jakozaur

I tested it, and it looks real but more nuanced. It is just for subagents; workflows do not seem affected. I guess many new users prefer workflows (ultracode), so the impact is less than the report suggested.

Moreover it is mostly cached reads, so subagents with short context cost little, while long-context ones cost much more.

In my Claude Code sessions, it looks more like ~8% at most.

Proxies are good if you pay API prices.

For subscriptions, it is easier to collect the JSONL files from ~/.claude/projects/ and similar directories. They are better organized (e.g., subagents) than what the proxy sees.

However, the best value comes from collecting agent sessions across the whole organization. You'll need to scrub credentials and handle security.

E.g., your prompt for simple auth works (e.g., decode JWT), but your sessions stop because you are flagged for cybersecurity risk.

The Fable is the worst offender, and it is not purely Anthropic's fault.

I worry we’ll have to approach this the other way around: verify that photos came from a camera, using hardware support like Apple’s Reference Image, rather than try to detect every AI generated one.

In many situations, photos are evidence. AI tools make convincing fakes, such as images of defect product.

A strong competitor in cybersecurity as an alternative to GLM-5.3 (Mistral reports 82% on CyberGym-E2E). Visual grounding is also impressive (42% on Dense 200 vs. 41% for GPT-6 Astra).

Otherwise, behind on the broader Pareto frontier, but not by much (Vals Index: 48.05% vs. GLM-5.3’s 53.51%; $13.78 vs. $7.25 per test). Many companies will prefer it over Chinease models.

Inflation in mentioned above example was 3.4% year over year for a professional at the higher end of the income scale. In that case it is inconvenience.

I agree that higher level of inflation might be brutal.

Typically, for AI training, you need human feedback. If AI were enough, then OpenAI would do it themselves. Though we all know how bad AI slop is, running it in a loop can lead to unreadable sentences.

They hired contractors on the condition that they provide human feedback without AI; those people broke the rules, so their contracts ended prematurely.

Not sure why this is news uncovered by an investigative journalist.

I don't believe we are anywhere close to AGI until I see 99.95% uptime at the frontier AI lab. Now they are undercounting. I hit 50x even when they claim they are up.

We now have alien intelligence that is very different from human intelligence.

I would say so. In this exercise, more tokens meant better results. Though DeepSeek tokens were as good as frontier ones.

In this hackathon, how much data you analyzed was correlated with tokens spent.

Analyzing the whole dataset gave the best insights, but cost more tokens. Using a small dataset could save tokens, but some insights were premature.

Yeah, real Jev got really weird, no benchmarking clause. Their Terms of Use (1(v)) and MCA (2.3(f)) both prohibit users from publishing "benchmarks or performance information about the Services". No major AI has it; we are back to Oracle-style legal.

Though Jev is original, it looks highly replicable.