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Founder @bubblspace — AI agent traffic intelligence. At FBA Lab 🧪 I build, train & run real AI models in real code. 👇 https://xitter.gq/t.co/mQsXyw7WIg

Joined April 2011
Photo by FireHacker (@thefirehacker)Photo by FireHacker (@thefirehacker)Photo by FireHacker (@thefirehacker)Photo by FireHacker (@thefirehacker)Photo by FireHacker (@thefirehacker)Photo by FireHacker (@thefirehacker)Photo by FireHacker (@thefirehacker)Photo by FireHacker (@thefirehacker)Photo by FireHacker (@thefirehacker)Photo by FireHacker (@thefirehacker)
Skills up for grab this week at FBA Lab speed run event 😱 🏀 LLM Modelling- Optimizer 🏀 Pytorch Request access —> Join event —> Complete Challenge bubblnet.com
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Weight Space Programming: a glimpse into continual learning. Agent edited Qwen 0.8B's weights to inject facts about my X profile directly into the model: my handle, bio, GitHub, and what i build. It edited 2% of the network (5 of 488 tensors). No training, no gradients. Result: Before, it had never heard of me ("I don't have access to private information…"). After, it tells you who i am, my bio and GitHub, answer project questions it was never trained on. Everything else it knew is untouched. Reproduce : Link to reproduction repo in comments. Point your agent to the repo and teach model your own facts.
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Engrams + Decision Models + In-weights programming = 👀👀👀
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Andrew Huberman on the best advice he ever got: figure out the most real work you can do every day, consistently, and find a way to reset every week. "The best advice I ever got: there was a neurologist out at UC Berkeley and UCSF, Bob Knight, some years ago, very talented. I asked him for advice starting graduate school." "He said, 'Figure out the maximum amount of real work that you can do every day consistently, and then figure out some non-destructive way to reset yourself every single week.'" "I said, 'How do you reset?' He said he likes fishing. I like to hike. I like to get into nature and spend time with people while I do it. I figured out I could work X number of hours per day, 5 days a week, and I just stuck to it for 25 years." "People who take a ton of stimulants and just blitz, blitz, blitz, blitz, blitz. These are the same people that crash and disappear. You have to figure out and build your capacity." "Yes, you have to work extremely hard. You have to be extremely focused. But you also have to figure out how long you can keep this up." @hubermanlab
Stanford neuroscientist Andrew Huberman built one of the most popular podcasts in the world by turning health research into protocols millions of people actually use. His top three habits are surprisingly straightforward: get bright light shortly after waking, resistance train, and make the sleep you can get as high-quality as possible. He joins a16z’s Daisy Wolf to walk through the protocols he actually follows, from his 35-year workout to the lockbox he keeps his social media phone in, and explains why packing in every protocol can backfire if it gets in the way of the rest of your life. For founders, he explains why the hours your brain does its best work are worth guarding like a fight, how to find the most real work you can sustain, and why sleep is where neurotech will win first. 0:47 Why zero drinks is easier than two 4:39 Meditation means watching the chaos 6:25 Sunlight first, caffeine if training 7:20 The workout he's done for 35 years 10:09 Two sets to failure, low volume 13:11 Find foods that make you feel good 15:55 Why eating too clean backfires 17:29 Why non-sleep deep rest beats a nap 19:05 A sunset habit from a sleep lab 21:18 Who needs blue-light glasses 23:32 Every supplement he takes 30:46 Why his phone lives in a lockbox 32:40 His top three habits for health 35:47 Guard your two daily focus windows 42:47 Neurotech that puts you to sleep 46:38 The best advice he ever got YouTube: youtu.be/Ezi-nwe6LlY @hubermanlab @daisydwolf
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Thousands of companies died at the altar of the internet for one business model to survive, the ad. The agentic internet will need a different one. @kumarharsha2212 and I spoke to The Economic Times about why you can't upsell an agent, vertical Al built for India, and how this wave compares with the early internet.
AI agents that shop and book on a person's behalf change who sees an ad, and who pays for the internet. Lightspeed Partners @kumarharsha2212 and @MohapatraHemant joined Himanshi Lohchab in @EconomicTimes podcast Corner Office Conversation to discuss which purchases agents will take over and which will stay with people, why an agent cannot be upsold, and why India may prefer to pay for an outcome rather than a subscription. They also discussed vertical AI built for India beyond its largest cities, how this wave compares with the early internet, and how they use AI assistants themselves. Watch the full conversation here: youtu.be/KNdl_uhht3g?si=Xzml…
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if you are a foreign investor in india, this is the most comprehensive guide to managing your investment - KYC, demat, transfers, sales, compliances, taxes and more. @ro_kapoor is brilliant and his firm has been a godsend for us to manage our india investments.
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Thoughts from my Shanghai field trip Sentiment among Greater China investors was far worse than I expected. Unlike the US market, A shares had not seen a single rebound since July. Still holding Chinese and Hong Kong AI stocks that had crashed and Korean memory stocks that had collapsed, investors kept pressing me on when memory would bounce back. Many investors said the AI trade was over, and a few said they doubted whether even 2027 capex, not just 2028, would go ahead as planned. Overall, the bearish sentiment was so strong that even I, a bull before coming to China, started to waver. It got to the point where, faced with the bear case that memory prices could start falling in the third quarter of next year, I took it seriously and couldn't refute it. As the conversations went on, I gradually began to understand where their pessimism was coming from. In the end, they were all asking the same question. So what comes after AI coding? What will be the next engine of ARR for Anthropic and OpenAI? Several candidates came up. AI4S, AI chip design, and so on... But none of them clearly convinced investors. I couldn't offer a clear answer either. Sure, there are rumors that Anthropic has made major breakthroughs in both AI4S and AI chip design, but what we actually want to know is when any of that turns into money. With AI4S, there could be beneficiaries right away, such as companies that sell lab monkeys or diagnostic kits. In fact, there already are. But while we can trade those names in the short term, what we ultimately want to know is whether Anthropic can make money by developing drugs. That is still several years away. As for AI chip design, the TAM itself is hard to calculate, and even if you do calculate it, it is hard to monetize. That makes it difficult for the market to believe in. Honestly, this feels like the hardest market in history. If a company isn't making money, selling makes sense. But right now the market sells even when companies are making money, even when they beat, and even when they say demand will stay solid all the way through 2028 and 2029. So my recommendation to investors was to buy memory, and within memory, DRAM. Not because I have conviction in the DRAM cycle, but because I see it as a relatively defensive choice within the AI trade. Memory stocks are now trading close to 1x P/B on 2027 BPS. Historically, memory stocks have staged technical rebounds when they fell below 1x P/B. And even if the broader market falls further from here, the three DRAM makers will keep buying back their own shares. I thought that would guarantee a certain level of buying and concluded the stocks would have downside support. Of course, I don't know whether memory stocks can break above their previous highs. That depends on whether sentiment across the broader market recovers. But I think there is a fairly good chance that memory outperforms the rest of the AI trade.
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🚨Agrani Labs in talks to raise $100 million from Groww founders' Stargazer fund, Google, Peak XV, others A $100 million investment will mark one of the largest rounds in India's deeptech ecosystem after the likes of Pixxel, Skyroot, Sarvam among a handful others. Moneycontrol was the first to report on September 1 that Agrani Labs was in talks to raise $50 million (Rs 475 crore), with existing investor Peak XV Partners looking to double down and new investor 360 One joining the round. That round has now been upsized to $100 million with Groww's founders too coming in, among others. Scoop with @Goenka_Tushar1 moneycontrol.com/news/busine…
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This is ragebait, do not fall for ragebait. Always remember, if Ace didn’t fall for Akainu ragebait then Marineford would have been a success.
sam: local inference is a terrible allocation of scarce resources “a lot of people want local inference, and somehow they assume that the problem is solved now because we have open-source models, we're going to be able to run them on our computers, and so we're not going to need all the stuff in the data centre but from a global allocation perspective, actually it's quite terrible to have everybody, like, have a bunch of the world's limited supply of DRAM sitting in their own computer and being used five percent of the time you want maximum utilisation in the cloud if you're trying to deal with this very supply-constrained environment somehow many people's intuition is the opposite of that”
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Mission Success! GSLV-F18 has successfully placed NVS-03 in the intended orbit. Congratulations! Watch live: youtube.com/live/qIiYr3tRSqE Mission details: isro.gov.in/Mission_GSLVF18.… #GSLVF18 #NVS03 #ISRO #NavIC
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Congratulations to our scientists and engineers at ISRO on the successful launch of GSLV-F18 carrying NVS-03. This mission marks an important step in strengthening NavIC, our indigenous satellite navigation system. Equipped with enhanced capabilities, NVS-03 will further our pursuit of self-reliance in critical technologies and support navigation services that benefit our people. The dedication and ingenuity of our space scientists continue to make our nation proud. @isro
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Can you RL design taste into a model? I explored training Qwen3.8-27B over some open-source design environments. The answer: you can, but that taste probably won't be yours or mine. And definitely not a real designer's. jasperlu.com/blog/rl-design-…
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back then I was forced to memorize the periodic table from a textbook. I hated chemistry for that. now I made it interactive in 3D: sael.net/periodic-table
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Europe wanted to play "everyone is equally responsible" narrative but Piyush Goyal didn't give them a single chance. Look at their uncomfortable expressions, still refusing to face the facts 😂
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Training large models with RL typically runs on a fixed homogeneous cluster of the latest accelerators for numerical stability and faster throughput. This throws away inference optimization work done for other vendors and generations, limits the capacity you can utilize, and is unnecessarily expensive. So we trained a model on agentic coding tasks using fully asynchronous RL and mixed-precision, mixed-accelerator samplers. With off-policy correction, training stayed stable through convergence, on par with homogeneous samplers, at a lower cost.
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Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: reflection.ai/beam
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43 days ago Marin kicked off the largest live-streamed pretraining run in history: a 535B MoE trained on 18T tokens. A scaling ladder was used to forecast eval loss along the entire trajectory, and the forecast was publicly pre-registered. Its halfway along, and despite being a 300x extrapolation, the run is within 0.3% of the forecast. The run was originally planned to be 360B parameters. Until @ravwojdyla found a hardware implementation with custom kernels to grow to 535B parameters while also speeding up tokens per second. Learn about how he did it on the Open Athena blog: openathena.ai/blog/expert-pa….
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New historic NanoGPT record at 39.9s (-27.7s) from @DevenPzak , obliterating the prior record of 67.6s! This record introduces a new paradigm of thinking to NanoGPT: instead of optimizing matmuls or adding more expressive operations, optimize at the individual flop level with incredibly clever engineering and ML judgement. If a flop is low value on a particular step, skip it. Specifically: -(~8s) Sampled softmax. If a token doesn’t appear in a batch, skip its lm_head fwd/bwd some fraction of the time. -Sparse values. Only run an optimizer step for ngram embeddings that occurred in the batch. Set beta1 to zero to enable this. Beta2 is applied retroactively when the row is later used. -Sparse updates. Only update ngram and value embeddings once every 4 steps instead of once every 2. -Sparse communication. Shard the n-gram table across GPUs, and only pass the rows receiving updates on each step. -Sparse optimizer states. For the n-gram table, reduce from 2 floats in Adam optimizer per param, to 1 float per 768 params. -Hand-rolled flash attention for 64 dim heads. There are several additions that add accuracy too: -(~4s) EMA during last 300 steps, combined with lifting final_lr to 0.3 instead of 0.15. -(~1s) A new optimizer, Anvil2, which expands muon via a second tracked momentum buffer, improves the ortho coefficients, and modifies the cautious weight decay application. -A couple additional dynamic skip connections in the network. The most striking consequence of the ‘flop aware paradigm’ is you can grow parameters arbitrarily large, only limited by the available memory, since you can selectively choose how to expend flops on those parameters on each step. NanoGPT has kept active parameters below 124M, but total is unbounded, and has grown to 640M through embedding sparsity over the last year. This PR takes that to its logical conclusion on the 8xH100, scaling up to 65B sparse embedding parameters, which accounts for 25% of the PR’s gains. At frontier scale, where one is not bounded by an 8xH100, one could imagine where this paradigm could lead. github.com/KellerJordan/modd… As this was a very notable PR, I spoke with Deven for an hour to learn how he did it. Here’s his story on the changes: hyperstition.cc/training-nan…
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keeps getting worse and worse for openai
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unfortunately i contacted them on the night of wednesday, september 2nd making the following points 1. i had intel they had information about math work i was doing and a few days before they had spun up a group to try to compete. 2. i emphasized and reemphasized mine was a strictly personal mathematical collaboration 3. that this was a collaboration that had been going on for a year 4. that in my personal collaboration we were happy users of codex as well as obviously claude (and i later complimented astra explicitly after it came out for its help, and reassured i would obviously be sprinkling acknowledgments for it, previous models, and codex liberally in the writeups) 5. that it would look terrible if openai were competing against mathematician consumers, and my collaborator (Tristan) wanted to email about this explicitly 6. that i expected none but in the worst case intended to refuse marketing around the result we were working towards, being the fruit of a personal external academic collaboration, and wanted to express this because i figured worries about such marketing by their leadership was why a team was spun up to extraordinarily directly compete. sebastien and i have in the past months had our fun pushing each other on twitter, but as i process it all i keep feeling bad about Tristan. 1. neither of us could sleep Sunday night, so we went on a walk around the city. i finally asked him why fluids, and he told me it was the navier-stokes problem. he told me parts of the story about his classic nonuniqueness result with Vicol i hadn’t heard yet, and also about how he too grew up writing shaders as a kid and then switched to math 2. tristan is a mathematician’s mathematician. he kept rock solid to his principles during the tense calls and negotiations, immediately turning down a career dream and million dollar prize for me, the sort of strange excited unclear-quasiuseful confused idiot in our collaboration. fifteen minutes later, his feet were up on his desk with three toes per foot peeking out of ripped socks 3. our periodic call after i’d texted him euler blowup was just laughing for an hour or two. just laughing. i’d seen fable deal correctly with monge ampere in the equality case of ehrhart’s conjecture, so in some call before that i’d really pushed him for the craziest shit he could think of that i might try. i texted him the pdf as an update on that “diego luis 3” line, in which following his incredible expression of intuition i hodgepodge slapped together a bunch of our recent work, in particular correcting a bunch of better and lesser known papers in the literature, as well as on diego and luis’ ipm paper, and hoped an analogous construction for boussinesq (thus axisym euler) might work out. i managed to wade through enough confusion to insist to the model it stop telling me stupid regularity stuff about kelvin waves, and a rayleigh-taylor ansatz stuck. like i said, just laughing. when i texted him the pdf, the worst thing either of us had seen so far, i didn’t even comment on the euler blowup claim, only explaining why skepticism should remain for boussinesq, because i found it so shocking. i mean what the hell did i know, i must’ve confused myself by misunderstanding one adjective like i was used to with algebraic geometry. im so lucky to have been able to live that 4. he managed all this this entire time with unclear sleep, timestamps continue to amaze me, due to just recently having a kid it is obvious he deserves the same sort of praise he heaped upon luis, perhaps mutatis mutandis:)
Replying to @__alpoge__
nothing at all was locked we were just willing to talk but you didn't talk to us ... sorry but that's just untrue, we were willing to go above and beyond and have as many discussions as you would have liked to reach resolution.
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Q: Navier-Stokes is just Newton's laws for fluids, and Newton's laws are linear, so why is Navier-Stokes nonlinear? (1/2)
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