We have benchmarks for our use cases, and every generation after Gemini 2.0 Flash has been a grim hit on price/performance. Costs have gone up, throughput has gone down, and performance has improved very slightly (and regressed on a few things).
As a professional programmer entering the final third of an enjoyable career, I would now place "learning to code" in the same category as "making a living as a poet." As in, it's truly enjoyable art and some people appreciate it, but you'd better plan for a day job.
Senior people who already know how to code are doing OKish for now, from the data I've seen, but the job is increasingly babysitting models like they were junior contributors.
Frontier models like Fable are mostly useful if you want to paste in one or two prompts, and receive a subtly broken application that looks impressive. That is very hard to do with local models today.
What current local models work fine for is delegating clearly-described tasks in a code base the programmer actually understands. Qwen3.6 27B and DeepSeek V4 Flash are both great little workhorses.
There's also GLM 5.2, which is kind of like "store brand Opus", and which might be considered a "near-frontier" model. I don't have as much experience with it.
> The same change could be affected by e.g. schools and businesses agreeing to open at 8am instead of 9am.
School starts at 8am everywhere that I know of in northern New England and always has? Does school start at 9am where you live?
And as noted, an 8am start to the working day is long established in certain parts of Vermont and New Hampshire. It has not been "widely unpopular." It's nice to get a few minutes of sunlight and twilight after work in winter.
Yeah, as someone who lives in Vermont, you could talk me into permanent DST. That would move the winter sunset from, say, 4:21pm to 5:21pm, which would mean I'd get enough twilight for a short walk after work. And Maine is even further east and north in the same time zone, so they have an even earlier sunset. On the other hand, Vermont's standard time sunrise around 7:20 is reasonable enough.
Parts of Vermont have traditionally coped with this by having an 8-4 workday instead of 9-5.
But the reality is that Vermont gets only about an hour of daylight outside working hours, depending on local customs. People have extremely strong preferences about how that hour gets split up.
> I saw what I forgot immediately; but soon after, with engagement, I saw how quickly I was able to remember.
We actually have pretty good models for how long it takes to forget things. It's the same basic math that powers Anki. To oversimplify, if you force yourself to remember something right before you would have otherwise forgetten it, you will remember it roughly 2.5 times as long before forgetting it again. (This changes at both the shortest time intervals and the longer ones, so treat it as a rough rule of thumb, not an exact formula.)
But this provides a handy bound! If you've been doing something professionally for 20 years, you should expect to remember it for another 50. At which point you're likely well into old-age, and memory performance may decrease for other reasons.
Where AI kills you is actually at the other end: initial learning. You are much less likely to need to recall something after 1 day, 2.5 days, 6.25 days, etc. And thanks to the lack of the "testing effect", memory formation will be much weaker.
In other words, I would naively expect AI to make long-used skills a bit rusty, but to drastically impede formation of new skills and knowledge.
> It's best not to blame the students. They are good at optimizing metrics; that's how they ended up here in the first place.
As an alumnus of Dartmouth College's CS program, I am sad to hear that my alma mater has sunken so low. Look, I know that the Committee on Students was historically bad at handling CS plagiarism cases back in the 90s (compared to ones in the humanities). But Dartmouth's historic solution to this sort of pernicious "optimization" was to reduce the expected value of cheating by imposing extreme negative consequences on anyone they caught, with a 3-term suspension and a permanent transcript notification for a first offense.
Allowing widespread cheating and LLM regurgitation will destroy a school's reputation with graduate schools and employers, and rightfully so.
> The penalty for cheating should be automatic expulsion.
Historically, the penalty for cheating at Dartmouth was a 9-month suspension for a first offense (no matter how small, in theory), and permanent "separation from the college" for a second offense. Back in the 90s, there were multiple incidents where this wasn't properly applied to the CS department because the academic committees in charge of punishment were bad at evaluating plagiarism of source code.
Dartmouth certainly should blame the students. Their policy is historically clear on that point. It is the responsibility of professors to use their syllabus to clearly define "cheating" for a particular course, and it is the responsibility of the students not to cheat. The only case where this should be even slightly complicated is if the professor hasn't been sufficiently about what constitutes cheating (and there was one major historic scandal related to this).
I certainly agree that any degree which allows rampant cheating will quickly become a joke to employers.
> May be it is that we are ingenious amd creative with tools and thats how we evolve.
And every time you use the AI to be ingenious or creative, that will be added to the training data. Then someday the AI can be ingenious and creative without you! (It might take a few more breakthroughs. But investors will literally spend trillions chasing those breakthroughs.)
The endgame here is to replace all human intelligence and labor with machines that are smarter and work cheaper. But who controls the machines?
A GPU with 24GBs of RAM is mostly useful for running a very carefully squeezed Qwen3.6 27B (4-bit Unsloth quants, 8-bit K/V cache, possibly MTP, 128k context). This is a fun little model that's smart enough to do debugging, refactoring, and implementing "clean" specs that don't force it to make complicated design choices. I've seen it rip through a 9-year-old Terraform AWS config, and (without using the network) correctly identify nearly everything that would need to be upgraded or migrated for modern AWS. But if I give it some poorly conceived spec with lurking design headaches, then it goes on an endless thinking binge and ultimately fails.
Speed-wise, I don't have numbers, but it feels subjectively faster than Opus in Claude Code. YMMV.
Once you go above "a used 3090 at a decentish price", then I strongly recommend renting cloud GPUs or at least testing models using paid APIs. This allows testing your use case before spending piles of money.
> If so, the thinking trace can be sort of nonsensical for a reader, though whether this is an idiosyncrasy of the model or a property of LLMs in general isn't clear to me yet.
This is a small step in the direction of something called "neuralese", where the model has stopped thinking in English and is thinking in internal vector spaces. Since this gets serialized through text, it isn't quite true neuralese, but it's moving in that direction.
I mean, I'm sympathetic towards the models. My internal thought process when writing code uses lots of intermediate steps that would be hard to write out in English.
The difference is that a compiler is a rigorous, (nearly) determinisic, heavily tested artrifact built by expert humans. I have only encountered genuine code generation bugs in compilers twice in my career. And yes, those bugs I did trace to the assembly.
An LLM prompt, even a huge one, is an incredibly vague document that leaves out most of the edge cases. And even Fable 5 happily ignores clear instructions in its prompt.
Now, to be fair, I absolutely expect the buggy slop to win, and to drive out the people that either write their own code or at least read the output. This will, in turn, make customers less willing to spend money on software after they get burnt a few times by buggy garbage. I think this is pretty much inevitable once Fable returns. It's just too damn good at long time horizon tasks, generating far more mostly sorta working code than any human could reasonably read.
> Telling people “you must read all the code generated by an LLM” is definitely meaningful—but it is not at all moderate (so most people won’t do it).
I am honestly heartbroken to live in a world where reading the code is seen as an unreasonable ask by either students or by professional working programmers.
Claude's writing style is at least as distinctive as any human's personal style. It has a long list of favorite words, verbal tics and common structures. On top of that, LLM writing is often bad in a very particular way: it's weak on actual things to say, but with an overheated style.
Some days, I spend over 4 hours a day reading walls of text written by Claude. If I couldn't recognize Claude's default "voice" by now, something would be wrong. It would be like a Hemingway fan not being able to recognize Hemingway. Except more so, because Claude's writing style is getting worse from version to version, descending into self parody.
On the statistical side, Pangram's model identifies AI-authored text with a 1-in-5,000 false positive rate, measured against hold-out texts from before 2022. My "ear" also agrees closely with Pangram. If I think something sounds AI written, Pangram virtually always comes back with "AI, confidence: high."
I live out in the countryside. If I run into someone in the road, I will nod my head, maybe introduce myself, and maybe chat, if the other person is interested. (To be fair, I know about 80% of the people I see in the road.) This is normal behavior. Sometimes, two cars will pass each other and stop to talk.
I have also lived in the city. If a stranger wants to talk to me in the city, either they're looking for directions (happy to help!), or they are deeply confused about appropriate social behavior in crowded spaces. In the latter case, I'm lucky if the stranger-with-no-boundaries merely wants to warn me about the dangers of the lizard people. So I've learned to ignore strangers.
As far as I can tell, much of Anthropic genuinely believes that someone will build an AI in the next 3-20 years that's significantly smarter than any human alive. Sounds wild, but a lot of their people have been saying this since 2018 or even earlier. I think they're true believers. Furthermore, they believe that building such an AI would be dangerous.
So their plan is:
1. We can't stop other people from building something dangerous.
2. But we can get there first.
3. If we build it, it has maybe a 15% chance of killing everyone alive. (I think that's a number I've seen Dario use before, but I may be wrong.) If OpenAI or China build it, the odds would be worse.
Obviously, if Anthropic is actually correct about (1) and (3), then nobody should allowed to build frontier AI.
People find it really hard to believe that (a) anyone believes in the possibility of dangerous AI in our lifetimes, and (b) that someone could believe what Anthropic seems to believe and then still go ahead and gamble with everyone's lives anyway.
> Amazon removes guardrails from Fable, getting access to Mythos.
Amazon did not remove any "guardrails" from Fable. They created a fake, obviously insecure program. And apparently their prompt was exactly, "Fix this code." And Fable fixed the bugs.
This is something that even dinky local Chinese models running on a high-end gaming GPU can often do. Certainly Opus, GPT 5.5 and Gemini can all do this. And any high-end Chinese "near-frontier" model can do this, too.
But either (1) the administration is too clueless to know most models can do this, (2) Trump wants to be paid a bribe, (3) someone thinks Anthropic is "woke" and should therefore be destroyed by the power of the state, or possibly, if you're really cynical, (4) maybe the NSA SIGINT wants access to Mythos so they can break into everyone's computers, but they don't want you to have a model good enough to keep them out. Take your pick, I guess.
Anyway, apparently we don't do free markets or rule of law in the United States any more?
COBOL was mostly outsourced to India, and it's a terrible professional path for anyone in the EU or US, and has been since the Y2K bugs got fixed at the last minute.
(And probably a bad path in India, too, but I have no data one way or the other. It's just that all the excellent Indian devs I know use almost exactly the same tech stacks I do.)
I write a lot of code in Rust, Python and TypeScript. Some of it is open source. Somehow I get paid for this.
I've done startups and consulting, but I'm pretty busy right now.