Jasmeet Singh

Local Search Ads @ Google 🙂

[Jasmeet Singh @jasmeet13n]

@hqmank Do you have this project in GitHub? Want to try it locally

[Curiosity @CuriosityonX]

Artemis II mission route in 3D

[Jasmeet Singh @jasmeet13n]

OpenClaw + On-Device LLMs directionally seem to be the best path for personal agents.

[Jasmeet Singh @jasmeet13n]

@OfficialLoganK Upload on YT.
X on Android is horrible, can't do background playback.

[App Economy Insights @EconomyApp]

Choose your fighter
$MSFT $AAPL $AMZN $GOOG

[Patrick Collison @patrickc]

"Pessimists sound smart. Optimists make money." —@natfriedman

[Jasmeet Singh @jasmeet13n]

@TheChowdhary The method I have been using more recently: Write one test as a guide and ask LLMs to generate more test cases with different conditions.

[Jasmeet Singh @jasmeet13n]

@djgovins @TheChowdhary Lol, kuch bhi 😀🙌

[Google Gemini @GeminiApp]

📣 Today we're releasing the world’s best AI model (as ranked by @lmarena_ai), 2.0 Flash Thinking Experimental, available at no cost, to Gemini app users.

We're also rolling out a version of 2.0 Flash Thinking that can interact with the apps you know and love, like @YouTube, @Google Search and @GoogleMaps.

Here’s what you can expect from these new models ↓🧵

[Andrew Ng @AndrewYNg]

Today's "DeepSeek selloff" in the stock market -- attributed to DeepSeek V3/R1 disrupting the tech ecosystem -- is another sign that the application layer is a great place to be. The foundation model layer being hyper-competitive is great for people building applications.

[Andrej Karpathy @karpathy]

I don't have too too much to add on top of this earlier post on V3 and I think it applies to R1 too (which is the more recent, thinking equivalent).

I will say that Deep Learning has a legendary ravenous appetite for compute, like no other algorithm that has ever been developed in AI. You may not always be utilizing it fully but I would never bet against compute as the upper bound for achievable intelligence in the long run. Not just for an individual final training run, but also for the entire innovation / experimentation engine that silently underlies all the algorithmic innovations.

Data has historically been seen as a separate category from compute, but even data is downstream of compute to a large extent - you can spend compute to create data. Tons of it. You've heard this called synthetic data generation, but less obviously, there is a very deep connection (equivalence even) between "synthetic data generation" and "reinforcement learning". In the trial-and-error learning process in RL, the "trial" is model generating (synthetic) data, which it then learns from based on the "error" (/reward). Conversely, when you generate synthetic data and then rank or filter it in any way, your filter is straight up equivalent to a 0-1 advantage function - congrats you're doing crappy RL.

Last thought. Not sure if this is obvious. There are two major types of learning, in both children and in deep learning. There is 1) imitation learning (watch and repeat, i.e. pretraining, supervised finetuning), and 2) trial-and-error learning (reinforcement learning). My favorite simple example is AlphaGo - 1) is learning by imitating expert players, 2) is reinforcement learning to win the game. Almost every single shocking result of deep learning, and the source of all *magic* is always 2. 2 is significantly significantly more powerful. 2 is what surprises you. 2 is when the paddle learns to hit the ball behind the blocks in Breakout. 2 is when AlphaGo beats even Lee Sedol. And 2 is the "aha moment" when the DeepSeek (or o1 etc.) discovers that it works well to re-evaluate your assumptions, backtrack, try something else, etc. It's the solving strategies you see this model use in its chain of thought. It's how it goes back and forth thinking to itself. These thoughts are *emergent* (!!!) and this is actually seriously incredible, impressive and new (as in publicly available and documented etc.). The model could never learn this with 1 (by imitation), because the cognition of the model and the cognition of the human labeler is different. The human would never know to correctly annotate these kinds of solving strategies and what they should even look like. They have to be discovered during reinforcement learning as empirically and statistically useful towards a final outcome.

(Last last thought/reference this time for real is that RL is powerful but RLHF is not. RLHF is not RL. I have a separate rant on that in an earlier tweet
https://t.co/RMIpFPVpuM)

[Aravind Srinivas @AravSrinivas]

Pixel 9 Pro is a really good phone. I bought it to test a new thing we’re working on. And realized the phone’s really well made that it doesn’t feel I’m not using something IPhone quality.

[Andrew Ng @AndrewYNg]

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future!

Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build.

This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products.

Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow.

This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow.

Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires:
- Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models.
- Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process.
- Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software.
- Skill in managing ambiguity. Because AI’s performance is hard to predict in advance, PMs need to be comfortable with this and have tactics to manage it.
- Ongoing learning. AI technology is advancing rapidly. PMs, like everyone else who aims to make best use of the technology, need to keep up with the latest technology advances, product ideas, and how they fit into users’ lives.

Finally, AI Product Managers will need to know how to ensure that AI is implemented responsibly (for example, when we need to implement guardrails to prevent bad outcomes), and also be skilled at gathering feedback fast to keep projects moving. Increasingly, I also expect strong product managers to be able to build prototypes for themselves.

The demand for good AI Product Managers will be huge. In addition to growing AI Product Management as a discipline, perhaps some engineers will also end up doing more product management work.

The variety of valuable things we can build is nearly unlimited. What a great time to build!

[Original text: https://t.co/OIeAQXpriK ]

[Jasmeet Singh @jasmeet13n]

Gemini 2.0 + Grounding = Make your own Perplexity

Wow🔥

[Ammaar Reshi @ammaar]

Just built a Perplexity clone using Gemini 2.0 + Grounding, and the wildest part? @Replit's Agent wrote ALL the code in 2 hours!

Search anything, get sources, ask follow-ups.

Google has all the pieces to make AI search incredible. Hope they productize it soon!

Demo + code 👇

[Jasmeet Singh @jasmeet13n]

@NVIDIAGeForce #GeForceRTX50

[Jasmeet Singh @jasmeet13n]

Nobody wants a subscription service for their garage door opener. Stop it myQ!

[Min Choi @minchoi]

Gemini 2.0 has really changed the AI game.

People are using it in wild ways beyond ChatGPT. There's a shift.

8 examples:

[Years Progress @YearsProgress]

2025 is 0% complete.

[Jeff Dean @JeffDean]

Introducing Gemini 2.0 Flash Thinking, an experimental model that explicitly shows its thoughts.

Built on 2.0 Flash’s speed and performance, this model is trained to use thoughts to strengthen its reasoning.

And we see promising results when we increase inference time computation!

[Jeff Dean @JeffDean]

What a way to celebrate one year of incredible Gemini progress -- #1🥇across the board on overall ranking, as well as on hard prompts, coding, math, instruction following, and more, including with style control on.

Thanks to the hard work of everyone in the Gemini team and elsewhere at Google! 🎊