Lying models, real planes, and the benchmarks that lie to you

The world's largest electric aircraft took to the air for the first time. The plane weighs 27 tons and has a wingspan of 42 meters. It's powered by a battery pack the size of a shipping container — roughly 1.5 megawatt-hours of energy, about the same as 20 Tesla Model 3s combined. The test flight lasted 40 minutes and reached 185 kilometers per hour at 1,000 feet. The developers behind the project are based in the UK, and they say this is the first electric plane of this scale to fly. They've been working on it for years, and the airframe itself was designed for electric propulsion from the start, not retrofitted from a gas engine.

Electric planes this size are a big deal because they point at a real gap in the world's transport system. Most commercial flights are short hops — 400 kilometers or less — and those are exactly the distances where batteries are starting to catch up with fossil fuel. A 27-ton plane can carry passengers and cargo where a turboprop would have been flying before. If battery energy density keeps improving, regional flights that now require kerosene could eventually run on batteries alone. The physics is unforgiving — energy density matters more than almost anything else in aviation — but the trajectory is clear. This flight is a proof of concept for a class of aircraft that could reshape regional transport within a decade.

Why this matters for us: the shift to electric transport isn't just about cars — it's moving up the weight ladder, and regional planes are next on the list, which means cheaper flights and less dependence on oil companies who've been calling the shots for generations.

Snowflake is swapping its billing metric from rows processed to bytes scanned. The idea sounds reasonable — charge by the data you actually touch instead of rows you churn through. But the reality is messier.

The problem is that bytes scanned doesn't account for data that's already been filtered at the file level by Snowflake's metadata indexing. You can end up paying for bytes the engine never touched. Meanwhile, the old rows metric at least gave you a rough sense of work done. This one gives you a number that feels arbitrary.

Some shops are already seeing their bills jump on queries that look lightweight. The kind of query you run a hundred times a day — filtering a table by date — now costs more because it has to read the underlying files even though Snowflake's predicate pushdown already narrowed the set. It's a quiet tax on the queries nobody notices until the invoice arrives.

Why this matters for us: if you're running anything on Snowflake right now, check your new bill before the next statement — the change is real and it's catching people off guard.

A new post argues that the benchmarks everyone uses to grade AI models are useless for the people who actually depend on them. The author is working on a system that runs a lot of real workloads, and the standard benchmarks simply don't map to what keeps the lights on.

The piece walks through how the usual test suites — the ones that measure how well a model writes code or answers trivia — miss the things that matter when you're actually using the model at scale. Latency, consistency, cost per output, how it fails under load: none of that shows up on the leaderboards. It turns out the gap between benchmark scores and real-world performance is wider than most people think, and the gap keeps growing as models get bigger.

Why this matters for us:
If your side gig or the shop you work at is starting to use AI tools, the benchmarks on the front page of Hacker News won't tell you whether they'll save you time or just cost you more.

Zhipu — the Beijing lab behind ChatGLM — has released GLM-5, and the weights are public on Hugging Face. It's the first model from the company to hit the top of the CyberScore leaderboard, edging out Mythos 5 by a margin. The model runs 4-bit quantized on a single 4090, which means the average tinkerer can spin it up at home without renting a cluster.

The timing is worth noting: the release arrived two weeks after CyberScore got reworked, so this is the first model benchmarked under the new scoring system. Zhipu has been quietly building a reputation in the open-source space — they ship weights fast, they don't gatekeep, and they tend to beat the bigger labs on raw quality per dollar of compute. GLM-5 fits that pattern.

For the comunidad, this means another capable model you can run locally. No cloud API, no per-token fees, no vendor lock-in. The 4-bit 4090 setup is the cheapest way to get real inference on a home GPU right now.

Why this matters for us: local models are the only ones we can actually own — no API key, no rate limit, no one turning off the tap when the politics get uncomfortable.

A creator with 100,000 followers posted a video reviewing an AI product, then quietly put the whole thing on a sponsored post with a $100,000 price tag. The company behind the tool doesn't actually have 100,000 users — the review was just a billboard wearing a personality.

This is how the new ad market works. Instead of paying for banner ads or even YouTube pre-roll, brands buy the influencer's face and let them read the copy. The influencer looks like they're genuinely recommending something. The audience never knows they're watching a paid ad. It's cheaper than traditional media and way more believable — which is the whole point.

The math is simple. A big influencer charges a flat fee, the brand gets the reach, and the product doesn't need to actually be good. The review lives on the internet forever, and every time someone clicks through, it looks like organic interest. The tool probably sits at a few thousand active users, but the ad makes it look like a hit.

Why this matters for us: la gente trusts the people they follow, and when that trust becomes a line item on a brand's budget, the next time someone tells you the new app is a game-changer, it might just be a paid post wearing a personality.