robsteranium

u/robsteranium@lemmy.world
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I've tried hardening off with a day release in the garden but tbh I'm just going through the motions as I can't really see any difference! I don't have a cold frame but I tried a poly tunnel and things just end up bolting (presumably they're too warm for the amount of sun in spring).

I've been wondering about grow lights in any case, that's worth a try. Good point about the variety, I'd been using the same seeds and maybe they just weren't right.

Thanks for the suggestions.

I've sown them indoors from seed 3 years running but they invariably died after planting out. I tried various locations.

It's possible I was a bit eager. There's still a risk of frost until May here but I prefer to get going in April. Since they overwinter I assumed it was safe but that might only be established plants.

Our soil is really sandy without much organic matter and poor moisture retension. The beans need watering daily in summer. We're with facing though so get plenty of sun. I may have watered inconsistently but I reckon I've generally got a good feel for that.

This year I got a seedling and it's done better (is still alive). I put a lot of compost into the bed last winter and was really on it with the aphids in spring (of which there were many). Maybe we'll see it flower next year...

They just wither and die without ever really getting established. The outer leaves seem to go brown faster than new inner ones can replace them.

I haven't been fertilising them as they've always been so small. Maybe I should try that

Any tips for growing them? I've been trying for years without success.

This is the first time I've made it this far into the summer after being pretty thorough with the aphids/ ants! have a few leaves but no signs of flowering yet...

Vibe coding is accepting changes from the model without reviewing them fully (or trusting models to do the verification).

She/he is presumably discussing ideas with Claude then reviewing any suggestions before integrating them.

This may still not be to your taste but it's not the same as vibe coding. Whether or not they are actually reviewing everything is also a separate question!

We spin up a VPS on demand then tear it down again once they've finished playing.

Some months we accrue so little use that Hetzner doesn't bother issuing an invoice!

We've shared the provisioning scripts on Codeberg if you want to try it ou: LuanParty.

Yeah the intelligence is still in the model. The promise of symbolic AI is about logic programming/ formal semantics not recursive loops.

To a large extent the idea has failed because it proved too hard to get non-experts to represent systems formally.

I still think there's potential value in a hybrid approach - e.g. get language models to do the representation then let them use formal reasoners/ verification instead of hallucinating.

That sounds about right to me! Trying to go full steam ahead for 8 hours a day isn't sustainable or even desirable.

I found Cal Newport's book Slow Productivity really helped me get some perspective on work. It has three main lessons: Do Fewer Things, Work at a Natural Pace and Obsess over Quality.

on Old ham is manc · c/dadjokes · 1 pts · 29d

Can't believe the strength of opinion on this post! I assume it's people frustrated that you dare post a pun in a (non-American) dialect... or perhaps you've touched a raw nerve in Oldham!

The paper link above 404s but I think it should be this: https://arxiv.org/abs/2607.05147

tl;dr DSpark improves the efficiency speculative decoding as it's able to verify longer draft sequences by accounting for inter-token dependencies.

Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification. While recent parallel drafters efficiently propose long token sequences in a single forward pass, they suffer from rapid acceptance decay due to a lack of inter-token dependencies. Furthermore, indiscriminately verifying these extended blocks wastes critical batch capacity on tokens with high rejection risks, severely degrading throughput in high-concurrency serving systems. We introduce DSpark, a speculative decoding framework that unifies high-throughput parallel generation with adaptive, load-aware verification. To maintain draft quality, DSpark utilizes a semi-autoregressive architecture, coupling a parallel backbone with a lightweight sequential module, to introduce intra-block dependency modeling and mitigate suffix decay. To optimize system efficiency, DSpark employs confidence-scheduled verification, dynamically tailoring the verification length for each request based on estimated prefix survival probabilities and engine-specific throughput profiles. On offline benchmarks across diverse domains, DSpark substantially improves the accepted length over state-of-the-art autoregressive and parallel drafters. When deployed within the DeepSeek-V4 serving system under live user traffic, DSpark successfully mitigates verification waste. Compared to the established production baseline (MTP-1), DSpark accelerates per-user generation speeds by 60 to 85 percent at matched throughput levels. More importantly, by preventing severe throughput degradation under strict interactivity constraints, it enables performance tiers that were previously unattainable, shifting the Pareto frontier of our serving system