u/Balance-

Just encountered this limit on Plus. Makes no sense.

The issue isn’t that I need five things running constantly. Some scheduled tasks might run once a week, once a month, or only check for something occasionally. Yet a monthly reminder apparently consumes the same scarce slot as a daily recurring task.

That makes the feature weirdly self-defeating: the more useful you find scheduled tasks, the faster you hit an arbitrary ceiling.

Limiting users to five active task definitions total, regardless of how rarely they run, feels like the wrong constraint.

At minimum, infrequent tasks should either count differently or the cap should be substantially higher.

Maybe 5 daily? Where a monthly tasks counts as 1/30th of a daily task?

Not a transformer. It's a recurrent latent reasoning setup that keeps "thinking" in latent space before answering. Sits completely outside the published cost/accuracy frontier for ARC-AGI, and something this size runs on basically anything. Paper is from the Pathway team, dropped 4 days ago. I want to see it scaled to 1-3B before getting too excited, but the shape of the result is wild.

> De Nederlandse gasopslagen zijn voor 40 procent gevuld, terwijl het ‘vulseizoen’ al een eind op weg is. Vorig jaar was de voorraad al in juni zo ver gevuld. De doelstelling, een vulgraad rond de 80 procent, is nog ver weg. En bijvullen is met de hoge gasprijzen een kostbare zaak.

I’m curious, are people using OpenAI’s Prism? How do you experience it? Are you using it in your regular work? Is it still being continued to be developed?

Deep Research launched Feb 2025 and felt like a real step change. Every lab shipped their own version within months. Since then, the changes seem mostly incremental: a newer base model, MCP connectors, source restrictions, nicer report UI. Useful, but not another step change.

What strikes me is that the known weaknesses from the launch post — hallucinated facts, trusting sketchy sources, poor uncertainty calibration — still show up in third-party benchmarks over a year later. The reports are impressive but you still have to verify everything, which eats most of the time savings.

Is this a hard capability wall (telling good sources from confident SEO junk might just be really hard)? Did the labs shift focus to general agents and browsers, leaving research modes as a maintained feature rather than a frontier? Or is progress happening but invisible (fewer hallucinations and better source picking don’t demo well)?

So why has progress on this front stalled?

TabFM is a zero-shot tabular foundation model from Google Research. It supports classification and regression on structured/tabular data with mixed numerical and categorical columns, requiring no fine-tuning or hyperparameter search - training examples are passed as context and predictions are made in a single forward pass.

Ik denk dat ik nog even lekker blijft zitten. Hoewel ik ook wel langzaam een beetje trek krijg.

Keuzes!

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