Deepak

[Pedro Domingos @pmddomingos]

Only AI can keep up with all the new developments in AI.

[Saurabh @sorukumar]

Spent the weekend in the grant thread. Same debate, over and over.

We boiled the whole debate down to four points.

Been thinking about this for months. A map of projects × contributors × grants; that’s what we’re building, and I think it helps. https://t.co/6mwwfqPs2L

[Harvard Business Review @HarvardBiz]

Leaders with toxic interpersonal behaviors can still make excellent first impressions. That’s why organizations often hire technically impressive leaders who ultimately damage team performance, culture, and retention.

A few steps can help hiring managers better evaluate candidates during the recruiting process:

1. Identify the interpersonal requirements of the role.
2. Draft interview questions.
3. Define your ideal responses.

To learn more about these three steps—and discover the remaining three—read the full article at HBR: https://t.co/WwUKhADsr6

[Deepak @usct01]

Do you remember when you joined X? I do! #MyXAnniversary

[Pedro Domingos @pmddomingos]

I love all these people who think they know AI because they've used a chatbot.

[Andrew Ng @AndrewYNg]

New: A map of the most important skills in AI Engineering. https://t.co/VVkn1Dqp1N

[Anil Kumble @anilkumble1074]

It was 36 years ago today that I received my first Test Cap as player number 192 for India. Taking the wicket of Allan Lamb was the start of a truly special journey, one that I cherish deeply to this day.
I am incredibly grateful for this journey and everyone who has been a part it. 🙏🏽

[Jeff Dean @JeffDean]

Announcing Discovery Loop!

I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor.

♾

Learn more at: https://t.co/Rv3LMdLluK

[Andrej Karpathy @karpathy]

# on shortification of "learning"

There are a lot of videos on YouTube/TikTok etc. that give the appearance of education, but if you look closely they are really just entertainment. This is very convenient for everyone involved : the people watching enjoy thinking they are learning (but actually they are just having fun). The people creating this content also enjoy it because fun has a much larger audience, fame and revenue. But as far as learning goes, this is a trap. This content is an epsilon away from watching the Bachelorette. It's like snacking on those "Garden Veggie Straws", which feel like you're eating healthy vegetables until you look at the ingredients.

Learning is not supposed to be fun. It doesn't have to be actively not fun either, but the primary feeling should be that of effort. It should look a lot less like that "10 minute full body" workout from your local digital media creator and a lot more like a serious session at the gym. You want the mental equivalent of sweating. It's not that the quickie doesn't do anything, it's just that it is wildly suboptimal if you actually care to learn.

I find it helpful to explicitly declare your intent up front as a sharp, binary variable in your mind. If you are consuming content: are you trying to be entertained or are you trying to learn? And if you are creating content: are you trying to entertain or are you trying to teach? You'll go down a different path in each case. Attempts to seek the stuff in between actually clamp to zero.

So for those who actually want to learn. Unless you are trying to learn something narrow and specific, close those tabs with quick blog posts. Close those tabs of "Learn XYZ in 10 minutes". Consider the opportunity cost of snacking and seek the meal - the textbooks, docs, papers, manuals, longform. Allocate a 4 hour window. Don't just read, take notes, re-read, re-phrase, process, manipulate, learn.

And for those actually trying to educate, please consider writing/recording longform, designed for someone to get "sweaty", especially in today's era of quantity over quality. Give someone a real workout. This is what I aspire to in my own educational work too. My audience will decrease. The ones that remain might not even like it. But at least we'll learn something.

[CM of Karnataka @CMofKarnataka]

ತುಂಗಭದ್ರಾ ಜಲಾನಯನ ಪ್ರದೇಶದ ರೈತರು ಮತ್ತು ಆ ಭಾಗದ ಜನರಿಗೆ ಸಿಹಿಸುದ್ದಿಯೊಂದನ್ನು ತಲುಪಿಸುವ ಉದ್ದೇಶದಿಂದ ಈ ವೀಡಿಯೋವನ್ನು ಹಂಚಿಕೊಳ್ಳುತ್ತಿದ್ದೇನೆ.

ಕಳೆದ ವರ್ಷ ತುಂಗಭದ್ರಾ ಜಲಾಶಯದ 19ನೇ ಗೇಟ್ ಕೊಚ್ಚಿಹೋದ ವೇಳೆ ಕೇವಲ 6 ದಿನಗಳಲ್ಲಿ ಹೊಸ ಗೇಟ್ ಅಳವಡಿಸಿ, ಜಲಾಶಯದಿಂದ ನೀರು ಪೋಲಾಗದಂತೆ ತಡೆದು ರೈತರ ಹಿತ ಕಾಪಾಡಿದ್ದೆವು. ಅಂದೇ ಮತ್ತೆ ಇಂತಹ ಘಟನೆಗೆ ಅವಕಾಶ ನೀಡಬಾರದು ಎಂಬ ಸಂಕಲ್ಪಗೈದಿದ್ದ ನಾವು ಈಗ ಅಣೆಕಟ್ಟಿನ ಎಲ್ಲಾ 33 ಗೇಟುಗಳಿಗೆ ಹೊಸ ಗೇಟ್ ಅಳವಡಿಕೆ ಮಾಡಿದ್ದೇವೆ.

ಜೂನ್ 25ನೇ ತಾರೀಖು ಕೇಂದ್ರ ಸಚಿವರಾದ ಶ್ರೀ ಸಿ.ಆರ್.ಪಾಟೀಲ್, ಆಂಧ್ರಪ್ರದೇಶದ ಮುಖ್ಯಮಂತ್ರಿ ಶ್ರೀ ಚಂದ್ರಬಾಬು ನಾಯ್ಡು ಹಾಗೂ ತೆಲಂಗಾಣದ ಮುಖ್ಯಮಂತ್ರಿ ಶ್ರೀ ರೇವಂತ ರೆಡ್ಡಿಯವರ ಜೊತೆಗೂಡಿ ಹೊಸ ಗೇಟ್‌ಗಳನ್ನು ಲೋಕಾರ್ಪಣೆಗೊಳಿಸುತ್ತಿದ್ದೇನೆ.

ಲಕ್ಷಾಂತರ ರೈತರ ಜಮೀನುಗಳಿಗೆ ನೀರಿನ ಆಸರೆಯಾಗಿರುವ, ಕೋಟ್ಯಂತರ ಜೀವಗಳಿಗೆ ಜೀವಜಲವುಣಿಸುತ್ತಿರುವ ತುಂಗಭದ್ರಾ ಜಲಾಶಯ ನಮ್ಮವರಿಂದ ಬಂದ ಬಳುವಳಿ. ಅದನ್ನು ಸುಸ್ಥಿತಿಯಲ್ಲಿಟ್ಟು, ಮುಂದಿನ ತಲೆಮಾರುಗಳಿಗೂ ಅದರ ಲಾಭ ದಕ್ಕುವಂತೆ ಮಾಡುವುದು ನಮ್ಮ ಕರ್ತವ್ಯ. ನಾವಿದನ್ನು ಅತ್ಯಂತ ಶ್ರದ್ಧೆಯಿಂದ ಮಾಡಿದ್ದೇವೆ.

ಈ ಯೋಜನೆಯೂ ನಿಮ್ಮದೇ, ಕಾರ್ಯಕ್ರಮವೂ ನಿಮ್ಮದೆ. ನಿಮ್ಮೆಲ್ಲರ ಶುಭ ಹಾರೈಕೆ ನಮ್ಮೊಂದಿಗಿರಲಿ.
- ಮುಖ್ಯಮಂತ್ರಿ ಶ್ರೀ @DKShivakumar

[WHITEFIELD TRAFFIC PS BTP @wftrps]

🚨 During heavy rain, PC Balram promptly cleared water stagnation on the roadway, ensuring smooth and safe movement of traffic at Forum Value Mall WF
His timely action helped prevent congestion and enhanced commuter safety. 02.06.2026 Tue

#WhitefieldTrafficPS
#WeCare

[Andrew Ng @AndrewYNg]

One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations.

The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below.

The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs.

However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality.

Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on.

What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities.

[Original text: The Batch newsletter]

[Andrew Ng @AndrewYNg]

There will be no AI jobpocalypse.

The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it.

I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines.

Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%.

Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable!

Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more.

Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus.

To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market.

Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades.

Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have).

Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future!

[Original text in The Batch newsletter.]

[Barack Obama @BarackObama]

What the Artemis II astronauts did over the last 10 days was a testament to their bravery. And the fact that they traveled farther from Earth than anyone ever has, re-entered our atmosphere at more than 24,000 mph, and splashed down safely was a testament to human ingenuity. Thanks to everyone at @NASA for making this mission possible, and for taking us along for the ride.

[Andrej Karpathy @karpathy]

Farzapedia, personal wikipedia of Farza, good example following my Wiki LLM tweet.

I really like this approach to personalization in a number of ways, compared to "status quo" of an AI that allegedly gets better the more you use it or something:

1. Explicit. The memory artifact is explicit and navigable (the wiki), you can see exactly what the AI does and does not know and you can inspect and manage this artifact, even if you don't do the direct text writing (the LLM does). The knowledge of you is not implicit and unknown, it's explicit and viewable.
2. Yours. Your data is yours, on your local computer, it's not in some particular AI provider's system without the ability to extract it. You're in control of your information.
3. File over app. The memory here is a simple collection of files in universal formats (images, markdown). This means the data is interoperable: you can use a very large collection of tools/CLIs or whatever you want over this information because it's just files. The agents can apply the entire Unix toolkit over them. They can natively read and understand them. Any kind of data can be imported into files as input, and any kind of interface can be used to view them as the output. E.g. you can use Obsidian to view them or vibe code something of your own. Search "File over app" for an article on this philosophy.
4. BYOAI. You can use whatever AI you want to "plug into" this information - Claude, Codex, OpenCode, whatever. You can even think about taking an open source AI and finetuning it on your wiki - in principle, this AI could "know" you in its weights, not just attend over your data.

So this approach to personalization puts *you* in full control. The data is yours. In Universal formats. Explicit and inspectable. Use whatever AI you want over it, keep the AI companies on their toes! :)

Certainly this is not the simplest way to get an AI to know you - it does require you to manage file directories and so on, but agents also make it quite simple and they can help you a lot. I imagine a number of products might come out to make this all easier, but imo "agent proficiency" is a CORE SKILL of the 21st century. These are extremely powerful tools - they speak English and they do all the computer stuff for you. Try this opportunity to play with one.

[Andrej Karpathy @karpathy]

Wow, this tweet went very viral!

I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs.

So here's the idea in a gist format: https://t.co/NlAfEJjtJV

You can give this to your agent and it can build you your own LLM wiki and guide you on how to use it etc. It's intentionally kept a little bit abstract/vague because there are so many directions to take this in. And ofc, people can adjust the idea or contribute their own in the Discussion which is cool.

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Andrej Karpathy @karpathy]

- Drafted a blog post
- Used an LLM to meticulously improve the argument over 4 hours.
- Wow, feeling great, it’s so convincing!
- Fun idea let’s ask it to argue the opposite.
- LLM demolishes the entire argument and convinces me that the opposite is in fact true.
- lol

The LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.

[François Chollet @fchollet]

In the era of algorithmic distraction, the ability to maintain a single thread of thought for four hours is a superpower. It is the only way to solve hard problems.