anshuman

@ry4nrodriguez_ what does that even mean? why do you need to train across tabs?

Working on a project called Swarm that enables distributed model training across browser tabs.

@sama @victormustar do you?

i want the US to win in AI both in open source and proprietary models, and i am glad to see this

@knowShubhangi go for a run

Hello people of Bangalore Where should a girl go if she doesn’t want to attend tech meetups this weekend? Museums? Art galleries? Craft food and coffee? Any nice experiences to must do?

@plastixcxx @StalwartCoder @1littlecoder

Hi, if you're in Bengaluru, planning to host engineering meetups/events soon and looking for a venue, would love to connect with you. Tag tech communities/organizers 👇🏻

in the next 2 minutes i'll teach you how to pick hardware for local LLMs. tl;dr > VRAM tells you what fits. and not how fast model runs. > decode is limited by how fast memory feeds the chip, not by raw compute. a smaller, better-fed GPU can beat a bigger one. inference is not one operation. it's two. "prefill" reads your whole prompt and builds the KV cache. it's heavy on compute. "decode" produces one token at a time, re-reading the weights and cache every step. it's bottlenecked on memory bandwidth. so your workload shape changes everything: short prompt, long answer, you live and die by memory bandwidth. long prompt, short answer, attention kernels and the prefill path matter most. many users at once, the scheduler is the whole game. when picking stack, i've seen people doing this - a) count the VRAM. b) find a benchmark screenshot with a big tokens per second number. c) pick the engine with the biggest number. d) buy the card with the most memory. but it's not the appropriate. let's say benchmark said 180 tok/s figure, it might have come from one user, one prompt shape, probably a 1K in, 128 out toy run. but the coding agent, you are gonna be using local-llm for will drag 80K of context for example you must not start from the engine. you must start from: what hardware is actually in the rack. whether the model sits in fast memory or spills into slow shared memory. whether your problem is prefill or decode. how long context runs and how many users hit it at once. whether prompts share prefixes you can cache. answer those and the engine picks itself: weird, offline, cpu-heavy, edge, GGUF, llama.cpp. mac with big unified memory, MLX. models fit that never would on a 24gb card, but memory is slower, so you trade speed for capacity. one 4090 or 5090 on low-bit weights, ExLlamaV2. a few consumer nvidia cards or local MoE, ExLlamaV3. serving open models in production, vLLM, the safe default. long context, MoE, routing, disaggregation, SGLang. all nvidia, squeezing the last drop, TensorRT-LLM. a whole fleet, put Dynamo on top. and remember - without a fast interconnect like NVLink, spraying tensor parallelism across multiple GPUs can be slower than plain pipeline parallelism. the moment a model crosses a GPU boundary you start paying a communication tax, and cheap PCIe links make you pay a lot of it. so before anyone argues about which engine wins, answer the fundamental questions first. the hardware and the workload decide. the choice of engine just follows.

@sachaarbonel Banger

showing her how i tell 5.6 sol high to just do it no fancy prompts. no examples. no chain of thought. just 'continue' and 'sounds good'

@dejavucoder let's try this.

i wrote "just follow" in my bio and more people started following me. how do i keep forgetting this that you can just ask for things.

@HaimantikaM 😭

@athleticKoder Tweet nahi padha na🌝 I wrote Asha tiffins

What I needed to start the morning and finish the second last assignment of this semester. 🤓 (You aren’t allowed to comment “carbs”, “no protein”) 📍Asha tiffins

@HaimantikaM on a 2nd thought arogya doesn't have circular tables😂

@HaimantikaM arogya ahara/asha tiffins?

@vadiamit woah

can't wait for this Saturday. see you soon, India :) 🇮🇳

@harshilmathur @kamath_sutra i can go on and on about how true this is based on lived realty

Every company is hiring AI-native engineers to move faster. What they actually need is AI-native leadership. Engineering isn’t the bottleneck anymore, leadership is.

@monali_dambre Manthan bhaiya spotted

A Stargate for Data Labs are on a trajectory towards >$100B/year of data spend by 2030. As we begin the trillion-dollar compute project, we need to think about the equivalent civilizational-scale effort for the other core ingredient: data. At the foundation of the scaling revolution is a simple empirical law: deep neural networks improve smoothly, near magically, as you scale two things in proportion — (1) the size of the model and (2) the amount of data you train on. And despite the scaling laws being brutally diminishing, we’ve successfully bitten the bullet of logarithmic scaling with exponentially larger clusters and datasets, and received incredible new capabilities in return. But this exponential scaling is bound to hit some limits. Oddly enough, compute has compounded fairly smoothly without limit, with trillions flowing into hypercluster buildout. Instead, we’re starting to hit the limits of an exponential demand for data. Gone are the days of being purely in the compute-limited regime, where we had effectively infinite internet data but never enough GPUs, we’re now entering a data-limited regime. Luckily, this limitation is coinciding with staggering improvements in AI capabilities. Incredibly, we seem to have a real line of sight towards automating a majority of knowledge work with the methods we have today. RL + pretraining, and the data for each, will be generally sufficient to achieve most economically valuable tasks, given some minimal algorithmic progress and continued compute scaling. In a data-limited world, economic progress & scientific acceleration will be directly bottlenecked by our coverage in each domain. We need to see data collection as imperative, deserving the same civilizational ambition we’ve given compute. The internet as a one-time subsidy It’s underrated how much all progress in AI owes everything to the blessing of the internet, this one-time civilizational subsidy to deep learning, decades of unintentional accumulation of a perfect dataset: every book, blog post, image, video, paper, discussion, etc. all digitized and freely available. Without the internet, we’d likely see comparably minimal progress in AI today, and in fact, if you notice where systems currently underperform, it’s almost always a domain where web coverage is limited and data is private, expensive, non-digitized, or non-existent. But we’re running out of it. There are only about 300 trillion tokens of useful public human text, and the internet doesn’t produce nearly enough new high-quality data to match what scaling demands — we’re soon to hit the limits of public data for pretraining. And though the advent of RL bought us reprieve — chain-of-thought RL needed a new form of untapped data, gradable math & coding tasks, also available online — we’re quickly running dry of hard tasks for RL as well. Why do we need so much data anyways? Humans learn comparably in far less time, needing just one textbook where language models might need the equivalent of hundreds to learn a new topic. It’s possible we discover methods that are massively more data efficient — synthetic data, data efficient architectures, other exotic algorithms — but fundamental progress is slow and highly unpredictable, and the recipe we have just works today. And, while I’m wary of getting too deep here, even arbitrary data efficiency can’t replace data that just doesn’t exist in the first place. There’s a massive amount of missing information on the web: the dark matter of the internet — tacit knowledge, undocumented processes, etc. — most of which was never published and lives only inside organizations, the physical world, or just in people’s heads. I’ll leave it here and say, for reasons far longer than I can fit in this post [1], it’s best to operate on the assumption that our insatiable desire for data will continue as it has for the last decade. There will be >$100B/year in data spend by 2030 We’re not screwed yet, of course. Only a fraction of useful data in the world is on the public internet, the rest is stored inside private datasets, corporations, personal archives, universities, governments, and otherwise. Labs can and will continue to license these private datasets, or create them from scratch, like Anthropic’s book scanning project. And we’ll increasingly task human experts to manufacture new high-quality data, with a large fraction of hard RL training tasks already being sourced this way. But collecting this data, unlike before, will be expensive. As the free internet dries up and demand for data rises, we should see labs investing equally in data as compute, likely spending a significant fraction of their compute budgets on data. As we see trillions spent on compute, we should also expect hundreds of billions spent on data (human data & collection budgets), given their equivalent importance. And, notably, data spend is already tracking this way: total data spend across vendors, not counting internal lab efforts, is already roughly $7 billion per year. It’s quite reasonable we’ll see >10x by 2030. Data is the moat Data becoming increasingly private will also majorly shift the competitive landscape. While compute is a commodity — everyone buys the same chips and builds the same clusters — data really isn’t. The big reason why frontier models have felt eerily similar to one another, until now, is they were trained on substantially the same internet (pretraining data variability across labs seems pretty low). As labs diverge onto more exclusive, manually collected corpora, I think models will begin to increasingly diverge. OpenAI pulling ahead in mathematics and Anthropic in cybersecurity isn’t an accident. I really think laser-focused collection of high-quality midtraining tokens, custom RL tasks, environments, with dedicated research effort, has driven much of the visible progress in the last year. James Betker has an excellent blog about “the ‘it’ in a model is the dataset”: model architecture and compute buy you efficiency and order-of-magnitude performance, but ultimately, models, of any architecture, are such incredible approximators of their dataset that the core meat of a model boils down to just that, nothing else. Data is a major moat. AGI long, ASI short As I’ve tweeted before, I’m confident that, despite the narrative, the data labeling industry will continue to fuel great businesses and be an excellent AGI long, ASI short. The argument is just: By the time the AGI labs no longer need data, it’s probably over for everything else too [2]. In this frame, the last companies left should be the data companies, as the last speck of economically relevant data is sucked in. And these companies are already among some of the fastest-growing companies in history: Mercor, founded three years ago, is rumored to be doing $2 billion in revenue with something like a few million expert labelers under contract. While these businesses are very non-stationary, what type of data is needed shifts constantly, I don’t think that diminishes their value. The long-tail of the economy is long, and the value isn’t diminishing as you extend farther into more obscure information: as models get more capable, the value of the marginal dataset goes up, not down. Automating a full job means covering its full distribution of tasks, tools, edge-cases, and long-horizon loops. There’s some O-ring logic to it: a dataset that buys a 1% bump can justify a previously unjustifiable collection cost when it’s the difference between a system that does 99% of a job and one that does all of it [3]. The competitive dynamics of the data industry are still evolving but as demand for data is increasingly niche, ultra high-quality, expert-generated, I think we’ll see real consolidation. Again, contra-narrative, we’ll probably see true competitive differentiation built on brand, quality control of data (which, from personal experience, can vary massively), as well as in network effects from the talent networks themselves over time. We’ve already seen rapidly shifting data type demand work in favor of incumbents, benefiting those with early knowledge of where the market is headed. The binding constraint It’s truly remarkable that we seem to have the recipe — pretraining + RL — to absorb most economically valuable work, despite being far from a lot of what we expected from “AGI”. The same way chess engines revealed we never needed general intelligence to solve chess, as we originally thought, we’ll soon realize that software, mathematics, and the vast majority of the economy (including physical, just running ~3 years behind!) are the same. If recursive self-improvement or some other algorithmic breakthrough arrives, that’s wonderful, but we really don’t have to wait for it. The binding constraint between here and an automated economy isn’t that, it’s data coverage: every app, workflow, edge case, process, etc. sitting in private stores or someone’s head. Ultimately, while we make tremendous strides in more efficient model architectures, and clusters like Stargate equip us with zettaflop-scale compute, we really aren’t making rapid progress collecting the data we lack. We’ll soon live in a world where we have the methods & compute to accelerate scientific progress or economic growth, but not the data. And we’re already there today: frontier models would surely be as good at accounting/many medical tasks/legal advice as they are at software engineering if we only had the same pretraining & RL coverage as we did for code. I really want to drill this in: The speed at which we automate the economy is going to be directly rate-limited by our ability to collect data about it. Worth noting that under this assumption, with data as defensible and directly proportional to economic & scientific progress, data should also be considered a national strategic asset like compute. Imagine what we’d do in a world where we had a Manhattan Project-effort for AI and needed to mobilize data collection as a limiting factor. We should be concerned about China, with greater state capacity and authoritarian economic control, being capable of mobilizing data collection at national scale, potentially compounding their economy and scientific output faster than us down the line. A Stargate for data I’m leaving my complete ideas for a future post, as this one is already far too long, so I’d really like to pose the question here. Stargate exists because we organized trillions of dollars, international strategy, gigawatts around compute as a fundamental ingredient. What would equivalent ambition look like for data? Obviously, scaling data collection, a heterogeneous mass of information across the economy, isn’t going to be as clear as scaling compute, as a homogenous infrastructural effort. A core division will be first, coverage — all uncaptured knowledge sitting across the economy/science/physical world and all that simply isn’t recorded — and, secondly, sheer volume in the domains we already train on: more hard math tasks, more high-quality web text, way more coding data, more legal drafts, etc. I have a post coming soon which breaks down my proposals. There’s a lot of room for creativity. Quickly, we’ll probably want to start with a deep census of what we have and what we’re missing, predict what the 2030 model will still be bad at and work backward to what we should be collecting today. You can probably license a large amount, leveraging high lab valuations to buy datasets or companies altogether. There’s an adversarial nature to a lot of this collection with firms, so there’s lots of engineering to do this correctly. We should go convince important companies to turn off deletion policies, even if we’re not buying from them yet. Data flywheels in consumer products will be massive. Confidential training, government legislation for grant-funded research, running companies at a loss for their data, etc. We’re headed towards hundreds of billions in expenditure, national prioritization, and major data limitation on the horizon. We have a great opportunity to think creatively about what a megaproject for data would look like: How do we, deliberately this time, construct the next internet’s worth of data? Footnotes: [1]: I’ll probably soon publish my much longer post explaining my position on data efficiency and why the value of this data is still pretty high in most worlds regardless of new algorithms. [2]: The “AGI freeroll” bet: heads you win, tails ASI flips the world upside down anyways. [3]: We already see a glint of validation of this point, given the data market is strongly tilting towards ultra-high-quality agentic data, rather than unskilled labeling — niche expert workflows, live environments, and evaluations requiring increasingly obscure talent & knowledge — yet shows increasing, not decreasing, revenues.

@madiator woah. congratss. knew you are gonna go big leagues.

Happy to finally make this announcement of our Seed and Series A raises. It's been a great journey since I left Google DeepMind 2.5 years ago with a goal to democratize post-training! Post-training gets easier if you have access to good data and now, RL environments. That's why we have poured our energy into doing data research. And we will continue to make Bespoke into one of the world's best data research labs. Thank you everyone for your support and thanks to the incredible Bespoke team that has done amazing work so far! PS1: Yes, we got busy with building after our Seed (MiniCheck, Curator, OpenThoughts, Terminal Bench..) so we didn't get a chance to announce the Seed raise! PS2: I will be at ICML starting Wed!

@alokbishoyi97 would love to join.

We now have a community of 45 of some of the smartest Indian origin folks spanning across inference engg, post training and ai infra - including folks from OAI, deepmind, meta, MSR, sarvam, a bunch of neolabs and some fresh grads who are arguably 99.9 %ile of folks in terms of agency we are looking for even more high caliber folks who are working at frontier or neo labs to join us! Spread the word!

@knowShubhangi check out - flent.in

Hellooo Twitter! 👋 Looking to sublet a 1BHK/2BHK apartment in Bangalore! 📍 Preferred: HSR, Koramangala, Indiranagar & nearby central areas 📅 Dates: 20th July – 31st August (flexible) 💸 Budget: ₹20-30k max 🏠 Preferably fully furnished, kitchen access & washing machine About me: Remote worker, neat & tidy (I clean whenever possible!) DM me with details/photos if you have something available! If you don’t have a place but know someone traveling in August, please comment/RT so it reaches them - they can earn some extra cash via sublet! 💃🏻 @BangaloreRoomi @fmrbangalore @peakbengaluru @flatshareblr

@HaimantikaM filter coffeeeee

@athleticKoder Haha. Happy to do that honestly. Woh last time jaha gaye the🤓

Will be in Bangalore for the next few weeks starting tomorrow. Excited to eat good dosa again 😭 Take a stroll around my favourite places in HSR, meet friends and of course attend some conferences + meetups!