Yeah, medical computer vision is a (fascinating) field with a lot of ongoing research. SOTA models are highly specialized, and are only getting good enough to be used by actual doctors and patients. Using a general purpose LLM to do this is similar to giving a credit card to Openclaw and telling it to make you rich through the stock market & cryptos.
Doesn't track with mine. I've been stuck with Sonnet 4.6 with one of the clients I work for. It writes code fine, but it's not nearly as good as the more recent models for everything else. It's fairly common for it to suddenly go off the rails for no good reason, so I can't really trust it with agentic loops.
It's also not very good at diagnosing non-trivial issues. It's not uncommon for it to suggest whole lists of irrelevant / nonsensical reasons for something not working. Then I copy/paste the code and some context into chatgpt and it hones in onto the correct issue right away, even with inferior tooling.
Having to lug 2 phones around has always seemed like more trouble than it's worth to me. I also don't like having multiple devices to do stuff that a single one could do, for environmental reasons, but that's not a very wide-spread opinion.
So I do have work stuff on my personal phone, but with no notifications whatsoever. Only works because I'm in a position where it's acceptable to require all communications to go through emails or messaging apps though.
Can't speak for 26, but a year ago I worked on a project that migrated from v5 to 11 because of improved image segmentation capabilities. My understanding is that the newer versions don't necessarily have better precision/recall, but they tend to be faster for equivalent results, and have increased capabilities.
Had a very similar experience. Opus went "look, t-sne shows your features are neatly clustered" (it didn't) and left it at that. Fable didn't fully explore the problem/data, but it did go much further, implementing models to check for correlations and adjust feature clusters. Opus was able to finish the job after Fable was cut, but required much prodding (doing exactly what you described: pointing it towards things that look off and asking it, are you sure that's all there is to this?).
Useful context for this is that token usage keeps rising at an exponential pace. I mean, we don't have numbers for the big labs, but Openrouter's numbers are quite telling (can't post link because corporate decided to block all "non-validated AI tools"), and I think they're probably representative of the global trend. +500% year to date, +50% over the month of May alone. It's unsurprising that providers are struggling to find and pay for the compute.
> In Bitcoin you don't generate cash, you earn block rewards for acting as a consensus broker which otherwise would require a central banking settlement layer. This activity, tied directly to the transaction layer, acts to maintain the equilibrium between increases in goods and services and expansion of the money supply.
Block rewards have no connection to transaction volume or economic activity, the protocol is designed such that bitcoin supply increases at a predictable (and diminishing) rate. Bitcoin is deflationary by design, which is one of the major issues that stopped it from becoming anything other than a speculative store of value.
> at it's core (pun intented) Dark matter is something to make equations fit without any other thought behind it or whether there might be several things behind it or god forbid that we juddge the equations themselves
Another way to interpret dark matter is that we can observe something using several different ways, but all those ways use gravity. When trying to observe this something using electromagnetism, we see nothing. It doesn't seem so crazy then to hypothesize that this something only interacts with gravity, and not electromagnetism.
Not GP, but BMAD has several interview techniques in its brainstorming skill. You can invoke it with /bmad-brainstorming, briefly explain the topic you want to explore, then when it asks you to if you want to select a technique, pick something like "question storming". I've had positive experience with this (with Opus 4.7).
As a data architect I dislike the term NoSQL and often recommend that my coworkers not use it in technical discussions, as it is too vague. Document, key-value and graph DBs are usually considered NoSQL, but they have fairly different use cases (and I'd argue that search DBs like Elastic / OpenSearch are in their own category as well).
To me write scaling is the main current advantage of KV and document DBs. They can generally do schema evolution fairly easily, but nowadays so can many SQL DBs, with semi-structured column types. Also, you need to keep in mind that KV and document DBs are (mostly) non-relational. The more relational your data, the less likely you are to actually benefit from using those DBs over a relational, SQL DB.
> Certainly can’t compete with using VS Code or TeXstudio locally, collaborating through GitHub, and getting AI assistance from Claude Code or Codex.
I have a phd in economics. Most researchers in that field have never even heard of any of those tools. Maybe LaTeX, but few actually use it. I was one of very few people in my department using Zotero to manage my bibliography, most did that manually.
Money is never backed by nothing, or it's worthless. It may not be backed by anything physical, but it's always backed by some form of trust. National currencies are backed by trust in the corresponding government and institutions.
Reserves matter even if reserve ratios are zero. If Bank A lends too much money, then when its customers spend that money, a lot of it will end up deposited at other banks. These banks will then ask Bank A for reserves (as in, central bank money) to clear the inter-bank transfers, which Bank A will need to borrow from the central bank, at a cost.
This probably won't make you feel any better, but banks don't really loan out money that's not theirs. When they lend money, they literally create it out of thin air. Creating that money has a cost, which is what ultimately limits how much they can lend, and having more deposits can lower that cost somewhat, but there's no direct connection between the money you deposit in your account and the money that the bank lends to someone else.
If I were to do this (and I might give it a try, this is quite an interesting case), I would try to run a detection model on the image, to find bounding boxes for the planets and their associated text. Even a small model running on CPU should be able to do this relatively quickly.
On the professional side, they also often let you interact with their experts and architects directly, as part of your support contract. With most other companies, you either have to go through front-office support exclusively, or pay extra for Professional Services.