HN user
https://dandaka.com/
Few cases I have found very useful myself
1/ Using GUI software. My agents are using headful Google Chrome and Figma. It helps a lot to have separate environment, which is not interfering my main machine.
2/ Running long processes (1h+), so I can leave main machine closed.
3/ Running intensive processes. I use Gemma, Whisper and Qwen, which could burn main machine CPU and resources.
Not at all, we love them all with Chinese labs. And wish them to continue competing and not winning. That is how we get best models, lower prices and better availability.
do they have a website? I have found only paper PDF and it seems more general than SWE
What is considered SOTA for SWE benchmarks now?
calculating the exact discount you get using a subscription could be difficult
Why can't you derive this discount from experiments? Example of such research
Can I connect it to my skills/tools? Example case, I have a knowledge base and event log in my company. I need a brainstorm companion, which will have full access to this knowledge, can converse about it and can invoke skills/tools available in the repo.
Another important benchmark would be — cost per benchmark task using subscription tokens. Since most of us are using subscriptions and cost per token there is quite different from API costs.
Naming is worrisome!
Codex, Claude Code, ZAI — they continue work in headless mode, when you close your laptop, if you have connected to remote machine
What is your top chart? I am using GUI harnesses from model providers and not happy with any of them.
Every country is moving in that direction
Imagine you only know how to cook (use fry pan skill) and know how to cook omelette (recipe). You get the task to cook doner kebab. How many Wikipedia pages do you need to read to get a good understanding? I guess its max 5.
I think grounding your abstract problem to an example makes it more trivial, than it sounds in general.
How would it know about Wikipedia and when to use it?
2 general concepts "You have to get good understanding of subject area before you do actions" + "Wikipedia is a good source of knowledge of subject areas" will get a model there.
spawning a baby human, have it spend an (instant) life learning
Humans spend 99% of their life on boring repeating tasks, not learning anything, just navigating on heuristics.
Model can use tools to get that knowledge. In your example, read Wikipedia page about table tennis. Imagine a reasoning engine with a big enough context, that knows nothing. A path built from first principles to understand "table tennis spin" — does not look very long for me.
Take mathematics as an example. Humanity has found math notation, which allowed to express math rules — distill them to the core. Before math was expressed in prose — a very inefficient way, very similar to current LLMs.
In my school, math teacher was giving me prose, which I was converting to math notation. I could argue, that this prose→reasoning conversion is not required at training, and can be obtained at inference time with search tools.
I think this is a well known concept, which we can't deliver yet. LLM/transformer give us reasoning engine as a byproduct of its design, but it is quite ineffective. If we can distill reasoning, if reasoning can be achieved without general knowledge, it will be a very effective machine.
Some amount of knowledge is required for reasoning. Maybe such model can dynamically knowledge domains to have taxonomy. For example, model can't effective reason about development task, if it has no knowledge about development best practices. But population of New York or recipies can definitely be loaded run time with tools.
"Free AI" will not fight existing knowledge or strong opinions. It will spread to empty spaces. Example with blood test — there is no podcaster there and will not be.
AI will not help by improving extremely smart people. AI will help dumb people and dumb processes with "free" expert-level intelligence. Anyone could still ignore intelligence and make ignorant decisions. But the default mode would be highly intelligent informed decision.
Example with health - a patient can read blood test results with Opus and get very good results for "free". This is far away from helping extremely smart people, still it improves society from the ground up.
How fast is it going to expand to other markets? Asking for a friend obviously
Going from crypto to fiat and back is an extremely monitored and regulated route. It might be an easy way to settle between counterparties, but a difficult one to launder.
I imagine the best distribution model like this. We split all tickets in 2 buckets 50/50:
1/ Sorted. Some buyers have priority. They can be sorted by price paid, by amount of minutes listened, depends on the sale.
2/ Random with KYC. Everyone has the same chance to purchase.
You are cooking them wrong. You absolutely must ask the model to do grounding work — search online, search in your files, cross-check with different agents. They are universal reasoning engines, not a fact recalling tool.
Check my 'deep research' skill, you will get the idea
https://github.com/dandaka/skills/blob/main/deep-research/SK...
It is called 'jagged intelligence'. A lot progress was made in the last 2 years. Most notably reasoning models, tools use, harness progress. It takes time to build the skill to make those models useful, but they do provide a lot of value.
In my experience, current agentic workflows are so slow, that for many cases it only makes sense to run them in parallel. So a lot of context switching. If we could have 10-100× faster token generation, we could have task delivery at the speed of human review.
Not sure I follow your point.
The fact that humankind grew from 5M to 8.3B, while dramatically improving longevity and quality of life speaks volumes. Multiply life quality × population × life duration, not only "misery and destruction" is not the case, but you could rather see powers of positive technology influence.
Do we have a list of open problems? Would love to see a chart, where AI solves such problems one by one in the upcoming years.
10000 years ago we had 10-15% deaths from violence (skeletal evidence). As well as infections, child mortality, starvation and injuries.
Benefits of civilization eliminated most of that + increased quality of life dramatically.
We can reach a different situation
1/ No one knows how even small components work, because their inner working mechanism is too hard to understand by human mind
2/ The whole society is run (in intelligence sense) by alien minds
The goal is not to record activities of others, it is to have full context of operator activity. More like a personal knowledge base, that camera pointing to a cop.
why not? if context stays on device and operator is in full control, what downsides are there? there is no observer here, only operator and full context of his activity at his fingers
Next generation of OS should have constant video and audio recognition by on device LLM. This will provide valuable context for a lot of scenarios. So instead of frequent copy-pasting we are used to, we can let agents access context of our whole workflows from different apps.
But Google is a very ill positioned candidate for such OS. I would rather trust Apple and local-first on-device models.