It bugs me how there can be an acknowledged live incident happening today with Actions, and yet down the page where it shows last 30d uptime for actions, today's little rectangle is a chipper green.
Easy: it isn't down, it is just "experiencing degraded performance" which impacts "a limited set of customers". It only counts as downtime when nobody can access any part of it!
My great hope is that we will look back on this as a tiny sliver of time, when everyone couldn't resist the awesome power that came with running our devtools over the internet through megacorps, willingly handing over our code and secrets and dollars, in exchange for those sweet lines of code.
But then in the waning months of 2026, some inspired enthusiasts figured out how to run a model called Qwen3.8-Flash-Next on modest consumer hardware. It was comparable to Opus-4.6, and the first such model that could run efficiently on a GPU with just 16GB VRAM. This was the breaking point.
Next came Qwen4-Flash, which ran with similar efficiency but significantly higher capability, comparable to Opus-4.8. Going into 2027, ternary bonsai builds, n-gram offloading, and hybrid diffusion models brought even more capable models to modest consumer hardware.
By early 2028, TPUs and unified memory became standard in Apple and Lenovo laptop computers, and the vast majority of software development was now done with LLMs running locally on users' devices.
In the 2030s came the humanoid robots. By the end of the decade, for a cool $10k, one could have a trusted personal assistant that would cook and clean around the house, walk the dog, play with the children, even go to the gym for you. All running with onboard TPUs and local models.
We have models right now such as Qwopus, which are Qwen with open weights and using distilled Claude responses in it's training data. Since AI can't be copywritten, distillation is the way forward to getting rid of these proprietary models.
Qwopus is a family of open-weight, reasoning-enhanced large language models developed by community fine-tuner Jackrong, designed to run efficiently on consumer hardware. The models are built on Alibaba’s Qwen3.5 or Qwen3.6 base architectures and are fine-tuned using Trace Inversion to distill the step-by-step reasoning chains of Anthropic’s Claude Opus (versions 4.6 and 4.7).[1]
Yeah, it is very suspicious. Each time they update their ranking algorithm, the closed models from US labs jump up and any previously high ranking open model jumps down. It happened with MiMo, Kimi, GLM. All were right there amongst the very top, and each time that happened, AA followed quickly with an index update that shunted them dramatically lower.
Check out vals.ai for an alternative. Not sure how independent they really are either, but it's at least good for a cross reference.
This could be another point informing your prior as to how much benchmaxxing and distilling of closed models is being performed by the open weights publishers as well.
My 8yo kid was spending a lot of time with the kindle fire on youtube. I spied on his watch history. Was full of videos about theory of relativity, big bang, history of physics, quantum entanglement; and then a good bit of 20th century American history, and learning to program in Lua tutorials.
My 7yo kid, on the other hand, was watching hours' worth of mindless youtubers playing various forms of scripted hide and seek, and that we had to quash.
"Screen time" can cover an extremely wide range of spectrum from turning you into a lobotomized zombie -- to filling your head with all sorts of understanding and curiosity and wonderment. It is what you make of it.
Well, not that one, because that one is incorrect.
Adoption of coding agents is motivated by understanding and productivity. That's not traditionally a religious aspiration.
In software engineering, and in so many other settings too, LLMs can act as a strong catalyst, or strong damper for curiosity and critical thinking. The same way that when you used to find a thread on StackOverflow about the exact problem you were facing, you could either copy/paste the solution and forge ahead, or else use those breadcrumbs to research and more deeply understand the problem and its prospective solutions -- you can drive the model to the same effect.
If you are looking to think critically, it can be an absolutely fantastic partner in that endeavor. If you are looking to slap together a solution for a problem you don't care to understand, it can do that too. How you want to use it is up to you. And how other people use it can and will be everywhere across that spectrum.
> If you are looking to think critically, it can be an absolutely fantastic partner in that endeavor.
Joining “thinking critically” and a tool that has no truth basis is concerning. You may as well put numbers inside a raffle box and just take one out instead of using a calculator.
There’s no definition of correct that LLM tooling enforces, so how can it be a partner in thinking?
That’s the main issue with slop coding. It would be way more useful to send pseudocode out if you can. Logical semantic is always independent of programming syntax. No need to generate code.
> Joining “thinking critically” and a tool that has no truth basis is concerning. You may as well put numbers inside a raffle box and just take one out instead of using a calculator.
LLMs can have as much truth basis as humans have. We humans have fallible memory, all manner of cognitive biases, varying degrees of deceptive tendencies, misunderstandings, heartfelt beliefs in things we cannot prove...
And nonetheless, looking at art, reading books, talking to children, interacting with nature -- can all prompt us to question our previous assumptions and productively challenge ideas we might have otherwise thought settled. Seeing these different perspectives, however grounded or not they may be in truth and physics, is part of critical thinking.
A calculator will only get you so far. If you're looking for LLMs to tell you the "correct" answer, then you may be disappointed. But if you want leads to follow, ideas to consider, pointers to existing research, experiments to run, then it can be very helpful, similarly to talking to a very knowledgeable colleague. In the end, so far at least, it's still up to the human to make good decisions, based on evidence.
Prices for everything are all in flux at this moment. Consumer cards like RTX 5090 are bonkers. Even 3090s have like doubled in the last six months. Meanwhile, prices for renting this class of hardware on vast.ai are spiking too, with rates increasing like 50% in September alone.
It's hard to know if we're at the beginning or the end of this trend. If manufacturing continues to focus on high end datacenter gpus at $40k a pop, and we continue to have supply constraints, this could last a while.
On the other hand, if Chinese models continue to nip at the tails of the frontier, cheaper by 20x, running on new Chinese chips, the whole bottom could fall out of the US market, and things could get very weird.
Yeah, you need both a smaller system prompt than OpenCode uses, and a larger context configured in llama.cpp. It's probably running into compaction cycles before it even gets to start thinking about the task at hand.
You'd have better luck with pi instead of OpenCode, and see how far you can get that context window higher. For any halfway actual work you need bare minimum 64k, ideally 128k+. You can use 8bit kv cache, despite whatever folklore you may hear otherwise.
Well, this is much better than nothing -- we should be grateful that whatever the market conditions and corporate incentives, a major lab is releasing a capable open-weight model. But still very far behind DeepSeek and GLM. Even on consumer hardware, Qwen 3.8 27B is going to compare favorably to this.
True, but the conditions are quite relaxed, so it can even be used commercially without worries by small businesses, while it is unconditionally free for non-commercial uses.
Other big open-weights LLMs have similar licensing terms and I believe that they are quite fair, i.e. at the point when someone uses such an LLM to obtain tens of million $ of revenue it becomes fair to demand a share of that for the creator of the model.
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