> Moving forward, where an original image is not present or available, the Company will ensure that website users are informed that antibody images may have been optimized for presentation and clarity on the website.
wut. Bro if you don't have an original valiation image then the answer is not to say "oh we'll make sure we communicate that we're making up a random image" - it's to say you don't have the damn image. It's validation data wtf. It's not a pretty background image it's validation data if you don't have the data wtf are you "optimizing for presentation?" This faq is unreal - pure CYA except by someone who doesn't seem to know what they're trying to cover. If you've got cut and pasted/rotated bands that's just fake data. Not "optimized for presentation."
Yes labs should and usually do always validate new antibodies as well. It's a waste of time and taxpayer money for them to spend their time on bad antibodies they purchased based on fake validation data. And just fundamentally - don't make up validation data. If it's not there it's not there. What are you optimizing for presentation if there's no original!? What does that say about the rest of your process?
What's wrong with the cloud? I get the point about a hype cycle but those two examples don't seem even remotely in the same universe of similarity. SQL is still around, but the cloud won pretty comprehensively no? If you're starting something new, there's still a debate around what's the best database to use and stuff, but I'm struggling to imagine a content where the idea of managing your own hardware would even make sense as something to consider outside of the context of an existing large company with pre-existing on prem infra? And at least in the web world, 90+% of the time you are just going to go and click some buttons in vercel and posthog and whatever and that's your infra. The generic saas webapps that make up a huge chunk of the new projects that would be making architecture decisions where you might theoretically consider on prem are pretty well covered by the point and click abstractions - most of them probably wouldn't exist with the friction and overhead of having to actually manage bare metal
It's not just about returns, it's also about risk. The role of a passive index fund is to be a passive index fund. If the s&p starts chasing returns, that will reduce its utility to the market. You get higher returns by being compensated for risks that passive investors/retirement funds don't want to take. And active investors use the S&P and similar indexes for the specific risks and asset class exposures they provide. You might think the economy is going to do poorly which would be good news for some company that's anti-correlated with the economy but you need to hedge that bet for if the economy does well, so in addition to buying shares of whatever that company is you buy into some market reflectiong mix of stocks bonds etc. The role of that hedge is to have a counterbalancing asset that moves opposite your primary bet to reduce volatility, and the role of the s&p 500 is to broadly reflect the american large cap publicly traded stock component of the market. If the S&P 500 begins behaving unpredictability to chase returns as an index then buying funds that track that index is no longer doing what you need it to do. S&P index loses utility, active investors just use some other index, but passive investors with 401ks locked in to tracking the s&p are suddenly forced to buy whatever bet the index creator is making en masse driving the stock price up. That's not a good outcome for anyone except the company muscling their way in and anyone that was somehow rewarded by that addition
The post training is meant to make it more steerable (usually). I might not want it to write tests. I might not have a dev environment set up for an agent to run tests in it's loop. A major goal in post training is to make it follow instructions, which doesn't have to mean have particular instincts for code organization
Why is that hard to believe? It's literally the prompt telling it what to do - if you want a poem about watermelons you tell it to write a poem about watermelons, if you want tests you tell it to write tests. It's not like TDD is some universal pattern that every llm will naturally optimize towards
this model is whack. Exclamation marks everywhere, sycophantic - not producing working code on prompts the other models handle fine.
"The reason it is echoing back your messages is because gpt-5.4-nano is a fictional model name!"
"Everything is in perfect order! Let's-Go-ready for the next phase, which will connect this durable infrastructure to the user-facing UI!"
It's like they RLed it on thumbs up and downs on ai overview responses and forgot to make it not be a sycophantic echo chamber machine. And like, the thing it built doesn't work because it's not actually in perfect order, but it doesn't seem to be able to figure out what's wrong because everything is clearly remarkably engineered
That's a list of like 6 things. And each of those less complicated a question then the seven thousand questions people throw at you when you complain about something not working right on a Linux distro or about speeding up build times for a new tool or configuring webpack or like pretty much any software tool. What lint rules are you using are you using poetry or uv are you running on Mac windows linux or wsl how are your security groups configured in aws - some tools are more plug and play but it's quite the stretch to say that asking "how is your code organized, do you have your agents.md config file set up, do you have tests, and how large is the codebase" is some sort of unmanageable list of questions for a software engineer to think through when figuring out wtf is going on with some new tooling they're using
My take is there was one big inflection point around opus 4.5 when they got the agentic stuff working and now whether or not it works depends on whether your use case/area of software engineering is profitable enough for the companies to have spent a bunch of money generating synthetic data to RL on, or if it's similar enough to areas that they've done that for. With similar enough being a very loose constraint given how much overlap there is in a lot of coding fundamentals. Tbh if the models aren't working for you now I don't think they're gonna be working for you in 6 months
It's very real but probably very domain specific. It got really good at a lot of traditional web dev stuff, bash, sql, and writing one off scripts to accomplish random tasks (hence all the agent stuff taking off). And they got good at staying on task. That may not translate to game dev because from what I understand a lot of these gains are basically around post training methods driven by synthetic data generation etc (with potential caveats on how synthetic that data actually is lol). I wouldn't be surprised if the areas of code the llms are good at now are straight up just product decisions of where to allocate budget for generating those synthetic data sets, and game dev stuff might not be at the top of the list because the customer base for that might not be as big
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