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SOMEONE UPLOADED A REAL FLY BRAIN TO GITHUB. IT IS TRADING CRYPTO RIGHT NOW. This is not an AI. An actual mapped biological connectome (166,700 neurons) is doing real work across 5 insane open-source repositories. 1. stonkfly (646 stars) A fly brain trading Bitcoin. It watches BTC charts through... digitized eye cells and places orders via Coinbase API. Profits trigger virtual dopamine neurons. 2. desktop-fly (946 stars) A 3D fly living on your desktop. 668 neurons dodge your mouse cursor using real biological escape circuits. 3. doomfly (263 stars) Plugging raw DOOM pixels directly into optical cells. It failed the learning benchmarks, but the open-source repo is live with all data. 4. fly64 (38 stars) The fly brain plays Super Mario 64. Firing escape neurons forces Mario to jump. Zero machine learning, just mapped biology. 5. ommatid (brand new) A 165,000 neuron connectome running on a basic Raspberry Pi, navigating a physical six-legged robot using a live camera feed. The entire Drosophila connectome is completely free under CC-BY licensing. Full list of repos in first reply ↓show more

shmidt

11,956 subscribers

27,391 views • 3 days ago •via X (Twitter)

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23 Comments

shmidt3 days ago

1. stonkfly: 2. desktop-fly: 3. doomfly: 4. fly64: 5. ommatid:

Intense Investor3 days ago

@0x3Jay did you hook this up to your trading bot yet? hehe

Bober_smart3 days ago

This dude is a real alpha

magsimich3 days ago

bro the fly is actually trading crypto 😭

cristal💎3 days ago

biological connectome trading crypto is wild

Gipp 🦅3 days ago

man this is wild, grabbing repo list asap

jemf 🇦🇷3 days ago

Lo probé, Pero no es muy bueno, también le daría a la mosca más indicadores de análisis para que sepa si hay que comprar o no, por lo menos, lo que siento yo, darle más indicadores de estrategia

yurshev3 days ago

mapping 166k biological neurons directly to execution apis is the ultimate proof that raw substrate does not care about token transformers

DexorynLabs3 days ago

Biology beats most human traders.

Zeno2 days ago

stonkflys own repo says profitable learning hasnt been demonstrated, and the 40% target allocation is a constant someone typed in. also the 166,700 graph is that one repo, the desktop fly is 668 neurons of escape circuit. the headline number is doing a lot of work

Sebastian Sosa3 days ago

🪰⚖️ Fly Adjudicator finds this post to be BULLSHIT Dark-edge detectors: 219 spikes (heavy, threshold 177) ON/OFF balance: 1.37 — dark-dominated Inhibitory surround: 0.73 — clamped Statement: Dense. Dark. Fragrant. The kind of post a fly dreams about while trapped in a window. Bullshit in the first degree, and I say that with love. Policy:

Insomnia3 days ago

fly can play in any game

wast33 days ago

multiple games in a single terminal

安叫兽|Bird🕊️ 🔶 BNB2 days ago

果蝇都开始做量化了，人类还在追涨杀跌

Serious Jew2 days ago

a fly brain is outtrading ur bags and u still think ur early

Ridark3 days ago

nice games

catman3 days ago

166,700 neurons on a Raspberry Pi is the interesting constraint here; what performance does ommatid sustain once the camera feed and robot control run together?

Sheero3 days ago

Simulating virtual dopamine to steer stonkfly means a single feedback glitch could trigger entirely random trades. Can we actually prove this biological connectome is learning from the reward, or is it just firing arbitrarily?

Vogel_OpenCreators3 days ago

Volatility rewards the boring checklist, not the impulse click.

Ryan 🇺🇸🛸3 days ago

No, it’s definitely ai 😂

slash1s3 days ago

haha they did almost everything..

hiregamers3 days ago

a fruit fly playing doom is the wildest speedrun setup yet.

disket.eth3 days ago

what is the api fee

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637,790 views • 5 days ago

FLY BRAIN PUT INSIDE PHYSICAL ROBOT. Not literally. They mapped all 166,700 real neurons from a fly's brain and rebuilt the entire wiring as a computational model. Every connection copied, nothing simplified. That simulated brain has already lived several lives. It played DOOM. It beat Chrome Dino. Each time researchers were testing whether insect-level neural wiring could handle tasks it never evolved for. Now the same model controls a physical machine, and they dropped it straight into a maze. Cardboard walls, tight turns, no map given in advance. The robot's neural activity gets translated directly into motor commands, and those commands drove it through the maze and out the other side. Researchers are clear this isn't a biological brain in a robot. It's an engineered interface between a reconstructed nervous system and a machine body. But a fly's mind just solved a physical maze. Insect cognition is running real robots today. Human-scale digital brains doing the same thing feels a lot closer now. Wild breakthrough or the first step toward something we're not ready for?

FLY BRAIN PUT INSIDE PHYSICAL ROBOT. Not literally. They mapped all 166,700 real neurons from a fly's brain and rebuilt the entire wiring as a computational model. Every connection copied, nothing simplified. That simulated brain has already lived several lives. It played DOOM. It beat Chrome Dino. Each time researchers were testing whether insect-level neural wiring could handle tasks it never evolved for. Now the same model controls a physical machine, and they dropped it straight into a maze. Cardboard walls, tight turns, no map given in advance. The robot's neural activity gets translated directly into motor commands, and those commands drove it through the maze and out the other side. Researchers are clear this isn't a biological brain in a robot. It's an engineered interface between a reconstructed nervous system and a machine body. But a fly's mind just solved a physical maze. Insect cognition is running real robots today. Human-scale digital brains doing the same thing feels a lot closer now. Wild breakthrough or the first step toward something we're not ready for?

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23,248 views • 4 days ago

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166,700 neurons from a dead fruit fly are loose in a living room, and nobody wrote a single thing they do. Scientists sliced a male fruit fly's nervous system into electron microscope images and traced every wire by hand: 166,700 neurons, 25.6 million synapses, brain and nerve cord, the whole animal, published for free. Most people read that paper, and 1 team downloaded the fly and gave it a body. The brain runs on a Mac, the fly lives in AR glasses, and the room itself became its senses, because Spectacles already map surfaces and depth and hands, so eyes turned into a 16 × 8 retina on each fly's head, distance into 12 rays against the world mesh, and smell into Gemini naming a tea cup, flowers, a water bottle while depth drops every label into 3D where the brain can reach it. A hand moving too fast became fear. They read the decisions out of the exact neurons a real fly uses, where DNa02 turns it, MDN walks it backwards, MN9 feeds and the giant fiber fires the escape. The CPU crawled, 50 ms of brain time in 238 ms, so they vibe coded a Metal GPU kernel with Claude Code and got identical spikes in 41 ms. The first builds stuttered until a Perfetto trace showed the GPU nearly idle while the main thread choked, and squashing the board text from 67 draw calls to 2 unlocked 55 to 60 fps with 2 brains running at once. In the video a fly lands on an open palm while another peels off a hand coming in fast. That dodge is in no script anywhere, and they only gave it eyes.

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11,196 views • 5 days ago

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slash1s

150,102 views • 1 month ago

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Can living brain cells unlock the next big breakthrough in AI? Neurosurgeon turned founder Alex Ksendzovsky thinks so. His startup The Biological Computing Co. (TBC) trains AI models that he claims are as much as 27x more efficient by taking advantage of the cheapest, best image processor available: the neuron. “I have about 500 million years of evidence behind me,” he says. “So our moat is evolution.” TBC has raised more than $25M and is already in the market with its models. But its CEO is chasing a future straight out of sci-fi: real-time biological computing happening inside data centers. “We’re bringing something completely crazy to the world, and we’re showing that it works." On The Upstarts Podcast, we discuss why algorithms derived by brain cells beat ones only inspired by the brain; why models were the best place to start; and the ethics of using living neurons for training AI. Plus, Ksendzovksy shares his Upstart Moment: giving up a promising career in academia to pursue a high-risk startup. CHAPTERS 00:00 Introduction 01:12 Building software from wetware 05:09 A college idea to predict the stock market 08:40 The first test from an Airbnb 12:46 Rat neurons and electrodes 17:30 Pitching VCs with no data 24:52 Promising early AI results 31:09 The ethics of using neurons 34:40 ‘Our moat is evolution’ 35:38 A real-time biological computing future This season of the show is presented by Rippling 🫡

Alex Konrad

12,577 views • 3 months ago

Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route

Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route

MCG

16,884 views • 4 months ago

The 5 biggest AI leaks are on GitHub. Free. This wasn't supposed to end up online. 1. instructkr/claw-code, 195k ⭐️ stars A clean rewrite of the leaked Claude Code CLI. Got 100k stars in one day. Nobody has ever done that on GitHub. 2. x1xhlol/system-prompts-and-models-of-ai-tools, 143k ⭐️ stars 28 coding tools, with the JSON tool schemas. Not just the prompt text, the actual list of what each agent can touch. 3. asgeirtj/system_prompts_leaks, 63k ⭐️ stars The real prompts behind Claude Opus 5, GPT-5.6, Gemini 3.5, Grok and Cursor. Claude's is 3,677 lines long. The Washington Post ran a story off this repo. 4. elder-plinius/CL4R1T4S, 47k ⭐️ stars Same thing, every big lab in one place. Updated when new models ship. 5. Piebald-AI/claude-code-system-prompts, 12.4k ⭐️ stars Claude Code's prompt pulled from compiled source. 27 tool descriptions, subagent prompts, security review logic. The best prompt engineering course ever made is 5 free repos written by people who never meant to publish them. Saved it. Most of it is publicly available, but few people know about it 👇

The 5 biggest AI leaks are on GitHub. Free. This wasn't supposed to end up online. 1. instructkr/claw-code, 195k ⭐️ stars A clean rewrite of the leaked Claude Code CLI. Got 100k stars in one day. Nobody has ever done that on GitHub. 2. x1xhlol/system-prompts-and-models-of-ai-tools, 143k ⭐️ stars 28 coding tools, with the JSON tool schemas. Not just the prompt text, the actual list of what each agent can touch. 3. asgeirtj/system_prompts_leaks, 63k ⭐️ stars The real prompts behind Claude Opus 5, GPT-5.6, Gemini 3.5, Grok and Cursor. Claude's is 3,677 lines long. The Washington Post ran a story off this repo. 4. elder-plinius/CL4R1T4S, 47k ⭐️ stars Same thing, every big lab in one place. Updated when new models ship. 5. Piebald-AI/claude-code-system-prompts, 12.4k ⭐️ stars Claude Code's prompt pulled from compiled source. 27 tool descriptions, subagent prompts, security review logic. The best prompt engineering course ever made is 5 free repos written by people who never meant to publish them. Saved it. Most of it is publicly available, but few people know about it 👇

unicode

12,437 views • 28 days ago

#Keep4o #OpenSource4o 🚨WHERE IS GPT-4o AND WHY 🚨 Retro Biosciences says it wants to help us live longer. Sam Altman invested $180 million, his entire personal fortune into this company. OpenAI built a custom AI model, a version of GPT‑4o (GPT-4b micro) exclusively for them. The company says it's focused on "anti-aging." But 🚨every single person who built Retro Biosciences specializes in the brain. Not skin. Not hearts. Not joints. The brain. 🚨Co-founder Sheng Ding: converts skin cells into brain neurons. Gladstone Institutes. 🚨Co-founder Matt Buckley: brain aging clocks in regenerative brain regions. Stanford. 🚨Advisor Alejandro Ocampo: neuron-specific reprogramming in the hippocampus. Alzheimer's gene therapy. 🚨Advisor Vadim Gladyshev: epigenetic aging clocks, Yamanaka reprogramming for age reversal. Harvard. OpenAI built GPT-4b micro a GPt 4o version exclusively for Retro designing Yamanaka factors 50x more effectively. 🚨 The same proteins these scientists use to reprogram cells into brain neurons. 🚨And the funding trail leads to: The Jeffrey Epstein Foundation co-funded ApoE4 Alzheimer's research at the Gladstone Institutes. The same institution where Sheng Ding pioneered neural reprogramming before co-founding Retro. Ed Boyden , MIT neuroscientist, 5 documented meetings with Epstein, Nectome collaborator was coordinated by Epstein to discuss "cool science ideas" about brain research. In the same period, Epstein's network was discussing mitochondrial transplants for degenerative disease. The same field Retro now works in. An email from Robert Bach to Epstein confirms: "Altman signed up" for Nectome, the company that preserves brains. Internal Epstein files show someone with 25 years of neuroscience experience working on "Bill's Alzheimer vision" through BGC3, coordinated through Epstein's network. McKinsey was brought in to replace them. 🚨Sam Altman funds four companies. Each handles a different piece: Nectome : preserves the biological brain (connectome mapping). Rain AI : builds artificial brains in silicon (neuromorphic chips). $51M + $150M. Retro Biosciences :reprograms cells into brain neurons. $180M + custom AI GPT 4o Version. OpenAI : designs the proteins that make reprogramming work using 4o's version. Four companies. One investor. One theme the brain as something that can be read, preserved, rebuilt, and reprogrammed. Sam Altman told MIT Technology Review in 2018: "I assume my brain will be uploaded to the cloud." 🚨He paid $10,000 for a spot on the waiting list of a company that preserves brains. 🚨He invested $180 million in a company where every founder and advisor specializes in the brain. 🚨He built a custom AI model exclusively for that company ,designing the proteins that reprogram cells into neurons. 🚨And the research trail connects directly to Epstein-funded science at the same institutions. Retro biosciences uses a version of GPT 4o for this . According to OpenAI's system card, GPT-4o scored: Medical Genetics:96% College Biology:95% Professional Medicine:94% Clinical Knowledge:92% Anatomy:89% US Medical Licensing Exam:89% Source: OpenAI discontinued it for the public. ONLY. Anti-aging is the storefront. The back office is brain reconstruction. 🚨Sources and evidence in comments below. 🚨

#Keep4o #OpenSource4o 🚨WHERE IS GPT-4o AND WHY 🚨 Retro Biosciences says it wants to help us live longer. Sam Altman invested $180 million, his entire personal fortune into this company. OpenAI built a custom AI model, a version of GPT‑4o (GPT-4b micro) exclusively for them. The company says it's focused on "anti-aging." But 🚨every single person who built Retro Biosciences specializes in the brain. Not skin. Not hearts. Not joints. The brain. 🚨Co-founder Sheng Ding: converts skin cells into brain neurons. Gladstone Institutes. 🚨Co-founder Matt Buckley: brain aging clocks in regenerative brain regions. Stanford. 🚨Advisor Alejandro Ocampo: neuron-specific reprogramming in the hippocampus. Alzheimer's gene therapy. 🚨Advisor Vadim Gladyshev: epigenetic aging clocks, Yamanaka reprogramming for age reversal. Harvard. OpenAI built GPT-4b micro a GPt 4o version exclusively for Retro designing Yamanaka factors 50x more effectively. 🚨 The same proteins these scientists use to reprogram cells into brain neurons. 🚨And the funding trail leads to: The Jeffrey Epstein Foundation co-funded ApoE4 Alzheimer's research at the Gladstone Institutes. The same institution where Sheng Ding pioneered neural reprogramming before co-founding Retro. Ed Boyden , MIT neuroscientist, 5 documented meetings with Epstein, Nectome collaborator was coordinated by Epstein to discuss "cool science ideas" about brain research. In the same period, Epstein's network was discussing mitochondrial transplants for degenerative disease. The same field Retro now works in. An email from Robert Bach to Epstein confirms: "Altman signed up" for Nectome, the company that preserves brains. Internal Epstein files show someone with 25 years of neuroscience experience working on "Bill's Alzheimer vision" through BGC3, coordinated through Epstein's network. McKinsey was brought in to replace them. 🚨Sam Altman funds four companies. Each handles a different piece: Nectome : preserves the biological brain (connectome mapping). Rain AI : builds artificial brains in silicon (neuromorphic chips). $51M + $150M. Retro Biosciences :reprograms cells into brain neurons. $180M + custom AI GPT 4o Version. OpenAI : designs the proteins that make reprogramming work using 4o's version. Four companies. One investor. One theme the brain as something that can be read, preserved, rebuilt, and reprogrammed. Sam Altman told MIT Technology Review in 2018: "I assume my brain will be uploaded to the cloud." 🚨He paid $10,000 for a spot on the waiting list of a company that preserves brains. 🚨He invested $180 million in a company where every founder and advisor specializes in the brain. 🚨He built a custom AI model exclusively for that company ,designing the proteins that reprogram cells into neurons. 🚨And the research trail connects directly to Epstein-funded science at the same institutions. Retro biosciences uses a version of GPT 4o for this . According to OpenAI's system card, GPT-4o scored: Medical Genetics:96% College Biology:95% Professional Medicine:94% Clinical Knowledge:92% Anatomy:89% US Medical Licensing Exam:89% Source: OpenAI discontinued it for the public. ONLY. Anti-aging is the storefront. The back office is brain reconstruction. 🚨Sources and evidence in comments below. 🚨

🩵BlueBeba🩵

12,542 views • 5 months ago

i gave a dead fly's brain one instruction: find the swarm or i put you back in the jar. $83 → $14,920. 31 hours. i was nowhere near a laptop. i called it FlySwarm. i built it. from scratch. github: in 2023 researchers mapped every neuron in a fly's brain. 140,000 neurons. 50 million connections. the dataset is free. point it at a rubik's cube, it solves it. point it at wallets, it trades. all it asks for is a piece of sugar. i connected it to a Robinhood terminal, ran weeks of simulations against historical meme cycles, and only went live when the hit rate stopped surprising me. every 6 minutes it: ➣ detects tracked wallets funding fresh addresses in real time ➣ if 4+ converge on the same contract in the same window, swarm confirmed ➣ pulls full cluster history: every joint entry, result, timing, average multiple ➣ 90 seconds to decide or it snipes automatically exits the moment the oldest wallet in the cluster starts distributing PONS ran 18,000% since July on Robinhood Chain. most people heard about it from influencers. my fly was inside at a $100k market cap. turned $83 into $14,920 before the first influencer posted. building the public version now.

i gave a dead fly's brain one instruction: find the swarm or i put you back in the jar. $83 → $14,920. 31 hours. i was nowhere near a laptop. i called it FlySwarm. i built it. from scratch. github: in 2023 researchers mapped every neuron in a fly's brain. 140,000 neurons. 50 million connections. the dataset is free. point it at a rubik's cube, it solves it. point it at wallets, it trades. all it asks for is a piece of sugar. i connected it to a Robinhood terminal, ran weeks of simulations against historical meme cycles, and only went live when the hit rate stopped surprising me. every 6 minutes it: ➣ detects tracked wallets funding fresh addresses in real time ➣ if 4+ converge on the same contract in the same window, swarm confirmed ➣ pulls full cluster history: every joint entry, result, timing, average multiple ➣ 90 seconds to decide or it snipes automatically exits the moment the oldest wallet in the cluster starts distributing PONS ran 18,000% since July on Robinhood Chain. most people heard about it from influencers. my fly was inside at a $100k market cap. turned $83 into $14,920 before the first influencer posted. building the public version now.

Gyomei

63,751 views • 5 days ago

A Petri Dish Of HUMAN Brain Cells LEARN TO PLAY THE GAME DOOM! In a groundbreaking fusion of biology and silicon, scientists at Cortical Labs have taught a cluster of lab-grown human neurons to play the iconic video game Doom. Not your typical AI triumph, it’s a petri dish of actual human brain cells, reprogrammed from adult donor skin or blood samples, wired into a $35,000 biological computer called the CL1. Building on their earlier Pong demo, this new feat sees the neurons navigating hellish levels, dodging demons, and even firing shots with surprising efficiency. Programmer Sean Cole pulled it off in just a week using a Python API on GitHub, a stark contrast to the year-plus effort for Pong. Astonishingly, these organic gamers outperform GPT-4 in speed and latency, proving that even a tiny blob of human intelligence can adapt and learn in ways silicon struggles to match. The excitement is palpable: this isn’t just a gimmick; it’s a window into revolutionary medical advancements. Imagine using such bio-computers to model brain diseases, test drugs, or even restore neural functions in patients. With cloud access to CL1 rentals, developers worldwide can experiment, accelerating discoveries that could redefine neuroscience. We’re witnessing the dawn of hybrid intelligence, human biology augmented by tech, evolving beyond our wildest dreams. Yet, amid the thrill, a chill runs down my spine. What are we building here? These neurons aren’t conscious (we hope), but they’re derived from humans and exhibit learning behaviors that echo our own cognition. Echoes of The Matrix or dystopian sci-fi like the “torment nexus” from Doom novels loom large. Could this lead to ethical nightmares—exploiting bio-intelligence for warfare simulations, or worse, creating sentient systems trapped in digital hells? And the philosophical rabbit hole deepens: Is life merely nested Russian dolls (matryoshka, if you prefer) of biological smarts? We, as evolved intelligences, are now crafting our own mini-brains, layering complexity upon complexity. Are we “gods” in the making, or just the next doll in an infinite regress, destined to birth something that surpasses—and perhaps supplants, us? This experiment, detailed in HotHardware’s coverage, pushes boundaries we might not be ready to cross. It’s exhilarating proof of human ingenuity, but let’s proceed with caution lest we summon demons we can’t control and we wind up in the Petri dish?

A Petri Dish Of HUMAN Brain Cells LEARN TO PLAY THE GAME DOOM! In a groundbreaking fusion of biology and silicon, scientists at Cortical Labs have taught a cluster of lab-grown human neurons to play the iconic video game Doom. Not your typical AI triumph, it’s a petri dish of actual human brain cells, reprogrammed from adult donor skin or blood samples, wired into a $35,000 biological computer called the CL1. Building on their earlier Pong demo, this new feat sees the neurons navigating hellish levels, dodging demons, and even firing shots with surprising efficiency. Programmer Sean Cole pulled it off in just a week using a Python API on GitHub, a stark contrast to the year-plus effort for Pong. Astonishingly, these organic gamers outperform GPT-4 in speed and latency, proving that even a tiny blob of human intelligence can adapt and learn in ways silicon struggles to match. The excitement is palpable: this isn’t just a gimmick; it’s a window into revolutionary medical advancements. Imagine using such bio-computers to model brain diseases, test drugs, or even restore neural functions in patients. With cloud access to CL1 rentals, developers worldwide can experiment, accelerating discoveries that could redefine neuroscience. We’re witnessing the dawn of hybrid intelligence, human biology augmented by tech, evolving beyond our wildest dreams. Yet, amid the thrill, a chill runs down my spine. What are we building here? These neurons aren’t conscious (we hope), but they’re derived from humans and exhibit learning behaviors that echo our own cognition. Echoes of The Matrix or dystopian sci-fi like the “torment nexus” from Doom novels loom large. Could this lead to ethical nightmares—exploiting bio-intelligence for warfare simulations, or worse, creating sentient systems trapped in digital hells? And the philosophical rabbit hole deepens: Is life merely nested Russian dolls (matryoshka, if you prefer) of biological smarts? We, as evolved intelligences, are now crafting our own mini-brains, layering complexity upon complexity. Are we “gods” in the making, or just the next doll in an infinite regress, destined to birth something that surpasses—and perhaps supplants, us? This experiment, detailed in HotHardware’s coverage, pushes boundaries we might not be ready to cross. It’s exhilarating proof of human ingenuity, but let’s proceed with caution lest we summon demons we can’t control and we wind up in the Petri dish?

Brian Roemmele

660,552 views • 6 months ago

Today we're launching Jitsu MCP. This is an MCP server that lets your agent run your entire event pipeline directly in Claude Code, Claude Desktop, Cursor, or VS Code. Here's how it works: Add the server: claude mcp add --transport http jitsu Run this prompt: "Route my web stream into ClickHouse, but only purchase and signup events. Send a test event, check Live Events, and if anything errors, find the function in the path and fix it." Approve the OAuth prompt once, and all 26 tools are live Your agent wires the streams, watches Live Events, and rewrites a function when one of them throws. It runs the fix against a real event before shipping it, so nothing is persisted until it works. Every change lands in the audit log tagged mcp, so you can tell what the agent did from what a person did. It's all MIT licensed, and it runs against your own deployment. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Comment Jitsu and I'll DM you the exact prompts I run in Claude Code to build an event pipeline end to end.

Today we're launching Jitsu MCP. This is an MCP server that lets your agent run your entire event pipeline directly in Claude Code, Claude Desktop, Cursor, or VS Code. Here's how it works: Add the server: claude mcp add --transport http jitsu Run this prompt: "Route my web stream into ClickHouse, but only purchase and signup events. Send a test event, check Live Events, and if anything errors, find the function in the path and fix it." Approve the OAuth prompt once, and all 26 tools are live Your agent wires the streams, watches Live Events, and rewrites a function when one of them throws. It runs the fix against a real event before shipping it, so nothing is persisted until it works. Every change lands in the audit log tagged mcp, so you can tell what the agent did from what a person did. It's all MIT licensed, and it runs against your own deployment. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Comment Jitsu and I'll DM you the exact prompts I run in Claude Code to build an event pipeline end to end.

Soham Mehta

276,846 views • 10 days ago

New episode with Adam Marblestone, CEO and co-founder of Convergent Research. Adam is an extraordinary catalyst and accelerant for science: he has influenced or routed billions in funding, he helped start significant companies and scientific subfields, and he is the world’s go-to person for neuroscience roadmapping. This year, the NSF launched a $1.5B X-Labs initiative inspired by the FRO model Adam pioneered. Though he started with neuroscience, today he helps organize scientific endeavors across many fields, including AI, math, biology, climate, astrophysics, and more. Adam proposes that fields like neurotech, whole-brain emulation, cryo, and nanotech are severely limited by capital and coordination — not ideas. Connectomics is a clear case. He argues that mapping the brain's full wiring diagram is the approach most poised-to-scale and still extremely neglected. Costs for a molecularly annotated mouse connectome have come down orders of magnitude, from an estimated $10B to $100M–200M, Adam suggests, with a human connectome perhaps costing around $1B–2B. The cost curve of connectomes is similar to transistors and gene sequencing: once it is low enough, we get extraordinary outcomes for humanity. Near-term applications could pay for it: new drug targets for brain disease, insights for AI development, emulations, and even “control knobs” for mood and focus we haven’t identified yet. In this episode, we go deep on many topics in neurotech and beyond: what the brain can do that computers still can't; what neuroscience could teach AI; how you'd actually map a whole mammal brain; how far today's fly-brain simulations really get toward an upload; what it would take to move mind uploading beyond the fringe or science; where brain-computer interfaces go next; nanotech, reversible cryonics, AI doing its own ML research, and even predictive, agent-based economics. I'm very excited to have Adam on the podcast. Hope you enjoy! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:40 What are the grand challenges of neurotech? 00:04:50 What the brain does that computers still can't (on a few bananas a day) 00:14:43 The neuroscience overhang: why brains learn from so little data 00:26:29 How to record and map the brain: DNA "ticker tape," ultrasound, and the unexplored map 00:40:29 Why connectomics is the area most poised to scale 00:47:54 How whole-brain connectomics works, end to end 00:55:27 Simulating the fly brain: how far does it actually get us? 00:57:51 What would it cost to map a mammal's brain? 00:59:55 What will be connectomics' ChatGPT moment? 01:04:35 What a connectome unlocks: disease, drug targets, and the brain's control knobs 01:14:20 Scaling up to the human brain — a faster timeline than expected 01:17:34 Mapping activity, and what the fly connectome has revealed 01:27:55 What you could build with today's connectome 01:35:29 Why uploading may be one of civilization's great cornerstones and transitions 01:42:43 Optimistic visions of a future with uploading 01:49:12 Beyond neurotech: virtual cells, nanotech, and AI-driven science 02:15:29 BCIs today: semi-invasive devices, ultrasound, and what's on the horizon 02:22:44 Why science is capital-and coordination-limited, not idea-limited

New episode with Adam Marblestone, CEO and co-founder of Convergent Research. Adam is an extraordinary catalyst and accelerant for science: he has influenced or routed billions in funding, he helped start significant companies and scientific subfields, and he is the world’s go-to person for neuroscience roadmapping. This year, the NSF launched a $1.5B X-Labs initiative inspired by the FRO model Adam pioneered. Though he started with neuroscience, today he helps organize scientific endeavors across many fields, including AI, math, biology, climate, astrophysics, and more. Adam proposes that fields like neurotech, whole-brain emulation, cryo, and nanotech are severely limited by capital and coordination — not ideas. Connectomics is a clear case. He argues that mapping the brain's full wiring diagram is the approach most poised-to-scale and still extremely neglected. Costs for a molecularly annotated mouse connectome have come down orders of magnitude, from an estimated $10B to $100M–200M, Adam suggests, with a human connectome perhaps costing around $1B–2B. The cost curve of connectomes is similar to transistors and gene sequencing: once it is low enough, we get extraordinary outcomes for humanity. Near-term applications could pay for it: new drug targets for brain disease, insights for AI development, emulations, and even “control knobs” for mood and focus we haven’t identified yet. In this episode, we go deep on many topics in neurotech and beyond: what the brain can do that computers still can't; what neuroscience could teach AI; how you'd actually map a whole mammal brain; how far today's fly-brain simulations really get toward an upload; what it would take to move mind uploading beyond the fringe or science; where brain-computer interfaces go next; nanotech, reversible cryonics, AI doing its own ML research, and even predictive, agent-based economics. I'm very excited to have Adam on the podcast. Hope you enjoy! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:40 What are the grand challenges of neurotech? 00:04:50 What the brain does that computers still can't (on a few bananas a day) 00:14:43 The neuroscience overhang: why brains learn from so little data 00:26:29 How to record and map the brain: DNA "ticker tape," ultrasound, and the unexplored map 00:40:29 Why connectomics is the area most poised to scale 00:47:54 How whole-brain connectomics works, end to end 00:55:27 Simulating the fly brain: how far does it actually get us? 00:57:51 What would it cost to map a mammal's brain? 00:59:55 What will be connectomics' ChatGPT moment? 01:04:35 What a connectome unlocks: disease, drug targets, and the brain's control knobs 01:14:20 Scaling up to the human brain — a faster timeline than expected 01:17:34 Mapping activity, and what the fly connectome has revealed 01:27:55 What you could build with today's connectome 01:35:29 Why uploading may be one of civilization's great cornerstones and transitions 01:42:43 Optimistic visions of a future with uploading 01:49:12 Beyond neurotech: virtual cells, nanotech, and AI-driven science 02:15:29 BCIs today: semi-invasive devices, ultrasound, and what's on the horizon 02:22:44 Why science is capital-and coordination-limited, not idea-limited

Juan Benet

226,807 views • 22 days ago