+1, the tokens from before a quantum hack will be transferred to a new fork. So at that moment, the value of BTC will go to 0 and the value of BTC v2 will take the value of BTC.
However at the moment, the community seems to be leaning towards pay-for-quantum-resistance [0]
I'm not so sure this will apply to BTC. It more or less has the staying power that it has because it hasn't changed at all, in fact pretty much everyone involved with it has resisted changing it, and it's not obvious there's anyone with enough political capital to make the fork, because anyone, right now, can make a fork of BTC and none of them have ever done well.
Stock buyback is effectively just a dividend with a different tax implication: reducing the number of shares in circulation raises the ownership stake of the remaining shares
> Stock buyback is effectively just a dividend with a different tax implication
Not just different, it specifically a lower tax rate assuming that the stockholder has held long enough to use the long term capital gains tax rate (which lower than the dividend tax rate).
Stock buyback isn't a total scam as it seems, but it does mean "we can't figure out any productive use case for this cash in advancing R&D or scaling our business anymore" which is still pretty worrying
It’s more like, “the executives with lots of shares can’t see how to make the company grow, so they’ll just use profits to pump up the share price for their gain”.
I deeply feel buybacks shouldn’t be illegal but treated shamefully.
Instead of using profits to build up long term savings or fund R&D, they basically choose to do as little as possible.
> Instead of using profits to build up long term savings or fund R&D, they basically choose to do as little as possible.
Isn't buying stock a form of long term savings? After all, they can always sell that stock when they want to "withdraw".
Sure, they may not get the same return as simply stashing it into a bank account, but it's also a statement of confidence in their future: "We are sure that our stock will outperform every other option there is for storing our money, because our long-term plans include extracting more profit from the market."
This may not be the actual reason in this case, but I think it's good to be aware of: A non-zero chunk of "ai for science" research done at tech companies is basically done for marketing. Even in cases where it's not directly beneficial for the companies products or is unlikely to really lead to anything substantial, it is still good for "prestige"
Local inference. I imagine they have an interest in making this and other cutting edge models small enough to be possible to do quick inference on their desktop machines. The article shows that, with Figure 1E demonstrating inference on an M2 Max 64 GB.
Frankly, it's a great idea. If you are a small pharma company, being able to do quick local inference removes lots of barriers and gatekeeping. You can even afford to do some Bayesian optimization or RL with lab feedback on some generated sequences.
In comparison, running AlphaFold requires significant resources. And IMHO, their usage of multiple alignments is a bit hacky, makes performance worse on proteins without close homologs, and requires tons of preprocessing.
A few years back, ESM from Meta already demonstrated that alignment-free approaches are possible and perform well. AlphaFold has no secret sauce, it's just a seq2seq problem, and many different approaches work well, including attention-free SSMs.
I think people often interpret a bit too much.
Perhaps it’s just some researchers who got enough freedom to run and publish interesting work within apple. For a company like apple it makes sense to have a research lab with considerable freedoms even if protein folding is not a core interest, which is why you see it published but not the formula for the new Corning Gorilla glass…
Will be fascinating to see how the market breaks down in the future, will enough people want a third best model they can run on prem, or will people all be fighting in line for the top models that are a few cents more per token on supercomputers.
To sell computers? 20 years ago, Apple had scientific poster sessions at WWDC and worked to bring PyMol to the Mac. The pictures of proteins you see in the paper were generated with PyMol as are probably >50% of the protein images in scientific papers for the last 15 years.
If Warren Delano (the author of PyMol) were still with us, I think he would be amazed at where we are now with AlphaFold, and all the rest. At least what he hoped for, that software like this would be open source and peer-reviewable, has mostly held true.
They have a much better reputation that most companies. I think they're doing okay compared to google, facebook, oracle, etc. Few people are going to think a corp is "doing good" but reputation does still matter somewhat.
You're confusing your opinion of the company with the perception by the general public. Apple's definitely not perceived as 'an office appliance company' by your average person. It's considered a high-end luxury brand by many[1].
I think their public sales data shows Apple sells mainly to consumers, and mainly iPhones at that.
Like 1980s SONY, they are the top of the line consumer electronics giant of the time. The iPhone is even more successful than the Walkman or Trinitron TVs.
They also sell the most popular laptops,to consumers as well as corporate. Like SONY’s VAIO but more popular again.
Ah, I see where we went wrong here, you never specified that you meant reputation in your mind only. FYI, “reputation” is usually considered to be related to a general public opinion, not your personal one.
To what extent is YC's current strategy a form of "cookie-licking"? I.e. capture a small fraction of every plausible startup created by the next generation of students?
100% at this point. The industry's leading AI for start-up ideas has run out of AI for _____ suggestions. It was pretty obvious they were scraping the bottom of the barrel a month ago when I made this list from their current batch:
Acrely — AI for HVAC administration
Aden — AI for ERP operations
AgentHub — AI for agent simulation and evaluation
Agentin AI — AI for enterprise agents
AgentMail — AI for agent email infrastructure
AlphaWatch AI — AI for financial search
Alter — AI for secure agent workflow access control
Altur — AI for debt collection voice agents
Ambral — AI for account management
Anytrace — AI for support engineering
April — AI for voice executive assistants
AutoComputer — AI for robotic desktop automation
Autosana — AI for mobile QA
Autotab — AI for knowledge work
Avent — AI for industrial commerce
b-12 — AI for chemical intelligence
Bluebirds — AI for outbound targeting
burnt — AI for food supply chain operations
Cactus — AI for smartphone model deployment
Candytrail — AI for sales funnel automation
CareSwift — AI for ambulance operations
Certus AI — AI for restaurant phone lines
Clarm — AI for search and agent building
Clodo — AI for real estate CRMs
Closera — AI for commercial real estate employees
Clueso — AI for instructional content generation
cocreate — AI for video editing
Comena — AI for order automation in distribution
ContextFort — AI for construction drawing reviews
Convexia — AI for pharma drug discovery
Credal.ai — AI for enterprise workflow assistants
CTGT — AI for preventing hallucinations
Cyberdesk — AI for legacy desktop automation
datafruit — AI for DevOps engineering
Daymi — AI for personal clones
DeepAware AI — AI for data center efficiency
Defog.ai — AI for natural-language data queries
Design Arena — AI for design benchmarks
Doe — AI for autonomous private equity workforce
Double – Coding Copilot — AI for coding assistance
EffiGov — AI for local government call centers
Eloquent AI — AI for complex financial workflows
F4 — AI for compliance in engineering drawings
Finto — AI for enterprise accounting
Flai — AI for dealership customer acquisition
Floot — AI for app building
Fluidize — AI for scientific experiments
Flywheel AI — AI for excavator autonomy
Freya — AI for financial services voice agents
Frizzle — AI for teacher grading
Galini — AI guardrails as a service
Gaus — AI for retail investors
Ghostship — AI for UX bug detection
Golpo — AI for video generation from documents
Halluminate — AI for training computer use
HealthKey — AI for clinical trial matching
Hera — AI for motion design
Humoniq — AI for BPO in travel and transport
Hyprnote — AI for enterprise notetaking
Imprezia — AI for ad networks
Induction Labs — AI for computer use automation
iollo — AI for multimodal biological data
Iron Grid — AI for hardware insurance
IronLedger.ai — AI for property accounting
Janet AI — AI for project management (AI-native Jira)
Kernel — AI for web agent browsing infrastructure
Kestroll — AI for media asset management
Keystone — AI for software engineering
Knowlify — AI for explainer video creation
Kyber — AI for regulatory notice drafting
Lanesurf — AI for freight booking voice automation
Lantern — AI for Postgres application development
Lark — AI for billing operations
Latent — AI for medical language models
Lemma — AI for consumer brand insights
Linkana — AI for supplier onboarding reviews
Liva AI — AI for video and voice data labeling
Locata — AI for healthcare referral management
Lopus AI — AI for deal intelligence
Lotas — AI for data science IDEs
Louiza Labs — AI for synthetic biology data
Luminai — AI for business process automation
Magnetic — AI for tax preparation
MangoDesk — AI for evaluation data
Maven Bio — AI for BioPharma insights
Meteor — AI for web browsing (AI-native browser)
Mimos — AI for regulated firm visibility in search
Minimal AI — AI for e-commerce customer support
Mobile Operator — AI for mobile QA
Mohi — AI for workflow clarity
Monarcha — AI for GIS platforms
moonrepo — AI for developer workflow tooling
Motives — AI for consumer research
Nautilus — AI for car wash optimization
NOSO LABS — AI for field technician support
Nottelabs — AI for enterprise web agents
Novaflow — AI for biology lab analytics
Nozomio — AI for contextual coding agents
Oki — AI for company intelligence
Okibi — AI for agent building
Omnara — AI for agent command centers
OnDeck AI — AI for video analysis
Onyx — AI for generative platform development
Opennote — AI for note-based tutoring
Opslane — AI for ETL data pipelines
Orange Slice — AI for sales lead generation
Outlit — AI for quoting and proposals
Outrove — AI for Salesforce
Pally — AI for relationship management
Paloma — AI for billing CRMs
Parachute — AI for clinical evaluation and deployment
PARES AI — AI for commercial real estate brokers
People.ai — AI for enterprise growth insights
Perspectives Health — AI for clinic EMRs
Pharmie AI — AI for pharmacy technicians
Phases — AI for clinical trial automation
Pingo AI — AI for language learning companions
Pleom — AI for conversational interaction
Qualify.bot — AI for commercial lending phone agents
Reacher — AI for creator collaboration marketing
Ridecell — AI for fleet operations
Risely AI — AI for campus administration
Risotto — AI for IT helpdesk automation
Riverbank Security — AI for offensive security
Saphira AI — AI for certification automation
Sendbird — AI for omnichannel agents
Sentinel — AI for on-call engineering
Serafis — AI for institutional investor knowledge graphs
Sigmantic AI — AI for HDL design
Sira — AI for HR management of hourly teams
Socratix AI — AI for fraud and risk teams
Solva — AI for insurance
Spotlight Realty — AI for real estate brokerage
StackAI — AI for low-code agent platforms
stagewise — AI for frontend coding agents
Stellon Labs — AI for edge device models
Stockline — AI for food wholesaler ERP
Stormy AI — AI for influencer marketing
Synthetic Society — AI for simulating real users
SynthioLabs — AI for medical expertise in pharma
Tailor — AI for retail ERP automation
Tecto AI — AI for governance of AI employees
Tesora — AI for procurement analysis
Trace — AI for workflow automation
TraceRoot.AI — AI for automated bug fixing
truthsystems — AI for regulated governance layers
Uplift AI — AI for underserved voice languages
Veles — AI for dynamic sales pricing
Veritus Agent — AI for loan servicing and collections
Verne Robotics — AI for robotic arms
VoiceOS — AI for voice interviews
VoxOps AI — AI for regulated industry calls
Vulcan Technologies — AI for regulatory drafting
Waydev — AI for engineering leadership insights
Wayline — AI for property management voice automation
Wedge — AI for healthcare trust layers
Workflow86 — AI for workflow automation
ZeroEval — AI for agent evaluation and optimization
Please don't spam the threads with massive lists. It's enough to link to the information.
I'm not sure what this is supposed to show, other than that YC funds a lot of startups and a lot of them are working in AI, which is what you'd expect during any major tech wave.
In fact, YC funds so many startups that your list is actually misleadingly short, unless you meant to argue that AI startups are a low percentage.
You should create a startup based around an AI for collapsing lists if clicking [-] is too difficult and they are so annoying.
110 of those were from their summer 2025 batch of 170, so your statement is actually misleading unless you meant to argue that 65% is a low percentage.
Tecto AI is my favourite, as it forms the "AI for AI" ouroboros. Outrove also sounds confusing, because doesn't Salesforce already purport to have their own native AI features? And when I look them up, it seems they're just AI for recruiting, which is also what VoiceOS purports to do.
Looking up the founders is fascinating too. So many of them seem to have graduated and immediately started trailing what appear to be a string of back-to-back failed businesses, rarely with more than a year of staying power per attempt. It's hard to tell if that's "failing quickly" (desirable) or "frantically trying to get-rich-quick" (undesirable and what I run into in Melbourne most often).
I'm actually the CEO of a startup that created the very first AI for AI for _____ list generation service. Already in our 347,985th round of funding, currently valued at $382,457,203.
I'm guessing they were looking for preferential delivery to certain cell types, and AAVs just happened to have best profile for those. If anything, LNPs might aggregate in the liver even more than AAVs, which can lead to even worse hepatotoxicity if an immune response happens.
Lipid nanoparticle toxicity has long been an industry concern.
In a profile of Moderna back in 2016, Katalin Karikó (instrumental in the development of mRNA vaccines) mentioned this issue:
“I would say that mRNA is better suited for diseases where treatment for short duration is sufficiently curative, so the toxicities caused by delivery materials are less likely to occur” [1]
The issue was not the gene therapy itself, but the delivery mechanism. They used a virus to administer the gene therapy, and this virus (like most bloodstream impurities) aggregates in the liver. At low doses this is fine, but at high doses, your body's immune response will be laser-focused on the liver, and you die from the side effects of this response.
if it's so obvious that this is going to produce these side effects, then why on earth did they gamble ?
(because, it definitely look like gambling, like "investors are behind us right now, so we have the money to do it, so let's do it before money runs out")
Yes, dialysis is surprisingly good at filtering out viral particles, but... that's not desirable in this case. After all these viruses are carrying the therapeutic payload, if you filter them out then you might as well not introduce them in the first place.
Ok, I was thinking more of injecting viruses upstream, and filtering them out downstream (preventing them from entering the liver in the first place). Maybe you could even recycle them.
I suppose it's possible at that point, possibly to try and stem the process. The question is just how rapidly this condition emerges, and I suspect (although this is just a suspicion) that the time between onset and a severe reaction is fairly brief. Mostly though the problem is that this is a really complex, whole immune system reaction that's triggered by the AAV in the liver, but simply removing the intial cause probably wouldn't stop the cascade.
I took a look at some of the aftermath reports (i.e. https://pmc.ncbi.nlm.nih.gov/articles/PMC10638066/ and some others) which get into specific details about the course of treatment in several patients who died from this complication. The through-line is an aggressive use of several immune suppressing and modulating therapies to calm the cascade.
I have to admit I can't find any specific discussion about dialysis in that context, so I can only assume that removal of the viral particles would be a case of closing the barn door after the horse escaped.
My bad, snark wasn't my intent. I meant it literally. There was an accumulation of toxins, and it seems (to me at least) extremely unlikely that the researchers were not aware of dialysis as a way to remove toxins. So then let's jump to the next level of question.
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