not need additional permissions.

In order to get there, here's what we recommended to the team a few weeks ago:

1. Take the time to try out the tools. The tools do sell themselves — many people have had amazing experiences with 5.2 in Codex, after having churned from codex web a few months ago. But many people are also so busy they haven't had a chance to try Codex yet or got stuck thinking "is there any way it could do X" rather than just trying.
- Designate an "agents captain" for your team — the primary person responsible for thinking about how agents can be brought into the teams' workflow.
- Share experiences or questions in a few designated internal channels
- Take a day for a company-wide Codex hackathon

2. Create skills and AGENTS[.md].
- Create and maintain an AGENTS[.md] for any project you work on; update the AGENTS[.md] whenever the agent does something wrong or struggles with a task.
- Write skills for anything that you get Codex to do, and commit it to the skills directory in a shared repository

3. Inventory and make accessible any internal tools.
- Maintain a list of tools that your team relies on, and make sure someone takes point on making it agent-accessible (such as via a CLI or MCP server).

4. Structure codebases to be agent-first. With the models changing so fast, this is still somewhat untrodden ground, and will require some exploration.
- Write tests which are quick to run, and create high-quality interfaces between components.

5. Say no to slop. Managing AI generated code at scale is an emerging problem, and will require new processes and conventions to keep code quality high
- Ensure that some human is accountable for any code that gets merged. As a code reviewer, maintain at least the same bar as you would for human-written code, and make sure the author understands what they're submitting.

6. Work on basic infra. There's a lot of room for everyone to build basic infrastructure, which can be guided by internal user feedback. The core tools are getting a lot better and more usable, but there's a lot of infrastructure that currently go around the tools, such as observability, tracking not just the committed code but the agent trajectories that led to them, and central management of the tools that agents are able to use.

Overall, adopting tools like Codex is not just a technical but also a deep cultural change, with a lot of downstream implications to figure out. We encourage every manager to drive this with their team, and to think through other action items — for example, per item 5 above, what else can prevent a lot of "functionally-correct but poorly-maintainable code" from creeping into codebases.

[Bill Madden @maddenifico]

Nvidia CEO Jensen Huang, with an incredibly thoughtful answer to the smartest person he's ever met. If you listen closely to the words he uses, including "empathy" and "wisdom," you'll realize a certain group of people he's not describing.

[Andrew Yeung @andruyeung]

This is fascinating... the HEIGHT of the ceiling in the room you're working in has a DIRECT impact on how creative you are

It's called the Cathedral Effect

How it works: Your brain borrows metaphors from the physical world (space is one of the strongest)

When a room feels tall and open, your mind unconsciously associates that with freedom and possibility - you zoom OUT

When a room feels tight or enclosed, your mind goes into precision mode… attention narrows. You notice typos, spot mistakes, and hone in on details - you zoom IN

Researchers found that people in high-ceiling rooms perform better on creativity. People in low-ceiling rooms perform better on detail orientation and error detection

Churches and museums have soaring ceilings - meant to inspire awe. Libraries and war rooms are tighter - meant for concentration

Startup brainstorms love lofts, and accounting teams love small rooms with doors

Even coffee shops do this. The ones designed for deep work tend to be lower and quieter. The ones designed for conversation tend to feel more open

So if you’re doing creative stuff - writing, designing, brainstorming - do it in a LARGE room with high ceilings. Then move to a smaller room to edit and proofread.

[Shane Legg @ShaneLegg]

AGI is now on the horizon and it will deeply transform many things, including the economy.

I'm currently looking to hire a Senior Economist, reporting directly to me, to lead a small team investigating post-AGI economics.

Job spec and application here: https://t.co/VAfwrMc8Tp

[Scott Adams @ScottAdamsSays]

A Final Message From Scott Adams