raphaelj

Well most of the increase in prices goes into petroleum companies' profits (at least the ones that can export). So it's technically not lost and will be invested somehow.

Like a Carbon tax, the money doesn't disappear. But to whom it gets distributed, that's another story...

That's only about 35% more than the main telecom operator here in Belgium (Proximus: $7.2B revenue in 2025, $2.5B market cap, positive earnings for 15+ years).

Obviously Starlink can and will growth. I'm just pointing out how insane the market cap is, when compared to similar scale "connectivity" businesses.

That might be part of the answer.

But IIRC, in Belgium at least, these plants are also remunerated on "secondary" markets for non-productive tasks. These secondary markets are mostly for grid balancing.

It is 800W, but the battery allows it to buffer the excess and never output more than the legal limit.

Mine (Ecoflow Stream Ultra) has two 1200W regular sockets I can use while the grid down.

These inverters have to switch off if they cannot detect a grid signal, or if the grid frequency/voltage goes out of the safe range.

Same legislation as the non-plug&play inverters.

You have to register to the utility, but that's just a form to fill with the model and power of the kit.

With a e-meter, you will get compensated when you're generating a surplus (+/- €0.04/kWh last time I checked).

However, thanks to the battery, I'm self-consuming almost all that electricity, saving around €0.30/kWh.

Expect 800kWh of annual production per 1kW of panels.

House is facing South, but I'm laying them flat on the floor for economic and aesthetic reasons, with some 3D printed brick-based ballast [1].

Buying supports for PV is actually less economical than buying additional PV panels.

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[1] https://imgur.com/a/tDPevmM

We legalized these in Belgium last year.

I bought a 1600Wc + 1.9KWh kit (Ecoflow Stream) for +/- 1300€ last summer. It took us about 2h to install (we had to setup a new plug outside), and I already saved 200€+ since July. I am expecting to save about 350€ per year.

Also, as u/jstch said, it's extremely fun to setup and generate your own power!

A 10c€/kWh CfD is not strictly speaking a subsidy, at the government will recover the average market price.

That being said, the total cost per kWh could well reach 20c/kWh, which is ridiculous. It's not only not competitive against renewables, but also not competitive with natural gas (CCGT are probably around 10-15c€/kWh).

Companies that do not reduce their workforce might outcompete you.

It might not be about bringing more revenues but retaining market share.

I like Scaleway a lot too.

Interestingly, the Renault 5 Turbo 3E is more Cyberpunk than anything Tesla is making!

Same experience here.

On some tasks like build scripts, infra and CI stuff, I am getting a significant speedup. Maybe I am 2x faster on these tasks, when measured from start to PR.

I am working on a HPC project[1] that requires more careful architectural thinking. Trying to let the LLM do the whole task most often fail, or produce low quality code (even with top models like Opus 4.5).

What works well though is "assisted" coding. I am usually writing the interface code (e.g. headers in C++) with some help from the agent, and then let the LLM do the actual implementation of these functions/methods. Then I do final adjustments. Writing a good AGENTS.md helps a lot. I might be 30% faster on these tasks.

It seems to match what I see from the PRs I am reviewing: we are getting these slightly more often than before.

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[1] https://github.com/finos/opengris-scaler

Could you try with some open-weighted models, e.g. Qwen3-coder, GLM-4.7 or Devstral-2?

The UK is actually world leading in wind electricity generation (especially offshore). So it's not all bad.

Why does he need to manually do the tracing or reference counting of all these nodes?

Instead, he could just use the references he needs in the new tree, delete/override the old tree's root node, and expect the Javascript GC to discard all the nodes that are now referenced.

Made a typo, I meant 00s. I edited it, thanks :)

> What is the basis of these revisions?

Ideology

Meanwhile, according to the IEA, renewables investments exceed those in fossil fuels since +/- 2022 [1], and are expected to be the top electric power generator by 2026 [2].

The Trump administration is basically following Kodak's strategy from the early 00s.

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[1] https://www.iea.org/reports/world-energy-investment-2025/exe...

[2] https://www.eco-business.com/news/iea-renewables-will-be-wor...

I've been trying to use other LLM providers than OpenAI over the past few weeks: Claude, Deepseek, Mistral, local Ollama ...

While Mistral might not have the best LLM performances, their UX is IMO the best, or at least a tie with OpenAI's:

- I never had any UI bug, while these were common with Claude or OpenAI (e.g. a discussion disappearing, LLM crashing mid-answer, long context errors on Claude ...);

- They support most of the features I liked from OpenAI, such as libraries and projects;

- Their app is by far the fastest, thanks to their fast reply feature;

- They allow you to disable web-search.

Couldn't the battery just do, as an example, 1 minute long charge then discharge cycles?

For example, if the electricity price is -28€/MWh (like today in Germany), and your battery efficacy is 80%, you could get paid 28€/MWh charging, then only pay back 22€ discharging, generating a 6€/MWh profit.

There might not even be any need for V2G or V2H.

Just charging your car when the demand is low is probably enough to drastically reduce the overall cost of the system. And this has basically no impact on the battery lifespan.

This is definitively possible. Are you thinking about YouTube or social media links?

I've an EcoFlow plug & play inverter, and it automatically shuts off if the grid comes down. That's a requirement for all these devices.

Do we know which changes made DeepSeek V3 so much faster and better at training than other models? DeepSeek R1's performances seem to be highly related to V3 being a very good model to start with.

I went through the paper and I understood they made these improvements compared to "regular" MoE models:

1. Latent Multi-head Attention. If I understand correctly, they were able to do some caching on the attention computation. This one is still a little bit confusing to me;

2. New MoE architecture with one shared expert and a large number of small routed experts (256 total, but 8 in use in the same token inference). This was already used in DeepSeek v2;

3. Better load balancing of the training of experts. During training, they add bias or "bonus" value to experts that are less used, to make them more likely to be selected in the future training steps;

4. They added a few smaller transformer layers to predict not only the first next token, but a few additional tokens. Their training error/loss function then uses all these predicted tokens as input, not only the first one. This is supposed to improve the transformer capabilities in predicting sequences of tokens;

5. They are using FP8 instead of FP16 when it does not impact accuracy.

It's not clear to me which changes are the most important, but my guess would be that 4) is a critical improvement.

1), 2), 3) and 5) could explain why their model trains faster by some small factor (+/- 2x), but neither the 10x advertised boost nor how is performs greatly better than models with way more activated parameters (e.g. llama 3).

Do we know which change(s) made DeepSeek V3 so much more efficient than other models?

I went through the paper and I understood they made these improvements compared to "regular" MoE models:

1. Latent Multi-head Attention. If I understand correctly, they were able to do some caching on the attention computation. This one is still a little bit confusing to me;

2. new MoE architecture with one shared expert and a large number of small routed experts (256 total, but 8 in use in the same token inference). This was already used in DeepSeek v2;

3. Better load balancing of the training of experts. During training, they add bias or "bonus" value to experts that are less used to make them more likely to be selected in the future training steps;

4. They added a few smaller transformer layers to predict not only the first next token, but a few additional tokens. Their training error/loss function then uses all these predicted tokens, not only the first one. This is supposed to improve the transformer capabilities of predicting sequences of tokens. Note that they don't use this for inference, except for some latency optimisation by doing speculative execution on the 2nd token.

5. They are using FP8 instead of FP16 when it does not impact accuracy.

My guess would be that 4) is the most impactful improvement. 1), 2), 3) and 5) could explain why their model train faster, but not how is performs greatly better than models with way more activated parameters (e.g. llama 3).

I've been using it for a few months (with Starcoder 2 for code, and GPT-4o for chat). I find the code completion actually better than Github Copilot.

My main complain is that the chat sometimes fails to correctly render some GPT-4o output (e.g. LaTeX expressions), but it's mostly fixed with a custom system prompt. It also significantly reduces the battery life of my Macbook M1, but that's expected.