Gaurav Parida

12/ The bittersweet part: I came in with a strong research bet, and reality had a different answer. The bitter lesson strikes again — data had the highest leverage, not the clever method. But here's what's sweet: the results are more exciting than my original thesis. Compositional generalization emerging naturally from data, working across embodiments and tasks, steerable through language and goals. That's a future worth being optimistic about.

Super interesting company: @anvil_robotics has raised $5.5M in seed funding to build 'Legos for robots.' Its robots are about the size of a middle-school-aged child, but big enough to do basic dextrous tasks. The robots typically cost $5K to $10K, but Anvil's least expensive model is just $1,900. Read more in the @crunchbasenews article in the comments.

We scale from one institution to multiple institutions, and eventually, the whole world will be collecting data.

"Everything in our world, in our cities, in our civilization – look around you – is mined or grown – manufactured and moved" atoms.co/vision

in a world of agents, the product role is going to split into two jobs: - one that organizes humans (stakeholders, design, eng) - one that organizes agents (prompts, evals, workflows, etc) Both will be in pursuit of offering the right products to customers, but how you get there will dramatically change. What happens to the typical product rituals? Instead of PRDs, OKRs, standups, product reviews, we'll need the equivalent for agents. Couple wild ideas here... instead of standups: the equivalent is that agents will report back to us based on run logs and anomaly flags. no one needs to say what they did yesterday, the system already did thousands of things. the question is where it broke, where it surprised you, and where it got better. Show us the patterns, the trends, the edge cases - particularly the ones the agents didn't fix automatically. the daily ritual becomes reviewing deltas, scanning failures, and deciding which ones matter. less reporting, more triage instead of OKRs: we’ll need adversarial agents that continuously monitor/grade the system and detect patterns, scoring outcomes on an hourly or daily basis. Rather than setting a quarterly goal of "increase X by 5%" and revisiting slowly -- instead, management will be able to monitor success in real-time and detect trends/patterns towards overall goals instead of PRDs: we won't need waterfall. Prototyping will rule the day, and we’ll need a living agentic loop that mediates customer feedback/ratings and what's being prioritized and built. you don’t hand it to eng, you deploy it into the agent loop. if it’s wrong, it fails visibly and you can revert. if it’s right, it produces the right output instead of product reviews: we'll need simulation systems to examine agent behavior in different scenarios. In an agentic world where UI shifts from buttons/menus to agents automatically doing things, you'll want to examine their behavior before you deploy. You rewind decisions, fork alternate paths, and see how different prompts or constraints would have changed outcomes. the review becomes interactive. less storytelling, more counterfactuals. The PM sits in the middle of this split. On the human side, still aligning taste, risk tolerance, and strategy across people. On the agent side, shaping the actual behavior of the system through prompts, evals, and feedback loops. one side is persuasion. The other is instrumentation. the best ones will collapse the gap, translating intent directly into systems that act on it. the fascinating part is that the agentic loop will run 10000x faster than the human one, and of course, you can "hire" them faster. Thus the “organizing humans” half starts to feel slow and lower impact unless it directly improves the agent loop. Eventually the PM will shift towards agents and maybe ignore the human coordination altogether...

This is wild. 143 million people thought they were catching Pokémon. They were actually building one of the largest real-world visual datasets in AI history. Niantic just disclosed that photos and AR scans collected through Pokémon Go have produced a dataset of over 30 billion real-world images. The company is now using that data to power visual navigation AI for delivery robots. Players didn't just walk around with their phones. They scanned landmarks, storefronts, parks, and sidewalks from every angle, at every time of day, in lighting and weather conditions that staged photography would never capture. They documented the physical world at a scale no mapping company with a fleet of vehicles could have replicated on the same timeline or budget. Niantic collected this systematically, data point by data point, across eight years, while users thought the only thing at stake was catching a rare Charizard. The most valuable AI training datasets in the world aren't being assembled in data centers. They're being built by people who have no idea they're building them.

@tejgw Chronicle

Cursor for Slides is finally here Watch the first 47 seconds. Then try going back to your old deck tool Reply "Chronicle" + RT to get two months of Pro for free. Make sure you follow so I can DM you asap.

New podcast on AI (full episode). Links below. A Motorcycle for the Mind 0:00 If you want to learn, do 2:13 Vibe coding is the new product management 6:49 Training models is the new coding 10:13 Is traditional software engineering dead? 13:07 There is no demand for average 14:12 The hottest new programming language is English 18:36 AI is adapting to us faster than we are adapting to it 22:56 No entrepreneur is worried about AI taking their job 26:46 The goal is not to have a job 29:49 AIs are not alive 32:55 AI fails the only true test of intelligence 36:49 Early adopters of AI have an enormous edge 39:37 AI meets you exactly where you are 43:02 Always leverage the best intelligence 44:37 If you can't define it, you can't program it 49:37 The solution to AI anxiety is action

It's somewhat unfortunate that virtually all software platforms are only using AI to go wide instead of deep, piling on more features to increase the value of their platform lock-in. More companies should use AI to master the long-tail edge cases of their core domain and increase their composability, rather than merely expanding into adjacent areas.

I spent time in Shenzhen last year and when I saw Merz come back from China saying Germans need to work more I immediately knew what broke his brain because I lived the exact same cognitive shock my first week in Huaqiangbei I burned through 4 prototype iterations of a motor controller board for less than a thousand bucks total, back home a friend was working on something similar and spent over 12 thousand for a single revision that took almost two months to arrive when you live that contrast in your own hands with your own project something permanently shifts in how you see the world and it goes way deeper than speed & cost what Shenzhen actually built is a collective learning organism, imagine 20 PCB fabs 15 injection mold shops 30 component distributors and a hundred firmware freelancers all within a 2km radius, looks insanely redundant from the outside until you realize redundancy is actually information density in disguise I watched this firsthand with an injection mold supplier I was working with, this guy had seen a hundred founders iterate similar thermal designs over 6 months so he proactively modified his tooling before I even opened my mouth, he knew what I needed before I knew what I needed, the intelligence lives in the relationships between the nodes and it compounds daily the west thinks about manufacturing as a cost center you optimize by centralizing… China accidentally built a distributed neural network of manufacturing intelligence where knowledge diffuses horizontally across thousands of agents faster than any single western company can process internally so when Merz comes back and says we need to work a bit more I think he saw the problem but COMPLETELY misdiagnosed the solution, telling Germans to work harder is like telling a horse to gallop faster when the other side built a combustion engine the gap is ARCHITECTURAL it’s ecosystem density, you need a custom connector in Shenzhen you walk 200 meters, in Munich you send an email and wait 3 weeks it’s iteration speed, parallel search vs sequential optimization at the system level, it’s risk tolerance, Chinese founders ship something broken on Monday fix it Tuesday ship again Wednesday while European companies are still in the approval phase for the pilot program of the feasibility study… and Merz only saw the surface, what he missed is the tier 2 cities like Hefei Chengdu Wuhan replicating the Shenzhen model at scale right now BYD going from irrelevant to outselling every european automaker combined in roughly 5 years, Huawei building its own 7nm chip under maximum sanctions when every analyst said it was physically impossible & behind all of that a government that treats advanced manufacturing as an existential national priority while europe debates whether AI needs another ethics committee I think what we’re watching is the most asymmetric economic competition in modern history and most western leaders are still framing it as a productivity problem when it’s actually an ontological one Europe & America are optimizing variables that China stopped tracking years ago meanwhile China is compounding on dimensions the west has no framework to even measure Merz at least had the courage to name it out loud and I respect that genuinely but working a bit more inside a broken architecture just means you arrive at the wrong destination slightly faster

India is now a wholesale funding station for Israeli, French and Russian mil R&D ;)

Just see how good or bad Indian police, netas, judiciary, babooze, PSUs and other instruments of state are. If you think that armed forces which come from the same set of people and systems are any better, then you're only fooling yourself. These are just like any other sarkari org in which 8-10% do all the heavy lifting and the rest just coast along. The grind, sacrifices and pain of individual soldiers does need respect and recognition. But the organisations must not be put on a pedestal, like it is by default here. These are as good as or as bad as any others: full of red tape, indecision, and other bad things, with only a small minority trying their best to do their jobs.

You want to know why do Bahubalis thrive in Indian politics? Or why does caste play such an important role as vote banks? Or why general public is so deferential towards crooked politicians? Much of the answer to all of these questions lies in the fact that Babudom and Police are cruelly corrupt and criminally obdurate. An ordinary person hopes the muscle power of Bahubali Politician will get him a hearing in the system. His caste MP or MLA would heed to him because of his caste. His FIR might get registered when Bhaiyyaji’s office calls the SHO. Yes there are also other dynamics at play and supporters do try to take undue advantage also but we will have a much cleaner politics if Govt officials worked sincerely and fairly.

is also nearing, especially thanks to the perfect performance by our very own, Indian mission systems and combat equipment. A pan-India endeavour, Tejas Mk1A is truly the pinnacle of engineering in our country.

A 2.26 min video that possibly can convince you why preventive measures for metabolic disorders can be cost effective (cheaper) solution. Please spare sometime to check this out.

Vibe coding is the new product management. Training and tuning models is the new coding.

Creator of @nodejs says humans writing code is over

And do you want to know how much we are spending on it? 1.25 Billion. Meanwhile, we have paid 2x for "India Specific Enhancements" on Rafales. If any of you find out anything which is India specific, please let me know. Because, Rafale can't carry a single Indian weapon.

Amount spent of Rafale deals until now: 4.5 lac cr. vs since inception - DoS (ISRO included): 1.6 lac cr (40%) - Tejas (TDs/LSPs/SPs/Mk1/Mk1A/NLCA/Trainer/Mk2 220+ jets): 1.2 lac cr (30%) - AMCA: 0.15 lac cr(3%) - Kaveri: 0.03 lac cr (0.6%) - Saras: 0.07 lac cr (1.5%)