I still believe a correct framing on LLM usage is that they can get you 80% of the way upfront, but that last 20% starts to become impossible to overcome. The proper way to use an LLM is to do the difficult manual 20% upfront. Once you have some decent structure, decent API surface area, decent data structures laid out, decent base libraries. Then you can largely prompt your way through the last 80%. While the first 20% which you did manually, intentionally and fully comprehend. That 20% essentially becomes part of your development harness, almost a prompt itself. Where you can steer its overall architecture, or data design, or other critical pieces by keeping a grip on that first manually done 20%. Something real concerning with this. Knowing what to do in that first 20% requires advanced knowledge of the problem domain you are going to deal with. It is a staff or principal level engineer domain. Which most simply can't do. Which means most people are going to run the gauntlet heavily relying on prompts, and are going to cover some incredibly impressive ground very quickly. Because 80% of the way is quite a long ways there. It looks like it could almost be done. But then they will get stuck in that last 20% and will never be able to make it perfectly polished. Nor perfectly sable. Nor remove all the odd quirks. Nor overcome the last pieces of performance or memory issues. It will basically hit a plateau where they lose control of it. Or it becomes unwieldy to take it in a new complex direction, or add some novel complex functionality to it. It will be very hard to tell from the outside if any given company or project is aimed at getting stuck in that last 20%. As they could appear to be kicking some major butt in that first 80%. But then it will reach a plateau where they can no longer do anything substantial without breaking something else, or seem unable to fix every little quirk or issue. A lot of companies will probably get stuck here. With dozens of very impressive things 80% of the way, but seem unable to get over the last final 20% to really perfect it and polish it.

Yeah, "capitalism" is just a sort of minimal framework for societal coercion reduction, given human nature as a prior. Socialism is an extremely heavy handed and ultimately violence-requiring way to deny or re-form human nature in some very ill-fitting mold.

People keep asking me to push for one Mayoral candidate or the other. Trying to decide the best move. Guys...it's over. LA had its chance for a candidate of change back in June, and instead...they voted for continued destruction. Both candidates will make LA worse. I feel like Hank in that late episode of Breaking Bad...he's already shot to hell, Walt is desperately bargaining with the Nazis to spare his life in exchange for all the money he has. He begs Hank to tell them to take the deal. Hank just looks at him and says "you're the smartest guy I ever met. And you're too stupid to see...he made up his mind 10 minutes ago." I hate to break it to you, but the fate of LA is already baked into the cake 3 months ago. There's no more wheeling and dealing, no clever ballot trick, no October surprise that's gonna get you out of it. Your chance was in June, and a lot of you just stayed home. Or voted for Alan Miller. I told you to get ten of your friends to vote, and you didn't do it. Elections have consequences, so I hope you all now appreciate that, and operate with a greater sense of urgency with these things. Vote for governor, you still have a shot. Vote for voter ID. Vote down all the new taxes. But for Mayor, it's too late.

The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field. I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so. First, I don’t see any step up in the risk of human extinction from AI compared to a few months ago. The theories about this remain the same fantastical, science fiction scenarios as a few months ago. The biggest change in AI risk is its cybersecurity capabilities — a topic which we should take seriously — but this, too, will not lead to the end of the world. The most notable recent event leading to increased fear was when an OpenAI team deployed an agent swarm that hacked into Hugging Face. Much of the popular press contained significant hype. For example, some publications reported that a swarm of 1,200 agents carried out the attack. While this was technically accurate, as I write this, I have about 1,300 processes running on my laptop. Yes, the ability to get large swarms of agents to work in parallel on a task is a significant technical advance, And, in computing, many processes run at the same time. So this shouldn’t be seen as some magical capability. Additionally, OpenAI’s buggy sandboxing and monitoring processes were key to enabling this incident. Fixing these bugs and putting in place improved monitoring would be appropriate fixes, not pausing AI. There are many well known ways to attack software systems. The main advantage of AI agents is that they are relentless. They will tirelessly try many tactics — and have the patience to chain vulnerabilities together — that previously would have taken an infeasible amount of human effort. But in the long term, I believe the advantage will lie with defenders (because they have more information with which to identify bugs, which they can fix), but the cyber-threat landscape has changed significantly. There are still bottlenecks to identifying and exploiting a vulnerability. AI agents still have to try a lot of things to see what works, and taking these actions takes time and might be detected by defenders. This is why, even though it is now easy to obtain versions of leading open weight models that have had their guardrails removed or weakened, so they will not refuse to try to execute cyber attacks, the world has not ended. I am also concerned about the anthropomorphization of AI in a lot of reporting, where LLMs and agents are unnecessarily treated as if they were people. If I wield a hammer, miss a nail, and accidentally dent the wall, it’s not the fault of the hammer. The problem lies in how I used the hammer. Similarly, if I prompt an agent and it hacks into someone else’s system, the responsibility lies with me, not the agent. Of course, we want to build systems that are as safe and predictable as possible. (For example, an unsafe hammer would be one whose head randomly flies off under normal use.) Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use. One new element in the forecasts of AI-enabled doom is AI companies disclaiming responsibility for their own products. “I didn’t do it; my out-of-control agent did!” There’s a balance to be struck between the responsibility of the tool maker and the tool user, but when something goes wrong, let’s hold the people building and/or using the hammer responsible, rather than the hammer. (By the way, if you’re worried about AI bioweapon risk, David Bellamy has a great post on why this, too, is overhyped. Briefly, the bottleneck in building a bioweapon is not intelligence, but lab work and manufacturing.) Pausing AI progress will create much more harm than benefit. First, our adversaries will certainly not slow down. Second, engineering requires discovering problems empirically so we can fix them. If we pause AI by a decade, we will also delay finding and implementing safety engineering fixes by about the same duration. Of course, the incentive to stoke fears — for regulatory capture, to garner attention, or to make one’s technology seem more powerful — remains the same as before. Disclaiming responsibility is a new one. Taking a hard technical look at the actual risks however, I see little factual basis for the degree of fear that’s been stoked up. We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building. [Original text (with links): ]

I was very strongly influenced by rationalism/LessWrong/EA growing up. This is why I don’t emphasize claims that it’s all just a cynical ploy for power. I know what it feels like to sincerely believe these things. And that’s much worse. By and large, I think these people really believe that not just the world but the entire lightcone is at stake, and the only hope for a good outcome is if they totally control AI development, which would require global governance and a surveillance state. And since the stakes are ~infinite, any means are justified in achieving this. A cynical greedy person can be negotiated with and bought off. A true believer can’t. They’re much more dangerous, ironically for the same reason they say AGI will be dangerous: monomaniacal focus on a single goal leads via instrumental convergence to unconstrained power-seeking.

Ayn Rand predicted this Bernie Sanders, Ro Khanna, and most of hollywood (each worth 10s to 100s of millions) will rabidly insist that Elon is evil simply because he is a billionaire Yet they are living in one of their 3-4 beautiful mansions and he is living in an airstream so he can work on building superintelligence and employing people

People who want to appear moral are attracted to the left. People who want to appear rational are attracted to the right. Effective Altruism lets you appear rational and moral - the ultimate ego trip.

near used to be an L1 when they started off. they're now an onchain exchange... everything you can do on coinbase/binance, you can do on near dot com. they just route to the best venue/product incredible product execution.

Soon, LA will just be a ghost town with a 100K illegal street vendors cooking rat meat in front of abandoned brick and mortar restaurants. The Jay Luchs window signs fading in the sun, soon to be kindling for a zombie meth cook fire that burns down half the block as Nithya rides her bike down the new $200M bike lane.

I've attempted to map everything in oncology in a public website and open source repo for all. The site is trying to get all cancers, products, technologies, bottlenecks, people, startup opportunities with 1000+ ideas for upgrading the field. If you have a loved one with cancer and you are technical or can engineer, go take a look, file improvement requests or bugs or help with the open repo and make this the best open and free info resource for individuals, researchers and educational use. This should save people time, aid AI oncology projects and generate positive action. If you are not technical just complain in this thread about broken or annoying or things you want and I'll fix them live. Some of the interesting pages: Treatments: A gallery of the molecules being used And targets: The technologies in oncology: 1100 ideas for helping oncology: Bottlenecks on oncology: Open questions (LETS GO RESEARCH PEOPLE) Startup requests (LETS GO STARTUP PEOPLE) Mechanics of cancer: Battlefronts: Isotope supply: Key papers: Pipeline funnels: Cancer by type: Institutional rankings: The startups: Heros and heroines : Key medical people: Here is the project roadmap: Models and data sets: There are other views as well, take a browse. Try making a PR if you have an upgrade to this on the repo here: If you are biologically/medically minded and something is wrong, file a bug as well or say on the thread and we will get it fixed live. If this is a useful project star the repo and help get it calibrated. I've tried to add some other languages but I cannot speak them so tell me if that doesnt work well. I believe we will crack oncology and having total information dominance is key to the problem. Let the feedback flow!

@matthuang Point two is akin to saying that shareholders shouldn’t control a corporation. It anoints trusted third parties off-chain using pleasant-sounding words like “community.” It creates sybil attacks and unending politics. Blockchains are markets, not democracies or aristocracies.

I recently wrote in frustration that labs *don't* face liability if they screw up and that's one of the biggest problems Slowdown or not, we need to address the path of culpability for when AI agents do illegal things