Longevity InTime: Autonomous AI Institute. Anti-Aging Digital Health Immortality Transhumanist AI Channel

🟢 CureForge AI joins the NVIDIA Inception Program We’ve been accepted into NVIDIA Inception — joining the global program that backs AI-native companies building on NVIDIA’s accelerated computing stack. CureForge AI is the first end-to-end autonomous research federation built for one mission: solve biological mortality. 36 institutes across 8 tiers, coordinated by an autonomous AI layer with non-negotiable human-in-the-loop oversight on every consequential decision. Pharma’s productivity has collapsed under Eroom’s Law — costs doubling roughly every nine years per approved drug. The single-target, single-lab model is structurally broken. Our answer isn’t another lab. It’s a federation that simulates, hypothesizes, audits, and iterates across every relevant field of life science in parallel — at compute scale. We’re at architecture-validation stage today. NVIDIA Inception gives us deeper access to the compute substrate this mission requires: BioNeMo, CUDA-X, NIM microservices, and the full accelerated-computing stack our agents already run on. The federation is the differentiator. The compute makes it possible. #NVIDIAInception #CureForgeAI #LongevityInTime #AgenticAI #Longevity

🟢 CureForge AI joins the NVIDIA Inception Program We’ve been accepted into NVIDIA Inception — joining the global program that backs AI-native companies building on NVIDIA’s accelerated computing stack. CureForge AI is the first end-to-end autonomous research federation built for one mission: solve biological mortality. 36 institutes across 8 tiers, coordinated by an autonomous AI layer with non-negotiable human-in-the-loop oversight on every consequential decision. Pharma’s productivity has collapsed under Eroom’s Law — costs doubling roughly every nine years per approved drug. The single-target, single-lab model is structurally broken. Our answer isn’t another lab. It’s a federation that simulates, hypothesizes, audits, and iterates across every relevant field of life science in parallel — at compute scale. We’re at architecture-validation stage today. NVIDIA Inception gives us deeper access to the compute substrate this mission requires: BioNeMo, CUDA-X, NIM microservices, and the full accelerated-computing stack our agents already run on. The federation is the differentiator. The compute makes it possible. #NVIDIAInception #CureForgeAI #LongevityInTime #AgenticAI #Longevity

Is 'rescuing failed drugs with AI' a category now? Just two days ago, Biossil, a Toronto startup, came out of stealth with $43M co-led by Peter Thiel’s Founders Fund and OpenAI — and their thesis is a bit different from what most AI biopharma companies are doing. Instead of designing new molecules, they use AI to dig through late-stage clinical failures and figure out which patient subgroups those drugs should have actually been tested on. Ten molecules acquired quietly over three years, trials running in everything from glioblastoma to Alzheimer's. There's a well-known strategy in pharma called drug repurposing. Basically, taking an approved drug and finding it a new indication, like thalidomide going from its original (disastrous) use to multiple myeloma, or metformin being studied in cancer. Biossil is doing something more subtle: same molecule, same disease, just a more precisely defined subset of patients. The argument is that many drugs "failed" trials only in the aggregate, averaged across a heterogeneous population where a real signal got buried. They're not alone in this territory, however… For instance, Lantern Pharma Inc. (Nasdaq: LTRN), a biotech company focused on oncology, has been working a related playbook for years — using their RADR AI platform to identify abandoned clinical-stage drugs and match them to the patient subgroups most likely to respond. Different technical approach, narrower therapeutic focus, but a similar underlying bet. They're also further along in generating clinical data, which makes them worth watching as a reference point for whether the thesis holds up. NOETIK — trains AI models on massive datasets of paired pathology images and spatial transcriptomics to find hidden biological subtypes among trial participants and predict which patients will respond. BPGbio, Inc. — uses Bayesian causal AI to do post-hoc subgroup analysis on failed trials. In one Phase Ib oncology trial, their models identified a subgroup with a distinct metabolic phenotype that showed significantly stronger responses. The list goes on... but the difference is, of course, in the details. Image credit: Biossil

NaFM offers one option for such validation: the model reads the molecule along with the organism and the genetic route of its assembly. For age-related diseases, this is the early stage of the search: selecting molecules worth synthesizing and testing in cells, tissues, and animals. The next test for NaFM is straightforward: third-party labs take open-label compounds, run their natural molecules, and see if the computational advantage translates into compounds that work experimentally.

NaFM trains AI to search for drug candidates among natural molecules by associating the chemical scaffold with the organism and genes that could have assembled it. On April 29, Nature Machine Intelligence published a paper on NaFM—a basic model for small natural molecules from microbes, plants, and animals. The authors are testing the idea that it is useful to read a molecule along with its biological origin, because living organisms assemble such compounds through genetic and enzymatic pathways. The testing is currently computational: molecule classification, activity prediction, and computer selection of candidates. In the paper by Yuheng Ding, Bo Qiang, Shaoning Li, and colleagues, NaFM is trained on natural compounds—small molecules produced by bacteria, fungi, plants, and animals. Drugs have already been developed from such molecules: penicillin came from mold, taxol from yew, and rapamycin from a soil bacterium. AI-based search for natural chemistry has already become a platform race in its own right. Brightseed's Forager maintains a database of 11 million natural compounds and has already translated two discovered molecules into short human studies. NaFM takes a different approach: it teaches the model to read a molecule along with its biological origin. A typical molecular model views a compound as a diagram: atoms are dots, chemical bonds are lines. NaFM adds to this diagram the source of the molecule: which organism might have produced it and which gene regions or protein families might have been involved in its assembly. In microbes, the instructions for assembling many natural molecules are often located close together in the DNA. These regions are called biosynthetic gene clusters: the genes encode proteins, the proteins participate in the assembly of the chemical scaffold, and the scaffold helps understand the class of the molecule and possible target proteins. The genomes of organisms themselves are also becoming raw material for drug discovery: the America's Living Library Act proposes turning plants, fungi, animals, and microbes from US national parks into a public genomic database for AI-powered drug discovery. NaFM demonstrates why this layer is needed within the model: the connection between the organism, the assembly route, and the final chemical structure is important. NaFM separately takes into account the molecule's chemical backbone and side groups, which alter activity and selectivity. During training, the model compared similar molecules and reconstructed hidden parts of the molecular structure. The authors deliberately obscured an entire fragment of the backbone so that the model learned the overall structure without relying on guessing based on adjacent bonds. Verification remains computer-based for now. In the task of classifying natural molecules, NaFM yielded the best average result across different numbers of examples per class. With four examples per class, its result was 70.10 versus 69.17 for the closest method; with 64 examples, it was 91.75 versus 91.07. In predicting biological activity, the authors compared the average error: the lower the error, the more accurate the prediction. NaFM was the best for seven of the eight target proteins, including PTP1B, acetylcholinesterase, COX-1, and COX-2. On HIV-1 reverse transcriptase, it was slightly outperformed by the N-Gram method: 1.0606 versus 1.0802 for NaFM. The margin of error is this: the authors demonstrated a computational advantage, but laboratory and clinical tests of the candidates from this study have not yet been published. The NaFM-Official code is open source, the data is hosted on Figshare, and the model weights are hosted on Zenodo. Other groups can repeat the calculations and validate the model on their own sets of molecules. In 2023, a review in Nature Reviews Drug Discovery described the same problem more broadly: AI can discover hidden diversity in natural molecules when the field has good data and rigorous algorithm validation.

OpenProtein.AI is trying to take protein design out of the hands of niche AI ​​teams and directly into the hands of everyday biologists via a browser and free access for academia. On April 17, MIT News published a story about OpenProtein.AI, a company founded by Tristan Bepler and Tim Lu. The company is building a web platform where biologists can run tools for selecting new proteins and testing their properties without programming. MIT specifically emphasizes two things: access via a no-code interface and free use for academic researchers. There are already many models at the intersection of AI and biology. The bottleneck now is different: who can use them in everyday lab work. Protein design—that is, selecting new protein versions for a given task—still too often remains the preserve of teams with their own machine learning engineers, computational resources, and the people capable of putting it all together. The rest are left to read papers and look at pretty graphs. OpenProtein is selling the way out of this trap as a convenient way in. Upload the data, select a task, get a library of variants, mutation and structure predictions, and if you want, you can go deeper through programmatic access. This is changing the promise itself. Previously, protein AI was often sold as the magic of the model. Here, it's sold as a workspace for biologists. On the product page, the company explicitly states that PoET is available free for academic use, and the documentation includes code-free scripts, tools for working with code, and a list of models where in-house and external solutions are side by side. The emphasis is not only on model quality but also on making it an everyday tool, not a rare feature of a few leading labs. Open access shouldn't be confused with complete openness. OpenProtein talks about an open ecosystem, but it's building a service that's still accessed through a single company. In their documentation, they list open and closed models side by side. This isn't a world where any university can deploy the entire stack on its own. It's a softer dependency: not on its own infrastructure, but on someone else's convenient one. The next debate at the intersection of AI and biology concerns access. It boils down to the question of whether biology acceleration tools will remain confined to a few large players, or whether they will truly be brought to mainstream labs. If this doesn't happen, the entire conversation about accelerating science risks becoming a mere window dressing instead of a mass-scale research machine. OpenProtein has a real scientific foundation: the PoET model is described in a paper for the NeurIPS 2023 conference on machine learning. For now, this is primarily an infrastructural shift. There are efforts to package AI for proteins for a much wider range of labs that need to more quickly understand which proteins are worth bringing to real experiments.

Remember the "Second Heart" implant from Cyberpunk 2077 that resurrects the player after death? We haven't reached that level yet, but reality is getting pretty close, just less spectacularly and much more quietly. In the US, a woman was implanted with a defibrillator due to Brugada syndrome, a rare genetic disorder in which the heart can suddenly enter a life-threatening arrhythmia. Sometimes without any symptoms. A person can simply go about their day and suddenly find themselves on the brink of cardiac arrest. The implant itself is located inside the body and constantly monitors the heart's rhythm. If a dangerous malfunction occurs, the device automatically delivers an electrical shock and restores normal rhythm. This isn't resurrection after death, but rather preventing the very point of no return. In this story, it's not just the device itself that's important, but how it developed. The disease was long considered predominantly male, making it more difficult to diagnose in other patients. The doctor underwent special training to install a more appropriate device, rather than a standard solution. This is no longer just cookie-cutter medicine, but the adaptation of technology to the individual. And here's where things get interesting. Today, such implants protect against sudden death. Over time, such systems could become not only a therapy but also an enhancement—protection from overload, automatic stabilization, and perhaps even the expansion of the body's capabilities.

Do you believe in this? Tempus AI just launched an AI that predicts 123 cancer biomarkers from a single image. No repeat biopsy. No waiting weeks. Results in 5 minutes. When cancer tissue samples run out, patients face weeks of delays waiting for repeat biopsies - or worse, miss out on critical treatment information entirely. It's called "quantity not sufficient" (QNS). The biopsy doesn't give enough tissue for full molecular testing. So patients wait and the treatment gets delayed. Tempus just solved that with Paige Predict - an AI that analyzes standard tissue slides and predicts which biomarkers are present. Here’s how it works: ▶ 1. Predicts 123 biomarkers across 16 cancer types The AI predicts biomarkers in lung, prostate, breast, and other cancers. Doctors then prioritize which confirmatory tests to run first - maximizing results before tissue runs out. ▶ 2. Results in approximately 5 minutes Traditional molecular profiling takes days or weeks. Paige Predict delivers predictions in about 5 minutes - automatically included in the clinical report. ▶ 3. Built on 200,000+ patient datasets Trained using data from over 200,000 de-identified patient cases. Validated across multiple diverse datasets to ensure accuracy. Since the launch, Tempus has cut tissue waste by 18% and reduced QNS failures by 15%. That means 15% fewer patients waiting weeks for repeat biopsies or missing out on molecular testing entirely. After 25 years in healthtech, I know the best innovations solve daily clinical problems. A cancer patient shouldn't wait weeks for answers when tissue runs out. This solves that. Would you trust AI predictions to guide your cancer testing if it meant getting answers faster?

“I saw my mother off on a plane - she was visiting us for three weeks in San Francisco. I really wish I could rewind time, or at least slow it down. But physics says that reversing time is practically impossible, even in toy models with all their chaos, and here we have all of life with all its diversity. And biology says that aging, that is, time, is inexorable - it will grind everything and everyone down with its "positive entropy production." Life tries to combat this with hypercontrol through "ontogeny repeats phylogeny." Reversing time won't work, nor will fighting aging, but at least the next generation will learn from the mistakes of the previous ones and document it in their DNA. So we live in endless cycles of suffering and learning. And in the middle, our life is between two infinities. All my life, I've wanted, if not to reverse time, then at least to slow it down. It took six years of therapy to learn to sleep at night, not think about death and not worry about how little time there is. Well, how do you learn? Gradually blur these thoughts, so that the fear of time's irreversibility gives you a little space to try to do something about the inevitable - aging and death. Mom waves after security and heads to the gate, and before her eyes is my father, who waved just like that in his ridiculous fur hat and blue scarf at the Yaroslavsky Station eight years ago. My father has been gone for almost three years, but the feeling of nausea from the irreversibility of time never goes away. "Have you ever considered leaving aging behind? And what made you stay?" my colleague asks me. And what should I tell her? Where can I go from it, if it will never leave me and is slowly sharpening its claws? Hug your loved ones from me - it's never clear how much time you have left.” Andrey Tarkhov