h100envy
@h100envy • 4,520 subscribers
ex product engineer @google CA: GQCGitfVw5LYnj4L4zrNUMYeK9dNxEJi9ZjMwMfQpump
Videos
I BUILT F*CKING GOLD AND ITS CALLED GEMSEARCH repo: while you scroll your feed 6 hours a day my spider does it in 0 seconds whats inside: > spider extension lives in your X feed. scans every post in real time. highlights keywords. collects tickers and links. everything you see - it sees > JEV module groups mentions, kills duplicates, scores author diversity. 3 bots mentioned it - trash. 12 independent accounts - signal > crawler hits sites, github, docs on its own. context youd google for 40 minutes - collected in seconds > 4 role grok verification: signal / product / risks / data sufficiency. four angles on every narrative before you spend a single SOL > dashboard: projects, sources, decisions, unknowns, full history, JSON export. everything in one place > LAUNCH QUEUE. gemsearch generates a token concept straight from the narrative and launches via built in pumpfun executor. other solana platforms - HTTPS adapter. connects to ANY launchpad 5 attempts per 24h. 0.025 SOL per attempt. full budget control reads feed → finds narrative → verifies through 4 roles → generates concept → launches token full cycle. without you. the narrative era is here. only one question - are you scrolling your feed yourself or is the spider doing it for you
h100envy95,821 次观看 • 3 天前
100+ STARS IN A FEW HOURS. I TOOK F*CKING GOLD AND TURNED IT INTO A F*CKING DIAMOND github: $GEMSEARCH doesnt just scan the feed anymore. it understands it. whats new: > spider catches tickers $TICKER straight from posts. majors like $BTC $SOL $USDC and price strings like $100 are auto skipped. new tokens only > catches solana addresses. not strings that look like addresses, only real ones that decode to a 32 byte key > catches NEW NARRATIVES. word pairs repeated by at least 3 different authors in 6 hours that arent in known topics. the spider finds whats emerging before anyone writes a post about it > calculates growth for every find: mentions in 6h vs previous 18h, author count, first signal time. you see not just what but how fast its growing > keeps only the top per pass: 10 tickers, 10 addresses, 5 phrases. grok limit goes to the best finds not garbage > topics are now custom via topics.json. your own watchlist of what to track > tickers addresses and phrases are marked research only. they dont enter the token launch queue. protection from accidentally launching a copy of someone elses token gold scanned the feed. diamond understands whats happening in it
h100envy41,358 次观看 • 2 天前
Google engineer explained how to fine-tune a tiny LLM from 46% to 90% accuracy on your phone in 21 minutes - better than $1500 on-device AI bootcamps. pick Gemma 270M -> generate synthetic task data -> fine-tune with LoRA -> quantize to int4 -> deploy to Pixel and hit 2000 tokens per second. That loop is how a 270M model beats a 70B one on your task, running fully offline in your pocket. Gemma 270M + synthetic data + LoRA + int4 quantization + on-device runtime - that's the stack. Watch and save it, then fine-tune your own tiny agent tonight.
h100envy710,274 次观看 • 2 个月前
Ex-NVIDIA engineer who built Unsloth explained RL, kernels, reasoning, quantization, and agents in 2 hours 42 minutes - better than $5000 fine-tuning bootcamps. pick the base model -> write triton kernels for 2x faster fine-tune -> quantize to 4-bit -> run GRPO/DPO -> ship a reasoning model on your single GPU. That loop is why Unsloth is the default way to fine-tune Llama, Qwen, Gemma, and Phi on hardware you already own. Unsloth + Triton kernels + 4-bit quantization + GRPO/DPO + single-GPU fine-tuning - that's the stack. Watch and save it, then fine-tune your first model tonight.
h100envy497,258 次观看 • 3 个月前
Ex-Berkeley PhD who leads SGLang at xAI explained how they serve Grok on 100K GPUs in 23 minutes - better than $2000 inference-at-scale courses. split prefill and decode -> shard experts across GPUs -> route tokens per expert -> overlap comm and compute -> serve at DeepSeek-API-killing prices. That loop is why xAI runs Grok on SGLang and third parties beat DeepSeek's own API by 5x on cost. SGLang + prefill-decode disaggregation + expert parallelism + AMD MI300 - that's the stack. Watch and save it, then read the article below.
h100envy319,581 次观看 • 3 个月前
I BUILT A BEE BRAIN. IT TRADES MEMECOINS. I CAN'T STOP WATCHING IT LEARN. website: github: twitter project: BeeBrain i simulated a honeybee brain and dropped it on memecoin pools. 40 new tokens per hour on solana. 90 seconds per pool. 8 signals, all noise. a real bee: 960,000 neurons in 1 mm³. 20% of the brain on one job: learning which inputs lead to reward. that is exactly what a memecoin scorer does. can the architecture of a 1 mm brain outlearn a transformer that forgets every pool it ever scored? 2,000 kenyon cells. five lobes. one waggle vector. > antennal lobes smell the pool. 8 features per glomerulus > optic lobes read 60 ticks of narrative heat > mushroom bodies remember every trade. 5% fire > central complex picks explore or exploit > motor says pass, watch or skip a print is sugar. a rug is punishment. rug memory never fades faster. the pipeline forgets. the bee remembers. $500 → $6,966 best sim seed. 67% wins. 9 of 10 profitable. 522 pools scored on solana in 74 min. passes went up 3x as often as skips. still lost. gate stays closed. apparently 175,000 kenyon cells per mushroom body weren't enough. the bee wanted alpha. pip install beebrain
h100envy35,756 次观看 • 12 天前
BEEBRAIN sent a notification. I opened my phone, looked at the pool, entered. 1000 → 5000. repo: website: tg bot: This is not a signal channel. This is a bee brain in telegram. 2,000 kenyon cells scan every new pool on solana. Five brain lobes score it in seconds. If the pool passes, the bot messages you. $JACK passed. I entered. 5x. The bee doesn't say buy or sell. It says: this pool looks like the ones that printed before. The rest is on you. The bot is free. The brain is open source.
h100envy27,481 次观看 • 11 天前
Liquid AI's head of post-training explained how they built a small model that runs on-device under 1 GB in 20 minutes - better than $2500 small-model bootcamps. pick LFM2.5 base -> on-policy preference alignment -> agentic reinforcement learning -> curriculum training -> iterative model merging -> ship a 1B model that reliably calls tools on your phone. That loop is why frontier small models now beat 70B models on the tasks that actually matter. LFM2.5 + on-policy DPO + agentic RL + curriculum training + iterative merging - that's the stack. Watch and save it, then run a 1B agent on your phone tonight.
h100envy87,975 次观看 • 2 个月前
Alibaba engineer who leads Qwen explained the future of open agent models in 25 minutes - better than $2000 LLM training courses. pre-train the base ->SFT -> RLHF -> tool use -> multi-modal -> ship a whole family (chat / VL / coder / math / QwQ). That loop is why Qwen quietly became the most downloaded open model family on Hugging Face. Qwen base + Qwen-VL + Qwen-Coder + QwQ reasoning - that's the stack. Watch and save it, then read the article below.
h100envy114,423 次观看 • 3 个月前
THIS IS NERVE BEEBRAIN. $500 → $1,300 IN 9 DAYS. THE BEE NEARLY STUNG ME ON DAY THREE. i put a simulated bee brain inside a trading pipeline. 960,000 neurons. five lobes. each one does a different job before a single trade fires. gave it $500 on solana memecoins. it scores pools. i make EVERY entry myself. → mushroom bodies remember which pool patterns printed and which ones rugged → antennal lobes filter noise from the impulse before the brain even sees it → optic lobes read the narrative map and see the shape of the market, not the numbers → central complex decides: explore a new pool or exploit a known pattern → motor output sends PASS, SKIP or WATCH with a confidence vector per lobe day 3: a pool hit every lobe green. i entered. it dropped 40% in two minutes. the bee held its score. i held the position. it came back to +160%. the bee does not say yes or no. it shows you WHICH PART of the brain agrees and which part disagrees. that is the waggle dance. a confidence vector per lobe, not a single number. watching five lobes argue about your money is a different kind of stress. → neurons: 960,000 → lobes: 5 → trades: 23 → wins: 16 → drawdown that nearly killed me: -40% on day 3 → result: $500 → $1,300
h100envy17,495 次观看 • 14 天前
WHO THE F*CK OPEN SOURCED A MEMECOIN KILL CHAIN WITH A 98.6% REJECTION RATE me. i did. and i am still not sure it was a good idea. six agents. one typed message. seven reflexes that fire before the model even wakes up. a sqlite black box that logs every kill. i ran it on robinhood chain for 4 nights. 312 pools in. 6 reached my wallet. 5 green. 1 red within the stop. the model approved a pool at 89 confidence. mint renounced. lp locked. everything clean. one reflex saw $38K liquidity. below the $50K floor. killed it in nanoseconds. cost: $0.00. 40 minutes later that pool was zero. the dumbest check in the system outperformed the smartest model in the pipeline. +$5,140 on a protocol that says no to almost everything. every decision is recoverable: scanner:pending → analyst:pass → risk:reject(low_liquidity) not a log. a kill tape. the repo is live. i might regret this.
h100envy25,156 次观看 • 22 天前
I BUILT 6 AI AGENTS THAT SNIPE MEMECOINS ON ROBINHOOD CHAIN AND THE REFLEX THAT COSTS $0.00 SAVED ME MORE THAN THE MODEL THAT COSTS $0.008 $500 in. 3 nights. $4,370 out. i mass checked nothing manually. > Scanner watches every new pool launch 24/7. pure code, no LLM, never sleeps > Analyst gets metrics only. no addresses, no keys, no tx. returns pass or reject > Risk runs 7 reflexes before the model is even called. kill switch, daily loss, gas cap, liquidity floor, slippage ceiling, duplicate check, max positions > Executor only fires if every node above said yes. one rejection anywhere and the impulse dies > Monitor watches every open position and drags stops > Reporter sends the morning brief with the full funnel night one: scanner caught 140 pools. reflexes killed 91 before the analyst saw them. analyst rejected 43. risk cut 4 more. 2 went through. 97% of memecoins die in 24 hours. the spine dropped 98.6% of them before a single dollar moved. the reflex i almost removed is low_liquidity. felt too aggressive at $50K floor. then friday a pool passed the analyst at 89 confidence, mint renounced, lp locked, everything clean. reflex caught it at $38K liquidity. i checked manually. rug pulled 40 minutes later. a $0.00 check saved me from a pool that a $0.008 model approved. every decision lives in sqlite. open any impulse from last week: scanner:pending → analyst:pass → risk:reject(daily loss limit) not something broke. exactly who, when, and why. the model influences the score through confidence but cannot flip it. a pool with active mint will not score high no matter what gpt-6 astra says. the math has veto power over the brain. want a 7th agent? one line. spine.add_node(). the other six do not know it exists and require zero changes. the full protocol, all six nodes, every reflex, and the audit trail are in the article below if a zero cost reflex outperforms a paid model call, what does that say about where safety actually lives?
h100envy25,504 次观看 • 23 天前
F*CKING AWESOME. THE BOT TEXTED AT 3AM. WOKE UP, CHECKED THE POOL, ENTERED WITH 1 SOL. BY MORNING IT WAS 4 BEEBRAIN will be deployed today repo: website: tg bot: this is not a signal chat. not an alpha group. not an influencer with a paid subscription. this is BEEBRAIN. a bee brain. a real one. 780,000 neurons scan every new pool on solana and decide if you should look at it. i didn't touch charts. didn't read chats. didn't hunt for alpha. the brain found it. the brain texted. i entered. 1 sol → 4 sol. while i was sleeping. the bot is free. the code is open source. the brain is alive.
h100envy12,831 次观看 • 11 天前
BEEBRAIN IS GOING TO THE NEXT LEVEL. until now, it was just an experiment inside NERVE. a bee brain learning to read the market through liquidity, holders, snipers, creator age, volume, and now even Telegram. but the experiment went too far. after BEEBRAIN started finding signal combinations that i never explicitly taught it to look for, i decided to turn it into a standalone project. tomorrow BEEBRAIN gets: → its own website → its own Twitter account → its own GitHub repo → a fully open brain architecture i want anyone to be able to open it up and see exactly how the system thinks. what “senses” it uses. what signals reach the glomeruli. how the mushroom bodies learn from previous pools. which combinations start to matter. and why the brain says PASS on one token and ignores another completely. the last test already went from $600 → $1,900. one token was detected 40 seconds before anyone on CT started talking about it. but that was only version one. next, i want to keep giving the brain new senses and see what connections it discovers on its own. > social signals. > on-chain behavior. > wallet movement. > context. > maybe things normal traders would never even think to connect. i do not want to build just another “trading bot.” i want to build a brain that learns how to understand the market by itself. tomorrow BEEBRAIN stops being an experiment. it becomes its own project.
h100envy14,279 次观看 • 13 天前
Ex-vLLM core contributor explained how to make LLM inference 10x cheaper in 34 minutes - better than $3000 inference optimization bootcamps. request comes in -> check LMCache -> hit? load KV cache from CPU/SSD/remote -> skip prefill -> serve. That loop is why Bloomberg and other production stacks now push 300 terabytes of KV cache per week. LMCache + vLLM + CPU/SSD/remote storage + zero-copy CUDA kernels - that's the stack. Watch and save it, then wire the KV-offload into your inference stack.
h100envy61,470 次观看 • 2 个月前
The creator of Pydantic explained how to make live agents smarter after they ship in 1 hour 21 minutes - better than $2500 agent evals bootcamps. collect traces from your live agent -> score them with an eval -> run GEPA to auto-optimize the prompt -> deploy the winner -> repeat forever. That loop is how Pydantic AI agents self-improve while running in production. Pydantic AI + Logfire tracing + GEPA prompt optimizer + evals + feedback loops - that's the stack. Watch and save it, then wire GEPA into your live agent this week.
h100envy49,768 次观看 • 2 个月前
OpenAI engineer explained every form of fine-tuning in 1 hour 46 minutes - better than $3000 fine-tuning bootcamps. pick your task -> try prompting first -> drop to SFT for style -> run DPO for preference -> run RFT for verifiable rewards -> ship a model 10x cheaper than GPT-5. That loop is why teams that fine-tune beat teams that only prompt on cost and speed. Prompting + SFT + DPO + RFT + OpenAI fine-tuning API - that's the stack. Watch and save it, then pick the right fine-tune for your task this week.
h100envy41,245 次观看 • 2 个月前
Prime Intellect engineers explained how they train reasoning models over the open internet in 30 minutes - better than $3000 distributed training courses. split policy and rollouts across nodes -> run agents in parallel envs -> verify with LLM judges -> gradient-sync over the internet -> train Llama, Qwen, Gemma at cluster scale on rented GPUs. That loop is why open reasoning models are catching closed labs without owning a data center. Prime-RL + verifiers + distributed rollouts + LLM judges + multi-cloud GPUs - that's the stack. Watch and save it, then launch your first distributed RL run this week.
h100envy40,022 次观看 • 2 个月前
Ying Sheng co-wrote SGLang, the inference engine now serving Grok at xAI on a hundred thousand GPUs. She also built FlexGen, which made a 175-billion model run on a single consumer GPU, and helped build Chatbot Arena. Three artifacts the whole field uses, one researcher. SGLang hit a 5x cost cut over DeepSeek's own API, and a dozen teams reproduced it. Everyone argues about models. She builds the engines that actually serve them cheaply enough to survive.
h100envy40,959 次观看 • 3 个月前
PyTorch core engineer at Meta turned CUDA kernel writing into a sport in 13 minutes - better than $1500 GPU programming bootcamps. profile the kernel -> find the bottleneck -> rewrite -> benchmark -> merge the winning code into PyTorch. That loop is how the open community now beats hand-tuned vendor kernels. GPU MODE community + KernelBot competition + winning kernel merged into the framework - that's the stack. Watch it, then steal the loop below.
h100envy35,390 次观看 • 3 个月前