TrenchGod

Said by the world’s loneliest person

When i will make it there’a gonna be signs

3 more years of Circus and people are treating $Downald like a daily runner. Its here to do the most retarded shit you have never thought about 😅

@BtcPuppet Lots of Trump coins went to $100m+ but the actual one $Downald is gonna topple them all

10hrs Later

$100m run starts now $PUNCH

$5M for $TripleT next. 🙏

@LexaproTrader Who thought his fall will be a sight to watch

So at first glance Zazu is the umpteenth iteration of Zazu and since I kind of fade dogs and cats in general I almost missed this - BUT luckily a friend tugged on my shirt sleeve and forced me hear him out / check out some content - and I was sold quickly. It’s actually really fucking smart and potent and a masterclass in how to meme (coin) lol So there’s two things that elevated this to S-tier for me - -First, Mog has been a central part of the push and he’s like the meme Mozart. Theres a genuine passion that defines every last detail. And I can see that with Zazu. If you take some time and look over the socials/ content you’ll realize this is the dev/ team that can uplift and define this brand into something bigger than it’s ever been. -Second, the catchphrase/ narrative of “this is the Zazu that will break the curse” is brilliant. Obviously Zazu needs to break the curse and ascend to its rightful lofty heights. But also this ethos applies to Bonk. It also applies to crypto as a whole. Binance? Coinbase? Most traders in our space? It’s the collective ethos most are living right now. So in a way Zazu is on its hero’s journey and it’s cathartic to be a part of. So ya: Dope af dev/ team/ artists/ holders Insanely top tier content/ videos/ memes etc. This is the first and last Zazu I’ve bid and it feels like the right move. When you feel it you feel it. 10/10 best Zazu of all time.

@slingoorio No

Legend can i have 5 sol ?

Yup @pxblocito it really did something funny 😂

@paul_conyngham @curiouswavefn Hey @paul_conyngham have you seen this?

@curiouswavefn this is it.

My take on the whole "AI cures cancer in dog in Australia". It's a very interesting story, but perhaps not for the reasons that are being noted. In 2007, Freeman Dyson published an essay in The New York Review of Books called “Our Biotech Future.” It contains one of the most memorable predictions about the future of biology I’ve ever read. “I predict that the domestication of biotechnology will dominate our lives during the next fifty years at least as much as the domestication of computers has dominated our lives during the previous fifty years.” Dyson believed biology would eventually follow the trajectory of computing. At first, powerful tools live inside large institutions - universities, government labs, major companies. Over time those tools get cheaper, easier to use, and more widely distributed. Eventually individuals start doing things that once required entire organizations. “Biotechnology will become small and domesticated rather than big and centralized.” He even imagined genome design becoming something almost artistic: “Designing genomes will be a personal thing, a new art form as creative as painting or sculpture.” Dyson's words rang in my mind as I read the "AI cures dog cancer" story. Much of the coverage framed this as an example of AI discovering new science. But that’s not really the interesting part of the story. The scientific pipeline involved here is actually well known. It closely mirrors the workflow used in personalized neoantigen vaccine research that has been under active development for years. The steps are fairly standard: sequence the tumor, identify somatic mutations, predict which mutated peptides might be recognized by the immune system, encode those sequences in an mRNA construct, and deliver them to stimulate an immune response. The biological targets themselves were almost certainly not new discoveries (I have been unable to find out what they are, but mutations in targets like KIT which are common might be involved). Partly therein lies the rub, since the hardest part of drug discovery, whether in humans or dogs, is target validation, the lack of which leads to lack of efficacy - the #1 reason for drug failure. In neoantigen vaccines, the proteins involved are usually ordinary cellular proteins that happen to contain tumor-specific mutations. AlphaFold which was used to map the mutations on to specific protein structures is now a standard part of drug discovery pipelines. The challenge is identifying which mutated peptides might plausibly trigger immunity. What is interesting though is how the pipeline was assembled. Normally, this type of workflow spans multiple domains - genomics, bioinformatics, immunology, and translational medicine - and in institutional settings those pieces are distributed across specialized teams, document sources and legal and technical barriers. Navigating the literature, selecting computational tools, interpreting sequencing results, and designing a candidate mRNA construct is typically a collaborative process. In this case, AI appears to have helped compress that process, pulling together data and tools from different sources. Instead of requiring multiple experts, a motivated individual was able to assemble the workflow with AI acting as a kind of guide through the technical landscape. I’ve seen something similar in my own work while building lead-optimization pipelines in drug discovery. The underlying science hasn’t changed, but the friction involved in assembling the workflow can drop dramatically. Tasks that once required stitching together multiple tools, papers, and areas of expertise can now often be executed much faster with AI helping navigate the terrain; and by faster I mean roughly 100x. That kind of workflow compression is powerful, to say the least. When the cost of navigating technical knowledge drops, more people can realistically assemble sophisticated research pipelines. This story is a great example of what naively seems like a boring quantitative acceleration of the research process. In that sense, therefore, the real novelty here is not the biology but the combination of three things: a non-specialist orchestrating a complex biomedical pipeline, AI acting as a navigational layer across multiple technical domains, and the resulting decentralization of capabilities that were once confined to institutional research environments. But I think the story also points to something deeper, which is a challenge to modern regulatory environments. Modern biomedical innovation does not operate solely according to what is scientifically possible. It is structured by regulatory frameworks - clinical trials, safety oversight, institutional review boards, and regulatory agencies. Those systems exist for important reasons, but they also assume that the development of therapies occurs primarily within large, regulated organizations. When individuals begin assembling pieces of these pipelines outside those institutions, the relationship between technological capability and regulatory oversight starts to shift. The dog in this story sits outside the human regulatory framework. That fact alone made the experiment possible. In other words, the story is not just about technological capability; it is also about how certain forms of experimentation can occur when they bypass the regulatory pathways that normally govern biomedical innovation. One is reminded of another Australian, Barry Marshall, who received a Nobel for demonstrating through self-experimentation that ulcers are caused by bacteria. This raises an interesting question: what happens when the tools for assembling sophisticated biological workflows become widely accessible while the regulatory structures governing them remain institution-centric? That tension may ultimately be the most important implication of this moment. Regulatory frameworks will need to adapt to this kind of citizen science. Seen in this light, the story about the AI-assisted vaccine is less about a breakthrough in cancer therapy and more about a glimpse of the early stages of something Dyson anticipated nearly two decades ago: the domestication of biotechnology. If AI continues to reduce the cognitive overhead required to navigate biological knowledge and assemble complex pipelines, the boundary between professional research and motivated individuals may begin to blur. That shift will require careful thinking about safety, governance, and responsibility. But it also carries an exciting possibility. Dyson imagined a world in which biological design might eventually become something like a creative craft practiced not only by institutions but also by curious individuals experimenting at smaller scales. For a long time that vision felt distant. Now, it feels like we may be seeing the first hints of it.

Maxxing is the term of the year, decade and century 2.5m mc Severely underpriced

@comparemcap $500M ? I will sell it all at $30m maxxx

marketcap $MAXXING 🔗 comparemarketcap.com/coin/maxxing

$Income could be lexapro’s comeback arc

#BNB season?

@BtcPuppet Dump Dump Sahur

The fuck is going on ?

@BtcPuppet My quant 🙏🏻

As Predicted the WTDD belongs to #BNB while $Catfu stays with Solana

Next time they talk about community just tell them to shut the fuck Up

Bro 💔

@BtcPuppet $TripleT

All you need is a 1% of a Billion Dollars Meme